United States Department of Transportation
i
Impact of Public Transit Market Share on Energy Consumption and the Environment: Developing Statistical Models for Validation and Gross Predictions
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2011-12-31
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Abstract:Policies that encourage the use of more efficient transportation modes are considered beneficial in terms of reducing carbon dioxide (CO2) emissions. In support of developing such policies, the impacts of various transportation demand, supply, and regulation variables on passenger travel related CO2 – the predominant greenhouse gas (GHG) – emissions are investigated. A methodology for integrating data from multiple sources in a consistent manner is conceived and implemented, producing a dataset consisting of 146 of the largest urbanized areas in the US. A preliminary model for CO2 emissions per capita in terms of various explanatory variables in this dataset is developed, and future improvements are suggested. While the effect of transit share is found to be statistically significant in the preliminary model, other variables exhibit a larger impact on CO2 emissions, which is understandable in light of the fairly low and narrowly varied values of transit service utilization across most urbanized areas in the US. Additional variables whose coefficients are significant are lane miles/capita, average travel time, vehicle occupancy, and the reciprocal of population density. There are some steps that can be taken next to enhance the reliability of both the dataset and model of interest. The data for certain variables need to be further verified and cross-checked to make sure that conclusions made are valid. In addition, incorporating a few more variables in the dataset that could improve the explanatory power of the model is desirable. Further improvement of the model could come from including additional and already available explanatory variables. In addition, there are various interactions that could be investigated. One aspect that has not yet been considered is the influence of government policies and regulations pertaining to CO2 emissions on CO2/capita. It could also be the case that the level of CO2/capita in an urbanized area influences the public policies and regulations that are put in place. Therefore, the inclusion of such explanatory variables would result in simultaneity that would have to be addressed in specifying and estimating the model of interest. All of the above reflect further dataset development and modeling considerations that will be considered following this reporting period.
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