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Water resource environmental carrying capacity-based reward and penalty mechanism: A DEA benchmarking approach

Zhou, Xiaoyang, Luo, Rui, An, Qingxian, Wang, Shouyang, Lev, Benjamin
Journal of cleaner production 2019 v.229 pp. 1294-1306
basins, carrying capacity, case studies, cities, models, pollutants, water resources, watersheds, China
Due to the discharge of pollutants, over-exploitation of water resources and large regional imbalances, it is critical to design an effective Reward and Penalty Mechanism (RPM) for the regions in a basin based on the Water Resource Environmental Carrying Capacity (WRECC) performance. To determine how to set the reference points of reward and penalty plans and design the RPM, the following approach is proposed. Firstly, since the evaluated regions always orient their activities towards certain goals at the initial stage of the evaluation period, this paper finds the closest goal for each indicator according to the previous performance using a DEA benchmarking model. Secondly, a reward and penalty plan addressing both reward and penalty within a unified benchmarking framework is designed according to the different alert levels determined from WRECC performances. Thirdly, an extended DEA model is developed to adjust the benchmarking of the current period to the goals to determine DEA targets that are achievable and represent best practices as reference points. The gaps between the DEA targets and actual observed values allow for us to identify the alert level of the evaluated region. The rewards or penalties of the current period can be obtained based on reward and penalty plans at the final stage. A case study concerning the WRECC-based PRM of the top 10 cities in China's Huaihe River basin is selected to demonstrate the validity of the proposed approach. The results indicate that the top 10 cities of the Huaihe River basin in 2016 are benchmarked against Zhengzhou, Yangzhou and Jining; Zhengzhou, Xuzhou, Yangzhou and Jining should receive rewards and the remainder of the evaluated cities should pay penalties. According to the results, the gap between the actual performance and the DEA targets can be defined; how much improvement the regions should make can be identified; and regions that should be rewarded or punished as well as the specific amount of money required can be determined.