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1999~2007年中国能源消费碳排放强度空间演变特征
摘要点击 3851  全文点击 2712  投稿时间:2010-12-30  修订日期:2011-03-25
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中文关键词  能源碳排放  碳排放强度  Theil指数  空间自相关  格局演变
英文关键词  energy carbon emission  carbon emission intensity  Theil index  spatial autocorrelation  spatial pattern evolution
作者单位
赵雲泰 南京大学地理与海洋科学学院南京 210093 
黄贤金 南京大学地理与海洋科学学院南京 210093江苏省土地开发整理工程技术中心南京 210093 
钟太洋 南京大学地理与海洋科学学院南京 210093江苏省土地开发整理工程技术中心南京 210093 
彭佳雯 南京大学地理与海洋科学学院南京 210093 
中文摘要
      采用Theil指数和空间自相关分析方法,研究1999~2007年国家、 区域和省际层面能源碳排放强度特征、 区域差异水平和空间格局演变.结果表明,①1999~2007年,全国能源碳排放总量从0.91 Gt逐年上升至1.83 Gt,碳排放强度从0.83 t·万元-1震荡下降至0.79 t·万元-1;②八大经济区域碳排放强度呈现三级分化趋势,东北、 黄河中游和大西北地区历年碳排放强度均在1.0 t·万元-1以上;北部沿海、 长江中游和大西南地区碳排放强度在0.7~1.0 t·万元-1之间;东部和南部地区碳排放强度在0.32~0.51 t·万元-1之间;③Theil指数分析表明区域内部碳排放强度水平相近,区域之间的碳强度分化是全国总体差异扩大的主要原因;④全局自相关Moran's Ⅰ值从0.19上升至0.25,表明省域之间碳排放强度呈现正相关的空间集聚分布;碳强度的“冷点”区相对稳定,主要集中在东部和南部沿海地区;“热点”区从大西北转至黄河中游和东北地区.⑤碳排放强度的空间差异与区域资源禀赋、 经济发展、 产业结构和能源利用效率等因素密切相关.区域差异和空间自相关关系的探讨有助于深入了解我国能源碳排放强度的空间异质性和格局演变,也为国家制定差异化的区域碳减排目标和碳排放调控政策提供有益参考.
英文摘要
      Using Theil index and spatial autocorrelation analysis methods, the characteristics, regional disparity and spatial pattern evolution of carbon emission intensity from energy consumption were analyzed on national, regional and provincial level from 1999 to 2007 in China. The results indicate that: ① total energy carbon emission in China has increased from 0. 91Gt in 1999 to 1.83Gt in 2007, while carbon emission intensity has decreased from 0.83 t·(104 yuan)-1 to 0.79 t·(104 yuan)-1; ② carbon emission intensity of eight major economic blocks showed the trend of three-level differentiation, with that of northeast regions, the middle reaches of Yellow River regions and northwest regions above 1.0 t·(104 yuan)-1; northern coastal regions, the middle reaches of Yangtze River regions and southwest regions 0.7-1.0 t·(104 yuan)-1; eastern and northern regions 0.32-0.51 t·(104 yuan)-1; ③ Theil index analysis indicates that the within-region carbon emission intensities were similar, and the expanding total disparity of carbon emission intensity was primarily due to between-region inequalities. ④ spatial autocorrelation analysis shows that Global Moran's Ⅰ has increased from 0.19 to 0.25, indicating that there were positive spatial correlations among provincial regions in China, and regions of similar carbon emission intensity agglomerated in space. The “cold spot” areas of carbon emission intensity were relatively stable, while the “hot spot” areas has gradually shifted from northwest regions to the middle reaches of Yellow River regions and northeast regions. ⑤ spatial disparity of carbon emission intensity is closely related to factors such as regional resources endowment, economic development, industrial structure and energy utilization efficiency. The study of regional disparity and spatial autocorrelation provides insight into spatial heterogeneity and spatial pattern evolution of carbon emission intensity in China, and also provides references for the development of differential regional objectives of carbon emission reduction and related regulation policies.

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