This article examines a crucial question that has been raised in recent years in both policy and economic literature: the role of uncertainty as a barrier to energy retrofit decisions. We develop a discrete choice experiment to elicit preferences for energy renovation measures. This methodologically innovative experiment design includes two insurance schemes covering potential sources of uncertainty as attributes of the energy retrofit alternatives. We use a mixed logit model to investigate the nature of systematic heterogeneity in household preferences for attributes of energy retrofit solutions. The article shows that uncertainties related to future energy pricing and retrofit quality are negatively perceived during energy-renova-tion decision making. This impact varies according to household characteristics such as risk aversion and perceptions of the economic context. The results suggest that public policies should support and accompany the development of insurance schemes to increase energy renovation rates.
Get full access to this article
View all access options for this article.
References
1.
AchtnichtM.MadlenerR. (2014). “Factors Influencing German House Owners’ Preferences on Energy Retrofits.” Energy Policy68: 254-263. https://doi.org/10.1016/j.enpol.2014.01.006.
2.
ADEME (2018). Travaux de Renovation Energétique des Maisons Individuelles. ADEME Edition.
3.
AlberiniA.BanfiS.RamseierC. (2013). “Energy Efficiency Investments in the Home: Swiss Homeowners and Expectations about Future Energy Prices.” The Energy Journal34: 49-86. https://doi.org/10.5547/01956574.34.L3.
4.
AlpízarF.CarlssonF.H.C.MartinssonP. (2001). “Using Choice Experiments for Non-Market Valuation.” EconomicIssues8(1): 83-110.
5.
BelaidF.RoubaudD.GalariotisE. (2019). “Features of residential energy consumption: Evidence from France using an innovative multilevel modelling approach.” Energy policy125: 277-285. https://doi.org/10.1016/j.enpol.2018.11.007.
6.
BonnieuxF.CarpentierA. (2007). “Preference pour le Statu Quo dans la Methode des Programmes : Illustration a Partir d’un Probleme de Gestion Forestiere.” Revue d'EconomiePolitique117: 699-717. https://doi.org/10.3917/redp.175.0699.
7.
BozdoganH. (1987). “Model Selection and Akaike’s Information Criterion (AIC): The General Theory and its Analytical Extensions.” Psychometrika52: 345-370. https://doi.org/10.1007/BF02294361.
8.
BrentD.A.WardM.B. (2018). “Energy Efficiency and Financial Literacy.” Journal of Environmental Economics and Management90: 181-216. https://doi.org/10.1016/j.jeem.2018.05.004.
9.
CalzadaJ.SanzA. (2018). “Universal access to clean cookstoves: Evaluation of a public program in Peru.” Energy policy118: 559-572. https://doi.org/10.1016/j.enpol.2018.03.066.
10.
DempsterA.PLairdN.M.RubinD.B. (1977). “Maximum Likelihood from Incomplete Data via the EM Algorithm.” Journal of the Royal Statistical Society Series B39: 1-38. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x.
11.
DixitA.K.PindyckR.S. (1994). Investment Under Uncertainty. Princeton, NJ: Princeton University Press. https://doi.org/10.1515/9781400830176.
12.
DohmenT.FalkA.HuffmanD.SundeU.SchuppJ.WagnerG.G. (2011). “Individual Risk Attitudes: Measurement, Determinants, and Behavioral Consequences.” Journal of the European Economic Association9: 522-550. https://doi.org/10.1111/j.1542-4774.2011.01015.x.
13.
EpanechnikovV. (1969). “Non-Parametric Estimation of a Multivariate Probability Density.” Theory Pro’a’ility. Applications14: 153-158. https://doi.org/10.1137/1114019.
14.
FischbacherU.SchudyS.TeyssierS. (2015). “Heterogeneous Preferences and Investments in Energy Saving Measures.” Working Paper No. 2015-11, Munich Discussion Paper.
15.
FlavinM.YamashitaT. (1998). “Owner-Occupied Housing and the Composition of the Household Portfolio Over the Life-Cycle.” Working Paper No. 6389. National Bureau of Economic Research. https://doi.org/10.3386/w6389.
16.
FowlieM.GreenstoneM.WolframC. (2015). “Are the Non-monetary Costs of Energy Efficiency Investments Large? Understanding Low Take-Up of a Free Energy Efficiency Program.” American Economic Review105: 201-204. https://doi.org/10.1257/aer.p20151011.
17.
FuerstF.McAllisterP. (2011). “The impact of Energy Performance Certificates on the rental and capital values of commercial property assets.” Energy policy39(10): 6608-6614. https://doi.org/10.1016/j.enpol.2011.08.005.
18.
GalassiV.MadlenerR. (2017). “The Role of Environmental Concern and Comfort Expectations in Energy Retrofit Decisions.” Ecological Economics141: 53-65. https://doi.org/10.1016/j.ecolecon.2017.05.021.
19.
GarrodG.RutoE.WillisK.PoweN. (2012). “Heterogeneity of Preferences for the Benefits of Environmental Stewardship: A Latent-Class Approach.” Ecological Economics76: 104-111. https://doi.org/10.1016/j.ecolecon.2012.02.011.
20.
GerardenT.NewellR.G.StavinsR.N. (2015). “Deconstructing the Energy-Efficiency Gap: Conceptual Frameworks and Evidence.” American Economic Review: Papers & Proceedings 105(5): 183-186. https://doi.org/10.1257/aer.p20151012.
21.
GillinghamK.PalmerK. (2014). “Bridging the Energy Efficiency Gap: Policy Insights from Economic Theory and Empirical Evidence.” Review of Environmental Economic Policy8: 18-38. https://doi.org/10.1093/reep/ret021.
22.
GillinghamK.RapsonD.WagnerG. (2016). “The Rebound Effect and Energy Efficiency Policy.” Review of Environmental Economic Policy 10, 68-88. https://doi.org/10.1093/reep/rev017.
23.
GiraudetL.-G.HoudeS.MaherJ. (2018). “Moral Hazard and the Energy Efficiency Gap: Theory and Evidence.” Journal of the Association of Environmental and Resource Economists5: 755-790. https://doi.org/10.1086/698446.
24.
GollierC. (2007). “Comment Integrer le Risque dans le Calcul Economique ?” Revue d’Economie Politique117: 209-223. https://doi.org/10.3917/redp.172.0209.
25.
GollierC. (2013). Pricing the Planet's Future: The Economics of Discounting in an Uncertain World. Princeton, NJ: Princeton University Press. https://doi.org/10.1515/9781400845408.
26.
GreeneW.HensherD. (2003). “A Latent Class Model for Discrete Choice Analysis: Contrasts with Mixed Logit.” Transportation Research Part B: Methodological37: 681-698. https://doi.org/10.1016/S0191-2615(02)00046-2.
27.
HanleyN.WrightR.E.AdamowiczV. (1998). “Using Choice Experiments to Value the Environment.” Environmental Resource Economics11: 413-428. https://doi.org/10.1023/A:1008287310583.
28.
HannonM.J.BoltonR. (2015). “UK Local Authority engagement with the Energy Service Company (ESCo) model: Key characteristics, benefits, limitations and considerations.” Energy Policy78:198-212. https://doi.org/10.1016zj.en-pol.2014.11.016.
29.
HoleA. (2016). “MIXLOGIT: Stata module to fit mixed logit models by using maximum simulated likelihood.” Statistical Software Components.
30.
HolmesT.P.AdamowiczW.L. (2003). “Attribute-Based Methods.” in: ChampP.A.BoyleK.J.BrownT.C. (Eds.), A Primer on Nonmarket Valuation: The Economics of Non-Market Goods and Resources. Dordrecht: Springer Netherlands, pp. 171-219. https://doi.org/10.1007/978-94-007-0826-6_6.
31.
HoltC.A.LauryS.K. (2002). “Risk Aversion and Incentive Effects.” American Economic Review92: 1644—1655. https://doi.org/10.1257/000282802762024700.
32.
JaffeA.B.StavinsR.N. (1994). “The Energy Paradox and the Diffusion of Conservation Technology.” Resource and Energy Economics16: 91-122. https://doi.org/10.1016/0928-7655 (94)90001-9.
33.
JessoeK.RapsonD. (2012). “Knowledge Is (Less) Power: Experimental Evidence from Residential Energy Use.” Working Paper No. 18344, National Bureau of Economic Research. https://doi.org/10.3386/w18344.
34.
JohnsonB.E. (1994). “Modeling Energy Technology Choices: Which Investment Analysis Tools Are Appropriate?” Energy Policy, Markets for Energy Efficiency22: 877-883. https://doi.org/10.1016/0301-4215(94)90147-3.
35.
JohnstonR.J.BoyleK.J.AdamowiczW.BennettJ.BrouwerR.CameronT.A.HanemannW.M.HanleyN.RyanM.ScarpaR.TourangeauR.VosslerC.A. (2017). “Contemporary Guidance for Stated Preference Studies.” Journal of the Association of Environmental and Resource Economists4: 319^05. https://doi.org/10.1086/691697.
36.
KahnM.E.KokN. (2014). “The Capitalization of Green Labels in the California Housing Market.” Regional Science and Urban Economics SI: Tribute to John Quigley47: 25-34. https://doi.org/10.1016/j.regsciurbeco.2013.07.001.
37.
KahnemanD.TverskyA. (1979). “Prospect Theory: An Analysis of Decision under Risk.” Econometrica47: 263-91. https://doi.org/10.2307/1914185.
KrinskyI.RobbA.L. (1986). “On Approximating the Statistical Properties of Elasticities.” The Review of Economics and Statistics68: 715-719. https://doi.org/10.2307/1924536.
40.
KullmannC.SiegelS. (2005). “Real Estate and its Role in Household Portfolio Choice.” SSRN Scholarly Paper No. ID 424322.
41.
KwakS.-YYooS.-H.KwakS.-J. (2010). “Valuing Energy-Saving Measures in Residential Buildings: A Choice Experiment Study.” Energy Policy38: 673-677. https://doi.org/10.1016/j.enpol.2009.09.022.
42.
LaffontJ.-J.MartimortD. (2009). The Theory of Incentives: The Principal-AgentModel. Princeton, NJ: Princeton University Press. https://doi.org/10.2307/j.ctv7h0rwr.
43.
LaurentM.H.AllibeB.OreszczynT.HamiltonI.GalvinR.TigchelaarC. (2013). “Back to Reality: How Domestic Energy Efficiency Policies in Four European Countries Can Be Improved by Using Empirical Data Instead of Normative Cal-culation.” ECEEE Summer Study Proceedings. URL http://proceedings.eceee.org/visabstrakt.php?event=3&doc=7-305-13 (accessed 2.17.19).
44.
LouviereJ.J.HensherD. A.SwaitJ.D. (2000). Stated Choice Methods: Analysis and Applications. Cambridge, UK: Cambridge University Press. https://doi.org/10.1017/CBO9780511753831 .
45.
MardonesC.SaavedraA. (2016). “Comparison of economic instruments to reduce PM2. 5 from industrial and residential sources.” Energy Policy98: 443-452. https://doi.org/10.1016/j.enpol.2016.09.011.
46.
MardonesC.SanhuezaL. (2015). “Tradable permit system for PM2. 5 emissions from residential and industrial sources.” Journal of environmental management157: 326-331. https://doi.org/10.1016/j.jenvman.2015.03.054.
47.
MardonesC. (2019). “Determining the ‘optimal’level of pollution (PM2. 5) generated by industrial and residential sources.” Environmental Impact Assessment Review74: 14-22. https://doi.org/10.1016/j.eiar.2018.09.003.
48.
MarinoA.BertoldiP.RezessyS.Boza-KissB. (2011). “A Snapshot of the European Energy Service Market in 2010 and Policy Recommendations to Foster a Further Market Development.” Energy Policy39(10): 6190-6198. https://doi.org/10.1016/j.enpol.2011.07.019.
49.
McCoyD.KotschR. (2018). “Why the energy efficiency gap is smaller than we think: quantifying heterogeneity and persistence in the returns to energy efficiency measures.” In Grantham Res. Inst. Clim. Chang. Environ. Work. Pap. No. 306.
50.
McFaddenD. (1974). “Conditional Logit Analysis of Qualitative Choice Behaviour.” in: ZarembkaP., Ed., Frontiers in Econometrics. New York: Academic Press.
51.
Neumann VonJ.MorgensternO.RubinsteinA. (1944). Theory of Games and Economic Behavior (60th Anniversary Commemorative Edition). Princeton, NJ: Princeton University Press.
52.
NewellR.G.SiikamakiJ.V. (2015). “Individual Time Preferences and Energy Efficiency.” Working Paper No. 20969, National Bureau of Economic Research. https://doi.org/10.3386/w20969.
53.
OkayN.AkmanU. (2010). “Analysis of ESCO Activities Using Country Indicators.” Renewable and Sustainable Energy Reviews14(9): 2760-2771. https://doi.org/10.1016/j.rser.2010.07.013.
54.
PacificoD.YooH.I. (2012). “lclogit: A Stata Module for Estimating a Mixed Logit Model with Discrete Mixing Distribution Via the Expectation-Maximization Algorithm.” SSRN Scholarly Paper No. ID 2205054.
55.
ParisA. (2018). “Les Biocarburants dans la Transition Energetique : Impacts Macroeconomiques et Perspectives de Deve-loppement.” thesis, Paris10.
56.
QiuY.KahnM.E. (2019). “Impact of Voluntary Green Certification on Building Energy Performance.” Energy Economics80: 461-475. https://doi.org/10.1016/j.eneco.2019.01.035.
57.
QiuY.ColsonG.GrebitusC. (2014). “Risk Preferences and Purchase of Energy-Efficient Technologies in the Residential Sector.” Ecological Economics107: 216-229. https://doi.org/10.1016/j.ecolecon.2014.09.002.
58.
ReyesR.NelsonH.ZerriffiH. (2018). “Firewood: Cause or consequence? Underlying drivers of firewood production in the South of Chile.” Energy for Sustainable Development42: 97-108. https://doi.org/10.1016/j.esd.2017.10.006.
SchueftanA.SommerhoffJ.GonzalezA.D. (2016). િirewood demand and energy policy in south-central Chile.” Energy for Sustainable Development33: 26-35. https://doi.org/10.1016/j.esd.2016.04.004.
61.
SorrellS. (2007). “The Economics of Energy Service Contracts.” Energy Policy35(1): 507-521. https://doi.org/10.1016/j. enpol.2005.12.009.
62.
SorrellS.O’MalleyE. (2004). The Economics of Energy Efficiency. Cheltenham, UK: Edward Elgar Publishing.
63.
StolyarovaE. (2016). “Renovation Energetique de l’Habitat en France : Analyse Micro-économétrique du Choix des Mé-nages.” thesis, Paris Sciences et Lettres.
64.
SutherlandR.J. (1991). “Market Barriers to Energy-Efficiency Investments.” The Energy Journal12: 15-34. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol12-No3-3.
65.
TerrellG.R.ScottD.W. (1992). “Variable Kernel Density Estimation.” The Annals of Statistics20: 1236-1265. https://doi.org/10.1214/aos/1176348768.
66.
ThompsonP.B. (1997). “Evaluating Energy Efficiency Investments: Accounting for Risk in the Discounting Process.” Energy Policy25: 989-996. https://doi.org/10.1016/S0301-4215 (97)00125-0.
67.
TrainK.E. (2009). Discrete Choice Methods with Simulation. Cambridge, UK: Cambridge University Press.
68.
TroncosoK.SeguradoP.AguilarM.daA.S. Silva (2019). “Adoption of LPG for cooking in two rural communities of Chiapas, Mexico.” Energy Policy133: 110925.
69.
VollandB. (2017). “The Role of Risk and Trust Attitudes in Explaining Residential Energy Demand: Evidence from the United Kingdom.” Ecological Economics132: 14-30. https://doi.org/10.1016/j.ecolecon.2016.10.002.
