Abstract
Over the last decade, households’ preferences about energy efficiency measures in the residential sector have been the focus of a growing body research employing models based on revealed and stated preferences. Analysis of households’ energy consumption and demand elasticities were carried out before with the intent to forecast the potential of energy efficiency programs, but the recent concerns about climate change have drawn attention to the causes of this problem. As a result, the residential and renewable energy sectors have become strategic for the human being’s future. Different retrofit measures and technical solutions are now available for the new buildings, but the existing residential stock is more difficult to improve. More specifically, this implies the investment decision of heterogeneous groups of homeowners and landlords who differ in terms of the characteristics of their assets, their financial possibilities and time preferences. Valuation models have helped to forecast the demand of both market and public goods. Based on different approaches and theories, these applications have opened new avenues of research, but leaving some questions unanswered. This work tries to take stock of a debate that is still open by comparing experiments based on revealed and stated preferences in this specific field.
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References
Achtnicht M (2011) Do environmental benefits matter? Evidence from a choice experiment among house owners in Germany. Ecol Econ 70:2191–2200
Achtnicht M, Madlener R (2014) Factors influencing German house owners’ preferences on energy retrofits. Energy Policy 68:254–263
Alberini A, Bigano A, Ščasný M, Zvěřinová I (2018) Preferences for Energy Efficiency vs. Renewables: What Is the Willingness to Pay to Reduce CO2 Emissions? Ecol Econ 144:171–185
Allcott H, Greenstone M (2013) Is there an energy efficiency gap? In: Energy efficiency. Elsevier, pp 133–161
Andrews CJ, Krogmann U (2009) Explaining the adoption of energy-efficient technologies in U.S. commercial buildings. Energy Build 41:287–294
Banfi S, Farsi M, Filippini M, Jakob M (2008) Willingness to pay for energy-saving measures in residential buildings. Energy Econ 30:503–516
Barthelmes VM, Becchio C, Bottero M, Corgnati SP (2016) Cost-optimal analysis for the definition of energy design strategies: the case of a Nearly-Zero Energy Building. Valori e valutazioni 21:61–76
Barthelmes VM, Becchio C, Fabi V, Corgnati SP (2017) Occupant behaviour lifestyles and effects on building energy use: investigation on high and low performing building features. Energy Procedia 140:93–101
Becchio C, Bertoncini M, Boggio A et al (2018) The Impact of Users’ Lifestyle in Zero-Energy and Emission Buildings: an Application of Cost-Benefit Analysis. In: New Metropolitan Perspectives. Springer International Publishing, pp 123–131
Becchio C, Bottero MC, Corgnati SP, Dell’Anna F (2018) Decision making for sustainable urban energy planning: an integrated evaluation framework of alternative solutions for a NZED (Net Zero-Energy District) in Turin. Land Use Policy 78:803–817
Ben-Akiva M, Mcfadden D, Train K et al (2002) Hybrid choice models: progress and challenges. Mark Lett 13:163–175
Boeri M, Longo A (2017) The importance of regret minimization in the choice for renewable energy programmes: evidence from a discrete choice experiment. Energy Econ 63:253–260
Bonifaci P, Copiello S (2015) Real estate market and building energy performance: data for a mass appraisal approach. Data Br
Borchers AM, Duke JM, Parsons GR (2007) Does willingness to pay for green energy differ by source? Energy Policy 35:3327–3334
Bottero MC, Bravi M, Cavana G, Dell’Anna F (2019, Forthcoming) Energy retrofit and investment decisions: individuals’ preferences valuation through a Choice Experiment. Geoing Ambient e Mineraria
Bottero M, D’Alpaos C, Dell’Anna F (2018a) Boosting Investments in Buildings Energy Retrofit: the Role of Incentives. In: New Metropolitan Perspectives. Springer International Publishing, pp 593–600
Bottero MC, Bravi M, Dell’Anna F, Mondini G (2018b) Valuing building energy efficient through Hedonic Prices Method: are spatial effects relevant? Valori e Valutazioni 21:27–40
BPIE (Buildings Performance Institute Europe) (2011) Europe’s buildings under the micro-scope: a country-by-country review of the energy performance of Europe’s buildings. http://bpie.eu/publication/europes-buildings-under-the-microscope/
Buso T, Dell’Anna F, Becchio C, Bottero M, Corgnati S (2017) Of comfort and cost: examining indoor comfort conditions and guests’ valuations in Italian hotel rooms. Energy Res Soc Sci 32:94–111
Cameron TA (1985) A nested logit model of energy conservation activity by owners of existing single family dwellings. Rev Econ Stat 67(2):205–211
Chorus C, van Cranenburgh S, Dekker T (2014) Random regret minimization for consumer choice modeling: assessment of empirical evidence. J Bus Res 67:2428–2436
Chorus CG (2010) A new model of Random Regret Minimization. Eur J Transp Infrastruct Res 10(2):181–196
D’Alpaos C, Bragolusi P (2018) Multicriteria prioritization of policy instruments in buildings energy retrofit. Valori e Valutazioni 21:15–25
Dell’Anna F, Vergerio G, Corgnati S, Mondini G (2019) A new price list for retrofit intervention evaluation on some archetypical buildings. Valori e Valutazioni 22:3–17
Denstadli JM, Lines R, de Dios Ortúzar J (2012) Information processing in choice-based conjoint experiments. Eur J Mark 46:422–446
European Commission (2016) Accelerating Clean Energy in Buildings. COM 2016 860 final Annex 1
Fabi V, Di Nicoli MV, Spigliantini G, Corgnati SP (2017) Insights on pro-environmental behavior towards post-carbon society. Energy Procedia 134:462–469
Fiebig DG, Keane MP, Louviere J, Wasi N (2010) The generalized multinomial logit model: accounting for scale and coefficient heterogeneity. Market Sci 29:393–421
Freeman AM III, Herriges JA, Kling CL (2003) The measurement of environmental and resource values: theory and methods. Resources for the Future, Routledge, Washington DC
Fregonara E, Rolando D, Semeraro P (2017) Energy performance certificates in the Turin real estate market. J Eur Real Estate Res 10(2):149–169
Galassi V, Madlener R (2017) The role of environmental concern and comfort expectations in energy retrofit decisions. Ecol Econ 141:53–65
Gerarden T, Newell RG, Stavins RN (2015) Deconstructing the energy efficiency gap: conceptual frameworks and evidence. Am Econ Rev 105(5):183–186
Gillingham K, Newell RG, Palmer K (2009) Energy efficiency economics and policy. Discussion Paper RFF DP 09-13, Resource for the Future, Washington DC
Gilovich T, Griffin D, Kahneman D (2002) Euristic and biases: the psychology of intuitive judgement. Cambridge University Press, Cambridge UK
Glenk K, Colombo S (2013) Modelling outcome-related risk in choice experiments. Aust J Agric Resour Econ 57:559–578
Greene WH, Hensher DA, Rose J (2006) Accounting for heterogeneity in the variance of unobserved effects in mixed logit models. Transp Res Part B Methodol. 40B(1):75–92
Hensher DA, Greene WH (2010) Non-attendance and dual processing of common-metric attributes in choice analysis: a latent class specification. Empir Econ. 39:413–426
Howarth R, Haddad BM, Paton B (2000) The economics of energy efficiency: insights from voluntary participation programs. Energy Policy 28:477–486
Islam T (2014) Household level innovation diffusion model of photo-voltaic (PV) solar cells from stated preference data. Energy Policy 65:340–350
Janssen EM, Marshall DA, Hauber AB, Bridges JFP (2017) Improving the quality of discrete-choice experiments in health: how can we assess validity and reliability? Expert Rev Pharmacoeconomics Outcomes Res 17:531–542
Kahneman D, Tversky A (1984) Choices, values, and frames. Am Psychol 399:341–350
Kwak S-Y, Yoo S-H, Kwak S-J (2010) Valuing energy-saving measures in residential buildings: a choice experiment study. Energy Policy 38:673–677
Lacetera N, Pope DG, Sydnor JR (2012) Heuristic thinking and limited attention in the car market. Am Econ Rev 102:2206–2236
Li J, Just RE (2018) Modeling household energy consumption and adoption of energy efficient technology. Energy Econ 72:404–415
Lundhede T, Jacobsen JB, Hanley N et al (2015) Incorporating outcome uncertainty and prior outcome beliefs in stated preferences. Land Econ 911(2):296–316
Ma C, Burton MP (2013) A nested logit model of green electricity consumption in Western Australia (No. 1784-2016-141890)
Magidson J, Vermunt J. (2007) Removing the scale factor confound in multinomial logit choice models to obtain better estimates of preference. 2007 Sawtooth software conference, Santa Rosa (CA), 17–19 Oct 2007
Marmolejo-Duarte C, Bravi M (2017) Does the energy label (EL) matter in the residential market? A stated preference analysis in Barcelona. Buildings 7:53
Marmolejo-Duarte C, Chen A (2019) The uneven price impact of energy efficiency ratings on housing segments. Implications for public policy and private markets. Sustainability 11:372
Marschak J (1960) Binary choice constraints on random utility indicators. In: Arrow K (ed) Stanford symposium on mathematical methods in the social sciences. Stanford University Press, Stanford, CA, pp 312–329
McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Paul Zarembka (ed) Frontiers in econometrics. Academic Press, New York, pp 105–142
McFadden D (2001) Economic Choices. Am Econ Rev 91:351–378
Michelsen CC, Madlener R (2012) Homeowners’ preferences for adopting innovative residential heating systems: a discrete choice analysis for Germany. Energy Econ 34:1271–1283
Napoli G, Gabrielli L, Barbaro S (2017) The efficiency of the incentives for the public buildings’ energy retrofit. The case of the Italian Regions of the “Objective Convergence”. Valori e valutazioni 18:25–39
Newell RG, Siikamki J (2015) Individual time preferences and energy efficiency. Am Econ Rev 105(5):196–200
Olaussen JO, Oust A, Solstad JT (2017) Energy performance certificates—Informing the informed or the indifferent? Energy Policy 111:246–254
Pascuas RP, Paoletti G, Lollini R (2017) Impact and reliability of EPCs in the real estate market. Energy Procedia 140:102–114
Payne JW, Bettman JR, Johnson EJ (1993) The adaptive decision maker. Cambridge University Press, New York, NY
Peón D, Antelo M, Calvo-Silvosa A (2017) An inclusive taxonomy of behavioral biases. Eur J Gov Econ 6:24–58
Perlaviciute G, Steg L (2014) Contextual and psychological factors shaping evaluations and acceptability of energy alternatives: integrated review and research agenda. Renew Sustain Energy Rev 35:361–381
Phillips Y (2012) Landlords versus tenants: Information asymmetry and mismatched preferences for home energy efficiency. Energy Policy 45:112–121
Rhead R, Elliot M, Upham P (2018) Using latent class analysis to produce a typology of environmental concern in the UK. Soc Sci Res 74:210–222
Rouvinen S, Matero J (2013) Stated preferences of Finnish private homeowners for residential heating systems: a discrete choice experiment. Biomass Bioenerg 57:22–32
Sadler M (2003) Applying stated choice modeling to a hybrid energy economy model. Report to Natural Resources Canada, Simon Fraser University
Schleich J, Mills B, Dütschke E (2014) A brighter future? Quantifying the rebound effect in energy efficient lighting. Energy Policy 72:35–42
Soekhai V, de Bekker-Grob EW, Ellis AR, Vass CM (2018) Discrete choice experiments in health economics: past, present and future. PharmacoEconomics 37:201–226
Sousa Lourenço J, Ciriolo E, Rafael Almeida S, Troussard X (2016) Behavioural in-sights applied to policy: European report 2016. JRC EUR 27726 EN. https://publications.europa.eu/en/publication-detail/-/publication/eb1f5ea2-d3ae-11e5-a4b5-01aa75ed71a1/language-en
Steg L, Perlaviciute G, van der Werff E (2015) Understanding the human dimensions of a sustainable energy transition. Front Psychol 6
Swait J, Adamowicz W (1996) The effect of choice environment and task demands on consumer behavior: discriminating between contribution and confusion. University of Alberta Libraries
Viscusi WK, Huber J (2012) Reference-dependent valuations of risk: why willingness-to-accept exceeds willingness-to-pay. J Risk Uncertain 44:19–44
Ward DO, Clark CD, Jensen KL et al (2011) Factors influencing willingness-to-pay for the ENERGY STAR® label. Energy Policy 39:1450–1458
Webber CA, Brown RE, Koomey J (2000) Savings estimates for the Energy Star® voluntary labeling program. Energy Policy 28:1137–1149
Acknowledgements
Part of the work illustrated in the present paper has been developed in the research projects titled EnerValor grant BIA 2015-63606-R (MINECO/FEDER) and VALIUM (Valuation for Integrated Urban Management—supported from the Department of Regional and Urban Studies and Planning—DIST of the Politecnico di Torino).
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Bottero, M., Bravi, M., Dell’Anna, F., Marmolejo-Duarte, C. (2020). Energy Efficiency Choices and Residential Sector: Observable Behaviors and Valuation Models. In: Mondini, G., Oppio, A., Stanghellini, S., Bottero, M., Abastante, F. (eds) Values and Functions for Future Cities. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-23786-8_9
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