An Update on Public Transportation's Impacts on Greenhouse Gas Emissions (2021)

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9   Research Approach The modeling for this project focused on producing clear, well­documented results related to the climate and sustainability impacts of transit, and in particular: • The national sustainability benefits associated with transit ridership, • The reduced carbon footprint of individuals using public transit, • Transit agency contributions to GHG emission reduction and sustainability, and • Public transit’s national and regional impact on GHG emissions and energy use related to land use and travel behavior. The approach was to document the impacts of transit today based on existing data and research. The most recent complete data at the time of analysis was for 2018, so the findings are for that year. The emissions calculations in this analysis are based on transit vehicle, energy use, and passenger data reported in the NTD (FTA 2020a). The GHG calculation methods follow best practices from the American Public Transportation Association (APTA 2018), the U.S. Environ­ mental Protection Agency (U.S. EPA 2020d), and the GHG Protocol (WBCSD and WRI 2004), as documented in the methodology descriptions in this chapter and Appendix A. The land use efficiency GHG savings were modeled using household travel survey data from 28 regions using a structural equation model documented in this methodology and Appendix B. Included Emissions. The emissions values in this report include direct, indirect, and upstream GHG emissions associated with vehicle travel. Direct GHGs are the carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions that occurred at the vehicle when fuel was consumed. Indirect GHG emissions occurred at the power plant when electricity was produced or in the process of producing hydrogen. Upstream emissions, sometimes referred to as “well­to­pump” emissions, are the GHG emissions that occurred during fuel production and distribution. Thus, the emissions values presented here are “well­to­wheels” values or the full life­cycle GHG emissions associated with using a fuel. The calculated GHG values were multiplied by their 100­year global warming potentials (GWPs) and summed to be presented as carbon dioxide equivalents (CO2e). Excluded Emissions. The emissions calculations presented here do not include the GHG impacts of transit operations, such as electricity use at stations or offices, non­transit vehicles, infrastructure, and staff commutes, nor do these calculations include the life­ cycle GHG impacts of transit vehicle production, maintenance, and disposal. The impact of fugitive emissions [such as sulfur hexafluoride (SF6), hydrofluorocarbons (HFCs), and perfluoro carbons (PFCs)] were also not modeled due to a lack of transit­specific data on these emissions sources. C H A P T E R 2

10 An Update on Public Transportation’s Impacts on Greenhouse Gas Emissions A transit agency calculating its greenhouse gas inventory will include energy use in offices, stations, and non­transit vehicles, but that is not typically a large source of its emissions impact relative to transit vehicle operations (Chester and Horvath 2007). An examination of five past GHG inventories of transit agencies found that revenue transit vehicles represented 65% to 95% of transit agency GHG emissions (McGraw et al. 2010, Southworth et al. 2011). Many transit agencies are examining these impacts in GHG inventories and reducing these emissions through energy efficiency and other improvements. Because there was not a national source for these data, they were left out of the analysis, but it is an area worthy of future research. Research Approach The direct, indirect, and upstream GHG emission impacts of public transportation are analyzed in three categories: • Transit Vehicle GHG Emissions, which are the GHGs that result from the use of fossil fuels, hydrogen, and electricity to propel buses, trains, and other transit vehicles; • Transportation Efficiency GHG Savings, which are the avoided personal vehicle emissions of transit passengers; and • Land Use Efficiency GHG Savings, which are generated as communities with transit enable residents to drive less. Calculating Emissions from Transit Vehicle Activity The primary calculation method was that used in GHG accounting in communities around the world: given an activity of x at emissions rate y, what are the GHG emissions of that activity? (For example, the energy use of diesel buses and the CO2 emission factor per gallon of diesel fuel.) Each transit agency’s service vehicle emissions were calculated separately by mode type and fuel. The calculations are described further in Appendix A. Vehicle Typology The NTD provides transit data by mode names and vehicle types. For the purposes of this study, these have summarized into six mode types: bus, commuter rail, ferry, heavy rail, light rail, and van (see Appendix A). Activity Data The activity data used to calculate emissions from transit revenue vehicles are the energy use data and vehicle mileage data reported in the NTD. Many smaller transit agencies do not report fuel use to the NTD. Where vehicle mileage data were available, fuel use was estimated based on average fuel efficiencies by mode and fuel type. The net result was a database of fuel use and vehicle mileage by mode type among 907 transit agencies. The NTD reports the following fuel types: • Biodiesel (gallons), • Compressed natural gas (gallon equivalents), • Diesel (gallons), • Electric battery (kWh), • Electric propulsion (kWh), • Ethanol (gallons), • Gasoline (gallons),

Research Approach 11   • Hydrogen (gallon equivalents), • Liquefied natural gas (gallon equivalents), and • Liquefied petroleum gas (gallon equivalents). Emissions Factors Emissions factors for direct, indirect, and upstream CO2, CH4, and N2O emissions were applied to each fuel type (U.S. EPA 2020a, U.S. EPA 2020b, Wang et al. 2020). The electricity grid subregion emissions factors associated with the zip code of each transit agency’s headquarters was used to calculate electricity emissions. Where appropriate, CH4 and N2O emissions were calculated on a per­vehicle­mile basis. Transportation Efficiency: Calculating Avoided Emissions from Transit Passenger Travel The net GHG benefits of transit as calculated for this project include the avoided GHG emis­ sions of private automobile use by transit passengers, also called transportation efficiency. NTD­reported passenger mile data were the basis for this analysis, which was done using the calculations described here: • Passenger miles × mode shift factor (0.329) = avoided vehicle miles. • Avoided vehicle miles/miles per gallon (22.5, 2018 FHWA on­road light­duty vehicles) = avoided gallons of fuel. The avoided vehicle miles and gallons of fuel were then used to calculate GHG emissions (see Appendix A). Mode Shift Factor One of the key GHG benefits of public transportation is that it enables passengers to avoid emissions that would have otherwise occurred if they had driven a private automobile. APTA calls this a “transportation efficiency” gain (APTA 2018). It may also be described as the direct effect of transit on vehicle miles traveled (VMT). The tradeoff between passenger miles and private automobile miles is not a 1­to­1 replacement. Some passengers would drive if they were not taking transit, but others would carpool, bicycle, walk, use a taxi, use a ridehailing service, or not take a trip at all. Transit agencies conduct passenger surveys to understand the modes of transportation that passengers would choose if they were not taking a transit trip. This mode shift is an important factor in estimating the GHGs avoided by transit passengers (see Figure 2). APTA gathers passenger survey data from transit agencies and compiles them into a national average, which is what was used for this research. An estimated 12% of passengers would use ridehailing if not on transit, 14% would otherwise drive alone, 10% would carpool (which is divided by 2.5 passengers per carpool for this analysis), and 3% would take taxis (APTA 2020). These data are used in the analysis to determine that for every 3 passenger miles on public transportation, 1 personal vehicle mile is avoided (a mode shift factor of 0.329). See Appendix A for more information.

12 An Update on Public Transportation’s Impacts on Greenhouse Gas Emissions Land Use Efficiency: Calculating Avoided Emissions from Community Travel A significant body of research has shown that transit’s impacts on emissions in a community expand beyond transit’s passengers (Ewing et al. 2007). Transit investments, service improve­ ments, and associated development can lead to location efficiency, where destinations like employment and shopping are closer to the households that need them [Ewing et al. 2015, Center for Neighborhood Technology (CNT) n.d.]. Residents in a location­efficient area are able to make shorter trips, fewer trips, or walk or bike to meet their daily needs (Ewing and Cervero 2010, Cervero and Murakami 2010). The GHG emissions savings associated with these impacts were calculated by transit agency using NTD­reported passenger mile data in the calculations described here: • Passenger miles × mode shift factor (0.329) = avoided passenger vehicle miles. • [(Avoided passenger vehicle miles × transit multiplier) – avoided passenger miles] = avoided community vehicle miles. • Avoided community vehicle miles/miles per gallon (22.5 2018 FHWA on­road light­duty vehicles) = avoided gallons of fuel. The avoided vehicle miles and gallons of fuel were then used to calculate GHG emissions (see Appendix A). Transit Multiplier The transit multiplier is the total VMT reduction associated with transit, including trans­ portation efficiency and land use efficiency VMT savings divided by the transportation efficiency VMT savings to create a multiplier (see Figure 3). The multiplier allows the research findings about transit’s impact on VMT in 28 communities to be applied to every transit agency in this study in a regionally specific way. Source: APTA 2020, adjusted to include other transit modes. Drive Alone, 14% Ridehailing, 12% Taxi, 3% Carpool (Ride with Someone), 10% Walk/Bike, 11% Other, 9% No Trip, 19% Other Transit, 22% Mode transit riders would take in place of their transit trip: Figure 2. Transit survey mode shift data.

Research Approach 13   • Transportation Efficiency: VMT reduction of transit passengers (also called transit direct effect on VMT). • Land Use Efficiency: VMT reduction in the community. Even residents who do not ride transit themselves save VMT, such as through shorter trips and fewer driving trips (also called transit indirect effect on VMT). The transit multipliers for this study were developed using a multilevel structural equation model and a database of household travel survey data in 28 regions matched with socio­ economic, built environment, and regional characteristics. The model found that the effect of transit in the community is much larger than the avoided auto use of transit passengers alone and that changes in the built environment in communities that are well served by transit create VMT savings several times larger than passenger impact alone. The advantages of this modeling approach over previous studies include: • The diverse set of regions examined allowed the researchers to study a wider variety of regional and local variables, and • The household­level data allowed the researchers to deploy place­based characteristics to the model at a fine grain to better determine impact, which gives this work a validity that was missing from some of the earlier studies and allowed the researchers to calculate custom transit multipliers for the transit agencies in the analysis. Transit multiplier = (transportation efficiency VMT) + (land use efficiency VMT) (transportation efficiency VMT) Figure 3. Transit multiplier equation. The range of transit multipliers calculated for this study was 5.97 to 13.04, with a median value of 6.03. The total impact across all transit agencies studied was equivalent to a multiplier of 8.35. Note that this is not the land use multiplier as usually defined, but a multiplier of total VMT reduction relative to VMT reduc- tion due to transit passengers. What does a transit multiplier of 6.03 mean? Consider a community with 1 million transit passenger miles. Using the transportation efficiency calculation and the mode shift factor of 0.329, it is estimated that those 1 million passenger miles avoided 329,000 personal vehicle miles. Applying the transit multiplier, this 329,000 VMT can be used to estimate the VMT avoided in the broader community by solving for land use efficiency in the equation in Figure 3. (329,000 VMT × 6.03 transit multiplier) − 329,000 VMT = 1,654,870 land use efficiency VMT savings. So, taken together, that community with 1 million transit passenger miles saw a total reduc­ tion of 1,983,870 miles traveled by both transit passenger and broader community members. This value would match closely with that of previous studies, where for every 1 passenger mile

14 An Update on Public Transportation’s Impacts on Greenhouse Gas Emissions of transit service there were 2 miles of vehicle travel avoided. An adjustment for vehicle occupancy is not required because there is an occupancy factor in the model that is used to develop the transit multiplier. If the transit multiplier value in this hypothetical community were the maximum among the transit agencies of 13.04, the outcome would be 4.3 miles of vehicle travel avoided for every passenger mile of transit service, which is a significant impact that is within the range of values found in previous studies. What the transit multiplier approach in this study allows that previous studies have not is to scale the multiplier for each transit agency using passenger miles and service area population to better estimate transit’s impact. Furthermore, the transit multiplier is anchored in real­world data from a diverse set of 28 regions, which gives it a high degree of statistical certainty. Because land use patterns take time to develop, the authors expect that areas with long­ established transit and location­efficient land use patterns may see different impacts than areas with more recent changes. The authors also expect that regional variation may play a role. Due to the spatial overlap of transit modes within many regions, mode­specific transit multi­ pliers were not developed as part of this study. Further unpacking of impacts using the transit multiplier modeling presented here is an area ripe for additional exploration. Public Transportation Scenarios for 2030 and 2050 Using the findings of the GHG analysis for 2018, the authors created a set of hypothetical scenarios of transit emissions for 2030 and 2050 to highlight the potential impacts of public transportation climate action. The assumptions these scenarios use are described in Chapter 3. The scenarios are not forecasts, but rather conceptual “what ifs” for further electrification of transit, expansion of clean power adoption, and significant increases in transit ridership. The spreadsheet tool published as part of this project allows the user to try similar scenarios at the individual transit agency level.