Transportation Ecoefficiency: Social and Political Drivers in U.S. Metropolitan Areas
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Transportation Ecoefficiency: Social and Political Drivers in U.S. Metropolitan Areas

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Presentation at the Association of American Geographers' annual meeting, April 9-13, Los Angeles, CA. Session: Cities, Transportation and Sustainability. ...

Presentation at the Association of American Geographers' annual meeting, April 9-13, Los Angeles, CA. Session: Cities, Transportation and Sustainability.
Abstract:
As environmental impacts from automobiles have grown, more research is needed to determine what social and policy forces can influence transportation ecoefficiency (TE). TE is the environmental impact per unit of travel, including accessibility and mobility, and it is measured by proxy as the index of four z-scores: percent drive-alone commuting (sign reversed); percent commuting by public transit; percent of commuters walking or riding a bicycle; and population density. A higher TE index indicates more ecoefficient transportation, compared to the average. This study presents a macro-level analysis of institutional and structural predictors of TE in a sample of 225 United States Metropolitan Statistical Areas. Specifically, Ordinary Least Squares regression with robust standard errors points to several conclusions. A New Political Culture, measured by education and income (real per capita income and % change in real per capita income) increases TE, although professional status could reverse this effect. High and rising incomes interact to increase TE, with an effect size over 10 times larger than other effects. State-mandated urban growth management increases TE, demonstrating the beneficial effects of comprehensive planning. This is enhanced by higher incomes, and the combination of high incomes and state-mandated planning also has an effect size over 10 times larger than other effects. Percent African American has a quadratic influence, presumably due to the effects of tolerance and racial threat. Overall, this analysis demonstrates that macro-level social processes, including race, comprehensive planning, and the presence of a new political culture, have a significant impact on TE.

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  • 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 metropolitan 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.
  • Transportation ecoefficiency (TE): a proxy for the environmental impact transportation, per unit of travel. A proxy measure is reasonable 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 more useful for holistically assessing transportation (a single metric can proxy a variety of environmental effects). Previous research has demonstrated the close connection between these factors and transportation’s environmental impacts.
  • Pop Density: approximates likely travel distances. Other lit. shows that this is a reasonable approach. Previous research has connected higher density with lower gasoline consumption (Newman and Kenworthy 1989) and transportation energy use (Naess 1996). Lower pop. density implies greater indirect impacts, due to more extensive road networks. 1. Ewing & Cervero 2010: a meta-analysis of transportation and built environment. Trip distance is primarily a function of the built environment, and trip lengths are shorter with higher densities. 2. Naess 2006: pop. density has no significant effect on commute distance in Copenhagen net of other built environment variables, but higher densities occur in areas with other features that reduce commute distances. Pop. density is thus a proxy for other built environment features that influence travel behaviors and trip distances.
  • Commuting: is the most basic daily travel, co-varies with other trips, etc. Driving alone: highly eco-inefficient, so its sign is reversed Public transit varies in ecoefficiency but is usually much more ecoefficient than driving alone Walking/bicycling have near-zero environmental impacts. They’re not strongly correlated with public transit use, and land use features that encourage them are different. A thorough lit. review indicates that the built environment and travel modes as the primary determinants of transportation’s environmental impact and ecoefficiency . 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, average the z-scores of the 4 components (with the sign reversed for the drive-alone commuting z-score). This captures important differences in the systemic environmental impact of transportation in a way that is effective for analyzing the higher-level social processes that influence transportation policies and practices.
  • Photo credits: http://bloximages.newyork1.vip.townnews.com/stltoday.com/content/tncms/assets/v3/editorial/c/e9/ce992098-0d5e-11e0-9cbd-0017a4a78c22/4d1140277e0fb.image.jpg http://census2010.georgetown.org/files/2011/02/acslogo.gif http://planning.utah.gov/super/mapping.htm
  • 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 purposes.
  • This is a huge increase from 1980-2008 in drive-alone commuting, at the expense of more ecoefficient modes. Drive-alone commuting increased by >10%, walking and bicycling commuting was ~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 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..
  • The overall TE index shows 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 4 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. 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 variation between metro areas. My analysis investigates that variation.
  • For this analysis, I used OLS regression with robust standard errors to predict 2008 TE in US MSAs ( heteroskedasticity problems that ruled out standard OLS regression ) . The analysis is a lagged panel design , predicting 2008 TE index while controlling for the 1980 index. (An alternative model specification was tested, predicting change in TE. This change model’s results did not differ substantively from those presented here, and produced r-squared values above 0.5.) Independent variables: social/demographic, economic, and political variables measured in or near 1980, and a control for the starting point (1980 TE). This long time lag is effective because many of the factors that determine travel behavior change slowly .
  • The New Political Culture theory is similar to Richard Florida’s Creative Class and Herman Boschken’s theory of Upper-Middle Class influence. In short, these theories predict that the presence of certain kinds of people 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. NOTES FOR ME: These results are from model 2, the pared down full-U.S. model with regional controls. Data: % Professional/Technical Occupations = % of workers in Professional/Technical occupations (excluding sales) % College Graduates (logged) = % over age 21 with a college degree Real income per capita, 1979 (logged) = Real income per capita, 1979 (2005 dollars) % real per capita income change, 1979-1989 (logged) = % change in Real per capita income, 1979-89 (2005 dollars)
  • Planning literature indicates that coordinated land use and transportation planning at the regional scale should improve planning outcomes , and therefore TE. However, there not much research on the outcomes of regional planning. 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 comprehensive planning). I chose state planning mandates mainly because state-mandated planning is more likely to be “planning with teeth”. MPOs without state mandates tend to be advisory bodies , and they’re less likely to have an impact because their recommendations aren’t enforceable. In general voluntary or advisory policies are generally less effective at achieving real changes. Image source: http://plannersweb.com/topics/basic-tools/comprehensive-planning/
  • First, state-mandated urban growth management does have a significant, positive effect. This is a small effect, but interaction effects that make it more interesting. NOTES FOR ME: These results are from model 1, the pared down full-U.S. model without regional controls (since controlling for census region masks the effect of state-mandated urban growth management) Data: State-mandated urban growth management = State mandates for urban growth management and land use planning are scored on four criteria: year of adoption, community planning requirement, principal plan review authority, authority for amendment of original plan and approval of amendments. Components are assigned a value 1-3, with higher values for greater state involvement. Components are then averaged, and those averages are used to assign a value of 1 for MSAs in states with weak state mandates, 2 in states with strong mandates, and 0 in states with no mandates (Yin and Sun 2007).
  • There is a significant quadratic effect of % African American, so that as the % African American increases, TE initially increases and then decline beyond the threshold of 8.44% African American.. 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 2, the full-U.S. model with census region controls.
  • Figure: Fitted plot of the relationship between % African American and the TE index, based on model 2 (with regional controls). As % African American increases, TE increases and then declines beyond the inflection point of 8.44% African American . This could be because a larger African American population initially leads to more tolerance , but beyond 8.44%, greater diversity leads to racial conflict in politics or residential location choices . Research and theory indicates that racial threat can affect the political process (Giles and Hertz 1994; Tolbert and Hero 2001), so it could also affect the politics of transportation. However, it is important to note that this interpretation is tentative , and there are complications that could affect this relationship.
  • 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. 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. Photo Credits: http://www.hatcountry.com/images/DesperadoStraw-1.jpg http://3.bp.blogspot.com/-Cr3GNRnH0Sg/TwyCWE8zrwI/AAAAAAAAChE/oUZ1r6NYSy8/s1600/rainy+bus+stop.jpg
  • The most interesting results: the interactions! There is 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. There is 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 . (p=0.015 for this interaction effect) NOTE FOR ME: These results are from Series 2 interaction models, the pared down full-U.S. model with regional controls.
  • The predictive power of these models is very high , and including the interactions slightly improves the predictive power compared to the base model.
  • A qualitative difference (in service quality, perceptions of riders and planners) between buses and rail or BRT . Perceptions render bus transit a second-class mode. Interpretation of race (i.e. that racial tolerance initially increases TE, but as % African American increases further, racial conflict or threat leads to TE declines): quadratic effect was unexpected, and social theory has not provided an explanation . Is this really a class effect (even though % of families in poverty was controlled for)? Exclusive focus on African Americans (other minority racial groups make up a growing % of the US population). Hispanic populations were not assessed consistently in census data from 1980, but still useful to evaluate many racial groups today. More nuanced/specific forms of segregation, a connection between race and the perceptions of rail versus bus, perceived associations between race and crime as a barrier to bus ridership, etc. Inadequate metrics , due to data limitations: not good to rely on state-level laws mandating urban growth management; Non-significant variables: city management capacity, local government fragmentation.
  • Values ranges for categories based on 2000 Transportation Ecoefficiency (TE) Scores, for U.S. Metropolitan Statistical Areas (MSAs). Well below average: TE = -0.5 and below, 7 MSAs (2.6%) A little below average: TE = -0.49 to -0.1, 100 MSAs (36.8%) Average: TE = + or - 0.09, 71 MSAs (26.1%) A little above average: TE = 0.1 to 0.49, 68 MSAs (25.0%) Well above average: TE = 0.5 to 1, 18 MSAs (6.6%) Very high above average: TE = >1, 8 MSAs (2.9%)

Transportation Ecoefficiency: Social and Political Drivers in U.S. Metropolitan Areas Transportation Ecoefficiency: Social and Political Drivers in U.S. Metropolitan Areas Presentation Transcript

  • TransportationEcoefficiency Social and Political Drivers in U.S. Metropolitan Areas Dr. Anna C. McCreery
  • Measuring Transportation Building smarter cities requires good research on transportation  Many micro-level studies in the literature  Macro-level research less well established Thismacro-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: Pop. Density Proxy for travel distance1 Associated with other built environment features that affect travel 21 Ewing and Cervero 20102 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 1 Different commute modes have vastly different environmental impacts:  Driving alone is very eco-inefficient  Public transit, walking, and cycling are generally more ecoefficient modes1 Lee et al. 2009; Naess 2006
  • 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 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 score: 0.6 0.5 0.4 0.3 0.2 0.1 0 -0.1 -0.2 -0.3 1980 1990 2000 2008 Mean 0.504 -0.068 -0.227 -0.211 TE index
  • Analyzing TE: data & methods Sample: 225 U.S. Metropolitan Statistical Areas (MSAs)1 Dependent variable: TE score, 2008 Analysis: Ordinary Least Squares regression with robust standard errors, predicting 2008 TE from various independent variables (measured around 1980). Controls for 1980 TE.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. Beta% prof / tech workers -0.04*** -0.31% college grads 0.58*** 0.24real income per capita 1.64*** 0.30% change in real income percapita 0.75** 0.09 * p<0.05 ** p<0.01 *** p<0.0011 Boschken 2003; Clark & Harvey 2010; DeLeon & Naff 2004
  • Results: Planning State-mandated comprehensive planning is expected to increase TE 1  State policies requiring coordinated urban growth management2 should increase TE  State mandated planning is more likely to be enforceable1 Cervero 2002, Ewing and Cervero 2010, Filion and McSpurren 2007, Handy 2005, Quinn 20062 Yin and Sun 2007
  • Results: Planning Variable Coef. Beta State-mandated urban growth 0.10** 0.10 management * 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. Interpretation is tentative.Variable Coef. Beta% African American 0.100** 0.12% African American, squared -0.001** -0.22 * p<0.05 ** p<0.01 *** p<0.001
  • Results: Race
  • Results: Census Region Westernregion showed significantly higher TE: coef. = 0.42***, beta = 0.22 Includingcensus region altered the significance of other variables  Indicating that other regional differences affect what factors influence TE Culture? Climate?
  • Results: InteractionsVariable Coef. BetaReal income per capita * % 5.20*** 7.74change in real income percapitaReal income per capita * 0.60* 6.04State-mandated urban growthmanagement * p<0.05 ** p<0.01 *** p<0.001
  • Results: Predictive Power 1 0.98 0.96 0.94 0.92 0.9 0.88 0.86 0.84 0.82 0.8 High * Rising Income * Base Model Incomes Planning R-squared 0.872 0.882 0.879
  • Limitations Qualitative differences between bus and rail transit (in service quality and perceptions) Interpretation of the effect of race is very tentative Data limitations and imperfect measurement of:  Planning (preferably regional planning)  Non-significant variables
  • Main Contributions The TE concept and metric is a useful empirical tool1 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 transportation1 McCreery forthcoming in Environment and Planning A
  • Recommendations for Practice Comprehensive planning can achieve real results, especially with enforceable plans Multi-pronged sustainability efforts are worth pursuing:  well-chosen investments in a strong, green economy might have indirect transportation benefits Influenceof planning plus higher incomes is dramatically larger than the effects of demographic and other factors that are beyond the influence of planners
  • 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 Department of SOCIOLOG
  • References Boschken H.L. 2003. “Global Cities, Systemic Power, and Upper-Middle-Class Influence.” Urban Affairs Review 38(6): 808-830. Cervero, R. 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. 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, P. and K. McSpurren. 2007. “Smart Growth and Development Reality: The Difficult Co- ordination of Land Use and Transport Objectives” Urban Studies 44(3): 501-523. Handy, S., L. Weston, and P. Mokhtarian. 2005. “Driving by choice or necessity?” Transportation Research Part A- Policy and Practice 39(2-3): 185-203. Lee, B., P. Gordon, H.W. Richardson, and J.E. Moore II. 2009. “Commuting Trends in U.S. Cities in the 1990s” Journal of Planning Education and Research 29(1): 78-89. McCreery, A.C. Forthcoming. “Transportation Ecoefficiency: Quantitative Measurement of Urban Transportation Systems with Readily Available Data.” Environment and Planning A. 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, B. 2006. “Transit-Oriented Development: Lessons from California” Built Environment 32(3): 311-322. Yin, M., and J. Sun. 2007. "The Impacts of State Growth Management Programs on Urban Sprawl in the 1990s" Journal of Urban Affairs 29(2): 149-179.
  • Mapping TE Scores (2000 data)