Spatial-temporal analysis of China's regional carbon intensity based on ST-IDA from 2000 to 2015

Abstract

Abstract To study the carbon intensity characteristics across regions in China, reveal the spatial differences and temporal dynamics of major impact factors, spatial-temporal index decomposition model (ST-IDA) based on a provincial panel data set for the period 2000–2015 was adopted to quantitatively explore the spatial-temporal differences of drivers contributed to the changes in carbon intensity across regions. Considering the spatial attributes of carbon emissions under regional economic association, spatial adjacency matrix was constructed and induced into a spatial autoregressive model to analyze the carbon intensity characteristics of different economic spatial agglomeration regions. 30 provinces were divided into 4 categories according to GDP per capita themselves and the surrounding provinces including high-value surrounded by high-value (HH region), high-value surrounded by low-value (HL region), low-value surrounded by high-value (LH region) and low-value surrounded by low-value (LL region). Results indicate that (a) there were significant differences in carbon intensity in different economic spatial agglomeration regions. The carbon intensity in LL region was significantly higher than that in other regions due to the much higher carbon intensity of industry sector; (b) the change in carbon intensity depends mainly on the industry and the transportation sector. In 2000–2005, the significant increase of transportation carbon intensity dominated the total change except in LL region. After 2005, the industry sector dominates the decline of carbon intensity in all regions; (c) energy intensity effect is the main reason for the spatial differences of carbon intensity. LL region has a higher potential for energy intensity decline; (d) temporally, energy intensity had a significant negative effect on carbon intensity growth of the regions. The industrial structure and energy structure effect were positive in all regions. Carbon coefficient effect showed a rise and then decline trend.