中国居民生活碳排放的区域差异及影响因素分析

论文在总结划分居民生活碳排放（HCEs）类别的基础上,以中国HCEs量为测度指标,从时间和空间两个角度对1997-2012年人均HCEs进行分析,同时对2012年人均HCEs的空间格局分布及影响因素进行分析。基本结论如下：根据划分方法,HCEs按照碳排放源可分为直接和间接碳排放;按照基本生活需求可分为基本和发展碳排放;按照人类消费行为可分为交通、居住、食品、商品及服务碳排放五大类。从时间尺度来看,中国各类别人均HCEs均呈现逐年上升趋势。从空间差异来看,1997-2012年,我国各省人均HCEs表现出共同的空间分布特征。从空间格局分布来看,2012年中国人均居民生活碳排放呈现从东到西递减的趋势。从空间计量模型角度看,2012年,人均居民生活基本碳排放比重是影响人均HCEs空间差异的主要驱动因子。此外,各地区人均收入和人均GDP的差异也对人均HCEs的空间差异起到正相关作用。基于对中国人均HCEs的时空格局及影响因素分析,为我国制定碳减排举措提供科学依据并提出有效建议。

Analyze on the Spatial-temporal Pattern and Influence Factors of China’s per Capita Household Carbon Emissions

This paper calculates per capita household carbon emissions (HCEs) based on IPCC’s reference approach and Input-output analysis (IOA) of different categories of carbon emissions in China from 1997 to 2012. Its driving factors are also analyzed with the Spatial Error Model (SEM) and the Spatial Lag Model (SLM). The main purpose of this work is to emphasize the characteristics of household carbon emissions based on temporal scale and spatial scale. The results show that: 1) Based on different carbon sources, HCEs can be divided into direct and indirect emissions; based on different human needs, HCEs can be classified as basic and development emissions; based on different consumers’ behaviors, HCEs can be divided into transportation, housing, food, goods and service emissions. 2) At the time scale, both direct and indirect per capita HCEs, basic and development per capita HCEs, and each item of per capita HCEs based on behaviors exhibit the increasing tendency. 3) From the spatial perspective, there is a common pattern in spatial distributions of per capita HCEs. The cluster effect of per capita HCEs is stable. 4) From the space point of view, the per capita HCEs in China shows a decreasing tendency from east to west in 2012. 5) Based on the spatial analysis model, the proportion of basic HCEs per capita in the whole is the main driving factor. Meanwhile, per capita income and per capita GDP are also affecting per capita HCEs. On the basis of analyzing the spatial-temporal patterns and driving factors of per capita household carbon emissions, we provide scientific evidences and put forward effective suggestions for carbon emissions reduction measures and policies.