Identification of the key factors affecting Chinese carbon intensity and their historical trends using random forest algorithm
文献类型：期刊论文
| 作者 | Tang Zhipeng1,2; Mei Ziao1,2; Liu Weidong1,2; Xia Yan3 |
| 刊名 | JOURNAL OF GEOGRAPHICAL SCIENCES
 |
| 出版日期 | 2020-05-01 |
| 卷号 | 30期号:5页码:743-756 |
| 关键词 | machine learning random forest carbon intensity key factors China |
| ISSN号 | 1009-637X |
| DOI | 10.1007/s11442-020-1753-4 |
| 通讯作者 | Xia Yan(xiayan@casipm.ac.cn) |
| 英文摘要 | The Chinese government ratified the Paris Climate Agreement in 2016. Accordingly, China aims to reduce carbon dioxide emissions per unit of gross domestic product (carbon intensity) to 60%-65% of 2005 levels by 2030. However, since numerous factors influence carbon intensity in China, it is critical to assess their relative importance to determine the most important factors. As traditional methods are inadequate for identifying key factors from a range of factors acting in concert, machine learning was applied in this study. Specifically, random forest algorithm, which is based on decision tree theory, was employed because it is insensitive to multicollinearity, is robust to missing and unbalanced data, and provides reasonable predictive results. We identified the key factors affecting carbon intensity in China using random forest algorithm and analyzed the evolution in the key factors from 1980 to 2017. The dominant factors affecting carbon intensity in China from 1980 to 1991 included the scale and proportion of energy-intensive industry, the proportion of fossil fuel-based energy, and technological progress. The Chinese economy developed rapidly between 1992 and 2007; during this time, the effects of the proportion of service industry, price of fossil fuel, and traditional residential consumption on carbon intensity increased. Subsequently, the Chinese economy entered a period of structural adjustment after the 2008 global financial crisis; during this period, reductions in emissions and the availability of new energy types began to have effects on carbon intensity, and the importance of residential consumption increased. The results suggest that optimizing the energy and industrial structures, promoting technological advancement, increasing green consumption, and reducing emissions are keys to decreasing carbon intensity within China in the future. These approaches will help achieve the goal of reducing carbon intensity to 60%-65% of the 2005 level by 2030. |
| WOS关键词 | DECOMPOSITION ANALYSIS ; BEHAVIOR |
| 资助项目 | National Natural Science Foundation of China[41771135] |
| WOS研究方向 | Physical Geography |
| 语种 | 英语 |
| WOS记录号 | WOS:000529539000004 |
| 出版者 | SCIENCE PRESS |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/159918]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Xia Yan |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Reg Sustainable Dev Modeling, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Sci & Dev, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tang Zhipeng,Mei Ziao,Liu Weidong,et al. Identification of the key factors affecting Chinese carbon intensity and their historical trends using random forest algorithm[J]. JOURNAL OF GEOGRAPHICAL SCIENCES,2020,30(5):743-756. |
| APA | Tang Zhipeng,Mei Ziao,Liu Weidong,&Xia Yan.(2020).Identification of the key factors affecting Chinese carbon intensity and their historical trends using random forest algorithm.JOURNAL OF GEOGRAPHICAL SCIENCES,30(5),743-756. |
| MLA | Tang Zhipeng,et al."Identification of the key factors affecting Chinese carbon intensity and their historical trends using random forest algorithm".JOURNAL OF GEOGRAPHICAL SCIENCES 30.5(2020):743-756. |
入库方式： OAI收割
来源：地理科学与资源研究所
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