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The driving forces and potential mitigation of energy-related CO2 emissions in China's metal industry

Chao Feng, Jian-Bai Huang and Miao Wang

Resources Policy, 2018, vol. 59, issue C, 487-494

Abstract: Utilizing the logarithmic mean Divisia index (LMDI) method, this paper decomposes the CO2 emissions of China's metal industry into five factors, namely, industrial output, industrial structure, energy intensity, energy consumption structure, and carbon dioxide emissions coefficient. Then, a scenario analysis is used to predict future CO2 emissions and estimate the emission mitigation potential from 2016 to 2020. The results indicate that (1) from 2000 to 2015, China's metal industry witnessed substantial growth in CO2 emissions, showing an increase of 1015.56 million tons (Mt). (2) Of the five driving factors, the industrial output effect was mainly responsible for CO2 emissions growth, with a contribution ratio of 245.51% over the total CO2 emission change, with other driving factors held constant. In contrast, the energy intensity decline was decisive for reducing CO2 emission, followed by the industrial structure. (3) The energy consumption structure had the weakest effect on CO2 emission changes and presented an overall positive effect with some volatility. (4) The CO2 emission mitigation potential under the moderate and aggressive emission mitigation scenarios is predicted to be 219.30 and 1445.26 Mt in 2020, respectively. These research findings propose that the Chinese government should take full advantage of the mitigating factors (i.e., decreasing energy intensity and industrial restructuring), paying particular attention to the subsector of the smelting and pressing of ferrous metals, and strive for breakthroughs.

Keywords: LMDI method; Decomposition analysis; CO2 emissions prediction; Scenario analysis (search for similar items in EconPapers)
Date: 2018
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