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

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

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

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

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

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%

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%

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

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

Respondent chooses attributes

Then works through choice survey on those attributes

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

Gender Age

Educational attainment Ethnic ‘New Zealander’

Respondent characteristics

Household income

Household size

Own without mortgage?

Household characteristics

Age of house

# of bedrooms

Insulation

House characteristics

Energy-related capabilities

Energy-related attitudes

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

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

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

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

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?

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