What Cause Large Spatiotemporal Differences in Carbon Intensity of Energy-Intensive Industries in China? Evidence from Provincial Data during 2000–2019

Abstract

China has been reported as the world’s largest carbon emitter, facing a tough challenge to meet its carbon peaking goal by 2030. Reducing the carbon intensity of energy-intensive industries (EIICI) is a significant starting point for China to achieve its emission reduction targets. To decompose the overall target into regions, understanding the spatiotemporal differences and drivers of carbon intensity is a solid basis for the scientific formulation of differentiated regional emission reduction policies. In this study, the spatiotemporal differences of EIICI are described using the panel data of 30 provinces in China from 2000 to 2019, and a spatial econometric model is further adopted to analyze its drivers. As indicated by the results: (1) from 2000 to 2019, China’s EIICI tended to be reduced continuously, and the spatial differences at the provincial and regional levels expanded continuously, thus revealing the coexistence of “high in the west and low in the east” and “high in the north and low in the south” spatial patterns. (2) There is a significant spatial autocorrelation in the EIICI, characterized by high and high agglomeration and low and low agglomeration types. Moreover, the spatial spillover effects are denoted by a 1% change in the local EIICI, and the adjacent areas will change by 0.484% in the same direction. (3) Technological innovation, energy structure, and industrial agglomeration have direct and indirect effects, thus affecting the local EIICI and the adjacent areas through spatial spillover effects. Economic levels and firm sizes only negatively affect the local EIICI. Environmental regulation merely has a positive effect on adjacent areas. However, the effect of urbanization level on EIICI has not been verified, and the effect of urbanization level on the EIICI has not been verified. The results presented in this study show a scientific insight into the reduction of EIICI in China. Furthermore, policymakers should formulate differentiated abatement policies based on dominant drivers, spatial effects, and regional differences, instead of implementing similar policies in all provinces.

The Effect of FDI Agglomeration on Carbon Emission Intensity: Evidence from City-Level Data in China

“…Carbon emission intensity refers to the carbon emissions generated per unit of gross domestic product (GDP), which is an important indicator to measure the quality of energy utilization and carbon emission efficiency. Academic research on carbon emission intensity mainly focusses on spatial and temporal distribution characteristics [3], spatial effects [4,5], and driving factors [6][7][8], investigating how financial geographic density affects carbon emission intensity by building a spatial model. On the one hand, financial geographic density can promote technological innovation and industrial structure upgrading, thus reducing carbon emission intensity; on the other hand, financial geographic density can promote industrial scale expansion, and thus increase carbon emission intensity.…”

“…On the one hand, financial geographic density can promote technological innovation and industrial structure upgrading, thus reducing carbon emission intensity; on the other hand, financial geographic density can promote industrial scale expansion, and thus increase carbon emission intensity. Xu et al [3], based on the data of high-energy-consuming industries in 30 provinces of China from 2000 to 2019, found that the carbon emission intensity of China's high-energy-consuming industries showed a spatial distribution pattern of "high in the west and low in the east" and "high in the north and low in the south", and there was spatial autocorrelation between high and low concentrations. Technological innovation, energy structure, and industrial agglomeration have significant direct and indirect effects, which affect the carbon emission intensity of local and surrounding areas, through spatial spillover effects.…”