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Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches

机译:中国工业碳排放的驱动力:来自PDA和LMDI联合方法的证据

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摘要

As the world’s top carbon-emitting country, China has placed great emphasis on understanding the driving factors of carbon emissions and developing appropriate emissions reduction policies. Due to the obvious variations in carbon emissions among various industries in China, corresponding policies need to be formulated for different industries. Through data envelopment analysis, this study introduced the Shephard distance function into the logarithmic mean Divisia index (LMDI) for decomposition analysis, built a carbon emissions decomposition model of 23 industries in China during 2003–2015, and analyzed the impact of 10 factors driving carbon emissions. The main results are as follows. (1) Potential gross domestic production (GDP) is a crucial factor for increasing carbon emissions, whereas potential energy intensity and technological advances of carbon emissions have a significant inhibitory effect on carbon emissions; (2) the technological progress of energy usage and the technological advances of GDP output are manifested by inhibiting carbon emissions at the early stage of development and increasing emissions at the later stage; (3) the structure of coal-based energy consumption is difficult to change in the long term, resulting in a weak effect of energy mix on carbon emissions and an increase in carbon emissions due to the potential energy carbon intensity factor.
机译:作为世界上最大的碳排放国,中国非常重视了解碳排放的驱动因素并制定适当的减排政策。由于中国各个行业之间碳排放量的明显差异,因此需要针对不同行业制定相应的政策。通过数据包络分析,本研究将谢泼德距离函数引入对数平均Divisia指数(LMDI)进行分解分析,建立了2003-2015年中国23个行业的碳排放分解模型,并分析了驱动碳的10个因素的影响。排放。主要结果如下。 (1)潜在的国内生产总值(GDP)是增加碳排放的关键因素,而潜在的能源强度和碳排放的技术进步对碳排放具有显着的抑制作用; (2)能源使用的技术进步和国内生产总值的技术进步表现为在发展的早期抑制碳排放,在发展的后期抑制排放; (3)煤炭能源消费的结构长期难以改变,由于潜在的能源碳强度因子,导致能源混合对碳排放的影响较弱,碳排放增加。

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