Abstract

Hydropower projects have contributed significantly to the world’s just energy transition, while at the cost of massive-scale resettlement and displacement. The principles of a just transition ensure that the low-carbon transition process is fair, inclusive and socially responsible. However, since hydropower resettlers are often involuntary, it incurs many socio-environmental disruptions for the affected. Whether the post-resttlement living environment of the resettlers can also be just and sustainable needs to be effectively evaluated. This paper establishes and validates an optimized deep neural network (DNN) approach for predictability and reliability. A comprehensive set of living environment development indicators is then used to assess the just transition in the living environment of resettlers, based on three typical hydropower station resettlement cases in the Wujiang River Basin in Guizhou Province, China. The results indicate that individual living condition, community development, and fairness in market and social participation have important impacts on the just transition of resettlers’ living environments. By summarizing the composite affiliation and score values of each sub-indicator, it is found that the Hongjiadu hydropower station has the lowest composite score of the resettlers’ living environment, ranging from 0.476 to 0.680, followed by the Dongfeng hydropower station, from 0.483 to 0.709. The resettlers of Qianzhong Hydropower Hub have the highest living environment status after resettlement, from 0.497 to 0.794. Although there are differences in the living environment scores of different hydropower stations, it is argued that the transition of the living environment in the stepped power stations along the Wujiang River is just and sustainable in the long term. This paper provides valuable insights into the just transition of hydropower and the living environment of the resettlers, so as to foster sustainable development policymaking.

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