Ensuring Greener Mobility is Fairer Mobility: a measurement of income’s effects on low-carbon transportation access in Seattle, WA

by Christopher Randels

Anthropogenic carbon dioxide (CO2) emissions are a principal cause of global warming (Schellnhuber et al. 2006). In Seattle, passenger vehicles account for over half of citywide CO2 emissions (Seattle OSE 2016), so reducing emissions in the urban mobility sector can be a key area for climate change mitigation. Emerging trends in micromobility and vehicle sharing provide new opportunities for lower-carbon transport, but not all alternate transportation modes to the single occupant vehicle (SOV) are equal in their emissions reductions, and access to these modes may not be distributed equally within cities. Inequities in transportation access can further exacerbate inequities in health and quality of life outcomes already present if higher income neighborhoods have better mobility access to goods and services (Allen & Farber 2019; Blumenberg 2017).

As Seattle continues to reduce its emissions and residents’ vehicle miles travelled (VMT), the city will need to determine which populations are primarily bearing the costs of this transition. With this study, I sought to measure such cost inequities by creating hypothetical routes to various destinations and services from Seattle’s highest and lowest income neighborhoods across multiple transportation modes: walking, biking, transit, driving, bikeshare (Lime and Jump Bike), carshare (Car2Go/ShareNow), and ridehailing (Uber). By measuring the costs, emissions, and travel times for a large number of destinations across transportation modes, I sought to answer two questions. First, how diferent are per-person emissions for the same routes between diferent travel modes? Secondly, are there disproportionate barriers for residents of lower income neighborhoods in using greener transportation options, as represented by cost and time?

I used city demographic data to select 10 neighborhoods (Community Reporting Areas) including the f-ve lowest income (Judkins Park, Pioneer Square/International District, South Beacon Hill/New Holly, South Park, and University District) and the five highest-income neighborhoods (Laurelhurst/Sand Point, Madison Park, Madrona/Leschi, Magnolia, and Montlake/Portage Bay). I then chose 10 points selected at random from residential areas in each neighborhood to serve as starting locations. For each point, I selected 14 destinations representing important points of interest and services a resident might access in their day-to-day life. Eight of these destinations represented places important to the entire city (e.g. major employment centers, universities, healthcare specialists, and stadiums) and thus remained the same across all neighborhoods. To represent access to local services, I used Google Earth to choose the nearest (that with the shortest straight line distance) post office, park, library, grocery store, supermarket, and super store to the starting location. Location data for these local destinations were gathered from the following sources: US Postal Service, Seattle Public Library, King County Public Library, City of Seattle Parks Department, and the Center on Budget and Policy Priorities websites.

In total, I analyzed 1400 routes across 7 different transportation modes and noted the distances, times, and calculated monetary costs and CO 2 emissions per route across modes. For walking, biking, transit, and driving, I used a script that called on real-time Google Maps data to determine the shortest route. For shared mobility routes, I used a GPS Location Modifier application to alter my smartphone’s perceived location and see realtime vehicle availability at each starting location across Lime, Jump, ShareNow, and Uber. I then calculated the requisite walking times/distances to access the closest vehicle available and the resulting trip times/ distances using a similar Google Maps script. For Uber, I also noted the waiting time returned by the app and added it to the total trip time. All data were collected between 10:00 am and 12:00 pm or 1:30 pm and 3:30 pm on Tuesdays, Wednesdays, and Thursdays between November 5th and December 19th , 2019 (excluding holiday weeks) to minimize variances due to weekend events and rush hour traffic. For each route, I used a combination of distance, time, or in-app data to calculate the total trip cost ($) and CO2 emissions. Walking, biking, and bikeshare were assessed without emissions, and the former two modes were assessed to be free. Driving costs were calculated at the 2019 IRS reimbursement rate of 58 cents per mile, and carshare emissions were assessed based on a vehicle MPG of 28 MPG (the average fuel economy of the Mercedes suite of vehicles used by ShareNow).

Figure 1: Study Area map, including the five highest-income Community Reporting Areas (CRAs) and the five lowest.

Figure 2: Emissions of all routes across four transportation modes. Walking, biking, and bikeshare trips were assessed to be without emissions. Modes marked with different letters are significantly different from each other at p < 0.05.

Uber trips result in more emissions than any other transportation mode (p < 0.001). In Seattle, emissions for an Uber trip are 88% higher than those for the same trip by private car. This diference is attributed to deadheading, the time during which ridehail drivers navigate to a pickup location without any passengers (Union of Concerned Scientists 2020). Uber trip emissions could be mitigated by incorporating a lower emission mode, such as walking, for part of the trip. However, all measured Uber trips never required any walking to a pickup destination. In contrast, some shared mode trips involved walking part of the route to access the vehicle, which in many cases reduced the distance traveled while using the vehicle (and thus total trip emissions). Additionally, all other modes use a vehicle that emits less CO2 per passenger-mile than private cars, further increasing the diference in emissions between these modes.

For local destinations, lower income neighborhoods mostly had fewer barriers in using alternate transportation modes than higher income neighborhoods, as measured both by trip cost and time. Access to all destination types (with the exception of parks) by lower emission transportation modes was easier in lower income than higher income neighborhoods. However, these trends are not distributed equally across neighborhoods within the same income class. For example, South Park, a lower income neighborhood in South Seattle, had the highest median time or median cost between all studied neighborhoods across 5 transportation modes (walking, biking, driving, bikeshare, and Uber). This stands in stark contrast with the more centrally-located lower-income neighborhood of Pioneer Square/International District, which had the lowest median time and cost across all mode metrics (except carshare trip cost). This indicates that other metrics, such as density, zoning, and distance to the city’s center, may play a more important role in destination access than income level exclusively. Also important is that the effect of these factors does not impact each destination type equally. For routes to parks, for example, Pioneer Square/International District exhibited some of the highest costs and times across all transportation modes (in spite of the neighborhood’s central location). With further analysis, dense development in this neighborhood means that the nearest park fitting the study criteria was Cal Anderson Park, over 2 kilometers away. This stands in contrast to higher-income neighborhoods like Magnolia and Laurelhurst/Sand Point; with a higher proportion of single family zoning in these neighborhoods, residents’ access to open spaces like parks and recreation opportunities are easier whereas access to goods and services are more restricted.

Although per-neighborhood modeshare data is not published by the City of Seattle, other studies have found that making lower-carbon travel modes more convenient by reducing their associated costs (monetary and time) relative to driving can increase their usage (Shaw 2015; Maghelal 2011; Brown et al. 2013). Elected officials in other cities such as Paris and London have advocated for denser local development and construction of bike infrastructure to encourage non-SOV transportation (O’Sullivan 2020; Reid 2020). Seattle can follow their example and encourage further modeshift by easing zoning restrictions, increasing local development, and implementing infrastructure modifications to make the associated costs of choosing alternate transportation modes closer to those of driving. Although the divide in modeshare access may not be as clearly determined by income as previously thought, such density-increasing proposals must ensure that the costs of such improvements (noise, gentrification, loss of neighborhood character, etc.) are not disproportionately borne by lower-income communities. By evaluating these transformations through an equity lens, city officials can ensure that the benefits of greener urban development and lower-carbon multimodal transportation options are reaped by all residents, regardless of income.

Figure 3: Route times for different destination classes between high and low income neighborhoods across modes. In pairs marked with *, route times from higher income neighborhoods were significantly greater than low income route times at p < 0.05. In pairs marked with °, route times from lower income neighborhoods were significantly greater than high income route times at p < 0.05.