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Study on Global Industrialization and Industry Emission to Achieve the 2 °C Goal Based on MESSAGE Model and LMDI Approach

Author

Listed:
  • Shining Zhang

    (Global Energy Interconnection Development and Cooperation Organization, Xicheng District, Beijing 100031, China)

  • Fang Yang

    (Global Energy Interconnection Development and Cooperation Organization, Xicheng District, Beijing 100031, China)

  • Changyi Liu

    (Global Energy Interconnection Development and Cooperation Organization, Xicheng District, Beijing 100031, China)

  • Xing Chen

    (Global Energy Interconnection Development and Cooperation Organization, Xicheng District, Beijing 100031, China)

  • Xin Tan

    (Global Energy Interconnection Development and Cooperation Organization, Xicheng District, Beijing 100031, China)

  • Yuanbing Zhou

    (Global Energy Interconnection Development and Cooperation Organization, Xicheng District, Beijing 100031, China)

  • Fei Guo

    (International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1-A, 2361 Laxenburg, Austria)

  • Weiyi Jiang

    (Faculty of science, Camperdown campus, University of Sydney, Camperdown, Sydney 2006, Australia)

Abstract

The industrial sector dominates the global energy consumption and carbon emissions in end use sectors, and it faces challenges in emission reductions to reach the Paris Agreement goals. This paper analyzes and quantifies the relationship between industrialization, energy systems, and carbon emissions. Firstly, it forecasts the global and regional industrialization trends under Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway2 (SSP2) scenarios. Then, it projects the global and regional energy consumption that aligns with the industrialization trend, and optimizes the global energy supply system using the Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE) model for the industrial sector. Moreover, it develops an expanded Kaya identity to comprehensively investigate the drivers of industrial carbon emissions. In addition, it employs a Logarithmic Mean Divisia Index (LMDI) approach to track the historical contributions of various drivers of carbon emissions, as well as predictions into the future. This paper finds that economic development and population growth are the two largest drivers for historical industrial CO 2 emissions, and that carbon intensity and industry energy intensity are the top two drivers for the decrease of future industrial CO 2 emissions. Finally, it proposes three modes, i.e., clean supply, electrification, and energy efficiency for industrial emission reduction.

Suggested Citation

  • Shining Zhang & Fang Yang & Changyi Liu & Xing Chen & Xin Tan & Yuanbing Zhou & Fei Guo & Weiyi Jiang, 2020. "Study on Global Industrialization and Industry Emission to Achieve the 2 °C Goal Based on MESSAGE Model and LMDI Approach," Energies, MDPI, vol. 13(4), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:825-:d:320400
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    References listed on IDEAS

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    Cited by:

    1. Liu, Xianmei & Peng, Rui & Bai, Caiquan & Chi, Yuanying & Liu, Yuxiang, 2023. "Economic cost, energy transition, and pollutant mitigation: The effect of China's different mitigation pathways toward carbon neutrality," Energy, Elsevier, vol. 275(C).
    2. Cheng Huang & Yang Qu & Lingfang Huang & Xing Meng & Yulong Chen & Ping Pan, 2022. "Quantifying the Impact of Urban Form and Socio-Economic Development on China’s Carbon Emissions," IJERPH, MDPI, vol. 19(5), pages 1-14, March.
    3. Feng-Fan Liao & Wun-Hwa Chen, 2021. "Will the Management Structure of Energy Administrators Affect the Achievement of the Electrical Efficiency Mandatory Target for Taiwan Factories?," Energies, MDPI, vol. 14(7), pages 1-14, April.
    4. Di Peng & Haibin Liu, 2022. "Measurement and Driving Factors of Carbon Emissions from Coal Consumption in China Based on the Kaya-LMDI Model," Energies, MDPI, vol. 16(1), pages 1-19, December.

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