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Indirect estimation of willingness to pay for energy technology adoption

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  • Ke, Jing
  • Khanna, Nina
  • Zhou, Nan

Abstract

Adopting energy-efficient and clean technologies is key to climate change mitigation and meeting long-term sustainability goals because they significantly reduce energy consumption and related carbon emissions. Understanding existing barriers and drivers for the adoption of these energy-efficient and clean technologies will be crucial to meeting ambitious national energy and emissions targets, and the customers’ willingness to pay (WTP) is a key factor in understanding the potential for scaling-up adoption. However, in practice, commonly-used WTP estimation methods such as survey or purchase experiments are not always practical or feasible due to budget, time, labor or data constraints. This study proposes a new constrained optimization-based indirect estimation of WTP for energy technology adoption using customers’ implicit life-cycle cost-benefit analysis and market data. The empirical probability distribution of WTP is estimated using the Monte Carlo methods. This new indirect estimation method provides a deeper understanding of the barriers and customers’ willingness to adopt high efficiency and clean energy technologies, and informs the development of supporting policies and programs needed to accelerate market adoption.

Suggested Citation

  • Ke, Jing & Khanna, Nina & Zhou, Nan, 2022. "Indirect estimation of willingness to pay for energy technology adoption," Applied Energy, Elsevier, vol. 312(C).
    • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s0306261922001647
    DOI: 10.1016/j.apenergy.2022.118701
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