| 52 | 0 | 41 |
| 下载次数 | 被引频次 | 阅读次数 |
在“双碳”目标背景下，识别资源型地区碳排放空间关联结构及其分区格局是推进区域协同减排与精准治理的重要前提。本文基于2000—2020年5个时期的山西省土地利用与能源碳排放数据，核算117个县域的直接和间接碳排放量，通过引力模型构建县际碳排放加权空间关联网络，运用社会网络分析法揭示整体网络密度与个体特征，并结合Louvain算法与共识聚类识别网络社团结构，进而实现碳排放空间关联网络分区。结果表明：(1)研究期内山西省碳排放总量显著增长，呈现“快速扩张—平台整理—恢复增长”的阶段性特征。(2)碳排放空间关联网络密度从2000年的9.17%升至2020年的38.37%，关联数量增加3倍以上；核心节点度数中心度持续居前，但中介中心度下降，网络结构由单核心主导向多中心联动转变。(3)2000—2020年山西省碳排放网络社团结构保持稳定，社团数量保持7个不变，跨期分区一致性较高，迁移县域主要集中于板块边界地带。研究结果可为区域协同减排和差异化治理提供参考。
Abstract：In the context of the "dual carbon" goals,identifying the spatial correlation structure and zoning pattern of carbon emissions in resource-based regions is a crucial prerequisite for promoting collaborative emission reduction and targeted governance.Based on land-use and energy-related carbon emission data for five periods in Shanxi Province from 2000 to 2020,this study estimated direct and indirect carbon emissions at the county level,constructed a weighted inter-county spatial correlation network of carbon emissions using a gravity model,applied social network analysis to reveal overall network density and individual node characteristics,and further identifies network communities through the Louvain algorithm combined with consensus clustering,thereby achieving the zoning of the carbon-emission spatial correlation network.The findings are as follows.(1) During the study period,total carbon emissions in Shanxi Province increased significantly,displaying a phased pattern of " rapid expansion-plateau adjustment-renewed growth".(2)The density of the carbon-emission spatial correlation network increased from 9.17% in 2000 to 38.37% in 2020,and the number of links increased by more than threefold;the degree centrality of core nodes remained persistently high,whereas betweenness centrality declined,indicating that the network structure shifted from single-core dominance to multi-center linkage.(3) From 2000 to 2020, the community structure of the carbon-emission network in Shanxi Province remained stable,with the number of communities unchanged at seven;the consistency of zoning across periods was high,and the counties that shifted communities were mainly concentrated in the boundary areas between communities.From the perspective of spatial correlation networks,this study identifies the structural characteristics and zoning pattern of carbon emissions in resource-based regions,and provides a reference for regional collaborative emission reduction and differentiated governance.
[1]董光龙,刘德玲,雷彤,等.基于多源遥感数据的中国县域土地利用碳排放时空演变与影响因素分析[J].环境科学,2026,47(8):5112-5125.
[2]曹翔,苏馨儿.碳排放权交易试点政策是否促进了碳中和技术创新[J].中国人口·资源与环境,2023,33(7):94-104.
[3]Barati A A,Zhoolideh M,Azadi H,et al.Interactions of landuse cover and climate change at global level:how to mitigate the environmental risks and warming effects[J].Ecological Indicators,2023,146:109829.DOI:10.1016/j.ecolind.2022.109829.
[4]杨雪璐.山西省土地利用碳排放时空特征及影响因素研究[D].太原:山西财经大学,2023.
[5]孙赫,梁红梅,常学礼,等.中国土地利用碳排放及其空间关联[J].经济地理,2015,35(3):154-162.
[6]吕倩,张林野,雒瑶.黄河流域县域土地利用碳排放时空格局及碳平衡分区[J].环境科学,2025,46(10):6209-6220.
[7]赵荣钦,张帅,黄贤金,等.中原经济区县域碳收支空间分异及碳平衡分区[J].地理学报,2014,69(10):1425-1437.
[8]魏燕茹,陈松林.福建省土地利用碳排放空间关联性与碳平衡分区[J].生态学报,2021,41(14):5814-5824.
[9]姚璐,於冉,吴雨桐,等.安徽省土地利用碳平衡分区及其隐性驱动力[J].环境监测管理与技术,2025,37(6):35-43.
[10]Cai Angzu,Guo Ru,Zhang Yuhao,et al.Assessing urban carbon health in China's three largest urban agglomerations:carbon emissions,energy-carbon emission efficiency and carbon sinks[J].Applied Energy,2025,383:125326.DOI:10.1016/j.apenergy.2025.125326.
[11]Dong Jia,Li Cunbin.Structure characteristics and influencing factors of China's carbon emission spatial correlation network:a study based on the dimension of urban agglomerations[J].Science of the Total Environment,2022,853:158613.DOI:10.1016/j.scitotenv.2022.158613.
[12]杨蕴琦,郝晋珉,张金懿,等.曲周县土地利用碳排放测算及情景预测研究[J].中国农业大学学报,2024,29(2):160-175.
[13]杨绪红,金晓斌,刘晶,等.土地利用碳排放核算系统设计与实现[J].测绘通报,2019(4):54-59.
[14]曾冰,李龙祺,魏玲.中国省际交界型县域碳排放时空演变及影响因素[J].地理与地理信息科学,2025,41(6):108-117.
[15]张金萍,秦耀辰,张丽君,等.黄河下游沿岸地市CO_2排放的时空分异[J].地理研究,2012,31(12):2229-2239.
[16]李平星.泛长三角地区碳生态效率的空间格局及影响因素[J].生态学报,2018,38(23):8500-8511.
[17]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.
[18]杨静媛,张明,多玲花,等.江西省土地利用碳排放空间格局及碳平衡分区[J].环境科学研究,2022,35(10):2312-2321.
[19]周波,许源溪,冯田.成渝城市群碳平衡空间分异与优化对策[J].长江流域资源与环境,2024,33(8):1650-1662.
[20]江越.“双碳”目标下长三角城市群土地利用碳排放路径分析[D].上海:上海师范大学,2023.
[21]柳梦琳,赵清华,马欲翔,等.中国多源碳排放演化与碳减排路径分析[J].环境科学,2026,47(5):2805-2819.
[22]李梓赫,周冬梅,江晶,等.甘肃省土地利用碳排放时空演变特征及影响因素[J].环境科学,2024,45(9):5040-5048.
[23]李璐,董捷,徐磊,等.功能区土地利用碳收支空间分异及碳补偿分区:以武汉城市圈为例[J].自然资源学报,2019,34(5):1003-1015.
[24]Fang Jingyun,Chen Anping,Peng Changhui,et al.Changes in forest biomass carbon storage in China between 1949 and 1998[J].Science,2001,292(5525):2320-2322.
[25]高翯,梁仕龙,姜雪.基于夜间灯光数据的县域碳排放空间关联网络特征研究:以吉林省为例[J].环境科学学报,2024,44(9):414-426.
[26]Fan Zhenggen,Xia Wentong,Yu Hu,et al.Spatiotemporal pattern and spatial convergence of land use carbon emission efficiency in the Pan-Pearl River Delta:based on the difference in land use carbon budget[J].Land,2024,13(5):634.DOI:10.3390/land13050634.
[27]Pan Lin,Yu Jing,Lin Lu.The temporal and spatial pattern evolution of land-use carbon emissions in China coastal regions and its response to green economic development[J].Frontiers in Environmental Science,2022,10:1018372.DOI:10.3389/fenvs.2022.1018372.
[28]孙宇栋,毛德华,邱冬冬.长沙市土地利用碳排放的时空演变特征及多情景模拟[J].农业资源与环境学报,2025,42(2):529-542.
[29]冯薇,赵荣钦,谢志祥,等.碳中和目标下土地利用碳排放效率及其时空格局:以黄河流域72个地级市为例[J].中国土地科学,2023,37(1):102-113.
[30]张娜,孙芳城,胡钰苓,等.长江经济带三大城市群土地利用碳排放的区域差异及空间收敛性[J].环境科学,2024,45(8):4656-4669.
[31]李梦倩,仇方道.淮海经济区工业碳排放效率空间网络结构特征分析[J].地理与地理信息科学,2024,40(6):62-68.
[32]郭辉,孙伟,余斌.基于主体功能区视角的土地利用碳收支时空分异与碳平衡分区:以江汉平原为例[J].长江流域资源与环境,2024,33(12):2632-2646.
[33]狄乾斌,侯智文,陈小龙.基于夜间灯光数据的中国海岛县碳排放时空分异及影响因素研究[J].地理与地理信息科学,2022,38(6):23-28.
[34] Wang Longke,Zhang Ming,Song Yan.Research on the spatiotemporal evolution characteristics and driving factors of the spatial connection network of carbon emissions in China:new evidence from 260 cities[J].Energy,2024,291:130448.DOI:10.1016/j.energy.2024.130448.
[35]马海涛,胡夏青.城市网络视角下的中国科技创新功能区划研究[J].地理学报,2022,77(12):3104-3124.
[36]徐新良,刘纪远,张树文,等.中国多时期土地利用土地覆被遥感监测数据集(CNLUCC)[Z].2018.
[37]Chen Jiandong,Gao Ming,Cheng Shulei,et al.County-level CO_2 emissions and sequestration in China during 1997-2017[J].Scientific Data,2020,7(1):391.DOI:10.1038/s41597-020-00736-3.
[38]赵明月,刘源鑫,张雪艳.农田生态系统碳汇研究进展[J].生态学报,2022,42(23):9405-9416.
[39]谭德明,谭卓成,李家裕,等.基于碳平衡分区的多目标土地利用格局优化:以长株潭城市群为例[J].水土保持通报,2025,45(1):276-285.
[40]高熳佳,崔佳.基于LUCC的山西省碳排放和生态系统服务价值时空关系[J].环境科学,2025,46(6):3546-3555.
[41]Blondel V D,Guillaume J L,Lambiotte R,et al.Fast unfolding of communities in large networks[J].Journal of Statistical Mechanics-Theory and Experiment,2008,2008:P10008.DOI:10.1088/1742-5468/2008/10/P10008.
[42] Newman M E J.Modularity and community structure in networks[J].Proceedings of the National Academy of Sciences of the United States of America,2006,103(23):8577-8582.
[43]万幼,刘耀林.基于地理加权中心节点距离的网络社区发现算法[J].武汉大学学报(信息科学版),2019,44(10):1545-1552.
[44] Monti S,Tamayo P,Mesirov J,et al.Consensus clustering:a resampling-based method for class discovery and visualization of gene expression microarray data[J].Machine Learning,2003,52(1):91-118.
基本信息:
中图分类号:X321;F124.5
引用信息:
[1]李豪,张蕾,张鹏飞,等.资源型地区碳排放空间关联网络与社团分区研究——以山西省为例[J].地理与地理信息科学().
基金信息:
国家自然科学青年基金项目（42001257）; 山西省留学回国人员科技活动择优项目（20250024）; 山西省留学回国人员科研项目（2024096）; 山西省社科联重点课题（SSKLZDKT2025179）
2026-09-23
2026-09-23
2026-09-23