I will begin this presentation by discussing the measure I’ve constructed to assess Transportation Ecoefficiency of urban areas by proxy. I will briefly discuss TE trends in US metro areas. I will finish by presenting the results of my analysis of social and political drivers of change in urban TE.
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 literature, and I aim to fill that gap with a macro-level study of broad social and political forces that help determine urban transportation’s environmental effects.
To conduct this research, I designed a measure called transportation ecoefficiency, or TE. This metric is a proxy designed to capture the environmental impact urban transportation, per unit of travel. 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. Although I don’t have time to discuss this measure in depth, a thorough literature review indicates that the built environment and travel modes as the primary determinants of transportation’s environmental impact, thus the ecoefficiency of urban transportation. 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). A higher TE index for a given metro area indicates lower environmental impact per unit of travel. Pop Density: Proxy for travel distance 1 Associated with other built environment features that affect travel 2 Pop density is included to approximate the likely travel distances in a metro area. Other literature shows that this is a reasonable approach. 1. 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. 2. 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. 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 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,
The Cronbach’s alpha for the 2008 index is 0.779, indicating high internal consistency within the metric. NOTE: The index means here were calculated using means and standard deviations from all 225 MSAs in both years, for comparison.
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. So the overall picture is one of increased driving alone, and therefore declining TE. This 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.. In other words, 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. However, despite this clear trend there is a decent degree of variation between metro areas, in terms of how they’ve changed over time. My analysis investigates that variation to determine how we might be able to encourage increase TE.
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 both 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.. In other words, 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. However, despite this clear trend there is a decent degree of variation between metro areas, in terms of how they’ve changed over time. My analysis investigates that variation to determine how we might be able to encourage increase TE.
For this analysis, I used a general linear regression to predict change in TE in US MSAs from 1980-2008. The independent variables include a variety of social, economic, and political variables measured in or near 1980. This long time lag is effective because many of the factors that determine travel behavior change slowly.
The New Political Culture theory is very similar to related theories such as Richard Florida’s Creative Class and Herman Boschken’s theory of Upper-Middle Class influence. In short, all of these theories predict that having certain kinds of people in a given metro area will affect the local political scene, and lead to an emphasis on public amenities like public transit, aesthetic investments, and similar things. So a “new political culture” should be associated with increased TE. Mostly, that’s what my results show. The big picture here is that professionalization, education and wealth are overall beneficial for TE, and these variables are all associated with the new political culture. Have more college grades, higher income, and rising income all produce an increase in TE (or a smaller decline in TE compare to the rest of the country). Although I decided not to show the numbers here, there is also a significant and positive interaction between real income per capita and % change in real income per capita. In other words, metro areas with high and rising incomes tend to have even more ecoefficient transportation systems, and this provides strong support for the theory. However, although the theory would predict a positive, linear relationship between TE and % professional workers, my results show a quadratic relationship. I expect that this is because metro area with a very high proportion of professional workers have a culture that emphasizes workplace status, and you can’t use that fancy car to gain status among your colleagues if you don’t drive it to work. NOTE FOR ME: These results are from model 4, the pared down full-U.S. model with regional controls. Also, the % prof/tech workers significant is from the f-test for joint significance, not from their individual significance.
A lot of the planning literature indicates that coordinated land use and transportation planning at the regional scale should improve planning outcomes, and therefore increase TE. However, there hasn’t been as much research on the outcomes of regional planning as I would like. To test the effects of regional planning I used a quantitative measure of state-mandated urban growth management (in other words, the state requires metro areas to engage in growth management, which is a kind of regional planning). State-centered theory predicts that state capacity for implementing innovative solutions is enhanced by cohesive local government, and that eliminated multiple competing governing bodies makes policy and outcomes more efficient. In this case, g overnment fragmentation at the local or state level implies less coordinated planning, which should be associated with lower TE.
First, state-mandated urban growth management does have a significant, positive effect. There is also a significant and positive interaction between state-mandated urban growth management and real income per capita, which probably means that community wealth helps with the implementation of growth management policies. Second, local government fragmentation actually has opposite the expected effect, and has a positive influence on TE change. This could be because there wasn’t adequate data for constructing a really good measure of the most relevant local government fragmentation, but this will have to be explored further in future research. Third, state government fragmentation has the predicted effect - MSAs that cross state boundaries, and are therefore subject to different state governance, declined in TE faster than MSAs contained within a single state. NOTE FOR ME: These results are from model 3, the pared down full-U.S. model without regional controls (since controlling for census region masks the effect of state-mandated urban growth management)
There is a significant quadratic effect of % African American, so that as the % African American increases, TE initially increases and then decline beyond a particular threshold. This could be because more racial diversity initially leads to more tolerance, but beyond the threshold more African Americans leads to more racial conflict in politics or residential location choices. There is also a significant, positive interaction between % African American and real income change. This could be because rising incomes reduces the potential for perceived racial threat, and makes White families less likely to flee to the suburbs. In other words, in MSAs with lower per capita income, a larger African American population is perceived as more of a threat (potentially driving white flight and longer, more car-dependent commutes) whereas a larger African American population in a metro area with higher per capita income is less threatening and more associated with tolerance (producing less white flight and more willingness to take the bus and potentially interact with diverse people). NOTE FOR ME: The significance is from the f-test for joint significance, not the separate significance for those variables. These results are from model 4, the full-U.S. model with census region controls.
Although census region wasn’t the only significant control, I’m going to discuss it here because it had a huge influence on the operation of other variables. First, western MSAs showed significantly higher TE change (and also higher TE in general). Additionally, controlling for census region altered the significance of other variables. In other words, the social forces that affect TE vary from region to region. This is probably because there is an unmeasured variable which varies by region, that influences the other relationships found in this analysis. I expect that this variable is either culture or climate, and this would be very interesting to investigate in future analyses.
Transportation Ecoefficiency: Social and Political Forces for Change in U.S. Metropolitan Areas
TransportationEcoefficiency Social and Political Forces for Change in U.S. Metropolitan Areas By Anna C. McCreery
Presentation Overview A new metric for Transportation Ecoefficiency (TE) 4 TE components TE trends in US metro areas Social & Political drivers of TE
Measuring Transportation Building smarter cities requires good research on transportation Many micro-level studies in the literature Macro-level research less well established This macro-level study investigates broad social forces that impact local transportation
Transportation Ecoefficiency Environmental impact of transportation, per unit of travel Measured by proxy as the index of: Population density1 % of commuters driving to work alone (sign reversed) % of commuters taking public transit % of commuters walking or bicycling1 Cervero 2007, Ewing and Cervero 2010, Naess 2006
Measuring TE: Data & Sample Sample: 225 U.S. Metropolitan Statistical Areas (MSAs), from 1980 to 2008 Source: Census data and American Community SurveyPhoto credits: http://bloximages.newyork1.vip.townnews.com/stltoday.com/content/tncms/assets/v3/editorial/c/e9/ce992098-0d5e-11e0-9cbd-0017a4a78c22/4d1140277e0fb.image.jpghttp://census2010.georgetown.org/files/2011/02/acslogo.gif
TE in US Metro Areas For 225 U.S. MSAs: 1980 2008 Variable mean meanPopulation Density* 320.3 360.0Commuters driving 67.9% 78.2%Commuters taking transit 3.21% 2.16%Commuters walking/bicycling 6.40% 3.35%TE Index 0.280 -0.204* People per square mile
TE Trends: Commuting 100% 16.26% 90% 22.48% other 3.35% other 2.16% walk 80% 6.40% transit bike walk 3.21 70% bike transit% 78.23% 60% 67.91% drive drive 50% 1980 2008 Other Modes % of commuters walking/bicycling % of commuters taking public transit % of commuters driving alone
TE Trends: the index Change in average TE index:0.40 0.2800.300.200.100.00-0.10 1980 2008-0.20 -0.204-0.30
Analyzing TE: data & methods Sample: 225 U.S. Metropolitan Statistical Areas (MSAs)1 Dependent variable: change in TE, 1980-2008 Analysis: general linear regression predicting TE change from various independent variables (measured around 1980)1 Data sources: U.S. Census, American Community Survey, National Historical GIS, and others
Results: New Political Culture New Political Culture theory: beneficial effects of educated professionals with high and rising incomes1 Variable Coef. % prof / tech workers 3.56 *** % prof / tech workers squared -0.71 *** % college grads 0.52 *** real income per capita 1.76 *** % change in real income per capita 0.70 * * p<0.05 ** p<0.01 *** p<0.0011 Boschken 2003; Clark & Harvey 2010; DeLeon & Naff 2004
Results: Government & Policy Coordinated regional planning expected to increase TE1 State policies requiring coordinated urban growth management2 should increase TE Government fragmentation (local and/ or state) implies less institutional coherence and less coordinated planning, so lower TE31 Cervero 2002, Ewing and Cervero 2010, Filion and McSpurren 2007, Handy 2005, Quinn 20062 Yin and Sun 20073 Amekudzi and Meyer 2006, Grodach 2011, Jenkins et al. 2006, Skocpol 1985
Results: Government & Policy Variable Coef. State-mandated urban growth 0.08 ** management Non-school local governments per 0.07 * capita MSA crosses state boundaries -0.16 * * p<0.05 ** p<0.01 *** p<0.001Photo Credits: http://www.memphistn.gov/media/images/gov2.jpghttp://soetalk.com/wp-content/uploads/2011/01/06senate2-600.jpg
Results: Race Race should impact local policy, housing, etc., and therefore also TE White Flight could reduce TE But….theory does not predict direction of influence Variable Coef. % African American 0.05 ** % African American, squared -0.03 ** * p<0.05 ** p<0.01 *** p<0.001
Results: Census Region Western region showed significantly higher TE change (increased TE or smaller decline): coef. = 0.36 Including census region altered the significance of other variables Indicating that other regional differences affect what factors influence TE Culture? Climate?Photo Credits: http://www.hatcountry.com/images/DesperadoStraw-1.jpghttp://3.bp.blogspot.com/-Cr3GNRnH0Sg/TwyCWE8zrwI/AAAAAAAAChE/oUZ1r6NYSy8/s1600/rainy+bus+stop.jpg
Main Contributions The TE concept and measure is a useful empirical tool Macro-level social forces impact urban transportation in significant and under- studied ways Grand sociological theories can lead to testable hypotheses and new insights about transportation
References Amekudzi, Adjo and Michael D. Meyer. 2006. “Considering the Environment in Transportation Planning: Review of Emerging Paradigms and Practice in the United States.” Journal of Urban Planning and Development 132(1): 42-52. Boschken H.L. 2003. “Global Cities, Systemic Power, and Upper-Middle- Class Influence” Urban Affairs Review 38(6): 808-830 Cervero, Robert. 2002. “Built environments and mode choice: toward a normative framework.” Transportation Research Part D- Transport and Environment 7(4): 265-284. Cervero, R. 2007. “Transit-Oriented Development’s Ridership Bonus: A Product of Self-Selection and Public Policies,” Environment and Planning A 39: 2068-2085. Clark, T.N. and R. Harvey. 2010. “Urban Politics” pp. 423-440 in: Kevin T. Leicht and J. Craig Jenkins, eds. Handbook of Politics: State and Society in Global Perspective New York: Springer. DeLeon, R.E. and K.C. Naff. 2004. “Identity Politics and Local Political Culture: Some Comparative Results from the Social Capital Benchmark Survey” Urban Affairs Review 39(6): 689-719.
References Ewing, R., and R. Cervero. (2010) “Travel and the Built Environment: A Meta- Analysis,” Journal of the American Planning Association 76(3): 265-294. Filion, Pierre and Kathleen McSpurren. 2007. “Smart Growth and Development Reality: The Difficult Co-ordination of Land Use and Transport Objectives” Urban Studies 44(3): 501-523. Grodach, Carl. 2011. “Barriers to Sustainable Economic Development: The Dallas-Fort Worth Experience” Cities 28: 300-309. Handy, S. 2005. “Smart growth and the transportation - Land use connection: What does the research tell us?” International Regional Science Review 28(2): 146-167. Holden, E., and K.G. Hoyer. (2005) “The Ecological Footprint of Fuels,” Transportation Research Part D 10: 395-403. Jenkins, J. Craig, Kevin T. Leicht and Heather Wendt. 2006. "Class Forces, Political Institutions and State Intervention: Subnational Economic Development Policy in the U.S., 1971-1990.” American Journal of Sociology 111(4): 1122-80. 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.
References 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. Quinn, Brian. 2006. “Transit-Oriented Development: Lessons from California” Built Environment 32(3): 311-322. Skocpol, Theda. 1985. “Bringing the State Back In: Strategies of Analysis in Current Research” (chapter) in Peter B. Evans, Dietrich Rueschemeyer, and Theda Skocpol, Bringing the State Back In. Cambridge, N.Y.: Cambridge University Press. 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. Yin, Ming, and Jian Sun. 2007. "The Impacts of State Growth Management Programs on Urban Spral in the 1990s" Journal of Urban Affairs 29(2): 149-179.
Acknowledgements Funding & ResourcesOhio State University Dept. of SociologyOhio State University Environmental ScienceGraduate ProgramThe Fay Graduate Fellowship Fund in EnvironmentalSciences Colleagues Dr. J. Craig Jenkins Dr. Ed Malecki Dr. Maria Conroy