中国家庭消费间接碳排放空间关联结构演变——基于社会网络分析法

doi:10.12118/j.issn.1000-6060.2024.035

Abstract

Abstract:

Understanding the spatial clustering and structural characteristics of indirect carbon emissions from household consumption is crucial for China to achieve the “carbon peaking and carbon neutrality” goal under its new development framework. This study calculates indirect carbon emissions from household consumption in China and examines the structural characteristics of the spatial correlation network for these emissions from 2013 to 2022 using social network analysis. The findings reveal the following: (1) Indirect carbon emissions from household consumption exhibit an overall upward trend, increasing 1.2-fold over ten years. Emissions from “food”, “housing”, “transport and communication”, and “education, culture, and entertainment” constitute 75% of the total. (2) Overall network characteristics: The overall network structure, centered on provinces and cities such as Jiangsu Province, Beijing City, Zhejiang Province, and Shanghai City, demonstrates a “core-edge” distribution pattern. Network density and the number of associations have declined, while grade gradient and association intensity have increased. (3) Characteristics of the block model: Regional network characteristics, based on node spillover and reception effects, are categorized into four segments: “net spillover”, “net benefit”, “broker”, and “two-way spillover”, and each segment plays different roles in the field of spatial correlation. (4) Individual network characteristics: Regarding individual network characteristics, provinces such as Shanghai City, Jiangsu Province, and Zhejiang Province, with the highest degree of centrality, occupy the core areas of the correlation network and exhibit significant spatial correlation and outward radiation effects. In contrast, provinces such as Qinghai Province and Heilongjiang Province, located on the periphery, exhibit weaker correlation effects.

Key words: indirect carbon emissions from household consumption, spatially associative networks, social network analysis, gravitational model

FU Wei, GONG Haixiu, CHEN Jiancheng. Evolution of spatial correlation structure of indirect carbon emissions from household consumption in China: Based on social network analysis[J].Arid Land Geography, 2025, 48(1): 119-129.

Figures/Tables 11

Tab. 1

Fig. 1

Tab. 2

Indices of overall network structure and individual structure characteristics"

整体网络结构	公式	个体网络结构	公式
网络密度（T）	$T = R / N N - 1$	点度中心度（C_RD）	$C R D = N / N - 1$
网络等级度（H）	$H = 1 - K / K m a x$	中介中心度（C_RB）	$C R B = 2 ∑ j = 1 N ∑ k = 1 N b j k i / N 2 - 3 N + 2$
网络效率（T）	$T = 1 - M / M m a x$	接近中心度（C_AP）	$C A P = ∑ j = 1 n d i j / N - 1$

Tab. 2

Tab. 3

Classification of economic growth segments in block model"

位置内部的关系比例	位置接受到的关系比例
位置内部的关系比例	$≈ 0$	$0$
$≥ (s k - 1 / (s - 1$	双向溢出板块	净受益板块
$(s k - 1 / (s - 1$	净溢出板块	经纪人板块

Tab. 3

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Tab. 4

Fig. 6

Tab. 5

References 29

[1]	吉雪强, 张跃松. 长江经济带种植业碳排放效率空间关联网络结构及动因[J]. 自然资源学报, 2023, 38(3): 675-693. doi: 10.31497/zrzyxb.20230308
	[Ji Xueqiang, Zhang Yuesong. Spatial correlation network structure and drivers of carbon emission efficiency of plantation industry in Yangtze River Economic Belt[J]. Journal of Natural Resources, 2023, 38(3): 675-693.] doi: 10.31497/zrzyxb.20230308
[2]	华怡婷, 石宝峰. 互联网使用与家庭间接碳排放: 测度及影响因素分析[J]. 重庆大学学报(社会科学版), 2023, 29(1): 117-134.
	[Hua Yiting, Shi Baofeng. Internet use and household indirect carbon emissions: Measurement and influencing factors analysis[J]. Journal of Chongqing University (Social Science Edition), 2023, 29(1): 117-134.]
[3]	邵帅, 徐俐俐, 杨莉莉. 千里“碳缘”一线牵: 中国区域碳排放空间关联网络的结构特征与形成机制[J]. 系统工程理论与实践, 2023, 43(4): 958-983. doi: 10.12011/SETP2022-1418
	[Shao Shuai, Xu Lili, Yang Lili. Structural characteristics and formation mechanism of spatial correlation network of regional carbon emissions in China[J]. Systems Engineering Theory and Practice, 2023, 43(4): 958-983.]
[4]	彭璐璐, 李楠, 郑智远, 等. 中国居民消费碳排放影响因素的时空异质性[J]. 中国环境科学, 2021, 41(1): 463-472.
	[Peng Lulu, Li Nan, Zhang Zhiyuan, et al. Spatial-temporal heterogeneity of influencing factors of carbon emissions from Chinese household consumption[J]. China Environmental Science, 2021, 41(1): 463-472.]
[5]	史琴琴, 鲁丰先, 陈海, 等. 中原经济区城镇居民消费间接碳排放时空格局及其影响因素[J]. 资源科学, 2018, 40(6): 1297-1306. doi: 10.18402/resci.2018.06.19
	[Shi Qinqin. Lu Fengxian, Chen Hai, et al. Temporal-spatial patterns and factors affecting indirect carbon emissions from urban consumption in the Central Plains economic region[J]. Resources Science, 2018, 40(6): 1297-1306.] doi: 10.18402/resci.2018.06.19
[6]	李治, 李培, 郭菊娥, 等. 城市家庭碳排放影响因素与跨城市差异分析[J]. 中国人口·资源与环境, 2013, 23(10): 87-94.
	[Li Zhi, Li Pei, Guo Ju’e, et al. Analysis of factors influencing urban household carbon emissions and cross-city differences[J]. China Population, Resources and Environment, 2013, 23(10): 87-94.]
[7]	庄贵阳, 魏鸣昕. 城市引领碳达峰、碳中和的理论和路径[J]. 中国人口·资源与环境, 2021, 31(9): 114-121.
	[Zhuang Guiyang, Wei Mingxin. Theory and pathway of city leadership in emission peak and carbon neutrality[J]. China Population, Resources and Environment, 2021, 31(9): 114-121.]
[8]	Belaid F, Rault C. Energy expenditure in Egypt: Empirical evidence based on a quantile regression approach[J]. Environmental Modeling & Assessment, 2021, 26(4): 511-528.
[9]	韩君, 牛士豪, 高瀛璐. 新发展阶段居民家庭碳排放核算及影响因素研究[J]. 兰州财经大学学报, 2023, 39(1): 68-80.
	[Han Jun, Niu Shihao, Gao Yinglu. Research on accounting and influencing factors of household carbon emissions in the new development stage[J]. Journal of Lanzhou University of Finance and Economics, 2023, 39(1): 68-80.]
[10]	Bin S, Dowlatabadi H. Consumer lifestyle approach to US energy use and the related CO₂ emissions[J]. Energy Policy, 2005, 33(2): 197-208.
[11]	Fan J, Guo X, Marinova D, et al. Embedded carbon footprint of Chinese urban households: Structure and changes[J]. Journal of Cleaner Production, 2012, 33: 50-59.
[12]	范玲, 汪东. 我国居民间接能源消费碳排放的测算及分解分析[J]. 生态经济, 2014, 30(7): 28-32.
	[Fan Ling, Wang Dong. Calculation and decomposition analysis on carbon emissions of indirect residents’ consumption in China[J]. Ecological Economy, 2014, 30(7): 28-32.]
[13]	陈为公, 程准, 张娜, 等. 山东省农村居民生活间接碳排放影响因素[J]. 沈阳大学学报(社会科学版), 2021, 23(3): 273-278, 286.
	[Chen Weigong, Cheng Zhun, Zhang Na, et al. Influencing factors of indirect carbon emissions in rural residents in Shandong Province[J]. Journal of Shenyang University (Social Science Edition), 2021, 23(3): 273-278, 286.]
[14]	吴茜, 陈强强. 甘肃省行业碳排放影响因素及脱钩努力研究[J]. 干旱区地理, 2023, 46(2): 274-283. doi: 10.12118/j.issn.1000－6060.2022.126
	[Wu Xi, Chen Qiangqiang. Influencing factors and decoupling efforts of industry-related carbon emissions in Gansu Province[J]. Arid Land Geography, 2023, 46(2): 274-282.] doi: 10.12118/j.issn.1000－6060.2022.126
[15]	吴开亚, 王文秀, 张浩, 等. 上海市居民消费的间接碳排放及影响因素分析[J]. 华东经济管理, 2013, 27(1): 1-7.
	[Wu Kaiya, Wang Wenxiu, Zhang Hao, et al. Indirect carbon emissions of Shanghai’s residents consumption and its influence factors[J]. East China Economic Management, 2013, 27(1): 1-7.]
[16]	杜娅明, 白永平, 梁建设, 等. 黄河流域旅游业碳排放效率综合测度及影响因素研究[J]. 干旱区地理, 2023, 46(12): 2074-2085. doi: 10.12118/j.issn.1000-6060.2023.193
	[Du Yaming, Bai Yongping, Liang Jianshe, et al. Comprehensive measurement and influencing factors of carbon emission efficiency of tourism in the Yellow River Basin[J]. Arid Land Geography, 2023, 46(12): 2074-2085.] doi: 10.12118/j.issn.1000-6060.2023.193
[17]	邹嘉龄, 刘卫东. 2001—2013年中国与“一带一路”沿线国家贸易网络分析[J]. 地理科学, 2016, 36(11): 1629-1636. doi: 10.13249/j.cnki.sgs.2016.11.004
	[Zou Jialing, Liu Weidong. Trade network of China and countries along “Belt and Road Initiative” areas from 2001 to 2013[J]. Scientia Geographica Sinica, 2016, 36(11): 1629-1636.]
[18]	Liu W, Xu J, Li J. The influence of poverty alleviation resettlement on rural household livelihood vulnerability in the western mountainous areas[J]. Sustainability, 2018, 10(8): 2793, doi: 10.3390/su10082793.
[19]	邵璇璇, 姚永玲. 长江中游城市群的空间网络特征及其影响机制[J]. 城市问题, 2019, 10: 15-26.
	[Shao Xuanxuan, Yao Yongling. Spatial network characteristics and influence mechanisms of city clusters in the middle reaches of the Yangtze River[J]. Urban Problems, 2019, 10: 15-26.]
[20]	Mi Z, Meng J, Green F, et al. China’s “exported carbon” peak: Patterns, drivers, and implications[J]. Geophysical Research Letters, 2018, 45: 4309-4318.
[21]	孙敏, 杨红娟, 刘海洋. 少数民族农户生活消费间接碳排放影响因素研究[J]. 经济问题探索, 2016(5): 51-58.
	[Sun Min, Yang Hongjuan, Liu Haiyang. Research on influencing factors of indirect carbon emissions from household consumption of ethnic minority farmers[J]. Exploration of Economic Issues, 2016(5): 51-58.]
[22]	王晓平, 冯庆, 宋金昭. 成渝城市群碳排放空间关联结构演化及影响因素[J]. 中国环境科学, 2020, 40(9): 4123-4134.
	[Wang Xiaoping, Feng Qing, Song Jinzhao. The spatial association structure evolution of carbon emissions in Chengdu-Chongqing urban agglomeration and its influence mechanism[J]. China Environmental Science, 2020, 40(9): 4123-4134.]
[23]	Mayer H M, Ullman E L. American commodity flow: A geographical interpretation of rail and water traffic based on principles of spatial interchange[J]. Geographical Review, 1959, 49(1): 142, doi: 10.2307/211582.
[24]	孙中瑞, 樊杰, 孙勇, 等. 中国绿色科技创新效率空间关联网络结构特征及影响因素[J]. 经济地理, 2022, 42(3): 33-43. doi: 10.15957/j.cnki.jjdl.2022.03.004
	[Sun Zhongrui, Fan Jie, Sun Yong, et al. Structural characteristics and influencing factors of spatial correlation network of green science and technology innovation efficiency in China[J]. Economic Geography, 2022, 42(3): 33-43.] doi: 10.15957/j.cnki.jjdl.2022.03.004
[25]	赵林, 高晓彤, 刘焱序, 等. 中国包容性绿色效率空间关联网络结构演变特征分析[J]. 经济地理, 2021, 41(9): 69-78, 90.
	[Zhao Lin, Gao Xiaotong, Liu Yanxu, et al. Evolution characteristics of inclusive green efficiency spatial association network structure in China[J]. Economic Geography, 2021, 41(9): 69-78, 90.]
[26]	Wasserman S, Faust K. Social network analysis: Methods and applications[J]. Contemporary Sociological, 1994, 91: 219-220.
[27]	杨上广, 王春兰, 刘淋. 上海家庭出行碳排放基本特征、空间模式及影响因素研究[J]. 中国人口·资源与环境, 2014, 24(6): 148-153.
	[Yang Shangguang, Wang Chunlan, Liu Lin. Study on the basic characteristics, spatial patterns and influencing factors of carbon emissions of household travel in Shanghai[J]. China Population, Resources and Environment, 2014, 24(6): 148-153.]
[28]	刘英恒太, 杨丽娜. 中国数字经济产出的空间关联网络结构与影响因素研究[J]. 技术经济, 2021, 40(9): 137-145.
	[Liu Yinghengtai, Yang Lina. Research on the structure and influencing factors of spatially correlate network of China’s digital economy output[J]. Technology Economics, 2021, 40(9): 137-145.]
[29]	孙亚男, 刘华军, 刘传明, 等. 中国省际碳排放的空间关联性及其效应研究——基于SNA的经验考察[J]. 上海经济研究, 2016(2): 82-92.
	[Sun Yanan, Liu Huajun, Liu Chuanming, et al. Study on spatial correlation and effect of interprovincial carbon emissions in China: An empirical investigation based on SNA[J]. Shanghai Economic Research Journal, 2016(2): 82-92.]

消费性支出项目	涉及内容	间接碳排放系数/kg CO₂·元^-1
食品	农林牧渔产品及服务；食品和烟草	0.049
衣着	纺织品；纺织服装鞋帽皮革羽绒及其制品	0.076
居住	非金属矿物制品；金属制品；电力、热力、燃气和水生产和供应；建筑	0.210
生活用品及服务	木材加工品和家具；电气机械和器材	0.068
交通通信	通信设备、计算机和其他电子设备；交通运输、仓储和邮政	0.198
教育文化娱乐	造纸印刷和文教体育用品	0.144
医疗保健	医药制品	0.117
其他用品及服务	批发和零售；住宿和餐饮	0.234

板块	接收关系		发出关系		期望内部关系比例/%	实际内部关系比例/%	板块属性
板块	板块内	板块外	板块内	板块外	期望内部关系比例/%	实际内部关系比例/%	板块属性
第一板块	6	98	6	16	10.00	27.27	净受益
第二板块	16	20	16	115	60.00	12.21	净溢出
第三板块	5	11	5	15	13.33	25.00	经纪人
第四板块	2	37	2	20	6.67	9.09	双向溢出

省市	点出度	点入度	点度中心度	中介中心度	接近中心度
江苏	27	27	90.00	63.30	90.91
上海	27	27	90.00	58.24	90.91
北京	25	25	83.33	74.74	85.71
浙江	25	25	83.33	44.90	85.71
广东	18	18	60.00	14.47	71.43
福建	17	17	56.67	16.32	68.18
甘肃	12	12	40.00	3.85	62.50
湖北	12	12	40.00	3.45	62.50
重庆	10	10	33.33	1.59	60.00
河南	10	10	33.33	2.39	60.00
山东	10	10	33.33	8.04	60.00
湖南	10	10	33.33	1.02	60.00
四川	9	9	30.00	0.91	58.82
广西	9	9	30.00	0.91	58.82
贵州	9	9	30.00	0.91	58.82
陕西	8	8	26.67	2.47	57.69
河北	7	7	23.33	3.59	56.60
云南	7	7	23.33	0.56	56.60
青海	7	7	23.33	0.56	56.60
宁夏	6	6	20.00	0.37	55.56
黑龙江	6	6	20.00	0.27	55.56
新疆	6	6	20.00	0.37	55.56
海南	6	6	20.00	0.37	55.56
西藏	6	6	20.00	0.37	55.56
山西	5	5	16.67	0.58	54.55
吉林	5	5	16.67	0.13	54.55
安徽	5	5	16.67	0.37	54.55
内蒙古	5	5	16.67	0.29	53.57
辽宁	4	4	13.33	0.13	53.57
江西	4	4	13.33	0.12	53.57
天津	3	3	10.00	0.00	50.85
均值	10	10	34.41	9.86	61.77

Evolution of spatial correlation structure of indirect carbon emissions from household consumption in China: Based on social network analysis

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