中国省际碳足迹广度、深度评价及时空格局

借鉴三维生态足迹方法构建了碳足迹广度、深度测算模型,对吸纳碳排放所占用的自然资本流量、存量进行区分,核算了2000—2016年中国30个省(市、自治区)碳足迹广度和碳足迹深度,并对其进行空间关联性分析。结果显示:①中国碳足迹广度受碳足迹和碳生态承载力的综合影响,由0.173 hm~2/人升至0.329 hm~2/人又降至0.301 hm~2/人;碳足迹广度高值区集中于东北、西北和西南地区,其自然资本流量尚未完全占用,低值区集中于东部沿海和中部,其自然资本流量已不足以补偿碳排放。②2008年起中国碳足迹深度突破自然原长1,数值由1.04升至1.42又降至1.31;研究期内碳足迹深度始终处于自然原长1的有10个地区,高值区集中于东部沿海和中部,尤其是上海可达298.83,以存量资本耗竭为主且生态持续性弱。吸纳碳排放所占用的流量资本和存量资本存在地域互补性。③中国碳足迹广度、深度呈显著的空间正相关。碳足迹广度H-H集聚区分布于东北和西北,该类集聚有减弱趋势;碳足迹深度H-H集聚区主要分布于东部沿海且向中部扩散,该类集聚有增强趋势。通过引入碳足迹广度、深度两项指标对碳足迹的研究方法进行了深化和完善,在碳排放对生态环境影响规模的刻画和表达上取得了较优于传统碳足迹的评价结果。

Assessment of carbon footprint size, depth and its spatial-temporal pattern at the provincial level in China

The relationship between carbon emissions and carbon absorption is unbalanced, and the carbon cycle system is facing tremendous ecological pressure at present in China. In this paper, a carbon footprint size and depth measurement model was firstly established based on the three-dimensional ecological footprint method. Then we used this model to distinguish the natural capital flow and stock occupied by absorbing carbon emissions. Furthermore, the carbon footprint size and depth of 30 provinces were calculated on the basis of energy consumption from 2000 to 2016. The spatial correlation of carbon footprint size (depth) was analyzed by spatial autocorrelation method. The results showed as follows:(1) The carbon footprint size in China was affected by carbon footprint and carbon ecological carrying capacity. It showed an increase from 0.173 hm²/person to 0.329 hm²/person and then decrease to 0.301 hm²/person. The high-value areas of carbon footprint size were the northeast, northwest and southwest regions where the natural capital flow was not yet fully occupied. The low-value areas were mainly distributed in the eastern coast and the central regions where the natural capital flow was insufficient to compensate for carbon emissions. (2) The provincial carbon footprint depth has exceeded the natural length of 1, and the depth increased from 1.04 to 1.42 and then decreased to 1.31 since 2008. The carbon footprint depth in 10 provinces was always at the natural length of 1 from 2000 to 2016. The high-value areas of carbon footprint depth were the eastern coast and the central regions where the natural capital stock was consumed by carbon emissions and the ecological sustainability was weak, while the highest depth occurred in Shanghai with 298.83. There existed the regional complementarity between natural capital flow and natural capital stock for absorbing carbon emissions. (3) Using the global spatial autocorrelation analysis, we found that carbon footprint size and depth showed the positive correlation and significant spatial agglomeration in all provinces of China. Through the local spatial autocorrelation analysis, the High-High agglomeration areas of carbon footprint size were mainly distributed in the northeast and northwest regions, and there was a tendency of number decrease of High-High agglomeration areas. Moreover, the High-High agglomeration areas of carbon footprint depth were mainly distributed in the eastern coastal areas and there was an obvious spread trend toward the adjacent regions. By introducing the two indexes of carbon footprint size and carbon footprint depth, the research methods of carbon footprint are further improved, and the evaluation results are more accurate and reasonable than the results by the traditional carbon footprint theory.