Consumers' willingness to pay for renewable energy: A meta-regression analysis

Chunbo Ma, Abbie Rogers, Marit Kragt, Fan Zhang, Maksym Polyakov, Fiona Gibson, Morteza Chalak Haghighi, Ram Pandit, Sorada Tapsuwan

    Research output: Contribution to journalArticlepeer-review

    124 Citations (Scopus)
    867 Downloads (Pure)

    Abstract

    Using renewable energy for domestic consumption has been identified as a key strategy by the Intergovernmental Panel on Climate Change to reduce greenhouse gas emissions. Critical to the success of this strategy is to know whether consumers are willing to pay to increase the proportion of electricity generated from renewable energy in their electricity portfolio. There are a number of studies in the literature that report a wide range of willingness to pay estimates. In this study, we used a meta-regression analysis to determine how much of the variation in willingness to pay reflects true differences across the population and how much is due to study design, such as survey design and administration, and model specification. The results showed that factors that influence willingness to pay, such as renewable energy type, consumers’ socio-economic profile and consumers’ energy consumption patterns, explain less variation in willingness to pay estimates than the characteristics of the study design itself. Because of this effect, we recommend that policy makers exercise caution when interpreting and using willingness to pay results from primary studies. Our meta-regression analysis further shows that consumers have significantly higher willingness to pay for electricity generated from solar, wind or generic renewable energy source (i.e. not a specific source) than hydro power or biomass.
    Original languageEnglish
    Pages (from-to)93-109
    Number of pages17
    JournalResource and Energy Economics
    Volume42
    Early online date21 Jul 2015
    DOIs
    Publication statusPublished - Nov 2015

    Fingerprint

    Dive into the research topics of 'Consumers' willingness to pay for renewable energy: A meta-regression analysis'. Together they form a unique fingerprint.

    Cite this