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Prediction and Analysis of CO 2 Emission in Chongqing for the Protection of Environment and Public Health

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  • Shuai Yang

    (National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China)

  • Yu Wang

    (National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China)

  • Wengang Ao

    (National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China)

  • Yun Bai

    (National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China)

  • Chuan Li

    (National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China)

Abstract

Based on the consumption of fossil energy, the CO 2 emissions of Chongqing are calculated and analyzed from 1997 to 2015 in this paper. Based on the calculation results, the consumption of fossil fuels and the corresponding CO 2 emissions of Chongqing in 2020 are predicted, and the supporting data and corresponding policies are provided for the government of Chongqing to reach its goal as the economic unit of low-carbon emission in the ‘13th Five-Year Plan’. The results of the analysis show that there is a rapid decreasing trend of CO 2 emissions in Chongqing during the ‘12th Five-Year Plan’, which are caused by the adjustment policy of the energy structure in Chongqing. Therefore, the analysis and prediction are primarily based on the adjustment of Chongqing’s coal energy consumption in this paper. At the initial stage, support vector regression (SVR) method is applied to predict the other fossil energy consumption and the corresponding CO 2 emissions of Chongqing in 2020. Then, with the energy intensity of 2015 and the official target of CO 2 intensity in 2020, the total fossil energy consumption and CO 2 emissions of Chongqing in 2020 are predicted respectively. By the above results of calculation, the coal consumption and its corresponding CO 2 emissions of Chongqing in 2020 are determined. To achieve the goal of CO 2 emissions of Chongqing in 2020, the coal consumption level and energy intensity of Chongqing are calculated, and the adjustment strategies for energy consumption structure in Chongqing are proposed.

Suggested Citation

  • Shuai Yang & Yu Wang & Wengang Ao & Yun Bai & Chuan Li, 2018. "Prediction and Analysis of CO 2 Emission in Chongqing for the Protection of Environment and Public Health," IJERPH, MDPI, vol. 15(3), pages 1-15, March.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:3:p:530-:d:136538
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    References listed on IDEAS

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    1. Wenxiu Wang & Yaoqiu Kuang & Ningsheng Huang, 2011. "Study on the Decomposition of Factors Affecting Energy-Related Carbon Emissions in Guangdong Province, China," Energies, MDPI, vol. 4(12), pages 1-24, December.
    2. Fernández González, P. & Landajo, M. & Presno, M.J., 2014. "Tracking European Union CO2 emissions through LMDI (logarithmic-mean Divisia index) decomposition. The activity revaluation approach," Energy, Elsevier, vol. 73(C), pages 741-750.
    3. Aviral Kumar TIWARI, 2011. "Energy Consumption, Co2 Emission and Economic Growth: A Revisit of the Evidence from India," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 11(2).
    4. Jalil, Abdul & Mahmud, Syed F., 2009. "Environment Kuznets curve for CO2 emissions: A cointegration analysis for China," Energy Policy, Elsevier, vol. 37(12), pages 5167-5172, December.
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    1. Ning Zhang & Zaiwu Gong & Kedong Yin & Yuhong Wang, 2018. "Special Issue “Decision Models in Green Growth and Sustainable Development”," IJERPH, MDPI, vol. 15(6), pages 1-8, May.
    2. Jia Wei & Hong Chen & Ruyin Long, 2018. "Determining Multi-Layer Factors That Drive the Carbon Capability of Urban Residents in Response to Climate Change: An Exploratory Qualitative Study in China," IJERPH, MDPI, vol. 15(8), pages 1-19, July.
    3. Yelin Wang & Ping Yang & Zan Song & Julien Chevallier & Qingtai Xiao, 2024. "Intelligent Prediction of Annual CO2 Emissions Under Data Decomposition Mode," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 711-740, February.
    4. Beibei Che & Chaofeng Shao & Zhirui Lu & Binghong Qian & Sihan Chen, 2022. "Mineral Requirements for China’s Energy Transition to 2060—Focus on Electricity and Transportation," Sustainability, MDPI, vol. 15(1), pages 1-23, December.

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