OCT Recovery Plan FAQs, by the folks who brought you Deep Decarbonization Pathways

The aim of this study was to evaluate the relative costs of multiple U.S. energy system transition scenarios that are consistent with returning atmospheric CO2 to 350ppm by the end of the century. 

The researchers studied six different scenarios: five that follow the 6% per year reduction path and one that follows the 12% path. All reach net negative CO2 by mid-century while providing the same energy services for daily life and industrial production as the Annual Energy Outlook (AEO), the Department of Energy’s long-term forecast. The scenarios explore the effects of limits on key decarbonization strategies: bioenergy, nuclear power, electrification, land NETs, and technological negative emissions technologies (“tech NETs”), such as carbon capture and storage (CCS) and direct air capture (DAC).

 

Though it may sound like it, the authors of this report do not propose increased use of natural gas. In all cases gas use goes down significantly and quickly.  Natural gas plants would be built, but they would be used rarely and only to fill in the gaps in electricity generation as renewable energy generation is built out as fast as humanly possible. (What are the assumptions in this?  Also depends on how many others are doing this at the same time, where your orders are in the queue, and what the supply capacity is?)

In most of the scenarios, by 2050 gas power plants sit idle and operate less than 10% of the time AND by that point they can operate on bio-based or synthetic natural gas instead of fossil-based fracked gas. (report says 15%?)

One scenario that does not rely on CCS – and both the price and pace of GHG emissions reductions in this scenario are not radically different than the others – suggesting that CCS is not a critical strategy to achieve the necessary reductions.  Having that scenario as one of several allows readers (including CCS advocates) to see the relative importance (or unimportance) of CCS (and other technologies.)

**

The main requirement of the transition is the construction of a low carbon infrastructure characterized by high energy efficiency, low-carbon electricity, and replacement of fossil fuel combustion with decarbonized electricity and other fuels, along with the policies needed to achieve this transformation. The findings of the present study are similar to the 2014 Deep Decarbonization Pathway Study but reflect both a more stringent emissions limit and the consequences of five intervening years without aggressive emissions reductions in the U.S. or globally.

What are the most important things that need to happen in the next 5 years? The next 10 years?

A: In the report we identify key actions by decade. For the 2020s we state the following as the most important actions both for near-term reductions and setting us up for a transition to a longer-term low emissions economy:

  • Begin large-scale electrification in transportation and buildings
  • Switch from coal to gas in electricity system dispatch
  • Ramp up construction of renewable generation and reinforce transmission
  • Allow new natural gas power plants to be built to replace retiring plants
  • Start electricity market reforms to prepare for a changing load and resource mix
  • Maintain existing nuclear fleet
  • Pilot new technologies that will need to be deployed at scale after 2030
  • Stop developing new infrastructure to transport petroleum fuels
  • Begin building carbon capture for large industrial facilities

Q: What kinds of government policies would be required to make this kind of change happen?

A: A recently published book, Legal Pathways to Deep Decarbonization in the United States, lists a host of local, state, and federal policy tools that could be used to achieve an 80% reduction in GHGs by 2050 in the United States. Similar tools would need to be applied more aggressively in order to achieve the reductions demonstrated in this study.

Q: How is Direct Air Capture different from Carbon Capture and Storage (CCS)?

A: Direct air capture is a technology that captures CO2 from the air, at atmospheric concentrations. Traditional carbon capture technologies rely on capturing a more concentrated stream of CO2 from a combustion source. Direct air capture technologies are considered “negative” emissions technologies because they extract CO2 from the atmosphere where carbon capture, when applied to sources of fossil fuel combustion, only avoids additional CO2 emissions.

Q: Why is Direct Air Capture an important component of deep decarbonization?

A: Direct Air Capture can be an important backstop technology if other strategies that we intend to rely on (zero-carbon biomass, electrification, etc.) don’t materialize at their expected scale. When powered by zero-carbon electricity, direct air capture can remove CO2 from the atmosphere and either sequester it (resulting in negative emissions) or utilize it with hydrogen to produce a carbon-neutral alternative to fossil fuels (gas, diesel, gasoline, etc.).

Q: Has Direct Air Capture of carbon dioxide been proven at scale? How do you know it will work?

A: Direct Air Capture hasn’t been proven at scale but has been demonstrated in pilot projects. It is not deployed in all scenarios and is only deployed at any volume in later years of the analysis to offset certain hard-to-decarbonize corners of the economy if other mitigation pathways fail to materialize. In this way, it’s an important “backstop” technology.

Q: What are the risks or downsides of Direct Air Capture technology?

A: Capturing CO2 from the atmosphere is an energy intensive process. Therefore, these direct air capture plants are most cost effective if they operate on excess low-carbon electricity – solar or wind, for example, when they are producing more than is needed. This electricity that would otherwise be “curtailed” is inexpensive from a system perspective. If there is not excess electricity available, the operation of Direct Air Capture facilities will require the construction of new renewables to support their energy needs. It isn’t possible to run these economically on the grid as it looks today because the emissions associated with fossil-fueled electricity would offset much of the benefit of the capture process. It only “fits” on a low-carbon grid. Like other new infrastructure, there will be land use and siting challenges, though there is no imperative to locate them near to human habitation.

Q: Why do the 350 ppm deep decarbonization pathways call for building more gas power generation capacity?

A: In the near-term, the priority is displacing electricity generation from the existing coal fleet. We assume real world limitations on the speed of renewable energy deployment in the near-term, which means while much of the displacement can be renewables, gas still plays a role in the rapid drawdown of coal generation. If planned well, in the longer-term these aren’t wasted investments, as gas-fired electricity generation, running very rarely to provide capacity, is the most cost-efficient way to maintain electric reliability, even as we transition to a system where most of the energy is being provided by renewable sources. With load growth from electrification, in the longer term, the need for capacity is growing, even with higher energy efficiency, flexible load, and the deployment of significant renewables.

Q: Will the transition to low carbon require construction of new natural gas transmission pipelines?

A: New pipelines that transport biogas, synthetic gas from renewables, hydrogen, or CO2 will be required. This is because the locations where these future sources of energy or carbon are sourced are not the same locations where we source natural gas today; (why is this? Gas is temporary and there should not be new mining?) and therefore, will require some new pipeline to connect them with loads or with carbon sequestration locations. As a general rule, new pipelines to transport natural gas are not needed in this transition, though exceptions may exist. As stated before, the priority is creating a switch from coal to gas as fast as possible and new pipelines, where they can make this transition happen faster, may be a part of the lowest cost strategy to reach 350 ppm.

Q: How often would gas powered plants be used in the low-carbon scenarios?

A: By the year 2050, gas generators operate less than 15% of the hours of the year. This is approximately a quarter of the time they operate today on a grid that delivers ~2x more electricity. This gas does not need to be fossil, either. It can burn electrically-derived or bio-based fuels.

Q: Why do most scenarios call for Carbon Capture and Storage (CCS) and Carbon Capture and Utilization (CCU)? Why can’t we transition off of fossil fuels quickly enough to avoid the need for carbon capture?

A: Carbon capture does not mean we are always capturing emissions from fossil energy. Most of our carbon capture is done in conjunction with biofuels production or accomplished through direct air capture, which is not a function of fossil emissions. These both contribute negative emissions without requiring fossil consumption so the use of carbon capture in these scenarios does not necessarily support continued use of fossil fuels.

When fuels (aviation, for example) or the chemical components of fuels (cement, petrochemicals) are required for an application, the use of carbon capture is critical. This can be used either in the production of fuels (CCS on biomass production, power-to-x using direct air capture) and/or at the point of use (CCS on cement and petrochemicals). This can result in zero or negative emissions fuels pathways, which contribute to overall emissions reductions in the economy.

Q: Are CCS and CCU already being used in the U.S.? How much more use would be needed?

A: CCS and CCU in the applications we’re describing is not common in the U.S. The only significant use for captured carbon, currently, is enhanced oil recovery, which is not an application we analyze in any great detail and is not the purpose of CCS and CCU in our scenarios. This would therefore be an almost entirely new industry, but one in which the technical implementation is already understood.

Q: How are synthetic liquid fuels made? Are there facilities making synthetic liquid fuels? Can they be used the same as petroleum products?

A: Synthetic liquid fuels are made by synthesizing a mixture of CO2 (produced through carbon capture) and hydrogen (produced primarily through electrolysis). This is synthesized into liquid hydrocarbons through the Fischer-Tropsch process in our modeling, though there are other synthesis routes as well.

Q: How does making synthetic gas by methanation work? Are there facilities making synthetic gas?

A: Synthetic gas is made by methanating a mixture of CO2 (produced through carbon capture) and hydrogen (produced primarily through electrolysis). There are dozens of projects in Europe but only one demonstration project in the U.S. as of 2019.

Q: Can existing fossil fuel infrastructure be used with synthetic fuels and gases?

A: Yes, these are what we refer to as “drop-in fuels” meaning all the components of the existing delivery (pipelines, storage facilities, etc.) and end-use consumption infrastructure (trucks, boilers, etc.) can use these fuels as if they were fossil-based.

Q: How are jet fuels made? Are there facilities producing low carbon jet fuels at scale?

A: In our modeling, jet fuel is made through Fischer-Tropsch processes, like the processes employed to make power-to-liquids. In this case, biomass is gasified to produce the mixture of CO2 and H2 (syngas) needed to then be synthesized into liquid fuels using Fischer-Tropsch.

Q: Can bio-based aviation fuels be produced at the quantity required without impinging on land needed for crop production?

A: Based on previous analysis we conducted, the level of biomass we employ is consistent with a level that would not impinge on food production domestically or internationally. Partly this is due to the fact that much of the biomass is waste or woody biomass, but also there is significant land that could be repurposed from corn ethanol production in our analysis. This corn ethanol production declines commensurate with the decline in gasoline demand as we electrify light duty vehicle travel.

Q: How much more biofuel production would be needed over the amount produced today?

A: Current biofuel production is approximately 1600 TBtu with the vast majority being corn ethanol. The most biofuel we see in any of our scenarios is 8400 TBtu. This would represent an approximately 5x increase in overall biofuel production. The low-biomass scenario uses 4,200 TBtu.

Q: How is hydrogen made from electricity? Are there large scale hydrogen production facilities in use in the United States?

A: Hydrogen is made from electricity through the process of electrolysis, which splits water molecules into H2 (hydrogen) and O2 (oxygen) molecules with an electric current. This is a well understood process that is used in some industrial applications. There are not currently large-scale electrolysis facilities in the U.S. because the economics currently support hydrogen production through natural gas reformation. With higher levels of renewables on the systems and an imperative to reduce carbon, economics would dictate the deployment of electrolysis or the employment of carbon capture on natural gas reformation.

Q: How does this study compare to a 100% renewable electricity study? Why is looking at electricity supply in isolation from other energy sectors counter-productive to stopping climate change?

A: 100% renewable electricity studies focus only on reducing emissions in the electric sector. This is important, but too limited when trying to understand how to reduce an entire economy’s emission to near-zero. The electric sector requires significant sectoral integration (production of electric fuels, direct electrification of transportation) both to meet its own targets and support the transition to zero-emissions economies for all sectors. Narrowly focusing on an electric sector without considering its interaction with other sectors or its role in supporting the emissions reductions from other sectors (e.g. transportation) is likely to lead to poor decision-making. For example, rather than forecasting declining electricity demand in the future, we need to be planning for a 2-fold increase in electricity demand. Even though we don’t directly model 100% renewable systems, we are modeling very high renewables and there are two key lessons to learn in terms of the economic operations of these types of systems in an economy-wide low carbon context:

  1. The use of electric fuels as a balancing resource in order to address seasonal balancing challenges is critical to avoiding large amounts of over generation of electricity that would result in temporarily shutting down renewable generators, a practice known as “curtailment”.  (See recent studies pointing out that over-generation of renewables may be the cleanest, fastest, and cheapest way to go)
  2. Allowing new gas-burning electricity generators to be constructed, even if requiring them to be fueled with biogas or power-to-gas when they run, is essential for operating a high renewables system reliably and economically.

Q: Do you have state-specific results? Which states are looking at their energy systems holistically? Can the EER results be tailored for state planning purposes?

A: This analysis did not produce state-specific results, but the underlying model, data, and analytical framework can and has been leveraged for statewide analyses. An increasing number of states have or are beginning to look at their energy systems holistically using studies similar to this one.

Q: Does this plan rely on offsets to meet the emissions reduction and carbon budget targets?

A: No, this does not rely on offsets to meet the targets. As explained by climate scientists like Dr. James Hansen, being on the 350 ppm by 2100 trajectory requires rapid emission reduction and carbon sequestration through improved forestry and agriculture.

Q: How does your study address natural carbon sequestration from forestry and agriculture?

A: This study did not model or evaluate sequestration potential. We stipulated a certain amount of carbon sequestration from forestry and agriculture based on recent research of what is feasible and based on the quantities necessary to return to 350 ppm in the atmosphere by 2100. These amounts provided a constraint that informed the energy system decarbonization scenarios.

Q: What would happen if we decommissioned nuclear power plants that are currently operating?

A: Decommissioning nuclear without the time to replace them with renewable energy would result in higher operating levels for other online thermal plants (coal and gas). In the longer-term, they can be technically replaced with renewables, but in many cases, this would increase the cost of achieving emissions targets. Still, this would not affect our conclusion of the scenario being technically possible.

Q: Do we need new nuclear to meet the emissions reduction and carbon budget targets?

A: No, we do not. We see that in our “No New Nuclear” sensitivity where we prohibit the construction of new nuclear facilities. Achieving the target requires more renewables, but this can be accomplished with little impact to overall compliance costs.

Q: Has this study been peer reviewed?

A: No, this study has not been peer reviewed. This analysis, however, is in the process of being prepared for submission to the academic literature for peer review.

**

350 PPM PATHWAYS

FOR THE UNITED STATES May 8, 2019

DEEP DECARBONIZATION PATHWAYS PROJECT

350 PPM Pathways for the United States U.S. Deep Decarbonization Pathways Project

Prepared by

Ben Haley, Ryan Jones, Gabe Kwok, Jeremy Hargreaves & Jamil Farbes

Evolved Energy Research

James H. Williams

University of San Francisco
Sustainable Development Solutions Network

May 8, 2019

Version 1

© 2019 by Evolved Energy Research 2

Table of Contents

Table of Contents……………………………………………………………………………………………………………. 3 List of Terms…………………………………………………………………………………………………………………… 4 Executive Summary…………………………………………………………………………………………………………. 6

1. 2.

3.

Introduction …………………………………………………………………………………………………………… 17 Study Design ………………………………………………………………………………………………………….. 23

  1. 2.1.  Scenarios ………………………………………………………………………………………………………… 23
  2. 2.2.  Modeling Methods and Data Sources…………………………………………………………………. 25
    1. 2.2.1.  EnergyPATHWAYS ………………………………………………………………………………………………… 25
    2. 2.2.2.  Regional Investment and Operations (RIO) Platform ………………………………………………… 27
    3. 2.2.3.  Key References and Data Sources…………………………………………………………………………… 27

Results…………………………………………………………………………………………………………………… 29

  1. 3.1.  Emissions ………………………………………………………………………………………………………… 29
  2. 3.2.  System Costs……………………………………………………………………………………………………. 32
  3. 3.3.  Energy Transition …………………………………………………………………………………………….. 35
  4. 3.4.  Infrastructure ………………………………………………………………………………………………….. 38
    1. 3.4.1.  Demand-Side Transformation………………………………………………………………………………… 38
    2. 3.4.2.  Low-Carbon Generation………………………………………………………………………………………… 40
    3. 3.4.3.  Biofuels Production ………………………………………………………………………………………………. 41
    4. 3.4.4.  Electricity Storage ………………………………………………………………………………………………… 44
    5. 3.4.5.  Electricity Transmission ………………………………………………………………………………………… 45
    6. 3.4.6.  Hydrogen Electrolysis……………………………………………………………………………………………. 48
    7. 3.4.7.  Direct Air Capture ………………………………………………………………………………………………… 49

Discussion ……………………………………………………………………………………………………………… 52

  1. 4.1.  Four Pillars ………………………………………………………………………………………………………. 52
  2. 4.2.  Regional Focus…………………………………………………………………………………………………. 53
  3. 4.3.  Electricity Balancing …………………………………………………………………………………………. 55
  4. 4.4.  Sector Integration ……………………………………………………………………………………………. 60
  5. 4.5.  Circular Carbon Economy ………………………………………………………………………………….. 62

Conclusions …………………………………………………………………………………………………………… 63

5.1. Key Actions by Decade ……………………………………………………………………………………… 64 2020s……………………………………………………………………………………………………………………….. 65 2030s……………………………………………………………………………………………………………………….. 67 2040s……………………………………………………………………………………………………………………….. 68

Bibliography …………………………………………………………………………………………………………………. 69 Technical Supplement……………………………………………………………………………………………………. 73 Appendix ……………………………………………………………………………………………………………………… 81

© 2019 by Evolved Energy Research 3

List of Terms

1.0oC – One degree Celsius (1.8oF) of global warming over pre-industrial temperatures. 1.5oC – One-and one-half degrees Celsius (2.7oF) of global warming over pre-industrial temperatures, an aspirational goal in the Paris Agreement climate accord.

2oC – Two degrees Celsius (3.6oF) of global warming over pre-industrial temperatures. The Paris Agreement states the intention of parties to remain “well under” this upper limit.

350 ppm – An atmospheric CO2 concentration of 350 parts per million by volume
80 x 50 – A target for reducing CO2 emissions used in U.S. states and in other countries, referring to an 80% reduction below 1990 levels by 2050.

AEO – The Annual Energy Outlook, a set of modeled results released annually by the U.S. government that forecasts the energy system under current policy for the next three decades. AZNM – eGRID region comprising most of Arizona and New Mexico
Base Case – The primary deep decarbonization pathway with all technologies and resources available according to best scientific estimates.

Baseline – A scenario derived from the U.S. Department of Energy’s Annual Energy Outlook projecting the future evolution of the energy system given current policies

BECCS – Bioenergy with carbon capture and geologic sequestration
BECCU – Bioenergy with carbon capture and utilization of that carbon somewhere in the economy
Bioenergy – Primary energy derived from growing biomass or use of organic wastes
CAMX – eGRID region comprising most of California
CCE – Circular carbon economy, a term that refers to the capture and reuse of CO2 within the energy system
CCS – Carbon capture and storage (also called carbon capture and sequestration)
CCU – Carbon capture and utilization (for economic purposes)
CO2 – Carbon dioxide, the primary greenhouse gas responsible for human caused warming of the climate
DAC – Direct air capture, a technology that captures CO2 from ambient atmosphere
DDPP – Deep Decarbonization Pathways Project
DOE – U.S. Department of Energy
EER – Evolved Energy Research, LLC.
eGRID – Emissions & Generation Resource Integrated Database maintained by the Environmental Protection Agency. eGRID divides the country into regions used in this study that are relevant for electricity planning and operations
EnergyPATHWAYS – An open-source, bottom-up energy and carbon planning tool for use in evaluating long-term, economy-wide greenhouse gas mitigation scenarios.
EPA – U.S. Environmental Protection Agency
ERCOT – Electricity interconnection and balancing authority comprising most of Texas

Gt(C) – Gigatons (billions of metric tons) of carbon

GW – Gigawatt (billion watts)
GWh – Gigawatt hour (equivalent to one million kilowatt hours)
IAM – Integrated Assessment Model, a class of model that models the energy system, economy, and climate system, to incorporate feedback between the three.
Intertie – Electric transmission lines that connect different regions
IPCC – Intergovernmental Panel on Climate Change, an international organization mandated to provide policy makers with an objective assessment of the scientific and technical information available about climate change.
Land NET – Negative CO2 emissions as the result of the update of carbon in soils and terrestrial biomass
Low Biomass – A scenario that limits the use of biomass for energy
Low Electrification – A scenario with a slower rate of switching from fuel combustion technologies to electric technologies on the demand-side of the energy system
Low Land NETs – A scenario with a lower uptake of carbon in land sinks, resulting in a more restricted emissions budget for the energy system.
MMT – Million metric tonnes
N-1 – A test to determine the reliability of a system by ensuring any single component of the system can fail without jeopardizing the system as a whole
NET – Negative emissions technology, one that absorbs atmospheric CO2 and sequesters it Net-zero – A condition in which human-caused carbon emissions equal the natural uptake of carbon in land, soils, and oceans such that atmospheric CO2 concentrations remain constant. No New Nuclear – A scenario that disallows new nuclear construction
No Tech NETs – A scenario that disallows use of the specific technologies of biomass with carbon capture and geologic sequestration and direct air capture with geologic sequestration NWPP – Northwest power pool
Pg(C) – Peta (1015) grams
ppm – parts per million
ReEDS – Renewable Energy Deployment System – a capacity planning and dispatch model build by the National Renewable Energy Laboratory
RFC – Three separate eGRID regions in the mid-Atlantic and extending west through Michigan RIO – Regional Investment and Operations Platform, an optimization tool built by Evolved Energy Research to explore electricity systems and fuels
SDSN – Sustainable Development Solutions Network
SR – eGRID region composing all of the Southeastern United States outside of Florida
TBtu – Trillion British thermal units, an energy unit typically applied to in power generation natural gas
Tech NET – Negative emission technologies composed of either biomass with carbon capture and sequestration or direct air capture with sequestration.
TX – Transmission
VMT – Vehicle miles traveled
WECC – Western electricity coordinating council

Executive SummaryThis report describes the changes in the U.S. energy system required to reduce carbon dioxide (CO2) emissions to a level consistent with returning atmospheric concentrations to 350 parts per million (350 ppm) in 2100, achieving net negative CO2 emissions by mid-century, and limiting end-of-century global warming to 1°C above pre-industrial levels. The main finding is that 350 ppm pathways that meet all current and forecast U.S. energy needs are technically feasible using existing technology, and that multiple alternative pathways can meet these objectives in the case of limits on some key decarbonization strategies. These pathways are economically viable, with a net increase in the cost of supplying and using energy equivalent to about 2% of GDP, up to a maximum of 3% of GDP, relative to the cost of a business-as-usual baseline. These figures are for energy costs only and do not count the economic benefits of avoided climate change and other energy-related environmental and public health impacts, which have been described elsewhere.1

This study builds on previous work, Pathways to Deep Decarbonization in the United States (2014) and Policy Implications of Deep Decarbonization in the United States (2015), which examined the requirements for reducing GHG emissions by 80% below 1990 levels by 2050 (“80 x 50”).2 These studies found that an 80% reduction by mid-century is technically feasible and economically affordable, and attainable using different technological approaches. The main requirement of the transition is the construction of a low carbon infrastructure characterized by high energy efficiency, low-carbon electricity, and replacement of fossil fuel combustion with decarbonized electricity and other fuels, along with the policies needed to achieve this transformation. The findings of the present study are similar but reflect both a more stringent emissions limit and the consequences of five intervening years without aggressive emissions reductions in the U.S. or globally.

1 See e.g. Risky Business: The Bottom Line on Climate Change, available at https://riskybusiness.org/ 2 Available at http://usddpp.org/.

The 80 x 50 analysis was developed in concert with similar studies for other high-emitting countries by the country research teams of the Deep Decarbonization Pathways Project, with an agreed objective of limiting global warming to 2°C above pre-industrial levels.3 However, new studies of climate change have led to a growing consensus that even a 2°C increase may be too high to avoid dangerous impacts. Some scientists assert that staying well below 1.5°C, with a return to 1°C or less by the end of the century, will be necessary to avoid irreversible feedbacks to the climate system.4 A recent report by the IPCC indicates that keeping warming below 1.5°C will likely require reaching net-zero emissions of CO2 globally by mid-century or earlier.5 A number of jurisdictions around the world have accordingly announced more aggressive emissions targets, for example California’s recent executive order calling for the state to achieve carbon neutrality by 2045 and net negative emissions thereafter.6

In this study we have modeled the pathways – the sequence of technology and infrastructure changes – consistent with net negative CO2 emissions before mid-century and with keeping peak warming below 1.5°C. We model these pathways for the U.S. for each year from 2020 to 2050, following a global emissions trajectory that would return atmospheric CO2 to 350 ppm by 2100, causing warming to peak well below 1.5°C and not exceed 1.0°C by century’s end.7 The cases modeled are a 6% per year and a 12% per year reduction in net fossil fuel CO2 emissions after 2020. These equate to a cumulative emissions limit for the U.S. during the 2020 to 2050 period of 74 billion metric tons of CO2 in the 6% case and 47 billion metric tons in the 12% case. (For comparison, current U.S. CO2 emissions are about 5 billion metric tons per year.) The emissions in both cases must be accompanied by increased extraction of CO2 from the atmosphere using land-based negative emissions technologies (“land NETs”), such as reforestation, with greater extraction required in the 6% case.

3 Available at http://deepdecarbonization.org/countries/.
4 James Hansen, et al. (2017) “Young people’s burden: requirement of negative CO2 emissions,” Earth System Dynamics, https://www.earth-syst-dynam.net/8/577/2017/esd-8-577-2017.html.
5 Available at https://www.ipcc.ch/sr15/.
6 Available at https://www.gov.ca.gov/wp-content/uploads/2018/09/9.10.18-Executive-Order.pdf.
7 Hansen et al. (2017).

p. 7

Figure ES1 Global surface temperature and CO2 emissions trajectories. Hansen et al, 2017.

We studied six different scenarios: five that follow the 6% per year reduction path and one that follows the 12% path. All reach net negative CO2 by mid-century while providing the same energy services for daily life and industrial production as the Annual Energy Outlook (AEO), the Department of Energy’s long-term forecast. The scenarios explore the effects of limits on key decarbonization strategies: bioenergy, nuclear power, electrification, land NETs, and technological negative emissions technologies (“tech NETs”), such as carbon capture and storage (CCS) and direct air capture (DAC).

Table ES1. Scenarios developed in this study

Scenario

Average annual rate of CO2 emission reduction

2020-2050 maximum cumulative fossil fuel CO2 (million metric tons)

Year 2050 maximum net fossil fuel CO2 (million metric tons)

Year 2050 maximum net CO2

with 50% increase in land sink (million metric tons)

Base

6%

73,900

830

-250

Low Biomass

6%

73,900

830

-250

Low Electrification

6%

73,900

830

-250

No New Nuclear

6%

73,900

830

-250

No Tech NETS

6%

73,900

830

-250

Low Land NETS

12%

57,000

-200

-450

p. 8

The scenarios were modeled using two new analysis tools developed for this purpose, EnergyPATHWAYS and RIO. As extensively described in the Appendix, these are sophisticated models with a high level of sectoral, temporal, and geographic detail, which ensure that the scenarios account for such things as the inertia of infrastructure stocks and the hour-to-hour dynamics of the electricity system, separately in each of fourteen electric grid regions of the U.S. The changes in energy mix, emissions, and costs for the six scenarios were calculated relative to a high-carbon baseline also drawn from the AEO.

Relative to 80 x 50 trajectories, a 350 ppm trajectory that achieves net negative CO2 by mid- century requires more rapid decarbonization of energy plus more rapid removal of CO2 from the atmosphere. For this analysis, an enhanced land sink 50% larger than the current annual sink of approximately 700 million metric tons was assumed.8 This would require additional sequestration of 25-30 billion metric tons of CO2 from 2020 to 2100. The present study does not address the cost or technical feasibility of this assumption but stipulates it as a plausible value for calculating an overall CO2 budget, based on consideration of the scientific literature in this area.9

8 U.S. EPA, Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016, available at https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2016
9 Griscom, Bronson W., et al. (2017) “Natural climate solutions.” Proceedings of the National Academy of Sciences 114.44 (2017): 11645-11650; Fargione, Joseph E., et al. (2018) “Natural climate solutions for the United States.” Science Advances 4.11: eaat1869.

p. 9

Figure ES2 Four pillars of deep decarbonization – Base case

Energy decarbonization rests on the four principal strategies (“four pillars”) shown in Figure ES2: (1) electricity decarbonization, the reduction in emissions intensity of electricity generation by about 90% below today’s level by 2050; (2) energy efficiency, the reduction in energy required to provide energy services such as heating and transportation, by about 60% below today’s level; (3) electrification, converting end-uses like transportation and heating from fossils fuels to low-carbon electricity, so that electricity triples its share from 20% of current end uses to 60% in 2050; and (4) carbon capture, the capture of otherwise CO2 that would otherwise be emitted from power plants and industrial facilities, plus direct air capture, rising from nearly zero today to as much as 800 million metric tons in 2050 in some scenarios. The captured carbon may be sequestered or may be utilized in making synthetic renewable fuels.

Achieving this transformation by mid-century requires an aggressive deployment of low-carbon technologies. Key actions include retiring all existing coal power generation, approximately doubling electricity generation primarily with solar and wind power (why just doubling?) and electrifying virtually all passenger vehicles and natural gas uses in buildings. It also includes creating new types of infrastructure, namely large-scale industrial facilities for carbon capture and storage, direct air capture of CO2, the production of gaseous and liquid biofuels with zero net lifecycle CO2, and the production of hydrogen from water electrolysis using excess renewable electricity. The scale of the infrastructure buildout by region is indicated in Figure ES3.

Figure ES3 Regional infrastructure requirements (Low Land NETS scenario)

p. 11

Figure ES4 shows that all scenarios achieve the steep reductions in net fossil fuel CO2 emissions to reach net negative emissions by the 2040s, given a 50% increase in the land sink, including five that are limited in one key area. This indicates that the feasibility of reaching the emissions goals is robust due to the ability to substitute strategies. At same time, the more limited scenarios are, the more difficult and/or costly they are relative to the base case with all options available. Severe limits in two or more areas were not studied here (so is all WWS and storage considered a limited use case?) but would make the emissions goals more difficult to achieve in the mid-century time frame. (is this true for WWS though?)

Figure ES4 2020-2050 CO2 emissions for the scenarios in this study

Figure ES5 shows U.S. energy system costs as a share of GDP for the baseline case and six 350 ppm scenarios in comparison to historical energy system costs. While the 350 ppm scenarios have a net cost of 2-3% of GDP more than the business as usual baseline, these costs are not out of line with historical energy costs in the U.S. The highest cost case is the Low Land NETs scenario, which requires a 12% per year reduction in net fossil fuel CO2 emissions. 

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By comparison, the 6% per year reduction cases are more closely clustered. The lowest increase is the Base scenario, which incorporates all the key decarbonization strategies. These costs do not include any potential economic benefits of avoided climate change or pollution, which could equal or exceed the net costs shown here.

Figure ES5. Total energy system costs as percentage of GDP, modeled (R.) and historical (L.)

A key finding of this study is the potentially important future role of “the circular carbon economy.” This refers to the economic complementarity of hydrogen production, direct air capture of CO2, and fuel synthesis, in combination with an electricity system with very high levels of intermittent renewable generation. If these facilities operate flexibly to take advantage of periods of excess generation, the production of hydrogen and CO2 feedstocks can provide an economic use for otherwise curtailed energy that is difficult to utilize with electric energy storage technologies of limited duration. 

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These hydrogen and CO2 feedstocks can be combined as alternatives for gaseous and liquid fuel end-uses that are difficult to electrify directly like freight applications and air travel. While the CO2 is eventually emitted to the atmosphere, the overall process is carbon neutral as it was extracted from the air and not emitted from fossil reserves. A related finding of this work is that bioenergy with carbon capture and storage (BECCS) for power plants appears uneconomic, while BECCS for bio-refineries appears highly economic and can be used as an alternative source of CO2 feedstocks in a low-carbon economy.

There are several areas outside the scope of this study that are important to provide a full picture of a low greenhouse gas transition. One important area is better understanding of the potential and cost of land-based NETs, both globally and in the U.S. Another is the potential and cost of reductions in non-CO2 climate pollutants such as methane, nitrous oxide, and black carbon. Finally, there is the question of the prospects for significant reductions in energy service demand, due to lifestyle choices such as bicycling over cars, structural changes such as increased transit and use of ride-sharing, or the development of less-energy intensive industry, perhaps based on new types of materials.

“Key Actions by Decade” below provides a blueprint for the physical transformation of the energy system. From a policy perspective, this provides a list of the things that policy needs to accomplish, for example the deployment of large amounts of low carbon generation, rapid electrification of vehicles, buildings, and industry, and building extensive carbon capture, biofuel, hydrogen, and synthetic fuel synthesis capacity.

Some of the policy challenges that must be managed include: land use tradeoffs related to carbon storage in ecosystems and siting of low carbon generation and transmission; electricity market designs that maintain natural gas generation capacity for reliability while running it very infrequently; electricity market designs that reward demand side flexibility in high-renewables electricity system and encourage the development of complementary carbon capture and fuel synthesis industries; coordination of planning and policy across sectors that previously had little interaction but will require much more in a low carbon future, such as transportation and electricity; coordination of planning and policy across jurisdictions, both vertically from local to state to federal levels, and horizontally across neighbors and trading partners at the same level; mobilizing investment for a rapid low carbon transition, while ensuring that new investments in long-lived infrastructure are made with full awareness of what they imply for long-term carbon commitment; and investing in ongoing modeling, analysis, and data collection that informs both public and private decision-making. These topics are discussed in more detail in Policy Implications of Deep Decarbonization in the United States.

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Key Actions by Decade

This study identifies key actions that are required in each decade from now to mid-century in order to achieve net negative CO2 emissions by mid-century, at least cost, while delivering the energy services projected in the Annual Energy Outlook. Such a list inherently relies on current knowledge and forecasts of unknowable future costs, capabilities, and events, yet a long-term blueprint remains essential because of the long lifetimes of infrastructure in the energy system and the carbon consequences of investment decisions made today. As events unfold, technology improves, energy service projections change, and understanding of climate science evolves, energy system analysis and blueprints of this type must be frequently updated.

2020s

  • Begin large-scale electrification in transportation and buildings
  • Switch from coal to gas in electricity system dispatch
  • Ramp up construction of renewable generation and reinforce transmission
  • Allow new natural gas power plants to be built to replace retiring plants
  • Start electricity market reforms to prepare for a changing load and resource mix
  • Maintain existing nuclear fleet
  • Pilot new technologies that will need to be deployed at scale after 2030
  • Stop developing new infrastructure to transport fossil fuels
  • Begin building carbon capture for large industrial facilities2030s
  • Maximum build-out of renewable generation
  • Attain near 100% sales share for key electrified technologies (e.g. EVs)
  • Begin large-scale production of bio-diesel and bio-jet fuel
  • Large scale carbon capture on industrial facilities
  • Build out of electrical energy storage
  • Deploy fossil power plants capable of 100% carbon capture if they exist

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Maintain existing nuclear fleet

2040s

  • Complete electrification process for key technologies, achieve 100% stock penetration
  • Deploy circular carbon economy using DAC and hydrogen to produce synthetic fuels
  • Use synthetic fuel production to balance and expand renewable generation
  • Replace nuclear at the end of existing plant lifetime with new generation technologies
  • Fully deploy biofuel production with carbon capture

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1. Introduction

This report describes the changes in the U.S. energy system that, in concert with related actions in land use, will be required to reduce U.S. carbon dioxide (CO2) emissions to a level consistent with returning atmospheric concentrations to 350 parts per million (350 ppm) in 2100, achieving net negative CO2 emissions by mid-century, and limiting end-of-century global warming to 1°C. This study builds on previous work, Pathways to Deep Decarbonization in the United States (Williams et al. 2014) and Policy Implications of Deep Decarbonization in the United States (Williams, Haley, and Jones 2015) which examined the requirements for reducing GHG emissions by 80% below 1990 levels by 2050 (“80 x 50”).10

In the 1980s, with atmospheric CO2 concentrations climbing rapidly, the U.S. government recognized the need to establish a safe CO2 target and determine what would be required to reach it. By 1991, at the request of Congress, both the U.S. EPA and the Office of Technology Assessment had issued roadmaps for maintaining CO2 concentrations near the then-current level of 350 ppm (Lashof and Tirpak 1990; Office of Technology Assessment 1991). Over the last decade, as CO2 concentrations have risen toward and then passed 400 ppm, the question of what constitutes a “safe” concentration relative to dangerous anthropogenic impacts on the climate system has become increasingly urgent. A recent report by the Intergovernmental Panel on Climate Change evaluated the increased risks of 2°C of warming compared to exceeding 1.5°C, and of 1.5°C of warming compared to present warming, and found that a temperature rise of 1.5°C is “not considered ‘safe’ for most nations, communities, ecosystems, and sectors and poses significant risks to natural and human systems as compared to current warming of 1°C (high confidence)” (Intergovernmental Panel on Climate Change 2018). The U.S. Government’s Fourth National Climate Assessment similarly documents an acceleration of climate change impacts already underway (U.S. Global Change Research Program 2017). Studies using global climate models and integrated assessment models (IAMs) indicate that limiting

10 Available at http://usddpp.org/.

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warming to a peak of 1.5°C will require reaching net-zero emissions of CO2 globally by mid- century or earlier (Intergovernmental Panel on Climate Change 2018). Reflecting these findings, a number of jurisdictions around the world have already announced more aggressive emissions targets, for example California’s recent executive order calling for the state to achieve economy-wide carbon neutrality by 2045 and negative net emissions thereafter (State of California 2018).

Several well-known climate studies have concluded that the best chance of avoiding the most catastrophic climate change impacts requires CO2 concentrations to be reduced to 350 ppm or less by the end of the 21st century ( Hansen et al. 2008; Veron et al. 2009; Hansen et al. 2013; Hansen et al. 2016; Hansen et al. 2017). The emission trajectories associated with reaching 350 ppm have lower allowable emissions (“emissions budgets”) in the 21st century than comparable trajectories that would peak at 2.0 or 1.5°C. These trajectories are intended to minimize the length of time the global temperature increase remains above 1°C to prevent the initiation of irreversible climate feedbacks indicated by paleoclimate evidence. In a recent article, Hansen and colleagues describe several possible trajectories for fossil fuel emission reductions that, in combination with specified levels of atmospheric CO2 removal, could achieve 350 ppm by 2100 (Hansen et al. 2017).

In this study we have modeled pathways – the sequence of technology and infrastructure changes – for the United States that result in net negative CO2 emissions before mid-century and that follow a global emissions trajectory consistent with a return to 350 ppm globally by 2100 (Figure 1). The cases modeled are a 6% per year and a 12% per year reduction in net fossil fuel CO2 emissions after 2020. These equate to a cumulative emissions limit for the U.S. during the 2020 to 2050 period of 74 billion metric tons of CO2 in the 6% case and 47 billion metric tons in the 12% case. (For comparison, current U.S. CO2 emissions are about 5 billion metric tons per year.) The emissions reductions in both cases must be accompanied by increased extraction of CO2 from the atmosphere. The 6% reduction case requires a global removal of 153 Pg.(C) incremental to the current global CO2 sink from 2020 to 2100, and the 12% reduction case requires an incremental removal of 100 Pg(C) during the same period. In our scenarios, the removal of the 100 Pg(C) or 153 Pg(C) is assumed to be accomplished through land-based negative emissions technologies (“land NETs”). These numbers imply an increase in the current

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global land sink of about 40% and 60%, respectively (Le Quéré et al. 2018). Additional extraction of atmospheric CO2 using technological negative emissions technologies (“tech NETs”), meaning direct air capture (DAC) and bioenergy with carbon capture and storage (BECCS), is deployed in some of our cases. DAC is the removal of diffuse CO2 directly from the air, while BECCS involves capture of concentrated streams of CO2 from the effluent at industrial facilities that use biofuels; in both cases, the captured CO2 is stored in geologic structures.

Figure 1 Global surface temperature and CO2 emissions trajectories11.

The goal of this study is to understand what realistic 350 ppm-compatible cases would mean concretely for changes in the U.S. energy system and industrial fossil fuel use. Our study differs from recent IAM studies of 1.5°C in that it has a tighter emissions budget, concentrates on a single country, and provides a greater level of technical detail on the transformation to a low carbon economy, including sectorally detailed treatment of costs (Rogelj et al. 2015). The principal research questions addressed by this study are the following:

11 The solid blue line in (b) illustrates a 350 ppm trajectory based on 6% per year reduction in net fossil fuel CO2 emissions combined with global extraction of 153 PgC from the atmosphere. Reprinted from Hansen, ESD, 2017.

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  1. Is it technically feasible to achieve a 350 ppm trajectory within the U.S. energy system, given realistic constraints?
  2. Is a 350 ppm trajectory robust against the absence of key carbon mitigation technologies, i.e. are there multiple technically feasible pathways?
  3. What is the cost of achieving a low-carbon energy system on a 350 ppm trajectory in the U.S?

To answer these questions, we have developed future scenarios using two new models built for this purpose, EnergyPATHWAYS and RIO. These are sophisticated analysis tools with a high level of sectoral, temporal, and geographic granularity. We use these tools to rigorously assess the technical feasibility and cost of rapidly reducing CO2 emissions through the deployment of low carbon technologies and NETs, year by year from the present out to 2050. Changes in energy mix, technology stocks, emissions, and costs for the 350 ppm scenarios were calculated relative to a high-carbon baseline drawn from the Department of Energy’s Annual Energy Outlook (AEO), the U.S. government’s official long-term energy forecast.

The first research question above, regarding technical feasibility, was addressed through the development of a scenario called the 350 ppm base case, which uses currently available technologies to decarbonize the energy system while providing all the same energy services needed to support the U.S. economy and daily life forecast in the AEO. This scenario draws on objective, nationally recognized studies for both current data and future forecasts of performance and costs for each kind of fuel and technology used, in both energy supply and end use. The analysis in EnergyPATHWAYS and RIO was designed to address all major feasibility concerns, ranging from energy balances at a variety of scales, to the inertia of infrastructure stocks, to the hour-to-hour dynamics of the electricity system, separately in each of fourteen electric grid regions of the U.S.

The second research question is based on the observation that since even the best studies cannot perfectly predict the future decades ahead, it is important to understand what options exist if some key decarbonization technology or strategy does not materialize. This was addressed by the simulation of five additional 350 ppm pathways that remove or limit five key strategies used in the base case, either because in the future they do not meet current expectations for performance or cost, or because they are otherwise unable to be deployed at

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scale, for example because they do not achieve social acceptance. The five scenarios start with the base case and then apply the following constraints separately: (i) limiting the availability of biomass for energy, (ii) limiting the rate of electrification of end uses, (iii) eliminating new nuclear plant construction, (iv) eliminating tech NETs, and (v) limiting the availability of land NETs.

In order to answer our third question, we calculate the costs of implementing this transition in the United States over the next three decades, with detailed year-by-year modeling of the energy economy. The 350 ppm-consistent scenarios are compared to a high-carbon case based on the AEO. This comparison is made “apples-to-apples” by ensuring that the energy services provided in the 350 ppm scenarios are the same as those provided in the AEO, and that the cost analysis reflects the differences in capital and operating costs for the low carbon technologies used in the 350 ppm scenarios relative to the business-as-usual technologies in the AEO.

The temporal, spatial, and sectoral detail in our modeling provides unique insights into how energy is supplied and used, and how carbon is, managed throughout the U.S. economy on a 350 ppm pathway. It improves current understanding of how energy and carbon removal interact technically, and how fossil fuel emissions, land NETs, and tech NETs trade off economically. Interactions between these different components of the energy-and-emissions system become increasingly important with tighter emissions constraints, so we account for them separately to avoid confusion and double-counting. Each of the scenarios demonstrates a different mode of utilizing infrastructure, balancing the electricity grid, and producing fuels as a single interactive system for least cost energy production. They also demonstrate how a 350 ppm-compatible energy system differs from one designed to achieve 80% reductions in CO2e below 1990 levels by 2050 (“80 x 50”), such as the pathways previously developed for the U.S. (Williams et al. 2014). 80 x 50 pathways are generally considered to be consistent with emissions scenarios (RCP 2.6) in the IPCC’s Fifth Assessment Report that give a 66% chance of not exceeding 2°C.

This study does not model land NETs, instead stipulating the global 100 Pg(C) and 153 Pg(C) cases mentioned above as boundary conditions for our scenarios. Some credible global evaluations indicate that achieving 153 Pg(C) of land-based C sequestration is potentially

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feasible (Griscom et al. 2017) Achieving this level of sequestration will require changes in current policy and practices that not only improve carbon uptake but address such concerns as indigenous land tenure and competition with food production. Recent assessments of U.S. land- based negative emission potential indicate that a significant share of the required global land NETs, 20 Pg(C) or more of additional land sinks in the 21st century, is possible in the U.S. (Fargione et al. 2018).

For this analysis, an enhanced land sink in the United States on average 50% larger than the current annual sink of approximately 700 million metric tons was assumed.12 This would require additional sequestration of 25-30 billion metric tons of CO2 from 2020 to 2100. The present study does not address the cost or technical feasibility of this assumption, but stipulates it as a plausible value for the purpose of calculating an overall CO2 budget, subject to revision as better information becomes available.

The costs calculated in this study include the net system cost of the transformation in the supply and end use of energy, including tech NETs. They do not include the cost of land NETs or the mitigation of non-CO2 greenhouse gases. Macroeconomic effects are not explicitly considered. There are a variety of other benefits (“co-benefits”) of avoided climate change that are not within the scope of this study, including impacts on human health, ecosystems, and economic productivity. Such co-benefits are addressed in other studies.

The remainder of this report is organized as follows: Chapter 2, Study Design, including descriptions of the EnergyPATHWAYS and RIO modeling platforms, key data sources used, and the scenarios studied; Chapter 3, Results, including emissions, energy supply and demand, infrastructure, bioenergy use, carbon capture, and costs; Chapter 4, Discussion, addressing regional differences, electricity balancing challenges and solutions, cross-sector integration, and the circular carbon economy; and Chapter 5, Conclusions, including key actions by decade. The Appendix describes the scenarios and modeling methodology in detail.

12 U.S. EPA, Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016, available at https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2016

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2. StudyDesign

2.1. Scenarios

This analysis explores the technical feasibility and cost of achieving a 350 ppm-compatible trajectory in the United States, transforming the energy system and achieving zero net emissions by mid-century. This is accomplished by developing a set of scenarios, subject to a variety of constraints (required outcomes and allowable actions), in the EnergyPATHWAYS and RIO models. In total we developed six 350 ppm- compatible scenarios: a core scenario called the Base Case, which is the least constrained, and five variants on this scenario to address concerns about the robustness of the results against implementation failures in key areas. The variants were designed specifically in response to criticisms of the assumptions made in other deep decarbonization analyses, typically conducted with global integrated assessment models. By doing this, we demonstrate the robustness of the result in a manner similar to “N-1” concept in electricity system planning, in which electricity systems must be designed with redundancy, so that even if the largest generator or transmission line fails, the system remains reliable. The contingencies that could threaten achieving 350 ppm are listed (1-5) below, then the business- as-usual baseline and the six 350 ppm-compatible scenarios are described quantitatively in Table 1.

Constraints

1. Restricted availability of zero-carbon primary biomass resources. This could be a result of inaccurate technical assessments of resource potential, unexpected difficulties in developing a bio-energy economy, or discovery of unintended impacts on other land- uses that lead to restrictions on biomass resource development.

2. Low rates of electrification. Direct electrification of end-uses such as light-duty vehicles and space heating is a key strategy of energy system decarbonization. Slower than expected rates of electrification would challenge a low carbon transition as it

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would result in higher residual fossil fuel emissions that must be mitigated in other ways.

3. No new nuclear plants. Based on current cost forecasts for advanced (4th generation) nuclear facilities, it is expected that they would play a role in energy system decarbonization, especially in regions with limited renewable resource potential. Restricting new nuclear plant construction means that their role in a low carbon generation portfolio must be accomplished by carbon capture power plants or renewables. In this scenario we assume that nuclear plants already in operation will be operated and retired based on the schedule in the 2017 AEO.

4. No technological negative emissions technologies. “Tech NETS” includes biomass facilities (either fuel production or power generation) with carbon capture and sequestration or direct air capture with sequestration. Both of these technologies remove CO2 from the atmosphere, helping to offset any residual fossil fuel use in the economy. They are heavily relied on in many integrated assessment modeling (IAM) studies of deep decarbonization, which has drawn criticism. This scenario can be interpreted as a contingency test of what happens in the case of technological failure or social refusal of these approaches. While this scenario doesn’t employ Tech NETS, it does employ both carbon capture and carbon sequestration in other forms.

5. Low land NETS. Land NETS are strategies that use land-use management practices to increase terrestrial carbon sequestration. In this study we employ estimates of land NETS potential based on the literature to determine the remaining emissions budget for energy and industrial CO2. This scenario is used to assess whether the necessary energy and industrial CO2 mitigation can be achieved in the event that changes in land management practices can only produce 100 PgC of carbon sequestration globally by 2100. The changes to land NETS result in a reduced cumulative energy and industrial CO2 target and a net negative CO2 emissions target in 2050.

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Table 1 Scenario definitions and emissions limits

Scenario

Average annual rate of CO2 emission reduction

2020-2050 maximum cumulative fossil fuel CO2 (million metric tons)

Year 2050 maximum net fossil fuel CO2 (million metric tons)

Year 2050 maximum net CO2

with 50% increase in U.S. land sink (million metric tons)

Base

Best case, all options available

6%

73,900

830

-250

Low Biomass

50% reduction in solid biomass feedstocks

6%

73,900

830

-250

Low Electrification

10-year delay in rates of electrification, all sectors

6%

73,900

830

-250

No New Nuclear

No new nuclear plants are constructed

6%

73,900

830

-250

No Tech NETS

No negative emissions from BECCS or DAC

6%

73,900

830

-250

Low Land NETS

Additional global land sink limited to 100 Pg(C)

12%

57,000

-200

-450

2.2. Modeling Methods and Data Sources

This section summarizes the modeling methods used in this analysis. Further detail on all modeling tools and data sources is available in the Technical Appendix to this report.

2.2.1. EnergyPATHWAYS

EnergyPATHWAYS is a bottom-up energy sector scenario planning tool. It performs a full accounting of all energy, cost, and carbon flows in the economy and can be used to represent both current fossil-based energy systems and transformed, low-carbon energy systems. It

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includes a granular technology representation with over 380 demand-side technologies and 100 supply-side technologies in order to represent all producing, converting, storing, delivering, and consuming energy infrastructure. It also has very high levels of regional granularity, with detailed representations of existing energy infrastructure (e.g., power plants, refineries, biorefineries, demand-side equipment stocks) and resource potential. The model is geographically flexible, with the ability to perform state-level and even county-level analysis. For this report, the model was run on a customized geography based on an aggregation of the EPA’s eGRID (U.S. Environmental Protection Agency 2018) geographies, as shown in Figure 2. The aggregation was done for computational purposes to reduce the total number of zones to a manageable number. EnergyPATHWAYS and its progenitor models have been used to analyze energy system transformations at different levels, starting in California (Williams et al. 2012) then expanding to U.S. wide analysis (Williams et al. 2014; Risky Business Project 2016; Jadun et al. 2017) and other state and regional analyses. The model has also been used internationally in Mexico and Europe. In each context, it has been successful in describing changes in the energy system at a sufficiently granular level to be understood by, and useful to, sectoral experts, decision makers, and policy implementers.

Figure 2 Regional granularity of analysis.

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2.2.2. Regional Investment and Operations (RIO) Platform

EnergyPATHWAYS, described in the previous section, focuses on detailed and explicit accounting of energy system decisions. These decisions are made by the user as inputs to the model in developing scenarios. The Regional Investment and Operations (RIO) platform operates differently, finding the set of energy system decisions that are least cost. The rationale for using two models in this study is that energy demand-side decisions (e.g. buying a car) are typically unsuited to least cost optimization, because they are based on many socioeconomic factors that do not necessarily result from optimal decisions and are better examined through scenario analysis. However, RIO’s strength is in optimization of supply-side decisions where least cost economic frameworks for decision making are either applied already (e.g., utility integrated resource planning) or are regarded as desirable in the future. RIO is therefore complementary to EnergyPATHWAYS. We use RIO to co-optimize fuel and supply-side infrastructure decisions within each scenario of energy demand and emissions constraints. The resulting supply-side decisions are then input into EnergyPATHWAYS for energy, emissions, and cost accounting of these optimized energy supplies. RIO is the first model we are aware of to integrate the fuels and electricity directly at a highly resolved temporal level, resulting in a co- optimization of infrastructure that is unique and critical for understanding the dynamics of low- carbon energy systems.

RIO works with the same geographic representation as EnergyPATHWAYS. Each zone contains: existing infrastructure; renewable resource potentials and costs; fuel and electricity demand (hourly); current transmission interconnection capacity and specified expansion potential and costs; biomass resource supply curves; and restrictions on construction of new nuclear facilities.

2.2.3. Key References and Data Sources

The parameterization of EnergyPATHWAYS and RIO to perform U.S. economy-wide decarbonization analysis requires a wide variety of inputs and data sources. We describe the full breadth of these data sources in the Appendix. There are, however, a few principal sources that are central to understanding and contextualizing our results. First and foremost, we utilized the 2017 Annual Energy Outlook (U.S. Energy Information Administration 2017), which

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includes detailed long-term estimates of economic activity, energy service demand, fuel prices, and technology costs. This allows us to compare our results to the principal energy forecast provided by the United States Government. Renewable costs and resource potentials are derived from National Renewable Energy Laboratory sources including the 2017 Annual Technology Baseline (National Renewable Energy Laboratory 2017) and input files to their ReEDS Model (Eurek et al. 2017). Biomass resource potential and costs are taken from the U.S. Department of Energy’s Billion Tons Study Update (Langholtz, Stokes, and Eaton 2016). In all cases we have sought to use thoroughly vetted public sources, which tend to be conservative about cost and performance estimates for low-carbon technologies.

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3. Results

3.1. Emissions

Emissions trajectories for energy and industrial CO2 emissions are shown below for all 350 ppm scenarios. (For net emissions including the negative emissions from Land NETS (enhanced sink), see Table 1). In all scenarios, we find it to be technically feasible, from the standpoint of a reliable energy system that meets all forecast energy service demand, to reach emission levels consistent with the 350 ppm target (Figure 3).

Figure 3 CO2 emissions trajectories

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All scenarios show broadly similar emissions trajectories, but they also contain differences in timing and carbon reduction magnitude. Scenarios that reduce the cost-effective mitigation options available before 2035 (i.e., Low Biomass and Low Electrification) show delayed emissions reductions, compensated by deeper reductions in later periods to hit the same cumulative CO2 budget. Those scenarios that remove options that are critical in the post-2035 time frame (i.e. No New Nuclear and No Tech NETS) show the opposite trend—larger reductions in near term CO2 reductions to accommodate the higher cost of deeper emissions cuts in the long-term. Scenario-specific findings include:

  • The Low Land NETS scenario, with a smaller cumulative CO2 budget by mid-century, requires a steeper and deeper trajectory of emissions reductions from energy and industry than do the other scenarios, and thus requires higher levels of mitigation. This scenario requires that energy and industrial emissions become net negative after 2040, reaching -200 MMT CO2 per year in 2050, and remaining at that level through the rest of the century in order to meet the cumulative budget for the whole 2020-2100 period.
  • The Low Electrification scenario shows the slowest rate of emissions reduction through 2035, with few cost-effective options for achieving the rate of transformation seen in the other cases with higher electrification rates. Post-2035, electrification levels catch up and the scenario employs more direct air capture than other scenarios to accelerate the mitigation trajectory.
  • The Low Biomass scenario also shows a slower rate of emissions decline, as the biomass resources needed to displace fossil fuels directly are not available in sufficient quantity. The alternative strategy of electric fuels does not become cost-effective as a mitigation option until later in the period, when renewable penetrations increase and the electric fuel load can contribute to electricity balancing. The scenario uses direct air capture in the later periods as well, in order to accelerate the mitigation trajectory.
  • The No New Nuclear scenario sees emissions decline faster than in other scenarios. This is because displacing residual fossil fuel on the electricity system is cheaper than attempting to achieve the same levels of electricity decarbonization in 2050 without the availability of nuclear. This scenario therefore finds a cost-effective route to have a slightly steeper slope but reach a less deep level by 2050.

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• The 350 – No Tech NETS scenario also has a steeper initial trajectory relative to the Base scenario, because without Tech NETS, it becomes more expensive to reduce emissions in the long-term. Thus, the model trades higher near-term emission reductions for additional emissions budget in 2050.

Figure 4 shows the cumulative 2020 – 2050 emissions path of each mitigation scenario. The shape of cumulative emissions show how critical early action is for achieving 350 ppm goals, even with very aggressive climate mitigation. The first five years, 2020-2025, consumes over one-third (25 MMT) of the Base scenario budget, and the first decade, 2020-2030, consumes almost two-thirds.

Figure 4 Cumulative CO2 emissions trajectories

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3.2. System Costs

Cost assessment of the 350 ppm scenarios is critical for assessing the potential economic and societal impacts of achieving a 350 ppm-compatible pathway, even if the technical feasibility of the pathway can be demonstrated. We apply a few different cost metrics to assess the economic feasibility of such a transition. First, we find the net cost of decarbonizing energy and industry to be consistent with results from other analyses of this type, using the metrics of incremental costs ($ per year) and incremental costs as a percentage of GDP per year (Figure 5). Incremental costs are calculated by comparing the cost of producing and using energy in each scenario compared to the baseline scenario derived from the AEO, which has no carbon constraint. Incremental cost includes the capital and operating costs of all low carbon energy supply infrastructure and demand-side equipment (e.g. electric vehicles and heat pumps) in comparison to the cost of the less efficient or carbon emitting reference technology that it replaces.

In all but one case these costs peak at less than 2% of the forecast GDP in 2040 (approximately $600B) annually. The Low Land NETS case is the only case that exceeds this value, with a 2040 value approaching 3% of GDP. This result emphasizes the value of negative emissions from land-use in managing the costs of decarbonizing the U.S. energy economy. All cases show the same peak in 2040, with continued cost declines of low-carbon technologies (renewables, electric vehicles, etc.) reducing the incremental cost compared to the fossil fuel baseline by 2050. We make no assessment of the human and environmental co-benefits (including, for example, avoided costs of climate impacts, national security benefits, and health benefits of improved air quality) associated with these emissions reductions, as such an assessment is outside of the scope of this analysis. However, such assessments have been made elsewhere (Risky Business 2015).

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Figure 5 Annual net system costs in $2016 and as % of GDP

Second, we assess the total spending on the energy system (including carbon capture costs) as a share of GDP and compare that to historical levels of spending on energy. Incremental demand-side costs, such as the cost premium to purchase a high efficiency appliance, are assessed as an energy resource in this context, so that the incremental costs of electrification and efficiency are also treated as spending on energy. Figure 6 shows the results for the six 350 ppm scenarios and the baseline scenario relative to historical U.S. energy spending as a % of GDP going back to 1970. In all cases, the spending on energy in 350 ppm-compatible scenarios is lower than historical peaks. Even in the highest cost case, the peak is only equivalent to 2009 spending levels as a % of GDP. This is a measure of the economic feasibility of energy system transformation, a result arising from cost declines in renewables and electric vehicles (batteries), the continued transition of the U.S. towards a service economy, and the expected continuation of low natural gas prices which helps to manage overall energy system costs even in the 350 ppm-compatible scenarios.

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Figure 6 Total energy system costs as % of GDP – modeled and historical

Modeled

Historical

Third, while the overall system costs of 350 ppm pathways are within the range of historical values for the U.S., the way that money flows within the energy economy changes substantially. In the low-carbon economies represented by the 350 scenarios, low-carbon technology investments are substituted for fossil fuels. This transformation is shown in Figure 7 with large new investments in biofuels, demand-side equipment, electric fuels, the electricity grid, and low-carbon generation being offset by dramatically reduced spending on coal, natural gas, and oil, especially the refined oil products gasoline, diesel, and jet fuel.

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Figure 7 Components of net energy system costs for all cases

3.3. Energy Transition

Transformation of the U.S. energy system occurs on both the demand and supply side of the system. Final energy consumption rapidly transitions away from direct combustion of fossil fuels towards the use of electricity (e.g. from gasoline powered vehicles to EVs) and other low carbon energy carriers, accompanied by a supply-side transition from primarily fossil sources of energy towards zero-carbon sources such as wind, solar, biomass, or uranium. Figure 8 shows these simultaneous transitions, with the left-hand side showing primary energy supply and the right-hand side showing final energy demand.

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Figure 8 Primary and final energy demand for all cases from 2020 – 2050

Figure 9 shows the transition of the energy mix over time, as reflected on both the supply and demand sides of the system. The three columns show energy divided into the main energy carrier types (liquids, gases, and electricity). The top row shows the transition in final energy demand over time, broken down by sector. The use of liquids and gases falls dramatically over

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time as a result of electrification, while electricity use increases for the same reason. The second row shows the evolving mix of energy types used to meet the final demand shown in the first row. The third row shows the average emissions intensity of the energy supply mix in the second row, which declines over time as lower carbon sources are used. The bottom row shows the total emissions over time from each of the main energy carriers, the product of the total amount of each used times its emissions intensity.

Figure 9 Components of emissions reduction for liquids, gas, and electricity in the 350 – Base case

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3.4. Infrastructure

Accomplishing the transformation of the energy sector requires significant investments in low- carbon infrastructure and a transition away from the extraction of fossil fuels. We’ve noted the costs of these investments, and this section details the scale of required infrastructure in the key areas of demand-side equipment, low-carbon electricity generation, biofuels production, electricity storage, electricity transmission, hydrogen electrolysis, and direct-air capture facilities.

3.4.1. Demand-Side Transformation

In addition to employing many efficiency measures, primarily in electric-only end-uses like lighting, ventilation, and household appliances, the demand-side undergoes a large transformation in end-uses where there are direct electric alternatives to fuel combustion. Transitions to electric technologies results in efficiency gains as well as a reduction in the amount of fuels that need to be displaces by bio-based or electric alternatives, reducing the overall cost of achieving emissions reduction goals. Figure 10 shows this transition for a variety of residential, commercial, productive, and transportation end-uses. Transportation electrification is the most critical sector to achieve these electrification goals in due to the volume of liquid fuels that it currently consumes.

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Figure 10 Electric Technology Stock Shares

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3.4.2. Low-Carbon Generation

All 350 ppm-compatible scenarios result in the addition of 2000 to 3000 gigawatts of renewable electricity capacity by mid-century – in comparison to total electricity generating capacity of all kinds of about 1000 gigawatts today – because renewables are the lowest-cost zero carbon resource available (Figure 11). This capacity takes the form primarily of new wind resources through 2030, since the majority of U.S. electricity demand (and population) is located in areas with better wind than solar resources, and thus provides better economics for wind. Wind generation is also able to reach higher shares of total generation (renewable penetration) than solar before encountering significant balancing challenges, because the production profile of wind is more evenly distributed throughout the day, in contrast to the concentration of solar generation within a narrow band of hours. Penetrations of solar beyond a certain percentage requires complementary balancing resources, such as energy storage or flexible loads, to avoid curtailment and enable full utilization of the resource.

New renewables built after 2040 are primarily solar PV because: (1) the supply of new low-cost wind resources is exhausted by this point; (2) solar costs continue to decline; and (3) the system’s growing electric fuels production capacity can utilize larger quantities of daytime solar electricity production. Offshore wind is used as a resource in the Low Land NETS and No New Nuclear scenarios, primarily in the Northeast (New York and New England). Higher penetrations of offshore wind would be seen if onshore wind becomes difficult to site, or cost declines for offshore wind turn out to be greater than anticipated.

The scenarios in which new nuclear generation is permitted to be built also see an expansion of nuclear, though the importance of new nuclear is less critical than some of the constraints in other scenarios, as the No New Nuclear scenario shows. A relatively modest increase in the deployment of new renewables above the level in the Base 350 ppm scenario compensates for the constraint imposed by No New Nuclear.

Carbon capture and storage (CCS) is much less important in power generation in all scenarios than it is for capturing the CO2 streams from biofuel refining and other industrial activities. High

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penetrations of renewables, which result in frequent surpluses of low marginal-cost energy, mean that CCS generators don’t achieve high capacity utilization. This makes CCS an expensive option for limiting emissions from electricity due to its high capital cost spread over a limited number of hours. CCS electricity generation is generally found in regions with restricted new nuclear build and within regions that have limited wind resources where it can provide a consistent source of off-peak power.

Figure 11 Low-Carbon generation capacity growth

3.4.3. Biofuels Production

3.4.3.1. Liquids

The expansion of biofuels production is a critical strategy for reducing emissions as the economy transitions towards high levels of electrification. Even at the conclusion of the electrification transition, liquid biofuels play an important role in mitigating emissions in hard- to-electrify end-uses such as heavy industry and aviation. The United States already has a biofuels industry of significant size, but it primarily produces corn-derived ethanol, a relatively

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high carbon form of biofuel over its lifecycle. As light-duty vehicle travel is electrified, the demand for liquid transportation fuels decreases, and this sector is reduced in importance. This analysis did not find cellulosic ethanol to be a critical strategy during the transition from gasoline to electricity due to the high cost of developing cellulosic refining and distribution, and the pace of electrification (the market-size for gasoline alternatives shrinks very quickly). This analysis also finds that the focus of biofuels should be on displacement of liquid fossil fuels, rather than gaseous fuels. This is due to: (a) natural gas has a lower cost per MMBtu than refined liquid fuels; and (2) natural gas CO2 emissions are lower than liquid fossil fuels on an energy basis. Liquid biofuels production is shown Figure 12. While this represents a rapid expansion of production capacity of up to 4 million barrels per day by 2040, it is still only a fraction of the current capacity of U.S. petroleum refineries. In cases where bio-energy carbon capture and storage (BECCS) is allowed, it dominates fuel production, which is understandable given the economic attractiveness of biorefineries for carbon capture, with concentrated CO2 streams and high utilization factors.

Carbon capture and storage (CCS) is distinct as a strategy from carbon capture and utilization (CCU) in this analysis. In CCU, the captured carbon is used in combination with electrically produced hydrogen to produce methane and other synthetic liquid or gaseous fuels that can be substituted for gasoline and diesel or natural gas, respectively. Bio-energy carbon capture and utilization (BECCU) is used either when the marginal cost of sequestration becomes high or in regions where there are substantial biomass resources but low sequestration potential, for example due to geographic unsuitability. In the No Tech NETS case, BECCS is primarily displaced by biofuels production without capture, but this case also has the highest BECCU production capacity.

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Figure 12 Liquid biofuels production capacity in Million Barrels Per Calendar Day (MBCD) and as a % of current U.S. petroleum refining capacity

3.4.3.2. Gaseous Fuels

The analysis finds that the priority use for biomass feedstocks is liquid fuel production, but in some scenarios, there is also limited production of gaseous biofuels. The Low Land NETS case requires a displacement of almost all fossil fuels, including gas in the pipeline, and so we see deployment of biogas in this case (as well as significant amounts of fuels produced from electricity). This is shown in Figure 13 both in units of TBTU/Year, which is more appropriate for gaseous fuels, and also on the right-hand axis in units of million barrels per calendar day, for purposes of comparison to the scale of liquid biofuels.

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Figure 13 Gaseous biofuels production capacity in TBTU/Year and as a % of current U.S. refining capacity

3.4.4. Electricity Storage

Electricity storage provides capacity to balance the electricity system during times of low renewable energy output. Battery storage is the lowest-cost capacity resource available to address system peaks of limited duration. For this reason, it is deployed on a significant scale even in the Baseline scenario which has no carbon constraints (Figure 14). We find that significant amounts of new electricity storage are needed in all 350 ppm-compatible scenarios starting in 2030, and this storage is deployed with an average duration of four to six hours. Without a significant technological breakthrough, however, the high cost of stored electricity

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limits its value as a long-duration balancing resource (i.e. on scales from days to months of energy shortfalls from renewables). Thus, it operates primarily as a diurnal resource, using excess solar generation in the middle of the day on a consistent basis to avoid curtailment and to displace thermal generation off-peak (capacity and energy).

Figure 14 Energy storage capacity in gigawatts, gigawatt-hours, and average duration

3.4.5. Electricity Transmission

Many deep decarbonization analyses emphasize the importance of transmission to match the supply and demand for renewable electricity spatially across the country. Our findings are consistent with these studies in terms of the value of transmission as a resource. However, transmission has historically proven difficult to permit, site, and build in the U.S., especially in the case of large inter-regional lines. For this reason, in our analysis we have constrained new transmission construction to a doubling of currently existing capacity between regions. This is likely conservative, as some regional interties are quite small at present, not because of being technically or societally difficult but due to a lack of economic justification. However, this

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admittedly arbitrary limit serves as a useful proxy for barriers to transmission construction, and at any rate is non-binding (does not constrain inter-regional flows) in almost every instance.

Limits on new transmission build may present less of a handicap in our analysis compared to some because our analysis employs other methods to transfer renewable energy between regions, namely through pipelines in the form of fuels produced from electricity (storage of such fuels within regions also provides a form of renewable energy storage). Still, we do see significant new interties between some regions, with almost all regions seeing some new economic transmission build by 2050, in all scenarios. Figure 15 shows all the regional intertie capacity built from 2020 to 2050. The largest such builds are between the CAMX region (California and northern Baja California), which requires imports of wind energy from NWPP (Northwest) and AZNM (Arizona and New Mexico) in all scenarios. There is also major development of transmission capacity between the RFC and SR (Mid-Atlantic and South- Atlantic) regions in the Low Land NETS scenario, related to the higher use of DAC and production of electric fuels in this scenario.

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Figure 15 Incremental electric transmission capacity (gigawatts) by corridor

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3.4.6. Hydrogen Electrolysis

The use of electricity to produce hydrogen from the electrolysis of water plays a key role in balancing the electricity system during periods of renewable energy overgeneration. The hydrogen produced is then used to create synthetic fuels that can be used in applications that are difficult to electrify. As illustrated in Figure 16, all pathways require more than 100 GW of electrolysis capacity, and the cases that require substantially more electric fuel production – Low Biomass, Low Electrification, Low Land NETS, and No Tech NETS – have up to 400 GW. This situation can be said to constitute a type of “hydrogen economy,” but not the type that has typically been discussed in the literature, in which hydrogen itself becomes a principal energy carrier for end uses. Many of the objections raised regarding that form of hydrogen economy center on the difficulty of developing a delivery infrastructure for this highly flammable fuel. Instead, in our 350 ppm scenarios electrically produced hydrogen is used as a feedstock in the production of renewable liquid and gaseous fuels that already have existing delivery mechanisms. Hydrogen can be combined with captured carbon dioxide to produce methane, the main component of natural gas, and further chemical synthesis using the Fischer-Tropsch process can produce synthetic liquid fuels comparable to (and interchangeable with) refined petroleum products, including diesel, gasoline, and jet fuel. Produced hydrogen can also be injected into a natural gas pipeline directly (limited to 7% by energy, which research has shown can be blended with fossil-based or synthetic natural gas without damaging end use equipment or delivery infrastructure). The hydrogen intended for pipeline injection is represented by the blue wedge in Figure 16. In sum, the “hydrogen economy” used in the 350 ppm scenarios avoids many of the infrastructure challenges typically associated with the use of hydrogen at large scale.

The production of electrolytic hydrogen and synthetic fuels provide the primary method of long-duration energy storage for a system with high penetrations of renewable generation. When peak electricity generation exceeds demand, the extra electricity is used to synthesize these fuels. These fuels can be used directly to meet demand for liquid and gaseous fuels and— to a limited extent— also be used to produce electricity at times of fallow renewable production. However, unlike previous hydrogen economy conceptions, in the 350 ppm scenarios the principal mechanism by which electric fuels balance the electricity system is not

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round-trip electricity storage (production and storage, then burning in a power plant to produce more electricity), but instead by enabling the economically efficient over-building of renewable resources, in which curtailment (wasted energy) is minimized because the energy is used to produce fuels used elsewhere.

Figure 16 Capacity of hydrogen electrolysis for pipeline injection and synthetic fuels

3.4.7. Direct Air Capture

Direct air capture (DAC) is the removal of CO2 directly from ambient air. It has traditionally been imagined as a post-2050 technology, as 2oC scenarios called for achieving net-zero and net-negative emissions levels in the 2070 time frame. This analysis, however, demonstrates a role for DAC even before a net-zero economy is reached (Figure 17). In scenarios where there are insufficient biomass-based alternatives to replace fossil fuels (Low Electrification and Low

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Biomass), DAC plays a role in accelerating the transition necessitated by the cumulative emissions cap, either by creating a carbon feedstock used for electric fuel production (DAC for utilization) or through geological carbon sequestration. We find that a heightened emphasis on the early commercialization of DAC is warranted due to its role as an accelerator of the overall transformation, as well as its obvious role as a technological backstop in the event of such contingencies as slower electrification or limited biomass deployment.

Figure 17 Direct air capture capacity for sequestration and utilization (MMT/Year)

Historically there has been reticence to treat DAC as a legitimate portfolio technology for achieving deep emissions reductions, not necessarily for reasons of technological maturity or acceptance but because of “moral hazard”: the not unwarranted concern that the presence of this technology could be used to justify continued unabated combustion of fossil fuels. Our analysis, however, shows that there is clearly a place for DAC in the rapid transition to low-

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carbon energy systems, not as an alternative to decarbonization but as a complementary technology to hasten energy decarbonization and increase sequestration. Our analysis also shows that DAC pairs best economically with low-cost zero carbon resources such as wind and solar, because DAC (like hydrogen electrolysis) is a large industrial load that has high variable costs relative to fixed costs, and can therefore operate flexibly at less than full utilization, taking advantage of periods of renewable overgeneration. Alternative carbon capture scenarios in which grid electricity continues to be provided by fossil thermal generation do not offer the same economic opportunities.

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4. Discussion

In addition to high-level summary results presented above, this discussion details additional features and components of low-carbon energy systems that can achieve 350 ppm trajectories in the U.S.

4.1. Four Pillars

Deep decarbonization analyses have relied on three primary strategies for achieving emissions targets: (1) electricity decarbonization, the reduction in the emissions intensity of electricity generation; (2) energy efficiency, the reduction in units of energy needed to provide energy service demands; and (3) electrification, the conversion of end-uses from fuel to electricity. These have been referred to as the “three pillars” and the use of these strategies to achieve deep decarbonization is a robust finding across many jurisdictions both domestically and internationally. Under our scenarios, which assume EIA projections for economic growth and increased consumption of “energy services”, achieving 350 ppm requires the inclusion of a fourth pillar, carbon capture, which includes the capture of otherwise emitted CO2 from power plants, industrial facilities, and biorefineries. It also includes the use of direct-air capture facilities to capture carbon from the atmosphere. Once captured, this CO2 can either be utilized in the production of synthesized electric fuels or it can be sequestered. Both strategies are used extensively in the scenarios analyzed here.

Figure 18 below shows the four pillars of decarbonization employed in the Base scenario. The emissions intensity of electricity has declined to less than 50 tonnes/GWh in 2050 from 350 tonnes/GWh in 2020, which is itself less than the current U.S. average of 424 tonnes/GWh in 2016 (U.S. Energy Information Administration 2018). One of the principal strategies employed in the early years is a change in the merit order dispatch (the prioritization used by system operators to determine the order in which electric generation is employed to meet demand) so that electricity generation from gas plants is prioritized over generation from coal plants. This

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accounts for the difference between 2020 Electricity Decarbonization in Figure 18 and 2016 historical. Energy consumption per dollar GDP, one metric for energy efficiency, decreases substantially from 3.2 in 2020 to 1.2 in 2050. This is due to significant same-fuel energy efficiency, economic transition towards services, and direct electrification of end-uses, which contributes efficiency gains over fuel alternatives. Electricity, used either directly (e.g. in electric vehicle) or as an electrically produced fuel represents almost 60% of final energy demand by 2050. Carbon capture contributes 800 MMT of emissions reductions by 2050, either when directly sequestered or when utilized for making synthetic electric fuel.

Figure 18 Four pillars of deep decarbonization in the 350 – Base case

4.2. Regional Focus

Our current energy economy exhibits significant regional variation in terms of energy demand, energy supply, and overall energy costs. The future energy economy will exhibit these same regional variations and it is worth identifying geographic regions that may be at the center of the new energy economy. While all regions require significant investment in generation resources to decarbonize their sources of supply, some regions, due to particular resource endowments, will see additional investment as they become both the center of the fuel

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production and direct air capture sectors. These regional dynamics are illustrated in Figure 19 for the Low Land NETS case, which requires the most significant infrastructure investments of all the cases and therefore shows the regional dynamics most clearly.

Figure 19 Regional infrastructure needs in the 350 – Low Land NETS case

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Zero-carbon generation follows a predictable pattern of resource endowments, with regions that have access to the wind-belt extending from Texas up through Wyoming developing wind- heavy portfolios. The Southwest and Southeast have solar-heavy portfolios. The Northeast relies primarily on wind, both onshore and offshore. The Southeast and Midwest also rely on nuclear energy in this case.

Biofuels production will be concentrated in areas with significant biomass resources, primarily the Midwest and the Southeast. In addition, biofuels production is best located in areas with available saline aquifers in which captured CO2 can be stored. Electric fuels production will be determined by availability of CO2 as well as grid conditions that support low-cost electrolysis. These conditions include either low-cost solar or wind generation, which explains the high concentration of electrolysis in the desert southwest, as well as in the wind-belt. One additional consideration not modeled explicitly here is the availability of water, which may affect siting of electrolysis facilities as well. Direct air capture facilities will depend similarly on low-cost renewables as well as the availability of saline aquifers for sequestration or hydrogen for synthetic electric fuels production.

4.3. Electricity Balancing

Electricity balancing, which is the matching of electricity supply and demand at all time-scales is one of the principal technical and economic challenges of decarbonization. The systems modeled here have a large percentage of non-dispatchable generation resources. On important characteristic is that variable costs for these resources are low and curtailing production represents lost economic value. In many studies of low-carbon electricity systems, the principal resource used to balance these types of systems is electricity storage (batteries, pumped hydro, etc.). However, this is an incomplete toolkit, specifically when dealing with imbalances that can persist over days and weeks. This analysis expands the portfolio of options available to address the balancing challenge, employing solutions such as flexible electric fuel production, dual-fuel boilers systems (i.e. gas and electric), and direct air capture in addition to traditional solutions such as batteries, thermal generation, and transmission expansion. Figure 20 shows balancing behavior in the ERCOT (Texas) dispatch region in 2050 in the Low Land NETS case.

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The range of daily system balancing operations are shown by the set of transparent lines, while the daily average behavior shown by the thicker opaque lines. It can be seen, in this scenario, the lion’s share of balancing needed is provided by direct air capture and electrolysis loads. Thermal generation is needed infrequently but must be maintained on the system for purposes of reliability. Storage exhibits a diurnal pattern, common across all resources, of increased load in the middle of the day responding to regular solar overgeneration conditions. Due to limited physical interties between ERCOT and other regions, transmission plays a relatively minor role here.

Figure 20 Electricity balancing from key technologies in ERCOT in 2050 in the 350 – Low Land NETS case

One can see the relative economics of building each type of capacity to balance load by looking at the average operations of each resource compared to the maximum operations in any single period. This average operation represents the utilization factor of each resource (this is referred

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to as a “capacity factor” for generation resources). More expensive capacity such as direct air capture facilities operate at a much higher utilization factor than do cheaper forms of capacity such as electrolysis or batteries.

All these solutions contribute to addressing the balancing challenges posed by large amounts of non-dispatchable resources. Figure 21 shows the overall contribution by resource type, case, and year. How a resource contributes to electricity balancing is a function of its unique characteristics. Thermal generation and hydro contribute to balancing the system by generating during periods of some combination of low renewable output and high load; storage moves energy from overgeneration periods to hours where thermal generation would otherwise be needed; flexible fuel production and direct air capture balance the system by soaking up overgeneration and turning it either into electric fuels or sequestering carbon directly; finally, renewable curtailment balances the system by reducing overgeneration when there is no economic case for utilizing it.

The relative contributions are unique to each case and resource build, but there are commonalities. First, the scale of balancing needs in 2050 compared to 2020 is drastically different. That’s because the net-load signal that the system is trying to balance is significantly more volatile, as renewables make up a larger portion of generation.

In all cases, thermal generation provides most of the balancing through 2030 before the significant renewable penetration that ramps up post-2030. Flexible electric loads (Ex. fuel production and duel fuel boilers) play a role in all cases and become the dominant resource in cases where they are needed to displace fossil fuels. Storage plays a key role but not a solitary one, as its primary use is to operate diurnally and balance out solar overgeneration. Renewable curtailment is present in all cases.

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Figure 21 Balancing contribution by resource (TWh)

Figure 22 shows the initial net-load signal for ERCOT across sample days in 2020, 2030, and 2050. In 2020, variation is primarily a result of the electricity demand shape. By 2030 and certainly by 2050, that load variation is swamped by variability in renewable output, with average daily swings in the net load of almost 100 GW.

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Figure 22 Net Load in 2050 in ERCOT in the 350 – Low Land NETS case

Even with the magnitude of the different balancing solutions employed in the 350 ppm scenarios, there is in all cases still some level of renewable curtailment that is economically efficient. That is, during periods of significant or sustained overgeneration, the capacity that would be needed to be built to fully utilize that renewable energy generation is not economic. Economic curtailment exhibits a distinctly seasonal pattern, with much of it occurring in the spring and fall, shown in Figure 23. This is due to either generally high renewable production (wind, in particular, has a strongly seasonal shape), generally low-load conditions (i.e. no heating or air conditioning load), or a combination of the two. In regions with significant hydro resources, seasonal release requirements can contribute to spring overgeneration conditions as well. The baseline scenario has very low curtailment because without carbon constraints, the impetus to push renewable penetrations to levels that result in curtailment is diminished.

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Figure 23. Renewable resource curtailment patterns in 2050

4.4. Sector Integration

The 350 ppm-compatible scenarios demonstrate the need for sectoral integration in deeply decarbonized economies. The lines between traditionally distinct sectors become blurred when decisions and their effect are so tightly linked across sectors. For example, the need for electric fuels to replace fossil liquid or gaseous fuels has a huge impact on renewable resource needs in the electricity sector, as well as on the need for supplementary balancing resources such as electric storage. Electric fuel production even competes with the need for transmission, as energy can instead be transferred between high renewable production zones either as gaseous or liquid fuels.

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The demand-side transformation, especially rapid electrification in buildings, transportation, and industry, will also require sectorally integrated planning both to ensure that new generation resources are developed to meet the growing demand, and also to plan distribution system upgrades and charging infrastructure, and to leverage the ability of new electric loads (specifically, space heating, water heating, and vehicle charging) to operate flexibly. Figure 24 shows the rate of load growth in each of our cases, with rates exceeding 4% in some cases during the 2030 to 2040 timeframe.

Figure 24 Electric load growth by year (%)

Allocation of limited biomass resources is another area in which cross-sector integration is critical. Some jurisdictions have undertaken policies that emphasize 100% renewable electricity. The ambition of these types of targets is consistent with the challenge of deep emissions reductions targets, but 100% renewable or zero-carbon electricity can be regarded de facto as a

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biofuels allocation policy—achieving a zero emissions target requires some portion of biomass to be burned in electric generators rather than used as liquid biofuels. Allocation of biomass towards liquids, however, might be lower cost and provide the same overall emissions reduction, illustrating the fungibility of emissions reductions between sectors.

4.5. Circular Carbon Economy

The circular carbon economy, or CCE, is a term for an energy economy that uses CO2 embodied in biomass feedstocks or through direct air capture to produce electric fuels. Given existing energy service delivery mechanisms, both fuel delivery and fuel consumption infrastructure, large portions of energy demand in 2050 is still met as it is today, with liquid and gaseous fuels. These fuels can no longer be fossil-based and so require drop-in, non-fossil-based alternatives.

These fuels begin as electrolyzed hydrogen before they are catalyzed with captured CO2. Critical sources of carbon for utilization in this analysis are biorefineries and direct air capture facilities. Biorefineries that are located in areas with limited sequestration potential are specifically good candidates as they can run at high utilization factors and have extremely concentrated sources of CO2 emissions for low-cost capture. DAC facilities with utilization are also employed to a lesser extent as seen in Figure 17. This is a critical strategy in the long-term, even before net-zero emissions economies have been achieved.

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5. Conclusions

Based on the analyses described in this report, we conclude that achieving U.S. emissions consistent with 350 ppm globally is technically feasible. This result is robust against five key strategies not materializing at the scale expected in the base case – biomass deployment, electrification, new nuclear deployment, technological NETS deployment, and land NETS implementation. While feasible, achieving the outcomes modeled here requires ambitious early action in order to maintain reasonable trajectories towards mid-century. Without this ambitious early action, it will require the achievement of net-negative emissions energy economies before mid-century and then sustain them at these low-levels through the end of the century.

These scenarios are intended to answer the question of whether the U.S. and its anticipated growth in consumption of energy services can develop an energy system that is consistent with 350 ppm in the atmosphere and we conclude that it can. We do not assert the necessity of, nor model the effects of, behavioral changes and energy service demand reductions (i.e. lower VMTs, lower temperature setpoints, lower consumption of material goods) though all would contribute to lower system costs, lower material requirements, lower infrastructure needs, and could improve quality of life in ways not measured by this analysis. There are co-benefits aside from CO2 including improved air quality, energy price predictability, job creation and energy security that are not modeled here.

We observe large shifts in energy spending away from fossil fuels towards fixed infrastructure, both demand-side (electric vehicles, heat pumps, etc.) and supply-side (low-carbon generation, hydrogen electrolysis, electric storage, etc.). That said, the overall net costs of decarbonization found here are well within the range that a major industrial economy can manage, and indeed that the U.S. has managed historically. Based on this analysis, achieving 350 ppm-compatible pathways would maintain energy system costs within the low-range of historical values.

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5.1. Key Actions by Decade

In conclusion, “Key Actions by Decade” below describes the sequence of actions needed to achieve a 350 ppm trajectory in the U.S. The list is by no means comprehensive, but it does highlight the most important physical transformations required and when each needs to occur. These actions make up a general blueprint for the U.S.—regional differences in resource endowment, existing infrastructure, and societal preferences will mean that not every step is universally relevant. In some cases, these actions need to build on one another, so that later actions are path dependent on earlier successes.

This and previous research have indicated that many pathways to decarbonize the energy system exist. The list below represents our current best understanding of how to achieve mid- century carbon targets at lowest cost while delivering the energy services projected in the 2017 AEO. Inherently this blueprint relies on projections of cost and performance that are unknowable. Despite this, a long-term blueprint is essential because of the long lifetimes of infrastructure in the energy system—making decisions that have long-term consequences using imperfect information is an enduring challenge. Uncertainty means an energy system plan is never static. Thus, we expect future work to revise this plan as decisions get made, technology improves, energy service projections change, and as our understanding of the climate science evolves.

From a policy perspective, this provides a list of the things that policy needs to accomplish, for example the deployment of large amounts of low carbon generation, rapid electrification of vehicles, buildings, and industry, and building extensive carbon capture, biofuel, hydrogen, and synthetic fuel synthesis capacity. Some of the policy challenges and opportunities that must be managed include: land use tradeoffs related to carbon storage in ecosystems and siting of low carbon generation and transmission; electricity market designs that maintain natural gas generation capacity for reliability while running it very infrequently; electricity market designs that reward demand side flexibility in high-renewables electricity system and encourage the development of complementary carbon capture and fuel synthesis industries; coordination of planning and policy across sectors that previously had little interaction but will require much

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more in a low carbon future, such as transportation and electricity; coordination of planning and policy across jurisdictions, both vertically from local to state to federal levels, and horizontally across neighbors and trading partners at the same level; mobilizing investment for a rapid low carbon transition, while ensuring that new investments in long-lived infrastructure are made with full awareness of what they imply for long-term carbon commitment; and investing in ongoing modeling, analysis, and data collection that informs both public and private decision-making. These topics are discussed in more detail in Policy Implications of Deep Decarbonization in the United States (Williams et al. 2015).

2020s

  • Begin electrification – Electrification of buildings, transportation, and industry is necessary for affordable decarbonization. The initial focus should be on making new buildings all electric and building markets to electrify vehicles of all types. The transportation electrification goal is not near-term carbon emissions reductions but instead transformation of an industry to eliminate carbon emissions in the long term as the carbon intensity of electricity drops. Replacing air conditioners or furnaces with heat pumps in existing buildings is also a priority, pushing a technology that has improved markedly in recent years to further maturation. Steps towards electrification also involve removing systemic bias preventing electrotechnology adoption that are often good intentioned around energy efficiency goals but self-defeating in the long term. Examples include providing incentives on high-efficiency gas furnaces but no such incentives on heat-pumps or policies that discourage electric utility load growth of any type.
  • Switch from coal to gas in electricity system dispatch – Dispatching gas in preference to coal is one of the most impactful and cost-effective ways to curtail carbon emissions in the near-term. Natural gas has approximately half the carbon intensity of coal but costs only slightly more on an energy basis at time of writing and is generally burned more efficiently than coal. Coal to gas switching in dispatch is distinct from retiring all coal, which will happen more gradually due to considerations on reliability and speed at which replacement generation can be built. Natural gas plants also are better complementary generation in the medium-term as renewable generation is deployed.
  • Build renewables and reinforce TX where possible – Due to their abundance and based on current cost projections, wind and solar will form the backbone of a future low carbon energy system. Meeting 2050 goals requires a truly enormous quantity of renewable deployment, which must accelerate. Complementarity between wind and solar profiles means both get built wherever possible, but regional specialization will

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occur depending on resource quality. Offshore wind should be emphasized in places, like the Northeast, where this resource holds promise as a vital part of the electricity system long-term. Transmission that connects renewable resources to loads takes time to permit and build and thus planning must start early for this critical infrastructure.

  • Allow gas build to replace retiring plants – Even in a future electricity system with 80%+ energy coming from renewables, difficult long-duration (seasonal) electricity balancing challenges mean that dispatchable thermal capacity that can be dispatched during fallow periods of renewable production will be a part of a low-cost energy system. Our modeling shows that an optimized pathway to deep decarbonization shows little change to gas capacity relative to today over the next 30 years but eventual retirement of all other fossil electricity generation.
  • Start electricity market reforms to prepare for a changing load & resource mix – Future electricity systems must accommodate rapid load growth from electrification, increasingly flexible demand, and increasingly inflexible supply resources. Fossil generation in the future without carbon capture will operate for far fewer hours than today making capacity markets more and more attractive. In those capacity markets the need to distinguish resources that can offer capacity over long durations will become important. Future energy markets must also compensate balancing services, with full symmetry between supply and demand side balancing.
  • Maintain nuclear – Nuclear is an important source of low-cost carbon free electricity and when possible to do safely, the lowest cost path to decarbonization involves maintaining these resources. Retiring nuclear to ‘make room’ for renewable resources is ultimately self-defeating. Reducing climate change should be the priority when weighed against nuclear accidents given relative risk and consequence except where specific circumstances dictate otherwise (E.x. reactors in active seismic zones). This is not an assertion of the safety of generation III nuclear but rather a recognition of the urgency of the latest climate science.
  • Pilot new technologies that will be deployed at scale after 2030 – Among these are carbon capture of many varieties including direct air capture, carbon storage and utilization including creating drop-in replacement fuels through methanation, and generation IV nuclear technologies.
  • No new infrastructure to transport fossil fuels – Consumption of every fossil fuel declines in a pathway to 350 ppm. Thus, new infrastructure to transport fossil fuels run a high risk of either becoming stranded or locking in a higher emission pathway. Some infrastructure built for a 20th century energy system is still useful in the 21st century such as natural gas storage and transmission pipelines and should be maintained.
  • Start building carbon capture on industrial facilities – Carbon capture on industrial processes should be prioritized because many processes result in higher CO2 concentrations than post-combustion capture on electricity generation and operate at

© 2019 by Evolved Energy Research 66

higher utilization factors, reducing cost, and because some industrial processes offer no ready alternatives making this type of carbon capture a necessary long-term strategy.

2030s

  • Large renewables push – The 2030s is when the bulk of new renewable generation is built. Renewable curtailment is a necessary if sometimes transient balancing solution while transmission is expanded, market rules with high variable generation mature, and other balancing solutions get built.
  • Reach near 100% sales on key electric technologies – All new vehicle sales must become electric or zero carbon compatible, for example fuel cells or biodiesel for heavy equipment. Similar transitions must occur in buildings for heating and cooking equipment. In industry electric or dual-fuel equipment should be installed for process heating and steam production which can be called upon based on electric system conditions (i.e. they can utilize overgeneration).
  • Start significant biofuel production in diesel & jet fuel – Diesel and jet fuel are two of the largest residual fuels after high electrification. Bio-fuels used as drop-in replacements for fossil are a major strategy for reducing emissions. In the 2030s both are beginning to be produced in significant quantities, often with carbon capture on the biorefineries.
  • Large scale carbon capture on industrial facilities – This completes the carbon capture on industry begun in the 2020s. By the late 2030s the marginal carbon abatement cost exceeds the capture cost for most industrial processes making this a cost-effective measure to pursue. The main challenge becomes geographic mismatch between where industry is located and where CO2 is sequestered or used.
  • Electrical energy storage for capacity – As fossil capacity retires, electric energy storage technologies are deployed at a modest scale for reliability and to assist with diurnal balancing between electricity supply and demand. The phrase ‘modest’ is used because energy storage technologies cannot cost effectively replace all types of other dispatchable generation without a major cost breakthrough in long duration storage. Just like in the 2020s, some new gas power plant capacity is needed. When the duration of need for dispatchable capacity is less than 8 hours, energy storage will most likely be the most cost-effective option, for anything longer than 8 hours, gas turbines are the cheapest option for the system.
  • Fossil power plants with 100% capture – If competitive with renewables and nuclear, fossil power plants with pre-capture or oxy technologies should start to be deployed. It’s possible that CCS technologies in electricity are unable to compete with a combination of renewables and energy storage, in which case most carbon capture stays focused on industry and refining.

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• Maintain nuclear – As in the previous decade, continue to maintain nuclear where safe to do so.

2040s

  • Reach near 100% stock penetration on electric technologies – The key building heating and transportation technologies that approached 100% new technology adoption in the 2030s have lifetimes of 10-15 years; and therefore, stock shares of these technologies should approach 100% in the 2040s based on natural replacement.
  • Deploy circular carbon economy – In the 2040s synthetic fuel production & direct air capture (DAC) become important strategies to further reduce emissions and to balance a system with high renewables. The degree to which each are needed is dependent on many factors including: how much sustainable biomass can be produced, how much electrification is achieved, how cheap and efficient can DAC become, how much annual sequestration potential is there and at what cost, and how cheap are renewables and competing balancing strategies?
  • Maintain/grow renewables together with new flexible loads – As synthetic fuel industrial loads grow it gives a new tool for balancing a grid composed of large amounts of variable generation. This, in turn, allows for further increases in renewables at low cost. Distributed fuel production also avoids the need for some new transmission.
  • Replace nuclear at the end of its lifetime – As generation three nuclear retires, it should be replaced with fourth generation nuclear technologies if possible. By the 2040s renewables make up most of all electricity generation. Because of high marginal balancing costs when installing further wind and solar, dispatchable zero-carbon technologies such a nuclear are highly competitive.
  • Fully deploy biofuels including bio-energy with carbon capture – Biofuel production and deployment reaches its limit in the 2040s. Biofuels find only marginal application in electricity because of higher value uses in transport and industry. Those industrial applications that can also deploy carbon capture allow opportunities of negative life- cycle emissions. Carbon capture on biofuel refining becomes an important technology.

© 2019 by Evolved Energy Research 68

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Technical Supplement

Figure 25 350 – Low Biomass Emissions Reductions Breakdown

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Figure 26 350 – Low Electrification Emissions Reductions Breakdown

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Figure 27 350 – Low Land NETS Emissions Reductions Breakdown

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Figure 28 350 – No New Nuclear Emissions Reductions Breakdown

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Figure 29 350 – No Tech NETS Emissions Reductions Breakdown

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Figure 30 Net Costs by Sector

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Figure 31 Four Pillars

Electricity Decarbonization

Energy Efficiency

Electrification

Carbon Capture

© 2019 by Evolved Energy Research 79

Figure 32 Final Energy Demand

© 2019 by Evolved Energy Research 80

Appendix

1. Scenario Descriptions

EnergyPATHWAYS scenarios consist of combinations of energy demand scenarios as well as emissions targets and other constraints applied to the entire energy economy. In this framework, we have three energy demand scenarios – Baseline, Base 350, and Low Electrification 350 – that are used for our seven energy economy scenarios. These relationships are described in the below table.

Table 2 Energy demand and energy economy scenarios

Energy Demand Scenarios

Energy Economy Scenarios

Baseline

Baseline

Base 350

Base 350

Base 350

No New Nuclear

Base 350

Limited Biomass

Base 350

No Tech NETS

Base 350

Low Land NETS

Low Electrification 350

Low Electrification

1.1 Energy Demand Scenario Descriptions

1.1.1 Baseline

This represents an assumption of stasis in terms of technology adoption. For example, gas storage water heaters in the residential sector are replaced with newer gas storage water heaters. These new technology vintages have changing parameters of cost and efficiency but

© 2019 by Evolved Energy Research 81

represent the same technology type and class (i.e. they use the same fuel and represent the same level of relative efficiency in the market).

1.1.2 Base 350

This scenario assumes rapid adoption of electrification technologies and high efficiency technologies where the end-use is already electric (i.e. refrigeration) or where complete electrification is infeasible. Adoption rates of these technologies accelerates through 2030, with the stock of these technologies lagging but making steady progress through 2050.

1.1.3 Low Electrification 350

This scenario assumes difficulty in inducing electrification of end-uses. Instead of adoption rates peaking by 2030, the adoption of these technologies is much slower, with peak adoption rates not being achieved until the 2050 timeframe. This slower rate of adoption leaves much more fuel combustion in intermediate years and also represents an incomplete electrification process by 2050, as much of the existing fuel combustion stock is still in service.

1.2 Demand-Side Mitigation Measures

1.2.1 Stock Rollover

The tables below show the stock shares (Table 3) and sales shares (Table 4) for three demand technology groups (Electrified Techs; HE Techs; Other Techs).13 The demand-side consists of over 380 technologies across all subsectors or end-uses, but we aggregate here for presentation purposes to show broader trends in our input values. The stock shares shown are determined by stock rollover assumptions specified in the measure for each technology as well as the lifetimes of the infrastructure and the methodology described in section 4.2.1.2.

13 Electrified Techs == Technologies that use electricity for end-uses where other fuels are competitors (i.e. water heating but not lighting); HE Techs == High efficiency technologies; Other Techs == Technologies not categorized as Electrified Techs or HE Techs.

© 2019 by Evolved Energy Research 82

Table 3 Stock shares

Sector

Subsector

Scenario

Technology Group

2020

2030

2040

2050

COMMERCIAL

COMMERCIAL AIR CONDITIONING

BASE 350

HE TECHS

11%

47%

84%

93%

COMMERCIAL

COMMERCIAL AIR CONDITIONING

BASE 350

OTHER TECHS

89%

53%

16%

7%

COMMERCIAL

COMMERCIAL AIR CONDITIONING

BASELINE

HE TECHS

11%

9%

9%

10%

COMMERCIAL

COMMERCIAL AIR CONDITIONING

BASELINE

OTHER TECHS

89%

91%

91%

90%

COMMERCIAL

COMMERCIAL AIR CONDITIONING

LOW ELECTRIFICATION 350

HE TECHS

11%

40%

79%

92%

COMMERCIAL

COMMERCIAL AIR CONDITIONING

LOW ELECTRIFICATION 350

OTHER TECHS

89%

60%

21%

8%

COMMERCIAL

COMMERCIAL COOKING

BASE 350

ELECTRIFIED TECHS

25%

57%

81%

81%

COMMERCIAL

COMMERCIAL COOKING

BASE 350

OTHER TECHS

75%

43%

19%

19%

COMMERCIAL

COMMERCIAL COOKING

BASELINE

ELECTRIFIED TECHS

25%

25%

25%

25%

COMMERCIAL

COMMERCIAL COOKING

BASELINE

OTHER TECHS

75%

75%

75%

75%

COMMERCIAL

COMMERCIAL COOKING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

25%

29%

55%

78%

COMMERCIAL

COMMERCIAL COOKING

LOW ELECTRIFICATION 350

OTHER TECHS

75%

71%

45%

22%

COMMERCIAL

COMMERCIAL LIGHTING

BASE 350

HE TECHS

39%

78%

81%

81%

COMMERCIAL

COMMERCIAL LIGHTING

BASE 350

OTHER TECHS

61%

22%

19%

19%

COMMERCIAL

COMMERCIAL LIGHTING

BASELINE

HE TECHS

43%

72%

75%

75%

COMMERCIAL

COMMERCIAL LIGHTING

BASELINE

OTHER TECHS

57%

28%

25%

25%

COMMERCIAL

COMMERCIAL LIGHTING

LOW ELECTRIFICATION 350

HE TECHS

39%

78%

81%

81%

COMMERCIAL

COMMERCIAL LIGHTING

LOW ELECTRIFICATION 350

OTHER TECHS

61%

22%

19%

19%

COMMERCIAL

COMMERCIAL REFRIGERATION

BASE 350

HE TECHS

10%

55%

96%

100%

COMMERCIAL

COMMERCIAL REFRIGERATION

BASE 350

OTHER TECHS

90%

45%

4%

0%

COMMERCIAL

COMMERCIAL REFRIGERATION

BASELINE

HE TECHS

10%

11%

14%

17%

COMMERCIAL

COMMERCIAL REFRIGERATION

BASELINE

OTHER TECHS

90%

89%

86%

83%

COMMERCIAL

COMMERCIAL REFRIGERATION

LOW ELECTRIFICATION 350

HE TECHS

10%

55%

96%

100%

COMMERCIAL

COMMERCIAL REFRIGERATION

LOW ELECTRIFICATION 350

OTHER TECHS

90%

45%

4%

0%

COMMERCIAL

COMMERCIAL SPACE HEATING

BASE 350

ELECTRIFIED TECHS

13%

43%

86%

98%

COMMERCIAL

COMMERCIAL SPACE HEATING

BASE 350

HE TECHS

1%

1%

1%

1%

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COMMERCIAL COMMERCIAL SPACE HEATING

BASE 350

OTHER TECHS

86%

56%

12%

1%

COMMERCIAL COMMERCIAL SPACE HEATING

BASELINE

ELECTRIFIED TECHS

13%

12%

12%

12%

COMMERCIAL COMMERCIAL SPACE HEATING

BASELINE

HE TECHS

1%

1%

1%

1%

COMMERCIAL COMMERCIAL SPACE HEATING

BASELINE

OTHER TECHS

86%

86%

87%

87%

COMMERCIAL COMMERCIAL SPACE HEATING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

13%

16%

44%

82%

COMMERCIAL COMMERCIAL SPACE HEATING

LOW ELECTRIFICATION 350

HE TECHS

1%

1%

1%

1%

COMMERCIAL COMMERCIAL SPACE HEATING

LOW ELECTRIFICATION 350

OTHER TECHS

86%

83%

55%

17%

COMMERCIAL COMMERCIAL VENTILATION

BASE 350

HE TECHS

11%

41%

80%

99%

COMMERCIAL COMMERCIAL VENTILATION

BASE 350

OTHER TECHS

89%

59%

20%

1%

COMMERCIAL COMMERCIAL VENTILATION

BASELINE

HE TECHS

11%

13%

11%

10%

COMMERCIAL COMMERCIAL VENTILATION

BASELINE

OTHER TECHS

89%

87%

89%

90%

COMMERCIAL COMMERCIAL VENTILATION

LOW ELECTRIFICATION 350

HE TECHS

11%

41%

80%

99%

COMMERCIAL COMMERCIAL VENTILATION

LOW ELECTRIFICATION 350

OTHER TECHS

89%

59%

20%

1%

COMMERCIAL COMMERCIAL WATER HEATING

BASE 350

ELECTRIFIED TECHS

5%

47%

96%

100%

COMMERCIAL COMMERCIAL WATER HEATING

BASE 350

HE TECHS

31%

28%

2%

0%

COMMERCIAL COMMERCIAL WATER HEATING

BASE 350

OTHER TECHS

64%

25%

2%

0%

COMMERCIAL COMMERCIAL WATER HEATING

BASELINE

ELECTRIFIED TECHS

5%

3%

3%

2%

COMMERCIAL COMMERCIAL WATER HEATING

BASELINE

HE TECHS

31%

51%

54%

55%

COMMERCIAL COMMERCIAL WATER HEATING

BASELINE

OTHER TECHS

64%

45%

44%

43%

COMMERCIAL COMMERCIAL WATER HEATING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

5%

8%

45%

86%

COMMERCIAL COMMERCIAL WATER HEATING

LOW ELECTRIFICATION 350

HE TECHS

31%

49%

29%

6%

COMMERCIAL COMMERCIAL WATER HEATING

LOW ELECTRIFICATION 350

OTHER TECHS

64%

43%

26%

8%

PRODUCTIVE INDUSTRIAL BOILERS

BASE 350

ELECTRIFIED TECHS

0%

26%

70%

75%

PRODUCTIVE INDUSTRIAL BOILERS

BASE 350

OTHER TECHS

100%

74%

30%

25%

PRODUCTIVE INDUSTRIAL BOILERS

BASELINE

ELECTRIFIED TECHS

0%

0%

0%

0%

PRODUCTIVE INDUSTRIAL BOILERS

BASELINE

OTHER TECHS

100%

100%

100%

100%

PRODUCTIVE INDUSTRIAL BOILERS

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

0%

3%

31%

65%

PRODUCTIVE INDUSTRIAL BOILERS

LOW ELECTRIFICATION 350

OTHER TECHS

100%

97%

69%

35%

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PRODUCTIVE INDUSTRIAL CURING

BASE 350

ELECTRIFIED TECHS

0%

29%

70%

75%

PRODUCTIVE INDUSTRIAL CURING

BASE 350

OTHER TECHS

100%

71%

30%

25%

PRODUCTIVE INDUSTRIAL CURING

BASELINE

ELECTRIFIED TECHS

0%

0%

0%

0%

PRODUCTIVE INDUSTRIAL CURING

BASELINE

OTHER TECHS

100%

100%

100%

100%

PRODUCTIVE INDUSTRIAL CURING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

0%

3%

31%

65%

PRODUCTIVE INDUSTRIAL CURING

LOW ELECTRIFICATION 350

OTHER TECHS

100%

97%

69%

35%

PRODUCTIVE INDUSTRIAL DRYING

BASE 350

ELECTRIFIED TECHS

0%

0%

0%

0%

PRODUCTIVE INDUSTRIAL DRYING

BASE 350

OTHER TECHS

100%

100%

100%

100%

PRODUCTIVE INDUSTRIAL DRYING

BASELINE

ELECTRIFIED TECHS

0%

0%

0%

0%

PRODUCTIVE INDUSTRIAL DRYING

BASELINE

OTHER TECHS

100%

100%

100%

100%

PRODUCTIVE INDUSTRIAL DRYING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

0%

0%

0%

0%

PRODUCTIVE INDUSTRIAL DRYING

LOW ELECTRIFICATION 350

OTHER TECHS

100%

100%

100%

100%

PRODUCTIVE INDUSTRIAL MACHINE DRIVES

BASE 350

ELECTRIFIED TECHS

87%

88%

91%

92%

PRODUCTIVE INDUSTRIAL MACHINE DRIVES

BASE 350

OTHER TECHS

13%

12%

9%

8%

PRODUCTIVE INDUSTRIAL MACHINE DRIVES

BASELINE

ELECTRIFIED TECHS

87%

87%

88%

89%

PRODUCTIVE INDUSTRIAL MACHINE DRIVES

BASELINE

OTHER TECHS

13%

13%

12%

11%

PRODUCTIVE INDUSTRIAL MACHINE DRIVES

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

87%

87%

89%

91%

PRODUCTIVE INDUSTRIAL MACHINE DRIVES

LOW ELECTRIFICATION 350

OTHER TECHS

13%

13%

11%

9%

PRODUCTIVE INDUSTRIAL PROCESS HEAT

BASE 350

ELECTRIFIED TECHS

21%

35%

57%

60%

PRODUCTIVE INDUSTRIAL PROCESS HEAT

BASE 350

OTHER TECHS

79%

65%

43%

40%

PRODUCTIVE INDUSTRIAL PROCESS HEAT

BASELINE

ELECTRIFIED TECHS

21%

21%

21%

22%

PRODUCTIVE INDUSTRIAL PROCESS HEAT

BASELINE

OTHER TECHS

79%

79%

79%

78%

PRODUCTIVE INDUSTRIAL PROCESS HEAT

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

21%

23%

38%

55%

PRODUCTIVE INDUSTRIAL PROCESS HEAT

LOW ELECTRIFICATION 350

OTHER TECHS

79%

77%

62%

45%

PRODUCTIVE INDUSTRIAL SPACE HEATING

BASE 350

ELECTRIFIED TECHS

0%

32%

81%

90%

PRODUCTIVE INDUSTRIAL SPACE HEATING

BASE 350

OTHER TECHS

100%

68%

19%

10%

PRODUCTIVE INDUSTRIAL SPACE HEATING

BASELINE

ELECTRIFIED TECHS

0%

0%

0%

0%

PRODUCTIVE INDUSTRIAL SPACE HEATING

BASELINE

OTHER TECHS

100%

100%

100%

100%

© 2019 by Evolved Energy Research 85

PRODUCTIVE INDUSTRIAL SPACE HEATING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

0%

4%

37%

76%

PRODUCTIVE INDUSTRIAL SPACE HEATING

LOW ELECTRIFICATION 350

OTHER TECHS

100%

96%

63%

24%

RESIDENTIAL RESIDENTIAL AIR CONDITIONING

BASE 350

HE TECHS

11%

50%

93%

100%

RESIDENTIAL RESIDENTIAL AIR CONDITIONING

BASE 350

OTHER TECHS

89%

50%

7%

0%

RESIDENTIAL RESIDENTIAL AIR CONDITIONING

BASELINE

HE TECHS

10%

11%

10%

11%

RESIDENTIAL RESIDENTIAL AIR CONDITIONING

BASELINE

OTHER TECHS

90%

89%

90%

89%

RESIDENTIAL RESIDENTIAL AIR CONDITIONING

LOW ELECTRIFICATION 350

HE TECHS

11%

46%

92%

100%

RESIDENTIAL RESIDENTIAL AIR CONDITIONING

LOW ELECTRIFICATION 350

OTHER TECHS

89%

54%

8%

0%

RESIDENTIAL RESIDENTIAL BUILDING SHELL

BASE 350

HE TECHS

0%

16%

38%

55%

RESIDENTIAL RESIDENTIAL BUILDING SHELL

BASE 350

OTHER TECHS

100%

84%

62%

45%

RESIDENTIAL RESIDENTIAL BUILDING SHELL

BASELINE

OTHER TECHS

100%

100%

100%

100%

RESIDENTIAL RESIDENTIAL BUILDING SHELL

LOW ELECTRIFICATION 350

HE TECHS

0%

16%

38%

55%

RESIDENTIAL RESIDENTIAL BUILDING SHELL

LOW ELECTRIFICATION 350

OTHER TECHS

100%

84%

62%

45%

RESIDENTIAL RESIDENTIAL CLOTHES DRYING

BASE 350

ELECTRIFIED TECHS

80%

88%

99%

100%

RESIDENTIAL RESIDENTIAL CLOTHES DRYING

BASE 350

OTHER TECHS

20%

12%

1%

0%

RESIDENTIAL RESIDENTIAL CLOTHES DRYING

BASELINE

ELECTRIFIED TECHS

80%

80%

80%

80%

RESIDENTIAL RESIDENTIAL CLOTHES DRYING

BASELINE

OTHER TECHS

20%

20%

20%

20%

RESIDENTIAL RESIDENTIAL CLOTHES DRYING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

80%

81%

88%

97%

RESIDENTIAL RESIDENTIAL CLOTHES DRYING

LOW ELECTRIFICATION 350

OTHER TECHS

20%

19%

12%

3%

RESIDENTIAL RESIDENTIAL CLOTHES WASHING

BASE 350

HE TECHS

0%

41%

96%

100%

RESIDENTIAL RESIDENTIAL CLOTHES WASHING

BASE 350

OTHER TECHS

100%

59%

4%

0%

RESIDENTIAL RESIDENTIAL CLOTHES WASHING

BASELINE

HE TECHS

0%

0%

0%

0%

RESIDENTIAL RESIDENTIAL CLOTHES WASHING

BASELINE

OTHER TECHS

100%

100%

100%

100%

RESIDENTIAL RESIDENTIAL CLOTHES WASHING

LOW ELECTRIFICATION 350

HE TECHS

0%

41%

96%

100%

RESIDENTIAL RESIDENTIAL CLOTHES WASHING

LOW ELECTRIFICATION 350

OTHER TECHS

100%

59%

4%

0%

RESIDENTIAL RESIDENTIAL COOKING

BASE 350

ELECTRIFIED TECHS

61%

74%

94%

100%

RESIDENTIAL RESIDENTIAL COOKING

BASE 350

OTHER TECHS

39%

26%

6%

0%

RESIDENTIAL RESIDENTIAL COOKING

BASELINE

ELECTRIFIED TECHS

61%

61%

61%

62%

© 2019 by Evolved Energy Research 86

RESIDENTIAL RESIDENTIAL COOKING

BASELINE

OTHER TECHS

39%

39%

39%

38%

RESIDENTIAL RESIDENTIAL COOKING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

61%

63%

75%

92%

RESIDENTIAL RESIDENTIAL COOKING

LOW ELECTRIFICATION 350

OTHER TECHS

39%

37%

25%

8%

RESIDENTIAL RESIDENTIAL DISHWASHING

BASE 350

HE TECHS

0%

41%

96%

100%

RESIDENTIAL RESIDENTIAL DISHWASHING

BASE 350

OTHER TECHS

100%

59%

4%

0%

RESIDENTIAL RESIDENTIAL DISHWASHING

BASELINE

OTHER TECHS

100%

100%

100%

100%

RESIDENTIAL RESIDENTIAL DISHWASHING

LOW ELECTRIFICATION 350

HE TECHS

0%

41%

96%

100%

RESIDENTIAL RESIDENTIAL DISHWASHING

LOW ELECTRIFICATION 350

OTHER TECHS

100%

59%

4%

0%

RESIDENTIAL RESIDENTIAL FREEZING

BASE 350

HE TECHS

0%

30%

73%

98%

RESIDENTIAL RESIDENTIAL FREEZING

BASE 350

OTHER TECHS

100%

70%

27%

2%

RESIDENTIAL RESIDENTIAL FREEZING

BASELINE

OTHER TECHS

100%

100%

100%

100%

RESIDENTIAL RESIDENTIAL FREEZING

LOW ELECTRIFICATION 350

HE TECHS

0%

30%

73%

98%

RESIDENTIAL RESIDENTIAL FREEZING

LOW ELECTRIFICATION 350

OTHER TECHS

100%

70%

27%

2%

RESIDENTIAL RESIDENTIAL LIGHTING

BASE 350

HE TECHS

4%

31%

77%

94%

RESIDENTIAL RESIDENTIAL LIGHTING

BASE 350

OTHER TECHS

96%

69%

23%

6%

RESIDENTIAL RESIDENTIAL LIGHTING

BASELINE

HE TECHS

4%

3%

3%

2%

RESIDENTIAL RESIDENTIAL LIGHTING

BASELINE

OTHER TECHS

96%

97%

97%

98%

RESIDENTIAL RESIDENTIAL LIGHTING

LOW ELECTRIFICATION 350

HE TECHS

4%

31%

77%

94%

RESIDENTIAL RESIDENTIAL LIGHTING

LOW ELECTRIFICATION 350

OTHER TECHS

96%

69%

23%

6%

RESIDENTIAL RESIDENTIAL REFRIGERATION

BASE 350

HE TECHS

0%

37%

86%

100%

RESIDENTIAL RESIDENTIAL REFRIGERATION

BASE 350

OTHER TECHS

100%

63%

14%

0%

RESIDENTIAL RESIDENTIAL REFRIGERATION

BASELINE

HE TECHS

0%

0%

0%

0%

RESIDENTIAL RESIDENTIAL REFRIGERATION

BASELINE

OTHER TECHS

100%

100%

100%

100%

RESIDENTIAL RESIDENTIAL REFRIGERATION

LOW ELECTRIFICATION 350

HE TECHS

0%

37%

86%

100%

RESIDENTIAL RESIDENTIAL REFRIGERATION

LOW ELECTRIFICATION 350

OTHER TECHS

100%

63%

14%

0%

RESIDENTIAL RESIDENTIAL SPACE HEATING

BASE 350

ELECTRIFIED TECHS

36%

57%

86%

97%

RESIDENTIAL RESIDENTIAL SPACE HEATING

BASE 350

OTHER TECHS

64%

43%

14%

3%

RESIDENTIAL RESIDENTIAL SPACE HEATING

BASELINE

ELECTRIFIED TECHS

36%

36%

37%

37%

© 2019 by Evolved Energy Research 87

RESIDENTIAL RESIDENTIAL SPACE HEATING

BASELINE

OTHER TECHS

64%

64%

63%

63%

RESIDENTIAL RESIDENTIAL SPACE HEATING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

36%

39%

58%

84%

RESIDENTIAL RESIDENTIAL SPACE HEATING

LOW ELECTRIFICATION 350

OTHER TECHS

64%

61%

42%

16%

RESIDENTIAL RESIDENTIAL WATER HEATING

BASE 350

ELECTRIFIED TECHS

44%

77%

98%

99%

RESIDENTIAL RESIDENTIAL WATER HEATING

BASE 350

OTHER TECHS

56%

23%

2%

1%

RESIDENTIAL RESIDENTIAL WATER HEATING

BASELINE

ELECTRIFIED TECHS

44%

44%

44%

44%

RESIDENTIAL RESIDENTIAL WATER HEATING

BASELINE

OTHER TECHS

56%

56%

56%

56%

RESIDENTIAL RESIDENTIAL WATER HEATING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

44%

48%

75%

96%

RESIDENTIAL RESIDENTIAL WATER HEATING

LOW ELECTRIFICATION 350

OTHER TECHS

56%

52%

25%

4%

TRANSPORTATION HEAVY DUTY TRUCKS

BASE 350

ELECTRIFIED TECHS

0%

20%

47%

50%

TRANSPORTATION HEAVY DUTY TRUCKS

BASE 350

HE TECHS

0%

20%

47%

50%

TRANSPORTATION HEAVY DUTY TRUCKS

BASE 350

OTHER TECHS

100%

60%

6%

0%

TRANSPORTATION HEAVY DUTY TRUCKS

BASELINE

OTHER TECHS

100%

100%

100%

100%

TRANSPORTATION HEAVY DUTY TRUCKS

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

0%

2%

21%

44%

TRANSPORTATION HEAVY DUTY TRUCKS

LOW ELECTRIFICATION 350

HE TECHS

0%

2%

21%

44%

TRANSPORTATION HEAVY DUTY TRUCKS

LOW ELECTRIFICATION 350

OTHER TECHS

100%

95%

58%

13%

TRANSPORTATION LIGHT DUTY AUTOS

BASE 350

ELECTRIFIED TECHS

1%

44%

94%

100%

TRANSPORTATION LIGHT DUTY AUTOS

BASE 350

HE TECHS

0%

0%

0%

0%

TRANSPORTATION LIGHT DUTY AUTOS

BASE 350

OTHER TECHS

99%

56%

6%

0%

TRANSPORTATION LIGHT DUTY AUTOS

BASELINE

ELECTRIFIED TECHS

1%

1%

2%

3%

TRANSPORTATION LIGHT DUTY AUTOS

BASELINE

HE TECHS

0%

0%

0%

0%

TRANSPORTATION LIGHT DUTY AUTOS

BASELINE

OTHER TECHS

99%

99%

97%

97%

TRANSPORTATION LIGHT DUTY AUTOS

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

1%

6%

44%

89%

TRANSPORTATION LIGHT DUTY AUTOS

LOW ELECTRIFICATION 350

HE TECHS

0%

0%

0%

0%

TRANSPORTATION LIGHT DUTY AUTOS

LOW ELECTRIFICATION 350

OTHER TECHS

99%

93%

55%

11%

TRANSPORTATION LIGHT DUTY TRUCKS

BASE 350

ELECTRIFIED TECHS

0%

39%

95%

100%

TRANSPORTATION LIGHT DUTY TRUCKS

BASE 350

HE TECHS

0%

0%

0%

0%

TRANSPORTATION LIGHT DUTY TRUCKS

BASE 350

OTHER TECHS

99%

60%

5%

0%

© 2019 by Evolved Energy Research 88

TRANSPORTATION

LIGHT DUTY TRUCKS

BASELINE

ELECTRIFIED TECHS

0%

0%

0%

0%

TRANSPORTATION

LIGHT DUTY TRUCKS

BASELINE

HE TECHS

0%

1%

1%

1%

TRANSPORTATION

LIGHT DUTY TRUCKS

BASELINE

OTHER TECHS

100%

99%

99%

99%

TRANSPORTATION

LIGHT DUTY TRUCKS

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

0%

5%

44%

88%

TRANSPORTATION

LIGHT DUTY TRUCKS

LOW ELECTRIFICATION 350

HE TECHS

0%

0%

0%

0%

TRANSPORTATION

LIGHT DUTY TRUCKS

LOW ELECTRIFICATION 350

OTHER TECHS

99%

94%

56%

12%

TRANSPORTATION

MEDIUM DUTY TRUCKS

BASE 350

ELECTRIFIED TECHS

0%

24%

63%

75%

TRANSPORTATION

MEDIUM DUTY TRUCKS

BASE 350

HE TECHS

0%

8%

21%

25%

TRANSPORTATION

MEDIUM DUTY TRUCKS

BASE 350

OTHER TECHS

100%

68%

16%

0%

TRANSPORTATION

MEDIUM DUTY TRUCKS

BASELINE

OTHER TECHS

100%

100%

100%

100%

TRANSPORTATION

MEDIUM DUTY TRUCKS

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

0%

3%

28%

61%

TRANSPORTATION

MEDIUM DUTY TRUCKS

LOW ELECTRIFICATION 350

HE TECHS

0%

1%

9%

20%

TRANSPORTATION

MEDIUM DUTY TRUCKS

LOW ELECTRIFICATION 350

OTHER TECHS

100%

96%

62%

19%

TRANSPORTATION

TRANSIT BUSES

BASE 350

ELECTRIFIED TECHS

0%

58%

100%

100%

TRANSPORTATION

TRANSIT BUSES

BASE 350

HE TECHS

17%

7%

0%

0%

TRANSPORTATION

TRANSIT BUSES

BASE 350

OTHER TECHS

82%

35%

0%

0%

TRANSPORTATION

TRANSIT BUSES

BASELINE

HE TECHS

17%

17%

17%

17%

TRANSPORTATION

TRANSIT BUSES

BASELINE

OTHER TECHS

83%

83%

83%

83%

TRANSPORTATION

TRANSIT BUSES

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

0%

7%

55%

95%

TRANSPORTATION

TRANSIT BUSES

LOW ELECTRIFICATION 350

HE TECHS

17%

16%

8%

1%

TRANSPORTATION

TRANSIT BUSES

LOW ELECTRIFICATION 350

OTHER TECHS

83%

77%

37%

4%

Table 4 Sales shares

Sector

Subsector

Scenario

Technology Group

2020

2030

2040

2050

COMMERCIAL

COMMERCIAL AIR CONDITIONING

BASE 350

HE TECHS

10%

96%

95%

94%

COMMERCIAL

COMMERCIAL AIR CONDITIONING

BASE 350

OTHER TECHS

90%

4%

5%

6%

COMMERCIAL

COMMERCIAL AIR CONDITIONING

BASELINE

HE TECHS

7%

9%

11%

13%

© 2019 by Evolved Energy Research 89

COMMERCIAL COMMERCIAL AIR CONDITIONING

BASELINE

OTHER TECHS

93%

91%

89%

87%

COMMERCIAL COMMERCIAL AIR CONDITIONING

LOW ELECTRIFICATION 350

HE TECHS

9%

94%

95%

94%

COMMERCIAL COMMERCIAL AIR CONDITIONING

LOW ELECTRIFICATION 350

OTHER TECHS

91%

6%

5%

6%

COMMERCIAL COMMERCIAL COOKING

BASE 350

ELECTRIFIED TECHS

26%

81%

81%

81%

COMMERCIAL COMMERCIAL COOKING

BASE 350

OTHER TECHS

74%

19%

19%

19%

COMMERCIAL COMMERCIAL COOKING

BASELINE

ELECTRIFIED TECHS

25%

25%

25%

25%

COMMERCIAL COMMERCIAL COOKING

BASELINE

OTHER TECHS

75%

75%

75%

75%

COMMERCIAL COMMERCIAL COOKING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

25%

36%

73%

81%

COMMERCIAL COMMERCIAL COOKING

LOW ELECTRIFICATION 350

OTHER TECHS

75%

64%

27%

19%

COMMERCIAL COMMERCIAL LIGHTING

BASE 350

HE TECHS

26%

82%

83%

83%

COMMERCIAL COMMERCIAL LIGHTING

BASE 350

OTHER TECHS

74%

18%

17%

17%

COMMERCIAL COMMERCIAL LIGHTING

BASELINE

HE TECHS

38%

70%

72%

72%

COMMERCIAL COMMERCIAL LIGHTING

BASELINE

OTHER TECHS

62%

30%

28%

28%

COMMERCIAL COMMERCIAL LIGHTING

LOW ELECTRIFICATION 350

HE TECHS

26%

82%

83%

83%

COMMERCIAL COMMERCIAL LIGHTING

LOW ELECTRIFICATION 350

OTHER TECHS

74%

18%

17%

17%

COMMERCIAL COMMERCIAL REFRIGERATION

BASE 350

HE TECHS

12%

99%

100%

100%

COMMERCIAL COMMERCIAL REFRIGERATION

BASE 350

OTHER TECHS

88%

1%

0%

0%

COMMERCIAL COMMERCIAL REFRIGERATION

BASELINE

HE TECHS

10%

12%

15%

17%

COMMERCIAL COMMERCIAL REFRIGERATION

BASELINE

OTHER TECHS

90%

88%

85%

83%

COMMERCIAL COMMERCIAL REFRIGERATION

LOW ELECTRIFICATION 350

HE TECHS

12%

99%

100%

100%

COMMERCIAL COMMERCIAL REFRIGERATION

LOW ELECTRIFICATION 350

OTHER TECHS

88%

1%

0%

0%

COMMERCIAL COMMERCIAL SPACE HEATING

BASE 350

ELECTRIFIED TECHS

13%

98%

99%

99%

COMMERCIAL COMMERCIAL SPACE HEATING

BASE 350

HE TECHS

1%

1%

1%

1%

COMMERCIAL COMMERCIAL SPACE HEATING

BASE 350

OTHER TECHS

86%

1%

0%

0%

COMMERCIAL COMMERCIAL SPACE HEATING

BASELINE

ELECTRIFIED TECHS

12%

12%

12%

12%

COMMERCIAL COMMERCIAL SPACE HEATING

BASELINE

HE TECHS

1%

1%

1%

1%

COMMERCIAL COMMERCIAL SPACE HEATING

BASELINE

OTHER TECHS

87%

87%

87%

87%

COMMERCIAL COMMERCIAL SPACE HEATING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

13%

28%

86%

98%

© 2019 by Evolved Energy Research 90

COMMERCIAL COMMERCIAL SPACE HEATING

LOW ELECTRIFICATION 350

HE TECHS

1%

1%

1%

1%

COMMERCIAL COMMERCIAL SPACE HEATING

LOW ELECTRIFICATION 350

OTHER TECHS

86%

71%

13%

1%

COMMERCIAL COMMERCIAL VENTILATION

BASE 350

HE TECHS

11%

99%

100%

100%

COMMERCIAL COMMERCIAL VENTILATION

BASE 350

OTHER TECHS

89%

1%

0%

0%

COMMERCIAL COMMERCIAL VENTILATION

BASELINE

HE TECHS

10%

10%

10%

10%

COMMERCIAL COMMERCIAL VENTILATION

BASELINE

OTHER TECHS

90%

90%

90%

90%

COMMERCIAL COMMERCIAL VENTILATION

LOW ELECTRIFICATION 350

HE TECHS

11%

99%

100%

100%

COMMERCIAL COMMERCIAL VENTILATION

LOW ELECTRIFICATION 350

OTHER TECHS

89%

1%

0%

0%

COMMERCIAL COMMERCIAL WATER HEATING

BASE 350

ELECTRIFIED TECHS

7%

99%

100%

100%

COMMERCIAL COMMERCIAL WATER HEATING

BASE 350

HE TECHS

49%

0%

0%

0%

COMMERCIAL COMMERCIAL WATER HEATING

BASE 350

OTHER TECHS

43%

0%

0%

0%

COMMERCIAL COMMERCIAL WATER HEATING

BASELINE

ELECTRIFIED TECHS

6%

5%

4%

4%

COMMERCIAL COMMERCIAL WATER HEATING

BASELINE

HE TECHS

50%

52%

53%

55%

COMMERCIAL COMMERCIAL WATER HEATING

BASELINE

OTHER TECHS

44%

43%

42%

41%

COMMERCIAL COMMERCIAL WATER HEATING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

7%

22%

83%

96%

COMMERCIAL COMMERCIAL WATER HEATING

LOW ELECTRIFICATION 350

HE TECHS

50%

42%

8%

0%

COMMERCIAL COMMERCIAL WATER HEATING

LOW ELECTRIFICATION 350

OTHER TECHS

44%

36%

9%

4%

PRODUCTIVE INDUSTRIAL BOILERS

BASE 350

ELECTRIFIED TECHS

1%

74%

75%

75%

PRODUCTIVE INDUSTRIAL BOILERS

BASE 350

OTHER TECHS

99%

26%

25%

25%

PRODUCTIVE INDUSTRIAL BOILERS

BASELINE

ELECTRIFIED TECHS

0%

0%

0%

0%

PRODUCTIVE INDUSTRIAL BOILERS

BASELINE

OTHER TECHS

100%

100%

100%

100%

PRODUCTIVE INDUSTRIAL BOILERS

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

1%

14%

64%

74%

PRODUCTIVE INDUSTRIAL BOILERS

LOW ELECTRIFICATION 350

OTHER TECHS

99%

86%

36%

26%

PRODUCTIVE INDUSTRIAL CURING

BASE 350

ELECTRIFIED TECHS

1%

74%

75%

75%

PRODUCTIVE INDUSTRIAL CURING

BASE 350

OTHER TECHS

99%

26%

25%

25%

PRODUCTIVE INDUSTRIAL CURING

BASELINE

ELECTRIFIED TECHS

0%

0%

0%

0%

PRODUCTIVE INDUSTRIAL CURING

BASELINE

OTHER TECHS

100%

100%

100%

100%

PRODUCTIVE INDUSTRIAL CURING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

1%

14%

64%

74%

© 2019 by Evolved Energy Research 91

PRODUCTIVE INDUSTRIAL CURING

LOW ELECTRIFICATION 350

OTHER TECHS

99%

86%

36%

26%

PRODUCTIVE INDUSTRIAL DRYING

BASE 350

ELECTRIFIED TECHS

0%

0%

0%

0%

PRODUCTIVE INDUSTRIAL DRYING

BASE 350

OTHER TECHS

100%

100%

100%

100%

PRODUCTIVE INDUSTRIAL DRYING

BASELINE

ELECTRIFIED TECHS

0%

0%

0%

0%

PRODUCTIVE INDUSTRIAL DRYING

BASELINE

OTHER TECHS

100%

100%

100%

100%

PRODUCTIVE INDUSTRIAL DRYING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

0%

0%

0%

0%

PRODUCTIVE INDUSTRIAL DRYING

LOW ELECTRIFICATION 350

OTHER TECHS

100%

100%

100%

100%

PRODUCTIVE INDUSTRIAL MACHINE DRIVES

BASE 350

ELECTRIFIED TECHS

88%

91%

92%

92%

PRODUCTIVE INDUSTRIAL MACHINE DRIVES

BASE 350

OTHER TECHS

12%

9%

8%

8%

PRODUCTIVE INDUSTRIAL MACHINE DRIVES

BASELINE

ELECTRIFIED TECHS

88%

87%

89%

89%

PRODUCTIVE INDUSTRIAL MACHINE DRIVES

BASELINE

OTHER TECHS

12%

13%

11%

11%

PRODUCTIVE INDUSTRIAL MACHINE DRIVES

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

88%

88%

91%

91%

PRODUCTIVE INDUSTRIAL MACHINE DRIVES

LOW ELECTRIFICATION 350

OTHER TECHS

12%

12%

9%

9%

PRODUCTIVE INDUSTRIAL PROCESS HEAT

BASE 350

ELECTRIFIED TECHS

23%

60%

60%

61%

PRODUCTIVE INDUSTRIAL PROCESS HEAT

BASE 350

OTHER TECHS

77%

40%

40%

39%

PRODUCTIVE INDUSTRIAL PROCESS HEAT

BASELINE

ELECTRIFIED TECHS

23%

20%

22%

22%

PRODUCTIVE INDUSTRIAL PROCESS HEAT

BASELINE

OTHER TECHS

77%

80%

78%

78%

PRODUCTIVE INDUSTRIAL PROCESS HEAT

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

23%

29%

55%

60%

PRODUCTIVE INDUSTRIAL PROCESS HEAT

LOW ELECTRIFICATION 350

OTHER TECHS

77%

71%

45%

40%

PRODUCTIVE INDUSTRIAL SPACE HEATING

BASE 350

ELECTRIFIED TECHS

1%

89%

90%

90%

PRODUCTIVE INDUSTRIAL SPACE HEATING

BASE 350

OTHER TECHS

99%

11%

10%

10%

PRODUCTIVE INDUSTRIAL SPACE HEATING

BASELINE

ELECTRIFIED TECHS

0%

0%

0%

0%

PRODUCTIVE INDUSTRIAL SPACE HEATING

BASELINE

OTHER TECHS

100%

100%

100%

100%

PRODUCTIVE INDUSTRIAL SPACE HEATING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

1%

17%

77%

89%

PRODUCTIVE INDUSTRIAL SPACE HEATING

LOW ELECTRIFICATION 350

OTHER TECHS

99%

83%

23%

11%

RESIDENTIAL RESIDENTIAL AIR CONDITIONING

BASE 350

HE TECHS

13%

99%

100%

100%

RESIDENTIAL RESIDENTIAL AIR CONDITIONING

BASE 350

OTHER TECHS

87%

1%

0%

0%

RESIDENTIAL RESIDENTIAL AIR CONDITIONING

BASELINE

HE TECHS

10%

11%

10%

11%

© 2019 by Evolved Energy Research 92

RESIDENTIAL RESIDENTIAL AIR CONDITIONING

BASELINE

OTHER TECHS

90%

89%

90%

89%

RESIDENTIAL RESIDENTIAL AIR CONDITIONING

LOW ELECTRIFICATION 350

HE TECHS

12%

99%

100%

100%

RESIDENTIAL RESIDENTIAL AIR CONDITIONING

LOW ELECTRIFICATION 350

OTHER TECHS

88%

1%

0%

0%

RESIDENTIAL RESIDENTIAL BUILDING SHELL

BASE 350

HE TECHS

9%

100%

100%

100%

RESIDENTIAL RESIDENTIAL BUILDING SHELL

BASE 350

OTHER TECHS

91%

0%

0%

0%

RESIDENTIAL RESIDENTIAL BUILDING SHELL

BASELINE

OTHER TECHS

100%

100%

100%

100%

RESIDENTIAL RESIDENTIAL BUILDING SHELL

LOW ELECTRIFICATION 350

HE TECHS

9%

100%

100%

100%

RESIDENTIAL RESIDENTIAL BUILDING SHELL

LOW ELECTRIFICATION 350

OTHER TECHS

91%

0%

0%

0%

RESIDENTIAL RESIDENTIAL CLOTHES DRYING

BASE 350

ELECTRIFIED TECHS

81%

100%

100%

100%

RESIDENTIAL RESIDENTIAL CLOTHES DRYING

BASE 350

OTHER TECHS

19%

0%

0%

0%

RESIDENTIAL RESIDENTIAL CLOTHES DRYING

BASELINE

ELECTRIFIED TECHS

80%

80%

80%

81%

RESIDENTIAL RESIDENTIAL CLOTHES DRYING

BASELINE

OTHER TECHS

20%

20%

20%

19%

RESIDENTIAL RESIDENTIAL CLOTHES DRYING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

80%

84%

97%

100%

RESIDENTIAL RESIDENTIAL CLOTHES DRYING

LOW ELECTRIFICATION 350

OTHER TECHS

20%

16%

3%

0%

RESIDENTIAL RESIDENTIAL CLOTHES WASHING

BASE 350

HE TECHS

2%

99%

100%

100%

RESIDENTIAL RESIDENTIAL CLOTHES WASHING

BASE 350

OTHER TECHS

98%

1%

0%

0%

RESIDENTIAL RESIDENTIAL CLOTHES WASHING

BASELINE

HE TECHS

0%

0%

0%

0%

RESIDENTIAL RESIDENTIAL CLOTHES WASHING

BASELINE

OTHER TECHS

100%

100%

100%

100%

RESIDENTIAL RESIDENTIAL CLOTHES WASHING

LOW ELECTRIFICATION 350

HE TECHS

2%

99%

100%

100%

RESIDENTIAL RESIDENTIAL CLOTHES WASHING

LOW ELECTRIFICATION 350

OTHER TECHS

98%

1%

0%

0%

RESIDENTIAL RESIDENTIAL COOKING

BASE 350

ELECTRIFIED TECHS

62%

100%

100%

100%

RESIDENTIAL RESIDENTIAL COOKING

BASE 350

OTHER TECHS

38%

0%

0%

0%

RESIDENTIAL RESIDENTIAL COOKING

BASELINE

ELECTRIFIED TECHS

61%

61%

61%

62%

RESIDENTIAL RESIDENTIAL COOKING

BASELINE

OTHER TECHS

39%

39%

39%

38%

RESIDENTIAL RESIDENTIAL COOKING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

62%

69%

94%

100%

RESIDENTIAL RESIDENTIAL COOKING

LOW ELECTRIFICATION 350

OTHER TECHS

38%

31%

6%

0%

RESIDENTIAL RESIDENTIAL DISHWASHING

BASE 350

HE TECHS

2%

99%

100%

100%

RESIDENTIAL RESIDENTIAL DISHWASHING

BASE 350

OTHER TECHS

98%

1%

0%

0%

© 2019 by Evolved Energy Research 93

RESIDENTIAL RESIDENTIAL DISHWASHING

BASELINE

OTHER TECHS

100%

100%

100%

100%

RESIDENTIAL RESIDENTIAL DISHWASHING

LOW ELECTRIFICATION 350

HE TECHS

2%

99%

100%

100%

RESIDENTIAL RESIDENTIAL DISHWASHING

LOW ELECTRIFICATION 350

OTHER TECHS

98%

1%

0%

0%

RESIDENTIAL RESIDENTIAL FREEZING

BASE 350

HE TECHS

2%

99%

100%

100%

RESIDENTIAL RESIDENTIAL FREEZING

BASE 350

OTHER TECHS

98%

1%

0%

0%

RESIDENTIAL RESIDENTIAL FREEZING

BASELINE

OTHER TECHS

100%

100%

100%

100%

RESIDENTIAL RESIDENTIAL FREEZING

LOW ELECTRIFICATION 350

HE TECHS

2%

99%

100%

100%

RESIDENTIAL RESIDENTIAL FREEZING

LOW ELECTRIFICATION 350

OTHER TECHS

98%

1%

0%

0%

RESIDENTIAL RESIDENTIAL LIGHTING

BASE 350

HE TECHS

2%

99%

100%

100%

RESIDENTIAL RESIDENTIAL LIGHTING

BASE 350

OTHER TECHS

98%

1%

0%

0%

RESIDENTIAL RESIDENTIAL LIGHTING

BASELINE

HE TECHS

0%

0%

0%

0%

RESIDENTIAL RESIDENTIAL LIGHTING

BASELINE

OTHER TECHS

100%

100%

100%

100%

RESIDENTIAL RESIDENTIAL LIGHTING

LOW ELECTRIFICATION 350

HE TECHS

2%

99%

100%

100%

RESIDENTIAL RESIDENTIAL LIGHTING

LOW ELECTRIFICATION 350

OTHER TECHS

98%

1%

0%

0%

RESIDENTIAL RESIDENTIAL REFRIGERATION

BASE 350

HE TECHS

2%

99%

100%

100%

RESIDENTIAL RESIDENTIAL REFRIGERATION

BASE 350

OTHER TECHS

98%

1%

0%

0%

RESIDENTIAL RESIDENTIAL REFRIGERATION

BASELINE

HE TECHS

0%

0%

0%

0%

RESIDENTIAL RESIDENTIAL REFRIGERATION

BASELINE

OTHER TECHS

100%

100%

100%

100%

RESIDENTIAL RESIDENTIAL REFRIGERATION

LOW ELECTRIFICATION 350

HE TECHS

2%

99%

100%

100%

RESIDENTIAL RESIDENTIAL REFRIGERATION

LOW ELECTRIFICATION 350

OTHER TECHS

98%

1%

0%

0%

RESIDENTIAL RESIDENTIAL SPACE HEATING

BASE 350

ELECTRIFIED TECHS

36%

97%

98%

98%

RESIDENTIAL RESIDENTIAL SPACE HEATING

BASE 350

OTHER TECHS

64%

3%

2%

2%

RESIDENTIAL RESIDENTIAL SPACE HEATING

BASELINE

ELECTRIFIED TECHS

35%

35%

36%

36%

RESIDENTIAL RESIDENTIAL SPACE HEATING

BASELINE

OTHER TECHS

65%

65%

64%

64%

RESIDENTIAL RESIDENTIAL SPACE HEATING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

36%

47%

89%

98%

RESIDENTIAL RESIDENTIAL SPACE HEATING

LOW ELECTRIFICATION 350

OTHER TECHS

64%

53%

11%

2%

RESIDENTIAL RESIDENTIAL WATER HEATING

BASE 350

ELECTRIFIED TECHS

37%

99%

99%

99%

RESIDENTIAL RESIDENTIAL WATER HEATING

BASE 350

OTHER TECHS

63%

1%

1%

1%

© 2019 by Evolved Energy Research 94

RESIDENTIAL RESIDENTIAL WATER HEATING

BASELINE

ELECTRIFIED TECHS

36%

36%

36%

36%

RESIDENTIAL RESIDENTIAL WATER HEATING

BASELINE

OTHER TECHS

64%

64%

64%

64%

RESIDENTIAL RESIDENTIAL WATER HEATING

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

36%

48%

90%

99%

RESIDENTIAL RESIDENTIAL WATER HEATING

LOW ELECTRIFICATION 350

OTHER TECHS

64%

52%

10%

1%

TRANSPORTATION HEAVY DUTY TRUCKS

BASE 350

ELECTRIFIED TECHS

1%

50%

50%

50%

TRANSPORTATION HEAVY DUTY TRUCKS

BASE 350

HE TECHS

1%

50%

50%

50%

TRANSPORTATION HEAVY DUTY TRUCKS

BASE 350

OTHER TECHS

98%

1%

0%

0%

TRANSPORTATION HEAVY DUTY TRUCKS

BASELINE

OTHER TECHS

100%

100%

100%

100%

TRANSPORTATION HEAVY DUTY TRUCKS

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

0%

9%

43%

50%

TRANSPORTATION HEAVY DUTY TRUCKS

LOW ELECTRIFICATION 350

HE TECHS

0%

9%

43%

50%

TRANSPORTATION HEAVY DUTY TRUCKS

LOW ELECTRIFICATION 350

OTHER TECHS

99%

81%

15%

1%

TRANSPORTATION LIGHT DUTY AUTOS

BASE 350

ELECTRIFIED TECHS

3%

99%

100%

100%

TRANSPORTATION LIGHT DUTY AUTOS

BASE 350

HE TECHS

0%

0%

0%

0%

TRANSPORTATION LIGHT DUTY AUTOS

BASE 350

OTHER TECHS

97%

1%

0%

0%

TRANSPORTATION LIGHT DUTY AUTOS

BASELINE

ELECTRIFIED TECHS

1%

2%

3%

3%

TRANSPORTATION LIGHT DUTY AUTOS

BASELINE

HE TECHS

0%

0%

0%

0%

TRANSPORTATION LIGHT DUTY AUTOS

BASELINE

OTHER TECHS

99%

97%

97%

97%

TRANSPORTATION LIGHT DUTY AUTOS

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

2%

22%

87%

99%

TRANSPORTATION LIGHT DUTY AUTOS

LOW ELECTRIFICATION 350

HE TECHS

0%

0%

0%

0%

TRANSPORTATION LIGHT DUTY AUTOS

LOW ELECTRIFICATION 350

OTHER TECHS

98%

78%

13%

1%

TRANSPORTATION LIGHT DUTY TRUCKS

BASE 350

ELECTRIFIED TECHS

2%

99%

100%

100%

TRANSPORTATION LIGHT DUTY TRUCKS

BASE 350

HE TECHS

0%

0%

0%

0%

TRANSPORTATION LIGHT DUTY TRUCKS

BASE 350

OTHER TECHS

98%

1%

0%

0%

TRANSPORTATION LIGHT DUTY TRUCKS

BASELINE

ELECTRIFIED TECHS

0%

0%

0%

0%

TRANSPORTATION LIGHT DUTY TRUCKS

BASELINE

HE TECHS

0%

1%

1%

1%

TRANSPORTATION LIGHT DUTY TRUCKS

BASELINE

OTHER TECHS

100%

99%

99%

99%

TRANSPORTATION LIGHT DUTY TRUCKS

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

1%

21%

87%

99%

TRANSPORTATION LIGHT DUTY TRUCKS

LOW ELECTRIFICATION 350

HE TECHS

0%

1%

0%

0%

© 2019 by Evolved Energy Research 95

TRANSPORTATION

LIGHT DUTY TRUCKS

LOW ELECTRIFICATION 350

OTHER TECHS

98%

79%

13%

1%

TRANSPORTATION

MEDIUM DUTY TRUCKS

BASE 350

ELECTRIFIED TECHS

1%

74%

75%

75%

TRANSPORTATION

MEDIUM DUTY TRUCKS

BASE 350

HE TECHS

0%

25%

25%

25%

TRANSPORTATION

MEDIUM DUTY TRUCKS

BASE 350

OTHER TECHS

98%

1%

0%

0%

TRANSPORTATION

MEDIUM DUTY TRUCKS

BASELINE

OTHER TECHS

100%

100%

100%

100%

TRANSPORTATION

MEDIUM DUTY TRUCKS

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

1%

14%

64%

74%

TRANSPORTATION

MEDIUM DUTY TRUCKS

LOW ELECTRIFICATION 350

HE TECHS

0%

5%

21%

25%

TRANSPORTATION

MEDIUM DUTY TRUCKS

LOW ELECTRIFICATION 350

OTHER TECHS

99%

81%

15%

1%

TRANSPORTATION

TRANSIT BUSES

BASE 350

ELECTRIFIED TECHS

2%

99%

100%

100%

TRANSPORTATION

TRANSIT BUSES

BASE 350

HE TECHS

17%

0%

0%

0%

TRANSPORTATION

TRANSIT BUSES

BASE 350

OTHER TECHS

81%

1%

0%

0%

TRANSPORTATION

TRANSIT BUSES

BASELINE

HE TECHS

17%

17%

17%

17%

TRANSPORTATION

TRANSIT BUSES

BASELINE

OTHER TECHS

83%

83%

83%

83%

TRANSPORTATION

TRANSIT BUSES

LOW ELECTRIFICATION 350

ELECTRIFIED TECHS

1%

19%

85%

99%

TRANSPORTATION

TRANSIT BUSES

LOW ELECTRIFICATION 350

HE TECHS

17%

14%

3%

0%

TRANSPORTATION

TRANSIT BUSES

LOW ELECTRIFICATION 350

OTHER TECHS

82%

67%

12%

1%

1.2.2 Energy Efficiency and Fuel Switching

The outputs of the stock rollover, when combined with the projections of service demand that the technology stocks must meet, contributes to the majority of final energy demand projections in our model. In subsectors where we do not have technology-level detail, we also employ subsector-level estimates of energy efficiency and fuel switching. Energy efficiency is a reduction in the same-fuel efficiency of providing an energy service. Fuel switching, which can also contribute to end-use efficiency, is a measure that changes the share of delivered energy service that is satisfied with a specific energy carrier.

© 2019 by Evolved Energy Research 96

Table 5

Sector

Subsector

Description

BASELINE

BASE 350

LOW ELECTRIFICATION 350

COMMERCIAL

OTHER

Reduction of 20% of all final energy demand by 2050. Levelized cost of efficiency for all fuel types assessed at $20/MMBTU.

X

X

TRANSPORTATION

AVIATION

Reduction of 48% of jet-fuel demand by 2050. Levelized cost of efficiency for all fuel types assessed at $20/MMBTU

X

X

PRODUCTIVE

various 14

Reduction of 32% of all final energy-demand by 2050. Levelized cost of efficiency for all fuel types assessed at $20/MMBTU

X

X

Sector

Subsector

Description

BASELINE

BASE 350

LOW ELECTRIFICATION 350

PRODUCTIVE

AGRICULTURE – CROPS

90% of pipeline gas and diesel energy demand for irrigation is converted to electricity.

X

PRODUCTIVE

AGRICULTURE – CROPS

60% of pipeline gas and diesel energy demand for irrigation is converted to electricity.

X

14 AGRICULTURE – CROPS; AGRICULTURE-OTHER; ALUMINUM; BALANCE of MANUFACTURING – OTHER; COMPUTER AND ELECTRONIC PRODUCTS; CONSTRUCTION; ELECTRICAL EQUIP., APPLIANCES, and COMPONENTS; FABRICATED METAL PRODUCTS; FOOD AND KINDRED PRODUCTS; GLASS AND GLASS PRODUCTS; MACHINERY; METAL AND OTHER NON-METALLIC MINING; PAPER AND ALLIED PRODUCTS; PLASTIC AND RUBBER PRODUCTS; TRANSPORTATION EQUIPMENT; WOOD PRODUCTS

© 2019 by Evolved Energy Research 97

RESIDENTIAL

SECONDARY HEATING

90% of fuel demand for pipeline gas and 100% of fuel demand for LPG and diesel fuel is converted to electricity.

X

RESIDENTIAL

SECONDARY HEATING

60% of fuel demand for pipeline gas and 66% of fuel demand for LPG and diesel fuel is converted to electricity.

X

1.3 Final Energy Demand

The combination of stock rollover, fuel switching, and energy efficiency measures results in different final energy demand trajectories across our energy demand scenarios. Final energy demand by sector and energy carrier are shown for each of our demand scenarios below.

Table 6 Final energy demand by sector and energy carrier for each scenario

Sector

Scenario

Final Energy

2020

2030

2040

2050

COMMERCIAL

BASE 350

DIESEL FUEL

0.38

0.31

0.21

0.15

COMMERCIAL

BASE 350

ELECTRICITY

4.64

5.09

5.75

6.19

COMMERCIAL

BASE 350

PIPELINE GAS

2.96

2.08

0.97

0.74

COMMERCIAL

BASE 350

SOLAR

0

0

0

0

COMMERCIAL

BASE 350

STEAM

0.1

0.11

0.12

0.13

COMMERCIAL

BASELINE

DIESEL FUEL

0.38

0.4

0.41

0.41

COMMERCIAL

BASELINE

ELECTRICITY

4.61

4.86

5.26

5.92

COMMERCIAL

BASELINE

PIPELINE GAS

2.97

3.01

3.09

3.17

COMMERCIAL

BASELINE

SOLAR

0

0

0

0

COMMERCIAL

BASELINE

STEAM

0.1

0.11

0.12

0.13

COMMERCIAL

LOW ELECTRIFICATION 350

DIESEL FUEL

0.38

0.38

0.31

0.21

© 2019 by Evolved Energy Research 98

COMMERCIAL LOW ELECTRIFICATION 350

ELECTRICITY

4.64

4.71

5.21

6.01

COMMERCIAL LOW ELECTRIFICATION 350

PIPELINE GAS

2.97

2.86

2.08

1.09

COMMERCIAL LOW ELECTRIFICATION 350

SOLAR

0

0

0

0

COMMERCIAL LOW ELECTRIFICATION 350

STEAM

0.1

0.11

0.12

0.13

PRODUCTIVE BASE 350

ASPHALT

0.88

0.89

0.99

1.06

PRODUCTIVE BASE 350

BIOMASS – WOOD

0.13

0.14

0.13

0.15

PRODUCTIVE BASE 350

COAL

0.37

0.34

0.3

0.32

PRODUCTIVE BASE 350

COKING COAL

0.55

0.55

0.53

0.45

PRODUCTIVE BASE 350

DIESEL FUEL

1.16

1.06

0.9

0.8

PRODUCTIVE BASE 350

ELECTRICITY

3.44

4.2

5.35

5.7

PRODUCTIVE BASE 350

GASOLINE FUEL

0.17

0.16

0.14

0.13

PRODUCTIVE BASE 350

LPG FEEDSTOCKS

0.51

0.6

0.65

0.68

PRODUCTIVE BASE 350

LPG FUEL

0.34

0.32

0.3

0.29

PRODUCTIVE BASE 350

MUNICIPAL SOLID WASTE

0.11

0.1

0.09

0.08

PRODUCTIVE BASE 350

NATURAL GAS FEEDSTOCKS

0.49

0.54

0.56

0.56

PRODUCTIVE BASE 350

OTHER PETROLEUM

0.35

0.3

0.26

0.25

PRODUCTIVE BASE 350

PETROCHEMICAL FEEDSTOCKS

0.34

0.42

0.46

0.49

PRODUCTIVE BASE 350

PETROLEUM COKE

0.24

0.19

0.13

0.13

PRODUCTIVE BASE 350

PIPELINE GAS

5.3

4.46

2.93

2.78

PRODUCTIVE BASE 350

RESIDUAL FUEL OIL

0.1

0.09

0.04

0.04

PRODUCTIVE BASE 350

STEAM

1.37

1.46

2.09

2.3

PRODUCTIVE BASELINE

ASPHALT

0.88

1

1.25

1.56

PRODUCTIVE BASELINE

BIOMASS – WOOD

0.13

0.15

0.15

0.17

PRODUCTIVE BASELINE

COAL

0.36

0.37

0.39

0.43

PRODUCTIVE BASELINE

COKING COAL

0.55

0.55

0.53

0.45

PRODUCTIVE BASELINE

DIESEL FUEL

1.16

1.29

1.37

1.49

PRODUCTIVE BASELINE

ELECTRICITY

3.43

3.73

3.98

4.32

PRODUCTIVE BASELINE

GASOLINE FUEL

0.17

0.18

0.18

0.19

© 2019 by Evolved Energy Research 99

PRODUCTIVE BASELINE

LPG FEEDSTOCKS

0.51

0.6

0.65

0.68

PRODUCTIVE BASELINE

LPG FUEL

0.34

0.35

0.37

0.39

PRODUCTIVE BASELINE

MUNICIPAL SOLID WASTE

0.11

0.11

0.11

0.12

PRODUCTIVE BASELINE

NATURAL GAS FEEDSTOCKS

0.49

0.54

0.56

0.56

PRODUCTIVE BASELINE

OTHER PETROLEUM

0.35

0.35

0.35

0.37

PRODUCTIVE BASELINE

PETROCHEMICAL FEEDSTOCKS

0.34

0.42

0.46

0.49

PRODUCTIVE BASELINE

PETROLEUM COKE

0.25

0.25

0.23

0.24

PRODUCTIVE BASELINE

PIPELINE GAS

5.3

5.51

5.76

6.1

PRODUCTIVE BASELINE

RESIDUAL FUEL OIL

0.11

0.12

0.1

0.11

PRODUCTIVE BASELINE

STEAM

1.36

1.39

1.46

1.6

PRODUCTIVE LOW ELECTRIFICATION 350

ASPHALT

0.88

0.89

0.99

1.06

PRODUCTIVE LOW ELECTRIFICATION 350

BIOMASS – WOOD

0.13

0.15

0.15

0.15

PRODUCTIVE LOW ELECTRIFICATION 350

COAL

0.37

0.37

0.35

0.34

PRODUCTIVE LOW ELECTRIFICATION 350

COKING COAL

0.55

0.55

0.53

0.45

PRODUCTIVE LOW ELECTRIFICATION 350

DIESEL FUEL

1.16

1.17

1.01

0.88

PRODUCTIVE LOW ELECTRIFICATION 350

ELECTRICITY

3.44

3.65

4.46

5.44

PRODUCTIVE LOW ELECTRIFICATION 350

GASOLINE FUEL

0.17

0.16

0.14

0.13

PRODUCTIVE LOW ELECTRIFICATION 350

LPG FEEDSTOCKS

0.51

0.6

0.65

0.68

PRODUCTIVE LOW ELECTRIFICATION 350

LPG FUEL

0.34

0.32

0.3

0.29

PRODUCTIVE LOW ELECTRIFICATION 350

MUNICIPAL SOLID WASTE

0.11

0.1

0.09

0.08

PRODUCTIVE LOW ELECTRIFICATION 350

NATURAL GAS FEEDSTOCKS

0.49

0.54

0.56

0.56

PRODUCTIVE LOW ELECTRIFICATION 350

OTHER PETROLEUM

0.35

0.32

0.28

0.25

PRODUCTIVE LOW ELECTRIFICATION 350

PETROCHEMICAL FEEDSTOCKS

0.34

0.42

0.46

0.49

PRODUCTIVE LOW ELECTRIFICATION 350

PETROLEUM COKE

0.24

0.23

0.18

0.14

PRODUCTIVE LOW ELECTRIFICATION 350

PIPELINE GAS

5.3

5.32

4.36

3.2

PRODUCTIVE LOW ELECTRIFICATION 350

RESIDUAL FUEL OIL

0.1

0.11

0.07

0.04

PRODUCTIVE LOW ELECTRIFICATION 350

STEAM

1.37

1.46

2.09

2.3

RESIDENTIAL BASE 350

BIOMASS – WOOD

0.43

0.43

0.44

0.43

© 2019 by Evolved Energy Research 100

RESIDENTIAL BASE 350

COAL

0

0

0

0

RESIDENTIAL BASE 350

DIESEL FUEL

0.49

0.34

0.13

0.01

RESIDENTIAL BASE 350

ELECTRICITY

4.6

4.84

5.27

5.32

RESIDENTIAL BASE 350

KEROSENE FUEL

0.02

0.01

0.01

0

RESIDENTIAL BASE 350

LPG FUEL

0.43

0.28

0.08

0

RESIDENTIAL BASE 350

PIPELINE GAS

4.61

2.9

0.72

0.05

RESIDENTIAL BASE 350

SOLAR

0.01

0.01

0.01

0.01

RESIDENTIAL BASELINE

BIOMASS – WOOD

0.43

0.43

0.45

0.44

RESIDENTIAL BASELINE

COAL

0

0

0

0

RESIDENTIAL BASELINE

DIESEL FUEL

0.48

0.49

0.54

0.53

RESIDENTIAL BASELINE

ELECTRICITY

4.55

4.59

4.85

4.97

RESIDENTIAL BASELINE

KEROSENE FUEL

0.02

0.02

0.02

0.02

RESIDENTIAL BASELINE

LPG FUEL

0.43

0.44

0.47

0.47

RESIDENTIAL BASELINE

PIPELINE GAS

4.59

4.75

4.97

4.96

RESIDENTIAL BASELINE

SOLAR

0.01

0.01

0.01

0.01

RESIDENTIAL LOW ELECTRIFICATION 350

BIOMASS – WOOD

0.43

0.42

0.42

0.4

RESIDENTIAL LOW ELECTRIFICATION 350

COAL

0

0

0

0

RESIDENTIAL LOW ELECTRIFICATION 350

DIESEL FUEL

0.49

0.46

0.32

0.12

RESIDENTIAL LOW ELECTRIFICATION 350

ELECTRICITY

4.6

4.35

4.58

4.99

RESIDENTIAL LOW ELECTRIFICATION 350

KEROSENE FUEL

0.02

0.02

0.01

0.01

RESIDENTIAL LOW ELECTRIFICATION 350

LPG FUEL

0.43

0.42

0.27

0.09

RESIDENTIAL LOW ELECTRIFICATION 350

PIPELINE GAS

4.61

4.32

2.65

0.77

RESIDENTIAL LOW ELECTRIFICATION 350

SOLAR

0.01

0.01

0.01

0.01

TRANSPORTATION BASE 350

COMPRESSED PIPELINE GAS

0.07

0.04

0.01

0

TRANSPORTATION BASE 350

DIESEL FUEL

7.42

5.15

3.24

3.21

TRANSPORTATION BASE 350

ELECTRICITY

0.05

2.99

6.1

6.65

TRANSPORTATION BASE 350

GASOLINE FUEL

19.9

10.6

1.11

0.29

TRANSPORTATION BASE 350

JET FUEL

2.17

2.2

2.17

2.05

© 2019 by Evolved Energy Research 101

TRANSPORTATION

BASE 350

LIQUEFIED PIPELINE GAS

0.01

0.08

0.17

0.25

TRANSPORTATION

BASE 350

LIQUID HYDROGEN

0

0

0

0

TRANSPORTATION

BASE 350

LPG FUEL

0.05

0.03

0

0

TRANSPORTATION

BASE 350

LUBRICANTS

0.14

0.14

0.14

0.14

TRANSPORTATION

BASE 350

RESIDUAL FUEL OIL

0.35

0.41

0.48

0.54

TRANSPORTATION

BASELINE

COMPRESSED PIPELINE GAS

0.07

0.07

0.07

0.07

TRANSPORTATION

BASELINE

DIESEL FUEL

7.44

7.29

7.17

7.65

TRANSPORTATION

BASELINE

ELECTRICITY

0.03

0.04

0.05

0.06

TRANSPORTATION

BASELINE

GASOLINE FUEL

19.93

18.11

16.79

16.39

TRANSPORTATION

BASELINE

JET FUEL

2.17

2.53

2.92

3.34

TRANSPORTATION

BASELINE

LIQUEFIED PIPELINE GAS

0.01

0.09

0.18

0.26

TRANSPORTATION

BASELINE

LIQUID HYDROGEN

0

0

0

0

TRANSPORTATION

BASELINE

LPG FUEL

0.05

0.06

0.06

0.07

TRANSPORTATION

BASELINE

LUBRICANTS

0.14

0.14

0.14

0.14

TRANSPORTATION

BASELINE

RESIDUAL FUEL OIL

0.35

0.41

0.48

0.54

TRANSPORTATION

LOW ELECTRIFICATION 350

COMPRESSED PIPELINE GAS

0.07

0.07

0.04

0.01

TRANSPORTATION

LOW ELECTRIFICATION 350

DIESEL FUEL

7.43

7.02

5.07

3.62

TRANSPORTATION

LOW ELECTRIFICATION 350

ELECTRICITY

0.04

0.4

3.01

5.95

TRANSPORTATION

LOW ELECTRIFICATION 350

GASOLINE FUEL

19.91

17.19

9.48

2.1

TRANSPORTATION

LOW ELECTRIFICATION 350

JET FUEL

2.17

2.2

2.17

2.05

TRANSPORTATION

LOW ELECTRIFICATION 350

LIQUEFIED PIPELINE GAS

0.01

0.09

0.18

0.25

TRANSPORTATION

LOW ELECTRIFICATION 350

LIQUID HYDROGEN

0

0

0

0

TRANSPORTATION

LOW ELECTRIFICATION 350

LPG FUEL

0.05

0.05

0.03

0.01

TRANSPORTATION

LOW ELECTRIFICATION 350

LUBRICANTS

0.14

0.14

0.14

0.14

TRANSPORTATION

LOW ELECTRIFICATION 350

RESIDUAL FUEL OIL

0.35

0.41

0.48

0.54

© 2019 by Evolved Energy Research 102

2. Energy Supply Scenario Descriptions

Energy supply portfolios are selected using the RIO optimization based on the economy-wide emissions constraint employed. The tables below show the cumulative and annual emissions constraint employed on energy and industrial process CO2 in this analysis. This also includes any contribution from direct air capture. The cumulative emissions caps from 2020 through 2050 for the Base 350, No New Nuclear 350, Limited Biomass 350, No Tech NETS 350, and Low Electrification 350 represent a cumulation of Hansen’s CO2 trajectories from 2020 through 2050. The annual target in 2050 of 828 MMT ensures that we are on the required low- emissions trajectory for post-2050 emissions. The Low Land NETS 350 case requires a different methodology, as achievement of this emissions target encourages net-negative emissions by 2050. Given this, we use the entire 2020 through 2100 emissions budget and additionally assume that at least the negative emissions achieved in 2050 persist through 2100. This results in a cumulative emissions target of 57 MMT (47 MMT represents the 2020-2100 emissions budget plus 10 MMT which represents 50 years of -200 MMT per year of emissions). The Baseline scenario is only required to maintain the 2020 emissions cap through 2050. This is not binding.

Table 7 Emissions targets for each scenario

Energy Economy Scenarios

Cumulative Emissions Target (2020- 2050)

Annual Emissions Target – 2050

Baseline

N/A

5300

Base 350

75 MMT

828

No New Nuclear 350

75 MMT

828

Limited Biomass 350

75 MMT

828

No Tech NETS 350

75 MMT

828

Low Land NETS 350

57 MMT

-200

Low Electrification 350

75 MMT

828

In addition to differing targets, the energy economy scenarios employ different constraints on potential energy supply options. Specifically, the scenarios constrain the availability of

© 2019 by Evolved Energy Research 103

technological NETS (No Tech NETS 350), primary biomass resources (Limited Biomass 350), and advanced nuclear plants (No New Nuclear 350). These constraints are shown in the table below.

Table 8 Additional scenario constraints

Energy Economy Scenarios

Additional Constraint

No New Nuclear

No additional nuclear resources are allowed to be built.

Limited Biomass

Supply of herbaceous and woody biomass is reduced by 50% in 2050.

No Tech NETS

No biomass with CCS or direct air capture with sequestration technologies are allowed to be built.

3. Model Overview

The EnergyPATHWAYS model is a comprehensive energy accounting and analysis frameworks specifically designed to examine the large-scale energy system transformations. It accounts for the costs and emissions associated with producing, transforming, delivering, and consuming energy in an economy. It has strengths in infrastructure accounting and electricity operations hat separate it from models of similar types. It is used, as it has been in this analysis, to calculate the impacts of energy system decisions out into the future in terms of infrastructure; emissions, and cost impacts to energy consumers and the economy more broadly.

The model works using decision-making “stasis” as a baseline. This means, for example, that when projecting energy demand for residential space heating, EnergyPATHWAYS implicitly assume that consumers will replace their water heater with a water heater of a similar type. This baseline does, however, include efficiency gains and technology development either required by codes and standards or reasonably anticipated based on techno-economic projections. If there are deviations from the current system in terms of technology deployment, these are made explicit in our scenario with the application of measures, which represent explicit user-defined changes to the baseline. These can take the form of adjustments of sales shares measures – changes in the relative penetration of technology adoption in a defined year; or stock measures, changes to the amount of technology deployment by a defined year. A

© 2019 by Evolved Energy Research 104

further description of measures is found in the Scenario section of the technical documentation.

4. Model Structure

EnergyPATHWAYS projects energy demand and costs in subsectors based on explicit user- decisions about technology adoption (I.e. electric vehicle adoption) and activity levels (I.e. reduced VMTs). These projections of energy demand across energy carriers are then sent to the supply-side of the model, which calculates upstream energy flows, primary energy usage, infrastructure requirements, emissions, and costs of supplying energy. These supply-side outputs are then combined with the demand-side outputs to calculate the total energy flows, emissions, and costs of the modeled energy system. Figure 33 shows the basic calculation steps for EnergyPATHWAYS as well as the outputs from each step.

© 2019 by Evolved Energy Research 105

Figure 33 EnergyPATHWAYS calculation steps

Initial Demand-Side Calculations

• Energy Demand

• Infrastructure Costs

Initial Supply-Side Calculations

• Energy Demand/Supply Mapping

• Energy Exports

Electricity Dispatch

• Grid Infrastructure

Needs
• Thermal

Final Supply-Side Calculations

• Infrastructure Costs

• Product and Primary Energy

Final Demand-Side Calculations

• Demand-Side Emissions

In the following section EnergyPATHWAYS separately detail the demand-side and supply-side of this calculation framework.

4.1 Subsectors

Subsectors represent separately modeled units of demand for energy services. These are often referred to as end-uses in other modeling frameworks. EnergyPATHWAYS is flexible in the configuration of these subsectors and the choices in the subsector detail rendered depends heavily on data availability. The high level of detail in subsectors in the US EnergyPATHWAYS database represents the availability of numerous high-quality data sources for the US energy

© 2019 by Evolved Energy Research 106

economy, which allows us to represent demand for energy services on a highly detailed, granular basis. We will describe the calculations for individual subsectors on the demand-side in this document, but assessing the total demand is simply the summation of these calculations for all subsectors.

4.2 Energy Demand Projection

Data availability informs subsector granularity and informs the methods used in each subsector. The flow diagram below represents the decision matrix used to determine the potential methods used to detail an individual energy demand subsector. The arrow downward indicates a progression from most-preferred to least-preferred methodology for detailing a subsector. More preferred methods allow for more explicit interventions of measures and better accounting for costs and energy impacts of concrete actions. Each method for projecting energy demand is described below.

4.2.1 Method A: Stock and Service Demand

This method is the most explicit representation of energy demand possible in the EnergyPATHWAYS framework. It has a high data requirement, however, as many end-uses are not homogenous enough to represent with technology stocks and others do not have

© 2019 by Evolved Energy Research 107

measurements of energy service demand. When they do EnergyPATHWAYS use the following formula to calculate energy demand from the subsector.

Equation 1

= �� ∗ ∗ ∗(1− ) Where ∈ =

E = Energy demand in year y of energy carrier c in region r

= Normalized share of service demand in year y of vintage v of technology t for energy carrier c in region r

= Efficiency (energy/service) of vintage v of technology t using energy carrier c = Total service demand input aggregated for year y in region r

= Unitized service demand reductions for year y in region r for energy carrier c. Service demand reductions are calculated from input service demand measures, which change the baseline energy service demand levels.

4.2.1.1 Service Demand Share (U)

The normalized share of service demand is calculated as a function of the technology stock (S), service demand modifiers (M), and energy carrier utility factors (C). Below is the decomposition of U into its component parts of S and M and C.

Equation2 ∗ ∗ =

∑∈ ∑∈ ∗

Where
= Technology stock in year y of vintage v of technology t in region r
= Service demand modifier in year y for vintage v for vintage t in region r = Utility factor for energy carrier c for technology t
The calculation of these are detailed in the sections below

© 2019 by Evolved Energy Research 108

4.2.1.2 Technology Stock (S)

The composition of the technology stock is governed by technology stock rollover mechanics in the model, technology inputs (lifetime parameters, technology decay parameters), initial technology stock states, and the application of sales share or stock measures. The section below describes the ways in which these model variables can affect the eventual calculation of technology share.

4.2.1.2.1 Initial Stock

The model uses an initial representation of the technology stock to project forward. This usually represents a single-year stock representation based on customer survey data (I.e. U.S. Commercial Building Energy Consumption Survey data informs 2012 technology stock estimates) but can also be “specified” into the future, where the composition of the stock is determined exogenously. At the end of this initial stock specification, the model uses technology parameters and rollover mechanics to determine stock compositions by year.

4.2.1.2.2 Stock Decay and Replacement

EnergyPATHWAYS allows for technology stocks to decay using linear representations or Weibull distributions, which are typical functions used to represent technology reliability and failure rates. These parameters are governed by a combination of technology lifetime parameters. Technology lifetimes can be entered as minimum and maximum lifetimes or as an average lifetime with a variance.

After the conclusion of the initial stock specification period, the model decays existing stock based on the age of the stock, technology lifetimes, and specified decay functions. This stock decay in a year (y) must be replaced with technologies of vintage (v) v = y. The share of replacements in vintage v is equal to the share of replacements unless this default is overridden with exogenously specified sales share or stock measures. This share of sales is also used to inform the share of technologies deployed to meet any stock growth.

4.2.1.2.3 Sales Share Measures

Sales share measures override the pattern of technologies replacing themselves in the stock rollover.

© 2019 by Evolved Energy Research 109

An example of a sales share measure is shown below for two technologies – A and B – that are represented equally in the initial stock and have the same decay parameters. EnergyPATHWAYS apply a sales share measure in the year 2020 that requires 80% of new sales in 2020 to be technology A and 20% to be technology B. The first equation shows the calculation in the absence of this sales share measure. The second shows the stock rollover governed with the new sales share measure.

S = Stock

D = Stock decay

G = Year on year stock growth

R = Stock decay replacement

N = New Sales

a = Technology A

b = Technology B

Before Measure (i.e. Baseline)

2019 = 100

2019 = 50

2019 = 50

2020 = 10

2020 = 5

2020 = 5

2020 = 110

2020 = 2020 −2019 =110−100=10

2020 = 2020 = 5

2020 = 2020 = 5

2020 = 2020 ∗ 2020 = 5/10 * 10 =5 2020

© 2019 by Evolved Energy Research 110

2020 =2020 ∗ 2020 =5/10*10=5 2020

2020 =2020 + 2020=5+5=10

2020 =2020 + 2020 =5+5=10

2020 =2019 + 2020 + 2020 =50–5+10=55

2020 =2019 + 2020 + 2020 =50–5+10=55 After Sales Share Measure

2019 = 100

2019 = 50

2019 = 50

2020 = 10

2020 = 5

2020 = 5

2020 = 110

2020 = 2020 −2019 =110−100=10

2020 =2020 ∗2020 =10*.8=8

2020 =2020 ∗2020 =10*.2=2

2020 =2020 ∗2020 =10*.8=8

2020 =2020 ∗2020 =10*.2=2

2020 =2020 + 2020=8+8=16

2020 =2020 + 2020 =2+2=4

2020 =2019 + 2020 + 2020 =50–5+16=61

2020 =2019 + 2020 + 2020 =50–5+4=49
This shows a very basic example of the role that sales share measures play to influence the

stock of technology. In the context of energy demand, these technologies can use different

© 2019 by Evolved Energy Research 111

energy carriers (i.e. gasoline internal combustion engine vehicles to electric vehicles) and/or have different efficiency characteristic.

Though not shown in the above example, the stock is tracked on a vintaged basis, so decay of technology A in 2020 in the above example would be decay in 2020 of all vintages before 2020. In the years immediately succeeding the deployment of vintage cohort, there is very little technology retirement given the shape of the decay functions. As a vintage approaches the end of their anticipated useful life, however, retirement accelerates.

4.2.1.2.4 Stock Specification Measures

EnergyPATHWAYS also allows for stock specification measures, which create exogenous specification of technology stocks along the year index (i.e. existing stock in a year), as opposed to sales share measures which operate along the vintage index (i.e. sales in a year). They both interact with the same basic stock rollover mechanics in the model but are interpreted differently by the model.

In the example below, EnergyPATHWAYS replicate the stock in 2020 of our previous sales share example where Technology A is 61 units in 2020 and Technology B is 49 Units.

After Stock Specification Measure

2019 = 100

2019 = 50

2019 = 50

2020 = 10

2020 = 5

2020 = 5

2020 = 110

2020 = 2020 −2019 =110−100=10

2020 =2020 − 2019 + 2020 =61–50+5=16 2020 =2020 −2020 =110–61=49

© 2019 by Evolved Energy Research 112

2020 =2020 − 2019 + 2020 =49–50+5=4 2020 =2020 = .8

2020 2020
=2020 =.2

2020
2020 =2020 ∗2020 =10*.8=8 2020 =2020 ∗2020 =10*.2=2 2020 =2020 ∗2020 =10*.8=8 2020 =2020 ∗2020 =10*.2=2

The model uses the stock specifications to produce sales shares that result in the specified stock. Where a stock specification measure requires more new sales than are available through natural rollover decay and stock growth, the model early-retires infrastructure to increase the pool of available sales based on the probability of retirement for given combination of vintage and technology. The model separately tracks physical and financial lifetimes, so even though technologies may be taken out of service, they are still paid for. Further discussion of this accounting can be found in 4.2.2.1.

4.2.1.3 Service Demand Modifier (M)

Many energy models use stock technology share as a proxy for service demand share. This makes the implicit assumption that all technologies of all vintage in a stock are used equally. This assumption obfuscates some key dynamics that influence the pace and nature of energy system transformation. For example, new heavy-duty vehicles are used heavily at the beginning of their useful life but are sold to owners who operate them for reduced duty-cycles later in their lifecycles. This means that electrification of this fleet would accelerate the rollover of electrified miles faster than it would accelerate the rollover of the trucks themselves. Similar dynamics are at play in other vehicle subsectors. In subsectors like residential space heating, the distribution of current technology stock is correlated with its utilization. Even within the same region, with the same climactic conditions, the choice of heating technology informs its

© 2019 by Evolved Energy Research 113

usage. Homes that have baseboard electric heating, for example, are often seasonal homes with limited heating loads.

EnergyPATHWAYS has two methods for determining the discrepancy between stock shares and service demand shares. First, technologies can have the input of a service demand modifier. This is used an adjustment between stock share and service demand share.

Using the example stock of Technology, A and B, the formula below shows the impact of service demand modifier on the service demand share.15

2019 = 100

2019 = 50

2020 = 50

2019 = 2019 = 50 = .5 =2019 =100=.5

2019 2019 50 2019 100

2019 = 2 2019 = 1

2019 = 2019∗2019 = 50∗2 = .667 ∑=.. 2019∗2019 150

2019 = 2019∗2019 = 50∗1 = .333 ∑= 2019∗2019 150

When service demand modifiers aren’t entered for individual technologies, they can potentially still be calculated using input data. For example, if the service demand input data is entered with the index of t, the model calculates service demand modifiers by dividing stock and service demand inputs.

15 EnergyPATHWAYS again ignore the index of vintage (v) for simplicity, but this is an important index to reflect technology utilization determined by age.

© 2019 by Evolved Energy Research 114

Equation 3

= Where

= Service demand modifier for technology t in year y in region r = Stock input data for technology t in year y in region r
= Energy demand input data for technology t in year y in region r 4.2.1.3.1 Energy Carrier Utility Factors (C)

Energy carrier utility factors are technology inputs that allocates a share of the technology’s service demand to energy carriers. The model currently supports up to two energy carriers per technology. This allows EnergyPATHWAYS to support analysis of dual-fuel technologies, like plug-in-hybrid electric vehicles. The input structure is defined as a primary energy carrier with a utility factor (0 – 1) and a secondary energy carrier that has a utility factor of 1 – the primary utility factor.

4.2.1.4 Method B: Stock and Energy Demand

Method B is like Method A in almost all its components except for the calculation of the service demand term. In Method A, service demand is an input. In Method B, the energy demand of a subsector is input as a substitute. From this input, EnergyPATHWAYS must take the additional step of deriving service demand, based on stock and technology inputs.

E = Energy demand in year y of energy carrier c in region r

U = Normalized share of service demand in year y of vintage v of technology t for energy carrier c in region r

f = Efficiency (energy/service) of vintage v of technology t using energy carrier c

Equation 4

= �� ∗ ∗ ∗(1−) Where ∈ =

© 2019 by Evolved Energy Research 115

D = Total service demand calculated for year y in region r
= Unitized service demand reductions for year y in region r for energy carrier c 4.2.1.4.1 Total Service Demand (D)

Total service demand is calculated using stock shares, technology efficiency inputs, and energy demand inputs. The intent of this step is to derive a service demand term (D) that allows us to use the same calculation framework as Method A.

= Total service demand in year y in region r
= Efficiency (energy/service) of vintage v of technology t using energy carrier c = Input energy data in year y of carrier c in region r
4.2.1.5 Method C: Service and Service Efficiency

Method C is used when EnergyPATHWAYS do not have sufficient input data, either at the technology level or the stock level, to parameterize a stock rollover. Instead EnergyPATHWAYS replace the stock terms in the energy demand calculation with a service efficiency term (j). This is an exogenous input that substitutes for the stock rollover dynamics and outputs in the model.

where
= Energy demand in year y for energy carrier c in region r
= Service efficiency (energy/service) of subsector in year y for energy carrier c in region r = Input service demand for year y in region r
= Unitized service demand multiplier for year y in region r for energy carrier c

Equation 5

= ��� ∗ ∗ Where ∈ ∈ =

Equation 6

= ∗ ∗ −

© 2019 by Evolved Energy Research 116

= Energy efficiency savings in year y in region r for energy carrier c

4.2.1.5.1 Energy Efficiency Savings (O)

Energy efficiency savings are a result of specified energy efficiency measures in the model. These take the form of prescribed levels of energy savings measures that are netted off the baseline projection of energy usage.

4.2.1.6 Method D: Energy Demand

The final method is simply the use of an exogenous specification of energy demand. This is used for subsectors where there is neither the data necessary to populate a stock rollover nor any data available to decompose energy use from its underlying service demand.

Equation 7

= −

Where

= Energy demand in year y for energy carrier c in region r

= Input baseline energy demand in year y for energy carrier c in region r

= Energy efficiency savings in year y in region r for energy carrier c

4.2.2 Demand-Side Costs

Cost calculations for the demand-side are separable into technology stock costs and measure costs (energy efficiency and service demand measures).

4.2.2.1 Technology Stock Costs

EnergyPATHWAYS uses vintaged technology cost characteristics as well as the calculated stock rollover to calculate the total costs associated with technology used to provide energy services.16

16 Levelized costs are the principal cost metric reported, but the model also calculates annual costs (i.e. the cost in 2020 of all technology sold).

© 2019 by Evolved Energy Research 117

= + + +

Where
= Total levelized stock costs in year y in region r

= Total levelized capital costs in year y in region r

= Total levelized installation costs in year y in region r

= Total levelized fuel switching costs in year y in region r

= Total fixed operations and maintenance costs in year y in region r

4.2.2.1.1 Technology Stock Capital Costs

The model uses information from the physical stock rollover used to project energy demand, with a few modifications. First, the model uses a different estimate of technology life. The financial equivalent of the physical “decay” of the technology stock is the depreciation of the asset. EnergyPATHWAYS uses a linear function with a maximum and minimum life of the mean technology life, meaning that all financial decay takes place in one year (i.e. the asset comes off of the financial books). This is referred to as the “book life” of the asset.

To provide a concrete example of this, a 2020 technology vintage with a book life of 15 years is maintained in the financial stock in its entirety for the 15 years before it is financially “retired” in 2035. This financial stock estimate, in addition to being used in the capital costs calculation, is used for calculating installation costs and fuel switching costs.

∗ ∈

Equation 8

= ∑

Where
= Total levelized technology costs in year y in region r

= Levelized capital costs for technology t for vintage v in region r

= Financial stock of technology t and vintage v in year y in region r

© 2019 by Evolved Energy Research 118

EnergyPATHWAYS primarily use this separate financial accounting so that EnergyPATHWAYS accurately account for the costs of early-retirement of technology. There is no way to financially early-retire an asset, so physical early retirement increases overall costs (by increasing the overall financial stock).

4.2.2.1.2 Levelized Capital Costs (W)

EnergyPATHWAYS levelized technology costs over the mean of their projected useful lives (referred to as book life). This is either the input mean lifetime parameter of the arithmetic mean of the technology’s max and min lifetimes. EnergyPATHWAYS additionally assess a cost of capital on this levelization of the technology’s upfront costs. While this may seem an unsuitable assumption for technologies that could be considered “out-of-pocket” purchases, EnergyPATHWAYS assume that all consumer purchases are made using backstop financing options. This is the implicit assumption that if “out-of-pocket” purchases were reduced, the amount needed to be financed on larger purchases like vehicles and homes could be reduced

in-kind. ∗ ∗ (1 + ) =

(1+) −1

Where
= Levelized capital costs for technology t for vintage v in region r

= Discount rate of technology t

= Capital costs of technology t in vintage v in region r

= Book life of technology t

4.2.2.2 Technology Stock Installation Costs

Installation costs represent costs incurred when putting a technology into service. The methodology for calculating these is the same as that used to calculate capital costs. These are levelized in a similar manner.

4.2.2.3 Technology Stock Fuel Switching Costs

© 2019 by Evolved Energy Research 119

Fuel switching costs represent costs incurred for a technology only when switching from a technology with a different primary energy carrier. This input is used for technologies like gas furnaces that may need additional gas piping if they are being placed in service in a household that had a diesel furnace. Calculating these costs requires the additional step of determining the number of equipment sales in a given year associated with switching fuels.

= ∑

∗ ∈

Where
= Financial stock associated with fuel-switched equipment installations

= Levelized fuel-switching costs for technology t for vintage v in region r

= Discount rate of technology t
= Fuel switching costs for technology t in vintage v in region r

4.2.2.4 Technology Stock Fixed Operations and Maintenance Costs

Fixed operations and maintenance (O&M) costs are the only stock costs that utilize physical and not financial representations of technology stock. This is because O&M costs are assessed annually and are only incurred on technologies that remain in service. If equipment has been retired, then it no longer has ongoing O&M costs.

= ∑ ∑ ∗ ∈ ∈

Where
= Technology stock of technology t in year y of vintage v in region r

= Fixed O&M costs for technology t for vintage v in region r

4.2.3 Measure Costs

Measure costs are assessed for interventions either at the service demand (service demand measures) or energy demand levels (energy efficiency measures). While these measures are abstracted from technology-level inputs, EnergyPATHWAYS uses a similar methodology for these measures as EnergyPATHWAYS do for technology stock costs. EnergyPATHWAYS use measure savings to create “stocks” of energy efficiency or service demand savings. These

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measure stocks are vintaged like technology stocks and EnergyPATHWAYS use analogous inputs like capital costs and useful lives to calculate measure costs.

4.2.3.1 Service Demand Measure Costs

Service demand measure costs are costs associated with achieving service demand reductions. In many cases, no costs are assessed for these activities as they represent conservation or improved land-use planning that occurs at zero or negative-costs.

Equation 9

= ∑

∗ ∈

Where
= Total service demand measure costs

= Financial stock of service demand reductions from measure m of vintage v in year y in

region r
= Levelized per-unit service demand reduction costs

4.2.3.2 Energy Efficiency Measure Costs

Energy efficiency costs are costs associated the reduction of energy demand. These are representative of incremental equipment costs or costs associated with non-technology interventions like behavioral energy efficiency.

Equation 10

= ∑

∗ ∈

Where
= Total energy efficiency measure costs

= Financial stock of energy demand reductions from measure m of vintage v in year y in

region r
= Levelized per-unit energy efficiency costs 5. Supply

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5.1 Supply Nodes

Supply nodes represent the fundamental unit of analysis on the supply-side and are analogous to subsectors on the demand-side. We will primarily describe the calculations for individual supply nodes in this document, but assessing the total costs and emissions from the supply-side is just the summation of all supply nodes for a year and region.

5.2 I/O Matrix

There is one principal difference between supply nodes and subsectors that explains the divergent approaches taken for calculating them; energy flows through supply nodes must be solved concurrently due to a number of dependencies between nodes. As an example, it is not possible to know the flows through the gas transmission pipeline node without knowing the energy flow through gas power plant nodes. This tenet requires a fundamentally different supply-side structure. To solve the supply-side, EnergyPATHWAYS leverages techniques from economic modeling by arranging supply nodes in an input-output matrix, where coefficients of a node represent units of other supply nodes required to produce the output product of that node.

Consider a simplified representation of upstream energy supply with four supply nodes:

  1. Electric Grid
  2. Gas Power Plant
  3. Gas Transmission Pipeline
  4. Primary Natural Gas

This is a system that only delivers final energy to the demand-side in the form of electricity from the electric grid. It also has the following characteristics:

  1. The gas transmission pipeline has a loss factor of 2% from leakage. It also uses grid electricity to power compressor stations and requires .05 units of grid electricity for every unit of delivered gas.
  2. The gas power plant has a heat rate of 8530 Btu/kWh, which means that it requires 2.5 (8530 Btu/kWh/3412 Btu/kWh) units of gas from the transmission pipeline for every unit of electricity generation.

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3. The electricity grid has a loss factor of 5%, so it needs 1.05 units of electricity generation to deliver 1 unit of electricity to its terminus.

The I/O matrix for this system is shown below in tabular form Table 9 as well as in matrix form below

Table 9 Tabular I/O Matrix

Natural Gas

Gas Transmission Pipeline

Gas Power Plant

Electric Grid

Natural Gas

1.02

Gas Transmission Pipeline

2.5

Gas Power Plant

1.05

Electric Grid

.05

Equation 11 1.05

⎛ 2.5⎞ A = ⎜ 1.05⎟ ⎝ .05 ⎠

With this I/O matrix, if we know the demand for energy from a node (supplied from the demand-side of the EnergyPATHWAYS model), we can calculate energy flows through every upstream supply node. To continue the example, if 100 units of electricity are demanded:

d=� 0 � 100

We can calculate the energy flow through each node using the equation, which represents the inverted matrix multiplied the demand term.

= ( − )−1 ∗

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This gives us the following result:

308

x = �302� 121

115

We use the I/O structure in much more complicated ways, and most of the supply-side calculations are focused on populating I/O coefficients and solving throughput through each node, which allows us to calculate infrastructure needs, costs, resource usage, and greenhouse gas emissions associated with energy supply

There are six distinct types of nodes that represent different components of the energy supply system. These will be examined individually in all of the supply-side calculation descriptions. The list below details some of their basic functionality

1. Conversion Nodes – Conversion nodes represent units of infrastructure specified at the technology level (i.e. gas combined cycle power plant) that have a primary purpose of converting the outputs of one supply node to the inputs of another supply node. Gas power plants in the above example are a conversion node, converting the output of the gas transmission pipeline to the inputs of the electric grid.

2. Delivery Nodes – Delivery nodes represent infrastructure specified at a non- technology level. The gas transmission pipeline is an example of a delivery node. A transmission pipeline system is the aggregation of miles of pipeline, hundreds of compressor stations, and storage facilities. We represent it as an aggregation of these components. The role of delivery nodes is to deliver the outputs of one supply node to a different physical location in the system required so that they can be used as inputs to another supply node. In the above example, gas transmission pipelines deliver natural gas from gas fields to gas power plants, which are not co-located with the resource.

3. Primary Nodes – Primary nodes are used for energy accounting, but they generally represent the terminus of the energy supply chain. That is, absent some exceptions, their coefficients are generally zero.

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4. Product Nodes – Product nodes are used to represent energy products where it is not possible to endogenously build up the costs and emissions through to their primary energy source. For example, we represent refined fuels as product nodes, generally, so that the price of these refined fuels can be divorced from the price of their primary oil inputs.

5. Blend Nodes – Blend nodes are non-physical control nodes in the energy supply chain. These are the locations in the energy system that we apply measures to change the relative inputs to other supply nodes. There are no blend nodes in the simplified example above, but an alternative energy supply system may add a biogas product node and place a blend node between the gas transmission pipeline and the primary natural gas node. This blend node would be used to control the relative inputs to the gas transmission pipeline (between natural gas and biogas).

6. Electric Storage Nodes – Electric storage nodes are nodes that provide a unique role in the electricity dispatch functionality of EnergyPATHWAYS.

5.3 Energy Flows

5.3.1 Coefficient Determination (A – Matrix)

The determination of coefficients is unique to supply-node types. For primary, product, and delivery nodes, these efficiencies are exogenously specified by year and region.

5.3.1.1 Conversion Nodes

Conversion node efficiencies are calculated as the weighted averages of the online technology stocks. We use both stock and capacity factor terms because we want the energy-weighted efficiency, not capacity-weighted.

Equation 12 ∗
=�� ∗

∈ ∈ ∑∈ ∑∈ ∗

Where
= Input coefficients in year y of node n in region r

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= Technology stock of technology t in year of vintage v in year y in region r
= Utilization rate, or capacity factor, of technology t of vintage v in year y in region r = Input requirements (efficiency) of technology t of vintage v using node n in region r 5.3.1.2 Blend Nodes

Blend node coefficients are user-determined. Blend measures determine the coefficients in each blend node in every year y and region r. Where measures haven’t been specified, or are incomplete (i.e. coefficients don’t sum to at least 1 as required) blend nodes have a user- specified “residual” supply node that supplies the remainder.

There are two blend nodes in the model that are treated differently than other blend nodes and both are related to the electricity dispatch functionality in EnergyPATHWAYS which will be described in further detail in the following sections. The primary purpose of the electricity dispatch functionality is to develop coefficients for the Electricity Blend Node and Thermal Dispatch Node.

5.3.1.2.1 Bulk Electricity Blend Node

The coefficients of the bulk electricity blend node, before EnergyPATHWAYS calculates an electricity dispatch, are user-determined. For example, a user may specify that they would like 50% of the bulk electricity energy to come from solar power plants and 50% of the energy to come from wind power plants. The electricity dispatch is used to calculate the feasibility of these selections given the hourly electricity profiles of the generation as well as the online balancing resources like energy storage, hydro, flexible electric fuel production (hydrogen electrolysis and power-to-gas), and flexible end-use loads. If sufficient balancing resources are available to balance the 50% wind and 50% solar system, in this case, then the coefficients of the node remain the same. If the dispatch finds, however, that residual thermal resources are required to supply electricity (i.e. the wind and solar generation cannot be completely balanced against demand) then the model calculates the need for residual energy supply from the Thermal Dispatch Node (which always functions as the residual node of the Bulk Electricity Blend Node). This results in a situation where the coefficients of the Bulk Electricity Blend Node are greater than 1 (.5 wind; .5 solar; >0 Thermal Dispatch). Coefficients greater than 1 in this case represent the curtailment of the unbalanced wind and solar generation.

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5.3.1.2.2 Thermal Dispatch Node

Energy requirements of the Thermal Dispatch Node are determined in the electricity dispatch process briefly described above. The coefficients of the Thermal Dispatch Node are determined in the thermal dispatch, which occurs after all other electricity dispatch processes and functions as the residual to the electricity dispatch. In this process, the share of the Thermal Dispatch Node output that come from different thermal resources like gas combined-cycle generators, gas combustion turbines, and coal power plants is determined using an economic dispatch stack model. Given the resource stack online in a year y, the model determines the share of generation that comes from each input node to the Thermal Dispatch Node and also determines the capacity factor of every vintage v and technology t combination in that supply node. The thermal dispatch process, therefore, influences both the overall flow through each node as well as the capacity factor term (U) in the efficiency determination.

5.3.2 Energy Demands

5.3.2.1 Demand Mapping

To help develop the (d) term in the matrix calculations described in section 5.2, EnergyPATHWAYS must map the demand for energy carriers calculated on the demand-side to specific supply-nodes. In the simplified energy system example, electricity as a final energy carrier, for example, maps to the Electric Grid supply node.

5.3.2.2 Energy Export Specifications

In addition to demand-side energy requirements, the energy supply system must also meet export demands, that is demand for energy products that aren’t used to satisfy endogenous energy service demands. These products aren’t ultimately consumed in the model, but their upstream impacts must still be accounted for.

5.3.2.3 Total Demand

Total demand is therefore the sum of endogenous energy demands from the demand-side of EnergyPATHWAYS as well as any specified energy exports.

Equation 13

= +

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127

Where

= Total energy demand in year y in region r for supply node n

= Endogenous energy demand in year y in region r for supply node n

= Export energy demand in year y in region r for supply node n

This total demand term is then multiplied by the inverted coefficient matrix to determine energy flows through each node.

5.4 Infrastructure Requirements

Infrastructure is represented only in delivery and conversion supply nodes. In delivery nodes, this infrastructure is represented at the aggregate node-level. In conversion nodes, infrastructure is represented in technology stocks similarly to stocks on the demand-side. The sections below detail the basic calculations used to determine the infrastructure capacity needs associated with energy flows through the supply node.

5.4.1 Delivery Nodes

The infrastructure capacity required is determined by Equation 14 below:

Equation 14

= Utilization (capacity) factor in year y in region r = Energy flow through node in year y in region r
h = Hours in a year, or 8760

=

∗ 8760 Where

17 Capacity factors of delivery nodes are endogenous inputs to the model except in the special cases of the Electricity Transmission Grid Node and the Electricity Distribution Grid node, where capacity factors are determined in the electricity dispatch.

P 17

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5.4.2 Conversion Nodes

Conversion nodes are specified on a technology-basis, and a conversion node can contain multiple technologies to produce the energy flow required by the supply system. The operations of these nodes are analogous to the demand-side in terms of stock rollover mechanics, with sales shares and specified stock measures determining the makeup of the total stock. The only difference is that the size of the total stock is determined by the demand for energy production for the supply node, which is different than on the demand-side, where the size of the total stock is an exogenous input.

The formula to determine the size of the total stock remains the essentially the same as the one used to determine the size of the total delivery stock. However, the average cap factor of the node is a calculated term determined by the weighted average capacity factor of the stock in the previous year:

Where
= Utilization (capacity) factor in year y in region r
−1 = Technology stock of technology t in year of vintage v in year y-1 in region r = Utilization rate, or capacity factor, of technology t of vintage v in year y in region r 5.5 Emissions

There are two categories of greenhouse gas emissions in the model. First, there are physical emissions. These are traditional emissions associated with the combustion of fuels, and they represent the greenhouse gas emissions embodied in a unit of energy. For example, natural gas has an emissions rate of 53.06 kG/MMBTU of consumption while coal has an emissions rate of 95.52 kG/MMBTU. Physical emissions are accounted for on the supply-side in the supply nodes where fuels are consumed, which can occur in primary, product, delivery, and conversion nodes. Emissions, or consumption, coefficients, that is the units of fuel consumed can be a subset of energy coefficients. While the gas transmission pipeline may require 1.03 units of

Equation 15

= ∑∈ ∑∈ −1 ∗

∑∈ ∑∈ −1

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natural gas, it only consumes .03 units. Gas power plants, however, consume all 2.5 units of gas required. Equation 16 shows the calculation of physical emissions in a node:

Equation 16

h=�∗ ∗h ∈

Where
h = Physical greenhouse gas emissions in year y in region r

Emissions rates are either a function of a direct connection in the I/O matrix to a node with an emissions coefficient or they are “passed through” delivery nodes, which don’t consume them. Gas powerplants in the supplied example take the emission rates from the Natural Gas Node, despite being linked in the I/O matrix only through the delivery node of Gas Transmission Pipeline.

The second type of emissions are accounting emissions. These are not associated with the consumption of energy products elsewhere in the energy system. Instead, these are a function of energy production in a node18. Accounting emissions rates are commonly associated with carbon capture and sequestration supply nodes or with biomass. Accounting emissions are calculated using:

18 For example, biomass may have a positive physical emissions rate, but if the biomass is considered to be zero-carbon, it would offset that with a negative accounting emissions rate. For accounting purposes, this would result in the Biomass Node showing negative greenhouse gas emissions and the supply nodes that use biomass, for example Biomass Power Plants, recording positive greenhouse gas emissions.

= Consumption coefficients in year y in region r of node n

= Energy flow through node in year y in region r
h = Emissions rates (emissions/energy) in year y in region r of input nodes n.

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Equation 17

= ∗

Where
= Accounting greenhouse gas emissions in the node in year y in region r

= Energy flow through the node in year y in region r

= Node accounting emissions rate

For primary, product, and delivery nodes, the accounting emissions rate in year y in region r is exogenously specified. For conversion nodes, this is an energy-weighted stock average.

= ∑ ∑ ∗ ∈ ∈

Where ∑∈ ∑∈
= Energy weighted average of node accounting emissions factor in year y in region r

= Stock of technology t of vintage v in year y in region r

= Exogenous inputs of accounting emissions rate for technology t of vintage v in year y in

region r

5.6 Costs

Costs are calculated using different methodologies for those nodes with infrastructure (delivery, conversion, and electric storage) and those without represented infrastructure (primary and product).

5.6.1 Primary and Product Nodes

Primary and product nodes are calculated as the multiplication of the energy flow through a node and an exogenously specified cost for that energy.

= ∗

Where
= total costs of supplying energy from node in year y in region r = Energy flow through node in year y in region r

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= Exogenous cost input for node in year y in region r

5.6.2 Delivery Nodes

Delivery node cost inputs are entered as per-energy unit tariffs. We use and adjust for any changes for the ratio of on-the-books capital assets and node throughput. This is done to account for dramatic changes in the utilization rate of capital assets in these nodes. This allows EnergyPATHWAYS to calculate and demonstrate potential death spirals for energy delivery systems, whereas the demand for energy from a node declines faster than the capital assets can depreciate. This pegs the tariff of the delivery node to the existing utilization rates of capital assets and increases them when that relationship diverges.

Equation 18
⎜ ∈1 ⎟

=⎛ ∗ ∑ ∗ ∗ +(1−)∗ ⎞∗ ∑

⎝ ∈1 ⎠

Where
= Total costs of delivery node in year y in region r = Physical stock of delivery node in year y in region r

= Financial stock of delivery node in year y in region r

= Exogenously specified utilization rate of delivery node in year y in region r
q = Share of tariff related to throughput-related capital assets, which are the only share of the

tariff subjected to this adjustment.
= Exogenous tariff input for delivery node in year y in region r = Energy flow through node in year y in region r
5.6.3 Conversion Nodes

Conversion node cost accounting is similar to the cost accounting of stocks on the demand-side with terms for capital, installation, and fixed O&M cost components. Instead of fuel switching costs, however the equation substitutes a variable O&M term.

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Equation 19

= + + +

Where
= Total levelized stock costs in year y in region r

= Total levelized capital costs in year y in region r

= Total levelized installation costs in year y in region r

= Total fixed operations and maintenance costs in year y in region r

= Total levelized variable operations and maintenance costs in year y in region r

There is no difference in the calculation of the capital, installation, and fixed O&M terms from the demand-side, so reference calculation for calculating those components of technology stocks in section 4.2.2.1.

5.6.3.1 Variable O&M Costs

Variable O&M costs are calculated as the energy weighted average of technology stock variable

O&M costs. ∗ = � �

∗ ∗ ∈ ∈ ∑∈ ∑∈ ∗

Where

= Total levelized variable operations and maintenance costs in year y in region r

= Technology stock of technology t in year of vintage v in year y in region r

= Utilization rate, or capacity factor, of technology t of vintage v in year y in region r = Exogenous input of variable operations and maintenance costs for technology t of

vintage v in region r in year y
= Energy flow through node in year y in region r

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5.6.4 Electric Storage Nodes

Electric storage nodes are a special case of node used in the electricity dispatch. They add an additional term, which is a capital energy cost, to the equation used to calculate the costs for conversion nodes. This is the cost for the storage energy capacity, which is additive with the storage power capacity.

= + + +

Where
= Total levelized stock costs in year y in region r

= Total levelized capital costs in year y in region r

= Total levelized energy capital costs in year y in region r

= Total levelized installation costs in year y in region r

= Total fixed operations and maintenance costs in year y in region r

= Total levelized variable operations and maintenance costs in year y in region r

5.6.4.1 Energy Capacity Costs

Energy storage nodes have specified durations, defined as the ability to discharge at maximum power capacity over a specified period of time, and also have an input of energy capital costs, which are levelized like all capital investments.

Equation 20

=�� ∗∗ ∈ ∈

Where
= Total levelized energy capacity capital costs in year y in region r

= Levelized energy capacity capital costs for technology t for vintage v in region r

= Exogenously specified discharge duration of technology t
= Financial stock of technology t and vintage v in year y in region r

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6. Regional Investment and Operations Platform and EnergyPATHWAYS integration

EnergyPATHWAYS is a scenario analysis tool, with a focus on detailed and explicit accounting of energy system decisions. There are advantages, however, in employing optimization approaches for a more limited subset of energy system decisions. The Regional Investment and Operations (RIO) platform is a complementary optimization approach where we develop a subset of decisions on the energy supply-side that benefit from linear optimization techniques to develop a co-optimization of fuel and supply-side infrastructure decisions under different scenarios of energy demand and emissions constraints. RIO is utilized to inform two types of EnergyPATHWAYS measures:

• Stock Measures
RIO can be used to optimize capacity decisions in electricity generation (e.g. wind, solar, etc.),

electricity storage, and fuel conversion processes.

• Blend Measures

RIO can also be used to optimize blend ratios for fuel. This allows for optimal determinations of bio-based, fossil-based, or electrically produced fuels (i.e. hydrogen, or power-to-gas synthetic natural gas).

RIO is also used as the tool for assessing the reliability of the electricity system, with hourly dispatch representations for all zones and resources including thermal, electricity storage, fixed output (i.e. renewables), and flexible loads (fuel production, direct air capture, etc.)

6.1 EnergyPATHWAYS/RIO Integration

The EnergyPATHWAYS/RIO integration is a multi-step process where:

  • EnergyPATHWAYS is used to define energy demand scenarios as parameterizations for RIO optimizations.
  • RIO is used to optimize investments in EnergyPATHWAYS conversion supply nodes and determine optimal blends of fuel components.
  • Optimized energy decisions are returned to EnergyPATHWAYS where they are input into the EnergyPATHWAYS accounting framework as stock measures or blend measures. This allows us to validate and represent the optimal scenario with the comprehensive accounting detail of EnergyPATHWAYS.

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6.2 RIO Features

The following sections will detail the specific features of the RIO optimization framework. The model is designed with a focus on electricity system operations and reliability. It also integrates fuels module that optimizes fuel production capacity expansion, storage, and use under emissions constraints.

Figure 34 EnergyPATHWAYS/RIO Integration Schematic

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6.2.1 Operations days selection

RIO utilizes the 8760 hourly profiles for electricity demand and generation from EnergyPATHWAYS and optimizes operations for a subset of representative days (sample days) and maps them to the rest of the year. Operations are performed over sequential hourly timesteps. To ensure that the sample days can reasonably represent the full set of days over the year, RIO uses clustering algorithms on the initial 8760 data sets. The clustering process is designed to identify days that represent a diverse set of potential system conditions, including different fixed generation profiles and load shapes. The number of sample days impacts the total runtime of the model. A balance is struck in the day selection process between representation of system conditions through number of sample days, and model runtime. Clustering and sample day selection occurs for each model year in the time horizon. This process is shown in Figure 35 below. The starting dataset is the EnergyPATHWAYS load and generation shapes, scaled to system conditions for the model year being sampled and mapped. Load shapes come directly from EnergyPATWHAYS accounting runs. The coincidence of fixed generation profiles (i.e. renewables) and load determine when important events for investment decision making occur during the year. For example, annual peak load and low load events may be the coincident occurrence of relatively high loads and relatively low renewables, and the inverse, respectively. However, renewable build is determined by RIO decision making. To ensure that the sample days in each model year are representative of the events that define investment decisions, renewable scaling happens for expected levels of renewables in future years as well as a range of renewables proportional builds (for example, predominantly wind, predominantly solar). The sample days are then selected to be representative of system conditions under all possible renewable build decisions by RIO.

As Figure 35 shows, the scaled historical days are clustered based on a number of characteristics. These include different metrics describing every day in the data set. Examples include peak daily load, peak daily net load, lowest daily solar output, largest daily ramping event etc. The result is a set of clusters of days with similar characteristics. One day within each cluster is selected to represent the rest by minimizing mean square error (MSE). As described in the previous section, RIO determines short-term operations for each of these representative

© 2019 by Evolved Energy Research 137

days. For long-term operations, each representative day is mapped back to the chronological historical data series, with the representative day in place of every other day from its cluster.

Figure 35 Conceptual diagram of sampling and day matching process

The clustering process depends on many characteristics of the coincident load and renewable shapes and uses statistical clustering algorithms to determine the best set of sample days. Figure 36 shows a simple, two characteristic, example of clustering. In this case the two characteristics are net load with high proportional solar build and net load with high proportional wind build. It is important to select sample days that both represent the full spectrum of potential net load, as well as be representative for both the solar and the wind case. The clustering algorithm has identified 5 clusters (a low number, but appropriate for the conceptual example) that ensure the sample days will represent the full range of net load

© 2019 by Evolved Energy Research 138

differences among days and remain representative regardless of whether RIO chooses to build a high solar system or a high wind system.

Figure 36 Simple, two characteristic, example of clustering

Mapping the clustered days back to the chronological historical dataset, the newly created year of sample days can be validated by checking that metrics describing the original historical dataset match those of the new set. Cumulative net load in Figure 37 is one example. These are related to the characteristics used to select the sample days in the clustering process such as peak load, largest ramp etc. and the distribution of these over the whole year.

Figure 37 Comparison of original and clustered load

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6.2.2 Operations

Time sequential operations are an important component of determining value of a portfolio of resources19. All resources have a set of attributes they can contribute to the grid, including, for example, energy, capacity, ancillary services, and flexibility. They work in complimentary fashion to serve the needs of the system. Whether a portfolio of resources is optimal or not depends on whether it can maintain system reliability, and whether it is cheaper than other portfolios. RIO determines the least cost dispatch for each one of the sample days to determine the least cost investments to make.

Operations are split into short-term and long-term operations in RIO. This is a division between those resources that do not have any multiday constraints on their operations, i.e. they can operate in the same way regardless of system conditions, and those resources that will operate differently depending on system condition trends that last longer than a day. An example of the former is a gas generator that can produce the same output regardless of system conditions over time, and an example of the latter is a long-duration storage system whose state of charge is drawn down over time when there is not enough energy to charge it. The long-term category includes all long-term storage mediums.

Operational decisions determine the value of one investment over another, so it is important to capture the detailed contributions and interactions of the many different types of resource that RIO can build.

Important factors captures in operations are:

19 Though typically an hour, the timestep of time sequential operations can be set to any length of time. For example, investment decisions in some systems may be insensitive to whether the time step is 1 hour or 2 hours. Having the option of setting timestep length for operations is another way of reducing model computation while preserving detail around important model components.

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  • Maximum operating levels – how many resources are needed to meet peak load conditions?
  • Planning reserves – are there enough resources to meet planning reserve margins?
  • Energy – what resources are required to ensure total daily energy budgets are met? RIO can constrain operations based on constraints that are similar to those used in productionsimulation. These include:
    • Resource minimum and maximum generation levels
    • Resource efficiency at different set points
    • Thermal generator linearized commitment constraints
    • Start up and shut down costs
    • Resource must run schedules
    • Resource contribution to reserves
    • Storage charge and discharge constraints
    • Storage efficiency constraints
    • Energy budgets and operational constraints for hydro resourcesFigure 38 below shows a conceptual daily dispatch. Thermal generation minimum generation level is constrained by Pmin and must run. RIO trades off the cost of starting up and shutting down generation, the available generator headroom for reserves, and the efficiency of operating the generators at sub optimal set points to find the best thermal generator dispatch. The short-term storage reservation is also optimally dispatched. These operational decisions drive concurrent capacity build decisions by determining the relative value of different potential resources.2020 In this integration with EnergyPATHWAYS, RIO is configured to run without enforcing constraints on thermal operating states. This means that constraints for minimum generation

© 2019 by Evolved Energy Research 141

Figure 38 Example RIO daily dispatch

6.2.2.1 Thermal Generator Operations

To reduce runtimes, generators are aggregated in RIO by common operating and cost attributes. These are by technology and vintage when the operating costs and characteristics vary significantly by installation year. Each modeled aggregation of generators contains a set of identical generators.

6.2.2.2 Hydro Operating Constraints

Hydro behavior is constrained by historical data on how fast the hydro system can ramp21, the minimum and maximum discharge by hour, and the degree to which hydro energy can be shifted from one period to another. Summed daily hydro output over user defined periods of

levels; startup and shutdown costs; efficiency penalties for deviation from optimal generator setpoints; and operating reserves are not included.

21 Hydro ramp constraints not enforced in this integration

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the year must fall within a cumulative energy envelope. For example, the energy envelope could be defined by 4 seasons: spring, summer, autumn, and winter. In this case, the cumulative energy envelope would have 4 sets of upper and lower bounds that constrain energy release in each period.

6.2.2.3 Storage Operating Constraints

Storage is constrained by maximum discharge rates dependent on built capacity. In addition, the model tracks storage state of charge hour to hour, including losses into and out of the storage medium. Storage, like all technologies, is dispatched with perfect foresight. Storage can operate through both short term and long term operations. In short term operations, storage is dispatched on an hourly basis within each sample day, as with all other dispatchable technology types. Short term storage dispatch shifts energy stored within a sample day and discharges it within the same sample day, such that the short term storage device is energy neutral across the day. In long term operations, storage can charge energy on one day and discharge it into another. This allows for optimal use of storage to address longer cycle reliability needs, such as providing energy on low renewable generation days, and participation in longer cycle energy arbitrage opportunities.

6.2.3 Planning reserve

Planning reserve is defined for each zone. A planning demand is specified for every hour that is equal to the demand in that hour net of the dependable contribution by local resources and flexible loads. The planning demand has to be met or exceeded by the contribution from system level resources that are also adjusted for dependability. Dependability is defined as the fraction of nameplate capacity of each resource that can be relied upon during peak load events. In this integration, we do not assume additional dependability derates on renewables past the coincidence of their generation profiles. For thermal resources, we assume derates equal to their forced outage rates.

6.2.4 Resource build decisions

Concurrently with optimal operational decisions, the model makes resource build decisions that together produce the lowest total system cost. There are three modes for resource build decisions, specified by aggregate generator. In all modes, the addition of new capacity is limited

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by the rate at which capacity can be constructed year on year, and the total quantity of capacity that can be constructed by a future year. The model builds resources when needed and those resources remain through the end of their useful life when they are retired. Resources are not economically retired early, repowered, or extended. Generators using this mode are built on top of a predefined MW schedule of existing resources in every year.

6.2.5 Transmission constraints

Transmission flows are constrained by the capacity of the line. If optimal transmission build by RIO is selected as an option, transmission additions are equal in flow capacity in both directions of the line. However, existing transmission does not have to have equally sized paths in each direction. Transmission additions are capped by a maximum addition by path and year.

The user specifies a schedule of transmission path flow capacities for every model year in the future. RIO can run with fixed transmission schedules or the user can select optimal transmission expansion.

6.2.6 Local Transmission and Distribution Capacity

RIO can incorporate local distribution feeders that are representative of the distribution system as a whole. These can be specified in different ways, for example representative urban and rural, or set of feeders representing different customer classes could be used. The constraints on local capacity track the local loads on the feeder. These net local generation from the local load shape and determine whether transmission capacity additions are needed to serve local load. Upgrade costs are determined as a penalty function for additional MW of local capacity.

6.2.7 Emissions cap/Emissions cost

As options, the user can include an emissions cap or an emissions cost to simulate future policy. Emissions accounting works through the fuel consumed by generators rather than the generators themselves. The total emissions are therefore the emissions factor per MMBTU of each fuel multiplied by the total consumption of that fuel, respectively.

6.2.8 Fuels

In addition to generator operating decisions, RIO also optimizes the fuel blend that a generator is eligible to receive, while also allowing fuels produced by electricity to contribute to fuel

© 2019 by Evolved Energy Research 144

stocks. This functionality is what allows RIO to extend beyond the electricity sector and optimize the entire energy supply side. Fuels can come from conventional fuel products (product fuels) or through converting something else into fuel using electricity (conversion fuels). By fueling generation with eligible blends, created from fuels that each have their own cost trajectory over time, or conversion infrastructure capacity costs, RIO can optimize the fuels burned as well as the generator investments and operations to burn them. One use of this is the realistic transition to clean fuels where fuel blends begin to include biofuels, and generation investments and operational decisions are driven by the changing costs of the blend over a generator’s lifetime.

Figure 39 RIO fuels schematic

7. United States EnergyPATHWAYS Database

The database of the United States energy economy used in this analysis has high geographical resolution on technology stocks; technology cost and performance; built infrastructure and resource potential as well as high temporal resolution on electricity loads by end-use as well as renewable generation profiles. EnergyPATHWAYS leverages many of the same input files used to populate the National Energy Modeling System (NEMS) used by the United States Energy Information Administration (EIA) to forecast their Annual Energy Outlook.

The model of the U.S. energy economy is separated into 65 energy-using demand subsectors. Subsectors, like residential space heating, represent energy-use associated with the performance of an energy-service. A description of the methods EnergyPATHWAYS use to

© 2019 by Evolved Energy Research 145

project energy-service demands, energy demands, and ultimately cost and emissions associated with the performance of that service is found in Demand. On the supply-side, the model is separated into interconnected nodes, which are associated with the production, transformation, and delivery of energy to demand subsectors. A description of how the data described in this section is used in the model calculations is found in the above sections.

7.1 Demand – Side Data Description

Table 10 lists all the subsectors in the US Database grouped by demand sector. It also specifies the methodology used to calculate energy demand in each subsector.
Table 10 Sectors, subsectors, and method of demand energy projection

Sector

Subsector

Method

residential

residential water heating

B

residential

residential furnace fans

D

residential

residential clothes drying

A

residential

residential dishwashing

A

residential

residential refrigeration

A

residential

residential freezing

A

residential

residential cooking

B

residential

residential secondary heating

D

residential

residential other appliances

D

residential

residential clothes washing

A

residential

residential lighting

A

residential

residential other – electric

D

residential

residential air conditioning

B

residential

residential space heating

B

commercial

commercial water heating

A

commercial

commercial ventilation

A

© 2019 by Evolved Energy Research 146

commercial

office equipment (p.c.)

D

commercial

office equipment (non-p.c.)

D

commercial

commercial space heating

A

commercial

commercial air conditioning

A

commercial

commercial lighting

A

commercial

district services

D

commercial

commercial refrigeration

A

commercial

commercial cooking

A

commercial

commercial other

D

transportation

heavy duty trucks

A

transportation

international shipping

D

transportation

recreational boats

D

transportation

transit buses

A

transportation

military use

D

transportation

lubricants

D

transportation

medium duty trucks

A

transportation

aviation

C

transportation

motorcycles

D

transportation

domestic shipping

D

transportation

passenger rail

C

transportation

school and intercity buses

A

transportation

freight rail

C

transportation

light duty trucks

A

transportation

light duty autos

A

industry

metal and other non-metallic mining

D

industry

aluminum industry

D

© 2019 by Evolved Energy Research 147

industry

balance of manufacturing other

D

industry

plastic and rubber products

D

industry

wood products

D

industry

bulk chemicals

D

industry

glass and glass products

D

industry

cement

D

industry

industrial space heating

B

industry

agriculture-other

D

industry

industrial drying

B

industry

industrial curing

B

industry

industrial machine drives

B

industry

agriculture-crops

D

industry

fabricated metal products

D

industry

machinery

D

industry

computer and electronic products

D

industry

transportation equipment

D

industry

construction

D

industry

iron and steel

D

industry

food and kindred products

D

industry

paper and allied products

D

industry

industrial boilers

B

industry

electrical equip., appliances, and components

D

industry

industrial process heat

B

The methods for representing demand-side subsectors are described in section 107. Table 11 describes the input data used to populate stock representations in the subsectors that employ Method A. and Table 13 describes the energy service demand inputs.

© 2019 by Evolved Energy Research 148

Table 11 Demand stock data

Subsector

Unit

Service Demand Dependent

Driver

Input Data: Geography

Input Data: Year(s)

Source

Residential Lighting

Bulbs per housing unit

No

Total square footage

US

2012

(U.S. Energy Information Administration 2017)

Residential Clothes Washing

Clothes washer

No

Households

Census division

2009

(U.S. Energy Information Administration 2013)

Residential Clothes Drying

Clothes dryer

No

Households

Census division

2009

(U.S. Energy Information Administration 2013)

Residential Dishwashing

Dishwashers per household

No

Households

Census division

2009

(U.S. Energy Information Administration 2013)

Residential Refrigeration

Cubic feet

No

Households

Census division

2009

(U.S. Energy Information Administration 2013)

Residential Freezing

Cubic feet

No

Households

Census division

2009

(U.S. Energy Information Administration 2013)

Commercial Water Heating

Capacity factor

No

Com square feet

Census division

2012

(U.S. Energy Information Administration 2012)

Commercial Space Heating

Capacity factor

No

Com square feet

Census division

2012-2013

(U.S. Energy Information Administration 2012)

Commercial Air Conditioning

Capacity factor

No

Com square feet

Census division

2012

(U.S. Energy Information Administration 2012)

Commercial Lighting

Capacity factor

No

n/a

Census division

2012

(U.S. Energy Information Administration 2012)

Commercial Refrigeration

Capacity factor

No

Com square feet

Census division

2012

(U.S. Energy Information Administration 2012)

Commercial Cooking

Capacity factor

No

Com square feet

Census division

2012

(U.S. Energy Information Administration 2012)

Commercial Ventilation

Capacity factor

No

Com square feet

Census division

2012

(U.S. Energy Information Administration 2012)

© 2019 by Evolved Energy Research 149

Light Duty Autos

Car per mile travelled

Yes

n/a

US

2012; 2020; 2030; 2040

(U.S. Energy Information Administration 2015)

Light Duty Trucks

Truck per mile travelled

Yes

n/a

US

2012; 2020; 2030; 2040

(U.S. Energy Information Administration 2015)

Medium Duty Trucks

Truck

Yes

n/a

US

2015

(TA Engineering Inc. 2012)

Heavy Duty Trucks

Truck

Yes

n/a

US

2011

(TA Engineering Inc. 2012)

Transit Buses

Bus

Yes

n/a

US

2014

(Brooker et al. 2015)

Subsector

Unit

Stock Dependent

Driver

Input Data: Geography

Input Data: Year(s)

Source

Residential Lighting

klm-hr per housing unit

No

Total square feet

US

2012

(Ashe et al. 2012)

Residential Clothes Washing

Cu. Ft. Cycle

Yes

n/a

Census division

2009

(U.S. Energy Information Administration 2013)

Residential Clothes Drying

Pound

Yes

n/a

Census division

2009

(U.S. Energy Information Administration 2013)

Residential Dishwashing

Cycle

Yes

n/a

Census division

2009

(U.S. Energy Information Administration 2013)

Residential Refrigeration

Cu. Ft.

Yes

n/a

Census division

2009

(U.S. Energy Information Administration 2013)

Residential Freezing

Cu. Ft.

Yes

n/a

Census division

2009

(U.S. Energy Information Administration 2013)

Commercial Water Heating

Terabtu

No

Com square feet

Census division

2012 – 2050

(U.S. Energy Information Administration 2017)

Commercial Space Heating

Terabtu

No

Com square feet

Census division

2012 – 2050

(U.S. Energy Information Administration 2017)

Commercial Air Conditioning

Terabtu

No

Com square feet

Census division

2012 – 2050

(U.S. Energy Information Administration 2017)

Commercial Lighting

gigalumen_year

No

Com square feet

Census division

2012 – 2050

(U.S. Energy Information Administration 2017)

Commercial Refrigeration

Terabtu

No

Com square feet

Census division

2012 – 2050

(U.S. Energy Information Administration 2017)

Commercial Cooking

Terabtu

No

Com square feet

Census division

2012 – 2050

(U.S. Energy Information Administration 2017)

© 2019 by Evolved Energy Research 150

Commercial Ventilation

gigacubic_foot

No

Com square feet

Census division

2012 – 2050

(U.S. Energy Information Administration 2017)

Light Duty Autos

Gigamile

No

n/a

US

2007; 2015- 2050

(U.S. Energy Information Administration 2017)

Light Duty Trucks

Gigamile

No

US

2012- 2050

(U.S. Energy Information Administration 2017)

Medium Duty Trucks

Mile

No

US

2015- 2050

(U.S. Energy Information Administration 2017)

Heavy Duty Trucks

Mile

No

N/A

US

2015- 2050

(U.S. Energy Information Administration 2017)

Transit Buses

Mile

No

Population

Census division

1995- 2008

(U.S. Energy Information Administration 2017)

Demand subsectors with technology stock also require technology-specific parameters for cost and performance. These input sources by subsector and technology-type are show below in Table 12.

Table 12 Demand technology inputs

Subsector

Technologies

Source

Residential Space Heating and Air Conditioning

Air source heat pump (ducted)

Cost: (Jadun et al. 2017)

Efficiency: NREL building simulations in support of (Jadun et al. 2017)

Ductless mini-split heat pump

Cost: (Dentz, Podorson, and Varshney 2014)

Efficiency: NREL building simulations in support of (Jadun et al. 2017)

Remainder

(Navigant 2014)

Residential Water Heating

Heat pump water heater

(Jadun et al. 2017)

Remainder

(Navigant 2014)

Residential Remaining Subsectors

All

(Navigant 2014)

Commercial Space Heating and Air Conditioning

Air source heat pump

(Jadun et al. 2017)

Remainder

(Navigant 2014)

Commercial Water Heating

Heat pump water heater

(Jadun et al. 2017)

© 2019 by Evolved Energy Research 151

Remainder

(Navigant 2014)

Commercial Lighting

All

(U.S. Energy Information Administration 2017)

Commercial Building Shell

All

(U.S. Energy Information Administration 2017)

Light-duty Vehicles

Battery electric vehicle and plug-in hybrid electric vehicle

(Jadun et al. 2017)

Hydrogen fuel cell vehicle

(TA Engineering Inc. 2012)

Remainder

Efficiency: (Navigant 2014) Cost: (TA Engineering Inc. 2012)

Medium Duty Vehicles

Battery electric

(Jadun et al. 2017)

Hydrogen fuel cell

(den Boer et al. 2013)

Remainder (CNG, diesel, etc.)

(TA Engineering Inc. 2012)

Heavy Duty Vehicles

Battery electric

(Jadun et al. 2017)

Hydrogen fuel cell

(Fulton and Miller 2015)

Reference diesel, gasoline and propane

(TA Engineering Inc. 2012)

Diesel hybrid and liquefied pipeline gas

(TA Engineering Inc. 2012)

Transit Buses

All

(Jadun et al. 2017; Brooker et al. 2015)

Industrial Space Heating

Air source heat pump

(Jadun et al. 2017)

Furnace

(Navigant 2014))

Industrial Boilers

All

(Jadun et al. 2017)

Industrial Process Heat

All

(Jadun et al. 2017)

Industrial Curing

All

(Jadun et al. 2017)

Industrial Drying

All

(Jadun et al. 2017)

Industrial Machine Drives

All

(Jadun et al. 2017)

© 2019 by Evolved Energy Research 152

Table 13 Service demand inputs

Subsector

Unit

Stock Dependent

Driver

Input Data: Geography

Downscaling method

Input Data: Year(s)

Source

Residential Lighting

klm-hr per housing unit

No

Total sq ft

US

Households 2010

2012

(Ashe et al. 2012)

Residential Clothes Washing

Cu. Ft. Cycle

Yes

n/a

Census division

Stock

2009

(U.S. Energy Information Administration 2013)

Residential Clothes Drying

Pound

Yes

n/a

Census division

Stock

2009

(U.S. Energy Information Administration 2013)

Residential Dishwashing

Cycle

Yes

n/a

Census division

Stock

2009

(U.S. Energy Information Administration 2013)

Residential Refrigeration

Cu. Ft.

Yes

n/a

Census division

Stock

2009

(U.S. Energy Information Administration 2013)

Residential Freezing

Cu. Ft.

Yes

n/a

Census division

Stock

2009

(U.S. Energy Information Administration 2013)

Commercial Water Heating

Terabtu

No

Com square feet

Census division

Employment in all industries (NAICS, no code) 2007

2012 – 2050

(U.S. Energy Information Administration 2017)

Commercial Space Heating

Terabtu

No

Com square feet

Census division

HDD x com_sq_ft

2012 – 2050

(U.S. Energy Information Administration 2017)

Commercial Air Conditioning

Terabtu

No

Com square feet

Census division

CDD x com_sq_ft

2012 – 2050

(U.S. Energy Information Administration 2017)

Commercial Lighting

gigalumen_year

No

Com square feet

Census division

Employment in all industries (NAICS, no code) 2007

2012 – 2050

(U.S. Energy Information Administration 2017)

Commercial Refrigeration

Terabtu

No

Com square feet

Census division

Employment in all industries (NAICS, no

2012 – 2050

(U.S. Energy Information Administration

© 2019 by Evolved Energy Research 153

code) 2007

2017)

Commercial Cooking

Terabtu

No

Com square feet

Census division

Employment in all industries (NAICS, no code) 2007

2012 – 2050

(U.S. Energy Information Administration 2017)

Commercial Ventilation

gigacubic_foot

No

Com square feet

Census division

Employment in all industries (NAICS, no code) 2007

2012 – 2050

(U.S. Energy Information Administration 2017)

Light Duty Autos

Gigamile

No

MD + HD VMT Historical

US

LDV VMT Share

2007; 2015- 2050

(U.S. Energy Information Administration 2017)

Light Duty Trucks

Gigamile

No

MD + HD VMT Historical

US

LDV VMT Share

2012- 2050

(U.S. Energy Information Administration 2017)

Medium Duty Trucks

Mile

No

gasoline sales volumes

US

MDV VMT Share

2015- 2050

(U.S. Energy Information Administration 2017)

Heavy Duty Trucks

Mile

No

US

HDV VMT Share

2015- 2050

(U.S. Energy Information Administration 2017)

Transit Buses

Mile

No

Population

Census division

Square miles

1995- 2008

(U.S. Energy Information Administration 2017)

Table 14 describes stock input data sources for subsectors that uses Method B (0). Table 15 describes energy demand input sources.
Table 14 Equipment stock data sources for Method B subsectors

Subsector

Unit

Service Demand Dependent

Driver

Input Data: Geography

Downscaling method

Input Data: Year(s)

Source

Residential Water Heating

Water heater

No

Households

Census division

Households 2010

2009

(U.S. Energy Information Administration 2013)

Residential Space Heating

Space heater

No

Households

Census division

Households 2010

2009- 2015

(U.S. Energy Information Administration 2017)

© 2019 by Evolved Energy Research 154

Residential Air Conditioning

Air conditioner

No

Households

Census division

Households 2010

2009

(U.S. Energy Information Administration 2013)

Residential Cooking

Cooktop

No

Households

Census division

Households 2010

2009

(U.S. Energy Information Administration 2013)

Industrial Boilers

Capacity factor 22

Yes

n/a

US

Value of Shipments

2015

By Assumption

Industrial Process Heat

Capacity factor

Yes

n/a

US

Value of Shipments

2015

By Assumption

Industrial Space Heating

Capacity factor

Yes

n/a

US

Value of Shipments

2015

By Assumption

Industrial Machine Drives

Capacity factor

Yes

n/a

US

Value of Shipments

2015

By Assumption

Industrial Curing

Capacity factor

No

n/a

US

Value of Shipments

2015

By Assumption

Industrial Drying

Capacity factor

No

n/a

US

Value of Shipments

2015

By Assumption

Table 15 Energy demand data sources for Method B subsectors

Subsector

Unit

Driver

Input Data: Geography

Downscaling method

Input Data: Year(s)

Source

Residential Water Heating

MMBTU

Households

Census division

Households 2010

2009

(U.S. Energy Information Administration 2013)

Residential Space Heating

MMBTU

HDD; occupied square feet

Census division

HDD x residential square footage

2009- 2015

(U.S. Energy Information Administration 2017)

Residential Air Conditioning

MMBTU

CDD

Census division

CDD x residential square footage

2009

(U.S. Energy Information Administration 2013)

22 The model uses an assumed capacity factor to translate energy service demand into equipment stocks in units of service demand/hour.

© 2019 by Evolved Energy Research 155

Residential Cooking

MMBTU

Households

Census division

Households 2010

2009

(U.S. Energy Information Administration 2013)

Industrial Boilers

USD

Value of shipments

Census region

Earnings in manufacturing (NAICS 31-33) 2007

2011- 2050

(U.S. Energy Information Administration 2017)

Industrial Process Heat

USD

Value of shipments

Census region

Earnings in manufacturing (NAICS 31-33) 2007

2011- 2050

(U.S. Energy Information Administration 2017)

Industrial Space Heating

USD

Value of shipments

Census region

Earnings in manufacturing (NAICS 31-33) 2007

2011- 2050

(U.S. Energy Information Administration 2017)

Industrial Machine Drives

USD

Value of shipments

Census region

Earnings in manufacturing (NAICS 31-33) 2007

2011- 2050

(U.S. Energy Information Administration 2017)

Industrial Curing

USD

Value of shipments

Census region

Earnings in manufacturing (NAICS 31-33) 2007

2011- 2050

(U.S. Energy Information Administration 2017)

Industrial Drying

USD

Value of shipments

Census region

Earnings in manufacturing (NAICS 31-33) 2007

2011- 2050

(U.S. Energy Information Administration 2017)

Table 16 includes the service demand projections for subsectors represented with Method C (4.2.1.5). Table 17 includes the service efficiency for Method C subsectors.

Table 16 Service demand data sources for Method C subsectors

Subsector

Unit

Stock Dependent

Driver

Input Data: Geography

Input Data: Year(s)

Source

Iron and Steel CO2 Capture

Tonnes of BOF Steel Production

No

Subsector value of output

Census region

2011-2050

(U.S. Energy Information Administration 2017)

Cement CO2 Capture

Tonnes of Clinker Production

No

Subsector value of output

Census region

2011-2050

(U.S. Energy Information Administration 2017)

© 2019 by Evolved Energy Research 156

Table 17 Service efficiency data sources

Subsector

Unit

Stock Dependent

Driver

Input Data: Geography

Input Data: Year(s)

Source

Iron and Steel CO2 Capture

MMBTU/Tonne of CO2

No

US

2018

(Kuramochi et al. 2012)

Cement CO2 Capture

MMBTU/Tonne of CO2

No

US

2018

(Kuramochi et al. 2012)

Table 18 shows baseline energy demand projection input data sources for subsectors employing Method D (4.2.1.6).
Table 18 Energy demand data sources for Method D subsectors

Subsector

Unit

Driver

Input Data: Geography

Downscaling method

Input Data: Year(s)

Source

Residential computers and related

MMBTU

Households

Census division

Households 2010

2009- 2050

(U.S. Energy Information Administration 2017)

Residential televisions and related

MMBTU

Households

Census division

Households 2010

2009- 2050

(U.S. Energy Information Administration 2017)

Residential Secondary Heating

MMBTU per household

Households; HDD

Census division

Households 2010

2010

(U.S. Energy Information Administration 2017)

Residential other uses

MMBTU

Households

Census division

Households 2010

2009- 2050

(U.S. Energy Information Administration 2017)

Residential Furnace Fans

MMBTU

Households

Census division

Households 2010

2009

(U.S. Energy Information Administration 2013)

Office Equipment (P.C.)

Quads

Office space

US

Employment in all industries (NAICS, no code) 2007

2015- 2050

(U.S. Energy Information Administration 2017)

Office Equipment (Non-

Quads

Office space

US

Employment in all industries (NAICS,

2015-

(U.S. Energy Information

© 2019 by Evolved Energy Research 157

P.C.)

no code) 2007

2050

Administration 2017)

Commercial Other

Quads

Commercial square footage

US

Employment in all industries (NAICS, no code) 2007

2015- 2050

(U.S. Energy Information Administration 2017)

Non-CHP District Services

kilobtu per square feet

Commercial square footage

Census division

Households 2010

2012

(U.S. Energy Information Administration 2017)

CHP District Services

Terabtu

Commercial square footage

US

Households 2010

2015- 2050

(U.S. Energy Information Administration 2017)

Domestic Shipping

Terabtu

n/a

US

Marine Fuel Use

2015- 2050

(U.S. Energy Information Administration 2017)

Military Use

Terabtu

n/a

US

Households 2010

2015- 2050

(U.S. Energy Information Administration 2017)

Motorcycles

Terabtu

Population

US

Households 2010

2012- 2050

(U.S. Energy Information Administration 2017)

Lubricants

Terabtu

Population

US

Households 2010

2015- 2050

(U.S. Energy Information Administration 2017)

International Shipping

Terabtu

n/a

US

Marine Fuel Use

2015- 2050

(U.S. Energy Information Administration 2017)

Recreational Boats

Terabtu

n/a

US

Households 2010

2015- 2050

(U.S. Energy Information Administration 2017)

School and intercity buses

Terabtu

Passenger miles, population

US

BUSES VMT Share

2015- 2050

(U.S. Energy Information Administration 2017)

Passenger rail

Terabtu

Rail passenger miles

Census division

Rail Fuel Use

2015- 2050

(U.S. Energy Information Administration 2017)

© 2019 by Evolved Energy Research 158

Freight rail

Terabtu

Gigaton mile service demand

Census division

Rail Fuel Use

2015- 2050

(U.S. Energy Information Administration 2017)

Aviation

Terabtu

Seat miles, population

US

Aviation Fuel Use

2015- 2050

(U.S. Energy Information Administration 2017)

Various Industrial Subsectors [1]

Terabtu

Subsector value of output

Census region

Value of shipments

2011- 2050

(U.S. Energy Information Administration 2017)

8. Supply – Side Data Description

Table 19 Supply-side data sources

Data Category

Data Description

Supply Node

Source

Resource Potential

Binned resource potential (GWh) by state with associated resource performance (capacity factors) and transmission costs to reach load

Transmission – sited Solar PV; Onshore Wind; Offshore Wind; Geothermal

(Eurek et al. 2017)

Resource Potential

Binned resource potential of biomass resources by state with associated costs

Biomass Primary – Herbaceous; Biomass Primary – Wood; Biomass Primary – Waste; Biomass Primary – Corn

(Langholtz, Stokes, and Eaton 2016)

Resource Potential

Binned annual carbon sequestration injection potential by state with associated costs

Carbon Sequestration

(U.S. Department of Energy: National Energy Technology Laboratory 2017)

Resource Potential

Domestic production potential of natural gas

Natural Gas Primary – Domestic

(U.S. Energy Information Administration 2017)

Resource Potential

Domestic production potential of oil

Oil Primary – Domestic

(U.S. Energy Information Administration 2017)

Product Costs

Commodity cost of natural gas at Henry Hub

Natural Gas Primary – Domestic

(U.S. Energy Information Administration 2017)

© 2019 by Evolved Energy Research 159

Product Costs

Undelivered costs of refined fossil products

Refined Fossil Diesel; Refined Fossil Jet Fuel; Refined Fossil Kerosene; Refined Fossil Gasoline; Refined Fossil LPG

(U.S. Energy Information Administration 2017)

Product Costs

Commodity cost of Brent oil

Oil Primary – Domestic; Oil Primary – International

(U.S. Energy Information Administration 2017)

Delivery Infrastructure Costs

AEO transmission and delivery costs by EMM region

Electricity Transmission Grid; Electricity Distribution Grid

(U.S. Energy Information Administration 2017)

Delivery Infrastructure Costs

AEO transmission and delivery costs by census division and sector

Gas Transmission Pipeline; Gas Distribution Pipeline

(U.S. Energy Information Administration 2017)

Delivery Infrastructure

AEO delivery costs by fuel product

Gasoline Delivery; Diesel Delivery; Jet Fuel; LPG Fuel Delivery; Kerosene Delivery

(U.S. Energy Information Administration 2017)

Technology Cost and Performance

Renewable and conventional electric technology installed cost projections

Nuclear Power Plants; Onshore Wind Power Plants; Offshore Wind Power Plants; Transmission – Sited Solar PV Power Plants; Distribution – Sited Solar PV Power Plants; Rooftop PV Solar Power Plants; Combined – Cycle Gas Turbines; Coal Power Plants; Combined – Cycle Gas Power Plants with CCS; Coal Power Plants with CCS; Gas Combustion Turbines

(National Renewable Energy Laboratory 2017)

Technology Cost and Performance

Electric fuel cost projections including electrolysis and fuel synthesis facilities

Central Hydrogen Grid Electrolysis; Power – To – Diesel; Power – To – Jet Fuel; Power – To – Gas Production Facilities

(Capros et al. 2018)

Technology Cost and Performance

Hydrogen Gas Reformation costs with and without carbon capture

H2 Natural Gas Reformation; H2 Natural Gas Reformation w/CCS

(International Energy Agency GHG Programme 2017)

Technology Cost and Performance

Nth plant Direct air capture costs for sequestration and utilization

Direct Air Capture with Sequestration; Direct Air Capture with Utilization

(Keith et al. 2018)

Technology Cost and Performance

Gasification cost and efficiency of conversion including gas upgrading.

Biomass Gasification; Biomass Gasification with CCS

(G. del Alamo et al. 2015)

Technology Cost and

Cost and efficiency of renewable Fischer- Tropsch diesel

Renewable Diesel; Renewable Diesel with CCS

(G. del Alamo et al. 2015)

© 2019 by Evolved Energy Research 160

Performance

production.

Technology Cost and Performance

Cost and efficiency of industrial boilers

Electric Boilers; Other Boilers

(Capros et al. 2018)

Technology Cost and Performance

Cost and efficiency of other, existing power plant types

Fossil Steam Turbines; Coal Power Plants

(Johnson et al. 2006)

© 2019 by Evolved Energy Research 161

2443 Fillmore Street,
No. 380-5034 San Francisco, CA, 94115

info@evolved.energy (844) 566-1366 www.evolved.energy

© 2019 by Evolved Energy Research 162

**

Summary In 100% Clean, Renewable Energy and Storage for Everything

Textbook in Preparation

https://web.stanford.edu/group/efmh/jacobson/WWSBook/WWSBook.html

Mark Z. Jacobson
April 18, 2019

Evaluation of Coal and Natural Gas With Carbon Capture as Proposed Solutions to Global Warming, Air Pollution, and Energy Security

Coal and natural gas with carbon capture have been advertised as zero-carbon sources of electric power that should be implemented as solu tions to global warming, air pollution, and energy security. Natural gas has also been proposed as a bridge fuel between coal and renewables. The purpose of this section is to evaluate these claims.

The main result is that neither coal nor natural gas with carbon capture is remotely close to a zero- carbon technology. At best they reduce ~22 percent carbon equivalent emissions (CO2e) over a 20 year time frame and ~34 percent over a 100-year time frame. However, at the same time, they increase air pollution and land degradation compared with no carbon capture by ~25 percent. In addition, the current use of the captured CO2 for enhancing oil recovery causes even greater damage to climate and human health. Finally, the cost of installing carbon capture equipment is still enormous.

Here are additional specific findings:

  • There is no low-carbon, let alone zero-carbon coal or natural gas power plant with carbon capture in existence.
  • In the Petra Nova coal with carbon capture plant in Texas, only 22 percent (rather than 90%) of the intended CO2 emissions are reduced over a 20-year time frame and only 34 percent are reduced over a 100-year time frame.
  • Even these emission savings may be offset fully by emissions from the oil recovered with the CO2.
  • New natural gas plants with carbon capture produce 27-86 times the 100-year CO2e emissions as new onshore wind.
  • New coal plants with carbon capture produce 33-183 times the 100-year CO2e emissions as new onshore wind.
  • Without carbon capture, open cycle natural gas turbines and combined cycle natural gas turbines cause 2.5 and 2 times, respectively, the global warming per unit energy over a 20 year time frame as a coal plant and only 8-29 percent less warming over 100 years than a coal plant.
  • The reason is the higher SO2 and NOx and lower CH4 emissions from coal. The higher SO2, NOx, and particulate emissions from coal result in coal causing about five times the premature mortality as gas.
  • As such, natural gas is not a bridge fuel.
  • Instead, coal and gas are both horrendous for climate, health, and land although coal causes greater health problems.
  • In comparison, wind, water, and solar power substantially address nearly all climate, health, and energy security problems.

3.1. Why Not Use Natural Gas as a Bridge Fuel?

Natural gas is a colorless, flammable gas containing a mass (mole) fraction of about 88.5 (93.9) percent methane plus smaller amounts of ethane, propane, butane, pentane, hexane, nitrogen, carbon dioxide, and oxygen (Union Gas, 2018). It is often found near petroleum deposits. Worldwide, it is usually either combusted in a gas turbine that is coupled with a generator to produce electricity or combusted in a burner to produce either building heat or high-temperature industrial heat.

Because natural gas is not very dense, it can be stored on its own only in a large container. As such, natural gas is often compressed or liquefied for transport and storage. Compressed natural gas (CNG) is natural gas compressed to less than 1 percent of its gas volume at room temperature. Liquefied natural gas (LNG) is natural gas that has been cooled to -162oC, the temperature at which it condenses to a liquid at ambient pressure. LNG has a volume that is 1/600th the volume of the original gas. Both CNG and LNG can be sent through pipelines, although different pipelines are needed for each. CNG and LNG can also be stored and used directly in automobiles that are designed to run on them. CNG and LNG can further be transported by truck or bus with a special fuel tank and can be stored at a power plant for backup use when pipeline gas is not available. In addition, pipeline CNG is often converted to LNG at a marine export terminal, put on a tanker ship with super-cooled cryogenic tanks, and shipped overseas. At the import terminal, it is re-gasified and piped to its final destination — either a power plant, industrial company, or company that transmits and distributes it to buildings for heating or other purposes.

Natural gas is obtained from underground conventional wells containing both oil and natural gas or by hydraulic fracturing. Hydraulic fracturing (fracking) is the process by which natural gas is extracted from shale rock formations instead of wells. Shale is sedimentary rock composed of a muddy mix of clay mineral flakes and small fragments of quartz and calcite. Large shale formations containing natural gas can be found in eastern North America, close to population centers, among many other locations worldwide. In the U.S., about 67 percent of natural gas in 2015 was extracted from shale rock (EIA, 2016). Extraction of natural gas from shale requires large volumes of water, laced with chemicals, forced under pressure to fracture and re-fracture the rock to increase the flow of natural gas. As the water returns to the surface over days to weeks, it is accompanied by methane that escapes to the air. As such, more methane leaks occur during fracking than during the drilling of conventional gas wells (Howarth et al., 2011, 2012; Howarth, 2019). Methane also leaks during the transmission, distribution, and processing of natural gas.

For electricity production, natural gas is usually used in either an open cycle gas turbine (OCGT) or a combined cycle gas turbine (CCGT). In an OCGT, air is sent to a compressor, and the compressed air and natural gas are both sent to a combustion chamber, where the mixture is burned. The hot gas expands quickly, flowing through a turbine to perform work by spinning the turbine’s blades. The rotating blades turn a shaft connected to a generator, which converts a portion of the rotating mechanical energy to electricity.

The main disadvantage of an OCGT is that that the exhaust contains a lot of waste heat that could otherwise be used to generate more electricity. A CCGT routes that heat to a heat recovery steam generator, which boils water with the heat to create steam. The steam is then sent to a steam turbine connected to the generator to generate 50 percent more electricity than the OCGT alone. Thus, a CCGT produces about 150 percent the electricity as an OCGT with the same input mass of natural gas thus carbon dioxide emissions in each case.

On the other hand, the ramp rate of an OCGT is 20 percent per minute, which is 2 to 4 times that of a CCGT (5 to 10 percent per minute) (Table 2.1). Thus, the less efficient OCGT, which also releases more CO2 per unit electricity generated (Table 3.1), is more useful for filling in short-term gaps in supply on the grid than is a CCGT.

It has long been suggested that natural gas could be used as a bridge fuel between coal and renewables (e.g., MIT, 2011). The two main arguments for this suggestion are (1) natural gas emits less carbon dioxide equivalent emissions per unit energy produced (CO2e – Section 1.2.3.5) than coal and (2) natural gas electric power plants are better suited to be used with intermittent renewables than coal.

However, the justifications for using gas as a bridge fuel are either incorrect or insufficient. Natural gas is not recommended for use together with WWS technologies for multiple reasons. These are discussed in the following sections.

3.1.1. Climate Impacts of Natural Gas Versus Other Fossil Fuels

First, as shown in Table 3.1, when used in an electric power plant, natural gas substantially increases, rather than decreases, global warming (by increasing CO2e) compared with coal over a 20-year time frame, and the difference over 100 years, while more favorable to gas, is relatively small. Regardless, CO2e emissions (and health-affecting air pollutant emissions) from both gas and coal are much larger than those from WWS technologies, so spending money on natural gas or coal represents an opportunity cost relative to spending the same money on WWS .

Over a 20-year time frame, the CO2e from using natural gas with a CCGT or an OCGT is 2 and 2.5 times, respectively, that using coal (Table 3.1). Over a 100-year time frame, the CO2e from a natural gas OCGT is only 8 percent less than that of coal; the CO2e from a natural gas CCGT is only 29 percent less than that of coal.

The fact that natural gas causes far more global warming than coal over a 20-year time frame is a significant concern because of the severe damage global warming is already causing that will only be made worse over the next two decades, including the triggering of some difficult-to-reverse impacts, such as the complete melting of the Arctic ice.

The reasons that the CO2e of natural gas exceeds that of coal over 20 years and is close to that of coal over 100 years are as follows.

First, although natural gas combustion in an OCGT or CCGT emits only 60 or 45 percent, respectively, of the CO2 per kilowatt-hour (kWh) of coal combustion, natural gas leaks during its mining and transport emit similar or more CH4 than do CH4 leaks during coal mining. CH4 has a high, positive 20- and 100-year GWP (Table 1.2). As such, the leaked CH4 from natural gas mining and transport contributes almost as much CO2e as do the direct CO2 emissions from natural gas combustion.

Second, and more important, coal combustion emits much more NOx and SO2 per kWh than does natural gas combustion (Table 3.1), and NOx and SO2 both produce cooling aerosol particles, which offset or mask much of global warming (Figure 1.2). The cooling impacts of these particles are through their direct reflection of sunlight back to space and their enhancement of cloud thickness. Thicker clouds reflect more sunlight back to space. As such, NOx and SO2, which are both short-lived, have very high negative GWPs over 20 years and even over 100 years (Table 3.1).

Howarth et al. (2011, 2012) identified the importance of methane leaks, particularly natural gas fracking of shale gas on the CO2e emissions of natural gas versus coal on a 20- versus 100-year lifetime. Wigley (2011), for one, estimated the cooling impact of SO2, but not NOx, when comparing CO2e from coal versus natural gas power plants.

Table 3.1. Comparison of 20- and 100-year lifecycle global CO2 equivalent (CO2e) emissions from coal versus natural gas used in either an open cycle gas turbine (OCGT) or a combined cycle gas turbine (CCGT) for electricity generation.

Natural Gas Combined Cycle Gas Turbine

Coal

Natural Gas
Open Cycle Gas Turbine

Chemical (X)

aCO2e-upstream bCH4-leak cCO2-plant dBC+OM-plant cNOx-N-plant cSO2-S-plant

20-y GWP

100-y GWP

Emis. factor (g-X/ kWh)

20-y CO2e (g-CO2e /kWh)

100-y

CO2e (g-CO2e /kWh)

Emis. factor (g-X/ kWh)

20-y CO2e (g-CO2e /kWh)

100-y

CO2e (g-CO2e /kWh)

Emis. factor (g-X/ kWh)

20-y CO2e (g-CO2e /kWh)

160 353 905 141 -129 -1,050

160 140 905 70 -37 -393

100 400 540 0.93 -84 -7

100 162 540 0.47 -24 -2

100 255 404 0.93 -8.4 -2.1

86
1 3,100 -560 -1,400

34 1 1,550 -159 -394

4.1 905 0.045 0.23 0.75

4.84 540 0.0003 0.15 0.005

3.1 404 0.0003 0.015 0.0015

Total

380

845

950

776

749

100-y

CO2e (g-CO2e /kWh) 100 103 404 0.47 -2.4 -2 603

All 20- and 100-year GWPs are from Table 1.2. Each CO2e is the product of the emission factor and a GWP, except for upstream totals, which are estimated from Skone (2015), slide 15, removing methane leaks since these are calculated here separately. Upstream emissions include emissions from fuel extraction, fuel processing, and fuel transport.

bCH4-leak emission factors for natural gas are obtained by multiplying the CH4 required per kWh of electricity by L/(1- L), where L is the fractional leakage rate of methane between mining and use in a power plant. The CH4 required per kWh for a combustion turbine is estimated from the volume of gas per unit electricity in an open cycle plant (0.270 m3-gas/kWh-electricity) and a combined cycle plant (0.172 m3-gas/kWh-electricity) (IGU, 2018), the natural gas mass density, 0.845 kg/m3, 0.2778 kWh/MJ, and the mass fraction of methane in natural gas, 0.885 (Union Gas, 2018). The results are 202 g-CH4/kWh-electricity for open cycle and 129 g-CH4/kWh-electricity for combined cycle. The overall U.S. methane leakage rate from natural gas, which includes leaks from drilling and from pipe transmission and distribution to electric power plants, industrial facilities, and buildings is ~3.7 percent for conventionally drilled natural gas and ~4.6 percent for shale gas (Howarth, 2019; Howarth et al., 2011, 2012). With shale gas at 2/3 of the U.S. natural gas production in 2015 (EIA, 2016), that gives a mean overall leakage rate of ~4.3 percent. However, the leakage rate for only drilling and transmission to large facilities may be ~2.3 percent (Alvarez et al., 2018). This number is used in this table, which is for electric power plant generation. For coal, the 100-year CO2e from CH4 leaks is estimated from Skone (2015), Slide 17. The emission factor is derived from this number and the 100-year GWP from the present table, and the 20-year CO2e is derived from the emission factor and the 20-year GWP .

cEmission factors from Figure 4 of de Gouw et al. (2014) for 2012 U.S. plants; For NOx-N, emission factors for NOx- NO2 were multiplied by the ratio of the molecular weight of N to that of NO2. For SO2-S, emission factors for SO2 were multiplied by the ratio of the molecular weight of S to that of SO2.

dThe emission factor of BC+OM for coal and natural gas were obtained from Bond et al. (2004) assuming, for coal, pulverized coal and a mix between hard and lignite coal.

Neither natural gas nor coal is recommended in a 100 percent WWS world because, among other reasons, the natural gas lifecycle 100-year CO2e for electricity generation (600 to 800 g-CO2e/kWh) (Table 3.1) is on the order of 60 to 80 times that of wind (~10 g-CO2e/kWh) (Table 3.5) and the 100-year coal CO2e (~850 g-CO2e/kWh) is ~85 times that of wind. Similarly, both coal and gas produce much more air pollution than do WWS sources (Section 3.1.2).

The CO2e emissions from natural gas versus other fossil fuels are higher for heating and transportation than for electricity. For building heat and industrial process heat, for example, natural gas offers less efficiency advantage over oil or coal than it does for electricity generation. As such, after accounting for all chemical emissions and their respective global warming potentials, natural gas may causes greater long-term global warming than do oil or coal for heating.

With respect to transportation fuels, the carbon dioxide equivalent emissions of natural gas may also exceed that of oil, since the efficiency of natural gas used in transportation is similar to that of oil. Thus, when methane leaks are added in, natural gas causes more overall warming than oil (Alvarez et al. 2012). In sum, in terms of climate, natural gas causes greater global warming than other fossil fuels over 20 years across all applications. Over a 100-year time frame, natural gas causes similar or less warming than coal used for electricity generation and greater warming than oil for heating and transportation over 100 years. All fossil fuels emit 1.5 to 2 orders of magnitude the CO2e as WWS sources.

3.1.2. Air Pollution Impacts of Natural Gas Versus Coal and Renewables

Whereas natural gas causes more CO2e emissions than coal over 20 years and a similar or slightly less level over 100 years, coal emits more health-affecting air pollutants than does natural gas, which is the main reason it has a lower CO2e over 20 years than does natural gas. Nevertheless, both natural gas and coal are much worse for human health than are WWS technologies, which emit no air pollutants during their operation, only during their manufacture and decommissioning. Such WWS emissions will disappear to zero as all energy transitions to WWS since even manufacturing will be powered by WWS at that point.

Table 3.2 provides U.S. emissions from all natural gas and coal uses in the United States in 2008. The table indicates that natural gas production and use in the U.S. emitted more CO, volatile organic carbon (VOC), CH4, and ammonia (NH3) than coal production and use, whereas coal emitted more NOx, SO2, and particulate matter smaller than 2.5- and 10-μm in diameter (PM2.5, PM10). Thus, both fuels resulted in significant air pollution, although the higher SO2, NOx, and particulate matter emissions from coal resulted in overall greater air pollution health problems from coal than natural gas.

Table 3.2. 2008 U.S emissions from natural gas and coal (metric tonnes/y). Bold indicates higher overall emissions between coal and natural gas (NG).

Source: U.S. EPA (2011). VOCs exclude methane. The methane emissions from the EPA inventory are likely underestimated (e.g., Alvarez et al., 2018).

Most SO2 and NOx emissions evolve to sulfate and nitrate aerosol particles, respectively. Natural gas also emits NOx, but less so than does coal (Tables 2.6 and 2.7). Natural gas, on the other hand, emits much less SO2 than does coal (Tables 2.6 and 2.7). Aerosol particles, including those containing sulfate and nitrate formed from gases in the atmosphere, and those emitted directly, cause 90 percent of the 4 to 9 million air pollution deaths that occur annually worldwide (Section 1.1.1). As such, coal in particular, but also natural gas, causes significant health damage.

Model simulations over the United States with the emission data from Table 3.2 suggests that emissions from all natural gas sources cause about 5,000 out of the 60,000 to 65,000 premature mortalities each year in the U.S. from air pollution (Jacobson et al., 2015a). Coal-related emissions are estimated to cause 20,000 to 30,000 premature mortalities in the U.S. Many of the remaining premature mortalities are due to

CO VOC CH4 NH3 NOx SO2 PM2.5 PM10

Coal All Uses 680 40
5
11 2,800 7,600 290 420

NG All Uses 900 1,130 310 54 1,540 123 61 71

pollution associated with oil (e.g., traffic exhaust, oil refinery evaporation), biofuels for transportation, and wood smoke emissions from open fires, fireplaces, and cooking.

As such coal causes more mortalities than does natural gas, but both cause far more mortalities than do WWS technologies. The combination of the much higher CO2e emissions and premature mortality due to natural gas than WWS technologies renders natural gas not an option as a bridge fuel.

3.1.3. Using Natural Gas for Peaking or Load Following

Another argument for using natural gas as a bridge fuel is that it can be used in a load-following or peaking plant (defined in Section 2.4), and WWS technologies will need load-following or peaking plants that use natural gas to back them up when not enough wind or solar is available.

However, whereas natural gas plants can help with peaking and load following, they are not needed (Section 8.2.1). Other types of WWS electric power storage options available include CSP with storage, hydroelectric dam storage, pumped hydropower storage, stationary batteries, flywheels, compressed air energy storage, and gravitational storage with solid masses (Section 2.7). As of 2019, the cost of a system consisting of wind and solar plus batteries costs less than using natural gas. For example, a Florida utility is replacing two natural gas plants with a combined solar-battery system due the lower cost of the latter (Geuss, 2019).

More important, a 100 percent WWS world involves electrifying or providing direct heat for all energy sectors, where the electricity or heat comes from WWS. Such a transition allows heat, cold, and hydrogen storage to work together with demand response to facilitate matching electric power demand with supply on the grid while also satisfying heat, cold, and hydrogen demands minute by minute at low cost. Chapter 8 discusses this issue in detail.

3.1.4. Land Required for Natural Gas Infrastructure

The continuous use of natural gas for electricity and heat results in the cumulative degradation of land for as long as the gas use continues. Wells must be drilled and pipes laid every year to supply a world thirsty for gas. When gas wells become depleted, new wells much be drilled. Allred et al. (2015) estimate that 50,000 new natural gas wells are drilled each year in North America alone to satisfy gas demand. The land area required for the well pads, roads, and storage facilities of these 50,000 new wells amounts to 2,500 km2 of additional land consumed per year (Allred et al., 2015). Once a gas well is depleted, it is sealed and abandoned, and a portion of the abandoned land cannot be used for any other purpose. The natural gas infrastructure also requires land for underground and aboveground pipes, power plants, fueling stations, and underground storage facilities. The flammability of natural gas further results in explosions in homes and urban areas that have had fatal consequences.

Table 3.3 shows the land required for the entire fossil fuel and nuclear infrastructure in California and the United States. The table indicates that the fossil fuel infrastructure takes up about 1.3 percent of the United States land area and 1.2 percent of California’s land area. Whereas all fossil fuels contribute to this land area degradation, natural gas’ share is growing due to the phase out of coal and growth of gas, particularly of hydraulically fracked gas. The damage due to fracking includes damage not only to the landscape but also to nearby groundwater, in which natural gas often leaks. Additional damage occurs to roads, which much carry heavy trucks associated with natural gas development. Gas flaring is another form of local environmental degradation, as the flaring emits soot (containing black carbon) that deposits downwind.

Table 3.3. Land areas required for the fossil fuel and nuclear infrastructure in California and the United States.

California

United States

Area per

Number

Area

Number

Area

installation (km2)

(km2)

(km2)

aActive oil and gas wells bAbandoned oil wells bAbandoned gas wells cCoal mines

dOil refineries
eKilometers of oil pipeline eKilometers of gas pipeline fCoal power plants
fGas power plants fPetroleum power plants fNuclear power plants fOther power plants gFueling stations
hGas storage facilities Total
Percent of CA or U.S.

0.05 0.00005 0.000025 50
7.28 0.006 0.006 1.74
0.12
0.93
14.9
0.93 0.0018 12.95

105,000 225,000 48,000 0

17 4,800 180,000 1
37
0
1
0 10,200 10

3,327 6.6 0.7
0

124 29 1,080 1.74 4.5

0 14.9 0
18 130 4,736 1.2

1.3 million 2.6 million 550,000 680

135 258,000 2.62 million 359
1,820
1,080
61
41
156,000 394

65,000 128.5 13.8 34,000 983 1,550 15,700 626 221 1,007 911

41
275 5,102 126,000 1.3

aNumber of active oil and gas wells, compressors, and processors from Oil and Gas (2018). The area of each is calculated from the 3 million ha of well pads, roads, and storage facilities required for 600,000 new wells from 2000 to 2012 (Allred et al., 2015).

bNumber of abandoned U.S. oil and gas wells from U.S. EPA (2017), slide 11. The California number is calculated as the U.S. number multiplied by the California to U.S. ratio of active wells. The area of each abandoned oil well is estimated as 50 m2, and of each gas well, 25 m2 from Jepsen (2018).

cNumber of coal mines from EIA (2018a). The area per mine is estimated from the total area among all mines from Sourcewatch (2011) divided by number of mines here.

dNumber of oil refineries from EIA (2018b). The area of each refinery is based on the area of the Richmond, California refinery.

eKilometers of oil and gas pipeline for the U.S. were from BTS (2018); for California were estimated. The area needed for each 1 km of pipeline is estimated to be 6 m (3 m on each side of the pipe) multiplied by 1 km.

fNumber of coal, gas, petroleum, nuclear and other power plants is from EIA (2018c). The areas for each coal, gas, and nuclear plant is derived from Strata (2017). For coal, the area includes those for the plant and waste disposal (mining is a separate line in this table). For gas, the area is just for the plant. For nuclear, the area includes the areas required for uranium mining, the plant itself, and waste disposal. The areas required for petroleum and other are an average of that for a coal and gas plant.

gNumber of retail fueling stations in the U.S. is from AFDC (2014) for 2012 and in California, from Statistica (2017) for 2016. The area of a fueling station is estimated from the area of a typical gas station.

fNumber of gas storage facilities is from FERC (2004). The area of a gas storage facility is estimated as that of the Aliso Canyon storage facility.

A transition to 100 percent WWS, on the other hand, eliminates the need and energy required to continuously mine, transport, and process fossil fuels and uranium. This activity consumes 12.6 percent of all energy worldwide (Jacobson et al., 2017). Wind, on the other hand, comes right to the turbine, and sunlight comes right to the solar panel. In other words, eliminating all fossil fuels and uranium will eliminate 12.6 percent of all energy needs worldwide immediately and will prevent the degradation of land used for the continuous mining of natural gas, coal, oil, and uranium.

3.2. Why Not Use Natural Gas or Coal With Carbon Capture?

Another proposal to help solve the climate problem is to capture the CO2 emitted from a coal or natural gas power plant before the CO2 is released from the stack. This would be done with carbon capture and sequestration (CCS) technology added to the plant. However, this solution is poor for four reasons: it increases emissions and health problems of all gases and particles aside from CO2 compared with no CCS, it only marginally reduces CO2, it increases the land degradation from the mining of fossil fuels compared with no CCS, and its high cost prevents more effective climate and pollution mitigation with lower-cost renewables.

Carbon capture and sequestration (CCS) is the separation of CO2 from other exhaust gases after fossil fuel or biofuel combustion, followed by the transfer of the CO2 to an underground geological formation (e.g., saline aquifer, depleted oil and gas field, or un-minable coal seam). The remaining combustion gases are emitted into the air or filtered further. Geological formations worldwide may theoretically store up to 2,000 Gt-CO2, which compares with a fossil fuel emission rate in 2017 of about 37 Gt-CO2/y.

Another proposed CCS method is to inject the CO2 into the deep ocean. The addition of CO2 to the ocean, however, results in ocean acidification. Dissolved CO2 in the deep ocean eventually equilibrates with CO2 in the surface ocean, reducing ocean pH and simultaneously supersaturating the surface ocean with CO2, forcing some of it back into the air.

A third type of sequestration method is to mix captured CO2 with concrete material, trapping the CO2 inside the concrete (Section 2.4.8.2).

Carbon capture and use (CCU) is the same as CCS, except that the isolated CO2 with CCU is sold to reduce the cost of the carbon capture equipment. To date, the major application of CCU has been enhanced oil recovery. With this process, CO2 is pumped underground into an oil field. It binds with oil, reducing its density and allowing it to rise to the surface more readily. Once the oil rises up, the CO2 is separated from it and sent back into the reservoir. About two additional barrels of oil can be extracted for every ton of CO2 injected into the ground.

Another proposed use has been to create carbon-based fuels to replace gasoline and diesel. The problem with this proposal is that it allows combustion to continue in vehicles. Combustion creates air pollution, only some of which can be stopped by emission control technologies.

3.2.1. Air Pollution Increases and Only Modest Lifecycle CO2e Decreases Due to Carbon Capture

Whereas carbon capture equipment is expected to capture 85 to 90 percent of the CO2 from a fossil fuel exhaust stream, several factors cause the overall CO2 and CO2e savings due to carbon capture to be much smaller than this but also cause an increase in emissions of health-affecting air pollutants relative to no carbon capture. The reasons for these impacts are summarized as follows:

  1. 1)  A fossil fuel with carbon capture power plant needs to produce 25 percent more energy, thus requires 25 percent more fuel, to run the carbon capture equipment than does a plant without the equipment (IPCC, 2005).
  2. 2)  Carbon capture equipment does not capture the upsteam CO2e emissions resulting from mining, transporting, or processing the fossil fuel used in the plant. Instead, such emissions increase 25 percent because 25 percent more fuel is needed. This offsets a portion of the captured CO2 from the plant exhaust and increases the air pollution emissions associated with the mining, transporting, and processing of the fuel.
  3. 3)  The carbon capture equipment does not capture any of the non-CO2 air pollutants from the fossil fuel exhaust. Such pollutants include CO, NOx, SO2, organic gases, mercury, toxins, BC, BrC, fly ash, and other aerosol components, all of which affect health. Instead, those pollutants increase 25 percent because 25 percent more fossil fuel from the plant is needed to run the CCS equipment.
  4. 4)  The chance that CO2 sequestered underground leaks increases over time and varies with geological formation.

One way to estimate the climate impact of carbon capture equipment when it is attached to a fossil fuel plant is to examine the plant’s lifecycle emissions before and after the equipment is added. Lifecycle emissions are carbon-equivalent (CO2e) emissions of a technology per unit electric power generation (kWh), averaged over a 20- or 100-year time frame. The emissions accounted for include those during the construction, operation, and decommissioning of the plant. For a fossil fuel (or nuclear) plant, the operation phase includes mining, transporting, and processing the fuel as well as running the plant equipment, repairing the plant over its life, and disposing of waste (e.g., coal residue or nuclear waste) over its life. Lifecycle CO2e is calculated as the lifecycle emission of CO2 plus the lifecycle emission of each other gas or particle pollutant from the technology multiplied by its respective 20- or 100-year GWP (Table 1.2).

Table 3.4 shows estimated 20- and 100-year lifecycle CO2e emissions from an average U.S. coal plant, a modern supercritical pulverized coal (SCPC) plant, and a natural gas combined cycle gas turbine (CCGT) plant, each with and without carbon capture. An SCPC plant operates at a high temperature and pressure than a normal coal plant. As such, the efficiency of combustion (electricity production per mass of coal) is higher. The table indicates that, even after carbon capture, the coal SCPC plant still emits 50.4 percent of its CO2e over 20 years and 28.7 percent over 100 years compared with no carbon capture. A natural gas CCGT emits 34 percent of its CO2e over 20 years and 35.4 percent over 100 years compared with no capture. These results reflect the fact that the carbon capture equipment increases the upstream emissions of CO2e due to increasing the fuel needed to be burned in the power plant. The results also reflect the fact that the carbon capture equipment lets 10 to 15 percent of the CO2 emitted by the stack escape.

Table 3.4. Lifecycle 20-year and 100-year CO2e emissions from average U.S. coal power plants, a supercritical pulverized coal (SCPC) power plant and a natural gas combined cycle gas turbine (CCGT) plant with and without carbon capture.

Average U.S. Coal Plant

Coal SCPC Plant

Natural Gas CCGT Plant

No Carbon Capture

With Carbon Capture

Percent

CO2e Remaining

No Carbon Capture

With Carbon Capture

Percent

CO2e Remaining

No Carbon Capture

With Carbon Capture

Percent

CO2e Remaining

20-y CO2e/kWh 100-y CO2e/kWh

1,316 1,205

664 346

50.4 28.7

1,188 965

599 277

50.4 28.7

896 506

305 179

34.0 35.4

All values are from Skone (2015), except the percent remaining for average U.S. coal was assumed the same as from Coal SCPC, and the CO2e values with carbon capture for average U.S. coal were calculated from the percent remaining and the no carbon capture values.

The results in Table 3.4 suggest that carbon capture does not come close to eliminating CO2e emissions from coal or gas power plants. Data from real world projects (Section 3.2.3) indicate even less reduction in CO2e emissions due to carbon capture than Table 3.4 suggests. Further, the lifecycle CO2e emissions from a natural gas or coal plant with carbon capture are not the only emissions associated with the plant. Lifecycle emissions can be placed in context only when all relevant emissions associated with a plant are accounted for and compared with emissions from other energy technologies, as discussed next.

3.2.2. Total CO2e Emissions Of Energy Technologies

Lifecycle emissions are one component of total carbon equivalent (CO2e) emissions. Additional components relevant to fossil fuels with carbon capture include opportunity cost emissions, emissions risk due to CO2 leakage, and emissions due to covering or clearing land for energy development. These are discussed next.

3.2.2.1. Opportunity Cost Emissions

Opportunity cost emissions are emissions from the background electric power grid, averaged over a defined period of time (e.g., either 20 years or 100 years), due to two factors. The first factor is the longer time lag between planning and operation of one energy technology relative to another. The second factor is the longer downtime needed to refurbish one technology at the end of its useful life when its useful life is shorter than that of another technology (Jacobson, 2009).

For example, if Plant A takes 4 years and Plant B takes 10 years between planning and operation, the background grid will emit pollution for 6 more years out of 100 years with Plant B than with Plant A. The emissions during those additional 6 years are opportunity cost emissions. Such additional emissions include both health-affecting air pollutants and pollutants that affect global climate.

Similarly, if Plant A and B have the same planning-to-operation time but Plant A has a useful life of 20 years and requires 2 years of refurbishing to last another 20 year and Plant B has a useful life of 30 years but takes only 1 year of refurbishing, then Plant A is down 2 y / 22 y = 9.1 percent of the time for refurbishing and Plant B is down 1 y / 31 y = 3.2 percent of the time for refurbishing. As such, Plant B is down an additional (0.091 – 0.032) × 100 y = 5.9 years out of every 100 for refurbishing. During those additional years, the background grid will emit pollution with Plant B.

Mathematically, opportunity cost emissions (EOC, in g-CO2e/kWh) are calculated as

EOC = EBR,H – EBR,L (3.1)

where EBR,H are total background grid emissions over a specified number of years due to delays between planning and operation and downtime for refurbishing of the technology with the more delays. EBR,L is the same but for the technology with the fewer delays. Background emissions (for either technology) over the number of years of interest, Y, are calculated as

EBR=EG ×([TPO +(Y–TPO)×TR /(L+TR)]/Y (3.2)

where EG is the emissions intensity of the background grid (g-CO2e/kWh for analyses of the climate impacts and g-pollutant/kWh for analyses of health-affecting air pollutants), TPO is the time lag (in years)

between planning and operation of the technology, TR is the times (years) to refurbish the technology, and L is the operating life (years) of the technology before it needs to be refurbished.

Example 3.1. Opportunity cost emissions.
What are the opportunity cost emissions (g-CO2e/kWh) over 100 years resulting from Plant B if its planning-to- operation time is 15 years, its lifetime is 40 years, and its refurbishing time is 3 years, whereas these values for Plant A are 3 years, 30 years, and 1 year, respectively? Assume both plants produce the same number of kWh/y once operating, and the background grid emits 550 g-CO2e/kWh.

Solution:
The opportunity cost emissions are calculated as the emissions from the background grid over 100 years of the plant with the higher background emissions (Plant B in this case) minus those from the plant with the lower background emissions (Plant A).

The background emissions from Plant B are calculated from Equation 3.2 with EG=550 g-CO2e/kWh, Y=100 y, TPO=15 y, L=40 y, and TR=3 y as EBR,H=550 g-CO2e/kWh × [15 y + (100 y – 15 y) × 3 y / 43 y)] / 100 y = 115 g-CO2e/kWh.

Similarly, the background emissions from Plant A averaged over 100 years are EBR,L=550 g-CO2e/kWh × [3 y + (100 y – 3 y) × 1 y / 31 y)] / 100 y = 33.7 g-CO2e/kWh. The difference between the two from Equation 3.1, EOC= EBR,H-EBR,L= 81.3 g-CO2e/kWh, is the opportunity cost emissions of Plant B over 100 years.

The time lag between planning and operation of a technology includes a development time and construction time. The development time is the time required to identify a site, obtain a site permit, purchase or lease the land, obtain a construction permit, obtain financing and insurance for construction, install transmission, negotiate a power purchase agreement, and obtain permits. The construction period is the period of building the plant, connecting it to transmission, and obtaining a final operating license.

The development phase of a coal-fired power plant without carbon capture equipment is generally 1 to 3 years, and the construction phase is another 5 to 8 years, for a total of 6 to 11 years between planning and operation (Jacobson, 2009). No coal plant has been built from scratch with carbon capture, so this could add to the planning-to-operation time. However, for a new plant, it is assumed that the carbon capture equipment can be added during the long planning-to-operation time of the coal plant itself. As such, Table 3.5 assumes the planning-to-operation time of a coal plant without carbon capture is the same as that with carbon capture. The typical lifetime of a coal plant before it needs to be refurbished is 30 to 35 years. The refurbishing time is an estimated 2 to 3 years.

No natural gas plant with carbon capture exists. The estimated planning-to-operation time of a natural gas plant without carbon capture is less than that of a coal plant. However, because of the shorter time, the addition of carbon capture equipment to a new natural gas plant is likely to extend its planning-to-operation time to that of a coal plant with or without carbon capture (6 to 11 years).

For comparison, the planning-to-operation time of a utility-scale wind or solar farm is generally 3 to 5 years, with a development period of 1 to 3 years and a construction period of 1 to 2 years (Jacobson, 2009). Wind turbines often last 30 years before refurbishing, and the refurbishing time is 0.25 to 1 year.

Table 3.5 provides the estimate opportunity cost emissions of coal and natural gas with carbon capture due to the time lag between planning and operation of those plants relative to wind or solar farms. The table indicates an investment in fossil fuels with carbon capture instead of wind and solar result in an additional 46 to 62 g-CO2e/kWh in opportunity cost emissions from the background grid.

Table 3.5. Total 100-year CO2e emissions from several different energy technologies. The total includes lifecycle emissions, opportunity cost emissions, anthropogenic heat and water vapor emissions, weapons and leakage risk

emissions, and emissions from loss of carbon storage in land and vegetation. All units are g-CO2e/kWh-electricity, except the last, column, which gives the ratio of total emissions of a technology to the emissions from onshore wind. CCS/U is carbon capture and storage or use.

Technology

aLifecycle emissions

bOpportuni ty cost emissions due to delays

cAnthro- pogenic heat emissions

dAnthro- pogenic water vapor emissions

eNuclear Weapons risk or 100-Y ear CCS/U leakage risk

fLoss of CO2 due to covering Land or clearing vegetation

gTotal 100-year CO2e

Ratio of 100-year CO2e to that of wind- onshore

Solar PV-rooftop Solar PV-utility CSP Wind-onshore Wind-offshore Geothermal Hydroelectric Wave

Tidal
Nuclear
Biomass
Natural gas-CCS/U Coal-CCS/U

15-34

10-29 8.5-24.3 7.0-10.8 9-17 15.1-55 17-22 21.7 10-20 9-70 43-1,730 179-336 230-800

-12 to -16 0
0
0
0 14-21 41-61 4-16 4-16 64-102 36-51 46-62 46-62

-2.2 -2.2 -2.2 -1.7 to -0.7 -1.7 to -0.7 0

0 0 0 1.6 3.4 0.61 1.5

0

0
0 to 2.8 -0.5 to -1.5 -0.5 to -1.5 0 to 2.8 2.7 to 26 0
0
2.8
3.2
3.7
3.6

0
0
0
0
0
0
0
0
0 0-1.4 0 0.36-8.6 0.36-8.6

0 0.054-0.11 0.13-0.34 0.0002-0.0004 0 0.088-0.093 0
0
0 0.17-0.28 0.09-0.5 0.41-0.69 0.41-0.69

0.8-15.8 7.85-26.9 6.43-25.2 4.8-8.6 6.8-14.8 29-79 61-109 26-38 14-36 78-178 86-1,788 230-412 282-876

0.1-3.3 0.91-5.6 0.75-5.3 1 0.79-3.1 3.4-16 7.1-22.7 3.0-7.9 1.6-7.5 9.0-37 10-373 27-86 33-183

aLifecycle emissions are 100-year carbon equivalent (CO2e) emissions that result from the construction, operation, and decommissioning of a plant. They are determined as follows:
Solar PV-rooftop: The range is assumed to be the same as the solar PV-utility range, but with 5 g-CO2/kWh added to

both the low and high ends to account for the use of fixed tilt for all rooftop PV versus the use of some tracking

for utility PV.
Solar PV-utility: The range is derived from Fthenakis and Raugei (2017). It is inclusive of the 17 g-CO2/kWh mean

for CdTe panels at 11 percent efficiency, the 27 g-CO2e/kWh mean for multi-crystalline silicon panels at 13.2 percent efficiency, and the 29 gCO2e/kWh mean for mono-crystalline silicon panels at 14 percent efficiency. The upper limit of the range is held at the mean for multi-crystalline silicon since panel efficiencies are now much higher than 13.2 percent. The lower limit is calculated by scaling the CdTe mean to 18.5 percent efficiency, its maximum in 2018.

CSP: The lower limit CSP lifecycle emission rate is from Jacobson (2009). The upper limit is from Ko et al. (2018). Wind-onshore and wind-offshore: The range is derived from Kaldelis and Apostolou (2017).
Geothermal: The range is from Jacobson (2009) and consistent with the review of Tomasini-Montenegro et al.

(2017).
Hydroelectric and wave: From Jacobson (2009).
Tidal: From Douglass et al. (2008).
Nuclear: The range of 9-70 g-CO2e/kWh is from Jacobson (2009), which is within the Intergovernmental Panel on

Climate Change (IPCC)’s range of 4-110 g-CO2e/kWh (Bruckner et al., 2014), and conservative relative to the 68 (10-130) g-CO2e/kWh from the review of Lenzen (2008) and the 66 (1.4-288) g-CO2e/kWh from the review of Sovacool (2008).

Biomass: The range provided is for biomass electricity generated by forestry residues (43 gCO2e/kWh), industry residues (46), energy crops (208), agriculture residues (291), and municipal solid waste (1730) (Kadiyala et al., 2016).

Natural gas-CCS/U: The lower bound is for the CCGT with carbon capture plant from Skone (2015), also provided in Table 3.4. The upper bound is CCGT value without carbon capture, 506 g-CO2e/kWh from Table 3.4, multiplied by 66.4 percent, which is the percent of CO2e emissions expected to be captured from the Petra Nova facility that will remain in the air over 100 years (Example 3.9).

Coal-CCS/U: The lower bound is for IGCC with carbon capture from Skone (2015). The upper bound is the coal value without carbon capture, 1,205 g-CO2e/kWh from Table 3.4, multiplied by 66.4 percent, which is the percent of CO2e emissions expected to be captured from the Petra Nova facility that will remain in the air over 100 years (Example 3.9).

bOpportunity cost emissions are emissions per kWh over 100 years from the background electric power grid, calculated from Equations 3.1 and 3.2 due to (a) the longer time lag between planning and operation of one energy technology relative to another and (b) additional downtime to refurbish a technology at the end of its useful life compared with

the other technology. The planning-to-operation times of the technologies in this table are 0.5-2 years for solar PV- rooftop; 2-5 years for solar PV-utility, CSP, wind-onshore, wind-offshore, tidal, and wave; 3-6 years for geothermal; 8-16 years for hydroelectric; 10-19 years for nuclear; 4-9 years for biomass (without CCS/U), and 6-11 years for natural gas-CCS/U and coal-CCS/U (Jacobson, 2009, except rooftop PV and natural gas-CCS/U values are added and solar PV-rooftop is updated here). The refurbishment times are 0.05-1 year for solar PV-rooftop; 0.25-1 year for solar-PV-utility, CSP, wind-onshore, wind-offshore, wave, and tidal; 1-2 years for geothermal and hydroelectric; 2-4 years for nuclear, and 2-3 years for biomass, coal-CCS/U, and natural gas-CCS/U. The lifetimes before refurbishment are 15 years for tidal and wave; 30 years for solar PV-rooftop, solar PV-utility, CSP, wind-onshore, wind-offshore; 30-35 years for biomass, coal-CCS/U, and natural gas-CCS/U; 30-40 years for geothermal; 40 years for nuclear; and 80 years for hydroelectric (Jacobson, 2009). The opportunity cost emissions are calculated here relative to the utility-scale technologies with the shortest time between planning and operation (solar-PV-utility, CSP, wind-onshore, and wind-offshore). The opportunity cost emissions of the latter technologies are, by definition, zero. The opportunity cost emissions of all other technologies are calculated as in Example 3.1 assuming a background U.S. grid emission intensity equal to 557.3 g-CO2e/kWh in 2017. This is derived from an electricity mix from EIA (2018d) and emissions, weighted by their 100-year GWPs, of CO2, CH4, and N2O from mining, transporting, processing and using fossil fuels, biomass, or uranium. The reason tidal power has opportunity cost emissions although its planning-to-operation time is the same as onshore wind is due to tidal’s shorter lifetime. Thus, it has more down time over 100 years than do other technologies. See Section 3.2.2.1. The opportunity cost emissions of offshore and onshore wind are assumed to be the same because new projects suggest offshore wind, particularly with faster assembly techniques and with floating turbines, are easier to permit and install now than a decade ago. Although natural gas plants don’t take so long as coal plants between planning and operation, natural gas combined with CCS/U is assumed to take the same time as coal with CCS/U.

cAnthropogenic heat emissions here include the heat released to the air from combustion (for coal or natural gas) or nuclear reaction, converted to CO2e (see Section 3.2.2.2). For solar PV and CSP, heat emissions are negative because these three technologies reduce sunlight to the surface by converting it to electricity. The lower flux to the surface cools the ground or a building below the PV panels. For wind turbines, heat emissions are negative because turbines extract energy from wind to convert it to electricity (Section 3.2.2.3 and Example 3.6). For binary geothermal plants (low end), it is assumed all heat is re-injected back into the well. For non-binary plants, it is assumed that some heat is used to evaporate water vapor (thus the anthropogenic water vapor flux is positive) but remaining heat is injected back into the well. The electricity from all electric power generation also dissipates to heat, but this is due to the consumption rather than production of power and is the same amount per kWh for all technologies so is not included in this table.

dAnthropogenic water vapor emissions here include the water vapor released to the air from combustion (for coal and natural gas) or from evaporation (water-cooled CSP, water-cooled geothermal, hydroelectric, nuclear natural gas, and coal), converted to CO2e (see Section 3.2.2.3). Air-cooled CSP and geothermal plants have zero water vapor flux, representing the low end of these technologies. The high end is assumed to be the same as for nuclear, which also uses water for cooling. The low end for hydroelectric power assumes 1.75 kg-H2O/kWh evaporated from reservoirs at mid to high latitudes (Flury and Frischknecht, 2012). The upper end is 17.0 kg-H2O/kWh from Jacobson (2009) for lower latitude reservoirs and assumes reservoirs serve multiple purposes. For biomass, the number is based only on the water emitted from the plant due to evaporation or combustion, not water to irrigate some energy crops. Thus, the upper estimate is low. The negative water vapor flux for onshore and offshore wind is due to the reduced water evaporation caused by wind turbines (Section 3.2.2.3 and Example 3.6).

eNuclear weapons risk is the risk of emissions due to nuclear weapons use resulting from weapons proliferation caused by the spread of nuclear energy. The risk ranges from zero (no use of weapons over 100 years) to 1.4 g-CO2e/kWh (one nuclear exchange in 100 years) (Section 3.3.2.1). The 100-year CCS/U leakage risk is the estimated rate, averaged over 100 years, that CO2 sequestered underground leaks back to the atmosphere. Section 3.2.2.4 contains a derivation. The leakage rate from natural gas-CCS/U is assumed to be the same as for coal-CCS/U.

fLoss of carbon, averaged over 100 years, due to covering land or clearing vegetation is the loss of carbon sequestered in soil or in vegetation due to the covering or clearing of land by an energy facility; by a mine where the fuel is extracted from (in the case of fossil fuels and uranium); by roads, railways, or pipelines needed to transport the fuel; and by waste disposal sites. No loss of carbon occurs for solar PV-rooftop, wind-offshore, wave, or tidal power. In all remaining cases, except for solar PV-utility and CSP, the energy facility is assumed to replace grassland with the organic carbon content and grass content as described in the text. For solar PV-utility and CSP, it is assumed that the organic content of both the vegetation and soil are 7 percent that of grassland because (a) most all CSP and many PV arrays are located in deserts with low carbon storage and (a) most utility PV panels and CSP mirrors are elevated above the ground. For biomass, the low value assumes the source of biomass is industry residues or contaminated wastes. The high value assumes energy crops, agricultural residues, or forestry residues. See Section 3.2.2.5.

gThe total column is the sum of the previous four columns.

3.2.2.2. Anthropogenic Heat Emissions

Anthropogenic heat emissions were defined in Section 1.2.3 to include the heat released to the air from the dissipation of electricity; the dissipation of motive energy by friction; the combustion of fossil fuels, biofuels and biomass for energy; nuclear reaction; and the heat from anthropogenic biomass burning. The relative worldwide contributions to each category of heat by each energy generating technology are provided in Jacobson (2014).

Table 3.5 includes the g-CO2e/kWh emissions from heat of combustion (for natural gas and coal) and from nuclear reaction. However, because the dissipation of electricity to heat per kWh is due to the consumption rather than production of electricity and is the same for all technologies, that term is not included in the table.

Solar PV and CSP convert solar radiation to electricity, thereby reducing the flux of heat to the ground or rooftop below PV panels. This is reflected in Table 3.5 as a negative heat flux.

The CO2e emissions (g-CO2e/kWh) due to the anthropogenic heat flux is calculated for all technologies (including the negative heat flux due to solar) as follows:

H = ECO2 × Ah / (FCO2 × Gelec) (3.3)

where ECO2 is the equilibrium global anthropogenic emission rate of CO2 (g-CO2/y) that gives a specified anthropogenic mixing ratio of CO2 in the atmosphere, FCO2 is the direct radiative forcing (W/m2) of CO2 at the specified mixing ratio, Ah is the anthropogenic heat flux (W/m2) due to a specific electric power producing technology, and Gelec is the annual global energy output of the technology (kWh/y).

The idea behind this equation is that the current radiative forcing (W/m2) in the atmosphere due to CO2 can be maintained at an equilibrium CO2 emission rate,

ECO2 = χCO2C/τCO2 (3.4)

where χCO2 (ppmv) is the specified anthropogenic mixing ratio that gives the current CO2 radiative forcing, C is a conversion factor (8.0055×1015 g-CO2/ppmv-CO2), and τCO2 is the data-constrained e-folding lifetime of CO2 against loss by all processes. As of 2019, τCO2 is ~50 years but increasing over time (e.g., Jacobson, 2012a, Figure 3.12).

Equation 3.4 is derived by noting that the time rate of change of the atmospheric mixing ratio of a well- mixed gas, such as CO2 is simply, dχ/dt = E – χC/τ. In steady state, this simplifies to E=χC/τ. Scaling the ratio of this equilibrium CO2 emission rate to the radiative forcing of CO2 by the ratio of the anthropogenic heat flux to the electricity generation per year producing that heat flux, gives Equation 3.3, the CO2e emission rate of the heat flux.

Thus, Equation 3.3 accounts for the emission rate of CO2 needed to maintain a mixing ratio of CO2 in the air that gives a specific radiative forcing. It does not use the present day emission rate because that results in a much higher CO2 mixing ratio than is currently in the atmosphere because CO2 emissions are not in equilibrium with the CO2 atmospheric mixing ratio. Equation 3.3 requires a constant emission rate that gives the observed mixing ratio of CO2 for which the current direct radiative forcing applies. Similarly, the energy production rate in Equation 3.3 gives a consistent anthropogenic heat flux.

Finally, whereas radiative forcing is a top-of-the-atmosphere value (and represents changes in heat integrated over the whole atmosphere) and heat flux is added to the bottom of the atmosphere, they both represent the same amount of heat added to the atmosphere. In fact, because the anthropogenic heat flux adds heat to near-surface air, it has a slightly greater impact on surface air temperature per unit radiative forcing than does CO2. For example, the globally averaged temperature change per unit direct radiative forcing for CO2 is ~0.6 K/(W/m2) (Jacobson, 2002), whereas the temperature change per unit anthropogenic heat plus water vapor flux is ~0.83 K/(W/m2) (Jacobson, 2014). As such, the estimated CO2e values for heat fluxes in particular in Table 3.5 may be slightly underestimated.

Example 3.2. Calculate the carbon equivalent heat emissions for coal and nuclear power worldwide.
In 2005, the anthropogenic flux of heat (aside from heat used to evaporate water) from all anthropogenic heat sources worldwide was Ah=0.027 W/m2 (Jacobson, 2014). Assume the percent of all heat from coal combustion was 4.87 percent and from nuclear reaction was 1.55 percent.

Estimate the CO2e emissions corresponding to the coal and nuclear heat fluxes given the energy generation of Gelec=8.622×1012 kWh/y from coal combustion and 2.64×1012 kWh/y from nuclear reaction.

Assume an anthropogenic CO2 direct radiative forcing of FCO2=1.82 W/m2, which corresponds to an anthropogenic mixing ratio of CO2 of χCO2=113 ppmv (Myhre et al., 2013). Also assume a CO2 e-folding lifetime of τCO2=50 years.

Solution:
From Equation 3.4, the equilibrium emission rate of CO2 giving the anthropogenic mixing ratio is

ECO2=1.809×1016 g-CO2/y.

Multiplying the total anthropogenic heat flux by the respective fractions of heat from coal combustion and nuclear reaction gives Ah=0.00132 W/m2 for coal and 0.00042 W/m2 for nuclear. Substituting these and the other given values into Equation 3.3 gives H = 1.52 g-CO2e/kWh for coal and 1.57 g-CO2/kWh for nuclear.

Example 3.3. Calculate the carbon-equivalent negative heat emissions of a solar PV panel.
Solar panels convert about 20 percent of the sun’s energy to electricity, thereby reducing the flux of sunlight to the ground. What is the reduction in heat flux (W/m2) per kWh/y of electricity generated by a solar panel and what is the corresponding CO2e emission reduction? The surface area of the Earth is 5.092×1014 m2.

Solution:
If a solar panel produces Gelec=1 kWh/y of electricity, the panel prevents exactly that much solar radiation from converting to heat compared with the sunlight otherwise hitting an equally reflective surface. Eventually, the electricity converts to heat as well (as does the electricity from all electric power generators). However, other electric power generators do not remove heat from the sun on the same time scale as solar panels do.

Multiplying the avoided heat (-1 kWh/y) by 1000 W/kW and dividing by 8760 h/y and by the area of the Earth gives Ah=-2.24×10-16 W/m2. Substituting this, Gelec=1 kWh/y, and ECO2 and FCO2 from Example 3.2 into Equation 3.3 gives H=-2.23 g-CO2e/kWh.

Finally, for hydropower, evaporation of water vapor at the surface of a reservoir by the sun increases anthropogenic water vapor emissions (Section 3.2.2.3). Because evaporation requires energy, it cools the surface of the reservoir. The energy used to evaporate the water becomes embodied in latent heat carried by the water vapor. However, the water vapor eventually condenses in the air (forming clouds), releasing the heat back to the air. As a result, the warming of the air offsets cooling at the surface, so hydropower causes no net anthropogenic heat flux. On the other hand, water vapor is a greenhouse gas, resulting in a net warming of the air due to evaporation. This warming is accounted for in the next section.

3.2.2.3. Anthropogenic Water Vapor Emissions

Fossil fuel, biofuel, and biomass burning release not only heat, but also water vapor. The water results from chemical reaction between the hydrogen in the fuel and oxygen in the air. In addition, coal, natural gas, and

nuclear plants require cool liquid water to re-condense the hot steam as it leaves a steam turbine. This process results in significant water evaporating out of a cooling tower to the sky. Many CSP turbines also use water cooling although some use air cooling. Similarly, whereas non-binary geothermal plants and some binary plants use water cooling, thus emit water vapor, binary plants that use air cooling do not emit any water vapor. Finally, water evaporates from reservoirs behind hydroelectric power plant dams. Table 1.1 indicates that anthropogenic water vapor from all anthropogenic sources causes about 0.23 percent of global warming.

On the other hand, as discussed in Chapter 7, wind turbines reduce water vapor, a greenhouse gas, by reducing wind speeds, and water evaporation is a function of wind speed (and temperature) (Jacobson and Archer, 2012; Jacobson et al., 2018a).

In this section, the positive or negative CO2e emissions per unit energy (M, g-CO2e/kWh) due to increases or decreases in water vapor fluxes resulting from an electric power source are quantified. The emissions are estimated with an equation similar to Equation, 3.3, except with the anthropogenic moisture energy flux (Am, W/m2) is substituted for the heat flux:

M = ECO2 × Am / (FCO2 × Gelec) (3.5)

In this equation, the globally averaged moisture energy flux can be obtained from the water vapor flux per unit energy (V, kg-H2O/kWh) by

Am =V×Le ×Gelec /(S×Ae) (3.6)

where Le=2.465×106 J/kg-H2O is the latent heat of evaporation, S=3.1536×107 seconds per year, and Ae=5.092×1014 m2 is the surface area of the Earth. For water evaporating from a hydropower reservoir, V = 1.75 to 17 kg-H2O/kWh (Table 3.5, footnote c).

Combining Equations 3.5 and 3.6 gives the globally averaged CO2e emissions per unit energy due to a positive or negative water vapor flux resulting from an energy generator as

M=ECO2 ×V×Le /(FCO2 ×S×Ae) (3.7)

This equation is independent of the total annual energy production (Gelec). Examples 3.4 to 3.6 provide calculations of anthropogenic water vapor fluxes for several of the generators in Table 3.5.

Example 3.4. Calculate the carbon-equivalent anthropogenic water vapor emissions from natural gas and nuclear plants.
The global anthropogenic water vapor flux from natural gas power plants in 2005 was Am=0.00268 W/m2 and from nuclear power plants was Am=0.000746 W/m2 (Jacobson, 2014). The total energy generation from natural gas use was Gelec=7.208×1012 kWh/y and from nuclear was 2.64×1012 kWh/y. Calculate the CO2e emissions associated with these fluxes.

Solution:
Substituting ECO2 and FCO2 from Example 3.2 and Am and Gelec provided in the problem into Equation 3.5 gives M=3.69 g-CO2e/kWh for natural gas and 2.81 g-CO2e/kWh for nuclear.

Example 3.5. Calculate the carbon-equivalent anthropogenic water vapor emissions from a hydropower reservoir.
If the evaporation rate of water from a hydropower reservoir is V=1.75 kg-H2O/kWh (Flury and Frischknecht, 2012), determine the CO2e emissions of water vapor from the reservoir.

Solution:

Substituting V into Equation 3.7 with ECO2 and FCO2 from Example 3.2 gives the carbon equivalent emissions due to hydropower reservoir evaporation as M=2.66 g-CO2e/kWh.

Wind turbines extract kinetic energy from the wind and convert it to electricity. Kinetic energy is the energy embodied in air due to its motion. For every 1 kWh of electricity produced, 1 kWh of kinetic energy is extracted. Like with all electric power generation, the 1 kWh of electricity eventually converts back to heat that is added back to the air. However, for purposes of assigning CO2e emissions or savings, the conversion of electricity back to heat is not assigned to any particular electric power generator in Table 3.5. However, the addition or extraction of heat and water vapor by the energy technology is.

When electricity dissipates to heat, some of that heat returns to kinetic energy. Heat is internal energy, which is the energy associated with the random, disordered motion of molecules. Higher temperature molecules move faster than lower temperature molecules. Some of the internal energy in the air causes air to rise since warm, low-density air rises when it is surrounded by cool, high-density air. To raise the air, internal energy is converted to gravitational potential energy (GPE), which is the energy required to lift an object of a given mass against gravity a certain distance. The lifted parcel is now cooler as a result of giving away some of its internal energy to GPE. Differences in GPE over horizontal distance create a pressure gradient, which recreates some kinetic energy in the form of wind (Section 6.8).

In sum, wind turbines convert kinetic energy to electricity, which dissipates to heat. Some of that heat converts to GPE, some of which converts back to kinetic energy. If a wind turbine did not extract kinetic energy from the wind, that energy would otherwise still dissipate to heat due to the wind bashing into rough surfaces, which are sources of friction. But, such dissipation would occur over a longer time.

However, wind turbines have an additional effect, which is to reduce water vapor, a greenhouse gas. When wind from dry land blows over a lake, for example, the dry wind sweeps water vapor molecules away from the surface of the lake. More water vapor molecules must then evaporate from the lake to maintain saturation of water over the lake. In this way, winds increase the evaporation of water over not only lakes, but also over oceans, rivers, streams, and soils. Because a wind turbine extracts energy from the wind, it slows the wind, reducing evaporation of water.

By reducing evaporation, wind turbines warm the water or soil near the turbine because evaporation is a cooling process, so less evaporation causes warming. However, because the air now contains less water vapor, less condensation occurs in the air. Since condensation releases heat, less of it means the air cools. Thus, the ground warming is cancelled by the air-cooling due to wind turbines reducing evaporation. However, because water vapor is a greenhouse gas, less of it in the air means that more heat radiation from the Earth’s surface escapes to space, cooling the ground, reducing internal energy. Since water vapor stays in the air for days to weeks, its absence due to a wind turbine reduces heat to the surface over that time more than the one-time dissipation of electricity, created by the wind turbine, increases heat.

In sum, wind turbines allow a net escape of energy to space by reducing water vapor. A portion of the lost energy comes from the air’s internal energy, resulting in lower air temperatures. The rest comes from kinetic energy, reducing wind speeds, and from gravitational potential energy, reducing air heights. As such, a new equilibrium is reached in the atmosphere. Section 6.9.1 quantifies the impacts of different numbers of turbines worldwide on temperatures and water vapor.

Thus, wind turbines reduce temperatures in the global average by reducing both heat fluxes and water vapor fluxes. Wind turbines do increase temperatures on the ground downwind of a wind farm because they reduce evaporation, but in the global average, this warming is more than offset by atmospheric cooling due

to less condensation plus the loss of more heat radiation to space due to the reduction in water vapor caused by wind turbines.

The energy taken out of the atmosphere temporarily (because it is returned later as heat from dissipation of electricity) by wind turbines is 1 kWh per 1 kWh of electricity production. The maximum reduction in water vapor, based on global computer model calculations (Chapter 7), due to wind turbines ranges from – 0.3 to -1 kg-H2O/kWh, where the variation depends on the number and location of wind turbines. Example 3.6 provides an estimate of the CO2e savings due to wind turbines from these two factors.

Example 3.6. Estimate the globally averaged CO2e emissions reductions due to wind turbines.
Assuming that wind turbines extract 1 kWh of the wind’s kinetic energy for each 1 kWh of electricity produced, estimate the CO2e savings per unit energy from reduced heat and water vapor fluxes due to wind turbines considering that, when the turbine is not operating, every 1 kWh of kinetic energy in the wind evaporates 0.3 to 1 kg-H2O/kWh and the rest of the energy remains in the atmosphere. Assume the equilibrium emission rate and resulting radiative forcing of CO2 from Example 3.2.

Solution:
Multiplying the latent heat of evaporation (Le=2.465×106 J/kg) and 1 kWh/3.6×106 J by -0.3 to -1 kg-H2O/kWh gives the reduction in energy available to evaporate water as -0.21 to -0.69 kWh per kWh of electricity-produced. Multiplying 1000 W/kW and dividing by 8760 h/y and by the area of the Earth, 5.092×1014 m2, gives Am/Gelec = – 4.6×10-17 to -1.53×10-16 (W/m2)/(kWh/y). Substituting this and ECO2 and FCO2 from Example 3.2 into Equation 3.5 gives the anthropogenic water vapor energy flux from wind turbines as -0.46 to -1.53 g-CO2e/kWh.

The heat flux is the difference between -1 kWh/kWh-electricity and -0.21 to -0.69 kWh/kWh-electricity, which is -0.79 to -0.31 kWh/kWh-electricity. Performing the same calculation as above gives the anthropogenic heat flux from wind turbines as -1.77 to -0.70 g-CO2e/kWh. The total heat plus water vapor energy flux savings due to wind turbines is thus -2.23 g-CO2e/kWh, the same as for solar panels (Example 3.3).

3.2.2.4. Leaks of CO2 Sequestered Underground

The sequestration of carbon underground due to CCS or CCU (e.g., from injecting CO2 during enhanced oil recovery) runs the risk of CO2 leaking back to the atmosphere through existing fractured rock or overly porous soil or through new fractures in rock or soil resulting from an earthquake. Here, a range in the potential emission rate due to CO2 leakage from the ground is estimated.

The ability of a geological formation to sequester CO2 for decades to centuries varies with location and tectonic activity. IPCC (2005, p. 216) references CO2 leakage rates for an enhanced oil recovery operation of 0.00076 percent per year, or 1 percent over 1000 years, and CH4 leakage from historical natural gas storage systems of 0.1 to 10 percent per 1000 years. Thus, while some well-selected sites could theoretically sequester 99 percent of CO2 for 1000 years, there is no certainty of this since tectonic activity or natural leakage over 1000 years is not possible to predict. Because liquefied CO2 injected underground will be under high pressure, it will take advantage of any horizontal or vertical fractures in rocks to escape as a gas to the air. Because CO2 is an acid, its low pH will also cause it to weather rock over time. If a leak from an underground formation to the atmosphere occurs, it is not clear whether it will be detected. If a leak is detected, it is not clear how it will be sealed, particularly if it is occurring over a large area.

The time-averaged leakage rate of CO2 from a reservoir can be calculated by first estimating how the stored mass of CO2 changes over time. The stored mass (S) of CO2 at any given time t in a reservoir, resulting from a constant injection at rate I (mass/y) and e-folding lifetime against leakage T (years) is

S(t)= S(0)e-t/T+TI(1-e-t/T) (3.8)

where S(0) is the stored mass at time t=0. The average leakage rate over t years is then simply the injection rate minus the remaining mass stored mass at time t divided by t years,

L(t)= I- S(t)/t (3.9) The average leakage rate of CO2 from an underground storage reservoir over a specified period is

calculated from Equations 3.8 and 3.9 given an injection rate and a lifetime against leakage.

Example 3.7. Estimating average leakage rates from underground storage reservoirs.
Assume a coal-fired power plant has a CO2 emission rate before carbon capture and storage ranging from 790 to 1,017 g-CO2/kWh. Assume also that carbon capture equipment added to the plant captures 90 and 80 percent, respectively, of the CO2 (giving a low and high, respectively, emission rate of remaining CO2 to the air). If the captured CO2 is injected underground into a geological formation that has no initial CO2 in it, calculate a low and high CO2 emission rate from leakage averaged over 100 years, 500 years, and 1000 years. Assume a low and high e-folding lifetime against leakage of 5,000 years and 100,000 years, respectively. The low value corresponds to 18 percent leakage over 1000 years, close to that of some observed methane leakage rates. The high value corresponds to a 1 percent loss of CO2 over 1000 years (e.g., IPCC, 2005).

Solution:
The low and high injection rates are 790 × 0.9 = 711 g-CO2/kWh and 1,017 × 0.85 = 864.5 g-CO2/kWh, respectively. Substituting these injection rates into Equation 3.8 (using the high lifetime with the low injection rate and the low lifetime with the high injection rate) and the result into Equation 3.9 gives a leakage rate range of 0.36 to 8.6 g- CO2/kWh over 100 years; 1.8 to 42 g-CO2/kWh over 500 years, and 3.5 to 81 g-CO2/kWh over 1000 years.

Thus, the longer the averaging period, the greater the average emission rate over the period due to CO2 leakage.

3.2.2.5. Emissions From Covering of Land or Clearing of Vegetation

Emissions from the covering of land or clearing of vegetation are emissions of CO2 itself due to (a) reducing the carbon stored in soil and in the vegetation above the soil by covering the land with impervious material or (b) reducing the carbon stored in vegetation by clearing the land so less vegetation grows. When soil is covered with impervious material, such as concrete or asphalt, vegetation can’t grow in the soil or decay and become part of the soil. Similarly, when land is cleared of vegetation, less carbon is stored in the vegetation and below ground. Energy facilities both cover land and reduce vegetation.

One estimate of the organic carbon stored in grassland and the soil under grassland, per unit area of land surface, is 1.15 kg-C/m2 and 13.2 kg-C/m2, respectively (Ni, 2002). Normally, when the grass dies, the dead grass contributes to the soil organic carbon. The grass then grows again, removing carbon from the air by photosynthesis. If the soil is covered instead with concrete, the grass no longer exists to remove carbon from the air or store carbon in the soil. However, existing carbon stored underground remains. Some of this is oxidized, though, over time and carried away by ground water.

The carbon emissions due to developing land for an energy facility can be estimated simplistically by first summing the land areas covered by the facility; the mine where the fuel is extracted (in the case of fossil fuels and uranium); the roads, railways, or pipelines needed to transport the fuel; and the waste disposal site associated with the facility. This summed area is then multiplied by the organic carbon content normally stored in vegetation per unit area that is lost plus the organic carbon content normally stored in soil under the vegetation per unit area that is lost. The latter value can be estimated as approximately one-third the original organic carbon content of the soil. The loss in carbon is then converted to a loss of carbon per unit electricity produced by the energy facility over a specified period of time. For purposes of Table 3.5, this period is 100 years. Example 3.8 provides an example calculation.

Example 3.8. Estimating the loss of carbon stored in vegetation and soil.

Assume a 425 MW coal facility has a 65 percent capacity factor and has a footprint of 5.2 km2, including the land for the coal facility, mining, railway transport, and waste disposal. Calculate the emission rate of CO2 from the soil and vegetation, averaged over 100 years, due to this facility, assuming that it replaces grass and 34 percent of the soil carbon is lost.

Solution:
The energy generated over one year from this plant is 425 MW × 8760 h/y × 0.65 × 1000 kW/MW = 2.42×109 kWh/y. Over 100 years, the energy produced is 2.42×1011 kWh.

The carbon lost in soil is 0.34 × 13.2 kg-C/m2 = 4.5 kg-C/m2 and that lost from vegetation is 1.15 kg-C/m2, for a total of 5.64 kg-C/m2. Multiplying by 1000 g/kg and the molecular weight of CO2 (44.0095 g-CO2/mol), then dividing by the molecular weight of carbon (12.0107 g-C/mol) give 20,700 g-CO2/m2. Multiplying this by the land area covered by the facility and dividing by the 100-year energy use gives an emission rate from lost soil and vegetation carbon as 0.44g-CO2/kWh, averaged over 100 years.

Because most of the carbon in soil and vegetation is lost immediately, the 100-year average loss of carbon from the soil provided in Table 3.5 underestimates the impact on climate damage of an energy facility that occupies land. Most climate impacts from the loss of carbon will begin to occur when the emissions occur. Thus, for example, the impacts over 10 years of carbon loss in soil are 10 times those in Table 3.5. However, for consistency with the other carbon-equivalent emissions, the emissions from carbon lost in land are averaged over 100 years in the table.

3.2.2.6. Comparison of Coal and Natural Gas With Carbon Capture With Other Energy Technologies

Table 3.5 compares the overall 100-year CO2 emissions from coal and natural gas power plants that have carbon capture (CCS or CCSU) with emissions from other electricity generating technologies. The table indicates that coal-CCS/U results in 33 to 183 times the CO2e emissions as onshore wind. Natural gas- CCS/U results in 27 to 86 times the emissions as onshore wind.

The reasons for the high CO2e emissions of coal and natural gas with carbon capture, are (1) coal and gas plants need 25 to 55 percent more energy to run the carbon capture equipment, and this increases the upstream emissions (fuel mining, transport, and processing) of coal and gas by 25 to 55 percent (Example 3.9), (2) the capture equipment allows 10 to 30 percent of the CO2 in the power plant exhaust to escape (Example 3.9), (3) CO2e emissions from the background grid occur due to the time lag between planning and operation of a coal or gas plant with capture relative to a wind or solar farm, (4) some leaks of CO2 occurs once CO2 is sequestered, and (5) coal and gas facilities reduce the storage of carbon in the ground.

Table 3.5 provides climate-relevant emissions, but not health-relevant emissions. Air pollution emissions of coal and gas without carbon capture are 100 to 200 times those of onshore wind per unit energy. Adding carbon capture to a coal or gas plant increases air pollution emissions another 25 to 55 percent.

The high air pollution and climate-relevant emission rates of coal and natural gas with carbon capture suggest that spending money on them represents an opportunity cost relative to spending money on lower- emitting technologies. Another issue is that, in a future WWS system, the number of hours of fossil fuel use at any given plant decreases, making CCS equipment, which is already costly, even more uneconomical (Lund and Mathiesen, 2012).

3.2.3. Carbon Capture Projects

To date, CO2 has been captured and separated primarily from mined natural gas or, in one case, from gasified coal. In all such cases, the CO2 has been used to enhance oil recovery.

As of 2019, only two fossil fuel power plants have operated with carbon capture equipment. In both cases, the separated CO2 was used for enhanced oil recovery, and the CCU equipment was installed at high cost.

One project experienced problems with the equipment, resulting in much more CO2 released to the air than anticipated. The other project required a natural gas plant to be built to power the CCU equipment, also resulting in much less benefit than anticipated. Future projects like these must also be in proximity to an oil and gas production field.

The first electric power plant with CCU equipment was the Boundary Dam power station in Estevan, Saskatchewan, Canada, which has been operating with CCU equipment on one coal boiler connected to a steam turbine since October 2014. The cost of the retrofit project was $1.5 billion ($13.6 million/MW for a 110 MW turbine). This cost included a $240 million subsidy from the Canadian government and was on top of the original coal plant cost. Whereas half the captured CO2 from the CCU equipment has been sold for enhanced oil recovery, the other half has been released to the air. In addition, since 2016, the CCU equipment has been operating only 40 percent of the time due to design problems.

The second plant with CCU equipment was the Texas Parish power plant in Thompsons, Texas. The plant was retrofitted with CCU equipment as part of the Petra Nova project and began operating with the equipment during January 2017. The CCU equipment (240 MW) is connected to and receives 37 percent of the emissions from a 654 MW pulverized coal boiler that produces steam for a steam turbine that generates electricity. The retrofit project cost $1 billion ($4,200/MW), including a $190 million grant from the U.S. government, on top of the cost of the coal plant itself.

The captured CO2 is compressed and piped to an oil field, where it is used to enhance oil recovery. However, a separate gas turbine was built just to provide electricity and steam for the carbon capture equipment, and the CO2 emissions from that turbine are not captured. In addition, the natural gas for the steam turbine has upstream CO2e emissions, including CH4 leaks, which are not captured. Those CO2e emissions, combined with the emissions of the additional oil produced by the captured CO2, result in the CCU equipment possibly increasing the overall CO2e from the plant by an estimated 2 percent (Scottmadden, 2017).

Calculations in Example 3.9 using data from EIA (2017) suggest that, of the CO2 reductions that were supposed to occur with the Petra Nova carbon capture equipment, only 21.6 percent are estimated to occur over a 20-year time horizon and 33.6 percent, over a 100-year time horizon. The reason is that CO2e emissions from the gas turbine needed to power the CCS equipment, from the upstream mining, transporting, and processing of the natural gas for the turbine, and from upstream methane leaks offset most of the benefits of the capture equipment.

Example 3.9. Calculating emission reduction due to coal with carbon capture.
According to EIA (2017), emissions of CO2 during January 2016 from the Texas Parish coal power plant, before carbon capture was implemented, were 934.4 kg-CO2/MWh. Emissions during January 2017, after carbon capture was implemented, were 680.4 kg-CO2/MWh, for a reduction of 254 kg-CO2/MWh. However, the natural gas plant needed to run the carbon capture equipment itself emitted 99.8 kg-CO2/MWh-coal-electricity.

First, estimate the upstream methane emissions from leaks associated with mining, transporting, and processing the natural gas used to run the gas plant. Also estimate the upstream fossil fuel combustion emissions of CO2 assuming such emissions are 10 percent of the combustion emissions from the natural gas turbine (ICF Consulting, 2012). Next, calculate the overall 20-year and 100-year CO2e of the upstream plus stack CH4 and CO2 emissions from the natural gas facility.

What percent of the CO2 captured was effectively reemitted due to the CO2e of CH4 + CO2 from natural gas, assuming a 20 year and a 100 year time horizon? What percent of the theoretical maximum emission reductions were actually obtained by the carbon capture equipment? Assume natural gas contains a 93.9 percent mole fraction of methane, and the upstream leakage rate of natural gas is 2.3 percent (Alvarez et al., 2018).

Solution:
Dividing the emission rate of CO2 from natural gas, 99.8 kg-CO2/MWh, by the molecular weight of CO2 (44.0098 g- CO2/mol) gives the moles of natural gas burned. Multiplying the moles burned by the fractional number of moles burned that are methane (0.939) and the molecular weight of methane (16.04276 g-CH4/mol) gives the mass intensity of methane in the natural gas burned, 34.2 kg-CH4-burned/MWh.

The upstream leakage rate of methane is then 34.2 * 0.023 / (1-0.023) = 0.804 kg-CH4/MWh. Multiplying by the 20- and 100-year GWPs of CH4 (86 and 34, respectively) from Table 1.2 gives CO2e emissions of the methane leaks as 69.2 and 27.3 kg-CO2e/MWh, respectively. The upstream CO2 combustion emissions rate is 10 percent of 99.8 kg- CO2/MWh, or 9.98 kg-CO2/MWh. Adding the upstream CO2+CH4 emissions to the gas turbine stack emissions gives 20- and 100-year CO2e emissions from the gas turbine as 179 and 137 kg-CO2e/MWh, respectively.

As such, averaged over 20 years, 179 / 254 = 70.4 percent of the CO2 captured by the capture equipment is effectively re-emitted (offset) by the gas plant. Averaged over 100 years, 137 / 254 = 54 percent is re-emitted. These re-emissions are on top of downstream leaks that may occur with the captured CO2.

The theoretical maximum reduction in emissions during January 2107 was 37 percent of the total coal plant emissions, 934.4 kg-CO2e/MWh = 347.7 kg-CO2e/MWh. The actual emission reduction from the coal stack was 254 kg- CO2e/MWh, so the carbon capture equipment itself reduced only 73 percent (254 / 347.7) of emissions. Conversely, the equipment allowed 27 percent of emissions to escape.

Further, of that 254 kg-CO2e/MWh, 179 kg-CO2e/MWh will be returned to the air over a 20-year time frame due to the gas plant, indicating that only a net of 75 kg-CO2e/MWh will be removed. Thus, only 75 / 347.7 = 21.6 percent of the maximum possible coal plant emission reduction will be realized over 20 years.

Over a 100-year time frame, 137 kg-CO2e/MWh will be returned to the air, thus 254 – 137 = 117 kg-CO2e/MWh will be removed. Thus, only 117 / 347.7 = 33.6 percent of the maximum possible coal plant emission reduction will be realized over 100 years. As such, 66.4 percent of CO2e from the plant will remain in the air after carbon capture.

In sum, this carbon capture project does not come close to achieving zero emissions or significant emissions reductions, even before accounting for additional emissions it causes from downstream underground leaks of sequestered CO2.

Example 3.10 illustrates that, because coal with CCS is (a) expensive, (b) results in more air pollution emissions than coal without CCS, and (c) only modestly decreases CO2 emissions, its cost to society is more than 10 times that of wind energy providing the same energy. As such, it is not a cost effective method of addressing climate change, and it worsens air pollution.

Example 3.10. Calculating the cost to society of using coal with CCS instead of wind.
Estimate the energy plus health plus climate change cost of a new coal plant with CCS versus that of wind energy under the following assumptions. The cost of wind energy is 4.25 ¢/kWh (Table 7.9), the cost of a new coal plant is 10.2 ¢/kWh (Table 7.9), the cost of CCS equipment is 7.5 ¢/kWh, the health cost of coal pollution is 12.7 ¢/kWh (Table 7.11), and the climate cost of coal pollution is 15.8 ¢/kWh (Table 7.11). Also assume that the CCS equipment requires 25 percent more energy thus increases all emissions by 25 percent. Finally, assume that the CCS equipment reduces the overall CO2 emission by 22 percent before CCS equipment was added.

Solution:
The social cost of the coal plant is the energy plus health plus climate cost of the plant. In this case, the energy cost of the plant plus equipment is 10.2 + 7.5 = 17.7 ¢/kWh. The health cost is 1.25 × 12.7 ¢/kWh = 15.9 ¢/kWh. The climate cost is 0.78 × 15.8 ¢/kWh = 12.3 ¢/kWh. Adding these three together gives 45.9 ¢/kWh. Dividing this by the cost of wind, 4.25 ¢/kWh, gives 10.8. Thus, the social cost of coal-CCS is 10.8 times that of wind. The direct energy cost of coal-CCS is 3.9 times that of wind.

Table 1.2. E-folding lifetimes, 20-year GWPs, and 100-year GWPs of several global warming agents.

Chemical

E-folding lifetime

20-Year GWP

100-Year GWP

aCO2
bBC+POC in fossil fuel soot bBC+POC in biofuel soot cCH4
cN2O
cCFCl3 (CFC-11)
dCF2Cl2 (CFC-12)
cCF4 (PFC-14)
dC2F6 (PFC-116) eTropospheric O3
fNOx-N
gSOx-S

50-90 years 3-7 days
3-7 days 12.4 years 121 years
45 years
100 years 50,000 years 10,000 years 23 days

< 2 weeks < 2 weeks

1 2,400-3,800 2,100-4,000 86
268
7,020 10,200 4,950
8,210

-560
-1,400

1 1,200-1,900 1,060-2,020 34
298
5,350 10,800 7,350 11,100

-159
-394

GWP=Global Warming Potential.
aLow-lifetime of CO2 is the data-constrained lifetime upon increasing CO2 emissions from Jacobson (2012a, Figure

3.12); high-lifetime of CO2 calculated from Figure 1 of Jacobson (2017), which shows CO2 decreasing by 65 ppmv (from 400 to 335 ppmv) over 65 years upon elimination of anthropogenic CO2 emissions. Since the natural CO2 is 275 ppmv, the anthropogenic CO2 = 400-275=125 ppmv, and the lifetime of anthropogenic CO2 ~ 65 y / -ln((125-65) ppmv/125 ppmv) = ~90 years. The GWP of CO2=1 by definition.

bPOC is primary organic carbon co-emitted with black carbon from combustion sources. In the case of diesel exhaust, it is mostly lubricating oil and unburned fuel oil. In all cases, POC includes both absorbing organic (brown) carbon (BrC) and less absorbing organic carbon. Soot particles contain both BC and POC. The lifetime is from Jacobson (2012b) and the GWP is from Jacobson (2010a, Table 4), which accounts for direct effects, optical focusing effects, semi-direct effects, indirect effects, cloud absorption effects, and snow-albedo effects.

cFrom Myhre et al. (2013) Table 8.7.
dFrom Myhre et al. (2013) Table 8.A.1.
eFrom Myhre et al. (2013), Section 8.2.3.1. Tropospheric ozone is not emitted so does not have a GWP.
fFrom Myhre et al. (2013), Table 8.A.3, including aerosol direct and indirect effects. Values are on a per kg nitrogen

basis
fFrom Streets et al. (2001) and Jacobson (2002), including aerosol direct and indirect effects. Values are on a per kg

sulfur basis.

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