中国居民消费碳排放影响因素的时空异质性

Spatial-temporal heterogeneity of carbon emissions and influencing factors on household consumption of China

研究我国居民消费间接碳排放的阶段性和区域性特征,对于制定社会经济新常态发展下的碳减排策略具有重要的现实意义.利用投入产出法和结构分解分析法,核算了2002~2017年中国居民消费间接碳排放水平,量化了影响因素对间接碳排放的贡献;利用地理加权回归模型定量描述了省域间接碳排放的时空分异特征.结果显示:2002~2017年中国居民消费间接碳排放呈现先增长后下降的趋势,“食品”类和“居住”类消费是其主要来源,占比42%~48%.新常态下,“直接碳排放强度”、“生产技术”和“消费倾向”因素的抑制作用显著加强,大大抵消了“收入规模”、“人口”及“消费结构”因素对碳排放的促进作用,促使碳排放降低了145MtC.省域间接碳排放从东至西呈现递减的分布特征,表现出一定的集聚性.碳排放的影响因素具有空间异质性,“生产技术”与碳排放呈现负相关关系,其他因素呈现正相关关系.其中,“人口”、“收入规模”及“直接碳排放强度”对碳排放的影响程度较为突出.

It is important to formulate a carbon reduction strategies under the phased and regional characteristics of indirect household consumption carbon emissions(IHCE)in China,especially the new social-economic development circumstance.With the input-output analysis(IOA)and the structural decomposition analysis(SDA),the IHCE of China from 2002 to 2017 was calculated and the contributions of different influencing factors was quantified.Based on which,a geographically weighted regression model(GWR)was used to quantitatively describe spatial-temporal differentiation characteristics of IHCE in provinces.The results showed that the IHCE increased first and then decreased from 2002 to 2017.In that period,food and residence consumption were the main sources of emissions,which accounted 42~48% of contribution.In the new social-economic development circumstance,the negative effects with the direct carbon emission intensity,the production technology and the consumption tendency were significantly strengthened,which greatly offset the positive effects with the income scale,the population and the consumption structure,and the net effect was a decline of 145MtC of the total IHCE.The provincial IHCE showed a decreasing from east to west as well as an agglomeration phenomenon.Furthermore,the influencing factors of carbon emissions showed a spatial heterogeneity.The production technology factor were negatively correlated with IHCE.Other factors were positively correlated with IHCE among which,the population,income scale,and direct carbon emission intensity factors had more prominent impacts on carbon emissions.