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Transportation Ecoefficiency: Quantitative Measurement of Urban Transportation Systems
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Transportation Ecoefficiency: Quantitative Measurement of Urban Transportation Systems

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Presentation at the 2012 Association of American Geographers Annual Meeting. Paper Session: Modelling and Visualizing Travel Behavior II (Sponsored by Transportation Geography Specialty Group).

Presentation at the 2012 Association of American Geographers Annual Meeting. Paper Session: Modelling and Visualizing Travel Behavior II (Sponsored by Transportation Geography Specialty Group).

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  • Hello. My name is Anna McCreery and I’m going to discuss a measure of transportation that I designed, and called Transportation Ecoefficiency (or TE).
  • I will begin this presentation by discussing the metrics currently used for measuring the environmental impact of transportation systems. I will then present a new metric that I designed, for measuring Transportation Ecoefficiency by proxy. I will discuss the components of this metric and the results of a factor analysis evaluating the components. I will then discuss transportation trends in US metro areas using the TE index measure, and conclude with some recommendations for using it in research. ** I welcome criticisms and comments on how to improve this metric, any other uses for it, and on how it fits with the rest of the literature.
  • Given the contribution of transportation to various environmental problems, we need a lot of research (a lot more, really) on transportation systems and how to improve them to work better for people and the environment. Although there are many great studies in the literature using micro-level details of transportation systems, there are fewer studies of macro-level processes. This is an important gap in the transportation toolbox, and we need a macro-level measure of transportation’s environmental impact that can be constructed for a variety of countries, time periods, and geographic levels.
  • Now, there are a few other measures of transportation that can be used for macro-level research, but they are subject to some limitations. I’m going to focus on comprehensive metrics that combine multiple transportation system characteristics to approximate the various environmental impact of transportation. First, the ecological footprint of transportation measures the land that would be needed to sustainably support a given transportation system in a given area. It can include a wide variety of features and environmental impacts, but it requires quite a lot of data to compute. Fuel or carbon intensity metrics focus on one kind of environmental impact, but they’re relevant because it’s a particularly large impact. This includes measures of fuel used or carbon emitted per unit of travel, such as vehicle kilometers or person kilometers. These measures are very good for comparing transportation modes or technologies, but somewhat less useful for macro-level studies.
  • In short, the metrics currently used in the literature are subject to some limitations. They often require data that isn’t available at smaller geographic scales such as counties or metro areas, or isn’t available historically or in some countries. Instead, I designed a metric that can be constructed with readily available data, to capture the overall environmental impact of transportation.
  • I’ve called this metric transportation ecoefficiency, or TE. Basically, I applied the concept of ecoefficiency to transportation, which hasn’t been done much before. This metric is designed to capture the environmental impact urban transportation, per unit of travel. To measure this concept, I constructed a proxy is based on based on 4 components, which I will discuss in the next few slides. A proxy measure is reasonable for this, because no direct measure of ecological impacts could capture the widely varying effects of transportation on climate, hydrologic systems, air quality, and other ecological system. A proxy measure is therefore more useful for holistically assessing transportation because a single metric can be used as a proxy for a variety of environmental effects. For example, increased drive-alone commuting will add to all of transportation’s harmful environmental effects. This TE proxy thus captures a more holistic assessment of the many, varied environmental impacts of transportation.
  • First, Pop density is included to approximate the likely travel distances in a metro area. Other literature shows that this is a reasonable approach. For example, Ewing & Cervero 2010 is a meta-analysis of studies on transportation and the built environment, concluding that trip distance is primarily a function of the built environment, and trip lengths are generally shorter with higher densities. Additionally, Naess 2006 found that although population density has no significant effect on commute distances in Copenhagen net of other built environment variables, higher densities tend to occur in areas with other features that reduce commute distances. Population density can therefore act as a proxy for other features of the built environment that influence travel behaviors, including trip distances.
  • Next, the 3 commuting components capture the expected ecoefficiency of transportation different modes. Commuting is the most basic daily travel, co-varies with other trips, etc. The 3 commuting modes have very different environmental impacts, and therefore different degrees of ecoefficiency Commuting by driving alone, which is highly eco-inefficient, so its sign it reversed Commuting by public transit varies in its ecoefficiency but is usually much more ecoefficient than driving alone Commuting by walking and bicycling both of have near-zero environmental impacts, so they’re combined to make the 4th component, To sum up: Taken together, these 4 components can be used as a proxy for the overall ecoefficiency of a given transportation system, by giving a broad overview of how people travel. To construct this measure, you simple average the z-scores of the 4 components (with the sign reversed for the drive-alone commuting z-score)
  • To test the effectiveness of this measure, data was compiled for a factor analysis of the components, and an analysis of trends in TE for US MSAs. I’ll begin with some summary statistics for the 4 components, in 2008. After that I’ll show you a confirmatory factor analysis of the 2008 data, and then discuss trends in TE from 1980-2008.
  • When we average the z-scores of these 4 components (with the sign for drive along commuting reversed), we get the TE index. A higher TE index for a given metro area indicates lower environmental impact per unit of travel.
  • The confirmatory factor analysis shows that these components load onto 2 factors. The 1st factor combines population density with commuting by public transit. This makes a lot of sense because the feasibility of high quality transit service is closely related to population density, and high quality service will be used more often.
  • The 2nd factor includes commuting by driving alone and commuting by walking or bicycling.
  • Although these components load onto 2 factors, it still makes sense to combine them into one index. The 4 components are so closely related that it would make less sense to separate them. Given what we know about the operation of cities and transportation, these 4 components are clearly connected enough to make a coherent index.
  • This is important because is the huge increase from 1980-2008 in drive-alone commuting. This increase is at the expense of more ecoefficient modes. So while drive-alone commuting increased by over 10%, walking and bicycling commuting was nearly cut in half. Note: The “other modes’ category includes things like working at home, commuting by taxi, motorcycles, and a number of others. Their degree of ecoefficiency is less clear in comparison to the modes that are included.
  • Bringing the discussion back to the index as a whole, the overall TE index captures these commuting trends and shows us a clear pattern of change over time. Z-scores for these indices were calculated using means and standard deviations from the full sample of 225 US MSAs in all four census years. The main point here is that the overall decline in average TE across US MSAs can be seen easily when looking at the TE index score, which declines by ~half a standard deviation from 1980-2000. Most of this trend is driven by increases in drive-alone commuting, at the expense of transit and walking or bicycling. Drive alone commuting follows a parallel trend to TE overall, increasing from 1980-2000 and stabilizing at that point. Population density follows a less consistent trend, declining slightly from 1980-1990, increasing by over 60 people per square mile from 1990-2008, and then declining slightly after that. The main point here is that the TE index shows a clear trend over time, that corresponds with the ecoefficiency of urban transportation. This information can then be used to analyze the drivers of this trend.
  • Analyses that must be completed quickly, unlike my dissertation. (haha) In short, this is a really practical metric, and is therefore useful outside the academic context, for organizations that need close-enough results as quickly as possible.
  • The other main strength of this metric is that it’s a macro-level measure. Most of the literature on transportation uses micro-level measures, and we need more research on broad social forces that impact transportation. I’ve noted some examples here from a variety of disciplines, of research that could make use of this metric. Final note: the most important aspect of any transportation system is how people use it. By measuring that directly, we can proxy the ecological efficiency of transportation.
  • Skip this slide if there’s no time. I’ve actually done a study using this measure, and I’m in the final stages of writing it. In short, I conducted a large-N analysis of change in TE from 1980-2008, in the sample of US MSAs. Some of the more interesting results include: -A significant quadratic of % African American -A positive effect of state policy requirement urban growth management, which implies that regional planning can have beneficial outcomes -The influence of the Creative Class, which is related to Terry Clark’s theory of the New Political Culture and Herman Boschken’s theory of Upper-Middle Class influence. There are several significant effects that provide some support for these theories -And census region, which is not only directly influential, but also alters the significance of other factors. This could be due to cultural variation. So basically, there’s some really interesting research that I’ve done with this metric, and more studies are needed.

Transportation Ecoefficiency: Quantitative Measurement of Urban Transportation Systems Transportation Ecoefficiency: Quantitative Measurement of Urban Transportation Systems Presentation Transcript

  • Transportation Ecoefficiency By Anna C. McCreery Quantitative Measurement of Urban Transportation Systems
  • Presentation Overview
    • Measures of urban transportation used in the literature
    • A new metric for Transportation Ecoefficiency (TE)
      • 4 TE components
      • Factor analysis of TE index
    • TE trends in US metro areas
    • Strength of TE for empirical research
  • Measuring Transportation
    • Studying transportation and building smarter cities requires good measures of transportation
      • Many micro-level metrics in the literature
      • Macro-level metrics less well established
    • We need a macro measure of transportation’s environmental impact with data available:
      • At many geographic levels
      • In many countries
  • Measuring Transportation
    • Ecological footprint of transportation
      • Land needed to sustainably support a given transportation system 1
    • Fuel or carbon intensity
      • Fuel used or carbon emitted per unit of transportation
      • Fuel used per vehicle Km or person Km,
      • Carbon emitted per fuel used
    1 Holden and Hoyer 2005, Wackernagel and Rees 1996 2 Kaufmen et al. 2010, Yang et al. 2009
  • Measuring Transportation
    • Limited data availability:
      • At some geographic levels
      • In some countries
      • Over time
    • Transportation Ecoefficiency (TE) a macro-level metric to capture the overall environmental impact, with readily available data
  • Transportation Ecoefficiency
    • Environmental impact of transportation, per unit of travel
    • Measured by proxy as the index of:
      • Population density
      • % of commuters driving to work alone
      • % of commuters taking public transit
      • % of commuters walking or bicycling
  • Measuring TE: Pop. Density
    • Proxy for travel distance 1
    • Associated with other built environment features that affect travel 2
    1 Ewing and Cervero 2010 2 Cervero 2007, Ewing and Cervero 2010, Naess 2006
  • Measuring TE: Commuting
    • Commuting:
      • A major share of personal travel
      • The most basic and fixed form of daily travel
      • Likely to co-vary with other trips
    • Different commute modes have vastly different environmental impacts:
      • Driving alone is very eco-inefficient
      • Public transit, walking, and cycling are generally more ecoefficient modes
  • Measuring TE: data & sample
    • Sample: 225 U.S. Metropolitan Statistical Areas (MSAs), from 1980 to 2008.
    • Source: Census data and American Community Survey
  • TE in US Metro Areas * People per square mile For 225 U.S. MSAs, in 2008: Variable Mean Std. Dev. Population Density* 359.95 400.50 Commuters driving 78.23% 4.54% Commuters taking transit 2.16% 2.92% Commuters walking/bicycling 3.35% 1.83% TE Index 0.023 0.783
  • Confirmatory Factor Analysis Variable Factor 1 Factor 2 Unique-ness Pop. Density (z-score) 0.760 0.048 0.420 % of commuters driving alone (z-score, sign reversed) 0.451 0.736 0.256 % of commuters taking public transit (z-score) 0.783 0.454 0.181 % of commuters walking or bicycling (z-score) 0.046 0.657 0.567 Eigenvalue 1.397 1.181
  • Confirmatory Factor Analysis Variable Factor 1 Factor 2 Unique-ness Pop. Density (z-score) 0.760 0.048 0.420 % of commuters driving alone (z-score, sign reversed) 0.451 0.736 0.256 % of commuters taking public transit (z-score) 0.783 0.454 0.181 % of commuters walking or bicycling (z-score) 0.046 0.657 0.567 Eigenvalue 1.397 1.181
  • Confirmatory Factor Analysis
    • Two factors:
      • Population density and % of commuters taking public transit
      • % of commuters driving alone (sign reversed) and % of commuters walking or bicycling
    • Close conceptual & statistical relationship between the 2 factors
  • TE Trends: Commuting
  • Overall TE Trends Change in average TE index: 0.280 0.134 -0.210 -0.204
  • Conclusions: Strengths of TE
    • Data for TE is widely available:
      • In many countries
      • At many geographic levels
    • Simple to calculate, so useful for analyses that must be completed quickly
  • Conclusions: Strengths of TE
    • For analyzing broad social forces:
      • Theories of politics and social change
      • Effect of planning processes
      • Economic choice theories
      • Distal processes, that happen before specific policy or behavioral choices are made
      • Factors likely to affect urban planning and individual behaviors.
    • Highly detailed data on specific transportation features less useful for studying macro-level social forces
  • Example TE Study
    • Large-N analysis of change in TE for US metro areas
    • Preliminary results show significant effects from:
      • race
      • state-level policy
      • creative class: % professional workers, high and rising real per capita income
      • census region, likely due to cultural variation
  • References
    • Cervero, R. (2007) “Transit-Oriented Development’s Ridership Bonus: A Product of Self-Selection and Public Policies,” Environment and Planning A 39: 2068-2085.
    • Ewing, R., and R. Cervero. (2010) “Travel and the Built Environment: A Meta-Analysis,” Journal of the American Planning Association 76(3): 265-294.
    • Holden, E., and K.G. Hoyer. (2005) “The Ecological Footprint of Fuels,” Transportation Research Part D 10: 395-403.
    • Kaufman, A.S., P.J. Meier, J.C. Sinistore, D.J. Reinemann. (2010) “Applying Life-Cycle Assessment to Low Carbon Fuel Standards: How Allocation Choices Influence Carbon Intensity for Renewable Transportation Fuels,” Energy Policy 38: 5229-5241.
    • Naess, P. (2006) “Accessibility, activity participation and location of activities: Exploring the links between residential location and travel behaviour,” Urban Studies 43(3): 627-652.
    • Wackernagel, M., W. Rees. (1996) Our Ecological Footprint: Reducing Human Impact on the Earth . New Society Publisher, Gabriola Island.
    • Yang, C., D. McCollum, R. McCarthy, and W. Leighty. (2009) “Meeting and 80% Reduction in Greenhouse Gas Emissions from Transportation by 2050: A Case Study in California,” Transportation Research Part D 14: 147-156.
  • Acknowledgements Colleagues Dr. J. Craig Jenkins Dr. Ed Malecki Dr. Maria Conroy Funding & Resources Ohio State University Dept. of Sociology Ohio State University Environmental Science Graduate Program The Fay Graduate Fellowship Fund in Environmental Sciences