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• W2616361773 abstract "This paper uses large-scale micro data to identify key factors affecting the decision to adopt renewable energy generation (wind, solar and biomass) on farms in Scotland. We construct an integrated dataset that includes the compulsory agricultural census and farm structural survey that cover almost all farms in Scotland. In addition to farm owner demographics and farm business structures, we also assess the effect of diversification activities such as tourism and forestry, as well as the spatial, biophysical and geophysical attributes of the farms on the adoption decision. We find that diversified farms are more likely to adopt renewable energy, especially solar and biomass energy. Farms are also more likely to adopt renewable energy if they have high local demand for energy, or suitable conditions for renewable energy production. We find that biophysical factors such as the agricultural potential of farm land are important in adoption decisions. We identify adopter profiles for each type of renewable energy, and map the geographic location of potential adopters. We argue that renewable energy policy should be more integrated with farm diversification policy and farm support schemes. It should also be tailored for each type of renewable energy, for the potential adopter profiles of wind, solar and biomass energy all differ in farm characteristics and geographic distribution." @default.
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• W2616361773 date "2017-08-01" @default.
• W2616361773 modified "2023-10-17" @default.
• W2616361773 title "Exploring factors affecting on-farm renewable energy adoption in Scotland using large-scale microdata" @default.
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• W2616361773 doi "https://doi.org/10.1016/j.enpol.2017.05.025" @default.
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