| 中国能源消费碳排放强度及其影响因素的空间计量 |
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| 引用本文： | 程叶青,王哲野,张守志,叶信岳,姜会明.中国能源消费碳排放强度及其影响因素的空间计量[J].地理学报,2013,68(10):1418-1431. |
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| 作者姓名： | 程叶青 王哲野 张守志 叶信岳 姜会明 |
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| 作者单位： | 1. 中国科学院东北地理与农业生态研究所, 长春 130102;2. 中国科学院大学, 北京 100049;3. 延边大学地理系, 延吉 133002;4. Department of Geography, Kent State University, Kent, Ohio 44242;5. 吉林农业大学管理学院, 长春 130018 |
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| 基金项目： | 中国科学院重点部署项目(KZZD-EW-06,KSZD-EW-Z-021);国家自然科学基金项目(41201159);教育部人文社会科学重点研究基地重大项目(13JJD790008)~~ |
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| 摘 要： | 碳排放所引起的全球气候变化对人类经济社会发展带来了严峻的挑战。中国政府承诺到2020 年GDP碳排放强度较2005 年降低40%~45%,这一目标的实现有赖于全国层面社会经济和产业结构的实质性转型,更有赖于省区层面节能减排的具体行动。基于联合国政府间气候变化专门委员会(IPCC) 提供的方法,本文估算了全国30 个省区1997-2010 年碳排放强度,采用空间自相关分析方法和空间面板计量模型,探讨了中国省级尺度碳排放强度的时空格局特征及其主要影响因素,旨在为政府制定差异化节能减排的政策和发展低碳经济提供科学依据。研究结果表明:① 1997-2010 年,中国能能源消费CO2排放总量从4.16 Gt 增加到11.29Gt,年均增长率为7.15%,而同期GDP年均增长率达11.72%,碳排放强度总体上呈逐年下降的态势;② 1997-2010 年,碳排放强度的Moran's I 指数呈波动型增长,说明中国能源消费碳排放强度在省区尺度上具有明显的空间集聚特征,且集聚程度有不断增强的态势,同时,碳排放强度高值集聚区和低值集聚区表现出一定程度的路径依赖或空间锁定;③ 空间面板计量模型分析结果表明,能源强度、能源结构、产业结构和城市化率对中国能源消费碳排放强度时空格局演变具有重要影响;④ 提高能源利用效率,优化能源结构和产业结构,走低碳城市化道路,以及实行节能减排省区联动策略是推动中国实现节能减排目标的重要途径。
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| 关 键 词： | 中国 碳排放强度 空间自相关 空间面板计量模型 |
| 收稿时间： | 2013-01-18 |
| 修稿时间： | 2013-06-05 |
Spatial econometric analysis of carbon emission intensity and its driving factors from energy consumption in China |
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| CHENG Yeqing;WANG Zheye;ZHANG Shouzhi;YE Xinyue;JIANG Huiming.Spatial econometric analysis of carbon emission intensity and its driving factors from energy consumption in China[J].Acta Geographica Sinica,2013,68(10):1418-1431. |
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| Authors: | CHENG Yeqing;WANG Zheye;ZHANG Shouzhi;YE Xinyue;JIANG Huiming |
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| Institution: | 1. Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Department of geography, Yanbian University, Yanji 133002, Jilin, China;4. Department of Geography, Kent State University, Kent, Ohio, 44242, USA;5. College of Economics and Management, Jilin Agricultural University, Changchun, 130118, China |
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| Abstract: | The economic and social development has been facing with serious challenge brought by global climate change due to carbon emissions. As a responsible developing country, China pledged to reduce its carbon emission intensity by 40%-45% below 2005 levels by 2020. The realization of this target depends on not only the substantive transition of society, economy and industrial structure in national scale, but also the specific action and share of energy saving and emissions reduction in provincial scale. Based on the method provided by the IPCC, this paper examines the spatio-temporal dynamic patterns and domain factors of China's carbon emission intensity from energy consumption in 1997-2010 using spatial autocorrelation analysis and spatial panel econometric model. The aim is to provide scientific basis for making different policies on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China's carbon emissions increased from 4.16 Gt to 11.29 Gt in 1997-2010, with an annual rate of 7.15%, which was much slower than that of annual growth rate of GDP (11.72%); therefore, China's carbon emission intensity tended to decline. Secondly, the changing curve of Moran's I indicated that China's carbon emission intensity from energy consumption has a continued strengthening tendency of spatial agglomeration at provincial scale. The provinces with higher and lower values appeared to be path-dependent or space-locked to some extent. Third, according to the analysis of spatial panel econometric model, it can be found that energy intensity, energy structure, industrial structure and urbanization rate were the domain factors that have impact on the spatio-temporal patterns of China's carbon emission intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, we should improve the utilizing efficiency of energy, and optimize energy and industrial structure, and choose the low-carbon urbanization way and implement regional cooperation strategy of energy conservation and emissions reduction. |
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| Keywords: | spatial autocorrelation spatial panel econometric model carbon emission intensity China |
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