11 Environmental Impact Assessment: Data, Methodological Approaches, and Examples

INTRODUCTION

This chapter is divided into two sections. The first section describes environmental, economic, or social impact assessments to evaluate the implications of transportation for cities, people, and greenhouse gas emissions. This section describes quantitative, qualitative, and mixed-method approaches to inform environmental, economic, or social impact assessments. The second section focuses on environmental impact assessments of GHG emissions in transportation. The third section presents exercises and examples for estimating the greenhouse gas emissions associated with transportation systems, commuting patterns, and city planning factors. The first exercise calculates the carbon footprint of faculty, students, and staff related to their commutes from their residences to the University of Texas at Arlington. The second exercise uses a life-cycle assessment perspective to compare GHG emissions associated with transportation systems, including cars, carpooling, the subway system, and buses.

Methodological Approaches

Research approaches can be categorized according to how planners address research questions. They can use qualitative, quantitative, or mixed-method approaches in planning research to inform environmental, economic, or social impact assessments. In addition, literature reviews may help reveal methods that previous scholars used to address similar research questions and issues. The following section briefly describes the qualitative, quantitative, and mixed process approaches commonly used in planning research.

Qualitative approaches

Qualitative approaches help researchers understand how and why communities and cities are impacted or benefited by planning. For example, researchers may use ethnographic methods to reveal people’s stories, perceptions, and everyday experiences (Creswell, 2003b). The following section discusses case studies and interviews as two methodological approaches of qualitative research that can inform environmental, economic, and social impact assessment in residential land use and transportation.

Researchers use case studies to explore a well-delimited community, city, or a set of communities over time through detailed and in-depth data collection processes involving multiple information sources (Creswell, 2003b). Researchers should strategically select case studies to make more generalized conclusions from the data collected (Francis et al., 2010). Also, interviewing is part of qualitative research that allows interaction between researchers and participants (Denzin & Lincoln, 2008). Interviews require more careful consideration of how to access the study context and an understanding of the language and culture of respondents. Participants could become partners who guide researchers in research design (Sletto, 2012).

Quantitative approaches

Quantitative research is based on empirical and statistical analyses conducted to understand the relationships among the variables that explain situations and phenomena (Shadish et al., 2002). For example, in transportation planning, scholars often use urban form variables, such as population density and interconnectivity and people’s income and ethnicity, to understand how transportation systems support mobility. One standard method that planners use to examine quantitative data is surveys. A survey or a questionnaire is a research tool, the primary function of which is to enable the measurement of data (Oppenheim, 1992). Some surveys inform databases that planners use, such as the American Community Survey, the origin-destination survey, or a survey that the researchers designed to explore commuting practices in a selected community. Databases may be publicly available or require empirical research and intensive fieldwork.

Mixed-method approaches

Mixed-method approaches include both qualitative and quantitative methods (Creswell, 2003a). Mixed-method approaches combine the advantages of both quantitative and qualitative methodologies to strengthen the validity and reliability of data. John Gaber highlights the benefits of mixed-method design, in which a researcher triangulates and corroborates outcomes from both quantitative and qualitative methods and increases the validity of results. A mixed-method research design complements the products obtained by qualitative and quantitative approaches. It enriches the understanding of the phenomenon being studied, allows comparisons and revisions, and improves the quality of questions and results from one research approach to others.

Environmental Justice Assessment in Transportation

Bullard (1997) argues that low-income communities of color disproportionately endure the highest transportation burdens. They usually live in communities that lack easy access to efficient and affordable systems and have less access to reliable private vehicles to get to places. Also, historically, low-income African American communities were disproportionately affected by the construction of highways (Bullard & Johnson, 1997). Despite the progress in transportation justice policies in the last five decades, low-income communities of color continue to live far from employment centers located in suburban locations. Low-income commuters often have unreliable cars and thus depend on public transit or affordable alternative car-sharing programs. On the other hand, wealthier communities benefit from transportation improvements, investments, policies, and subsidies, while others remain neglected (Bullard & Johnson, 1997).

To support transportation justice for vulnerable communities, planners should conduct empirical environmental justice assessments to reveal how transportation infrastructure supports or undermines community economic and social development. More importantly, ecological justice assessments should focus on low-income communities and have a comprehensive approach that includes housing and transportation. The evaluation should identify the obstacles that undermine mobility. This approach may also help reveal opportunities to increase affordable housing developments with access to transportation systems.

The National Environmental Policy Act (NEPA) provides a helpful guide to community-based environmental justice assessments[1]. According to NEPA, these assessments should use a comprehensive and dynamic approach that includes the following concepts:

• Meaningful engagement of vulnerable communities
• A careful understanding of the affected area or environment
• Identification of minority and low-income populations
• Impact Analysis of Communities
• Identification of disproportionately high and adverse impacts on vulnerable communities and ecosystems,
• Alternatives to mitigate the social and environmental harms
• Mitigation and monitoring.

Planners play a significant role in encouraging the meaningful involvement of vulnerable communities in environmental justice assessments. However, engagement may overlook and severely affect identified minority and low-income populations. Although the ecological justice assessment promoted by NEPA and the U.S. Department of Transportation represents a significant step toward supporting environmental justice issues, these assessments lack a comprehensive socio-ecological approach. Thus, the scope of the NEPA analysis should be expanded to understand better how transportation projects influence the living conditions and environmental quality of communities and the built environment. This comprehensive approach may help envision effective mitigation solutions (NEPA, 2019).

Cost-benefit analysis

Cost-benefit analysis may also serve as a practical methodology to understand and balance investment and operational costs of transportation and their potential benefits and impacts on cities, communities, and the environment. For instance, public transit may improve communities’ and cities’ access to transit and provide efficient and affordable alternatives to private vehicles. The potential benefits of transit include reducing car driving and traffic and, thus, improving air pollution since some drivers may switch to transit or drive less. Another potential impact is improving the household economies of low-income commuters who may have better access to transit services. Those using cost-benefit analysis should carefully determine the scope of their research to understand how city investments in transportation provide economic development, environmental quality, and social justice benefits. (Chapter 7 provides insights into the cost-benefit analysis of railroad and high-speed rail systems.)

Environmental Impact Assessments in Transportation: The Exercises

The third section presents exercises and examples for estimating the greenhouse gas emissions associated with transportation systems, commuting patterns, and city planning factors. One of the most essential methodological considerations is to explain the scope of the analysis and determine the units of study. Next, researchers may proceed to identify data that may inform their assessments. The exercises presented in this section solely use quantitative research approaches and publicly available data. These assessments may use the Census Bureau on transportation statistics or the American Community Survey. Finally, researchers can develop their calculations or use calculation sheets to help them elaborate on environmental impact assessments.

The Carbon Footprint of Daily Commutes to Work or School

The first exercise calculates the carbon footprint of students from the University of Texas at Arlington (UTA). This exercise seeks to show the necessary data that needs to be collected to address the problem. Data includes the average distance from students’ homes in the Dallas-Fort Worth Metroplex to UTA. Another essential element in determining the transportation mode these commuters use to get to school is their vehicles’ fuel-efficiency characteristics. Table 11.1 shows some GHG emission factors to estimate the carbon dioxide emissions per passenger mile developed by EIU (2008).

The following equation helps estimate carbon dioxide emissions (one-way) trips from a student’s residence to UTA. This equation uses the daily one-way distance between home and work in (km) and then multiplies this distance by two for a round-trip estimate. Then, researchers can use Table 11.1 to identify vehicle-specific emission factors (kg CO2/vehicle-km or kg CO2 / passenger-km).

Carbon footprint in a trip:

Sources: Demographia, 2005; EIU, 2008; O’Toole, 2008.

Table 11.2 shows the carbon footprint of a typical trip conducted by a former student in a class titled “Green Cities and Transportation.” The student usually commutes 29 miles from his Dallas, Texas, home to UTA. On average, his one-way trip’s carbon footprint is 10.61 kg CO2. Table 11.2 also shows scenarios considering carpooling and using alternative, fuel-efficient vehicles. Overall, carpooling in a Toyota Prius would reduce the carbon footprint from 10.61 to 0.86 kg CO2 for a daily, one-way trip. This, in turn, means that carpooling an efficient vehicle is nearly 12 times more efficient than driving solo in an average car. Highly occupied vehicles incentivizing sustainable and perhaps more equitable commuting practices may also significantly reduce GHG emissions.

The Carbon Footprint of Commutes to UTA (Using the GHG Protocol Initiative)

The GHG Protocol Initiative offers calculation sheets and methodologies to help cities, communities, industries, and companies develop inventories of greenhouse gas (GHG) emissions associated with the energy-use practices of people in buildings and vehicles. Assessment of GHG emissions in transportation considers three categories of related GHG emissions. Scope 1 includes vehicles owned/controlled by organizations, companies, or institutions. For instance, at UTA, the MAV Movers are governed by the university and may be considered vehicles in Scope 1. Scope 2 emissions include GHG emissions associated with the organization’s electricity consumption, although emissions are produced elsewhere. Scope 3 refers to the trips conducted by the people who work in an organization. For example, students and staff who commute from their homes to UTA may be considered Scope 3 because they are associated with the university and thus indirectly impact GHG emissions.

The GHG Protocol uses two approaches to estimate GHG emissions: 1) the average distance of trips and 2) fuel consumption. The first approach uses the same methodology already explained in the previous exercise. The latter requires a careful understanding of vehicles’ characteristics and fuel economies. In this section, we explain methods to estimate vehicles’ fuel consumption.

Vehicles’ Fuel Consumption

The U.S. Department of Energy, Office of Energy, and Renewable Energy offer calculation sheets to compare the fuel usage associated with vehicles according to their make, year, and characteristics. For example, the MPG estimates for a 2017 Ford F150 pickup range from 22 to 23 MPG, whereas those for a 2017 Toyota Prius range from 49 – 64 MPG. This, in turn, suggests that the Toyota Prius is twice as fuel-efficient as the 2017 Ford. As a result, this fuel-efficient vehicle would allow commuters to use less gasoline than the pickup.

To address this exercise, researchers can use the average estimate of fuel efficiency: 22.6 MPG for the 2017 Ford F150 and 56.3 MPG for the 2017 Prius. Then, we estimate the gasoline consumption of these two vehicles using a daily commuting distance of 29 miles for a trip from Dallas to the UTA campus.

1.  Link to download the Transportation Calculation Sheets developed by the GHG Protocol: https://ghgprotocol.org/calculation-tools#sector_specific_tools_id
2. https://www.fueleconomy.gov/
3. https://www.fueleconomy.gov/feg/browseList.jsp?src=feg

To estimate fuel consumption associated with the one-way trip, we can use the following equation: Fuel consumption = distance in miles/fuel efficiency (miles per gallon). For example, the fuel consumption of the 2017 Ford F150 is 1.31 gallons, whereas the 2017 Toyota Prius is 0.51 gallons.

Table 11.4 illustrates the GHG estimate associated with a one-way trip (29 miles) from Dallas to UT Arlington using different methods. The first approach uses the distance method, considering a gasoline car (2005-present model) and a 29-mile trip. The second approach uses the fuel consumption approach. It considers the estimates of gasoline consumption for the 2017 Ford F150 and Toyota Prius, while the third approach considers both distance and fuel consumption. Table 11.4 shows that the assessment of GHG emissions associated with the trip resembles the third approach, which carefully considers the distance traveled and the specific fuel consumption associated with the vehicle. Using the GHG Protocol calculation tool for mobile sources, we estimate a carbon footprint of 11 kg CO2eq associated with a journey to UTA. The GHG Protocol tool provides a disaggregated estimate for CO2 emissions and other greenhouse gases, including CH4 and N2O. This tool can help us develop scenarios that compare the implications of a vehicle’s MPG and distance traveled. For instance, the contribution to GHG emissions of a 2017 Toyota Prius (4 kg CO2e) is only a third of the assistance of a 2017 Ford F150 (12kg CO2e). This, in turn, reveals the GHG mitigation potential of fuel-efficient vehicles. Planners can use this calculation tool to estimate GHG emissions related to transportation. However, fine-grained data on distance and fuel consumption could inform a more accurate and specific analysis, but it requires additional research.

Source: Author (2022) using the GHG Protocol Calculator, Mobile Combustion

Second Exercise: The Journey to Work in U.S. Cities Journey to Work in U.S. Cities

The second exercise compares commuting patterns associated with workers across various U.S. cities, including Dallas, Minneapolis-Saint Paul, and Philadelphia. This exercise contrasts the journey to work, the means of transportation they use, and the distance traveled while controlling for the socioeconomic characteristics of commuters. This exercise seeks to teach researchers to gather and explore transportation and commuting data on transportation.

Planners can use various databases on transportation and commuting practices to understand how commuters get to places in the U.S. For this exercise, we explore the U.S. Census Data’s American Community Survey: Journey to Work.[4]

To address this exercise, you may use some of the questions that participants respond to in the ACS, which include S0802 Means of transportation to work by selected characteristics. Then, the metropolitan statistical area will be used as a geography of analysis. Table 11.5 summarizes the means of transportation workers older than 16 who were used to get to places in three urban statistical areas used by race. This table reveals significant differences in the share of private vehicles used by white workers and their Hispanic, African American, and Asian counterparts.