Transportation Ecoefficiency: Social and Political Drivers in U.S. Metropolitan Areas
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Transcript of Transportation Ecoefficiency: Social and Political Drivers in U.S. Metropolitan Areas
McCreery, A.C. 13 April 2013. Annual Meeting of the Association of
American Geographers
1
Transportation Ecoefficiency
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
� This macro-level study investigates broad social forces that impact local transportation
McCreery, A.C. 13 April 2013. Annual Meeting of the Association of
American Geographers
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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 bicycling
1 Cervero 2007, Ewing and Cervero 2010, Naess 2006
Measuring TE: Pop. Density
� Proxy for travel distance1
� Associated with other built environment features that affect travel2
1 Ewing and Cervero 20102 Cervero 2007, Ewing and Cervero 2010, Naess 2006
McCreery, A.C. 13 April 2013. Annual Meeting of the Association of
American Geographers
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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 trips1
� Different commute modes have vastly different environmental impacts:� Driving alone is very eco-inefficient
� Public transit, walking, and cycling are generally more ecoefficient modes
1 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
McCreery, A.C. 13 April 2013. Annual Meeting of the Association of
American Geographers
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TE in US Metro Areas
Variable1980 mean
2008 mean
Population Density* 320.3 360.0
Commuters 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
For 225 U.S. MSAs:
TE Trends: Commuting
78.23%
drive67.91%
drive
2.16%
transit3.21
transit%
6.40%
walk
bike
3.35%
walk
bike
16.26%
other22.48%
other
50%
60%
70%
80%
90%
100%
1980 2008
Other Modes
% of commuters walking/bicycling
% of commuters taking public transit
% of commuters driving alone
McCreery, A.C. 13 April 2013. Annual Meeting of the Association of
American Geographers
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TE Trends: the indexChange in average TE index:
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
Mean TE index
0.504 -0.068 -0.227 -0.211
1980 1990 2000 2008
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
McCreery, A.C. 13 April 2013. Annual Meeting of the Association of
American Geographers
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Results: New Political Culture
� New Political Culture theory: beneficial effects of educated professionals with high and rising incomes1
1 Boschken 2003; Clark & Harvey 2010; DeLeon & Naff 2004
Variable Coef. Beta
% prof / tech workers -0.04*** -0.31
% college grads 0.58*** 0.24
real income per capita 1.64*** 0.30
% change in real income per capita
0.75** 0.09
* p<0.05 ** p<0.01 *** p<0.001
Results: Planning
� State-mandated comprehensive planning is expected to increase TE1
� State policies requiring coordinated urban growth management2 should increase TE
� State mandated planning is more likely to be enforceable
1 Cervero 2002, Ewing and Cervero 2010, Filion and
McSpurren 2007, Handy 2005, Quinn 20062 Yin and Sun 2007
McCreery, A.C. 13 April 2013. Annual Meeting of the Association of
American Geographers
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Results: Planning
Variable Coef. Beta
State-mandated urban growth management
0.10** 0.10
* p<0.05 ** p<0.01 *** p<0.001
Photo Credits: http://www.memphistn.gov/media/images/gov2.jpg
http://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. Beta
% African American 0.100** 0.12
% African American, squared -0.001** -0.22
* p<0.05 ** p<0.01 *** p<0.001
McCreery, A.C. 13 April 2013. Annual Meeting of the Association of
American Geographers
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Results: Race
Results: Census Region
� Western region showed significantly higher TE: coef. = 0.42***, beta = 0.22
� 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
McCreery, A.C. 13 April 2013. Annual Meeting of the Association of
American Geographers
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Results: Interactions
Variable Coef. Beta
Real income per capita * % change in real income per capita
5.20*** 7.74
Real income per capita * State-mandated urban growth management
0.60* 6.04
* p<0.05 ** p<0.01 *** p<0.001
Results: Predictive Power
0.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
R-squared 0.872 0.882 0.879
Base ModelHigh * Rising
IncomesIncome * Planning
McCreery, A.C. 13 April 2013. Annual Meeting of the Association of
American Geographers
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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 inadequate 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 transportation
1 McCreery forthcoming in Environment and Planning A
McCreery, A.C. 13 April 2013. Annual Meeting of the Association of
American Geographers
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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
� Influence of planning * income is dramatically larger than the effects of demographic and other factors that are beyond the influence of planners
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
Acknowledgements
Department of
SOCIOLOGY
McCreery, A.C. 13 April 2013. Annual Meeting of the Association of
American Geographers
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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 Environment32(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.
Classifying MSAs by TE Score