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How landscape-technology fit affects public evaluations of renewable energy infrastructure scenarios. A hybrid choice model

可再生能源 环境经济学 持续性 环境资源管理 适度 离散选择 业务 计算机科学 经济 计量经济学 工程类 生态学 生物 电气工程 机器学习
作者
Boris Šalak,Kreg Lindberg,Félix Kienast,Marcel Hunziker
出处
期刊:Renewable & Sustainable Energy Reviews [Elsevier]
卷期号:143: 110896-110896 被引量:19
标识
DOI:10.1016/j.rser.2021.110896
摘要

In response to the effects of climate change, many countries are realigning their energy systems to the principle of sustainability. An energy system change will lead to the development of substantial renewable energy infrastructure (mostly wind and photovoltaic) in landscapes with effects on perceived landscape quality and socio-political acceptance. Both direct perceptive effects of physical landscape structures and latent meanings associated with those structures potentially affect their acceptance. This work evaluates the role of landscape-technology fit (derived from place-technology fit) representing the extent to which alternatives within each of these two components “fit” together (e.g., does a given type of renewable energy infrastructure fit well within some landscapes but not others?). It also evaluates the role of latent meanings ascribed to landscapes and renewable energy infrastructure within that mentioned “fit” decision as well as the role of prior experience (exposure) to both. The study is based on a survey of Swiss citizens in a representative online panel (n = 1062). To estimate preferences for diverse renewable energy infrastructure scenarios across landscape types, a discrete choice model was implemented. Meanings ascribed to landscapes and renewable energy infrastructure were included in a second component of the survey. An innovative hybrid choice model approach facilitated integration of latent and observed variables in a hierarchy of predictors. Results show that most effects were statistically significant. Landscape-technology fit functioned as a moderator between choice attributes and preferences; in turn, it is predicted by landscape and renewable energy meanings, which are predicted by relevant prior experience (exposure).
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