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Analysis of Influencing Factors and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model

Author

Listed:
  • Zhen Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Yanbin Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Shuangshuang Shao

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China′s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China′s carbon emissions from energy consumption will reach 302.82 million tons in 2020.

Suggested Citation

  • Zhen Li & Yanbin Li & Shuangshuang Shao, 2019. "Analysis of Influencing Factors and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model," Energies, MDPI, vol. 12(16), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3054-:d:255797
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    References listed on IDEAS

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    1. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.
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    Cited by:

    1. Marwa Elsherif, 2024. "Nexus between Blue Economy, Renewable Energy and Environmental Sustainability in the MENA Region: Evidence from Panel Threshold Regression," International Journal of Energy Economics and Policy, Econjournals, vol. 14(4), pages 169-185, July.
    2. Tiangui Lv & Han Hu & Hualin Xie & Xinmin Zhang & Li Wang & Xiaoqiang Shen, 2023. "An empirical relationship between urbanization and carbon emissions in an ecological civilization demonstration area of China based on the STIRPAT model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(3), pages 2465-2486, March.
    3. Jianbo Dong & Min Zhang & Guangbin Cheng, 2022. "Impacts of Upgrading of Consumption Structure and Human Capital Level on Carbon Emissions—Empirical Evidence Based on China’s Provincial Panel Data," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    4. Wen-Hsien Tsai, 2020. "Carbon Emission Reduction—Carbon Tax, Carbon Trading, and Carbon Offset," Energies, MDPI, vol. 13(22), pages 1-7, November.
    5. Huiping Wang & Yi Wang, 2022. "Estimating per Capita Primary Energy Consumption Using a Novel Fractional Gray Bernoulli Model," Sustainability, MDPI, vol. 14(4), pages 1-22, February.

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