Household Energy Use and Travel: Opportunities for Behavioral Change Sashank Musti, Katherine Kortum and Dr. Kara Kockelman The University of Texas at Austin

Questionnaire Design Cover letter (English and Spanish) Five sections –Travel Choices –Vehicle Ownership –Home Design and Energy Use –Energy Policy Opinions –Demographics

Survey Distribution Westlake Sunset Valley East Austin Manor Hyde Park Far West

Survey Distribution (2) Central Market Grocery Flyers and URL cards Community organizations Web links via CapMetro and City sites Internet version of the survey:

Data Weighting Sample was compared to PUMS Six control attributes: 720 categories –Gender (male, female) –Student status (student, non-student) –Worker status (worker, non-worker) –Age (18-24, 25-34, 35-44, 45-54, 55-64, 65+) –Household Size (1, 2, 3, 4, 5+) –Income ( $75k) Categories with few observations combined

Sample vs. Austin Workers are under-represented (nearly 2 to 1). Students are very over-represented. VariableSampleAustin Female49.6%50.4% Age %38.7% High-income46.6%39.5% Employed37%70.3% Students82%13%

What Should We Do?

Where Do We Stand?

Yearly VMT per Person (WLS) VariableCoefficientT-statistic Mean Elasticity Constant College-educated Income per person Number of children Distance to CBD Population density Population density & distance to CBD > Transit stops Age of respondent R2R Adjusted R

Yearly Fuel Use per Person (WLS) VariableCoefficientT-statistic Mean Elasticity Constant College-educated Income per person9.25 E Number of children Distance to CBD Population density & distance to CBD > Transit stops Age of respondent R2R Adjusted R

Yearly VMT and Fuel Use Both increase as… –Distance to CBD increases –Age increases Both decrease as… –Education level rises –Number of children increases –Number of transit stops increases

Home Size and Monthly kWh (WLS) SQUARE FOOTAGEMONTHLY ELECTRICITY Independent VariablesCoefficientT-statistic Mean Elasticity CoefficientT-statistic Mean Elasticity Constant Household size Worker status Income ($1,000)3.9 E E College-educated Age of home Own home Number of vehicles Number of adults Job density Population density Two- & three-story detached home Home size R2R Adjusted R

Home Size and Monthly kWh (WLS) Both increase as… –Income increases –Household size increases Both decrease as… –The area grows denser Older homes tend to be smaller but use more electricity. College graduates tend to have smaller home sizes.

Comparison to EIA’s RECS Data ComparisonAustin Energy Survey RECS, 2001 Average home size1,645 sq.ft.2,100 sq.ft. Average monthly kWh1,200 kWh900 kWh +1 household member+ 77 kWh+ 104 kWh +100 square feet+ 49 kWh+ 22 kWh

Opinions on Climate Change (Binary Probit) REGULATIONS SHOULD BE IMPOSED ADAPT TO A WARMER CLIMATE Explanatory VariablesCoefficientT-statisticCoefficientT-statistic Number of household vehicles Age of respondent Female Worker status Middle income indicator (between $80,000 & $112,500) College-educated Own home Rooms in home Age of home Average annual VMT E Constant Log Likelihood at Convergence Pseudo R

Opinions on Climate Change (Binary Probit) Regulations preferred by… –Women –Homeowners Adaptation preferred by… –Workers –Households with many vehicles Those with older homes acknowledge the importance of both regulations and adaptation.

Energy Reduction Strategies (Bivariate Ordered Probit) CAP ON ENERGY USETAX ALL ENERGY USE Explanatory VariablesCoefficientT-statisticCoefficientT-statistic Number of household vehicles Age of respondent Female Number of workers College-educated Income per household member E Household size Worker status VMT per household member-1.19 E E Age of home Rooms in home Own home Threshold Threshold Threshold Threshold Log Likelihood at Convergence Covariance across equations’ residuals

Energy Reduction Strategies CAPPING is preferred by… –Households with many vehicles –Older respondents –Workers TAXATION is preferred by… –College graduates –Large households –Homeowners

Conclusions Long-term behavioral changes are difficult to implement. Most agree climate change is a concern, but are unwilling to change their own behavior. Increasing income and education lead to greater (stated) concern about one’s impact on the environment.

Conclusions (2) Electricity usage increases by 77 kWh/month for an additional person in a household & by 49 kWh/month for an additional 100 square feet of living space. Average electricity consumption can be reduced by moving into newer, smaller homes. Fuel consumption increases by 16.6 gallon/person with a one mile increase in driving distance to the CBD. VMT per person per year increases by 307 miles with every additional mile a household lives from the CBD.

Thank You for your attention. Questions and Suggestions?

Sashank needs to rename & recluster/list variables, & get elasticities alongside, but there is enough info in these results for Katherine to start inferring meaningful results & she has done a nice job of that in the ppt she sent. (E.g., what's most pract signif/relevant, what is not in there that you thought you'd see, & *how can OTHERS make use of these results* ;-) Katherine: I'm afraid 22 slides is probably too much for the allotted time (I think it would take me close to 20 min., & you should aim for 18. You can tell how long it takes by practicing slowly.) I'm in room 2415 in case you want to try & reach us. I'd highlight 2 to 3 variables you'd like to talk about in a table. (Also, I think you could get away from such a slide altogether, though I do like how you accomplish/review two models with a single slide, as with slide 15. E.g., you could highlight 2 or 3 that signif increase the response with "red" [bad things, increasing c02/energy, for example], and the good practically significant variables [the ones that reduce vmt/fuel] with green.) On slide 19, what I think is interesting is who supports the first & not the 2nd (e.g., females & non-workers, in hh's with fewer vehicles). The college educated may simply realize that there will have to be adaptation (i.e., climate change is here to stay).

Vehicles per Household (Poisson) Independent Variables CoefficientT-statistic Household income3.93 E Income per person-4.42 E Number of workers in the household Own home Annual VMT1.16 E Age of respondent Region-Specific Variables Distance to UT campus Household density Population density Job density-2.07 E Constant

Vehicles per Household (Poisson) Comments (the model included in this presentation is currently an old model; among other things, it includes two income variables)

Energy Reduction Strategies (Ordered Probit) CAP ON ENERGY USETAX ALL ENERGY USE Explanatory Variables CoefficientT-statisticCoefficientT-statistic Number of household vehicles Age of respondent Female Number of workers College-educated Income per household member E Household size Worker status VMT per household member-1.16 E E Age of home Rooms in home Own home Threshold Threshold Threshold Threshold Log Likelihood at Convergence Pseudo R