An evaluation of greenhouse gas emission efficiency in China's industry based on SFA
As one of the largest countries with sound industrial systems in the world, China is a major emitter of greenhouse gases. In order to achieve sustainable development, the analysis of greenhouse gas emissions from industry is of great significance. This research evaluates the greenhouse gas emission...
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Veröffentlicht in: | The Science of the total environment 2019-11, Vol.690, p.1190-1202 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | As one of the largest countries with sound industrial systems in the world, China is a major emitter of greenhouse gases. In order to achieve sustainable development, the analysis of greenhouse gas emissions from industry is of great significance. This research evaluates the greenhouse gas emission efficiency (GHG efficiency) at industry level in 26 sectors of China and analyzes the impacts of its determinants using stochastic frontier approach. Furthermore, the correlations for GHG efficiency with its determinants and other proxies are estimated by Kendall's rank analysis. Industry level data from Chinese processing and manufacturing industries spanning over the period 2000–2016 are used for this analysis. Results show that there has little potential to improve GHG efficiency, then technology progress is necessary. The GHG efficiency performance responds both to changes in the proportion of net electricity use and the ratio of electricity price to coal price; meanwhile the technology capacity also has a positive impact. Besides, three main proxies of GHG intensity, energy efficiency and energy intensity have their own significance respectively, but could not represent GHG efficiency.
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•China's GHG efficiency at industry level is assessed by stochastic frontier analysis.•At night, net electricity is better than small-scale on-site coal-fired power plant.•Technology advance is better to improve efficiency and mitigate GHG emissions.•Energy efficiency and GHG efficiency are dependent but loosely related indexes. |
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ISSN: | 0048-9697 1879-1026 |
DOI: | 10.1016/j.scitotenv.2019.07.093 |