Auren Clarke and Paul Thorsnes presented this in August 2013 at the International Association for Energy Economics (IAEE) conference in Anchorage Alaska.

1. The many attributes of energy efficiency improvements:
Variation across households in the attributes of most value
Auren Clarke and Paul Thorsnes
Dept. of Economics
University of Otago
Dunedin, New Zealand
Presented August 2013 at the International
Association for Energy Economics (IAEE)
conference in Anchorage Alaska

2. Introduction
General issue: slow uptake of residential energy efficiency improvements
E.g., rate of EE improvements in Europe less than half that of other
types of renovations (Jakob, 2006)
Similar problem in NZ, despite subsidies/social marketing
A growing literature focuses on understanding the relative values
households place on various aspects, or ‘attributes’, of EE improvements
Some report results of discrete choice survey experiments
e.g., Poortinga (2003), Banfi et al. (2008), Farsi (2010), Nair et al.
(2010), Achtnict (2011), Achtnicht and Madlener (2012)
Plus earlier work of our own which focus on heterogeneity across
households in the relative values of attributes
In this study
We focus on heterogeneity in the attributes themselves

3. The New Zealand context
Household energy use has historically been inefficient
Low prices due to abundant local energy resources
Hydro-electricity, wood, coal, natural gas w/small population
Many houses are poorly insulated and heated
No insulation requirements until 1978
Efficient heating systems are rarely installed at construction
Interest is growing in cleaner/more efficient energy use
Higher prices as local energy resources become more scarce
Concerns about the health impacts of cold/damp houses
Concerns about negative environmental impacts
Particulate emissions
Green-house gases
Development in sensitive areas

4. Unique decision survey software
1000Minds
Web-based multiple-attribute decision software
Key feature: an efficient algorithm for presenting choices
Identifies all ‘undominated pairs’ of two attributes
Presents one choice pair for the respondent to evaluate
Eliminates from the survey all other choices implied by transitivity
Which reduces considerably the number of choices required to rank
all combinations of two attributes
Continues until all pairs are evaluated explicitly or implicitly
Relative values (or utilities) are then estimated using a linear program
The result is a complete set of relative utility values for each
respondent

5. Screenshot of a survey choice pair from a standard choice
survey of preferences for attributes of water heating

6. Mean relative values of attributes of water heating systems
Average of 30 choices to rank 80 undominated pairs
Means
Upfront cost 14.6
Running cost 16.4
Reliable supply 17.7
Confident in technology 12.4
Fits with house 12.2
Doesn't disturb neighbours 13.8
Off grid 7.3
Upgradable 5.6
Respondents/cluster 586
Size as % of sample 100%

7. Cluster analysis to explore preference heterogeneity
Average of 30 choices to rank 80 un-dominated pairs of two attributes
Means Thrifty Reliable Considerate Independent
Upfront cost 14.6 22.8 15.1 12.4 12.1
Running cost 16.4 25.5 16.7 13.8 14.1
Reliable supply 17.7 11.0 26.0 19.1 12.8
Confident in technology 12.4 9.9 14.3 12.0 12.7
Fits with house 12.2 7.9 10.6 14.9 12.8
Doesn't disturb neighbours 13.8 8.4 7.9 20.0 14.0
Off grid 7.3 9.4 4.3 3.3 14.0
Upgradable 5.6 5.1 5.2 4.7 7.6
Respondents/cluster 586 94 134 203 155
Size as % of sample 100% 16.0% 22.9% 34.6% 26.5%

8. Choice algorithm strengths and weaknesses
Strengths
Each choice is as simple as possible
Just two profiles defined on just two attributes (at a time)
A relatively small number of choices
To get respondent-specific utility weights
Ideal for investigating preference heterogeneity
e.g., can cluster respondents on the basis of utility weights
Weaknesses
Imposes a simple additively separable utility function
No interactions across the attributes as included in the model
Potentially sensitive to inaccurate choices
Each choice eliminates choices implied by transitivity

9. Next step…
The researcher conventionally chooses the attributes of interest
Estimates their relative values with data from a choice survey
But identifying the attributes of interest may itself be of interest
The number of attributes of EE improvements is relatively large
A review of the literature reveals more than 20
In this pilot study, we take advantage of the web-based interface to:
Allow each respondent to choose from a list the 6 attributes most
important to him or her
A 7th attribute – upfront cost – was imposed on everyone
Then work the respondent through a choice survey based on those 7
attributes
The choice model becomes tailored to the respondent

10. Respondent chooses attributes

11. Then works through choice survey on those attributes

12. Pilot study sample
Owner-occupiers in Dunedin, New Zealand
Mid-latitude coastal climate
Recruited in three census neighbourhoods
Analogous to census tracts
Combined demographics similar to NZ as a whole
Initial contact through an invitation letter in early winter 2012
The letter directs the householder to the survey web site
Inducement
A $10 shopping voucher upon completion, OR
A 10% chance of winning a $100 voucher
450 letters sent
About 15% response rate in the first week
Rate increased to 33% after follow-up telephone calls
149 responses, overall

13. Gender Age
Educational attainment Ethnic ‘New Zealander’
Respondent characteristics

14. Household income
Household size
Own without mortgage?
Household characteristics

15. Age of house
# of bedrooms
Insulation
House characteristics

16. Energy-related capabilities
Energy-related attitudes

17. Clusters of respondents based on the 6 attributes chosen
Cluster One Two Three Four Five Six
% in cluster who chose attribute 30.2% 22.1% 17.5% 14.8% 8.7% 6.7%
Value for money 0.87 0.85 0.92 0.82 0.85 0.70
As energy efficient as advertised 0.84 0.94 0.85 0.23 0.46 0.30
Works reliably 0.89 0.97 0.54 1.00 0.15 0.20
No structural alterations 0.09 0.24 0.50 0.55 0.85 0.00
Lifespan 0.71 0.30 0.08 0.73 0.38 0.30
Environmental benefits 0.07 0.73 0.31 0.32 0.54 0.40
Independence from the grid 0.20 0.58 0.31 0.05 0.23 0.90
Capitalizes into home value 0.73 0.06 0.42 0.36 0.23 0.60
Frequency of maintenance 0.62 0.36 0.15 0.14 0.77 0.30
DIY install 0.07 0.09 0.19 0.14 0.08 0.90
Time for daily operation 0.20 0.03 0.00 0.05 0.69 0.30
Well-ventilated home 0.20 0.33 1.00 0.18 0.00 0.10
Home safety 0.13 0.03 0.46 0.82 0.08 0.30
Not too fiddly 0.02 0.18 0.08 0.09 0.00 0.20
Appearance 0.11 0.12 0.04 0.14 0.31 0.20
Potential to disturb me 0.13 0.15 0.08 0.09 0.15 0.00
Potential to disturb neighbours 0.02 0.03 0.08 0.09 0.08 0.00
Large size 0.04 0.00 0.00 0.05 0.15 0.20

18. Summary of the cluster analysis
Some significant similarities
Nearly everyone cared about value for money
More than two-thirds were concerned that the improvement works
reliably and as energy efficiently as advertised
Also considerable heterogeneity
Every attribute was chosen as important by someone
Clusters were distinguished based on preferences for:
The extent to which the investment capitalises into home value
Concerns about impact on the environment
Effects on home ventilation (mould is a problem in NZ)
Safety in the home
Independence from the energy grid
DIY installation

19. The relative importance of cost
This figure shows the distribution of the utility from not spending $15,000
on an EE improvement relative to that from gaining an EE improvement
with the best levels of all other attributes chosen combined.
The red line indicates just average concern for spending $15k
There’s remarkable variation in the relative value of $15,000
And surprisingly strong willingness to pay for EE improvements
Upfront cost doesn’t seem a strong barrier to investment

20. Willingness to pay for energy efficiency
These graphs show the:
mean (orange square),
median (black diamond),
range in estimated utilities
for upfront cost and e
expected EE gain
On average, estimated
utility from even a 25%
increase in EE is higher
than that from saving $15k

21. Policy implications
• Upfront cost not a big concern for most
– Consistent with limited response to subsidies
• A relatively large group concerned about functional reliability
– Suggests aggressive independent testing and certification
• A relatively large group concerned about recovering cost upon sale of house
– Suggests perhaps home energy audit and certification program
• A large proportion concerned about environmental benefits
– Need for clear, unbiased information
• A fairly large group concerned about structural alterations
– Suggests support for customised installations
• Significant concern for impacts on other aspects of the house (damp, safety)
– Suggests a need for clear, unbiased information
• A small but significant group interested in independence from the grid
– Support for solar systems, bio fuels?

22. An information tool?
Any EE improvement can be defined in terms of its attributes
Various sources assist household decision-makers by describing
attributes of potential improvements
But the list of improvements can be long
The choice survey provides information about the household
This information can be used to rank-order potential improvements
Based on their attributes and the household’s preferences
That rank ordering helps reduce the information burden on households
By helping prioritise the information search
Or, the information could be useful to energy consultants