Journal of Geographical Sciences ›› 2020, Vol. 30 ›› Issue (5): 743-756.doi: 10.1007/s11442-020-1753-4

• Research Articles • Previous Articles     Next Articles

Identification of the key factors affecting Chinese carbon intensity and their historical trends using random forest algorithm

TANG Zhipeng1,2, MEI Ziao1,2, LIU Weidong1,2, XIA Yan3,*()   

  1. 1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    3. Institutes of Science and Development, CAS, Beijing 100190, China
  • Received:2019-12-22 Accepted:2020-02-20 Online:2020-05-25 Published:2020-07-25
  • Contact: XIA Yan E-mail:xiayan@casipm.ac.cn
  • About author:Tang Zhipeng (1978–), PhD and Associate Professor, specialized in economic geography and regional sustainable development. E-mail: tangzp@igsnrr.ac.cn
  • Supported by:
    National Natural Science Foundation of China(41771135)

Abstract:

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.

Key words: machine learning, random forest, carbon intensity, key factors, China