Unleashing China's coal conservation potentials by analyzing efficiency of energy intensive industries: A Logarithm Mean Divisia Index (LMDI) model

Zulqarnain Mushtaq, Wei Wei, Jie Liu
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Abstract

Considering China's ambitions for carbon peaking as of 2030 to ensure environmental protection and energy security, the present study is intended to explore sustainable pathways to reduce coal consumption by enhancing energy efficiency. The current article estimates coal consumption efficiency and radial super-efficiency by applying DEA-CCR and radial super-efficiency models. In the second stage, the Logarithm Mean Divisia Index (LMDI) and DEA-Malmquist models were used to explore the components of coal consumption in China's six key energy-intensive industries from 2000 to 2020. Findings indicate that (1) there is a substantial coal consumption efficiency gap among these industries, and they are working well below the production frontier. (2) Findings of DEA-Malmquist indicate that technological changes positively contributed to total productivity changes, while technical efficiency negatively impacted coal consumption growth. (3) The results of the LMDI model reveal that industrial output growth and structural changes are the key factors accelerating coal consumption. In contrast, the coal intensity had deaccelerated the coal consumption in the energy intensive industries. The current study provides several policy proposals to enhance coal conservation and consumption efficiency to achieve the aspiring goals of sustainable development.
通过分析高耗能行业的效率,挖掘中国的节煤潜力:对数均值指数(LMDI)模型
考虑到中国在 2030 年实现碳封顶,以确保环境保护和能源安全,本研究旨在探索通过提高能源效率减少煤炭消耗的可持续路径。本文运用 DEA-CCR 模型和径向超效率模型估算煤炭消费效率和径向超效率。第二阶段,采用对数平均除法指数(LMDI)和 DEA-Malmquist 模型探讨了 2000-2020 年中国六大重点高耗能行业的煤炭消费构成。研究结果表明:(1)这些行业之间的煤炭消费效率存在巨大差距,其工作效率远低于生产前沿。(2)DEA-Malmquist 的研究结果表明,技术变革对总生产率的变化有积极的促进作用,而技术效率对煤炭消费的增长有负面影响。(3) LMDI 模型的结果显示,工业产出增长和结构变化是加速煤炭消费的关键因素。与此相反,煤炭密集度降低了能源密集型产业的煤炭消费。本研究为提高煤炭节约和消费效率提供了若干政策建议,以实现可持续发展的预期目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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