This paper provides novel evidence on the main factors behind consumer choices regarding investments in energy efficiency and renewable energy technologies using the OECD Survey on Household Environmental Behaviour and Attitudes. The empirical analysis is based on the estimation of binary logit regression models. Empirical results suggest that households’ propensity to invest in clean energy technologies depends mainly on home ownership, income, social context and household energy conservation practices. Indeed, home owners and high-income households are more likely to invest than renters and low-income households. In addition, environmental attitudes and beliefs, as manifest in energy conservation practices or membership in an environmental non-governmental organisation, also play a relevant role in technology adoption.

1. DETERMINANTS OF
HOUSEHOLDS’ INVESTMENT IN
ENERGY EFFICIENCY AND
RENEWABLES
EVIDENCE FROM THE OECD SURVEY
Nadia Ameli and Nicola Brandt

2. Outline
• Data
• Methodology
• Results
• Policy conclusions

3. Motivation and Background
• Credit constraints
• Principal-agent problems (owner effect)
• Information problems
• Bounded rationality
“The Efficiency Gap” and households’
technology adoption

4. The HH survey data
• 12’000 households
• Australia, Canada, Chile, France,
Israel, Japan, Korea, Netherlands,
Spain, Sweden, Switzerland
• Survey on Household Environmental
Behaviour and Attitudes (2011) from
the Environment Directorate

5. The HH survey data: Technologies
• Energy-efficient appliances (62%)
• Light bulbs (82%)
• Heat pumps (4%)
• Solar panels (11%)
• Thermal insulation (34%)
• Heat thermostat (33%)
• Energy-efficient windows (38%)

6. • Socioeconomic (age, education, income, HH size,
no cope, gender)
• Dwellings (house, tenure, owner, rural)
• Attitudes, beliefs and behaviour (green
growthers/altruist/sceptics, NGO, cost bias)
• Household’s knowledge about energy use,
spending and exposure to price (metered, energy
bill known, kWh known, energy behaviour index)
The HH survey data: variables

7. • The probability of household’s investing in good i is
modelled as:
𝑃 𝑦𝑖 = 1|𝑥𝑖 =
exp 𝛽𝑋 𝑖
1+exp 𝛽𝑋 𝑖
= Λ 𝛽𝑋𝑖
where Λ denotes the logistic cumulative distribution
function
Econometric Model: Logit

8. • APPL: income, owner, NGO, Beh_Index, kWh, metered
• BULB: age, HHsize, owner, house, tenure, rural, sceptics, NGO, Beh_Index, Ebill
• THERM: age, income, owner, house, tenure, rural, NGO, Beh_Index, kWh, Ebill, cost bias
• SOLAR: HHsize, house, rural, Env_NGO, Beh_Index, Ebill, cost bias
• PUMPS: age, income, owner, rural, altruist, green-growther, Env_NGO
• INSULATION: age, income, owner, house, tenure, sceptics, NGO, Beh_Index
• WINDOWS: age, income, owner, house, tenure, rural, NGO, Beh_Index, Ebill, metered
Bayesian Model Averaging method

9. Technologies Explanatory variable: Owner
Marginal effects Obser.
Energy-efficient appliances
0.0785***
(0.0122)
8 605
Heat thermostats
0.0714***
(0.0134)
7 334
Thermal insulation
0.141***
(0.0136)
6 807
Light bulbs
0.0186***
(0.00789)
10 951
Heat pumps
0.00884***
(0.00302)
7 645
Energy-efficient windows
0.131***
(0.0143)
7 269
Some results: owner effect

10. Technologies Explanatory variable: Income
Marginal effects Obser.
Energy-efficient appliances
0.0833***
(0.00978)
8 605
Thermal insulation
0.0508***
(0.0107)
6 807
Heat thermostats
0.00306***
(0.00053)
7 334
Energy-efficient windows
0.0474***
(0.0115)
7 269
Some results: capital constraints
the influence of income on the ability to invest

11. Some results: capital constraints
Predicted probability of investing in energy-efficient
appliances depending on changes in income
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 20000 40000 60000 80000 100000 120000 140000 160000 180000
Predicted probability of investing

12. Technologies Explanatory variable: NGO
Marginal effects Obser.
Energy-efficient appliances
0.0797***
(0.0112)
8 605
Light bulbs
0.0511***
(0.00696)
10 951
Thermal insulation
0.0638***
(0.0123)
6 807
*Solar panels
0.0536***
(0.0121)
6 485
*Heat pumps
0.0209***
(0.00659)
7 645
Heat thermostats
0.0535***
(0.0115)
7 334
Energy-efficient windows
0.0546***
(0.0126)
7 269
Some results: attitudes, beliefs and
behaviour
*Variable: environmental NGO

13. Some results: Attitudes, beliefs and
behaviours
Technologies
Explanatory variable: Energy
behaviour index
Marginal effects Obser.
Energy-efficient appliances
0.0322***
(0.00319)
8 605
Light bulbs
0.0196***
(0.00186)
10 951
Heat thermostats
0.0182***
(0.00332)
7 334
Solar panels
0.00634***
(0.00167)
6 485
Thermal insulation
0.0309***
(0.00358)
6 807
Energy-efficient windows
0.0232***
(0.00364)
7 269

14. Some conclusions
• Evidence for credit constraints
• Evidence for the owner effect
• Attitudes and beliefs are important

15. Targeted policies are required
Capital constraints
• Financing/Loan (PACE, Green Deal, KfW)
• Direct subsidies, tax credit, rebates

16. Policy recommendations
Principal-agent problem
• Energy labels/product standards
• Explicitly allowing owners to increase
the rent after implementing EE and RE

17. Policy recommendations
Promoting energy conservation actions
and pro-environmental behaviour
• Information programs (peer comparison
feedback)
• “Nudge” (loft clearance scheme, UK)