基于不同需求层次的中国城镇居民消费隐含碳排放时空演变机制

正确认识不同需求水平下人均隐含碳排放量的变化,对实现低碳经济及低碳生活具有重要的参考价值。基于居民消费需求层次建立不同需求层次与隐含碳排放的对应关系,将人均隐含碳排放分解为生存型、发展型、奢侈型三类,并运用投入产出法进行核算,在对不同需求层次人均隐含碳排放的空间格局演变分析的基础上采用空间面板方法对其驱动机制进行甄别。结果显示,在全国层面,各需求层次人均隐含碳排放均呈现上升趋势,空间分布不均衡性主要体现在南北差异上,北部地区始终是各需求层次人均隐含碳排放的主要空间载体,其中多数省分生存型人均隐含碳排放上升势头较强,发展型和奢侈型的高值区在省份数量上分别呈现先减后增与逐渐增加的变化趋势;不同需求层次人均隐含碳排放水平相似的地区在空间上呈集聚分布,具有较强的"马太效应";空间面板模型结果显示技术减排是降低不同需求层次人均隐含碳排放的重要举措,而人口规模在各需求层次上的负向减排作用远小于正向的人口结构效应,宏观经济因素主要表现为增排效应,而居民消费因素的作用通道存在差异。此外,部分因素在各需求层次上存在显著空间外溢效应,应重视区域间的横向联动减排效应,做好隐含碳减排的统筹协调工作。

Spatio-temporal evolution and driving mechanism of per capita indirect carbon emissions based on different demand levels from urban residents' consumption in China

There is an important reference value for realization of low carbon economy and low carbon life to understand the change of per capita indirect carbon emissions based on different demand levels. Based on the demand-level of residents'' consumption, this paper established the corresponding relationship between different demand levels of per capita indirect carbon emissions. We decomposed per capita indirect carbon emissions into three types including survival type, developmental type and luxurious type, and estimated them by the input output method. The temporal and spatial evolution of per capita indirect carbon emissions in different demand levels were studied and the driving mechanisms were identified by the spatial panel method. The corresponding emission reduction measures were also proposed. The results indicate that (1) at the national level, there is an upward trend in per capita indirect carbon emissions in different demand levels. The spatial imbalance is mainly reflected in the difference between the north and the south. The northern region is always the main spatial carrier of them. In most provinces, per capita indirect carbon emissions of the survival type have a strong upward momentum. The high value areas of developmental type and luxurious type have a trend of decreasing first, then increasing, and increasing gradually in the number of provinces separately. (2) The global Moran''s I shows that there is a significant spatial autocorrelation of the per capita indirect carbon emissions in different demand levels in 30 provinces of China. There is a strong "Matthew effect". (3) The results of spatial panel regression show that the technological progress is a key factor to achieve indirect carbon emissions'' reduction. The negative emission reduction effect of population size of each demand level is much less than that of the positive structure effect. Macroeconomic factors have increasing emission effects, and the influence of consumption factors is different. In addition, some factors have significant spatial spillover effects on each level of demand. Therefore, we should pay attention to the effect of linkage emission reduction between regions, and do a good job of coordination in indirect carbon emission reduction.