黄河下游沿岸地市CO2排放的时空分异

The spatio-temporal differentiation of CO_2 emissions in cities along the Lower Yellow River

地市级尺度的碳排放结构演变与区域差异分析对通过技术学习与知识流动短期内抑制碳排放的快速增长具有重要意义。以地市级行政区作为基本空间单元,对黄河下游沿岸区、两大子区及21地市化石能源与水泥工业生产过程排放的CO2进行科学核算,发现CO2排放量变化于2000年的364.12～4426.95万t至2009年的1238.98～10411.91万t,呈现出典型的"S型"增长特征,但不同尺度区域排放结构的时空差异显著。2006年以来,工业化进程较快的区域水泥排碳比例有显著增长,产业结构优化策略促使少数地市水泥排碳比例有所下降。CO2排放强度与人均排放量的变动符合碳排放EKC曲线关系,但时空分异特征并不一致。排放强度的全区总体差异较为合理,而人均排放量的区域差异偏大,从而为涓滴效应的加速作用创造了空间。地区内差异分别为两指标全区总体差异变动的主导因素,整体上,中原区内地市间差异对两指标全区差异变动的贡献更大。

It is important for the accounting, structural evolution and regional differences analysis of the municipal-scale carbon emissions for rational utilization of geographical potential in regions to inhibit the rapid growth of carbon emissions in the short term by technological learning and knowledge flows. In this study, CO2 emissions from fossil fuel and cement industrial processes have been accounted in the region along the Lower Yellow River based on the municipal administrative units from 2000 to 2009, along with the accounting in the two sub-region and 21 cities. Study shows that, firstly, the total CO2 emissions at different spatial scales shows a typical ＂S-type＂ growth pattern, but there are significant spatial and temporal differences in the emission structures. Generally, emissions from the cement industrial processes have a rapid growth for the rapid industrialization in some cit- ies. Optimization of industrial structure in a few cities has promoted a decline in the pro- portion of emissions from the cement industrial production. Secondly, the change of CO2 intensity and CO2 emissions per capita is in line with the theory of the carbon EKC. However, the degree and characteristics of spatio-temporal differentiation of the two indicators are not consistent. The weighted coefficient of variation, Gini coefficient, Moran＇s I and Theil index reveal that the region＇s total variance of COz intensity are reasonable, while there is a larger variation for CO2 emissions per capita, which will create conditions for ac- celerating the trickle-down effect. Thirdly, the decomposition of Theil T and L index indi- cates that difference in cities of the two sub-region is the dominant factors for changes of the region＇s total variance of the two indicators during the study period. At the same time, the unbalanced development of the Zhongyuan region is much stronger and the main contribution for changes of the region＇s total variance of the two indicators.