The Structure and Evolution of Industrial Embodied Carbon Emission Flow Network in China under the Constraint of Carbon Peaking

Abstract

Abstract: The upstream and downstream of the industrial production process are highly correlated, so changing the existing production process is bound to bring about transformations in the whole industrial system. Therefore, in order to achieve the carbon peak in the industrial sector, it is necessary to study the embodied carbon emission flow relationship between different industries to promote carbon emission reduction collaboratively. In this paper, we compile China’s input-output tables of 2025 and 2030 based on GRAS method and use expanded environment input-output model (EEIO) and social network analysis method (SNA) to identify the characteristics of the structure and evolution of the embodied carbon emission flow networks of China’s industrial chain during 2010 to 2030. The results show that during 2010 to 2030, the embodied carbon emissions of the power sector show a rapid rising trend, while those of petrochemical, chemical, non-metallic mineral products, smelting and pressing of mental decrease between 8% and 30%. As for the perspective of network structure, chemical, non-metallic mineral products and mental smelting and pressing sectors are the main embodied carbon “importers”, while electric power, chemical and special equipment manufacturing sectors are the main embodied carbon “suppliers”. Electric power sector, chemicals and specialized equipment manufacturing contribute 40% of the intermediation capacity in embodied carbon emission network. Under the constraint of carbon peak target, the center of embodied carbon network is gradually transferred to the tertiary industry, and electric power, service and transportation sectors become important “bridge” in the process of embodied carbon transfer.

Key words: industrial sectors, carbon peaking, embodied carbon emission flow, EEIO model, network structure

Wang Zhaohua, Wang Shuohan, Li Hao, Wang Song, Zhang Shuang. The Structure and Evolution of Industrial Embodied Carbon Emission Flow Network in China under the Constraint of Carbon Peaking[J]. Management Review, 2025, 37(4): 3-16.

References

[1] 习近平. 在第七十五届联合国大会一般性辩论上的讲话[J]. 中华人民共和国国务院公报, 2020, (28):5-7 Xi J. P. Statement at the General Debate of the 75th Session of the United Nations General Assembly[J]. Gazette of the State Council of the People’s Republic of China, 2020, (28):5-7
[2] IEA. CO₂ Emission in 2022[R]. Paris: IEA, 2023
[3] Pan X. Z., Ma X. Q., Zhang Y. R., et al. Implications of Carbon Neutrality for Power Sector Investments and Stranded Coal Assets in China[J]. Energy Economics, 2023, 121:106682
[4] Xu R., Tong D., Davis S. J., et al. Plant-by-Plant Decarbonization Strategies for the Global Steel Industry[J]. Nature Climate Change, 2023, 13(10):1067-1074
[5] 杨冕, 谢泽宇, 徐飞. 区域梯次有序碳达峰下我国省际碳转移研究[J]. 系统工程理论与实践, 2023, 43(7):2102-2121 Yang M., Xie Z. Y., Xu F. A Study of Inter-provincial Carbon Transfer under Regional Sequential CO₂ Peak in China[J]. Systems Engineering-Theory & Practice, 2023, 43(7):2102-2121
[6] 赵忠秀, 闫云凤, 裴建锁. 生产分割、区域间贸易与CO₂排放:基于IRIO模型的研究[J]. 管理评论, 2018, 30(5):47-57 Zhao Z. X., Yan Y. F., Pei J. S. Production Fragmentation, Interregional Trade and CO₂ Emissions: An IRIO Model Approach[J]. Management Review, 2018, 30(5):47-57
[7] 潘晨, 李善同, 何建武, 等. 考虑省际贸易结构的中国碳排放变化的驱动因素分析[J]. 管理评论, 2023, 35(1):3-15 Pan C., Li S. T., He J. W., et al. Analysis of the Driving Forces of Changes in China’s CO₂ Emissions Considering Inter-provincial Trade Structure[J]. Management Review, 2023, 35(1):3-15
[8] Yang L., Wang Y. T., Wang R. R., et al. Environmental-Social-Economic Footprints of Consumption and Trade in the Asia-Pacific Region[J]. Nature Communications, 2020, 11(1):1-9
[9] 杨顺顺. 中国工业部门碳排放转移评价及预测研究[J]. 中国工业经济, 2015, (6):55-67 Yang S. S. Evaluation and Prediction on Carbon Emissions Transferring across Industrial Sectors in China[J]. China Industrial Economics, 2015, (6):55-67
[10] Jiang J. J., Ye B., Liu J. Research on the Peak of CO₂ Emissions in the Developing World: Current Progress and Future Prospect[J]. Applied Energy, 2019, 235:186-203
[11] Yu S. W., Zheng S. H., Li X., et al. China can Peak Its Energy-related Carbon Emissions Before 2025: Evidence from Industry Restructuring[J]. Energy Economics, 2018, 73:91-107
[12] 章高敏, 王腾, 娄渊雨, 等. 基于LSTM神经网络的中国省级碳达峰路径分析[J]. 中国管理科学, 2025, 33(3):339-350 Zhang G. M., Wang T., Lou Y. Y., et al. Research on China’s Provincial Carbon Emission Peak Path Based on a LSTM Neural Network Approach[J]. Chinese Journal of Management Science, 2025, 33(3):339-350
[13] Zhou S., Kyle G. P., Yu S., et al. Energy Use and CO₂ Emissions of China’s Industrial Sector from a Global Perspective[J]. Energy Policy, 2013, 58:284-294
[14] 杨冕, 卢昕, 段宏波. 中国高耗能行业碳排放因素分解与达峰路径研究[J]. 系统工程理论与实践, 2018, 38(10):2501-2511 Yang M., Lu X., Duan H. B. Analysis on the Determinants and Peaking Paths of CO₂ Emissions in China’s High Energy-consuming Industries[J]. Systems Engineering-Theory & Practice, 2018, 38(10):2501-2511
[15] Liu L., Wang K., Wang S. S., et al. Assessing Energy Consumption, CO₂ and Pollutant Emissions and Health Benefits from China’s Transport Sector through 2050[J]. Energy Policy, 2018, 116:382-396
[16] 王丽娟, 张剑, 王雪松, 等. 中国电力行业二氧化碳排放达峰路径研究[J]. 环境科学研究, 2022, 35(2):329-338 Wang L. J., Zhang J., Wang X. S., et al. Pathway of Carbon Emission Peak in China’s Electric Power Industry[J]. Research of Environmental Sciences, 2022, 35(2):329-338
[17] Zhou N., Khanna N., Feng W., et al. Scenarios of Energy Efficiency and CO₂ Emissions Reduction Potential in the Buildings Sector in China to Year 2050[J]. Nature Energy, 2018, 3(11):978-984
[18] Lyu Y. Z., Gao H. B., Yan K., et al. Carbon Peaking Strategies for Industrial Parks: Model Development and Applications in China[J]. Applied Energy, 2022, 322:119442
[19] 禹湘, 娄峰, 谭畅. 基于CIE-CEAM模型的中国工业“双碳”路径模拟[J]. 中国人口·资源与环境, 2022, 32(7):49-56 Yu X., Lou F., Tan C. A Simulation Study of the Pathway of Achieving the ‘Dual Carbon’ Goal in China’s Industrial Sectors Based on the CIE-CEAM Model[J]. China Population, Resources and Environment, 2022, 32(7):49-56
[20] 张国兴, 高晚霞, 张振华, 等. 产业协同是否有助于提升节能减排的有效性?——基于1052条节能减排政策的研究[J]. 中国管理科学, 2017, 25(3):181-189 Zhang G. X., Gao W. X., Zhang Z. H., et al. Can Industrial Collaboration Promote the Effectiveness of Energy Conservation and Emission Reduction? Based on the Research of 1052 Energy Conservation and Emissions Reduction Policies[J]. Chinese Journal of Management Science, 2017, 25(3):181-189
[21] Stone R., Champernowne D. G., Meade J. E. The Precision of National Income Estimates[J]. The Review of Economic Studies, 1942, 9(2):111-125
[22] Günlük- Şenesen G., Bates J. M. Some Experiments with Methods of Adjusting Unbalanced Data Matrices[J]. Journal of the Royal Statistical Society, 1988, 151(3):473-490
[23] Junius T., Oosterhaven J. The Solution of Updating or Regionalizing a Matrix with both Positive and Negative Entries[J]. Economic Systems Research, 2003, 15(1):87-96
[24] Temurshoev U., Miller R. E., Bouwmeester M. C. A Note on the GRAS Method[J]. Economic Systems Research, 2013, 25(3):361-367
[25] 张红霞, 夏明, 苏汝劼, 等. 中国时间序列投入产出表的编制:1981—2018[J]. 统计研究, 2021, 38(11):3-23 Zhang H. X., Xia M., Su R. J., et al. The Compilation of the Time Series Input-output Tables in China: 1981-2018[J]. Statistical Research, 2021, 38(11):3-23
[26] 贺晋瑜, 何捷, 王郁涛, 等. 中国水泥行业二氧化碳排放达峰路径研究[J]. 环境科学研究, 2022, 35(2):347-55 He J. Y., He J., Wang Y. T., et al. Pathway of Carbon Emissions Peak for Cement Industry in China[J]. Research of Environmental Sciences, 2022, 35(2):347-355
[27] 汪旭颖, 李冰, 吕晨, 等. 中国钢铁行业二氧化碳排放达峰路径研究[J]. 环境科学研究, 2022, 35(2):339-346 Wang X. Y., Li B., Lü C., et al. China’s Iron and Steel Industry Carbon Emissions Peak Pathways[J]. Research of Environmental Sciences, 2022, 35(2):339-346
[28] 庞凌云, 翁慧, 常靖, 等. 中国石化化工行业二氧化碳排放达峰路径研究[J]. 环境科学研究, 2022, 35(2):356-367 Pang L. Y., Weng H., Chang J., et al. Pathway of Carbon Emissions Peak for China’s Petrochemical and Chemical Industries[J]. China Environmental Science, 2022, 35(2):356-367
[29] 刘淳森, 曲建升, 葛钰洁, 等. 基于LSTM模型的中国交通运输业碳排放预测[J]. 中国环境科学, 2023, 43(5):2574-2582 Liu C. S., Qu J. S., Ge Y. J., et al. LSTM Model-based Prediction of Carbon Emission from China’s Transportation Sector[J]. Research of Environmental Sciences, 2023, 43(5):2574-2582