Jobs, water, health benefits and more: A retrospective analysis of benefits and impacts of U.S. renewable portfolio standards

Energy PolicyVolume 96, September 2016, Pages 645–660 by Galen Barbose, Ryan Wiser, Jenny Heeter et al.  doi:10.1016/j.enpol.2016.06.035.  The prior study in this series focused on historical RPS compliance costs, and future work will evaluate costs, benefits, and other impacts of RPS policies prospectively.  See Report PDF, Presentation PDF, Factsheet PDF, and Press Release PDF.

  • Benefits of satisfying U. S. renewable portfolio standards in 2013 were evaluated.
  • Carbon dioxide (equivalent) was cut by 59 million metric tons (worth $2.2 billion).
  • Reduced air pollution provided $5.2 billion in health and environmental benefits.
  • Water withdrawals (830 billion gal) and consumption (27 billion gal) were reduced.
  • Job/economic, electricity price, and natural gas price impacts were also evaluated.

As states consider revising or developing renewable portfolio standards (RPS), they are evaluating policy costs, benefits, and other impacts. We present the first U. S. national-level assessment of state RPS program benefits and impacts, focusing on new renewable electricity resources used to meet RPS compliance obligations in 2013. In our central-case scenario, reductions in life-cycle greenhouse gas emissions from displaced fossil fuel-generated electricity resulted in $2.2 billion of global benefits. Health and environmental benefits from reductions in criteria air pollutants (sulfur dioxide, nitrogen oxides, and particulate matter 2.5) were even greater, estimated at $5.2 billion in the central case. Further benefits accrued in the form of reductions in water withdrawals and consumption for power generation. Finally, although best considered resource transfers rather than net societal benefits, new renewable electricity generation used for RPS compliance in 2013 also supported nearly 200,000 U. S.-based gross jobs and reduced wholesale electricity prices and natural gas prices, saving consumers a combined $1.3–$4.9 billion. In total, the estimated benefits and impacts well-exceed previous estimates of RPS compliance costs.

1. Introduction

State renewable portfolio standards (RPS), which require electricity load-serving entities (LSEs) to meet a growing portion of their load with eligible forms of renewable electricity (RE), exist in 29 U.S. states and Washington, D.C. They have been one of the policy drivers for RE growth in the United States (Leon, 2015). Collectively, 58% of all non-hydroelectric RE capacity built in the United States from 1998 through 2014 is being used to meet RPS requirements (Barbose, 2015). In aggregate, existing state RPS policies require that by 2025, when most RPS requirements will have reached their maximum percentage targets, at least 8% of total U.S. generation supply will be met with RPS-eligible forms of RE, equivalent to roughly 106 gigawatts (GW) of renewable generation capacity (Wiser and Bolinger, 2015).

As RPS programs approach their terminal percentage targets and state policymakers consider revising existing programs or developing new ones, increasing attention is being paid to RPS costs, benefits, and other impacts. Our previous work found that RPS compliance costs during 2010–2013 averaged about $1 billion per year, nationally (Barbose et al., 2015, Heeter et al., 2014a and Heeter et al., 2014b). Compliance costs in individual states were generally equivalent to less than 2% of average statewide retail electricity rates, but they varied substantially, with the net cost ranging from −0.4 to 4.8¢ per kilowatt-hour of RE (kWh-RE). These RPS compliance costs represent the incremental cost to the utility or other LSE with RPS obligations, net of avoided conventional generation costs.

Aside from the cost savings from reduced purchases of conventional generation, which accrue directly to utilities and their ratepayers, RPS policies may yield broader societal benefits and other impacts, which may also have bearing on policy decisions.1 A number of states have conducted their own state-level assessments of RPS benefits, but the widely varying scopes and methods of those analyses limit the ability generalize to RPS programs more broadly, and therefore to compare costs and benefits (Heeter et al., 2014a and Heeter et al., 2014b).2Chen et al. (2008) summarize state-level forecasts of RPS outcomes. Other researchers have used statistical methods to analyze the impacts of state RPS programs on greenhouse gas (GHG) emissions (Eastin, 2014 and Yi, 2015), air pollution (Eastin, 2014 and Werner, 2014), jobs (Bowen et al., 2013 and Yi, 2013), and retail electricity prices (Caperton, 2012, Johnson, 2014 and Morey and Kirsch, 2013). Still others have explored a subset of the possible effects of RPS programs, sometimes coming to widely varying conclusions about the merits of these policies (Holt and Galligan, 2013, Michaels, 2008a, Michaels, 2008b and UCS, 2013).

This paper—which draws from a larger technical report (Wiser et al., 2016)—provides the detailed, national-level evaluation of state RPS benefits and impacts that is missing within the existing literature. We quantify a number of the potential societal benefits and impacts of compliance with state RPS programs, relying in part on the U.S. Environmental Protection Agency’s (EPA’s) AVoided Emissions and geneRation Tool (AVERT) and applying methods initially employed and vetted in the U.S. Department of Energy’s Wind Vision Report ( DOE, 2015). This is a retrospective analysis, focusing on “new” RE resources built after RPS enactment and used to meet RPS compliance obligations in 2013, the most recent year for which the necessary data were available. We evaluate potential benefits associated with reductions in GHG emissions, air pollution emissions, and water use. We also evaluate several other RPS “impacts,” including effects on gross jobs and economic development, wholesale electricity market prices, and natural gas prices. These impacts are resource transfers rather than net societal benefits, because they benefit some stakeholders at the expense of others—nevertheless they are often important considerations for policymakers. For each benefit and impact category, we quantify the effects in physical units (e.g., tons of pollutants, gallons of water) and, where possible, in dollar-value terms as well, and we estimate key sources of uncertainty. Our analysis estimates the aggregate effects of all state RPS programs in combination, reporting results nationally and, where appropriate, by region and state; however, we do not analyze the effects of individual state RPS programs.

Our study includes a number of general caveats and limitations. We quantify uncertainty in our estimates where possible, but in some cases we can only highlight or qualitatively describe areas of uncertainty. Although we address several potentially important benefits and impacts of RPS policies, we do not evaluate all possible effects, for example, those associated with grid integration, land use, etc. Moreover, our study does not address the direct costs of RPS procurement to LSEs or the cost savings from reduced fuel, capital, and operations and maintenance (O&M) expenses associated with non-renewable generation. These were evaluated retrospectively in Heeter et al., 2014a, Heeter et al., 2014b and Barbose et al., 2015 and will be evaluated prospectively in future work. We do not assess whether RPS programs are the most cost-effective way to achieve the benefits and impacts discussed. Finally, our analysis evaluates the benefits and impacts of new RE used to meet RPS compliance obligations in 2013, but it does not seek to attribute the estimated benefits and impacts solely to RPS policies; because of leakage and spillover effects as well as the multiple drivers for RE additions, our estimates might overstate the incremental benefits and impacts attributable only to state RPS programs. In addition to these cross-cutting considerations, various other caveats and limitations are associated with each individual benefit and impact analysis, as described further in the remainder of this paper and in more detail in Wiser et al. (2016).

Section 2 of this paper describes the foundational data and analysis on RPS resources and displaced fossil generation that, in turn, underlies the analyses of each of the six benefits and impacts evaluated. The results of those benefit and impact analyses are detailed in Section 3 through 8, along with additional methodological details. We conclude and discuss the policy implications of our results in Section 9.

2. Foundational data and analysis: RPS resources and displaced fossil generation

The RPS benefits and impacts estimated in this study require a common basis of information about renewable resources used for RPS compliance in 2013 and the associated displacement of fossil-fuel generation, capacity, fuel use, and emissions. We compile data on the specific fuel types, location, and quantities of RE generation used to meet 2013 RPS compliance obligations, drawing from state and utility RPS compliance filings, renewable energy certificate (REC) tracking systems, and other relevant sources. Based on these varied data sources, 98 terawatt-hours (TWh) of new RE generation were used to meet RPS compliance obligations in 2013, representing 2.4% of total U.S. electricity generation in that year.

We then rely on EPA’s AVERT to estimate the associated displaced generation, fuel, and emissions—carbon dioxide (CO2), nitrogen oxides (NOx), and sulfur dioxide (SO2)—from other (primarily fossil-fueled) power plants (EPA, 2014a).3 These outputs from the AVERT modeling, as well as the underlying data on RE generation used to comply with RPS obligations, constitute key inputs to the individual benefit and impact analyses described in the remainder of this paper.

Based on specified RE generation, the AVERT model estimates associated fossil generation displaced in each of ten regions (see Fig. 1). Using this model, we estimate that new RE generation used for RPS compliance obligations in 2013 resulted in a 3.6% reduction in total U.S. fossil fuel generation. The percentage of fossil generation displaced varies widely across regions, from just 0.1% of 2013 fossil generation in the Southeast to 13.9% in California, reflecting the varying stringency and prevalence of RPS policies, as well as the location of renewable generation used to meet RPS obligations (Fig. 2). Most displaced fossil generation was gas-fired (55% of the total), though substantial amounts of coal-fired generation were also displaced, especially in regions with a coal-heavy fossil generation mix.

Fig. 1.


Fig. 1.

AVERT regions (Source: EPA, 2014a).


Fig. 2.


Fig. 2.

Displaced fossil generation by AVERT region.


Data on RPS-related RE capacity additions are another set of input assumptions required across multiple benefit and impact analyses. Over the 2-year period from 2013 through 2014, we estimate an average of almost 5600 MW of new RE capacity per year was built to service RPS requirements. These data form a key foundation of the analysis of gross jobs and economic development impacts, as described in Section 6. Those average annual RE capacity additions are also estimated to displace more than 2500 MW of fossil generation capacity. Both RPS-related RE capacity additions and associated displaced fossil generation capacity are also used within a secondary aspect of the analyses of GHG benefits.4Wiser et al. (2016) detail each of these data elements and analytical steps.

3. Greenhouse gas emissions and climate change damage reduction benefits

EPA, (2015a) finds that efforts to limit climate change damages through reductions in GHGs can offer many benefits to the United States, and there is growing recognition of the desirability of near-term actions to limit emissions (IPCC (Intergovernmental Panel on Climate Change), 2014a, Luderer et al., 2013 and Nordhaus, 2013). RE technologies generally have very low life-cycle GHG emissions compared with fossil energy sources (DOE, 2015; see also Previous research has found a statistical link between RPS programs and carbon emission reductions (Eastin, 2014 and Yi, 2015).5

3.1. Methods

To value the potential GHG benefits from state RPS programs, we first estimate the life-cycle GHG reductions from new RE serving RPS compliance in 2013 and then quantify the economic value of those reductions based on a range of social cost of carbon (SCC) estimates. These methods are similar to those used in DOE’s recent Wind Vision Report (DOE, 2015) and are broadly consistent with methods used by U.S. regulatory agencies (GAO, 2014) and academic researchers ( Buonocore et al., 2015, Callaway et al., 2015,Cullen, 2013, Graff Zivin et al., 2014, Johnson et al., 2013, Kaffine et al., 2013, McCubbin and Sovacool, 2013, Novan, 2014, Shindell, 2015 and Siler-Evans et al., 2013).

We start with AVERT-estimated power sector CO2 emissions reductions from new RE used to meet RPS compliance obligations in 2013. We account for life-cycle impacts by combining AVERT combustion-related emissions with life-cycle, non-combustion emission values for each generation technology, based on results developed in the National Renewable Energy Laboratory’s (NREL’s) LCA Harmonization study ( By applying these life-cycle adjustments, we capture avoided fuel cycle and construction emissions from displaced fossil generation and capacity while accounting for increased fuel cycle and construction emissions from RE generation and capacity used to meet RPS standards in 2013.

We then estimate the economic benefits of RPS compliance in the form of reduced climate change damages using SCC estimates from the U.S. Interagency Working Group (IWG) on the Social Cost of Carbon (IWG, 2010 and IWG, 2015).6 The IWG (2015)provides four SCC estimates: a “low” case, a “central value” case, a “high” case, and a “higher-than-expected” case intended to account for a much less likely outcome with a more extreme impact. We base the monetary value of RPS compliance on all four IWG estimates, for 2013. The central value SCC for 2013 is $37 per metric ton (MT) of CO2 (in 2013$). The low case is $12/MT, the high case is $59/MT, and the higher-than-expected case is $106/MT.7

3.2. Results

New RE used to meet RPS obligations in 2013 reduced life-cycle GHG emissions by an estimated 59 million MT of CO2 equivalent (CO2e, Fig. 3). These reductions are driven almost entirely by direct combustion-related GHG emissions reductions from avoided fossil fuel generation, equal to 61 million MT of CO2 (3% of 2013 power sector emissions), as estimated using AVERT. RPS programs also displace non-combustion fuel-cycle emissions from fossil energy supply and, to a much lesser extent, construction-related life-cycle emissions from fossil plants. These upstream emissions savings, however, are more than offset by non-combustion life-cycle emissions from the construction and—to a lesser degree—operations of RE plants.

Fig. 3.


Fig. 3.

Life-cycle GHG emissions impacts of RPS compliance.


These GHG emissions reductions reduce climate change damages substantially (Fig. 4). Based on the IWG central-value SCC estimate, the global benefits from avoided future damages associated with new RE used to meet RPS obligations in 2013 are equal to $2.2 billion (2.2¢/kWh-RE).8 The low estimate is $0.7 billion (0.7¢/kWh-RE), the high $3.5 billion (3.6¢/kWh-RE), and the higher-than-expected $6.3 billion (6.4¢/kWh-RE). Note that the ¢/kWh-RE values are the monetary benefits per unit of RE generation used to meet 2013 RPS requirements.

Fig. 4.


Fig. 4.

Estimated benefits of RPS compliance due to reduced global climate change damages.


4. Air pollution emissions and human health and environmental benefits

Combusting fuels to generate electricity produces air pollutants that harm human health and cause environmental damage (NRC, 2010). Epidemiological studies have shown a causal association between increased mortality and morbidity and exposure to air pollution; for examples of the association with mortality, see Dockery et al., 1993, Krewski et al., 2009 and Lepeule et al., 2012. Lim et al. (2012) estimate more than three million premature deaths globally, each year, from outdoor particulate air pollution. In the United States, various studies have shown the air quality and public health benefits of reducing combustion-based electricity generation (Buonocore et al., 2015, Driscoll et al., 2015 and Siler-Evans et al., 2013). For example, EPA has estimated that its Clean Power Plan (CPP) would provide $14 billion to $34 billion of monetized health benefits in 2030 based mostly on reduced premature mortality (EPA, 2015b). Because most RE sources have no direct, and low life-cycle, air pollution emissions (IPCC (Intergovernmental Panel on Climate Change), 2011 and Turconi et al., 2013), RPS programs can be used to reduce air pollution emissions. Some previous research has found a link between RPS programs and air pollution concentrations (Eastin, 2014 and Werner, 2014).

4.1. Methods

Our overall approach is similar to that used in DOE’s Wind Vision Report ( DOE, 2015), and it is broadly consistent with methods used in NRC (National Research Council), 2010, Fann et al., 2012, Cullen, 2013, Johnson et al., 2013, McCubbin and Sovacool, 2013, Siler-Evans et al., 2013, EPA (U.S. Environmental Protection Agency), 2015b,Novan, 2014, Buonocore et al., 2015 and Driscoll et al., 2015. We first use AVERT to estimate power-sector SO2 and NOx emissions reductions from state RPS programs in 2013; because fine particulate matter (PM2.5) reductions are not reported by AVERT, we calculate these reductions by estimating emissions by plant as a function of avoided generation (from AVERT) and average emissions rates (from Cai et al., 2012 and Cai et al., 2013) by generation type and U.S. state. Those estimates consider avoided emissions due to fossil generation displaced by renewable generation but do not include any offsetting increases in emissions associated with biomass generation used to meet RPS programs. We therefore separately estimate emissions from RPS-related biomass combustion, and subtract those amounts from the AVERT-derived estimates of reduced emissions from non-renewable generation, to calculate the net change in emissions.

We then calculate reduced morbidity and mortality outcomes and total monetary value from these net emissions changes based on three different peer-reviewed approaches. Each approach accounts for pollutant transport and chemical transformation as well as population exposure and response: (1) the Air Pollution Emission Experiments and Policy analysis model (AP2, formerly APEEP, described in Muller et al., 2011), (2) EPA’s benefit-per-ton methodology developed for the CPP ( EPA, 2015b), and (3) EPA’s COBRA model (EPA, 2014b). While approaches (2) and (3) both come from EPA, they differ from each other and from AP2 in multiple respects. Approaches (2) and (3) also each include two estimates of the health impacts that reflect uncertainty in the underlying epidemiological studies. Henceforth, we refer to the multiple outputs from the EPA approaches as “EPA Low” and “EPA High” for the EPA benefit-per-ton methodology developed for the CPP and as “COBRA Low” and “COBRA High” for the COBRA model approach. The “high” and “low” classification corresponds to differences only between the underlying health impact functions employed by the particular EPA approach. We take the simple average of all five benefit estimates as the “central” value. One important assumption across all methods used is the monetary value of preventing a premature mortality (or the value of statistical life, VSL). Consistent with the broader literature, all use a VSL of approximately $6 million in 2000, but updated for year 2013.

4.2. Results

Compliance with RPS obligations in 2013 reduced national power sector emissions of SO2, NOx, and PM2.5 by an estimated 77,400, 43,900, and 4800 MT in 2013, respectively, with reductions in each pollutant equivalent to 2% of the corresponding total U.S. power sector emissions in 2013. In that year, these emissions reductions produced total continental U.S. health and environmental benefits of $2.6 billion (2.6¢/kWh-RE) to $9.9 billion (10.1¢/kWh-RE), with a “central” estimate (the average of the five primary estimates) of $5.2 billion (5.3¢/kWh-RE) (Fig. 5). Reduction of SO2 (primarily from coal) and the subsequent reduction of particulate sulfate concentrations accounted for most of the monetized benefits (Fig. 5). For example, SO2 emissions reductions accounted for 77%, 86%, and 83% of the AP2, EPA Low, and EPA High benefit estimates, respectively.9The benefits of reduced tropospheric ozone (from reduced NOx emissions) were relatively small, accounting for 4% and 7% of the EPA Low and EPA High benefit estimates, respectively.10 This shows that exposure to particulates (directly or indirectly from emissions of SO2, NOx, and PM2.5) is the primary driver of health outcomes. The benefits reported here are net of emissions from biomass electricity used for RPS compliance.11

Fig. 5.


Fig. 5.

National benefits of RPS compliance due to reduced health and environmental damages.


Most of the health benefits come from avoided premature mortality, primarily associated with reduced chronic exposure to ambient PM2.5 (which largely derive from the transformation of SO2 to sulfate particles). For example, 98% of COBRA-estimated benefits come from reduced mortality with the rest attributed to morbidity benefits. Based on the two EPA methods (and four EPA-based estimates), new RE meeting RPS programs in 2013 prevented 320–1100 deaths that year.12 We also estimate that RPS compliance in 2013 generated a range of benefits in the form of reduced morbidity, including avoiding between 160 and 290 emergency department visits for asthma, 195–310 hospital emissions for respiratory and cardiovascular symptoms, 40–560 non-fatal heart attacks,13 and 38,000–64,000 lost work days.

The greatest benefits accrue to the eastern half of the country, where higher-emitting fossil plants are displaced and population densities are relatively high. For example, Fig. 6 illustrates the regional allocation of monetized benefits from fine particulate exposure calculated using the COBRA Low approach.14 Note that fossil plant displacement and the resulting benefits occur not only within RPS states, but also outside of those states, given the regional nature of electricity markets, the fact that some RPS states may meet their compliance obligations with RE generation located in non-RPS states, and the dispersion of air pollutants across state borders.

Fig. 6.


Fig. 6.

Regional benefits of RPS compliance due to reduced health and environmental damages from particulate matter under the “COBRA Low” estimates.


5. Water use reduction benefits

The electric sector depends heavily on water—primarily for thermal plant cooling—and can affect water resources through water withdrawals, water consumption, changes in water quality, and changes in water temperature. Withdrawals are defined as the amount of water removed or diverted from a water source for use, while consumption refers to the amount of water that is evaporated, transpired, incorporated into products or crops, or otherwise removed from the immediate water environment (Kenny et al., 2009). In the case of thermal power plants, withdrawals occur when water is taken from a nearby water source to cool steam in the power plant condensers and for other processes. For once-through cooling technologies, most of the water withdrawn is returned to the source at a warmer temperature, and consumption is relatively low and is confined to induced evaporation in the water body and other on-site processes. For recirculating-cooled systems, most of the water withdrawn is consumed as the water in the cooling towers evaporates into the ambient environment and must be replaced.

The U.S. electric sector is the largest withdrawer of water (38% of total) in the nation (Maupin et al., 2014), whereas its share of consumption is around 3% (Solley et al., 1998). As such, water availability affects the electric system, including impacts on new capacity decisions and on power plant operations and reliability (DOE (U.S. Department of Energy), 2013 and Rogers et al., 2013). In turn, water availability and quality for other competing uses are impacted by the electricity sector. Moreover, future uncertainties with regard to water availability and temperature, including those associated with climate change, may exacerbate vulnerabilities in the electric sector (DOE, 2013). Many RE technologies have low water withdrawal and consumption intensities compared to fossil and nuclear generating technologies, whether only considering operational demands or the entire life cycle (Macknick et al., 2012a and Meldrum et al., 2013). Although RPS programs are generally not designed around water-reduction goals, water savings could be a significant co-benefit in some regions.

5.1. Methods

We first estimate, with AVERT, the quantity, type, and location of fossil-based generation displaced by new RE generation used for RPS compliance in 2013. Water withdrawal and consumption impacts from changes in fossil generation are calculated by applying fuel/prime mover/cooling system-specific water intensity rates (in gallons per MWh of electricity generated) to generating unit-level changes in monthly generation. A similar process is used to calculate increases in water use resulting from RE technologies, inclusive of all operational water demands, e.g., cooling for biomass and module cleaning for photovoltaics (PV). Water intensity rates are based on a literature review of nationally applicable estimates of power plant water use (Macknick et al., 2012b). Water use increases and decreases are aggregated, with results summarized on a net basis nationally, by month, and by state.

This approach builds on established methods for assigning generation from individual electric generating units or aggregate regional generation to corresponding cooling system types (Averyt et al., 2013 and UCS, 2012) that have been applied in multiple studies evaluating national and regional water impacts of the U.S. electricity sector (Clemmer et al., 2013, DOE (U.S. Department of Energy), 2015, Macknick et al., 2012b and Rogers et al., 2013). We do not quantify the benefits of water use reductions in monetary terms due to challenges associated with quantifying the value of water resource services (DOE, 2015).

5.2. Results

New RE used for RPS obligations in 2013 reduced net national water withdrawals by an estimated 830 billion gallons and net national water consumption by 27 billion gallons (Fig. 7). These reductions both amount to 2% of the corresponding power sector totals for 2013. Each MWh of electricity generated for RPS compliance obligations in 2013 represents an average savings of 8420 gallons of water withdrawal and 270 gallons of water consumption.

Fig. 7.


Fig. 7.

National water withdrawal (left) and consumption (right) benefits of RPS compliance.


Additional withdrawal needs from new RPS generation are less than 1% of the withdrawal reductions from decreased fossil-based generation. This is largely because fossil-based generators that use once-through cooling systems have high water withdrawal intensity, and renewable generators typically do not use once-through cooling (with exceptions for some biomass-based systems). Additional consumption needs from RPS-compliant new renewable generation are equal to 15% of water consumption reductions associated with displaced fossil-based generation. This higher percentage of water consumption from RPS-compliant generation, as compared with that for withdrawals, is largely a result of the water use characteristics of displaced fossil-based generation, which have relatively low water consumption rates and relatively higher water withdrawal rates. In addition, some biomass, concentrating solar power, and geothermal technology configurations that use recirculating cooling systems similar to those used by fossil-based generators can have water consumption rates similar to the displaced fossil generators. In any case, RE generation reduces net water withdrawals and consumption (Fig. 7).

Water withdrawal and consumption savings are not uniform throughout the continental United States (Fig. 8), though almost all states realized reduced withdrawal and consumption in 2013 as a result of new RE generation used for RPS compliance, even those state without an RPS. Importantly, water use was reduced in many drought-prone regions, with the largest withdrawal savings in California and the largest consumption savings in Texas. Certain states experience large withdrawal savings but lower consumption savings (e.g., New York), whereas others experience lower withdrawal savings but larger consumption savings (e.g., Pennsylvania). This regional variation reflects the type of generators and cooling systems being displaced and the water requirements of local renewable technologies deployed.

Fig. 8.


Fig. 8.

Water withdrawal and consumption benefits of RPS compliance, by state.


Six states experienced slight net increases in water consumption: Maine, New Hampshire, North Carolina, Nevada, Virginia, and Vermont. For Nevada, water consumption requirements of new geothermal facilities outweighed the water consumption savings at displaced coal- and gas-fired generators. For the five other states with an increase in net water consumption, the assumed water consumption requirements of biomass-fired technologies were greater than the water consumption savings of the displaced fossil technologies. Only one state (Vermont, which did not have an RPS in 2013) experienced a slight increase in water withdrawals, the result of in-state biomass projects serving RPS demands in other states.

Freshwater sources accounted for 82% of the withdrawal reductions and 97% of the consumption reductions. Saline water savings were concentrated in California, Connecticut, Florida, New York, and Texas. Most water consumption savings in even these states come from freshwater sources. However, withdrawal savings consist primarily of saline water in California, Connecticut, and Massachusetts owing to the types and coastal location of generators in those states. Reducing saline water withdrawals can have important impacts on the health of marine ecosystems (EPA, 2011).

Though standard methods for assigning monetary value do not exist, water use reductions from RPS policies can be considered a benefit, especially where water is scarce. Reduced water demand also reduces the vulnerability of electricity supply to the availability or temperature of water, potentially avoiding electric-sector reliability events and/or the effects of reduced thermal plant efficiencies—concerns that might otherwise grow as the climate changes (DOE, 2013). Reduced power-sector water use also frees water for other uses, whether for other productive purposes (e.g., agricultural, industrial, or municipal use) or to strengthen local ecosystems (e.g., benefiting wildlife owing to greater water availability, lack of temperature change, etc.). Finally, by avoiding upstream water demands from fossil fuel supply, RE may help alleviate other energy-sector impacts on water resource quality and quantity, e.g., water otherwise used in mining, coal washing, and hydraulic fracturing (Averyt et al., 2011).

6. Gross jobs and economic development impacts

Renewable electricity generation infrastructure requires various workers and expenditures, and the workers spend money that further supports jobs and other economic activity. The physical location of supply chain impacts—and related induced impacts—are greatly affected by where materials, manufacturing, and business services occur. We estimate the potential gross, domestic jobs, and other economic impacts supported by RPS policies. Our analysis does not, however, assess economy-wide net impacts. We therefore make no claim of net economy-wide benefits or costs, and there is little reason to believe that net impacts are sizable on a global or national level (see, e.g.,Rivers, 2013).

6.1. Methods

We use NREL’s Jobs and Economic Development Impacts (JEDI) suite of models to estimate the gross jobs and economic development impacts associated with the operation and construction (including manufacturing of equipment subsequently installed) of RE facilities used to support state RPS programs. IMPLAN, a proprietary input-output model, is used instead for landfill gas, because no JEDI model exists for that technology.15 Both JEDI and IMPLAN have been commonly used for the type of analysis conducted here (e.g., Adelaja and Hailu, 2008, Bamufleh et al., 2013, Croucher, 2012,DOE (U.S. Department of Energy), 2015, Navigant, 2013, Slattery et al., 2011 and You et al., 2012).

Operational impacts are based on RE facilities used to meet RPS compliance obligations in 2013, while construction impacts are based on average annual RPS capacity additions in 2013 and 2014. Average annual additions from 2013 through 2014 are used for the construction-related impacts to mitigate annual variability in RE capacity build rates and to acknowledge that construction-related jobs and economic impacts in 2013 may be associated partly with projects completed the following year, given lags between manufacturing, construction, and commercial operations.

JEDI produces impact estimates for four metrics: gross jobs (as full-time equivalents), employee earnings, output (e.g., company revenue), and gross domestic product (GDP). JEDI reports these four metrics in three categories: onsite, supply chain, and induced (due to onsite and supply chain workers making expenditures within the United States). In all cases, JEDI reports domestic jobs and impacts. All results produced by JEDI and IMPLAN are for the equivalent of a single year; although O&M jobs can be assumed to be ongoing, construction-related jobs are inherently of limited duration. Results are reported on a national and, for onsite jobs only, state-by-state basis.16

6.2. Results

We estimate that new RE serving state RPS policies supported nearly 200,000 gross domestic jobs in 2013, each earning an average annual salary of $60,000, with RE expenditures driving over $20 billion in gross U.S. GDP (Table 1). Of the total gross domestic jobs in 2013, nearly 170,000 are associated with building new facilities, with the remaining 30,000 associated with operating renewable projects serving RPS compliance obligations in 2013. Most gross jobs are onsite or within the supply chain, with a smaller number of induced jobs, reflecting the predominance of jobs tied to construction of new facilities. Similar trends are also apparent when comparing gross GDP impacts between construction-related and O&M activities as well as among onsite, supply chain, and induced jobs (Table 1).


Table 1.Estimated gross domestic jobs and economic impacts from RPS compliance.


Jobs (FTE) Total Earnings (Million $2013) Annual Earnings Per FTE ($2013) Output (Million $2013) GDP (Million $2013)
Onsite 67,000 $4500 $67,000 $7100 $6000
Supply Chain 62,700 $3800 $60,000 $10,500 $6200
Induced 37,700 $1700 $46,000 $5100 $3000
Sub-Total 167,400 $10,000 $60,000 $22,700 $15,200
Onsite 5700 $400 $70,000 $570 $450
Supply Chain 14,000 $840 $60,000 $5260 $3340
Induced 12,500 $670 $53,000 $2020 $1180
Sub-Total 32,200 $1910 $59,000 $7840 $4970
Total Construction + O&M
Onsite 72,700 $4900 $67,000 $7670 $6450
Supply Chain 76,700 $4640 $60,000 $15,760 $9540
Induced 50,200 $2370 $47,000 $7120 $4180
Total 199,600 $11,910 $60,000 $30,540 $20,170


Note: Totals may not sum due to rounding. Similarly, average annual earnings may not divide precisely due to rounding. FTE = full-time equivalent.


The distribution of jobs among renewable technologies reflects the contribution of each technology to RPS generation and capacity additions as well as the labor intensity of their construction and operation phases. As such, PV (rooftop and utility-scale) accounted for most construction jobs in 2013 (left panel in Fig. 9), driven by its large fractional share of recent RPS capacity additions and the relatively high labor intensity of installation and construction (especially for rooftop PV).17 Of the total PV-induced construction jobs, 62% are associated with rooftop applications (compared to 38% from utility-scale PV systems), despite the fact that rooftop installations accounted for just 26% of the underlying RPS-related PV capacity additions. In contrast, wind energy supported the largest portion of O&M jobs in 2013, followed by landfill gas, biomass, and geothermal (right panel in Fig. 9). During O&M, PV installations supported a disproportionately small fraction of jobs (4%, compared to PV’s 6% share of RPS generation in 2013) due to the low labor intensity of PV O&M.

Fig. 9.


Fig. 9.

Gross domestic construction and O&M jobs from RPS compliance, by technology.


Only onsite jobs can be readily linked to specific states. The distribution of onsite jobs (construction and O&M) across states largely corresponds to the distribution in RPS capacity additions, given the dominance of construction-related jobs (Fig. 10). Nearly 50% of RPS capacity additions in 2013 through 2014—primarily PV—was built in California, so most onsite job impacts from construction activities occurred in California, followed by Arizona, North Carolina, and Massachusetts. California also led states in RPS-related onsite O&M jobs in 2013, followed by New Jersey and New York.

Fig. 10.


Fig. 10.

Gross onsite jobs from construction and O&M from RPS compliance, by state.


7. Wholesale electricity price reduction impacts

RE generation with low marginal costs pushes out the wholesale power supply curve, an impact sometimes referred to as the “merit-order effect” (Sensfuß et al., 2008). In the short run, shifting the supply curve reduces market clearing prices, because more-expensive units are no longer needed to meet demand in hours with RE generation. Retail electricity rates are impacted by wholesale power prices insofar as the utility serving electricity consumers purchases power on wholesale spot markets or via contracts with generators that are indexed to wholesale power market prices. Reduced wholesale market prices resulting from increased deployment of RE could thereby reduce retail electricity prices and bills paid by end-use consumers. We quantify the potential effects of state RPS programs on wholesale electricity prices and estimate the associated bill savings to electricity consumers. However, these cost savings to electricity consumers come at the expense of owners or shareholders of electricity generating companies, because lower wholesale prices entail reduced revenues for generators. Any RPS-induced reduction in wholesale prices thus represents a transfer of wealth from producers to consumers, rather than a net societal benefit ( Felder, 2011).

7.1. Methods

Our methods are broadly similar to those used in previous analyses of the wholesale price effect of renewables and demand-side measures (e.g., Barbose et al., 2015, Fagan et al., 2012, Fagan et al., 2013, GE Energy Consulting, 2014, Gil and Lin, 2013, Heeter et al., 2014a, Heeter et al., 2014b, Hornby et al., 2013, IPA, 2013, Perez et al., 2012,Sensfuß et al., 2008, Woo et al., 2011 and Woo et al., 2013). First, we develop supply curves for each AVERT region by calculating the wholesale price in each hour as the change in production cost for that hour’s change in total generation. Specifically, the change in production cost in each hour is estimated based on the change in fuel consumption, derived from AVERT outputs, and U.S. Energy Information Administration (EIA) data on historical fuel prices for each AVERT region.18 We then plot the calculated wholesale price and the corresponding demand level for all hours of the year (see the “Raw AVERT” points in the left panel of Fig. 11, which illustrates a single AVERT region). The final regional supply curve is estimated by fitting a third-order polynomial with a positive slope to the price/demand pairs (the line labeled “Smoothed” in the left panel ofFig. 11). We then use that smoothed supply curve to estimate hourly wholesale prices with and without RPS generation in each AVERT region (right panel of Fig. 11). As expected, lower wholesale prices occur more frequently with RPS generation than without it (indicated by a narrower probability density curve with a shorter upper tail).

Fig. 11.


Fig. 11.

Example supply curve (left) and frequency distribution of wholesale prices (right). These figures are based on AVERT output for the Upper Midwest region. The left-hand figure shows the supply curve generated from these outputs, and the right-hand figure shows the “unadjusted” wholesale prices with and without RPS generation, in the form of a frequency distribution.


We then adjust these effects for two factors: (1) the degree to which the impact of RPS generation on wholesale prices persists into 2013 (i.e., the decay factor), and (2) the extent to which retail electricity consumer bills are exposed to wholesale prices.

The first adjustment is required because the impact of RPS generation on wholesale prices may erode. In the short term, lower wholesale prices reduce revenues for generators, dampening the incentive for new generators to enter the market or for existing generators to remain in operation (Traber and Kemfert, 2011 and Woo et al., 2012). Over time, the market will re-equilibrate as lower wholesale prices delay the entry of new generators and accelerate the retirement of others, shifting the supply curve back toward its original position. In addition, increasing levels of variable generation may shift the generation mix away from technologies with high upfront costs and low variable costs (i.e., coal and nuclear plants) and toward technologies with low upfront costs and higher variable costs (i.e., natural gas plants) (Bushnell, 2010, Lamont, 2008 and Sáenz de Miera et al., 2008), suggesting that long-term wholesale market prices may decline less than short-term prices. We account for this impact through a “decay factor” that reduces the short-term price effect based on the length of time the market has had to readjust to RPS generation. The first step in this adjustment is to specify when the decay begins. In the absence of significant literature to guide our assumptions, we bound our results by considering two bookends: the decay either begins at the initial date of RPS enactment (RPS Vintage) or is staggered based on when new renewable generators are built (RE Project Vintage). The RPS Vintage assumption implies that market participants alter their investment and retirement decisions when the RPS becomes law, whereas the RE Project Vintage assumption implies that market participants only begin to change investment and retirement decisions once new RPS generation enters the market and affects dispatch and power prices.19 The second step in the adjustment is to specify the rate at which decay occurs. Here, we bound our results based on the range of decay factors used in the literature (e.g., Hornby et al., 2011 and Hornby et al., 2013) and assume that the wholesale price effect decays linearly over 5, 10, or 20 years, with no effect after the final year.

The second adjustment reflects the fact that not all end-use customer demand is supplied by generation purchased at wholesale spot market prices. Rather, some portion of customer demand is hedged through long-term power contracts or utility ownership of generation; only the portion supplied by purchases tied to wholesale prices is impacted by reduced wholesale prices. To approximate the portion of retail electricity generation costs exposed to wholesale market prices, we use historical EIA data on changes in retail rates and changes in average wholesale prices to estimate the relationship between the two.20 We find that historical changes in wholesale prices flow through to the energy portion of retail electricity rates at 30–80% of the wholesale price changes, depending on the region and analysis approach. We assume that regions with organized wholesale power markets tend to feature greater levels of purchases tied to those prices than elsewhere.21 Thus we define two cases: a “Low Share” case, where 50% of energy purchases are based on wholesale prices in regions with organized markets and 30% elsewhere; and a “High Share” case, where 80% of energy purchases are based on the wholesale price in regions with organized markets and 50% elsewhere. Because these are broadly applied estimates, we focus on aggregate national results and do not present results on a regional basis.

7.2. Results

Across AVERT regions, the unadjusted wholesale price effect ranges from 0.8 to 4.7¢/kWh-RE; in other words, that new renewable electricity used to meet RPS compliance obligations reduced the cost of wholesale electricity market purchases by 0.8–4.7¢ per kWh of renewable electricity. The magnitude of this unadjusted effect is within the range suggested by the broader literature. 22 The variation across regions also aligns with expectations based on market fundamentals. For example, the effects of additional renewable generation on wholesale prices are greater in regions with steeper supply curves, which occur when the wholesale electricity market is supplied by many different types of generators with various fuel types and efficiencies.

More importantly, after adjustments, the magnitude of aggregate, national consumer savings resulting from wholesale price reductions range from an estimated $0 (0.0¢/kWh-RE) to $1.2 billion (1.2¢/kWh-RE) (Fig. 12). The uncertainty reflected in these ranges corresponds with the broad range of assumptions used for the decay of price effects and the portion of retail electricity sales exposed to wholesale spot market prices. If wholesale price impacts begin to decay at the date of RPS enactment (the left panel ofFig. 12), the aggregate consumer savings are relatively small, given the age of most RPS policies. Assuming, instead, that wholesale price effects begin to decay only once RE projects are built (the right panel of Fig. 12), the estimated consumer savings are larger.

Fig. 12.


Fig. 12.

Estimated consumer savings from wholesale price reductions associated with new renewable electricity used for RPS compliance. The two figures differ in their assumption about when price decay commences: with enactment of the RPS (left) vs. with operation of RE projects (right). Both figures show how the wholesale price savings vary with duration of decay (5, 10, or 20 years) and with the assumptions about what portion of energy purchases are tied to wholesale spot market prices (as described in the paper).


8. Natural gas price reduction impacts

Natural gas-fired generation is commonly the marginal electricity resource in the United States, so RPS-driven renewable generation with low marginal costs tends to displace gas-fired generation from the bid stack, which could reduce natural gas prices (e.g.,Wiser and Bolinger, 2007).23 Therefore, though RPS programs can drive up retail electricity prices (Barbose et al., 2015), these increases could be partially or entirely offset by reduced natural gas prices (e.g., Fischer, 2009). We estimate this effect while recognizing that reduced natural gas prices benefit consumers at the expense of natural gas producers. While individual states may experience net benefits from these transfers—e.g., states that consume more natural gas than they produce—others will experience the opposite.

8.1. Methods

We base our methods on the approaches summarized in Wiser et al., 2005 and Wiser and Bolinger, 2007 and recently applied in DOE’s Wind Vision Report ( DOE, 2015). These methods depend on data and assumptions about the amount of natural gas demand reduction, the shape of the natural gas supply curve, and total natural gas demand in the contiguous United States. The amount of RPS-induced natural gas demand reduction in 2013 is estimated through the AVERT modeling process described in Section 2, while total natural gas demand comes from the EIA. The shape of the supply curve—measured by its elasticity24—is more uncertain.

If the supply curve is steep (or “inelastic”), reduced demand will reduce price more than if the supply curve is flatter (or more “elastic”). Supply is relatively fixed in the near term but can adjust to market conditions over time, so supply curves are typically thought to be steeper in the near term than over longer terms. Presuming that gas supply will eventually adjust to gas-displacing renewable generators (or RPS policies), the older the renewable generator (or RPS policy), the less of an impact it should have on gas prices in 2013.

We use modeling output from the EIA’s National Energy Modeling System (NEMS) to derive an implied inverse elasticity curve for natural gas supply. Fig. 13 shows two curves derived by comparing different EIA-modeled scenarios of natural gas demand,25 along with the corresponding smoothed curve used for the rest of this analysis. These inverse elasticity curves conform to expectations: prices are more responsive in the short term than over the long term. Rather than eventually trending to zero (no price response) over time, however, the inverse elasticity curve eventually settles on a long-term steady-state price impact that is larger than zero, reflecting the fact that natural gas is an exhaustible resource.26

Fig. 13.


Fig. 13.

Derived inverse price elasticity of supply curve.


Next, we apply this derived inverse elasticity curve to the natural gas demand reductions estimated by the AVERT model (and attributed to specific renewable generators and RPS policies, each of which is associated with a particular year) in two different ways, to establish a range of potential gas price reductions in 2013. Under the first approach, we assume that RPS enactment caused gas suppliers to adjust supply in response to anticipated demand reductions. Under the second approach, we assume that gas suppliers do not adjust supply until each new renewable generator is built. The first approach results in a muted natural gas price response in 2013, because many RPS policies were enacted in the late 1990s, which places them within the lower, steady-state portion of the inverse elasticity curve. The second approach produces a larger price response, because even RPS policies enacted long ago have still, in many cases, driven the addition of new renewable generators through 2013, impacting prices through the higher, steeper portion of the inverse elasticity curve. Finally, we apply the range of natural gas price reductions to nationwide and state-level natural gas demand across all sectors of the economy in 2013.27 The results indicate, in dollars, the total consumer benefits stemming from natural gas price reductions at the sector, state, and national level.

8.2. Results

Compliance with RPS obligations in 2013 reduced demand for natural gas among gas-fired generators by about 0.42 quads (422 million MMBtu), representing 1.6% of total natural gas consumption in the contiguous United States (and 5% of natural gas consumption within the electricity sector). This demand reduction reduced natural gas prices by about $0.05 to $0.14/MMBtu, depending on whether the decay of natural gas price effects is tied to RPS vintage or RE project vintage, respectively.

When applied to all gas-consuming sectors of the economy within the contiguous states,28 the aggregate consumer savings in 2013 range from $1.3 billion (1.3¢/kWh-RE ) to $3.7 billion (3.7¢/kWh-RE) (Fig. 14). These values are broadly consistent with those reported in Wiser et al., 2005 and Wiser and Bolinger, 2007. Primary beneficiaries include gas consumers within the electricity, industrial, and residential sectors, followed by the commercial sector. Importantly, while individual states may experience net gains or losses, these potential price reductions and consumer savings are likely to be primarily, or even exclusively, transfer payments from gas producers 29 to consumers on a national basis. Many of the largest state beneficiaries—e.g., Texas, Louisiana, and Pennsylvania—are also large gas-producing states, so those states are also impacted by the offsetting negative effect on natural gas producers (Fig. 15).30

Fig. 14.


Fig. 14.

Estimated impacts of RPS compliance on natural gas consumer bill savings by sector under two bounding scenarios.


Fig. 15.


Fig. 15.

Natural gas consumer bill savings by state under two bounding scenarios.


9. Conclusions and policy implications

In 2013, new RE generation from RPS compliance obligations represented 2.4% of nationwide electricity generation that year, which reduced emissions of GHGs, SO2, NOx, and PM2.5. Though there is considerable uncertainty in these effects and their economic value, our central estimates suggest a monetary value of the GHG and air pollution emissions reduction totaling approximately $7.4 billion in 2013. Additional benefits, in the form of reduced water withdrawals and consumption, also occurred but were not evaluated in monetary terms, owing to a lack of available data and consistent methodology for such valuation.

Prior studies have found that RPS compliance costs—that is, the incremental costs net of avoided conventional generation costs—averaged approximately $1 billion per year during 2010–2013 ( Barbose et al., 2015, Heeter et al., 2014a and Heeter et al., 2014b). We find that nationwide monetary benefits in 2013 solely from reduced GHG, SO2, NOxand PM2.5 emissions outweigh the approximate compliance costs that year—in addition to the non-monetized benefits from water-use reductions. This loose comparison notwithstanding, for the reasons discussed in Section 1, additional research is needed to compare the costs of RPS policies formally and accurately with the associated benefits and impacts, and considerable uncertainty and nuance underlies any such comparison.

In addition, RE used to meet 2013 RPS compliance obligations yielded a range of additional impacts: though these are most appropriately considered resource transfers, rather than net societal benefits, they are nonetheless often important considerations for policy-makers. Among these impacts, renewables used to meet RPS obligations in 2013 are estimated to have supported nearly 200,000 U.S.-based gross jobs, centered largely in those regions with the greatest new capacity under construction. In addition, renewables used for RPS compliance reduced wholesale electricity prices and natural gas prices, together saving consumers $1.3–$4.9 billion, with natural gas price reductions comprising the bulk of those impacts.

These findings may inform evaluations of new or existing RPS programs. That said, multiple drivers exist for RE additions; thus the benefit and impact estimates presented in this study are not fully attributable to state RPS programs. Moreover, state RPS policies are not necessarily the least-cost approach to achieving the benefits assessed in this report.

Although we do not evaluate individual state RPS programs, our standardized methods may serve as a model for states evaluating the benefits and impacts of their policies: for example, by highlighting which benefits or impacts may be most significant in a particular region (and therefore most-deserving of close attention), by demonstrating the use of several publicly available tools, and by highlighting key methodological issues and challenges that must be addressed. Depending on the particular benefit or impact, access to more state-specific data or more specialized tools may reduce some of the uncertainty surrounding our results; for example, more advanced modeling tools could yield more precise estimates of wholesale market price suppression impacts. Finally, state-level tools and methods may enable an assessment of a broader set of benefits, costs, and impacts than covered in the present paper.

To compare benefits and costs properly, additional work is needed to understand the cost implications of RPS compliance. Past state-level assessments, using diverse methods, have shown that RPS compliance costs constituted less than 2% of average retail rates in most U.S. states during 2010–2013, albeit with a sizable range over time and across states, and with data unavailable for some key states (Barbose et al., 2015, Heeter et al., 2014a and Heeter et al., 2014b). Future work is also needed to understand future benefits, impacts, and costs of RPS polices—as currently enacted and as plausibly expanded. Such prospective analysis could leverage many of the same analytical tools and methods employed in the present study.


The authors would like to thank DOE’s Office of Energy Efficiency and Renewable Energy’s (EERE) Strategic Programs Office for primary funding support for this analysis (Contract Nos. DE-AC36-08GO28308 (NREL) and DE-AC02-05CH11231 (LBNL)). In particular, the authors are grateful to Steve Capanna of the Strategic Programs Office for his support of this project. We also wish to thank Jarett Zuboy for his excellent editorial support.



Ackerman and Stanton, 2012

F. Ackerman, E.A. Stanton


Climate risks and carbon prices: revising the social cost of carbon


Econ.: Open. -Access Open. -Assess. E-J., 6 (2012–10) (2012), pp. 1–25




Adelaja and Hailu, 2008

Adelaja, S., Hailu, Y.G., 2008. Renewable Energy Development and Implications to Agricultural Viability., accessed September 2015

    • Averyt et al., 2013
    • K. Averyt, J. Macknick, J. Rogers, N. Madden, J. Fisher, J. Meldrum, R. Newmark
    • Water use for electricity in the United States: an analysis of reported and calculated water use information for 2008
    • Environ. Res. Lett., 8 (2013), p. 015001
    • Averyt et al., 2011
    • K. Averyt, J. Fisher, A. Huber-Lee, A. Lewis, J. Macknick, N. Madden, J. Rogers, S. Tellinghuisen
    • Freshwater Use by U.S. Power Plants: Electricity’s Thirst for a Precious Resource
    • UCSUSA,, Cambridge, MA (2011)
    • Barbose, 2015
    • Barbose, G., 2015. Renewables portfolio standards in the United States: a status update. Presented at the 2015 National RPS Summit, Washington, D.C.
    • Bushnell, 2010
    • J. Bushnell
    • Building blocks: investment in renewable and non-renewable technologies
    • B. Moselle, J. Padilla, R. Schmalensee (Eds.), Harnessing Renewable Energy in Electric Power Systems: Theory, Practice, Policy, RFF Press, Washington, D.C (2010)
    • Cai et al., 2012
    • H. Cai, M. Wang, A. Elgowainy, J. Han
    • Updated Greenhouse Gas and Criteria Air Pollutant Emission Factors and Their Probability Distribution Functions for Electric Generating Units
    • Argonne National Laboratory, Lemont, IL (2012)
    • Cai et al., 2013
    • H. Cai, M. Wang, A. Elgowainy, J. Han
    • Updated Greenhouse Gas and Criteria Air Pollutant Emission Factors of the U.S. Electric Generating Units in 2010
    • Argonne National Laboratory, Lemont, IL (2013)
    • Callaway et al., 2015
    • D. Callaway, M. Fowlie, G. McCormick
    • Location, Location, Location: The Variable Value of Renewable Energy and Demand-Side Efficiency Resources
    • Energy Institute At Haas, Berkeley, CA (2015)
    • Caperton, 2012
    • R. Caperton
    • Renewable Energy Standards Deliver Affordable, Clean Power
    • Center For American Progress, Washington, D.C (2012)
    • Chen et al., 2008
    • C. Chen, R. Wiser, M. Bolinger
    • Weighing the Costs and Benefits of State Renewables Portfolio Standards: A Comparative Analysis of State-Level Policy Impact Projections
    • Lawrence Berkeley National Laboratory, Berkeley, CA (2008)
    • Clemmer et al., 2013
    • S. Clemmer, J. Rogers, S. Sattler, J. Macknick, T. Mai
    • Modeling low-carbon US electricity futures to explore impacts on national and regional water use
    • Environ. Res. Lett., 8 (2013), p. 015004
    • DOE (U.S. Department of Energy), 2013
    • DOE (U.S. Department of Energy)
    • U.S. Energy Sector Vulnerabilities to Climate Change and Extreme Weather
    • U.S. Department Of Energy, Washington, D.C (2013)
    • EPA (U.S. Environmental Protection Agency), 2011
    • EPA (U.S. Environmental Protection Agency)
    • Environmental and Economic Benefits (Analysis) for Proposed Section 316(b) Existing Facilities Rule
    • U.S. Environmental Protection Agency, Washington, D.C. (2011)
    • EPA (U.S. Environmental Protection Agency), 2014b
    • EPA (U.S. Environmental Protection Agency)
    • User’s Manual for the Co-Benefits Risk Assessment (COBRA) Screening Model Version: 2.61
    • U.S. Environmental Protection Agency, Washington, D.C (2014)
    • GAO, 2014
    • GAO
    • Regulatory Impact Analysis: Development of Social Cost of Carbon Estimates
    • U.S. Government Accountability Office, Washington, D.C (2014)
    • Heeter et al., 2014b
    • J. Heeter, K. Belyeu, K. Kuskova-Burns
    • Status and Trends in the U.S. Voluntary Green Power Market (2013 Data)
    • National Renewable Energy Laboratory, Golden, CO (2014)
    • Heeter et al., 2014a
    • J. Heeter, G. Barbose, L. Bird, S. Weaver, F. Flores, K. Kuskova-Burns, R. Wiser
    • A Survey of State-Level Cost and Benefit Estimates of Renewable Portfolio Standards
    • National Renewable Energy Laboratory, Golden, CO (2014)
    • IPA, 2013
    • IPA (Illinois Power Authority), 2013. Annual Report: The Costs and Benefits of Renewable Resource Procurement in Illinois under the Illinois Power Agency and Illinois Public Utilities Acts. Illinois Power Authority, Chicago.
    • IPCC (Intergovernmental Panel on Climate Change), 2014a
    • IPCC (Intergovernmental Panel on Climate Change)
    • Climate Change 2014: Mitigation of Climate Change: Working Group III Contribution to the Fifth Assessment Report of the IPCC
    • Cambridge University Press, Cambridge, UK (2014)
    • IPCC (Intergovernmental Panel on Climate Change), 2014b
    • IPCC (Intergovernmental Panel on Climate Change)
    • Climate Change 2014: Impacts, Adaptation, and Vulnerability: Working Group II Contribution to the Fifth Assessment Report of the IPCC
    • Cambridge University Press, Cambridge, UK (2014)
    • IWG, 2010
    • IWG (U.S. Interagency Working Group on the Social Cost of Carbon), 2010. Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866. U.S. Interagency Working Group on the Social Cost of Carbon, Washington D.C.
    • IWG, 2015
    • IWG (U.S. Interagency Working Group on the Social Cost of Carbon), 2015. Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866, July 2015 Revision. U.S. Interagency Working Group on the Social Cost of Carbon, Washington D.C.
    • Kenny et al., 2009
    • J.F. Kenny, N.L. Barber, S.S. Hutson, K.S. Linsey, J.K. Lovelace, M.A. Maupin
    • Estimated Use of Water in the United States in 2005
    • United States Geological Survey, Reston, VA (2009)
    • Krewski et al., 2009
    • D. Krewski, M. Jerrett, R.T. Burnett, R. Ma, E. Hughes, Y. Shi, M.C. Turner, C.A. Pope III, G. Thurston, E.E. Calle, M.J. Thun, B. Beckerman, P. DeLuca, N. Finkelstein, K. Ito, D.K. Moore, K.B. Newbold, T. Ramsay, Z. Ross, H. Shin, B. Tempalski
    • Extended Follow-Up and Spatial Analysis of the American Cancer Society Study Linking Particulate Air Pollution and Mortality
    • Health Effects Institute, Boston (2009)
    • Leon, 2015
    • W. Leon
    • Clean Energy Champions: The Importance of State Programs and Policies
    • Clean Energy States Alliance, Montpelier, VT (2015)
    • Lew et al., 2013
    • D. Lew, G. Brinkman, E. Ibanez, A. Florita, M. Heaney, B.-M. Hodge, M. Hummon, G. Stark, J. King, S.A. Lefton
    • The Western Wind and Solar Integration Study Phase 2
    • National Renewable Energy Laboratory, Golden, CO (2013)
    • Lim et al., 2012
    • S.S. Lim, T. Vos, A.D. Flaxman, G. Danaei, K. Shibuya, H. Adair-Rohani, M.A. AlMazroa, M. Amann, H.R. Anderson, K.G. Andrews, M. Aryee, C. Atkinson, L.J. Bacchus, A.N. Bahalim, K. Balakrishnan, J. Balmes, S. Barker-Collo, A. Baxter, M.L. Bell, J.D. Blore, F. Blyth, C. Bonner, G. Borges, R. Bourne, M. Boussinesq, M. Brauer, P. Brooks, et al.
    • A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study
    • Lancet, 380 (2012), pp. 2224–2260
    • |



    • Luckow et al., 2015
    • P. Luckow, E. Stanton, S. Fields, B. Biewald, S. Jackson, J. Fisher, R. Wilson
    • 2015 Carbon Dioxide Price Forecast
    • Synapse Energy Economics, Cambridge, MA (2015)
    • Luderer et al., 2013
    • G. Luderer, R.C. Pietzcker, C. Bertram, E. Kriegler, M. Meinshausen, O. Edenhofer
    • Economic mitigation challenges: how further delay closes the door for achieving climate targets
    • Environ. Res. Lett., 8 (3) (2013), p. 034033
    • Maupin et al., 2014
    • M.A. Maupin, J.F. Kenny, S.S. Hutson, J.K. Lovelace, N.L. Barber, K.S. Linsey
    • Estimated Use of Water in the United States in 2010
    • U.S. Geological Survey, Washington, D.C. (2014)
    • Meldrum et al., 2013
    • J. Meldrum, S. Nettles-Anderson, G. Heath, J. Macknick
    • Life cycle water use for electricity generation: a review and harmonization of literature estimates
    • Environ. Res. Lett., 8 (2013), p. 015031
    • Melillo et al., 2014
    • J.M. Melillo, T.C. Richmond, G.W. Yohe
    • Climate Change Impacts in the United States: The Third National Climate Assessment
    • U.S. Global Change Research Program, Washington D.C (2014)
    • Navigant, 2013
    • Navigant
    • Offshore Wind Market and Economic Analysis: Annual Market Assessment
    • Prepared For The U.S. Department Of Energy, Navigant, Burlington, MA (2013)
    • Nordhaus, 2013
    • W.D. Nordhaus
    • The Climate Casino: Risk, Uncertainty, and Economics for a Warming World
    • Yale University Press, New Haven, CT (2013)
    • Novan, 2014
    • K. Novan
    • Valuing the Wind: Renewable Energy Policies and Air Pollution Avoided
    • University Of California, Davis, Davis, CA (2014)
    • NRC (National Research Council), 2010
    • NRC (National Research Council)
    • Hidden Costs of Energy: Unpriced Consequences of Energy Production and Use
    • National Academies Press, Washington, D.C (2010)
    • Perez et al., 2012
    • R. Perez, B.L. Norris, T.E. Hoff
    • The Value of Solar to New Jersey and Pennsylvania
    • Clean Power Research, Napa, CA (2012)
    • Rogers et al., 2013
    • J. Rogers, K. Averyt, S. Clemmer, M. Davis, F. Flores-Lopez, D. Kenney, J. Macknick, N. Madden, J. Meldrum, S. Sattler, E. Spanger-Siegfried, D. Yates
    • Water-Smart Power: Strengthening the U.S
    • Electricity System In A Warming World, UCSUSA, Cambridge, MA (2013)
    • Solley et al., 1998
    • W.B. Solley, R.R. Pierce, H.A. Perlman
    • Estimated Use of Water in the United States in 1995
    • U.S. Geological Survey, Reston, VA (1998)
    • UCS, 2012
    • UCS
    • UCS EW3 Energy-Water Database, V.1.3
    • (Union of Concerned Scientists) Union Of Concerned Scientists, Cambridge, MA (2012)
    • UCS, 2013
    • UCS
    • How Renewable Electricity Standards Deliver Economic Benefits
    • (Union of Concerned Scientists) Union Of Concerned Scientists, Cambridge, MA (2013)
    • Werner, 2014
    • D. Werner
    • The Local Environmental Impacts of Renewable Portfolio Standards
    • University Of Maryland, College Park, College Park, MD (2014)
    • Wiser and Bolinger, 2015
    • R. Wiser, M. Bolinger
    • 2014 Wind Technologies Market Report. U.S
    • Department Of Energy, Washington, D.C (2015)
    • Wiser et al., 2005
    • R. Wiser, M. Bolinger, St.M. Clair
    • Easing the Natural Gas Crisis: Reducing Natural Gas Prices through Increased Deployment of Renewable Energy and Energy Efficiency
    • Lawrence Berkeley National Laboratory, Berkeley, CA (2005)
    • Wiser et al., 2016
    • R. Wiser, G. Barbose, J. Heeter, T. Mai, L. Bird, M. Bolinger, A. Carpenter, G. Heath, D. Keyser, J. Macknick, A. Mills, D. Millstein
    • A Retrospective Analysis of the Benefits and Impacts of U.S. Renewable Portfolio Standards
    • Lawrence Berkeley National Laboratory And National Renewable Energy Laboratory, Berkeley, CA and Golden, CO (2016)
Corresponding author.
Throughout the paper, we use the term “impacts” to refer effects that might best be considered resource transfers, rather than net societal costs or benefits, but that are nevertheless potentially relevant to policy-making.
The present study is specifically intended to build on these state-level assessments by applying a more consistent analytical framework. In addition to state-sponsored studies, a variety of more-academic studies have been conducted for specific states (e.g., for Michigan, see: Johnson and Novacheck, 2015a, Johnson and Novacheck, 2015b and Novacheck and Johnson, 2015).
AVERT uses historical data to estimate changes in generation and emissions within each AVERT region resulting from an increase in RE or energy efficiency. This enables a consistent analytical approach but also entails limitations. AVERT is more complex than using a simple regional marginal displacement (or emission) rate, but it is less complex than a full production cost model or a highly region-specific and detailed statistical analysis. Among some of the model limitations discussed in EPA, (2014a), the model is insensitive to the location of renewable generation within a given region and therefore may not accurately model the impacts of highly localized RE policies. It also does not capture interactions across regions. Given its calibration to historical data, AVERT also has limited ability to accurately model the impacts of very large RE programs. See EPA, (2014a) for additional information on AVERT’s methods, capabilities, and limitations.
In relation to GHG benefits, the capacity information is used only to estimate GHG emissions associated with the construction phase of renewable generation capacity and the avoided GHG emissions associated with the construction phase of new fossil generation facilities. As will be shown, these lifecycle GHG emission impacts are a minute component of the overall GHG benefits.
Current actions to limit GHGs can also be considered as one method of reducing the longer-term cost of future policies to reduce GHGs. Some U.S. states and regions have already enacted carbon-reduction policies; the U.S. Congress has considered such policies in the past; and EPA has proposed emission limits for power plants (Luckow et al., 2015). As a result, many utilities already regularly consider the possibility of future policies to reduce GHGs in resource planning and thereby treat RE sources as options for reducing the possible future costs of climate mitigation (Barbose et al., 2008, Bokenkamp et al., 2005 and Luckow et al., 2015).
Research has sought to estimate the magnitude and timing of climate-change impacts, damages, and associated costs (IPCC (Intergovernmental Panel on Climate Change), 2014a, IPCC (Intergovernmental Panel on Climate Change), 2014b, IWG, 2010, IWG, 2015, Melillo et al., 2014 and Weitzman, 2012). Because of the uncertainties involved, estimates of the SCC span a wide range (IPCC (Intergovernmental Panel on Climate Change), 2014b, Tol, 2011 and Weyant, 2014), leading some to suggest possible improvements to SCC estimates and procedures (Ackerman and Stanton, 2012, Arrow et al., 2013, Johnson and Hope, 2012, Kopp et al., 2012, Pizer et al., 2014 and Weyant, 2014) or even to question the use of these estimates (Pindyck, 2013).
Our methods do not fully consider the possible erosion of GHG benefits due to increased cycling, ramping, and part loading required of fossil generators in electric systems with higher penetrations of variable renewable generation; previous studies (e.g., Lew et al., 2013) suggest this impact is small. Our assessment of construction-related life-cycle emissions is based on average renewable capacity additions during 2013–2014 and avoided fossil capacity. Although application of these emissions to 2013 is somewhat speculative, these benefits are arguably attributable to the additional RE supported by the RPS policies. We assume that biomass combustion emissions (other than landfill gas) are entirely offset by carbon absorption to produce the biomass feedstocks; because of the lack of consensus in the literature, we do not seek to estimate land-use related emissions. We assume that landfill gas used for electricity production would otherwise have been flared rather than vented. Finally, we assume carbon cap-and-trade programs were effectively non-binding in 2013, and thus the benefits of RPS compliance should be valued at the estimated cost of climate damages rather than at carbon allowance prices. See Wiser et al. (2016) for additional detail on caveats and limitations.
Throughout the paper, we express benefits in units of cents per kWh of renewable electricity (¢/kWh-RE). For the purpose of cost-benefit analysis, this can be compared to any incremental cost of renewable electricity, relative to conventional resources, expressed in terms of the levelized cost of energy.
Benefits were not separated by pollutant with the COBRA model.
An estimate of ozone benefits, separate from total benefits, corresponding to the AP2 valuation was unavailable in the model.
We estimated emissions of 1,800, 6,200, and 900 MT of SO2, NOx, and PM2.5, respectively, from new biomass meeting RPS requirements in 2013. Biomass electricity therefore reduced total RPS emissions benefits by 2.3%, 12.3%, and 15.8% for SO2, NOx, and PM2.5, respectively. Because health benefits are dominated by SO2reductions, the biomass emissions only marginally reduce total monetized benefits; for example, the EPA Low value would have been 4% larger without the biomass emissions.
An estimate of mortality reduction corresponding to the AP2 valuation was not available within the model; similarly, AP2 does not report morbidity results.
This morbidity outcome has a wide range relative to the other outcomes as it is estimated based on two different studies, reflecting the uncertainty related to this outcome.
These monetized benefits (based on the COBRA Low results) focus exclusively on fine particulates. Ozone is also found to be reduced in many locations but is not accounted for in the COBRA model used to generate this figure. The monetized benefits from ozone reductions are of second order compared to the benefits of PM2.5reductions, however, so the exclusion of ozone in the figure does not create substantial bias. The EPA CPP benefit-per-ton approach and APP model do not output the locations of the resultant benefits, so they are not shown here.
The methodologies employed by the two models are broadly consistent and use the same set of economic multipliers. Costs and assumptions for the “domestic content” of all renewable technologies other than landfill gas are based on JEDI default data.
We do not estimate supply chain or induced economic impacts by state because of uncertainty relating to where in the United States those impacts occur. A solar installer in New Jersey, for example, may purchase information technology services from a company in California but could just as easily purchase these services from a company in another state.
In earlier years, wind would have been the largest source of construction jobs, given its historical dominance among new RPS capacity additions (Barbose, 2015).
The change in production cost is based on AVERT’s estimates of the change in fuel consumption at each plant for each hour and quarterly estimates of fuel costs from the EIA. Fuel prices from each quarter of 2013 by region were collected from
In reality, decay likely commences at some point in between these two bookends. On the one hand, given frequent revisions to RPS policies and the inherent uncertainty and unreliability of long-term forecasts, it is unlikely that market participants would be able to fully anticipate, at the time of RPS enactment, the impact of RPS resources on regional supply and demand 10 or 20 years into the future. On the other hand, it is also unlikely that market participants would not anticipate the effects until the renewable generation comes online, given the transparency of RPS planning processes and generation development in most states.
Retail price data were collected from, and wholesale price data were collected from
The AVERT regions with organized wholesale market exchanges are CA, EMW, NE, SC, TX, and WMW (see Fig. 1).
For example, IPA (2013) estimated a wholesale price effect of 2.1¢/kWh-RE for wind in the Midwest, while our AVERT method estimated 2.5¢/kWh-RE for the WMW and EMW region. More generally, a broad literature review conducted by Würzburg et al. (2013) created a common metric of $/MWh-RE per % of renewables across many studies, primarily from Europe. The median value across studies was $0.73/MWh-RE per % RE. Using this value would lead to estimates of 0–1.0¢/kWh-RE, depending on the region, for the RE penetration levels we are considering. On the other hand, other studies find much larger possible effects. A report on transmission in the Midcontinent Independent System Operator (MISO) area (Fagan et al., 2012), for example, estimated a price suppression benefit of $7.9 billion for 20 GW of wind or $12.2 billion for 40 GW of wind, implying a wholesale price impact of 9.9–12.9¢/kWh-RE. Moreover, Perez et al. (2012) estimate the wholesale price effect of solar in the mid-Atlantic region to be around 5.5¢/kWh-RE, comparable to the high end of our range.
Within a simple economics supply/demand framework, this gas displacement can be represented by the demand curve for natural gas shifting inward along the supply curve. Presuming the supply curve has an upward slope and does not change in response to the demand shift, the demand shift will result in a lower price. Although demand for coal within the electricity sector also declines as a result of increased RE generation, per the earlier AVERT results, we do not analyze potential impacts on coal prices, because the long-term inverse price elasticity of supply is generally thought to be lower for coal than for natural gas (Wiser and Bolinger, 2007).
Elasticity is a measure of how much demand changes in response to a change in price (%∆Q/%∆P). For this analysis, however, we are more interested in how prices change in response to a change in demand (%∆P/%∆Q), which we refer to as inverse elasticity.
The comparisons involve two sets of scenarios from the EIA’s Annual Energy Outlook 2015 ( EIA, 2015), to characterize how changes in gas demand impacted modeled prices. Specifically, we compared the High Economic Growth scenario (i.e., a high gas demand scenario) to the Reference scenario, and also to the Low Economic Growthscenario (i.e., a low gas demand scenario). In both comparisons, the inverse price elasticity of supply starts out relatively high at around 4.0 (implying an initial 4% change in price for every 1% change in demand) and declines somewhat smoothly over the next 10 years before settling somewhere between 0.5 and 1.0.
This point is perhaps best understood by considering an increase, rather than decrease, in gas consumption: a unit of exhaustible natural gas that is consumed today will not be available for future consumption, and hence it drives up the price of all remaining natural gas.
Data on natural gas consumption is provided by the EIA.
The application of the price reduction to the contiguous states reflects an implicit assumption that these states constitute a single market for natural gas (as opposed to multiple different regional markets) (see Wiser and Bolinger, 2007).
The price reductions also represent transfers from those that benefit from gas production, such as owners of mineral rights (through rents) and governments and taxpayers (through taxes).
These results by state could be misleading, because some of the commercial and industrial facilities that reap gas savings may be publicly traded and widely held by shareholders across the United States. However, many gas producers also may be publicly traded companies with shareholders located across the United States, in which case attributing lost production revenue to a specific state may also be misleading.