The Joint Effects of Income, Vehicle Technology, and Rail Transit Access on Greenhouse Gas Emissions

New paper published: The Joint Effects of Income, Vehicle Technology, and Rail Transit Access on Greenhouse Gas Emissions

Marlon G. Boarnet, Raphael W. Bostic, Andrew Eisenlohr, Seva Rodnyansky, Raúl Santiago-Bartolomei, Huê-Tâm Webb Jamme

First Published July 28, 2018 Research Article *


This paper examines the relationship between income, vehicle miles traveled (VMT), and greenhouse gas (GHG) emissions for households with varying access to rail transit in four metropolitan areas—Los Angeles, the San Francisco Bay Area, San Diego, and Sacramento—using data from the 2010–2012 California Household Travel Survey. Daily vehicle GHG emissions are calculated using the California Air Resources Board’s 2014 EMFAC (emission factors) model. Two Tobit regression models are used to predict daily VMT and GHG by income, rail transit access (within or outside 0.5 miles of a rail transit station in Los Angeles and the Bay Area, and linear distance to rail in San Diego and Sacramento), and metropolitan area. Comparing predicted VMT and GHG emissions levels, this paper concludes that predicted VMT and GHG emission patterns for rail access vary across metropolitan areas in ways that may be related to the age and connectivity of the areas’ rail systems. The results also show that differences in household VMT due to rail access do not scale proportionally to differences in GHG emissions. Regardless, the fact that GHG emissions are lower near rail transit for virtually all income levels in this study implies environmental benefits from expanding rail transit systems, as defined in this paper.

Many metropolitan areas see rail transit expansion as an opportunity to pursue multiple policy goals simultaneously. Such policy goals could be: (1) building more affordable housing, taking advantage of infill opportunities near rail transit stations, and (2) reducing vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions. The latter is achieved, in theory, by providing near-transit residences for lower income persons who are more likely to depend on rail transit and drive less, whereas higher income persons drive more and take transit less (1). Recent evidence suggests a potential challenge to this approach as higher income households might reduce their driving more if they live near rail precisely because they drive more when living far from rail, and therefore can make larger reductions when near it. In fact, Chatman et al. show in cross-sectional comparisons that the VMT difference among households within and beyond a half-mile area from rail stations is 9.86 miles per day for households below $50,000 in income, and 20.47 miles per day for households above $100,000 in income (2).

We expand on Chatman et al. by considering GHG emissions directly instead of using VMT as a proxy. Older vehicles are generally more polluting, so assuming that vehicle vintage and technology are closely related to income (3), greatest VMT reduction may not necessarily parallel GHG reduction. This study’s objective is twofold: (1) To begin to bridge the gap between VMT and GHG emissions in travel behavior studies, and (2) To examine the relationship between income, VMT, vehicle GHG emissions, and rail transit access. This study observes four California metropolitan areas; considers how the relationships between income, VMT, vehicle GHG emissions, and rail transit access vary by metropolitan area when accounting for vehicle technology; and posits mechanisms suggested by the varying relationships.

Using data from the 2010–2012 California Household Travel Survey (CHTS), two Tobit regression models are used to predict the effects of household characteristics (income and location, i.e., rail transit access) first on household VMT and then on household vehicle GHG emissions. The primary finding is that the pattern of GHG emissions across households and incomes, living near versus far from rail transit, is different from the pattern of VMT. This indicates that VMT may be an incomplete policy proxy when analyzing environmental impacts of travel behavior for those living near rail transit. That pattern also varies across regions in ways that suggest that differences in metropolitan land use and rail transit networks are important for the income–VMT–GHG relationship. It would be valuable for the literature to return to analyses that include vehicle GHG emissions and vehicle stock more directly.


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