Deciphering flows: Spatial correlation characteristics and factors influencing carbon emission intensity in the Yangtze River Delta

Abstract

Carbon leakage caused by factor flow is detrimental to the realization of global climate change goals, and the carbon reduction effect of factor flow is not yet clear. Reductions in the carbon emission intensity (CEI) can support ecologically and economically sustainable development efforts in developing countries. In this study, exploratory data analysis was used to investigate the spatial correlation characteristics of the CEI in the Yangtze River Delta, and the network of economic, data, and innovation flows was constructed via a gravity model. The heterogeneity impact of factor flows on CEI was analyzed via the STIRPAT model as multiple factors needed consideration. The results show that the CEI decreased from 0.375 tons/million yuan in 2010 to 0.173 tons/million yuan in 2022, with an average annual decrease of 6.719%, a significant positive spatial correlation, and an overall spatial pattern of decentralized distribution for low-value zones and banded agglomeration for high-value zones. Multidimensional factor flow was active, with the unbalanced development characteristics of "dense in the middle and sparse in the north and south" and a chain network "centered on the capital city of the province as the center of the preferential phase chain, multicenter flow". Data flow was the main factor influencing the change in CEI, followed by innovation flow and economic flow. However, the influence of industrial structure and landscape construction on CEI should not be neglected. This study deepens the understanding of the relationship between multidimensional factor flow and CEI. They can be used to build a platform for data circulation and technology transfer and promote integrated economic development.