Willingness to pay for renewables: Insights from a meta-analysis of choice experiments
Emilio Cerdá,
Xiral López-Otero,
Sonia Quiroga and
Mario Soliño
Energy Economics, 2024, vol. 130, issue C
Abstract:
Renewable energy policies contribute to solving some of the current environmental challenges, such as climate change mitigation and decarbonization, and people around the world are usually willing to pay for a greater presence of renewable energies. That willingness to pay (WTP) can be translated into a gain in social well-being. But not all renewables are equally valued, nor do all people experience the same gain in terms of well-being. Over the last 25 years, many choice experiments have focused on analyzing social preferences for different external effects associated with renewables. This article presents a meta-analysis of choice experiments to examine the evidence on WTP for renewable energies. We examined 63 articles across 28 countries, with a total sample of 52,948 participants and 84,821 observations. We used three econometric approaches (Generalized Least Squares, Weighted Least Squares, and a Panel Data Model) to estimate the willingness to pay of households for renewables around the world. Results show that the promotion of renewable energies is socially demanded, with an average minimum WTP of between €113 and €124 per household per year. The results also show that onshore wind farms are the preferred renewable energy source for electricity generation, whereas people are more reluctant about offshore wind in comparison with other renewable technologies. Finally, some geographic patterns are found, with people from Asia and southern Europe showing, respectively, a lower and higher willingness to pay for renewable energies.
Keywords: Electricity; Generalized least squares; Meta-regression; Panel data; Renewables; Weighted least squares (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:130:y:2024:i:c:s0140988324000094
DOI: 10.1016/j.eneco.2024.107301
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