Roxana J. Javid (corresponding author) Department of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA, firstname.lastname@example.org The Environmental Impacts of Carpooling in the United States
Policies to reduce GHG emissions from road transportation encompass a wide range of strategies (1–3). Common examples of the policies include
- high efficiency vehicles (4),
- travel demand management (flexible hours, teleworking) (3),
- shifting from personal car to public transport (3, 5),
- alternative fuels (hydrogen, electricity, biofuel) (4, 6)
- various types of taxation (toll road, parking charges) (7)
- reducing engine weight (8)
- zero-carbon alternatives (cycling and walking) (5, 7)
- High Occupancy Vehicle (HOV) lanes (5, 7).
All of the strategies for reducing emissions can be classified into three groups:
- Reduce (Reducing GHG emissions per passenger kilometer)
- Avoid (Avoiding unnecessary energy consumption and promote other modes of transportation), and
- Replace (replacing fossil fuels with low-emission alternative fuels) as described in (9).
This study focuses on the contribution of High Occupancy Vehicle (HOV) lanes and carpooling. Potential factors influencing carpooling propensity can be classified into three main categories:
- cost-related, and
- sociodemographic factors.
This paper explores 7 variables in these three categories that are assumed to affect carpooling behavior and employs several datasets to encompass these variables. We define independent variable as the rate of carpooling in each state. In this study, carpooling refers to share of carpoolers to work in total workers. The source datasets for this variable is the State Transportation Statistics (STS), released by the US Department of Transportation.
One of the main independent variables in this study is the HOV lane infrastructure as it can contribute to the carpooling rate within a given state. We define the percentage of existing HOV lane-miles to the total road miles as one of the infrastructural variables. Results for model (1) indicate that infrastructural variable HOV have a statistically significant impact on the carpooling rate, a positive impact of 0.447 (see TABLE 1, column 1). Results for model (2) highlight the fact that incorporating cost-related variables does not lower the significance of infrastructural variable, and HOV lane infrastructure remains highly significant in increasing the carpooling rate (0.393).
In this model, travel time to work plays an important negative role in carpooling propensity (-0.314) that means
- the shorter the average trip to work, the higher will be the carpooling rate. However,
- gasoline price plays an important positive role in carpooling rate (0.331) and higher gasoline prices yield more interest in carpooling.
Finally, when socio-demographic variables are added to model (3), the HOV lanes infrastructure variable at state level still plays an important roll in carpooling rate (0.322). Gasoline price remains highly significant (0.307). Interestingly, presence of socio-demographic variables in the model (3) eliminates the importance of travel time duration, while gas price is still highly significant in the model. Average number of vehicle in household does not have a statistically significant impact on carpooling rate; while average household size and HD index show significant impacts on carpooling rate (0.256 and -0.495 respectively).
The most significant factor is gas price, which results in 2.332% increase in carpooling rate for any added dollar in gas price per gallon.
Carpooling increases by 1.654% an additional person in the household.
As HD index level in the state increases by one, the carpooling rate would decrease by 0.829%. One of main factors that positively correlated with the HDI index is education. It is found that educated peoples have more intention to drive alone and less carpool, which is the same result as what was concluded in (11). Moreover, this index is positively correlated with income. Therefore, the results show that solo drivers are associated with higher income. This result was concluded in previous research on the demographics of carpooling (12).
The results show that District of Columbia stands out as showing the greatest potential to reduce annual CO2e under the expansion scenario, by 4.53%. Increased HOV lane kilometers in the next three states – Hawaii, New York, and New Jersey – have a moderate impact on reducing CO2e by 1.64%, 1.37%, 1.35%, respectively. The rest of the states including Maryland, California, Massachusetts, Connecticut, and Rhode Island have lower influence on GHG emission mitigation by 1.13% to 0.68% of CO2e reduction; however, they still contribute to climate change, as the cumulative impact is still significant.
The smallest reduction belongs to North Dakota, South Dakota, and Montana by 0.02%, 0.03%, and 0.05% respectively, implying the incompatibility of this strategy with those states.
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