Quantile analysis of carbon emissions in China metallurgy industry

被引:36
|
作者
Benjamin, Nelson, I [1 ]
Lin, Boqiang [2 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Business, Nanjing 210044, Jiangsu, Peoples R China
[2] Xiamen Univ, Collaborat Innovat Ctr Energy Econ & Energy Polic, Sch Management, China Inst Studies Energy Policy, Xiamen 361005, Fujian, Peoples R China
关键词
Metallurgy industry; Carbon dioxide emissions; Quantile estimates; CO2; EMISSIONS; CONSUMPTION;
D O I
10.1016/j.jclepro.2019.118534
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The metallurgy industry that was crucial in developing Chinese economy also enhanced energy consumption and carbon emissions. This paper examines the impact of economic variables on carbon emissions from the metallurgy industry of China, where energy structure, energy intensity, carbon intensity, industrial structure, and labor productivity were utilized in analyzing carbon dioxide emissions within a quantile models framework. Quantile estimates unveiled varying effects of variables across spectrum of carbon emissions and averagely, a unit increase in the above economic variables will influence carbon emissions by 97.2 percent, 100.3 percent, 118.6 percent, 98.4 percent and 100.2 percent approximately. Across all quantiles, results showed that carbon intensity had the greatest impact on carbon dioxide emissions, then energy intensity, labor productivity, industrial structure, and energy structure, most but not all the industry was plagued by carbon intensity, while industrial scale should be minimized optimally, labor productivity should be improved too. Energy intensity is the most influencing factor, prompting an urgent need for technology advancement The uniqueness of the metallurgy industry must be considered when administering economic policies across the industry in China. The enhancement of clean energy technology and optimizing energy structure are crucial for carbon emissions reduction and a review of energy consumption by the industry to accommodate renewable energy is recommended. (C) 2019 Elsevier Ltd. All rights reserved.
引用
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页数:10
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