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

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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)