An empirical analysis of spatial effects and their driving factors in urban green development using spatial econometric models

Evaluation of the spatial effect of urban green development can provide spatial clues for solving urban governance problems. The existing research lacks an assessment of urban green development from the perspective of productivity. In this study, 285 cities in China from 2008 to 2020 were selected as research objects. An indicator system for urban green development was innovatively constructed from a productivity perspective. Meanwhile the spatial autocorrelation effects of its development under the adjacency relationship weight matrix (ARWM), geographical distance weight matrix (GDWM), and economic distance weight matrix (EDWM) were clarified. Spatial Durbin Models (SDM) partial differential method was used to decompose the spatial effects of the influencing factors of urban green development, and the spatial correlation effects and spatial spillover effects of urban green development were clarified. The findings reveal that: (1) From the perspective of spatial correlation, urban green development has global spatial autocorrelation, which indicates that the level of urban green development is affected by at least 13% of cities with similar economic levels, 84% by cities with close geographical distance, and 63% by adjacent cities. (2) From the perspective of spatial spillover effect, urban green development has significant spatial spillover effect under ARWM and GDWM, specifically, the level of economic development under different spatial weight matrices has a significant spillover effect on urban green development. There are spillover effects on the impact of industrial structure, innovation level, infrastructure conditions, government intervention and foreign trade openness on urban green development under the influence of the ARWM. There is a significant spillover effect of environmental regulation on urban green development under the influence of EDWM.