| 我国碳排放区域差异性分析 |
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| 引用本文： | 董锋,徐喜辉,龙如银,韩宇. 我国碳排放区域差异性分析[J]. 长江流域资源与环境, 2014, 23(11): 1526. DOI: 10.11870/cjlyzyyhj201411006 |
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| 作者姓名： | 董锋 徐喜辉 龙如银 韩宇 |
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| 作者单位： | （中国矿业大学管理学院，江苏 徐州 221116） |
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| 基金项目： | 国家自然科学基金项目(41101569,71273258);国家社会科学基金重大项目(12&ZD062);中国博士后基金项目(2013T60570,2011M500965);江苏省社会科学基金项目(11EYC023);教育部高等学校博士点基金项目(20110095120002);江苏省博士后基金项目(1102088C);中央高校基本科研业务费专项资金(2013W01);江苏省“青蓝工程”中青年学术带头人人才项目;国家留学基金资助项目 |
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| 摘 要： | 在对我国各地区的碳排放总量、人均碳排放和碳排放强度进行计算的基础上，运用Theil系数等方法对我国碳排放区域差异性进行分析。Theil系数分析结果表明我国碳排放强度呈现区域差异性，区域内差异是总体差异的主要原因，而东部和中部地区内部差异是导致区域内差异的主要原因；变异系数分析结果表明：碳排放总量东部地区差异最大、中部最小，人均碳排放西部地区差异最大、东部最小，碳排放强度中部地区差异最大、东部最小；基尼系数分析结果表明：西部地区碳排放差异最大，中部次之，东部最小；运用聚类分析将全国各省碳排放现状分为优、良、中、差四大类。针对研究结果，提出了转化经济发展方式、优化产业结构等节能减排建议
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| 关 键 词： | 碳排放 Theil系数 基尼系数 变异系数 聚类分析 |
ANALYSIS OF CARBON EMISSION STATUS IN CHINA |
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| DONG Feng,XU Xi hui,LONG Ru yin,HAN Yu. ANALYSIS OF CARBON EMISSION STATUS IN CHINA[J]. Resources and Environment in the Yangtza Basin, 2014, 23(11): 1526. DOI: 10.11870/cjlyzyyhj201411006 |
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| Authors: | DONG Feng XU Xi hui LONG Ru yin HAN Yu |
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| Affiliation: | (School of Management， China University of Mining and Technology， Xuzhou 221116， China) |
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| Abstract: | We employed the Theil index in a comparative analysis of regional carbon emission intensity based on carbon emissions, per capita carbon emissions and carbon intensity concerning various regions in China from 1997 to 2010, followed by an inter regional comparison based on variation coefficient and a Gini based analysis of regional carbon emission fairness. A comprehensive study on how Chinas regions differ in carbon emission was conducted at macro , medium , and micro levels. Next, a cluster approach was adopted and the 29 provinces were grouped in accordance with total carbon emission volume of each. From 1997 to 2010 carbon emissions and per capita carbon emissions grew gradually in Eastern China, Central China and Western China. The carbon emissions of Eastern China were larger than that of Central China, and that of Central China were larger than Western China. However, Western China intends to catch up with Central China in this respect. The situation of per capita carbon emissions is identical to carbon emissions. Eastern China, Central China, and Western China were ranked the first, the second, and the third in the per capita carbon emissions, whereas in 2009, the ranking changed to be Eastern China, Western China, and Central China. In addition, the per capita carbon emissions in Western China exceeded those of Central China in 2009. The carbon intensity of Eastern China was much lower than that of Central China and Western China, while Central China was close to Western China. There appeard a downward trend of the carbon intensity. As the Theil index showed, the China wide carbon emission intensity is characterized by regional diversity, which mainly results from the gap existing inside a region. Inter regional differences are the secondary causes, of which Eastern China and Central China contributes more greatly to the overall differences, with a contribution rate of nearly 25%. Whereas the contribution rate of Western China is relatively low. The variation coefficients indicate that Eastern China ranked the first but Central China the last in carbon emission volume, Western China the first but Eastern China the last in per capita carbon emissions, and Central China the first but Eastern China the last in carbon emission intensity. As the calculation result shows, the Gini coefficient is 0154 3 in Eastern China, 0249 2 in Central China, 0315 2 in Western China, and 0244 6 in the whole nation. Among them, Western China has the highest Gini coefficient, followed by Central China and Eastern China in sequence. Gini coefficient demonstrates that Central China is in the first place and Eastern China in the last with respect to carbon emission diversity, of which the differences inside Inner Mongolia, Ningxia, Shanxi, Guangxi contribute greatly to the diversity of the regions they are related to. Furthermore, the cluster analysis classifies all provinces into four levels of excellent, good, moderate, and poor. Cluster analysis demonstrates that Hainan and Qinghai are excellent in carbon emission situation; 18 provinces represented by Beijing, Tianjin and Jilin are graded as good; other 8 provinces represented by Hebei and Shanxi are proved as moderate; only Shandong belongs to poor region in carbon emission situation. Finally, the authors suggest formulation of diverse objectives and strategies aimed at emission reduction |
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