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• W4225006987 abstract "Hydropower dams could benefit the three dimensions of sustainability of the river basins where they are deployed. However, experiences from large hydropower projects worldwide taught that large hydropower dams may cause serious social and environmental concerns that ultimately derive into public disapproval. Hydropower industries are increasingly aware that public support is a determining factor that could restrict the development of large hydropower projects, and hence industries have started to take action. Recently, a large hydropower industry from Colombia has implemented actions aimed at sustainability to improve the environmental and social welfare of territories without deviating from the business core. However, formulating effective sustainability policies that consider the different viewpoints of industrials, policymakers, and the general community is challenging. Hence, here we aim to identify cause-effect correlations between the impact management actions taken by the firm and the public perception of the impacts caused by two large hydropower dams in the Colombian Andes. To this end, we performed 50 stepwise logistic regressions between ad-hoc impact management perception indices (predictor variables) and impact perception variables (response variables). The data is based on a 25-question survey assessing the public perceptions of 694 respondents from two river basins and three social groups. Findings revealed 28 significant correlations between impacts and impact management, showing that the corporate actions for environmental protection play a crucial role in the public attitudes on sustainability. Our findings may provide guidelines to develop tailored policies aimed at sustainability in the hydropower sector of developing countries." @default.
• W4225006987 created "2022-04-29" @default.
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• W4225006987 date "2022-08-01" @default.
• W4225006987 modified "2023-10-02" @default.
• W4225006987 title "Social perception assessment of hydropower sustainability: A stepwise logistic regression modeling" @default.
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• W4225006987 doi "https://doi.org/10.1016/j.envsci.2022.03.026" @default.
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