Spatial Analysis and Prediction of Ecological Welfare Performance: Evidence from 284 Prefecture-level Cities in China

The general debate of the 78th session of the United Nations reaffirmed the importance of the Sustainable Urban and Community Development Goal (SDG 11), however, the pervasive accounting gaps still lead to significant challenges in the monitoring and evaluation of SDG 11 indicators. This paper focuses on ecological welfare performance accounting, aiming to assess the weaknesses and internal gaps of regional SDG 11 at the aggregate level, so as to promote eco-city planning and management, improve the well-being potential of urban residents, and reduce the adverse impact on the ecological environment. Taking China as a developing country as a case, the two-stage DEA model is used to measure the ecological welfare performance of 284 cities in China from 2007 to 2022, and the spatiotemporal pattern of efficiency at each stage of the ecological welfare transformation process is described, a modified gravitational model is constructed to transform the "attribute data" of ecological welfare performance into "relationship data", and the characteristics of network structure are described with the help of social network analysis, and the formation mechanism of the spatial correlation network of ecological welfare performance is revealed and its dynamic evolution characteristics are revealed through the spatial Markov chain.