School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China.
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China.
Sci Total Environ. 2019 Jan 15;648:1411-1420. doi: 10.1016/j.scitotenv.2018.08.183. Epub 2018 Aug 20.
To address climate change effectively, it is essential to quantify CO emissions and the driving factors in high-energy-consuming countries. China is the top CO-emitting country; moreover, there is a lack of comprehensive analytical studies on quantifying the contributions of key drivers to high-energy-consuming countries' CO emissions. Therefore, based on data of China's energy consumption from 2005 to 2016, this paper combines the extended Kaya identity with the logarithmic mean Divisia index (LMDI) decomposition method to construct an optimized carbon emission decomposition model. Carbon emission and carbon emission intensity are measured and decomposed. Then, the results of the decomposition are discussed, and the effects of various drivers on carbon emissions from energy consumption in China are analysed. Furthermore, we demonstrate real applications of decomposition analysis in policy-making using examples from China and present some ideas to reduce CO. The results show that from 2005 to 2016, China's total carbon emissions accounted for nearly one-third of the world's total carbon emissions, and the intensity of carbon emissions in China was generally higher than that of worldwide. The rapid development of economy and acceleration of urbanization are not conducive to reduction of carbon emissions. Reducing the intensity of energy consumption, adjusting the internal structure of the industry and perfecting the economic policy system should be important means used to promote the development of China's low-carbon economy in the future.
为了有效应对气候变化，有必要量化高能耗国家的 CO 排放和驱动因素。中国是最大的 CO 排放国；此外，对于量化关键驱动因素对高能耗国家 CO 排放的贡献，缺乏全面的分析研究。因此，本文基于中国 2005 年至 2016 年的能源消费数据，结合扩展的 Kaya 恒等式和对数平均迪氏分解（LMDI）分解方法，构建了一个优化的碳排放分解模型。对碳排放和碳排放强度进行了测量和分解。然后，讨论了分解结果，并分析了各种驱动因素对中国能源消费 CO 排放的影响。此外，我们通过中国的实例说明了分解分析在政策制定中的实际应用，并提出了一些减少 CO 的想法。结果表明，2005 年至 2016 年期间，中国的碳排放量占全球总量的近三分之一，中国的碳排放强度普遍高于全球水平。经济的快速发展和城市化进程的加快不利于减少碳排放。降低能源消费强度、调整产业内部结构和完善经济政策体系应是未来推动中国低碳经济发展的重要手段。
