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
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
establishedThis macro-level study investigates
broad social forces that impact local transportation
Transportation EcoefficiencyEnvironmental 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
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
TE in US Metro Areas
Variable 1980 mean
2008 mean
Population 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
For 225 U.S. MSAs:
TE Trends: Commuting
78.23%drive67.91%
drive
2.16%transit
3.21transit%
6.40%walkbike
3.35%walkbike
16.26%other22.48%
other
50%
60%
70%
80%
90%
100%
1980 2008Other Modes% of commuters walking/bicycling% of commuters taking public transit% of commuters driving alone
TE Trends: the indexChange in average TE score:
-0.3-0.2-0.1
00.10.20.30.40.50.6
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, 2008Analysis: 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
1 Boschken 2003; Clark & Harvey 2010; DeLeon & Naff 2004
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 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
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 TEBut….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
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 TECulture? Climate?
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 Model High * Rising Incomes
Income * Planning
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 transportation
1 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
Influence of planning plus higher incomes 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 MaleckiDr. 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
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)