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The many attributes of residential energy efficiency improvements: How do households vary in the attributes they value most? Auren Clarke and Paul Thorsnes.

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Presentation on theme: "The many attributes of residential energy efficiency improvements: How do households vary in the attributes they value most? Auren Clarke and Paul Thorsnes."— Presentation transcript:

1 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 Otago Dunedin, New Zealand

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 Organization of the presentation 1.Describe the survey software 2.Describe the sample 3.Report results 4.Next steps

4 Unique decision survey software 1000Minds 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

5 Screenshot of a survey choice pair

6 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

7 Screenshot of a survey choice pair

8 Relative values of attributes of water heating systems Average of 30 choices to rank 80 undominated pairs Means Upfront cost14.6 Running cost16.4 Reliable supply17.7 Confident in technology12.4 Fits with house12.2 Doesn't disturb neighbours13.8 Off grid7.3 Upgradable5.6 Respondents/cluster586 Size as % of sample100%

9 ‘Clusters’ of respondents with similar relative values Average of 30 choices to rank 80 undominated pairs MeansThriftyReliableConsiderateIndependent Upfront cost14.622.815.112.412.1 Running cost16.425.516.713.814.1 Reliable supply17.711. Confident in technology12.49.914.312.012.7 Fits with house12.27.910.614.912.8 Doesn't disturb neighbours13.88.47.920.014.0 Off grid7. Upgradable5. Respondents/cluster58694134203155 Size as % of sample100%16.0%22.9%34.6%26.5%

10 Next step… Conventionally, the researcher chooses the attributes of interest To obtain estimates of their relative values 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 7 th 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

11 Respondent chooses attributes

12 Screenshot of a choice pair

13 Recruiting a sample for the pilot study Owner-occupiers in Dunedin, New Zealand Climate similar to Seattle’s Recruited in three census neighborhoods 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)





18 Clusters on attributes ClusterOneTwoThreeFourFiveSix Proportion of respondents30.2%22.1%17.5%14.8%8.7%6.7% Value for money0.870.850.920.820.850.70 As energy efficient as advertised0.840.940.850.230.460.30 Works reliably0.890.970.541.000.150.20 No structural alterations0.090.240.500.550.850.00 Lifespan0.710.300.080.730.380.30 Environmental benefits0.070.730.310.320.540.40 Independence from the grid0.200.580.310.050.230.90 Capitalizes into home value0.730.060.420.360.230.60 Frequency of maintenance0.620.360.150.140.770.30 DIY install0. Time for daily operation0. Well-ventilated home0.200.331. Home safety0.130.030.460.820.080.30 Not too fiddly0. Appearance0. Potential to disturb me0. Potential to disturb neighbours0. Large size0.040.00


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

21 More heterogeneity…

22 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

23 Policy implications Policy 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?

24 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

25 Policy issue: slow up-take Policy 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


27 Clusters on attributes ClusterSixOneFourFiveTwoThree Proportion of respondents30.2%22.1%17.5%14.8%8.7%6.7% M. Value for money0.870.850.920.820.850.70 E. As energy efficient as advertised0.840.940.850.230.460.30 D. Works reliably0.890.970.541.000.150.20 S. No structural alterations0.090.240.500.550.850.00 L. Lifespan0.710.300.080.730.380.30 O. Environmental benefits0.070.730.310.320.540.40 P. Independence from the grid0.200.580.310.050.230.90 N. Capitalizes into home value0.730.060.420.360.230.60 F. Frequency of maintenance0.620.360.150.140.770.30 G. DIY install0. H. Time for daily operation0. Q. Well-ventilated home0.200.331. R. Home safety0.130.030.460.820.080.30 J. Not too fiddly0. B. Appearance0. C. Potential to disturb me0. I. Potential to disturb neighbours0. K. Large size0.040.00

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