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.

1. Transportation
Ecoefficiency
Social and Political Drivers in U.S.
Metropolitan Areas
Dr. Anna C. McCreery

2. 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

3. 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

4. Measuring TE: Pop. Density
 Proxy for travel distance1
 Associated with other built
environment features that affect travel 2
1
Ewing and Cervero 2010
2
Cervero 2007, Ewing and Cervero 2010, Naess 2006

5. 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 modes
1
Lee et al. 2009; Naess 2006

6. Measuring TE: Data & Sample
 Sample: 225 U.S. Metropolitan
Statistical Areas (MSAs), from 1980 to
2008
 Source: Census data and American
Community Survey

7. TE in US Metro Areas
For 225 U.S. MSAs:
1980 2008
Variable
mean 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

8. 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

9. 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

10. 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

11. 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*** 0.24
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
1
Boschken 2003; Clark & Harvey 2010; DeLeon & Naff 2004

12. 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 enforceable
1
Cervero 2002, Ewing and Cervero 2010, Filion and
McSpurren 2007, Handy 2005, Quinn 2006
2
Yin and Sun 2007

13. Results: Planning
Variable Coef. Beta
State-mandated urban growth 0.10** 0.10
management
* 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

14. 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

15. Results: Race

16. 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?

17. Results: Interactions
Variable Coef. Beta
Real income per capita * % 5.20*** 7.74
change in real income per
capita
Real income per capita * 0.60* 6.04
State-mandated urban growth
management
* p<0.05 ** p<0.01 *** p<0.001

18. 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

19. 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

20. 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

21. 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

22. Acknowledgements
Funding & Resources
Ohio State University Dept. of Sociology
Ohio State University Environmental Science
Graduate Program
The Fay Graduate Fellowship Fund in Environmental
Sciences
Colleagues
Dr. J. Craig Jenkins
Dr. Ed Malecki
Dr. Maria Conroy
Department of
SOCIOLOG

23. References
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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.

24. Mapping TE Scores (2000 data)