May 2017 – BRISKEE project reports
Determinants of households adoption of energy efficient technologies in Europe: focussing on preferences for risk, time and losses
Authors: Joachim Schleich, Xavier Gassmann, Corinne Faure, Thomas Meissner
[D2.3 – Scientific working paper on energy efficiency technology adoption]
This paper empirically studies the relation between household adoption of EETs (LEDs, energy efficient appliances, retrofit measures) and risk aversion, standard time preferences, present bias, and loss aversion. The analysis relies on a large representative sample drawn from eight EU countries. Time, risk, and loss aversion preferences were elicited and jointly estimated from participant choices in context-free MPLs.
Improving energy efficiency is commonly considered to be the cheapest short- to medium-term option for meeting energy and climate targets (e.g. IEA 2016), and it is a prime policy goal in many countries. For example, the European Union aims to reduce energy use by at least 30 percent compared to the projected use of energy in 2020. To address environmental externalities such as global warming or resource use, governments employ policies such as technology standards, information measures (e.g. labeling), rebates, tax credits, or subsidized loans. In addition, well-designed policies may also help overcome the so-called “energy efficiency paradox”, according to which decision-makers may fail to invest in energy-efficient technologies even though these appear to pay off under prevailing market conditions (e.g. Jaffe and Stavins 1994, Allcott and Greenstone 2012, Gillingham and Palmer 2014).
For household technology choices, insights from the psychology and behavioral economics literatures suggest that both time and risk preferences may help explain the energy efficiency paradox (e.g. Allcott and Mullainathan 2010, Allcott 2011, Gerarden et al. 2015, Ramos et al. 2015, or Schleich et al. 2016). There is some growing body of evidence on the effects of time discounting and risk preferences on energy-efficient technology adoption; however, a comprehensive test of these effects is missing. Researchers have also used different methods to capture time and risk preferences. Some rely on multiple price lists, following Coller and Williams (1999) and Holt and Laury (2002), while others have used Likert scales, following Dohmen et al. (2010, 2011) and Falk et al. (2016). Moreover, the effects of other domains of preference, such as loss aversion, that can be expected to affect energy-efficient technology adoption remain largely unstudied. This paper aims to fill that gap. To our knowledge, we are the first to empirically investigate the effects of loss aversion on household adoption of energy efficient technologies. We are also the first to simultaneously consider the effects of risk aversion, standard time discounting, present bias, and of loss aversion on energy efficient technology adoption to avoid mistakenly conflating their effects. Methodologically, to get internally consistent parameter estimates, the parameters reflecting standard time discounting, risk aversion, loss aversion, and of present bias are calculated jointly at the individual level. Preferences for time discounting, present bias, risk and loss aversion were elicited via (partly incentivized) decontextualized multiple price list (MPL) lotteries. As an alternative, we compare findings when time and risk preferences are elicited via Likert scales. To address concerns with previous literature, adoption decisions were surveyed from decision-makers for low- (LED light bulbs), medium- (appliances) and high- (retrofit) –stake energy-efficient technologies. Further, the study includes many household control variables (such as intention to move, renting, socio-demographics and individual traits), as well as dwelling characteristics such as size or dwelling age. Finally, empirically, our study is the first to utilize representative samples in a cross-country comparison and with roughly 15,000 respondents, our sample size is much larger than in previous studies, allowing for more generalizable results. Thus, our study contributes to the emerging literature that relate preference measures employed in laboratory experiments to actual behavior for representative samples (e.g. Dohmen et al. 2011). The remainder of the paper is organized as follows. Section 2 provides a discussion of the existing literature that links preferences to energy-efficient technology adoption. Section 3 briefly presents the theoretical model of individual preferences, describes the survey and the elicitation of time preferences, risk preferences, loss aversion, and present bias via multiple price lists, and outlines the variables used in the econometric analysis. Section 4 presents and discusses the findings of the econometric analysis. The final section summarizes the main findings and discusses their implications.
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