Building Energy Big Data Group, International Research Center for Sustainable Built Environment, School of Management Science and Real Estate, Chongqing University, Chongqing 400045, PR China.
Building Energy Big Data Group, International Research Center for Sustainable Built Environment, School of Management Science and Real Estate, Chongqing University, Chongqing 400045, PR China; Energy Analysis and Environmental Impacts Division, Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Sci Total Environ. 2020 Nov 1;741:138722. doi: 10.1016/j.scitotenv.2020.138722. Epub 2020 Apr 19.
Reducing energy-related carbon emissions has become the essential measure to mitigate global climate change. Based on decoupling analyses and index decomposition, this study is the first to explore whether carbon emission per capita decouples from the human development index from 2000 to 2015 at the provincial level in Southwest China [Chongqing (CQ), Sichuan (SC), Guizhou (GZ), and Yunnan (YN)]. We demonstrate the following. (1) The economic output and energy intensity effects among the five drivers are the strongest to promote and suppress the growth of carbon emission per capita from 2001 to 2015. (2) At the provincial level, we observed four decoupling statures, and the decoupling impact was organized in decreasing order: CQ > GZ > SC > YN (2001-2005), GZ > YN > CQ > SC (2006-2010), YN > SC > GZ > CQ (2011-2015). (3) The overall decoupling effect of Southwest China has been generally reinforced from 2000 to 2015, and finally entered a strong decoupling status in 2013-2015; an environmental Kuznets curve explained that this finding is related to historical peaks in total carbon emissions. Overall, this study provides guidance for the government on carbon emissions mitigation strategies and a valuable decision-making reference for other regions attempting to accelerate low-carbon development.
减少与能源相关的碳排放已成为缓解全球气候变化的必要措施。本研究基于脱钩分析和指数分解，首次探讨了 2000 年至 2015 年期间中国西南地区（重庆、四川、贵州和云南）的人均碳排放量是否与人类发展指数脱钩。研究结果表明：（1）五个驱动因素中的经济产出和能源强度效应是促进和抑制人均碳排放量增长的最强因素；（2）在省级层面上，我们观察到了四种脱钩状态，脱钩影响的组织顺序为：CQ>GZ>SC>YN（2001-2005 年），GZ>YN>CQ>SC（2006-2010 年），YN>SC>GZ>CQ（2011-2015 年）；（3）2000 年至 2015 年期间，中国西南地区的整体脱钩效应总体得到加强，最终在 2013-2015 年进入强脱钩状态；环境库兹涅茨曲线表明，这一发现与总碳排放量的历史峰值有关。总的来说，本研究为政府提供了有关碳减排策略的指导，为其他地区加速低碳发展提供了有价值的决策参考。
