|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|5064213||1476712||2015||8 صفحه PDF||سفارش دهید||دانلود رایگان|
- A large WTP per household is not necessarily accompanied by a large WTP per kWh.
- WTP for green electricity differs by source, hydropower being the least valued.
- WTP for renewables increases if they substitute conventional energy sources.
- WTPs differ when a respondent's personal characteristics are accounted for.
- The out-of-sample value transfer leads to a median error of around 21%.
At present, electricity generated from power plants using renewable sources costs more than electricity generated from power plants using conventional fuels. Consumers bear these expenses directly or indirectly through higher prices for renewable energy or taxes. The number of studies published over the last few years focusing on people's preferences for renewables has increased steadily, making it more and more difficult to identify key explanatory factors that determine people's willingness-to-pay (WTP) for renewables. We present results of a meta-regression on valuation of consumer preferences for a larger share of renewable energy in their electricity mix. Our meta-regression results reveal a number of important factors that explain the differences in WTP values for renewable energy. Different valuation methods show widely different values, with choice experiments producing the highest estimates. Our results further indicate that consumers' WTP for green electricity differs by source, with hydropower being the least valued. Variables that are often omitted from primary valuation studies are important in explaining differences in values. These variables describe individual and household characteristics as well as information on the type of power plant that will be replaced by renewables. Further, the marginal effect of a survey conducted in the US is pronounced. We also assess the potential for using the results for out-of-sample value transfer and find a median error of 21%.
Journal: Energy Economics - Volume 51, September 2015, Pages 1-8