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Peaking Industrial CO 2 Emission in a Typical Heavy Industrial Region: From Multi-Industry and Multi-Energy Type Perspectives

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  • Haiyan Duan

    (Key Lab of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China
    College of New Energy and Environment, Jilin University, Changchun 130021, China)

  • Xize Dong

    (College of New Energy and Environment, Jilin University, Changchun 130021, China)

  • Pinlei Xie

    (People’s Government of Daqiao Town, Jiangdu District, Yangzhou 225211, China)

  • Siyan Chen

    (College of New Energy and Environment, Jilin University, Changchun 130021, China)

  • Baoyang Qin

    (College of New Energy and Environment, Jilin University, Changchun 130021, China)

  • Zijia Dong

    (College of New Energy and Environment, Jilin University, Changchun 130021, China)

  • Wei Yang

    (Key Lab of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China
    College of New Energy and Environment, Jilin University, Changchun 130021, China)

Abstract

Peaking industrial carbon dioxide (CO 2 ) emissions is critical for China to achieve its CO 2 peaking target by 2030 since industrial sector is a major contributor to CO 2 emissions. Heavy industrial regions consume plenty of fossil fuels and emit a large amount of CO 2 emissions, which also have huge CO 2 emissions reduction potential. It is significant to accurately forecast CO 2 emission peak of industrial sector in heavy industrial regions from multi-industry and multi-energy type perspectives. This study incorporates 41 industries and 16 types of energy into the Long-Range Energy Alternatives Planning System (LEAP) model to predict the CO 2 emission peak of the industrial sector in Jilin Province, a typical heavy industrial region. Four scenarios including business-as-usual scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS) and low-carbon scenario (LCS) are set for simulating the future CO 2 emission trends during 2018–2050. The method of variable control is utilized to explore the degree and the direction of influencing factors of CO 2 emission in four scenarios. The results indicate that the peak value of CO 2 emission in the four scenarios are 165.65 million tons (Mt), 156.80 Mt, 128.16 Mt, and 114.17 Mt in 2040, 2040, 2030 and 2020, respectively. Taking ELS as an example, the larger energy-intensive industries such as ferrous metal smelting will peak CO 2 emission in 2025, and low energy industries such as automobile manufacturing will continue to develop rapidly. The influence degree of the four factors is as follows: industrial added value (1.27) > industrial structure (1.19) > energy intensity of each industry (1.12) > energy consumption types of each industry (1.02). Among the four factors, industrial value added is a positive factor for CO 2 emission, and the rest are inhibitory ones. The study provides a reference for developing industrial CO 2 emission reduction policies from multi-industry and multi-energy type perspectives in heavy industrial regions of developing countries.

Suggested Citation

  • Haiyan Duan & Xize Dong & Pinlei Xie & Siyan Chen & Baoyang Qin & Zijia Dong & Wei Yang, 2022. "Peaking Industrial CO 2 Emission in a Typical Heavy Industrial Region: From Multi-Industry and Multi-Energy Type Perspectives," IJERPH, MDPI, vol. 19(13), pages 1-30, June.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:7829-:d:848152
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    References listed on IDEAS

    as
    1. Zuo, Na & Zhong, Hua, 2020. "Can resource policy reverse the resource curse? Evidence from China," Resources Policy, Elsevier, vol. 68(C).
    2. Yu, Shiwei & Zheng, Shuhong & Li, Xia & Li, Longxi, 2018. "China can peak its energy-related carbon emissions before 2025: Evidence from industry restructuring," Energy Economics, Elsevier, vol. 73(C), pages 91-107.
    3. Xiaoyu Liu & Xian’en Wang & Junnian Song & Haiyan Duan & Shuo Wang, 2019. "Why Are the Carbon Footprints of China’s Urban Households Rising? An Input–Output Analysis and Structural Decomposition Analysis," Sustainability, MDPI, vol. 11(24), pages 1-18, December.
    4. Wang, Qunwei & Hang, Ye & Zhou, P. & Wang, Yizhong, 2016. "Decoupling and attribution analysis of industrial carbon emissions in Taiwan," Energy, Elsevier, vol. 113(C), pages 728-738.
    5. Xu, Bin & Chen, Jianbao, 2021. "How to achieve a low-carbon transition in the heavy industry? A nonlinear perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    6. You, Jianmin & Zhang, Wei, 2022. "How heterogeneous technological progress promotes industrial structure upgrading and industrial carbon efficiency? Evidence from China's industries," Energy, Elsevier, vol. 247(C).
    7. Jiancheng Qin & Hui Tao & Chinhsien Cheng & Karthikeyan Brindha & Minjin Zhan & Jianli Ding & Guijin Mu, 2020. "Analysis of Factors Influencing Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China," Sustainability, MDPI, vol. 12(3), pages 1-15, February.
    8. Duan, Haiyan & Chen, Siyan & Song, Junnian, 2022. "Characterizing regional building energy consumption under joint climatic and socioeconomic impacts," Energy, Elsevier, vol. 245(C).
    9. Liu, Lei & Wang, Ke & Wang, Shanshan & Zhang, Ruiqin & Tang, Xiaoyan, 2018. "Assessing energy consumption, CO2 and pollutant emissions and health benefits from China's transport sector through 2050," Energy Policy, Elsevier, vol. 116(C), pages 382-396.
    10. Lin, Boqiang & Xu, Bin, 2018. "Growth of industrial CO2 emissions in Shanghai city: Evidence from a dynamic vector autoregression analysis," Energy, Elsevier, vol. 151(C), pages 167-177.
    11. Zhao, Jun & Jiang, Qingzhe & Dong, Xiucheng & Dong, Kangyin & Jiang, Hongdian, 2022. "How does industrial structure adjustment reduce CO2 emissions? Spatial and mediation effects analysis for China," Energy Economics, Elsevier, vol. 105(C).
    12. Liu, Xiaoyu & Duan, Zhiyuan & Shan, Yuli & Duan, Haiyan & Wang, Shuo & Song, Junnian & Wang, Xian'en, 2019. "Low-carbon developments in Northeast China: Evidence from cities," Applied Energy, Elsevier, vol. 236(C), pages 1019-1033.
    13. Xianen Wang & Baoyang Qin & Hanning Wang & Xize Dong & Haiyan Duan, 2022. "Carbon Mitigation Pathways of Urban Transportation under Cold Climatic Conditions," IJERPH, MDPI, vol. 19(8), pages 1-16, April.
    14. Zhi-Fu Mi & Yi-Ming Wei & Bing Wang & Jing Meng & Zhu Liu & Yuli Shan & Jingru Liu & Dabo Guan, 2017. "Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030," CEEP-BIT Working Papers 103, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    15. Wu, Linfei & Sun, Liwen & Qi, Peixiao & Ren, Xiangwei & Sun, Xiaoting, 2021. "Energy endowment, industrial structure upgrading, and CO2 emissions in China: Revisiting resource curse in the context of carbon emissions," Resources Policy, Elsevier, vol. 74(C).
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