2017
DOI: 10.1016/j.erss.2017.04.008
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Making ‘Smart Meters’ smarter? Insights from a behavioural economics pilot field experiment in Copenhagen, Denmark

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Cited by 62 publications
(33 citation statements)
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“…Denmark’s cooperative, not‐for‐profit ownership policy has also supported R&D interests by encouraging local innovation, widening the market by benefit‐sharing, and serving regional interests by improving local acceptance, which, in turn, helps to meet the costs of the transition (Eikeland & Inderberg, , p. 172). Prospective policy innovation to involve consumers and prosumers, however, needs to pay more attention to how information is presented and designed, as shown in a study on smart‐metering initiatives in Copenhagen (Bager & Mundaca, ).…”
Section: Resultsmentioning
confidence: 99%
“…Denmark’s cooperative, not‐for‐profit ownership policy has also supported R&D interests by encouraging local innovation, widening the market by benefit‐sharing, and serving regional interests by improving local acceptance, which, in turn, helps to meet the costs of the transition (Eikeland & Inderberg, , p. 172). Prospective policy innovation to involve consumers and prosumers, however, needs to pay more attention to how information is presented and designed, as shown in a study on smart‐metering initiatives in Copenhagen (Bager & Mundaca, ).…”
Section: Resultsmentioning
confidence: 99%
“…The heterogeneity of household characteristics and perceptions may partly explain the 'energy efficiency gap' (Allcott and Greenstone, 2012;Jaffe and Stavins, 1994) between a hypothetical optimum as seen from an outside economics-engineering perspective and observed household decisions, which seem to undervalue future energy savings relative to upfront investment costs, resulting in a typically lower than expected uptake of energy-efficient technologies (reflected in high empirical estimates of implicit discount rates, as reviewed by Hausman, 1979;Train, 1985). Various explanations are discussed in the literature (for a review, see Gillingham and Palmer, 2014), which can be grouped into market barriers (e.g., hidden costs), market failures (e.g., asymmetric or imperfect information, split incentives) and systematic behavioural biases, as described by behavioural economics (e.g., loss aversion, suboptimal decision heuristics, status-quo bias, saliency, procrastination) (Allcott and Mullainathan, 2010;Bager and Mundaca, 2017;Frederick et al, 2002;Kahneman and Tversky, 1979;Lillemo, 2014;Shogren and Taylor, 2008;Simon, 1955).…”
Section: Household Decision Makingmentioning
confidence: 99%
“…Overall, the limited evidence we found of policy interventions that explicitly address behavioural factors was confined to non-governmental actions. Typically, interventions are deployed via experiments, pilot studies or small-scale utility-driven initiatives in (mostly) industrialised countries [21,30,33,66,68,69,71,77,78]. A priori, this situation suggests a divide between governmental and non-governmental initiatives and indicates that local behavioural interventions may need to be coupled or coordinated with national policy instruments.…”
Section: Resultsmentioning
confidence: 99%
“…12 It seems that policy efforts are limited to approaches that reduce the use and implementation of LCETs to the application of economic theory [cf 80]. Consequently, one could argue that current policy is not very effective because, among several factors, it fails to explicitly take account of mounting evidence of suboptimal decision-making and normative, emotional, moral and social influences affecting energy choices and climate action [21,30,32,43,66,78,[81][82][83]. With respect to our review of the documentation, the limited consideration of behavioural factors, and the lack of behavioural-oriented interventions may be driven by a lack of transdisciplinary approaches designed to support policymaking [64,79,84], political and ethical concerns regarding the integration of libertarian paternalistic approaches [85], and a poor understanding of what behavioural science has to offer [86][87][88].…”
Section: Resultsmentioning
confidence: 99%