基于多层网络层间关联的中国省域碳排放关键行业辨识

doi:10.12005/orms.2025.0347

摘要/Abstract

摘要： 通过精准识别碳排放关键行业,实现分省有序协同减排,是中国实现“双碳”目标的关键。因此,与现有碳排放关键行业研究及实践中忽略省域差异不同,本文考虑省域内行业的产品和服务的流动特征及省域间经济发展水平和社会因素的差异特征,基于2012,2015及2017年中国省级投入产出数据构建碳排放多层网络模型,并改进PageRank算法对多层网络节点重要性进行排序,以识别具有省域内交互、省域间关联特征的碳排放关键行业。实证结果表明:我国排名靠前的碳排放行业包括金属冶炼和化工业等高碳行业,其主要分布于河北、辽宁等省份,表明我国行业减排仍聚焦在部分重工业省份。同时,电热力的生产和供应行业因其基础性、民生性和技术含量高等特点,逐渐演变为我国大多数省份的碳排放关键行业,表明对该行业减排需跨省协同联动。此外,与北京、上海等碳排放强度较低的省份不同,宁夏、内蒙古等碳排放强度较高省份的碳排放关键行业主要有电子、机械制造业和交通运输等行业。研究结论为我国各省精准的协同减排策略提供理论支撑。

关键词: 碳排放, 多层网络, 关键行业, 超邻接矩阵, 区域协同减排

Abstract: It is crucial for China to achieve the dual-carbon goal by precisely identifying key industries of carbon emissions and achieving orderly and coordinated synergistic emission reduction across provinces. Due to regional disparities in resource conditions and the level of economic and social development in China, each province has formulated low-carbon development goals on the basis of its actual situation, which determines the fact that key industries of carbon emissions have provincial differences and specific spatial distribution. The existing research and practice on key industries of carbon emissions are mostly done from a national perspective, lacking comprehensive consideration of economic and social factors in different provinces.
Based on this, this paper comprehensively considers the flow characteristics of products and services in industries within provinces, as well as the differential characteristics in the level of economic development and social factors among provinces, from two dimensions: intra-provincial and inter-provincial. And it defines the intra-layer interaction relationships and inter-layer association strength in multi-layer networks. Additionally, by constructing a multi-layer network model of provincial carbon emissions in China based on the provincial input-output data of China in 2012, 2015, and 2017, this paper further improves the PageRank algorithm to rank the importance of nodes in the multi-layer network to identify the key industries of carbon emissions with the characteristics of intra-provincial interactions and inter-provincial associations.
The empirical results indicate that: (1)Overall, China’s top-ranking industries of carbon emission include high-carbon industries such as smelting and pressing of metals, transportation, storage, and post, as well as chemical industry and other industries, which are mainly distributed in provinces such as Hebei, Liaoning, Hunan, Jiangsu, and so forth, indicating that China’s industrial emission reduction is still concentrated in partial heavy industry provinces. Therefore, carbon reduction strategies for these provinces need to focus on improving carbon efficiency. Meanwhile, the generation and supply industries of electricity and heat play a crucial role in promoting inter-provincial industrial linkages due to their distinctions in fundamentals, livelihood, and high technology content. They have gradually evolved into key industries of carbon emissions in most provinces of China, indicating that ignoring the effect of correlation between economic and social factors in different provinces between industries will have an impact on precise control of carbon emissions in China; nevertheless, reducing emissions in this industry requires cross-provincial synergistic linkage, like power transmission from west to east. (2)Locally, the key industries of carbon emissions in China exhibit certain regional disparities on account of different carbon emission intensities. The key industries of carbon emissions in provinces and cities with lower carbon emission intensities include the production and supply of tap water, construction, and the generation and supply industries of electricity and heat, such as Beijing, Shanghai, Guangdong, and Zhejiang. However, the key industries of carbon emissions in provinces and cities with higher carbon emission intensities include the electronic equipment and machinery manufacturing industry, transportation, and the generation and supply industries of electricity and heat, such as Ningxia, Inner Mongolia, Xinjiang, and Shanxi. This shows that we need to boost the linkage effect between adjacent and cross provincial industries in various Chinese provinces and cities as well as improve the horizontal and vertical correlation relationships in key industries of carbon emissions, in order to achieve regional integrated development of industrial ecology. (3)In the spatial and temporal dimensions, China’s carbon-emitting key industries show certain ‘plate’ characteristics, as well as an increasing proportion of carbon-emitting industries in the tertiary and primary industries, indicating that it is crucial to formulate a personalized regional synergistic emission reduction strategy. The results of this research provide theoretical support for precise synergistic emission reduction strategies in various Chinese provinces.

Key words: carbon emission, multi-layer network, key industry, super-adjacency matrix, regional synergistic emission reduction

中图分类号:

F407
O157.6

许丽鹏, 王文平. 基于多层网络层间关联的中国省域碳排放关键行业辨识[J]. 运筹与管理, 2025, 34(11): 88-94.

XU Lipeng, WANG Wenping. Identification of Key Industries of Provincial Carbon Emissions in ChinaBased on Inter-layer Association of Multi-layer Network[J]. Operations Research and Management Science, 2025, 34(11): 88-94.

参考文献

[1] CHEN W D, WU F Y, GENG W X, et al. Carbon emissions in China’s industrial sectors[J]. Resources, Conservation and Recycling, 2017, 117: 264-273.
[2] AN P L, QU S, YU K, et al. Mapping analytical methods between input-output economics and network science[J]. Journal of Industrial Ecology, 2024, 28(4): 648-679.
[3] GUO J, ZHANG Y J, ZHANG K B. The key sectors for energy conservation and carbon emissions reduction in China: Evidence from the input-output method[J]. Journal of Cleaner Production, 2018, 179: 180-190.
[4] WEN W, WANG Q. Identification of key sectors and key provinces at the view of CO₂ reduction and economic growth in China: Linkage analyses based on the MRIO model[J]. Ecological Indicators, 2019, 96(Part 1): 1-15.
[5] WEN L, ZHANG Y X. Study on carbon transfer and carbon emission critical pathsin China: I-O analysis with multidimensional analytical framework[J]. Environmental Science and Pollution Research, 2020, 27: 9733-9747.
[6] HU Y, YU Y, MARDANI A. Selection of carbon emissions control industries in China: An approach based on complex networks control perspective[J]. Technological Forecasting & Social Change, 2021, 172: 121030.
[7] XIA Q, TIAN G L, WU Z. Examining embodied carbon emission flow relationships among different industrial sectors in China[J]. Sustainable Production and Consumption, 2022, 29: 100-114.
[8] XU D X, ZHANG Y, CHEN B, et al. Identifying the critical paths and sectors for carbon transfers driven by global consumption in 2015[J]. Applied Energy, 2022, 306(15): 118137.
[9] LIU L J, LIANG Q M, SHUAI Y X. Common driving forces of provincial-level greenhouse gas and air pollutant emissions in China[J]. Environmental Science & Technology, 2023, 57(14): 5806-5820.
[10] GAZZOTTI P, EMMERLING J, MARANGONI G, et al. Persistent inequality in economically optimal climate policies[J]. Nature Communication, 2021, 12: (Article)3421.
[11] YU B Y, ZHAO Z H, WEI Y M, et al. Approaching national climate targets in China considering the challenge of regional inequality[J]. Nature Communications, 2023, 14: (Article)8342.
[12] ALBERT R, JEONG H, BARABÁSI A L. The diameter of the world wide web[J]. Nature, 1999, 401(6): 130-131.
[13] LEI M L, CHEONG K H. Embedding model of multilayer networks structure and its application to identify influential nodes[J]. Information Sciences, 2024, 661: 120111.
[14] TAYLOR D, MYERS S A, CLAUSET A, et al. Eigenvector-based centrality measures for temporal networks[J]. Multiscale Modeling & Simulation, 2017, 15(1): 537-574.
[15] YUAN X L, SHENG X R, CHEN L P, et al. Carbon footprint and embodied carbon transfer at the provincial level of the Yellow River Basin[J]. Science of the Total Environment, 2022, 803: 149993.
[16] 蒋伟杰,张少华.中国工业二氧化碳影子价格的稳健估计与减排政策[J].管理世界,2018,34(7):32-49+183-184.
[17] WANG W Z, HU Y. The measurement and influencing factors of carbon transfers embodied in inter-provincial trade in China[J]. Journal of Cleaner Production, 2020, 270(19): 122460.
[18] LEONTIEF W. Environmental repercussions and the economic structure: An input-output approach[J]. The Review of Economics and Statistics, 1970, 52(3): 262-271.
[19] 刘耀彬,邓伟凤,李硕硕,等.基于碳汇潜力的碳排放空间关联网络结构特征及影响因素—以长江中游城市群为例[J].中国人口·资源与环境,2024,34(3):1-15.
[20] YU H, JONCHHE P, NG K, et al. Novel coal char-based cement grout: An experimental study on geotechnical and rheological properties[J]. Cement & Concrete Composites, 2023, 141: 105117.
[21] 胡钢,许丽鹏,徐翔.基于时序网络层间同构率动态演化的重要节点辨识[J].物理学报,2021,70(10):355-366.
[22] LI S L, SANG Y Z, WEI C P, et al. Dynamic social influence analysis and opinion evolution law under certain influence network structures[J]. IEEE Transactions on Fuzzy Systems, 2024, 32(6): 3629-3642.
[23] ZHENG H, BAI Y, WEI W, et al. Chinese provincial multi-regional input-output database for 2012, 2015, and 2017[J]. Scientific Data, 2021,8: (Article)244.
[24] GUAN Y R, SHAN Y L, HUANG Q, et al. Assessment to China’s recent emission pattern shifts[J]. Earth’s Future, 2021, 9(11): e2021EF002241.