中国省域碳排放及其驱动因子的时空异质性研究  

Research on Space-time Heterogeneity of Carbon Emission and Influencing Factors in Provinces of China

作　　者：李丹丹[1,2] 刘锐[1,3] 陈动[1,4] 

机构地区：[1]北京师范大学地理学与遥感科学学院,北京100875 [2]北京城市学院信息学部,北京100083 [3]中科宇图资源环境科学研究院,北京100101 [4]南京林业大学土木工程学院,江苏南京210037

出　　处：《中国人口·资源与环境》2013年第7期84-92,共9页China Population,Resources and Environment

基　　金：北京师范大学海外人才引进基金

摘　　要：本文选取30省(自治区、直辖市)行政单位作为基本空间单元,依据2003-2010年中国统计年鉴和中国能源统计年鉴数据,采用探索性空间分析(ESDA)方法和地理加权回归模型(GWR),分析八年间碳排放影响因素及其影响程度的时空分布,揭示我国碳排放的区域差异及其驱动因子的时空异质性。研究结果表明:在2003-2010年间,碳减排潜力相对较大区域主要集中在中东部地区,并有向四周扩展的趋势;全国省域碳排放存在较为显著的空间正相关性,且相关性的总体趋势呈现出先上升后下降的空间格局,表明在碳减排的进程中,全国碳排放量的总体空间差异在逐步缩小;碳排放的驱动因子存在明显的时空异质性,如GDP驱动因子在不同省域影响程度不同,而且2006年的GDP驱动因子的回归系数普遍高于2003年,人口影响因子也存在省域的异质性,2010年人口回归系数高于2003年;GDP对碳排放的影响最为显著,影响显著的区域从2003年西部转移到中东部,2010年又移回西部,这进一步说明碳减排与经济增长的复杂关系,意味着碳减排和经济发展并重需要优化经济发展方式,提高能源利用效率。This research proposed a method of evaluating the difference of carbon emissions among provinces of China and the spatial heterogeneity of driving factors. First, the 30 provinces （ autonomous regions and municipalities） were selected as the basic space unit. Then, the exploratory spatial data analysis（ESDA） and geographically weighted regression （GWR） methods were employed to discover the factors and its spatial and temporal distribution for carbon emissions. Finally, the data from China Statistical Yearbook and China Energy Statistical Yearbook between 2003 and 2010 was adopted to evaluate the reasonability of the proposed method. Our research findings were shown as follows： The regions with a large amount of carbon emissions were concentrated in mid-east region and its surrounding regions in central and eastern China between 2003 and 2010. There was a significant positive correlation of carbon emission among adjacent provinces and the trend for correlation first was increased, then decreased. This result showed that the difference of carbon emissions among provinces of China was gradually reduced. Impact factors of carbon emission had spatial temporal heterogeneity. For example, influence extent of GDP was diverse in different provinces, and that the regression coefficients of GDP in 2006 was higher than that in 2003. Populational influence factors also had heterogeneity among provinces. Population coefficients in 2006 was higher than that in 2003. For all of the influence factors, GDP was a significant factor to affect carbon emissions. The evident regions affected by GPD were transferred from western to central and eastern regions in 2003, while those evident regions were transferred back to western regions in 2010. This variation had convincingly proven the complicated relations between carbon emissions and economic growth. To achieve carbon emission reduction effectively, it is significant to adjust economic structure development and improve the energy utilization efficiency.

关 键 词：地理加权回归 探测性空间分析 碳排放 时空异质性 驱动因素 

分 类 号：TP208[自动化与计算机技术—检测技术与自动化装置]