Onshore wind and the likelihood of planning acceptance: Learning from a Great Britain context. (May 2019)

• Record Type: Journal Article
• Authors: Harper, Michael; Anderson, Ben; James, Patrick A.B.; Bahaj, AbuBakr S.
• Is Part Of: Energy policy. Volume 128(2019)
• Page Start: 954
• Page End: 966
• Publication Date: 2019-05
• Subjects: Planning -- Demographics -- GIS -- Great Britain -- Logistic regression; Energy policy -- Periodicals; Politique énergétique -- Périodiques; Electronic journals; 333.79
• DOI: 10.1016/j.enpol.2019.01.002
• Languages: English
• ISSNs: 0301-4215

Abstract

Abstract: Geospatial modelling is extensively used to identify suitable sites for the installation of onshore wind turbines, with the starting point being the estimate of exploitable resource. However, there are concerns that such approaches do not accurately consider the social issues surrounding such projects, resulting in large numbers of projects subsequently being rejected at the planning permission stage. Using the location of 1721 historic wind turbine planning applications in Great Britain, this paper explores whether the planning success of proposed wind turbine projects can be better predicted using a range of geospatial, social and political parameters. The results indicate that the size of the project, local demographics and the proximity to existing wind turbines are key influences affecting planning approval. The paper demonstrates that quantitatively linking local social and political data enhances the prediction of the planning outcome of wind turbine proposals, and highlights that geospatial parameters are necessary but in isolation, not sufficient for assessing the suitability of potential sites. These results also suggest that national policy is restricting the development of onshore wind energy in regions which appear generally supportive of wind energy. Abstract : Highlights: Geospatial parameters in isolation are not sufficient for assessing onshore wind. Demographics linked to the acceptance rates of wind turbines. Large regional variation in planning acceptance rates.