Sensitivity analysis and spatial-temporal heterogeneity of CO2 emission intensity: Evidence from China

Due to the Chinese reduction targets of CO2 emission intensity (CEI), it is of great significance to explore its determinants and mechanism. Therefore, the purpose of this study is to find how to effectively reduce CEI. At the national level, we investigate the influencing factors of CEI by using the Logarithmic Mean Divisia Index method (LMDI). Then, this study examines the sensitivity of CEI change to various factors. At the regional level, we study the spatial-temporal heterogeneity of the influencing factors through Geographically and Temporally Weighted Regress (GTWR). The main results are as follows. (1) At the national level, the positive contribution of the energy mix is the largest. The energy intensity of the production sector is the main negative driving factor in the early stage, and in the later stage, the principal negative contribution comes from the combined action of the CO2 emission coefficient and economic structure. (2) A dynamic change is observed in the sensitivity of CEI to various factors. (3) At the regional level, various determinants of CEI show spatial-temporal heterogeneity based on GTWR. For example, in analysing the impact of energy intensity in the industrial sector on CEI in various regions, Shandong Province has the largest coefficient. The findings are of considerable interest for China's policy makers to effectively formulate more appropriate emission-reduction measures for each region.