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Exploring the global and local social sustainability of wind energy technologies: An application of a social impact assessment framework

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  • Buchmayr, A.
  • Verhofstadt, E.
  • Van Ootegem, L.
  • Thomassen, G.
  • Taelman, S.E.
  • Dewulf, J.

Abstract

A transition to renewable energy sources is needed in the EU countries to achieve their goal of a low-carbon economy. This transition may come with potentially negative impacts on the well-being of the population, both globally and locally. Such social impacts are not yet systematically assessed for renewable energy technologies. In this paper, a social impact assessment framework for renewable energy technologies is developed and applied for a wind energy case study. The assessed social categories comprise impacts on human health, human rights infractions, working conditions, local job creation, quality of residential life, landscape quality. In order to cover this broad field of social impacts, four distinct social impact assessment methods were combined in a common social impact assessment framework. The application of the framework was demonstrated by means of the wind energy case study. The results are presented in the form of a social sustainability dashboard comprising 23 social impact indicators covering both global and local well-being impacts. The analysis showed that the life cycle material demand of offshore wind projects has larger impacts on global well-being than the onshore alternatives. For the local dimension, the offshore case was found to be less intrusive for the local population.

Suggested Citation

  • Buchmayr, A. & Verhofstadt, E. & Van Ootegem, L. & Thomassen, G. & Taelman, S.E. & Dewulf, J., 2022. "Exploring the global and local social sustainability of wind energy technologies: An application of a social impact assessment framework," Applied Energy, Elsevier, vol. 312(C).
    • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s0306261922002549
    DOI: 10.1016/j.apenergy.2022.118808
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    2. Yu, Chuanjin & Li, Yongle & Chen, Qian & Lai, Xiaopan & Zhao, Liyang, 2022. "Matrix-based wavelet transformation embedded in recurrent neural networks for wind speed prediction," Applied Energy, Elsevier, vol. 324(C).
    3. Buchmayr, A. & Taelman, S.E. & Thomassen, G. & Verhofstadt, E. & Van Ootegem, L. & Dewulf, J., 2023. "A distance-to-sustainability-target approach for indicator aggregation and its application for the comparison of wind energy alternatives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).

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