Looking beyond time preference: Testing potential causes of low willingness to pay for fuel economy improvements

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2023-10-01 DOI:10.1016/j.reseneeco.2023.101404
Féidhlim P. McGowan , Eleanor Denny , Peter D. Lunn
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Abstract

Time preferences are considered a leading cause of the energy efficiency gap. We test two cognition-based mechanisms (concentration bias and underestimation bias) which are distinct from time preferences but can produce identical behaviour when costs are paid upfront and benefits are spread over time. We use an experiment that measures willingness-to-pay for an improvement in fuel economy to test the explanatory power of these mechanisms. The sample is large, nationally representative and comprised only of car buyers (n = 2368). The experiment varies between-subjects (i) the payment schedule for the fuel economy improvement, and (ii) the temporal framing of its monetary benefit. We combine the payment schedules and the benefit frames so that the pattern of results predicted by time preferences differs from the pattern predicted by cognitive mechanisms. Results support the preregistered hypotheses: willingness-to-pay increases as the payment schedule becomes more dispersed across time and decreases when the benefit is presented as more disaggregated (i.e. a monthly saving instead of annual or multi-year saving). The findings are consistent with the predictions of the two cognitive mechanisms, which may explain part of the energy-efficiency gap currently attributed to pure time preference.

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超越时间偏好:测试改善燃油经济性的低意愿的潜在原因
时间偏好被认为是能源效率差距的主要原因。我们测试了两种基于认知的机制(集中偏差和低估偏差),这两种机制不同于时间偏好,但当成本提前支付,收益随着时间的推移而分散时,它们可以产生相同的行为。我们使用一个实验来衡量为提高燃油经济性而付费的意愿,以测试这些机制的解释力。样本量大,具有全国代表性,仅包括购车者(n=2368)。实验因受试者而异(i)燃油经济性改善的付款时间表,以及(ii)其货币效益的时间框架。我们将支付时间表和福利框架结合起来,使时间偏好预测的结果模式与认知机制预测的模式不同。结果支持了预先登记的假设:支付意愿随着支付时间表在时间上变得更加分散而增加,而当福利更为细分时(即每月储蓄而不是每年或多年储蓄)则减少。这些发现与这两种认知机制的预测一致,这可能解释了目前归因于纯粹时间偏好的部分能效差距。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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