Research on the embodied carbon transfer network of China's inter-provincial trade: Based on directed weighted network

doi:10.3969/j.issn.1004-9479.2025.11.20240053

Abstract

Abstract:

As inter-provincial trade grows, the impact of trans-provincial carbon flows on China's carbon reduction effectiveness is becoming increasingly apparent. To foster inter-provincial cooperation in reducing carbon emissions, it is necessary to account for provincial carbon emissions and clear up the inter-provincial carbon transfer relationship. Using the MRIO model, this study calculated the embodied carbon emissions based on production and consumption in 30 provinces of China in 2012 and 2017. Based on this, networks of embodied carbon transfers in inter-provincial trade were constructed by the SNA method, and the characteristics of the networks were analyzed from the "relationship" perspective. The results show that: ① In China, inter-provincial carbon transfer is a regular occurrence, and Beijing, Shanghai, Guangdong, Zhejiang, Jiangsu, Anhui, Henan, Hebei, Liaoning, Shanxi, Inner Mongolia, Xinjiang, Chongqing, and Shaanxi are more influential in China's inter-provincial carbon transfer system than the others. ② The embodied carbon transfer network of China's inter-provincial trade is intricate in nature, and the network could be divided into three major communities with "Hebei-Inner Mongolia-Beijing", "Jiangsu-Zhejiang-Henan", and "Guangdong" as the community cores, and the provinces in the same community have stronger relationships for carbon transfer. The results indicate that reducing carbon emissions requires not just controlling "quantity" but also managing "relationships", establishing regional collaborative mechanisms, and enhancing efficiency to reduce carbon emissions requires the combination of controlling "quantity" and managing "relationships".

Key words: production-based carbon emissions, consumption-based carbon emissions, transfer of carbon emissions embodied in trade, multi-regional input-output model, social network analysis

摘要：

碳排放的空间转移问题对我国碳排放总量、碳排放空间格局和区域协调发展具有不可忽视的影响。在此背景下，科学核算各省域的碳排放水平，厘清省际的碳转移关系，对于推动省际碳减排合作来说具有重要意义。本研究利用MRIO模型测算了中国30个省域生产侧、消费侧的隐含碳排放规模和省际贸易隐含碳转移规模，并运用SNA分析方法构建了中国省际碳转移的有向加权网络，从关系视角出发对中国省际碳转移网络的结构特征进行了分析。研究发现：①碳转移现象普遍存在于中国各省级行政区域之间，其中，北京、上海、广东、浙江、江苏、安徽、河南、河北、辽宁、山西、内蒙古、新疆、重庆和陕西14个省域的碳转移量较大。②中国省际贸易隐含碳转移关系呈现出复杂的网络形态，网络中的30个省域可划分为以“河北-内蒙古-北京”“江苏-浙江-河南”和“广东”为核心的三大社团，处于同一社团内的省域间的碳流动关系更为紧密。研究结果为中国当前自上而下聚焦于规模控制的碳减排政策体系的优化方向提供了参考，碳减排政策的制定在“规模”约束的基础上应充分考虑省际碳转移的空间关联“关系”，建立区域协同减排机制，通过“规模-关系”的双重控制进一步提升中国的碳减排效率。

关键词: 生产侧碳排放, 消费侧碳排放, 隐含碳转移, 多区域投入产出模型, 社会网络分析

Li HUANG, Ruilin ZHANG. Research on the embodied carbon transfer network of China's inter-provincial trade: Based on directed weighted network[J]. World Regional Studies, 2025, 34(11): 140-153.

黄丽, 张瑞琳. 中国省际隐含碳转移的空间关联研究[J]. 世界地理研究, 2025, 34(11): 140-153.

Figures/Tables 9

References 37

[1]	ZHOU D, ZHOU X, XU Q, et al. Regional embodied carbon emissions and their transfer characteristics in China. Structural Change and Economic Dynamics, 2018, 46: 180-193.
[2]	张玺, 郭细根, 李光勤. 中国省域国内与全球价值链的时空演变及耦合协调关系. 世界地理研究, 2023, 32(7): 1-12.
	ZHANG X, GUO X, LI G. Spatio-temporal evolution and coupling coordination relationship of China's provincial domestic and global value chains. World Regional Studies, 2023, 32(7): 1-12.
[3]	ZHONG Z, JIANG L, ZHOU P. Transnational transfer of carbon emissions embodied in trade: Characteristics and determinants from a spatial perspective. Energy, 2018, 147: 858-875.
[4]	WANG Z, LI Y, CAI H, et al. Regional difference and drivers in China's carbon emissions embodied in internal trade. Energy Economics, 2019, 83: 217-228.
[5]	WEI W, HAO S, YAO M, et al. Unbalanced economic benefits and the electricity-related carbon emissions embodied in China's interprovincial trade. Journal of Environmental Management, 2020, 263: 110390.
[6]	PETERS G P, HERTWICH E G. CO₂ embodied in international trade with implications for global climate policy. Environmental Science & Technology, 2008, 42(5): 1401-1407.
[7]	ARCE G, LOPEZ L A, GUAN D. Carbon emissions embodied in international trade: The post-China era. Applied Energy, 2016, 184: 1063-1072.
[8]	ZHAO Y, WANG S, ZHAN Z, et al. Driving factors of carbon emissions embodied in China-US trade: A structural decomposition analysis. Journal of Cleaner Production, 2016, 131: 678-689.
[9]	ANDERSSON F N G. International trade and carbon emissions: The role of Chinese institutional and policy reforms. Journal of Environmental Management, 2018, 205: 29-39.
[10]	李晖, 刘卫东, 唐志鹏. 全球贸易隐含碳净转移的空间关联网络特征. 资源科学, 2021, 43(4): 682-692.
	LI H, LIU W, TANG Z. Spatial correlation network of net carbon transfer in global trade. Resources Science, 2021, 43(4): 682-692.
[11]	DRUCKMAN A, JACKSON T. The carbon footprint of UK households 1990-2004: A socio-economically disaggregated, quasi-multi-regional input-output model. Ecological Economics, 2009, 68(7): 2066-2077.
[12]	GUO J, ZHANG Z, MENG L. China's provincial CO₂ emissions embodied in international and interprovincial trade. Energy Policy, 2012, 42: 486-497.
[13]	姚亮, 刘晶如. 中国八大区域间碳排放转移研究. 中国人口·资源与环境, 2010, 20(12): 16-19.
	YAO L, LIU J. Transfer of carbon emissions between China's eight major regions. China Population, Resources and Environment, 2010, 20(12): 16-19.
[14]	SU B, ANG B W. Input-output analysis of CO₂ emissions embodied in trade: The effects of spatial aggregation. Ecological Economics, 2010, 70(1): 10-18.
[15]	刘红光, 范晓梅. 中国区域间隐含碳排放转移. 生态学报, 2014, 34(11): 3016-3024.
	LIU H, FAN X. CO₂ emissions transfer embodied in inter-regional trade in China. Acta Ecologica Sinica, 2014, 34(11): 3016-3024.
[16]	石敏俊, 王妍, 张卓颖, 等. 中国各省区碳足迹与碳排放空间转移. 地理学报, 2012, 67(10): 1327-1338.
	SHI M, WANG Y, ZHANG Z, et al. Regional carbon footprint and interregional transfer of carbon emissions in China. Acta Geographica Sinica, 2012, 67(10): 1327-1338.
[17]	陈晖, 温婧, 庞军, 等. 基于31省MRIO模型的中国省际碳转移及碳公平研究. 中国环境科学, 2020, 40(12): 5540-5550.
	CHEN H, WEN J, PANG J, et al. Research on the carbon transfer and carbon equity at provincial level of China based on MRIO model of 31 provinces. China Environmental Science, 2020, 40(12): 5540-5550.
[18]	CLARKE-SATHER A, QU J, WANG Q, et al. Carbon inequality at the sub-national scale: A case study of provincial-level inequality in CO₂ emissions in China 1997-2007. Energy Policy, 2011, 39(9): 5420-5428.
[19]	李富佳. 区际贸易隐含碳排放转移研究进展与展望. 地理科学进展, 2018, 37(10): 1303-1313.
	LI F. Progress and prospects of research on transfer of carbon emissions embodied in inter-regional trade. Progress in Geography, 2018, 37(10): 1303-1313.
[20]	杨桂元, 吴齐, 涂洋. 中国省际碳排放的空间关联及其影响因素研究——基于社会网络分析方法. 商业经济与管理, 2016, 294(4): 56-68.
	YANG G, WU Q, TU Y. Research of China's regional carbon emission spatial correlation and its determinants: based on the method of social network analysis. Journal of Business Economics, 2016, 294(4): 56-68.
[21]	张德钢, 陆远权. 中国碳排放的空间关联及其解释——基于社会网络分析法. 软科学, 2017, 31(4): 15-18.
	ZHANG D, LU Y. Study on the spatial correlation and explanation of carbon emission in China. Soft Science, 2017, 31(4): 15-18.
[22]	DOMENECH T, DAVIES M. The social aspects of industrial symbiosis: The application of social network analysis to industrial symbiosis networks. Progress in Industrial Ecology, 2009, 6(1): 68-99.
[23]	闫敏, 孙慧. 经济内循环视角下中国省域间隐含碳转移网络结构特征研究. 生态经济, 2022, 38(2): 13-21.
	YAN M, SUN H. Research on the network structural characteristics of China's provincial implicit carbon transfer from the perspective of economic internal circulation. Ecological Economy, 2022, 38(2): 13-21.
[24]	张琦峰, 方恺, 徐明, 等. 基于投入产出分析的碳足迹研究进展. 自然资源学报, 2018, 33(4): 696-708.
	ZHANG Q, FANG K, XU M, et al. Review of carbon footprint research based on input-output analysis. Journal of Natural Resources, 2018, 33(4): 696-708.
[25]	刘军. 社会网络模型研究论析. 社会学研究, 2004(1): 1-12.
	LIU J. Research and analysis of social network models. Sociological Studies, 2004(1): 1-12.
[26]	LV K, FENG X, KELLY S, et al. A study on embodied carbon transfer at the provincial level of China from a social network perspective. Journal of Cleaner Production, 2019, 225: 1089-1104.
[27]	LI Y, CHEN B, CHEN G. Carbon network embodied in international trade: Global structural evolution and its policy implications. Energy Policy, 2020, 139: 111316.
[28]	SHAN Y, HUANG Q, GUAN D, et al. China CO₂ emission accounts 2016-2017. Scientific Data, 2020, 7(1): 1-9.
[29]	李善同, 何建武, 祝坤福, 等. 中国多区域投入产出模型:1987-2017年. 北京: 经济科学出版社, 2023.
	LI S, HE J, ZHU K . et al. Multi Regional Input-output Model in China: 1987-2017. Beijing: Economic science Press, 2023.
[30]	黎峰. 增加值视角下的中国国家价值链分工——基于改进的区域投入产出模型. 中国工业经济, 2016(3): 52-67.
	LI F. Division of China's national value chain on the perspective of value-added——Based on the modified regional input-output model. China Industrial Economics, 2016(3): 52-67.
[31]	王育宝, 何宇鹏. 中国省域净碳转移测算研究. 管理学刊, 2020, 33(2): 1-10.
	WANG Y, HE Y. Research on the calculation of provincial net carbon transfer in China. Journal of Management, 2020, 33(2): 1-10.
[32]	NEWMAN M E J. Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 2006, 103(23): 8577-8582.
[33]	李晓佳, 张鹏, 狄增如, 等. 复杂网络中的社团结构. 复杂系统与复杂性科学, 2008, 5(3): 19-42.
	LI X, ZHANG P, DI Z, et al. Community structure in complex network. Complex System and Complex Science, 2008, 5(3): 19-42.
[34]	王文治, 胡雍, 张晓宇. 中国省域碳排放责任分配方法比较与碳补偿设计. 资源科学, 2023, 45(10): 1913-1930.
	WANG W, HU Y, ZHANG X. Comparison of carbon emission responsibility allocation methods and carbon compensation design among provinces in China. Resources Science, 2023, 45(10): 1913-1930.
[35]	王育宝, 何宇鹏. 增加值视角下中国省域净碳转移权责分配. 中国人口·资源与环境, 2021, 31(1): 15-25.
	WANG Y, HE Y. Responsibility allocation of China's provincial net carbon transfer from the perspective of value-added. China Population, Resources and Environment, 2021, 31(1): 15-25.
[36]	安琪儿, 安海忠, 王朗. 中国产业间隐含能源流动网络分析. 系统工程学报, 2014, 29(6): 754-762.
	AN Q, AN H, WANG L. Analysis of the embodied energy flow network between Chinese industries. Journal of Systems Engineering, 2014, 29(6): 754-762.
[37]	李辉, 陈福才, 张建朋, 等.复杂网络中的社团发现算法综述. 计算机应用研究, 2021, 38(6): 1611-1618.
	LI H, CHEN F, ZHANG J, et al. Survey of community detection algorithms in complex network. Application Research of Computers, 2021, 38(6): 1611-1618.

分类标准	省际碳转移量≥30省均值	省际碳转移量<30省均值
碳排放量≥均值	类型Ⅰ：高排放，高转移	类型Ⅳ：高排放，低转移
碳排放量<均值	类型Ⅱ：低排放，高转移	类型Ⅲ：低排放，低转移

分类标准	省际碳转移量≥30省均值	省际碳转移量<30省均值
碳排放量≥均值	类型Ⅰ：高排放，高转移	类型Ⅳ：高排放，低转移
碳排放量<均值	类型Ⅱ：低排放，高转移	类型Ⅲ：低排放，低转移

类型	特征	特征侧	省份	政策要点
Ⅰ	高排放，高转移	仅消费侧	北京、上海	省内减排
		消费侧和生产侧	广东、浙江、江苏、安徽、河南、河北、辽宁	省际协同
		仅生产侧	山西、内蒙古、新疆
Ⅱ	低排放，高转移	仅消费侧	重庆	省际协同
		消费侧和生产侧	陕西
Ⅳ	高排放，低转移	消费侧和生产侧	山东、湖北	省内减排

类型	特征	特征侧	省份	政策要点
Ⅰ	高排放，高转移	仅消费侧	北京、上海	省内减排
		消费侧和生产侧	广东、浙江、江苏、安徽、河南、河北、辽宁	省际协同
		仅生产侧	山西、内蒙古、新疆
Ⅱ	低排放，高转移	仅消费侧	重庆	省际协同
		消费侧和生产侧	陕西
Ⅳ	高排放，低转移	消费侧和生产侧	山东、湖北	省内减排

序号	等级	边权	2012年			2017年
序号	等级	边权	边数	边数占比	边权占比	边数	边数占比	边权占比
合计			870	100.00%	100.00%	870	100.00%	100.00%
1	低值区	0.00~3.85	524	60.23%	17.74%	493	56.67%	13.35%
2	次低值区	3.86~10.20	230	26.44%	28.85%	218	25.06%	23.43%
3	中值区	10.21~21.38	80	9.19%	23.99%	100	11.49%	24.73%
4	次高值区	21.39~46.30	26	2.99%	15.47%	45	5.17%	23.45%
5	高值区	46.31~134.86	10	1.15%	13.95%	14	1.61%	15.04%