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Impacts of Transit Use on Greenhouse Gas Emissions

Policies that encourage the use of more efficient transportation modes are considered beneficial in terms of reducing greenhouse gas (GHG) emissions. In support of developing such policies, the impacts various transportation demand, supply, and policy and regulation variables have on passenger travel related carbon monoxide CO2 (the primary GHG) emissions are investigated by the Transit Lab team. A methodology for integrating data from multiple sources in a consistent manner is developed and implemented, producing a dataset consisting of 146 of the largest urbanized areas in the US. The effects various explanatory variables have on CO2 emissions per capita are modeled in a manner that takes into account the simultaneity between CO2 emissions and policies and regulations aimed at reducing such emissions. Based on the developed model, the magnitudes of the impacts that certain variables have on CO2 emissions in select urbanized areas are quantified. The results indicate that the variable used as a proxy for policies and regulations aimed at addressing environmental concerns influences the impacts other variables have on CO2 emissions. Depending on the absence or presence of this proxy variable, the effect of either average private vehicle occupancy or roadway lane-miles per capita is the strongest. While the effect of transit share is found to be statistically significant, other variables exhibit a larger impact on CO2 emissions, which is understandable in light of the fairly low values of transit service utilization accompanied with very small variability across most urbanized areas in the US. Finally, the relative magnitudes of the impacts across the different explanatory variables vary by urbanized area, implying that policies aimed at reducing CO2 emissions should focus on different variables depending on the overall characteristics of the specific urbanized area.

Based on these results, the effects that these variables have on CO2 emissions are modeled in a manner that takes into account the selectivity bias of urbanized areas with high CO2 emissions being more likely to require automobile emissions inspection programs, which are viewed as a proxy for other policies and regulations aimed at reducing GHG emissions. The full details of the model estimation are covered in (Mishalani, 2012). In addition, the magnitudes of the impacts that changes in certain variables have on CO2 emissions in select urbanized areas are quantified.

Table below shows the estimated CO2 change (metric tons) for a 10% increase in each explanatory variable

 

The results indicate the following:

•     The variables from the integrated database that are found to have a statistically significant influence on CO2 emissions are transit market share, lane-miles per capita, private vehicle occupancy, automobile emissions inspection, average travel time, and population density.

•     Automobile inspection in urbanized areas appears to be a proxy of other policies and regulations that influence the impact other variables have on reducing CO2 emissions. The variables that have appreciably different effects on CO2 emissions in the presence and absence of auto emissions inspection programs as a proxy variable are transit share, average travel time, and average private vehicle occupancy.

•     In the absence of proxy inspection programs, average private vehicle occupancy has by far the largest impact on CO2 emissions.

•     In the presence of proxy inspection programs, the variable that has the largest impact is roadway lane-miles/capita.

•     The relatively smaller magnitudes of impact on reduced CO2 emissions with respect to transit share increasing by 10% (all else equal) are understandable in light of the fairly low and narrowly varying values of transit service utilization across most urbanized areas in the US.

•     Policies aimed at reducing CO2 emissions would have to focus on different variables depending on the overall characteristics of the specific urbanized area.