The many attributes of residential energy efficiency improvements: How do households vary in the attributes they

value most? 

Auren Clarke and Paul Thorsnes

Dept. of Economics

University of OtagoDunedin, New Zealand

 

IntroductionGeneral 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 experimentse.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 studyWe focus on heterogeneity in the attributes themselves

Organization of the presentation

1. Describe the survey software

2. Describe the sample

3. Report results

4. Next steps

Unique decision survey software1000Minds

Web-based multiple-attribute decision software

Key feature: an efficient algorithm for presenting choices

Starts by identifing all ‘undominated pairs’ of two attributes

An undominated pair forces the respondent to make a trade-off

Then presents one such choice pair for the respondent to evaluate

Screenshot of a survey choice pair

Unique decision survey software1000Minds

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

Screenshot of a survey choice pair

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%

‘Clusters’ of respondents with similar relative values

Average of 30 choices to rank 80 undominated pairs

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%

Next step…

Conventionally, the researcher chooses the attributes of interestTo obtain estimates of their relative values

But identifying the attributes of interest may itself be of interestThe 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

Respondent chooses attributes

Screenshot of a choice pair

Recruiting a sample for the pilot studyOwner-occupiers in Dunedin, New Zealand

Climate similar to Seattle’s

Recruited in three census neighborhoodsAnalogous to census tractsCombined demographics similar to NZ as a whole

Initial contact through an invitation letter in early winter 2012The letter directs the householder to the survey web siteInducement

A $10 shopping voucher upon completion, ORA 10% chance of winning a $100 voucher

450 letters sentAbout 15% response rate in the first weekRate increased to 33% after follow-up telephone calls(149 responses)

Clusters on attributesCluster One Two Three Four Five Six

Proportion of respondents 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

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

More heterogeneity…

Choice algorithm strengths and weaknessesStrengths

Each choice is as simple as possibleJust two profiles defined on just two attributes (at a time)

A relatively small number of choicesTo get respondent-specific utility weights

Ideal for investigating preference heterogeneitye.g., can cluster respondents on the basis of utility weights

Weaknesses

Imposes a simple additively separable utility functionNo interactions across the attributes as included in the model

Potentially sensitive to inaccurate choicesEach choice eliminates choices implied by transitivity

Policy implicationsPolicy targeted toward characteristics of each group:

• A relatively small cost-constrained group– Consistent with limited response to subsidies; subsidies

necessary for some but not sufficient for many

• A group willing to invest, but concerned about recovering upfront cost upon sale of house– Suggests perhaps home energy audit and certification program

• A relatively large group concerned about functional reliability– Suggests aggressive independent testing and certification

• Another group concerned about aesthetics– Suggests support for customized installations

• A surprisingly large group interested in independence from the grid– Support for solar systems?

The New Zealand contextHousehold energy use has historically been inefficient

Low prices due to abundant local energy resourcesHydro-electricity, wood, coal, natural gas w/small population

Many houses are poorly insulated and heatedNo insulation requirements until 1978Efficient heating systems are rarely installed at construction

Interest is growing in cleaner/more efficient energy useHigher prices as local energy resources become more scarceConcerns about the health impacts of cold/damp housesConcerns about negative environmental impacts

Particulate emissionsGreen-house gasesDevelopment in sensitive areas

Policy issue: slow up-takePolicy efforts to encourage domestic investment

Subsidies

Persuasive advertising

There has been some consumer response

Partial insulation retrofits

Installation of un-ducted heatpumps and efficient wood burners

Limited information?

Heterogeneous households in heterogeneous houses

Difficult to know what works in context

Good information can be difficult to obtain

Dissatisfaction with relatively expensive improvements is common

Clusters on attributesCluster Six One Four Five Two Three

Proportion of respondents 30.2% 22.1% 17.5% 14.8% 8.7% 6.7%

M. Value for money 0.87 0.85 0.92 0.82 0.85 0.70

E. As energy efficient as advertised 0.84 0.94 0.85 0.23 0.46 0.30

D. Works reliably 0.89 0.97 0.54 1.00 0.15 0.20

S. No structural alterations 0.09 0.24 0.50 0.55 0.85 0.00

L. Lifespan 0.71 0.30 0.08 0.73 0.38 0.30

O. Environmental benefits 0.07 0.73 0.31 0.32 0.54 0.40

P. Independence from the grid 0.20 0.58 0.31 0.05 0.23 0.90

N. Capitalizes into home value 0.73 0.06 0.42 0.36 0.23 0.60

F. Frequency of maintenance 0.62 0.36 0.15 0.14 0.77 0.30

G. DIY install 0.07 0.09 0.19 0.14 0.08 0.90

H. Time for daily operation 0.20 0.03 0.00 0.05 0.69 0.30

Q. Well-ventilated home 0.20 0.33 1.00 0.18 0.00 0.10

R. Home safety 0.13 0.03 0.46 0.82 0.08 0.30

J. Not too fiddly 0.02 0.18 0.08 0.09 0.00 0.20

B. Appearance 0.11 0.12 0.04 0.14 0.31 0.20

C. Potential to disturb me 0.13 0.15 0.08 0.09 0.15 0.00

I. Potential to disturb neighbours 0.02 0.03 0.08 0.09 0.08 0.00

K. Large size 0.04 0.00 0.00 0.05 0.15 0.20