Renewable energy is worldwide seen as a key element necessary to address climate change. However, finding socially acceptable locations for renewable energy facilities and the accompanying infrastructure increasingly often faces fierce opposition. This paper quantifies the landscape externalities of renewable energies employing a choice experiment. In addition, it is investigated how accounting for non-compensatory choice behavior, i.e. attribute cut-offs, affects welfare measures and subsequently policy recommendations. The empirical application is Germany where we conducted a nationwide survey on the development of renewable energies. We first show that cut-off elicitation questions prior to the choice experiment at least partially influence preferences. We further find that most participants state cut-off levels for attributes. Many are, however, at the same time willing to violate the self-imposed thresholds when choosing among the alternatives. To account for this effect, stated cut-offs are incorporated into a mixed logit model following the soft cut-off approach. Model results indicate substantial taste heterogeneity in preferences and in the use of cutoffs. Also, welfare estimates are substantially affected. We conclude that welfare changes from renewable energy development could be strongly underestimated when cut-offs are ignored.

可再生能源在世界范围内被视为应对气候变化所必需的关键要素。然而，寻找可再生能源设施及其配套基础设施在社会上可以接受的位置越来越面临激烈的反对。本文通过选择实验对可再生能源的景观外部性进行了量化。此外，还研究了如何考虑非补偿性选择行为，即属性界限，影响福利措施以及随后的政策建议。经验的应用是德国，我们在德国进行了可再生能源发展的全国性调查。我们首先表明，选择实验之前的截断启发问题至少会部分影响偏好。我们进一步发现，大多数参与者都声明了属性的截止级别。但是，许多人同时愿意在选择替代方案时违反自我施加的阈值。为了解决此影响，遵循软截断方法，将规定的截断值合并到混合logit模型中。模型结果表明，偏好和临界值的使用在口味上存在很大的异质性。此外，福利估计数也受到很大影响。