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中国电力行业的碳排放:驱动因素与减排路径。

Carbon emission of China's power industry: driving factors and emission reduction path.

机构信息

Business School, Hohai University, Nanjing, 211100, China.

Business School, Hohai University, Changzhou, 231022, China.

出版信息

Environ Sci Pollut Res Int. 2022 Nov;29(52):78345-78360. doi: 10.1007/s11356-022-21297-5. Epub 2022 Jun 11.

DOI:10.1007/s11356-022-21297-5
PMID:35690704
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9188421/
Abstract

The low-carbon development of power industry is the key to low-carbon development of the whole society. In order to determine appropriate and feasible emission reduction policies, it is necessary to identify the contribution of different drivers to the change of carbon emissions in China's power sector and to simulate the potential evolution trend of carbon emissions. This paper constructs LMDI model to analyze the driving factors of carbon emission changes in China's power industry from 2000 to 2018 and uses Monte Carlo algorithm to simulate the evolution trend of carbon emission under different scenarios. We can find (1) economic output effect reached 3.817 billion tons from 2000 to 2018, which was the primary factor to increase the carbon emission. Population scale effect reached 251million tons, which had a weak promotion impact on carbon emission. (2) Conversion efficiency effect played a role in restraining carbon emissions, reaching 699 million tons from 2000 to 2018. (3) Emission factor effect and power intensity effect have obvious volatility. The power structure effect showed great volatility before 2013 and mainly played a role in restraining carbon emission after 2013. (4) Under the baseline scenario, the carbon emission of China's power industry shows a growth trend. Under green development scenario and enhanced carbon reduction scenario, the carbon emission shows a trend of first increasing and then decreasing.

摘要

电力行业的低碳发展是全社会低碳发展的关键。为了确定适当可行的减排政策,有必要确定不同驱动因素对中国电力部门碳排放变化的贡献,并模拟碳排放的潜在演变趋势。本文构建 LMDI 模型,分析了 2000 年至 2018 年中国电力行业碳排放变化的驱动因素,并利用蒙特卡罗算法模拟了不同情景下碳排放的演变趋势。我们可以发现:(1)经济产出效应从 2000 年到 2018 年达到 38.17 亿吨,是增加碳排放的主要因素。人口规模效应达到 2.51 亿吨,对碳排放的促进作用较弱。(2)转换效率效应在抑制碳排放方面发挥了作用,从 2000 年到 2018 年达到 6.99 亿吨。(3)排放因子效应和电力强度效应具有明显的波动性。电力结构效应在 2013 年前表现出较大的波动性,2013 年后主要起到抑制碳排放的作用。(4)在基准情景下,中国电力行业的碳排放呈增长趋势。在绿色发展情景和强化减排情景下,碳排放呈先增后减的趋势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0e5/9188421/529577bcbc48/11356_2022_21297_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0e5/9188421/d877ea0b1b0e/11356_2022_21297_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0e5/9188421/3f47bc7074fe/11356_2022_21297_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0e5/9188421/0f878b663e35/11356_2022_21297_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0e5/9188421/03b84fc87779/11356_2022_21297_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0e5/9188421/529577bcbc48/11356_2022_21297_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0e5/9188421/d877ea0b1b0e/11356_2022_21297_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0e5/9188421/3f47bc7074fe/11356_2022_21297_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0e5/9188421/0f878b663e35/11356_2022_21297_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0e5/9188421/03b84fc87779/11356_2022_21297_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0e5/9188421/529577bcbc48/11356_2022_21297_Fig5_HTML.jpg

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