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Identifying key impact factors on carbon emission: Evidences from panel and time-series data of 125 countries from 1990 to 2011

机译:确定影响碳排放的关键因素:1990年至2011年间125个国家/地区的面板数据和时间序列数据

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摘要

Global warming caused by carbon emission has been recognized as a threat to public health and welfare. Carbon emission reduction is therefore a necessary task for each country to address the severe challenges arising from global warming. This research combines the STIRPAT model with the use of the panel and time-series data to analyze the impacts of population, affluence and technology on the carbon emission of 125 countries at different income levels over the period of 1990-2011. The results show that the key impact factor (KIF) at global level is affluence, followed by technology and population in the order of their impacts on carbon emission. For countries at high-income (HI) level, technology has the greatest impact on carbon emission, while affluence has the least. Affluence, prior to technology and population, is identified as the KIF of carbon emission for countries at upper-middle-income (UMI) and lower middle-income (LMI) levels. When it comes to the low-income (LI) level, affluence serves as the factor greatest affecting on carbon emission, and technology has the least impact. In particular, two generic patterns are identified based on the empirical results: higher income leads to greater impact of the technology and lower impact of the affluence on carbon emission. The KIFs of different income level countries identified in this study provide policy-makers and practitioners with valuable references for adopting effective policies and strategies to stimulate the global carbon emission reduction. (C) 2016 Elsevier Ltd. All rights reserved.
机译:由碳排放引起的全球变暖已被认为是对公共健康和福利的威胁。因此,减少碳排放量是每个国家应对全球变暖带来的严峻挑战的一项必要任务。这项研究将STIRPAT模型与面板数据和时间序列数据相结合,以分析人口,富裕程度和技术对1990-2011年期间不同收入水平的125个国家碳排放的影响。结果表明,全球范围内的关键影响因子(KIF)是富裕程度,其次是技术和人口对碳排放的影响顺序。对于高收入国家而言,技术对碳排放的影响最大,而富裕程度最小。在技​​术和人口方面,富裕程度被确定为中等偏上收入(UMI)和中等偏下收入(LMI)水平国家的碳排放的KIF。当谈到低收入(LI)时,富裕是影响碳排放的最大因素,而技术的影响最小。特别是,根据经验结果确定了两种通用模式:较高的收入导致技术的影响更大,而富裕程度对碳排放的影响则较小。这项研究确定了不同收入水平国家的关键绩效指标,为决策者和从业者提供宝贵的参考,帮助他们采取有效的政策和策略来刺激全球碳排放量的减少。 (C)2016 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Applied Energy》 |2017年第1期|310-325|共16页
  • 作者单位

    Chongqing Univ, Sch Construct Management & Real Estate, Chongqing, Peoples R China|Chongqing Univ, Int Res Ctr Sustainable Built Environm, Chongqing, Peoples R China;

    Chongqing Univ, Sch Construct Management & Real Estate, Chongqing, Peoples R China|Chongqing Univ, Int Res Ctr Sustainable Built Environm, Chongqing, Peoples R China;

    Chongqing Univ, Sch Construct Management & Real Estate, Chongqing, Peoples R China|Chongqing Univ, Int Res Ctr Sustainable Built Environm, Chongqing, Peoples R China;

    Chongqing Univ, Sch Construct Management & Real Estate, Chongqing, Peoples R China|Chongqing Univ, Int Res Ctr Sustainable Built Environm, Chongqing, Peoples R China;

    Hong Kong Polytech Univ, Dept Bldg & Real Estate, Kowloon, Hong Kong, 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

    Global carbon emission; Carbon emission reduction; KIFs; STIRPAT model; Panel data; Time-series data;

    机译:全球碳排放;碳减排;KIF;STIRPAT模型;面板数据;时间序列数据;

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