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

Abstract

• Spatio-temporal analysis of the decoupling of urban development levels and CO 2 emissions. • Spatial decomposition of CO 2 emissions for 335 cities. • Chinese cities can be clustered into nine types according to their development status and resource endowment. • The critical intra-group decomposition factors differed for the nine types of cities. • Population intensity is the most critical factor contributing to the inter-group differences in urban CO 2 emissions. The decoupling of urban development levels (UDL) and CO 2 emissions is an essential representation of sustainable urban development. The spatial decomposition of urban CO 2 emissions is an essential reference for achieving differential CO 2 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 CO 2 emissions. Second, the Logarithmic Mean Divisia Index (LMDI) method is used to spatially decompose urban CO 2 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 CO 2 emissions. The results show that the decoupling status can be divided into three stages. CO 2 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 CO 2 emissions, followed by land size and output intensity.