The conflict between wind energy expansion and local environmental protection has attracted attention from society and initiated a fierce discussion about the community acceptance of wind farms. There are various empirical studies on factors affecting the public acceptance of wind farms, but little concerning the correlation and significance of factors, especially in a close distance to the wind farms. This paper aims to identify, classify, and analyze the factors affecting community acceptance through literature review, questionnaire, variance analysis, and linear regression analysis. A total of 169 questionnaires was conducted in 17 villages around the Zhongying Wind Farm in Zhejiang Province, China. The factors are categorized into four groups: Location-related factors, demographic factors, environmental impact factors, and public participation factors. Through the analysis of variance (ANOVA) and linear regression analysis, the outcome shows the universal rule of community acceptance under the Chinese social background. Finally, recommendations for improving wind farm planning procedures are put forward.
Under the background of the energy transition, the ecosystems are threatened by the rapid expansion of renewable power plants, especially wind turbines with enormous sizes. While the potential ecological influence near wind farms has not been extensively studied. In the process of wind farm site selection, there is no uniform methodology to assess the ecological impact on the local ecosystem. This paper proposed a research framework to quantify the impacts on ecological corridors under the influence of wind farms. Taking five wind farms in Ningbo Peninsula as cases, the geographical data from Landsat 8, NDVI, DEM, and wind turbine data from Vortex were selected from 2010 to 2020. The ecological sources and resistance surfaces were analyzed with and without wind farms under least-cost distance and least-cost path models. The results revealed that wind farms could increase the migration resistance that prevents the species' ecological corridors, and change the number and routes of main ecological corridors. Additionally, the cumulative resistance value positively correlated with the distance between ecological sources. Through GIS spatial quantification assessment, this paper helps balance wind energy development and regional ecological security and provides scientific and orderly planning suggestions for the healthy development of the regional ecological network.
Wind energy is an effective solution for achieving the carbon-neutrality target and mitigating climate change. The expansion of onshore wind energy evokes extensive attention to environmental impact in the locality. The landscape visual impact has become the critical reason for the local protest. This paper proposed a landscape visual impact evaluation (LVIE) model that combines the theoretical framework and practical solutions and optimizes the onshore wind farm planning procedures. Based on the theoretical research on landscape connotation, the evaluation principles, criteria, and a quantitative indicator set is constructed for LVIE model with three dimensions: landscape sensitivity, the visual impact of WTs, and viewer exposure. The practicality of this evaluation model is conducted through multi-criteria GIS analysis by the case study of Friedrich-Wilhelm Raiffeisen Wind Farm in Germany. The evaluation results illustrate detailed, visualized outcomes of landscape visual impact that are deeply combined with planning procedures. The innovation of this paper is to refine the form of evaluation results, optimize the procedures of wind farm planning, and enable cooperation between different planning departments and stakeholders with definite, visible, user-friendly evaluation results. This research provides precise comparison opportunities for different projects or the same project at different periods to obtain quantitative conclusions and feedback information. This paper enhances the accurate processing of multiple information and standardization process in wind energy visual impact evaluation.
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