Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels

Abstract

Due to the continuing industrialization and urbanization, China’s CO2 emissions have experienced a rapid increase in recent 30 years. The increase of CO2 emissions will not only effect country’s own sustainable development, but also potentially pose a negative impact on the global climate stability. Since the socioeconomic development is sensitive to geographic scales and regional heterogeneity, a systematic investigation of spatiotemporal variations (SV) of CO2 emissions and their impact factors (IF) across different levels will help to develop more effective and reasonable policies and measures for CO2 emissions mitigation. However, multi-scale analysis of those issues is still lacking. Hence, using two administrative levels (e.g., prefectures or provinces) in China as experimental objects, this study attempted to quantify and compare SV and IF of CO2 emissions from nighttime light images and socioeconomic data at different levels using the variation coefficient (VC), spatial autocorrelation spatial model, and spatial econometric model. Our results show that the VC of CO2 emissions is uninterruptedly increases from 0.66 in 1997 to 0.73 in 2006, and then gradually decreases to 0.69 in 2012 at the provincial level, and it consistently decreases from 1.29 in 1997 to 1.03 in 2012 at the prefectural level. The Global Moran’s I of CO2 emissions increases from 1997 to 2012 at the provincial and prefectural levels. Specifically, the Global Moran’s I gradually increases from 0.23 in 1997 to 0.27 in 2012 at the provincial level, while it shows a rapid growth trend, from 0.23 in 1997 to 0.34 in 2012 at the prefectural level. The proportion of second industry has been demonstrated as a major factor influencing CO2 emissions at different levels, while gross domestic product, urbanization rate, and population play more important roles in CO2 emissions at the prefectural level. This study illustrates that China’s CO2 emissions are sensitive to the spatial-temporal hierarchy of multi-mechanisms, and suggests that “proceed in the light of local conditions” strategies can help Chinese government for CO2 emissions mitigation.