Household Energy Use and Travel: Opportunities for Behavioral Change

1
Household Energy Use and TravelOpportunities
for Behavioral Change

• Sashank Musti, Katherine Kortum and Dr. Kara
  Kockelman
• The University of Texas at Austin

2
Questionnaire Design

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

3
Survey Distribution
4
Survey Distribution (2)

• Central Market Grocery
• Flyers and URL cards
• Community organizations
• Web links via CapMetro and City sites
• Internet version of the survey
  www.energysurvey.co.nr

5
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 (lt30k, 30k-75k, gt75k)
• Categories with few observations combined

6
Sample vs. Austin

• Workers are under-represented (nearly 2 to 1).
• Students are very over-represented.

7
What Should We Do?
8
Where Do We Stand?
9
Yearly VMT per Person (WLS)
10
Yearly Fuel Use per Person (WLS)
11
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

12
Home Size and Monthly kWh (WLS)
13
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.

14
Comparison to EIAs RECS Data
15
Opinions on Climate Change(Binary Probit)
16
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.

17
Energy Reduction Strategies(Bivariate Ordered
Probit)
18
Energy Reduction Strategies

• CAPPING is preferred by
• Households with many vehicles
• Older respondents
• Workers
• TAXATION is preferred by
• College graduates
• Large households
• Homeowners

19
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 ones impact on the
  environment.

20
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.

21
Thank Youfor your attention.
Questions and Suggestions?
22
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).
23
Vehicles per Household (Poisson)
24
Vehicles per Household (Poisson)

• Comments (the model included in this presentation
  is currently an old model among other things, it
  includes two income variables)

25
Energy Reduction Strategies(Ordered Probit)