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Multi-province comparison and typology of China’s CO2 emission: A spatial–temporal decomposition approach
- Wu, Feng, Huang, Ningyu, Zhang, Qian, Qiao, Zhi, Zhan, Ni-ni
- Energy 2019
- carbon, carbon dioxide, coasts, greenhouse gas emissions, industry, issues and policy, spatial variation, temporal variation, China
- Understanding regional characteristics of CO2 emissions and its drivers are crucial to formulating China’s regional CO2 mitigation strategy. Previous studies have proven that the structural decomposition analysis (SDA) method is an effective way to examine and identify the drivers of CO2 emissions. Nevertheless, the traditional SDA approach does not entirely consider the spatio-temporal variation of CO2 driving factors. We thus propose a new spatial-temporal structure decomposition analysis (ST-SDA) method in this study to identify the driving factors and examine the spatio-temporal variation of CO2 emissions at provinces level from 1997 to 2012. It is prominent because it integrates the temporal and spatial dimensions to decompose the influencing factors across regions and over time simultaneously. The decomposition results for the whole China and its 30 provinces show that the final demand aggregate effect is the main driver of boosting CO2 emissions except for Ningxia, which indicates that the expansion of industrial production scales driven by demand of product consumption promotes the increase of CO2 emissions. The results also present that the carbon intensity effect is the only factor to offset the growth of CO2 emissions, which demonstrates that technological progress contributes continuously to the reduction of CO2 emissions, especially in eastern and northern coastal areas. Within the positive contribution of CO2 emissions in most provinces, the Leontief structural effect is mainly reflected in the northeast and central China, which indicates that the industries in these areas are gradually transforming. We then generate the typology in terms of different influencing factors on CO2 emissions, and then put forth potential policy options for regional CO2 reduction accordingly.