Uncertainty, loss aversion, and markets for energy efficiency

By David Greene. 2011

185 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/98d6c71ae6ef41da9bc64e5093d7b13e5753d892

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Citations in the corpus

Estimating direct and indirect rebound effects for U.S. households with input–output analysis Part 1: Theoretical framework (2013, background): “…energy ef ﬁ ciency because of market failures, market barriers, stock turnover issues, behavioral patterns (Azevedo, 2009; Gillingham et al., 2009; Greene, 2011; Howarth and Sanstad, 1995; Jaffe and Stavins, 1994; NAS, 2009; Sanstad et al., 1995; Sorrell, 2004), and the difference between…”
Assessing the Energy-Efficiency Gap (2017, background): “Other relevant sources of uncertainty include future product use, product efficiency, and product life (Greene 2011, 610).”
Improving the behavioral realism of global integrated assessment models: An application to consumers’ vehicle choices (2017, background): “, upfront costs and future cost savings) (Greene, 2011); (ii) decisions are boundedly rational in 6 the sense that finding and processing information is costly and imperfect; (iii) decision making uses 7 heuristic (short-cut) rules which are non-optimizing (e.”
The role of information for energy efficiency in the residential sector (2015, unknown): “Qiu et al. (2014), Greene (2011), Erdem et al. (2010) and Farsi (2010) empirically analyze time and risk preferences and find evidences of the effects of these factors on household energy efficiency adoption.”
How Consumers Value Fuel Economy: A Literature Review (2010, background): “Greene, German and Delucchi (2009) and Greene (2009) applied the theory of context dependent loss aversion from behavioral economics to the question of valuing fuel economy and concluded that a typically loss averse consumer would behave as if he or she required a simple 3-year payback for…”
Achieving California's 80% greenhouse gas reduction target in 2050: Technology, policy and scenario analysis using CA-TIMES energy economic systems model (2015, methodology): “In order to improve the behavioral realism of the system optimization based solely on economic costs, a number of modeling techniques are employed, including growth constraints (to limit “winner-take-all” behavior), share constraints (to account for consumer heterogeneity), and hurdle rates (to acco”
A large-scale test of the effects of time discounting, risk aversion, loss aversion, and present bias on household adoption of energy-efficient technologies (2019, background): “Because decision-makers often evaluate the initial EET investment costs as a loss, loss aversion may affect EET adoption and therefore help explain the energy efficiency paradox (Greene et al., 2009; Greene, 2011).”
Energy Efficiency Investments in the Home: Swiss Homeowners and Expectations about Future Energy Prices (2013, methodology): “This latter ﬁnding is consistent with a number of possible explanations, including the notion that uncertainty about future energy prices hinders energy efﬁciency investments (Hassett and Metcalf, 1993; Metcalf and Rosenthal, 1995; D.L. Greene, 2011).”
Effects of providing total cost of ownership information on consumers’ intent to purchase a hybrid or plug-in electric vehicle (2015, background): “Research also indicates that people apply a high discount rate to future savings associated with lower operating costs, i.e., they value current outlays much more than long term savings (Loewenstein and Thaler, 1989; Frederick et al., 2002; Greene, 2011).”
Prospect theory and energy efficiency (2019, background): “Greene (2011) argues that loss aversion can explain the "energy paradox" or "energy efficiency gap," where individuals appear to neglect ostensibly cost-effective efficiency investments (Allcott and Greenstone 2012, Gillingham and Palmer 2014).1 While prospect theory has been shown to predict…”