A large-scale test of the effects of time discounting, risk aversion, loss aversion, and present bias on household adoption of energy-efficient technologies

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. AbstractThis paper empirically and jointly analyses the relations between risk aversion, standard time discounting, present bias, and loss aversion and household stated adoption of low to high stake energy efficiency technologies (EETs) (light emitting diodes (LEDs), energy efficient appliances, and retrofit measures). The analysis relies on a large representative sample drawn from eight European Union countries. Preferences over time, risk and losses were elicited and jointly estimated from participant choices in incentivized, context-free multiple price list experiments. The findings from econometrically estimating EET adoption equations suggest that present-biased individuals are less likely to adopt EETs. They also provide (weak) evidence that individuals which are more risk-averse, or more loss-averse, or which exhibit a lower discount factor are less likely to adopt EETs. Finally, omitting one or several of the time and risk or loss-aversion parameters when estimating the EET adoption equations did not appear to cause omitted variable bias.

Poor energy ratings when appliances convey?

“…The existing literature provides mixed results on the association of age and household adoption of energy-efficient technologies. While Ameli and Brandt (2015), or Schleich et al (2019b) find older households to be more likely to adopt energy-efficient technologies, Michelsen and Madlener (2012), Mills and Schleich (2014), and Ramos et al (2016) find the opposite. These inconsistent results may be explained by opposite effects of age on factors affecting energy efficiency adoption: on one hand, older age is typically associated with lower preferences for state-of-the art technologies as well as higher uncertainty about return on investment within one's lifetime (Carlsson-Kanyama et al, 2005); both of these factors would lower energy efficiency adoption.…”

“…Empirical analyses often find renters to be less likely than homeowners to adopt energy-efficient technologies (e.g., Davis, 2011;Ameli and Brandt, 2015;Krishnamurthy and Kriström, 2015;Schleich et al, 2019b). This result is typically explained through the landlord-tenant problem, i.e., the fact that because of split incentives, investors in energy-efficient technologies may not be able to appropriate the benefits of the investment; if the landlord provides the technology but the tenant benefits from a smaller energy bill, the landlord has no financial incentive to invest in energy-efficient technologies which have higher upfront costs than non-energy-efficient technologies.…”

“…The extant empirical literature typically finds a positive correlation of household income and the purchase of energy-efficient technologies (e.g., Michelsen and Madlener, 2012;Ramos et al, 2015;Schleich, 2019;Schleich et al, 2019b;Trotta, 2018) because, for instance, more affluent households are less likely to suffer from capital constraints. In the empirical specification, we used a dummy variable which we set equal to 1 if the household net income was above the country median.…”

“…Likewise, empirical studies typically find a positive relation between education and household adoption of energy-efficient technologies (e.g. Di Maria et al, 2010;Michelsen and Madlener, 2012;Ramos et al, 2015;Schleich et al, 2019b) because a higher level of education is expected to lower the costs of information acquisition and to improve the processing of information (Schultz et al, 1975). For the econometric analysis, we employed a dummy variable which took on the value of 1 if the respondent's level of education exceeded the country median.…”

“…The majority of empirical analyses find pro-environmental attitudes to be positively related with household adoption of low-cost energy-efficient technologies such as light bulbs or appliances (e.g. Di Maria et al, 2010;Mills and Schleich, 2014;Ramos et al, 2016;Schleich et al, 2019b), but appear to be less relevant for predicting high-cost investments such as thermal retrofit or low-energy houses (e.g., Whitmarsh, 2009;Ramos et al, 2016;Olsthoorn et al, 2019), thus suggesting a trade-off between environmental and financial concerns. Our analysis employs environmental identity to capture environmental attitudes.…”