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      Urban Agglomeration Ecological Welfare Performance and Spatial Convergence Research in the Yellow River Basin

      ,
      Land
      MDPI AG

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          Abstract

          As human society is entering an era of scarce natural resources, realizing the welfare level of human society is a fundamental requirement to improve sustainable development, while being restrained by the ecological environment. In this paper, we divided ecological welfare performance (EWP) into an ecological economic system and an economic welfare system from the perspective of ecological welfare, and predicted the EWP in the Yellow River Basin Urban Agglomerations (YRBUAs) using the US-NSBM model in two stages. We further explored the dynamic change trend and spatial convergence characteristics in the YRBUAs using the Dagum Gini coefficient, the kernel density estimation method, and the spatial convergence models. The results indicate that there are great spatial variations in EWP in the YRBUAs, where the spatial variation in the downstream is higher than that in the upstream, and the spatial distribution pattern in large- and medium-sized cities is higher than that in small cities. The DEA efficiency could not be realized overall throughout the study period, but it shows an improving trend. At the same time, absolute β convergence and conditional β convergence were observed in the YRBUAs, both overall and between the UAs. This paper provides a basis for analyzing the spatial pattern of EWP and for promoting the coordinated development of urban agglomerations in the YRBUAs, thus serving as a reference for the sustainable development of ecologically sensitive regions in countries across the world.

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                Author and article information

                Journal
                Land
                Land
                MDPI AG
                2073-445X
                November 2022
                November 17 2022
                : 11
                : 11
                : 2073
                Article
                10.3390/land11112073
                cd05bb01-69b7-4648-8bc3-a58edeff1d86
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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