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

Shining Zhang, Fang Yang, Changyi Liu, Xing Chen, Xin Tan, Yuanbing Zhou, Fei Guo and Weiyi Jiang
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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

Energies, 2020, vol. 13, issue 4, 1-21

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.

Keywords: industrialization; industrial CO 2 emission; MESSAGE model; Kaya identity; LMDI approach (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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