@article{35495,
  author = {Jing Ke and Nina Khanna and Nan Zhou},
  title = {Indirect estimation of willingness to pay for energy technology adoption},
  abstract = {<p><span>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.</span></p>},
  year = {2022},
  journal = {Applied Energy},
  volume = {312},
  pages = {118701},
  month = {04/2022},
  issn = {03062619},
  doi = {10.1016/j.apenergy.2022.118701},
  language = {eng},
}