“双碳”目标下黄河流域资源型城市碳足迹时空特征及组态路径

宋梅; 王子莎; 陈晨; 张加

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中国环境科学 ›› 2025, Vol. 45 ›› Issue (8) : 4677-4687.

碳排放控制

“双碳”目标下黄河流域资源型城市碳足迹时空特征及组态路径

宋梅, 王子莎, 陈晨, 张加

作者信息 +

Spatiotemporal characteristics and configuration paths of carbon footprint in resource-based cities of the Yellow River Basin under the “dual-carbon” goal

SONG Mei, WANG Zi-sha, CHEN Chen, ZHANG Jia

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文章历史 +

摘要

通过三维碳足迹模型、TOE框架及模糊集定性比较分析方法对黄河流域资源型城市三维碳足迹进行测算并明确其未来减排降迹组态路径.研究发现:研究期内黄河流域资源型城市平均碳足迹呈波动下降趋势,具体表现出“持续下降-略有回弹-波动下降”的特征;高碳足迹城市数量明显减少,上、中、下游资源型城市碳足迹存在显著差异且呈“上游<下游<中游”的顺序排列;黄河流域资源型城市减排降迹路径可归结为覆盖44%城市的“技术-结构主导型”路径、覆盖50%城市的“组织主导型”路径、覆盖37%城市的“环境主导型”路径.基于上述结论,未来应基于全局视角持续关注黄河流域碳生态系统,实时监测资源型城市碳足迹值,结合组态效应多策并举降低资源型城市碳足迹.

Abstract

The three-dimensional carbon footprint of resource-based cities in the Yellow River Basin was measured, and their configuration paths of carbon reduction and footprint minimization were clarified through the three-dimensional carbon footprint model, the TOE framework, and fuzzy-set qualitative comparative analysis. It was found that during the study period, the average carbon footprint of resource-based cities in the Yellow River Basin exhibited a fluctuating downward trend, characterized by "continuous decline-slight rebound-fluctuating decline". The number of high-carbon footprint cities significantly decreased, and there were significant differences in the carbon footprint of resource-based cities in the upper, middle, and lower reaches, which were arranged in the order of "upper reaches < lower reaches < middle reaches". The carbon reduction and footprint minimization paths of resource-based cities in the Yellow River Basin could be summarized into three types: the "technology-structure dominant" path covering 44% of the cities, the "organization dominant" path covering 50% of the cities, and the "environment dominant" path covering 37% of the cities. Based on the above conclusions, it was suggested that a global perspective should be maintained to continuously monitor the carbon footprint values of resource-based cities in the Yellow River Basin, and a combination of measures should be taken to reduce the carbon footprint of these cities based on the configuration effects.

导出引用

宋梅, 王子莎, 陈晨, 张加. “双碳”目标下黄河流域资源型城市碳足迹时空特征及组态路径[J]. 中国环境科学. 2025, 45(8): 4677-4687

SONG Mei, WANG Zi-sha, CHEN Chen, ZHANG Jia. Spatiotemporal characteristics and configuration paths of carbon footprint in resource-based cities of the Yellow River Basin under the “dual-carbon” goal[J]. China Environmental Science. 2025, 45(8): 4677-4687

中图分类号： X171.1

参考文献

[1] Xu G, Li S, Gong Y. Study on stochastic convergence of China’s carbon emission efficiency [J]. Climate and Development, 2024,17(5): 407-419.
[2] 陈红梅,李建豹.长三角城市群高质量发展对碳排放强度的空间溢出效应[J].生态经济, 2024,40(4):77-86. Chen H M, Li J B. Spatial spillover effect of high-quality development on carbon emission intensity in the Yangtze River Delta urban agglomeration [J]. Ecological Economy, 2024,40(4):77-86.
[3] 徐可西,詹冰倩,姜春,等.碳排放约束下的城市空间格局优化:理论框架、指标体系与实践路径[J].自然资源学报, 2024,39(3):682- 696. Xu K X, Zhan B Q, Jiang C, et al. Optimization of urban spatial pattern under carbon emission constraints: Theoretical framework, index system and practical paths [J]. Journal of Natural Resources, 2024,39(3):682-696.
[4] 李晓静,郝建花,蔡音飞,等.黄河流域资源型城市城镇化和产业协同集聚对土地利用效率的影响[J].北京大学学报(自然科学版), 2023, 59(4):681-694. Li X J, Hao J H, Cai Y F, et al. Impact of urbanization and industrial collaborative agglomeration on land use efficiency of resource-based cities in the Yellow River Basin, China [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2023,59(4):681-694.
[5] 白玮.韧性城市建设的实践与启示[J].宏观经济管理, 2020, (12):77-84. Bai W. Practices and enlightenments of the construction of resilient city [J]. Macroeconomic Management, 2020,(12):77-84.
[6] 穆佳音,王金满,刘彪,等.资源型城市碳排放演变及影响因素研究进展[J].煤炭学报, 2024,49(S2):1130-1142. Mu J Y, Wang J M, Liu B, et al. Research progress on the evolution and influencing factors of carbon emissions in resource-based cities [J]. Journal of China Coal Society, 2024,49(S2):1130-1142.
[7] Wang S J, Gao S, Huang Y Y, et al. Spatio-temporal evolution and trend prediction of urban carbon emission performance in China based on super-efficiency SBM model [J]. Acta Geographica Sinica, 2020, 75(6):1316-1330.
[8] 孔潇扬,李琦.能源碳排放的空间估算研究进展[J].测绘科学, 2022,47(8):146-156,185. Kong X Y, Li Q. Research progress of spatial estimation of energy carbon emissions [J]. Science of Surveying and Mapping, 2022,47(8):146-156,185.
[9] Doll C N H, Muller J P, Elvidge C D. Night-time imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions [J]. AMBIO: a Journal of the Human Environment, 2000, 29(3):157-162.
[10] 杨屹,张园园.呼包鄂榆资源型城市群“水-能-碳”耦合变化及影响因素[J].中国环境科学, 2023,43(11):6212-6224. Yang Y, Zhang Y Y. Analysis of the coupling process and the influencing factors of the “water-energy-carbon” relationship in the Hohhot-Baotou-Ordos-Yulin resource-based urban agglomeration [J]. China Environmental Science, 2023,43(11):6212-6224.
[11] 朱向梅,王子莎.黄河流域碳水足迹评价及时空格局研究[J].环境科学与技术, 2020,43(10):200-211. Zhu X M, Wang Z S. Study on the assessment of carbon and water footprint and its spatial-temporal pattern in the Yellow River Basin [J]. Environmental Science & Technology, 2020,43(10):200-211.
[12] Cui Y, Li L, Lei Y, et al. The performance and influencing factors of high-quality development of resource-based cities in the Yellow River basin under reducing pollution and carbon emissions constraints [J]. Resources Policy, 2024,88:104488.
[13] 胡剑波,闫烁,韩君.中国产业部门隐含碳排放效率研究——基于三阶段DEA模型与非竞争型I-O模型的实证分析[J].统计研究, 2021,38(6):30-43. Hu J B, Yan S, Han J. Study on the carbon emissions efficiency embodied in China’s industrial sector——Empirical analysis based on three-stage DEA model and non-competitive I-O model [J]. Statistical Research, 2021,38(6):30-43.
[14] 王凯,张淑文,甘畅,等.中国旅游业碳排放效率的空间网络结构及其效应研究[J].地理科学, 2020,40(3):344-353. Wang K, Zhang S W, Gan C, et al. Spatial network structure of carbon emission efficiency of tourism industry and its effects in China [J]. Geographical Science, 2020,40(3):344-353.
[15] 崔艳芳,张国兴.黄河流域资源型城市碳排放影响因素与达峰预测研究[J].人民黄河, 2023,45(2):9-14. Cui Y F, Zhang G X. Research on the influencing factors and peak prediction of carbon emission of resources-based cities in the Yellow River Basin [J]. Yellow River, 2023,45(2):9-14.
[16] 潘思羽,张美玲.基于BP神经网络的甘肃省二氧化碳排放预测及影响因素研究[J].环境工程, 2023,41(7):61-68,85. Pan S Y, Zhang M L. Prediction of carbon dioxide emission in Gansu Province based on BP neural network and its influencing factors [J]. Environmental Engineering, 2023,41(7):61-68,85.
[17] 常建波,付兴涛,郑美君,等.能源消耗碳排放的时空演变与预测:基于山西省夜间灯光数据[J].环境科学与技术, 2023,46(3):81-89. Chang J B, Fu X T, Zheng M J, et al. Temporal and spatial evolution and prediction of energy consumption carbon emissions based on night light data in Shanxi Province [J]. Environmental Science & Technology, 2023,46(3):81-89.
[18] Köne A Ç, Büke T. Factor analysis of projected carbon dioxide emissions according to the IPCC based sustainable emission scenario in Turkey [J]. Renewable Energy, 2019,133:914-918.
[19] 陈锋,张晶,任娇,等.基于LMDI模型的黄河流域碳排放时空差异及影响因素研究[J].地球环境学报, 2022,13(4):418-427. Chen F, Zhang J, Ren J, et al. Spatiotemporal variations and influencing factors of carbon emissions in the Yellow River Basin based on LMDI model [J]. Journal of Earth Environment, 2022,13(4): 418-427.
[20] Espoir D K, Sunge R. CO₂ emissions and economic development in Africa: Evidence from a dynamic spatial panel model [J]. Journal of environmental management, 2021,300:113617.
[21] 李在军,尹上岗,姜友雪,等.长三角经济增长与碳排放异速关系及形成机制[J].自然资源学报, 2022,37(6):1507-1523. Li Z J, Yin S G, Jiang Y X, et al. Analysis of allometric relationship and formation mechanism between economic growth and carbon emissions in the Yangtze River Delta [J]. Journal of Natural Resources, 2022,37(6):1507-1523.
[22] 孙秀梅,闫肃.TOE框架下资源型城市低碳转型驱动路径——基于我国108个资源型城市的fsQCA研究[J].科技进步与对策, 2023,40(23):72-81. Sun X M, Yan S. The driving path for low-carbon transformation of resource-dependent cities under the TOE framework: An fsQCA research based on 108 resource-dependent cities in China [J]. Science & Technology Progress and Policy, 2023,40(23):72-81.
[23] 冯相昭,蔡博峰,王敏,等.中国资源型城市CO₂排放比较研究[J].中国人口·资源与环境, 2017,27(2):5-9. Feng X Z, Cai B F, Wang M, et al. A comparative study on CO₂ emission of resource-based cities in China [J]. China Population, Resources and Environment, 2017,27(2):5-9.
[24] 张明斗,席胜杰.资源型城市碳排放效率评价及其政策启示[J].自然资源学报, 2023,38(1):220-237. Zhang M D, Xi S J. Evaluation of carbon emission efficiency of resource-based cities and its policy enlightenment [J]. Journal of Natural Resources, 2023,38(1):220-237.
[25] 方恺.生态足迹深度和广度:构建三维模型的新指标[J].生态学报, 2013,33(1):267-274. Fang K. Ecological footprint depth and size: new indicators for a 3D model [J]. Acta Ecologica Sinica, 2013,33(1):267-274.
[26] Wackernagel M, Rees W. Our ecological footprint: reducing human impact on the earth [M]. Philadelphia: New society publishers, 1998.
[27] 郑德凤,刘晓星,王燕燕,等.中国省际碳足迹广度、深度评价及时空格局[J].生态学报, 2020,40(2):447-458. Zheng D F, Liu X X, Wang Y Y, et al. Assessment of carbon footprint size, depth and its spatial-temporal pattern at the provincial level in China [J]. Acta Ecologica Sinica, 2020,40(2):447-458.
[28] 景侨楠,侯慧敏,白宏涛,等.自上而下的城市能源消耗碳排放估算方法[J].中国环境科学, 2019,39(1):420-427. Jing Q N, Hou H M, Bai H T, et al. A top-bottom estimation method for city-level energy-related CO₂ emissions [J]. China Environmental Science, 2019,39(1):420-427.
[29] 康韵婕,郑国璋,杨建平.基于碳足迹方法的山西省土地可持续利用研究[J].山西师范大学学报(自然科学版), 2021,35(2):23-28. Kang Y J, Zheng G Z, Yang J P. Study on sustainable land use in Shanxi Province based on carbon footprint method [J]. Journal of Shanxi Normal University (Natural Science Edition), 2021,35(2):23-28.
[30] IPCC. (2022). Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report. Cambridge University Press.
[31] 何勇.中国气候、陆地生态系统碳循环研究[M].北京:气象出版社, 2006. He Y. Research on China’s climate, Carbon cycle in terrestrial ecosystems [M]. Beijing: Meteorological Publishing House, 2006.
[32] 方精云,郭兆迪,朴世龙,等.1981~2000年中国陆地植被碳汇的估算[J].中国科学(D辑:地球科学), 2007,37(6):804-812. Fang J Y, Guo Z D, Piao S L, et al. Estimation of carbon sinks in terrestrial vegetation in China, 1981~2000[J]. Scientia Sinica (D: Terrae), 2007,37(6):804-812.
[33] 熊鹰,艾赣雄,周晨,等.基于改进三维生态足迹模型的洞庭湖区生态可持续时空演化研究[J].生态学报, 2022,42(3):1165-1179. Xiong Y, Ai G X, Zhou C, et al. Temporal and spatial evolution of ecological sustainability in Dongting Lake area based on the improved three-dimensional ecological footprint model [J]. Acta Ecologica Sinica, 2022,42(3):1165-1179.
[34] 钱凤魁,徐欢,逄然然,等.基于三维生态足迹模型辽宁省耕地生态补偿额度估算分析[J].中国农业资源与区划, 2023,44(6):97-109. Qian F K, Xu H, Pang R R, et al. Estimation and analysis of cultivated land ecological compensation in Liaoning Province based on three-dimensional ecological footprint model [J]. Chinese Journal of Agricultural Resources and Regional Planning, 2023,44(6):97-109.
[35] 刘某承,李文华,谢高地.基于净初级生产力的中国生态足迹产量因子测算[J].生态学杂志, 2010,29(3):592-597. Liu M C, Li W H, Xie G D. Estimation of China ecological footprint production coefficient based on net primary productivity [J]. Chinese Journal of Ecology, 2010,29(3):592-597.
[36] TORNATZKY L G, FLEISCHER M. The processes of technological innovation [J]. The Journal of Technology Transfer, 1991,(1):45-46.
[37] Chau P Y K, Tam K Y. Factors affecting the adoption of open systems: an exploratory study [J]. MIS quarterly, 1997,21(1):1-20.
[38] 王飞航,本连昌.创新生态系统视角下区域创新绩效提升路径研究[J].中国科技论坛, 2021,38(3):154-163. Wang F H, Ben L C. Research on the improvement path of regional innovation performance from the perspective of innovation ecosystem [J]. Forum on Science and Technology in China, 2021,38(3):154-163.
[39] 李从欣,刘佳源.“双碳”战略背景下数字经济碳减排效应的实证研究[J].城市问题, 2024,(12):74-85. Li C X, Liu J Y. Empirical study on the carbon emission reduction effect of digital economy under the background of “Dual-Carbon” strategy [J]. Urban Problems, 2024,(12):74-85.
[40] 赵涛,张智,梁上坤.数字经济、创业活跃度与高质量发展——来自中国城市的经验证据[J].管理世界, 2020,36(10):65-76. Zhao T, Zhang Z, Liang S K. Digital economy, entrepreneurship, and high-quality economic development: Empirical evidence from urban China [J]. Journal of Management World, 2020,36(10):65-76.
[41] 郭峰,王靖一,王芳,等.测度中国数字普惠金融发展:指数编制与空间特征[J].经济学(季刊), 2020,19(4):1401-1418. Guo F, Wang J Y, Wang F, et al. Measuring China’s digital financial inclusion: Index compilation and spatial characteristics [J]. China Economic Quarterly, 2020,19(4):1401-1418.
[42] 张艳,张雨,孙哲远.资源依赖、政府治理能力对资源型城市绿色经济转型的影响[J].南京财经大学学报, 2022,40(2):76-85. Zhang Y, Zhang Y, Sun Z Y. Impact of resource dependence and government governance capacity on transformation of resource-based cities’ green economy [J]. Journal of Nanjing University of Finance and Economics, 2022,40(2):76-85.
[43] Muhamad G M, Heshmati A, Khayyat N T. How to reduce the degree of dependency on natural resources? [J]. Resources Policy, 2021, 72:102047.
[44] Yang X, He L, **a Y, et al. Effect of government subsidies on renewable energy investments: The threshold effect [J]. Energy Policy, 2019,132:156-166.
[45] 于楠,孙仁金,石红玲,等.环境规制与能源生态足迹——基于绿色技术创新的中介效应[J].调研世界, 2023,(12):27-35. Yu N, Sun R J, Shi H L, et al. Environmental regulation and energy ecological footprint——Mediating effects based on green technology innovation [J]. The World of Survey and Research, 2023,(12):27-35.
[46] 朱洁西,李俊江.数字经济如何赋能城市绿色发展:基于区域创新产出和要素配置效率的视角[J].兰州学刊, 2023,(1):31-48. Zhu J X, Li J J. How can digital economy empower urban green development——From the perspective of regional innovation output and factor allocation efficiency [J]. Lanzhou Academic Journal, 2023, (1):31-48.
[47] 曾经纬,李柏洲.组态视角下企业绿色双元创新驱动路径[J].中国人口·资源与环境, 2022,32(2):151-161. Zeng J W, Li B Z. Driving paths of enterprise green ambidextrous innovation from the configuration perspective [J]. China Population, Resources and Environment, 2022,32(2):151-161.
[48] 闫华飞,牛兰兰,肖静.TOE框架下地区碳减排的组态路径研究[J].管理学刊, 2023,36(3):35-48. Yan H F, Niu L L, Xiao J. Research on the Configuration Path of Regional Carbon Emission Reduction Under the Framework of TOE [J]. Journal of Management, 2023,36(3):35-48.
[49] 张莹,陈涛峰,陈洪波,等.扶持政策对资源枯竭型城市高质量发展的促进效果[J].中国人口·资源与环境, 2022,(5):46-56. Zhang Y, Chen T F, Chen H B. Implementation effect of supporting polices on the high-quality development of resource-exhausted cities [J]. China Population, Resources and Environment, 2022,(5):46-56.
[50] 徐英启,程钰,王晶晶.中国资源型城市碳排放效率时空演变与绿色技术创新影响[J].地理研究, 2023,42(3):878-894. Xu Q Y, Cheng Y, Wang J J. The impact of green technological innovation on the spatiotemporal evolution of carbon emission efficiency of resource-based cities in China [J]. Geographical Research, 2023,42(3):878-894.
[51] 夏海力,朱诗晗.中国省域创新绩效提升的多元路径选择——基于TOE框架的组态分析[J/OL].科技进步与对策, 1-10[2025-04-09]. Xia H L, Zhu S H. Multiple path choice of improving provincial innovation performance in China: A configuration analysis based on TOE framework [J/OL]. Science & Technology Progress and Policy, 1-10[2025-04-09].
[52] Schneider C Q, Wagemann C. Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis [M]. Cambridge: Cambridge University Press, 2012.