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Decomposition of energy-related CO2 emissions in China: An empirical analysis based on provincial panel data of three sectors

机译:中国能源相关的二氧化碳排放量分解:基于三个部门的省级面板数据的经验分析

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摘要

To grasp the characteristics of CO2 emissions across provinces in China and to determine changes in the centre of gravity of CO2 emissions over the 2000-2014 period, a gravity model is first used to examine the spatial distribution and centre of gravity of energy-related CO2 emissions. Then, to explore the main factors driving CO2 emission changes and to uncover feasible ways to reduce CO2 emissions, this paper decomposes changes in energy-related CO2 emissions into a population effect (Delta C-P), an economic output effect (Delta C-Q), an industrial structure effect (Delta C-S), an energy intensity effect (Delta C-I), an energy structure effect (Delta C-M) and a carbon dioxide emission coefficient effect (Delta C-U) at both the national and provincial levels based on the Log-Mean Divisia Index (LMDI) method. The results indicate that (1) energy related CO2 emissions rose by approximately 5.46 billion tonnes during the 2000-2014 period, with secondary industry accounting for approximately 80% of total CO2 emissions. (2) Economic output (Q) was the dominant positive driving factor, and energy intensity (I) was the dominant negative driving factor. The population changes had a weak positive effect on CO2 emissions, but the industrial structure effect and energy structure effect varied considerably over the years without showing clear trends. (3) Over multiple spatial scales, the contribution ratios of the factors varied significantly across provinces; in general, the positive driving effects outweighed the negative inhibiting effects. Based on these empirical findings, policy recommendations to further reduce CO2 emissions are provided. The Chinese central and local governments should make full use of the important inhibiting factors, i.e., energy intensity and energy structure, and strive for breakthroughs in secondary sector. (C) 2017 Elsevier Ltd. All rights reserved.
机译:为了掌握中国各省的二氧化碳排放特征并确定2000-2014年期间二氧化碳排放重心的变化,首先使用引力模型来检查与能源有关的二氧化碳的空间分布和重心排放。然后,为探讨驱动CO2排放变化的主要因素并发现减少CO2排放的可行方法,本文将与能源有关的CO2排放变化分解为人口效应(Delta CP),经济产出效应(Delta CQ),基于Log-Mean Divisia的国家和省级工业结构效应(Delta CS),能量强度效应(Delta CI),能量结构效应(Delta CM)和二氧化碳排放系数效应(Delta CU)索引(LMDI)方法。结果表明:(1)在2000-2014年期间,与能源有关的CO2排放量增加了约54.6亿吨,第二产业约占CO2排放总量的80%。 (2)经济产出(Q)是主要的正驱动因素,而能源强度(I)是主要的负驱动因素。人口变化对CO2排放的积极影响较弱,但多年来的产业结构效应和能源结构效应变化很大,而没有显示出明显的趋势。 (3)在多个空间尺度上,各省的因素贡献率差异很大;通常,积极的驱动作用要大于消极的抑制作用。基于这些经验发现,提供了进一步减少CO2排放的政策建议。中国中央和地方政府应充分利用重要的制约因素,即能源强度和能源结构,争取第二产业的突破。 (C)2017 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Applied Energy》 |2017年第15期|772-787|共16页
  • 作者

    Wang Miao; Feng Chao;

  • 作者单位

    Cent S Univ, Sch Business, Changsha 410083, Peoples R China|Cent S Univ, Inst Met Resources Strategy, Changsha 410083, Peoples R China;

    Cent S Univ, Sch Business, Changsha 410083, Peoples R China|Cent S Univ, Inst Met Resources Strategy, Changsha 410083, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Energy-related CO2 emissions; Driving factors; Centre of gravity; LMDI method;

    机译:与能源有关的二氧化碳排放量;驱动因素;重心;LMDI方法;
  • 入库时间 2022-08-18 00:07:46

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