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如何实现“碳达峰、碳中和”双碳目标，推动低碳经济发展，成为我国经济社会发展需要解决的重大问题。在复杂网络视角下，基于2005—2020年我国省域居民能源消费碳排放量数据，运用空间计量和社会网络分析方法，从空间分布和网络关联两个方面对我国居民能源消费碳排放的空间关联关系进行研究。结果表明：我国居民能源消费碳排放具有明显的空间聚集性，省域间高-高聚集和低-低聚集明显，呈现空间正自相关性；并且我国居民能源消费碳排放空间关联不仅仅是地理距离上的相关，而是呈现复杂的网络结构形态；我国东部、中部、西部地区的居民能源消费碳排放的空间关联程度依次递减，东部沿海省（区、市）作为网络中心，对其他省（区、市）控制力较强，中西部省（区、市）位于网络边缘，对其他省（区、市）控制力较弱。该研究结果可以给相关部门衡量各省（区、市）的减排责任并制定有针对性的协同减排方案提供有效参考。
Abstract：How to achieve the dual carbon goals of "peak carbon and carbon neutrality" and promote the development of a low-carbon economy has become a major issue that needs to be addressed in China′s economic and social development. In the perspective of complex networks, based on the carbon emissions data of residential energy consumption in China′s provincial regions from 2005 to 2020, spatial econometrics and social network analysis methods are used to study the spatial correlation of residential energy consumption carbon emissions in China from both spatial distribution and network association aspects. The results show that the carbon emissions from residential energy consumption in China exhibit obvious spatial clustering, with significant high-high clustering and low-low clustering among provincial regions, demonstrating positive spatial autocorrelation. Moreover, the spatial correlation of residential energy consumption carbon emissions in China is not only related to geographical distance but also exhibits a complex network structure. The degree of spatial correlation of carbon emissions from residential energy consumption decreases from the eastern, central, to western regions of China, with the eastern coastal provinces serving as network centers exerting strong control over other provinces, while the provinces in the central and western regions are located at the network periphery and have weaker control over other provinces. The findings can provide effective references for relevant departments to assess the emission reduction responsibilities of each province and develop targeted collaborative emission reduction plans.
[1] CHAO Q C, FENG A Q. Scientific basis of climate change and its response[J]. Global Energy Interconnection, 2018,1(4):420-427.
[2] FANG K, LI C L, TANG Y Q, et al. China′s pathways to peak carbon emissions:new insights from various industrial sectors[J]. Applied Energy, 2022, 306:118039.
[3] TANG R, ZHAO J, LIU Y F, et al. Air quality and health co-benefits of China′s carbon dioxide emissions peaking before 2030[J]. Nature Communications, 2022, 13:1008.
[4]童霞，高申荣.中国工业企业低碳生产转型路径研究[J].南通大学学报（社会科学版），2018, 34(2):60-66.TONG X, GAO S R. Research on the path of low-carbon production transformation of Chinese industrial enterprises[J]. Journal of Nantong University(Social Sciences Edition),2018, 34(2):60-66.(in Chinese)
[5]马彩云，徐林，赖芨宇，等.基于灰色预测模型的安徽省建筑业碳排放量预测[J].南通大学学报（自然科学版），2019, 18(1):89-94.MA C Y, XU L, LAI J Y, et al. Carbon emission prediction of construction industry in Anhui Province based on the grey prediction model[J]. Journal of Nantong University(Natural Science Edition), 2019, 18(1):89-94.(in Chinese)
[6] WEI Y M, LIU L C, FAN Y, et al. The impact of lifestyle on energy use and CO2emission:an empirical analysis of China′s residents[J]. Energy Policy, 2007, 35(1):247-257.
[7] LU H L, LIU G F. Spatial effects of carbon dioxide emissions from residential energy consumption:a county-level study using enhanced nocturnal lighting[J]. Applied Energy, 2014, 131:297-306.
[8] WANG S J, LIU X P. China′s city-level energy-related CO2emissions:spatiotemporal patterns and driving forces[J]. Applied Energy, 2017, 200:204-214.
[9] COMMON M S, SALMA U. Accounting for changes in Australian carbon dioxide emissions[J]. Energy Economics,1992, 14(3):217-225.
[10] MUNKSGAARD J, PEDERSEN K A, WIER M. Changing consumption patterns and CO2reduction[J]. International Journal of Environment and Pollution, 2001, 15(2):146.
[11]傅京燕，李存龙.中国居民消费的间接用能碳排放测算及驱动因素研究：基于STIRPAT模型的面板数据分析[J].消费经济，2015, 31(2):92-97.
[12]计志英，赖小锋，贾利军.家庭部门生活能源消费碳排放：测度与驱动因素研究[J].中国人口·资源与环境，2016, 26(5):64-72.JI Z Y, LAI X F, JIA L J. Measurement and driving factors of carbon emissions from household energy consumption[J]. China Population, Resources and Environment,2016, 26(5):64-72.(in Chinese)
[13] IPCC. 2006 IPCC guidelines for national greenhouse gas inventories[Z].[2022-10-17]. https：//www.ipcc-nggip.iges.or.jp/public/2006gl/index.html.
[14]张梅，黄贤金，揣小伟.中国城市碳排放核算及影响因素研究[J].生态经济，2019, 35(9):13-19.ZHANG M, HUANG X J, CHUAI X W. Research on China′s urban carbon emission accounting and influencing factors[J]. Ecological Economy, 2019, 35(9):13-19.(in Chinese)
[15] CAO Q R, KANG W, XU S C, et al. Estimation and decomposition analysis of carbon emissions from the entire production cycle for Chinese household consumption[J].Journal of Environmental Management, 2019, 247:525-537.
[16]柴士改.城镇居民生活碳排放驱动因素的实证分析[J].统计与决策，2016(18):95-99.
[17] XIA Y, WANG H J, LIU W D. The indirect carbon emission from household consumption in China between 1995-2009 and 2010-2030:a decomposition and prediction analysis[J]. Computers&In dustrial Engineering, 2019, 128:264-276.
[18]王倩，高翠云.中国试点碳市场间的溢出效应研究：基于六元VAR-GARCH-BEKK模型与社会网络分析法[J].武汉大学学报（哲学社会科学版），2016, 69(6):57-67.WANG Q, GAO C Y. Research on the spillover effect of the pilot carbon trading markets in China:based on sextuple VAR-GARCH-BEKK model and social network analysis[J]. Wuhan University Journal(Philosophy&Social Sciences), 2016, 69(6):57-67.(in Chinese)
[19] PRELL C, FENG K S. Unequal carbon exchanges:the environmental and economic impacts of iconic U.S. consumption items[J]. Journal of Industrial Ecology, 2015, 20(3):55-62.
[20] LIU J B, PENG X B, ZHAO J. Analyzing the spatial association of household consumption carbon emission structure based on social network[J]. Journal of Combinatorial Optimization, 2023, 45(2):79.
[21]如何正确计算电力间接排放[EB/OL].(2022-07-07)[2022-10-17]. http：//www.cpem.org.cn/list100/17278.html.
[22]赵丽萍，王瑶瑶.中国居民直接生活能源消费碳排放时空特征分析[J].消费经济，2018, 34(2):45-51.ZHAO L P, WANG Y Y. Spatial-temporal characteristic analysis on carbon emissions of residents′direct life energy consumption in China[J]. Consumer Economics, 2018, 34(2):45-51.(in Chinese)
[23]孙耀华.中国省际碳强度空间相关性与统计特征分析[J].统计与决策，2021, 37(14):49-53.SUN Y H. Analysis on spatial correlation and statistical characteristics of China′s interprovincial carbon intensity[J]. Statistics&Decision, 2021, 37(14):49-53.(in Chinese)
[24]罗雨森，路正南，赵喜仓.中国省域知识产权资源水平评价及空间统计分析[J].统计与决策，2020, 36(1):62-66.
[25]鲁邦克，金大卫.我国经济发展平衡性充分性的测度与空间动态演进[J].统计与决策，2022, 38(5):96-100.
[26] HE Y Y, WEI Z X, LIU G Q, et al. Spatial network analysis of carbon emissions from the electricity sector in China[J]. Journal of Cleaner Production, 2020, 262:121193.
[27]国家统计局.东西中部和东北地区划分方法[EB/OL].(2011-06-13)[2022-10-17]. http：//www.stats.gov.cn/zt_18555/zthd/sjtjr/dejtjkfr/tjkp/202302/t20230216_1909741.htm.
[28] LIU S N, XIAO Q T. An empirical analysis on spatial correlation investigation of industrial carbon emissions using SNA-ICE model[J]. Energy, 2021, 224:120183.
[29] LI H, ZHANG M, LI C, et al. Study on the spatial correlation structure and synergistic governance development of the haze emission in China[J]. Environmental Science and Pollution Research, 2019, 26(12):12136-12149.
基本信息:
DOI：
中图分类号:F426.2;X322
引用信息:
[1]彭鑫蓓,刘家保.复杂网络视角下我国居民能源消费碳排放的空间关联分析[J].南通大学学报(自然科学版),2023,22(04):50-61.
基金信息:
安徽高校自然科学研究重点项目（KJ2020A0478）;; 省级重点教育教学研究项目（2022jyxm304）