Spatio-temporal analysis of decoupling and spatial clustering decomposition of CO2 emissions in 335 Chinese cities

The decoupling of urban development levels (UDL) and CO2 emissions is an essential representation of sustainable urban development. The spatial decomposition of urban CO2 emissions is an essential reference for achieving differential CO2 emission reduction. This study first evaluates the UDL of 335 cities from 2009 to 2019 using the entropy weight-TOPSIS method. The Tapio decoupling method is used to analyze the spatio-temporal evolution of the decoupling relationship between UDL and CO2 emissions. Second, the Logarithmic Mean Divisia Index (LMDI) method is used to spatially decompose urban CO2 emissions and explore their key influencing factors. Finally, cities with the same development status and resource endowment are spatially clustered by density peaks. Moreover, the proposed spatial LMDI method is used for intra-group and inter-group decomposition of CO2 emissions. The results show that the decoupling status can be divided into three stages. CO2 emission intensity and electricity consumption intensity show mainly north-south differences, while other factors show mainly east-west differences. Cities can be classified into nine types by clustering, and the critical intra-group decomposition factors are different. Population intensity is the most critical factor contributing to the inter-group differences in urban CO2 emissions, followed by land size and output intensity.

Automated summary

This study explores the spatial decomposition and influencing factors of urban CO2 emissions by evaluating the decoupling relationship between urban development levels and CO2 emissions. The results show regional differences in CO2 emission intensity and electricity consumption intensity. Cities are also classified into nine types based on their development status and resource endowment. Population intensity is identified as the most critical factor contributing to inter-group differences in urban CO2 emissions.