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Increasing carbon emissions efficiency (CEE) is essential for Chinese cities to achieve their dual carbon goals. For cities with higher CEE, it is crucial to comprehensively evaluate their carbon emissions reduction potential (CERP) and develop targeted carbon reduction paths. This study first determined the input, expected output, and non-expected output indicators and used the super-efficiency Slack Based Measure model to calculate the CEE of 336 cities. Secondly, we evaluated the urban development level, structural optimization space, and natural resource endowment using the BWM-CRITIC-TOPSIS method and combined them with CEE to evaluate the CERP. Finally, we used K-means clustering to group cities according to the carbon emissions level and CERP and proposed targeted emission reduction recommendations. The results showed that cities in the northern part of China had lower CEE, while the coastal areas had higher CEE. In the primary indicators of CERP, structural optimization space was the most important, followed by efficiency improvement space. The eastern region has a greater potential for carbon reduction, with an average score of 63.71 points (out of 100 evaluation points) for the 336 cities. The cities could be classified into nine types, with the ninth category being the key focus cities for achieving the 2030 carbon peak goal with high carbon emissions and CERP. [ABSTRACT FROM AUTHOR]

The coordinated development of upstream, midstream, and downstream of wind power industry chain is the key to solve the problem of wind power consumption. Therefore, this paper firstly constructs the performance evaluation index system of the upstream, midstream, and downstream of the wind power industry chain, and evaluates the performance of the upstream, midstream, and downstream of the wind power industry based on entropy weight-TOPSIS. Secondly, the coupling coordination degree (CCD) model between subsystems of wind power industry chain is established to dynamically evaluate the CCD between upstream and midstream, midstream and downstream, and among three subsystems of wind power industry chain. Finally, taking the development data of China's wind power industry from 2010 to 2019 as an example, the coupling coordination degree between subsystems is empirically studied. The results show that: the overall performance of wind power industry chain has a tortuous upward trend; the coupling degree (CD) of each subsystem is better than that of 2011 and 2012; The CCD of upstream and midstream of wind power industry chain fluctuates more than that of midstream and downstream and that of three subsystems. • The performance evaluation index of the upstream, midstream and downstream of the wind power industry chain is established. • Entropy weight-TOPSIS method is used to evaluate the performance of wind power industry chain. • The coupling coordination degree model between subsystems of wind power industry chain is established. • The coupling coordination degrees between subsystems of wind power industry chain are dynamically evaluated. [ABSTRACT FROM AUTHOR]