Multi-dimensional dynamic spatio-temporal evolution of the green development efficiency of water-energy-food in China. Issue 2 (13th February 2023)

• Record Type: Journal Article
• Authors: Li, Jinqiu; Huang, Dechun
• Is Part Of: Water policy. Volume 25:Issue 2(2023)
• Page Start: 122
• Page End: 145
• Publication Date: 2023-02-13
• Subjects: Green development efficiency -- Policy drivers -- Spatial effect -- Super-EBM -- Water-energy-food; Water-supply -- Management -- International cooperation; Water resources development -- International cooperation; Periodicals; 333.911505
• DOI: 10.2166/wp.2023.145
• Languages: English
• ISSNs: 1366-7017

Abstract

Abstract: This paper constructs a green development efficiency index framework of water-energy-food in China, and uses the Super-EBM model to measure it more accurately and scientifically. The existing studies on water-energy-food efficiency lack the analysis of regional differential decomposition and spatial state transition. In this paper, two kinds of models are used for complementary analysis. One is kernel density map, Dagum spatial Gini coefficient decomposition and traditional Markov chain, which does not contain spatial factors. The other is the global Moran index, spatial Markov chain and spatial spillover effect, including spatial factors. The spatio-temporal dynamic evolution of the green development efficiency of water-energy-food (GWEF) in China is compared from the perspective of national, regional and provincial dimensions. The conclusion is more scientific and comprehensive, which is conducive to the green collaborative development among water-energy-food, economy and environment in China. The study found that GWEF had a lot of room for improvement. The overall spatial difference was mainly derived from the regional difference. GWEF had a significant positive spatial autocorrelation. The development of GWEF maintained the convergence characteristics of clubs. The spatial spillover effect of the main influencing factors was studied. HIGHLIGHTS: This paper adopts two categories of models. One is kernel density map, Dagum spatial Gini coefficient decomposition and traditional Markov chain, which does not contain spatial factors. The other is global Moran index, spatial Markov chain and spatial spillover effect, including spatial factors. The spatio-temporal dynamic evolution of GWEF in China was compared from national, regional and provincial dimensions. Graphical Abstract