元耦合下的山水林田湖草复合生态系统隐含碳流耦合研究

张燕, 官冬杰, 周李磊, 和秀娟, 朱旭森

中国环境科学 ›› 2026, Vol. 46 ›› Issue (2) : 1114-1124.

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中国环境科学 ›› 2026, Vol. 46 ›› Issue (2) : 1114-1124.
碳排放控制

元耦合下的山水林田湖草复合生态系统隐含碳流耦合研究

  • 张燕1, 官冬杰2, 周李磊2, 和秀娟3, 朱旭森4
作者信息 +

The coupling of embodied carbon flows in the composite ecosystem of Mountains, Rivers, Forests, Farmlands, Lakes, and Grasslands under the metacoupling framework

  • ZHANG Yan1, GUAN Dong-jie2, ZHOU Li-lei2, HE Xiu-juan3, ZHU Xu-sen4
Author information +
文章历史 +

摘要

通过MRIO和PLE-SEM模型,测算重庆市山水林田湖草复合生态系统(CEMRFFLG)四个子系统的隐含碳,分析各子系统间在元耦合框架下的隐含碳流动,并探究隐含碳流的影响机制.结果表明:(1)2020年重庆市的CEMRFFLG的子系统中隐含碳排序为田>草>林>水,田子系统占比为68.88%.(2)重庆市的CEMRFFLG的隐含碳流入量是流出量的3.45倍,呈现出显著的“净输入型”特征.(3)直接碳流的远程系统流入量贡献率达99.28%,流出量贡献率达78.82%,呈现远距离传输为主的特征;隐含碳流则呈现出“远程输入、近程输出”的结构性特征,远程系统的流入量贡献率为85.61%,近程系统的流出量贡献率为91.36%.(4)消费水平对CEMRFFLG的隐含碳流具有最强的正向影响,路径系数为0.845.对直接碳与隐含碳进行分析比较,为重庆市的CEMRFFLG隐含碳流减排优化建议提供科学依据,为实现生态系统复合格局的绿色发展具有重要意义.

Abstract

In Chongqing's composite ecosystem of Mountains, Rivers, Forests, Farmlands, Lakes, and Grasslands (CEMRFFLG), the embodied carbon of four subsystems (farmland, grassland, forest, and water) was quantified using Multi-Regional Input-Output (MRIO) and Partial Least Squares Structural Equation Modeling (PLS-SEM) approaches. The flows of embodied carbon among these subsystems were analyzed within a metacoupling framework, and their driving mechanisms were systematically explored. The results indicated that: (1) In 2020, embodied carbon of CEMRFFLG subsystems in Chongqing ranked as farmland > grassland > forest > water, with the farmland subsystem accounting for 68.88% of the total; (2) The embodied carbon inflow to the CEMRFFLG of Chongqing was 3.45 times greater than its outflow, exhibiting a distinct net-input characteristic; (3) Direct carbon flows were characterized by inflow and outflow contribution rates from distant systems of 99.28% and 78.82%, respectively, indicating predominance of long-distance transmission. Conversely, embodied carbon flows presented a distinctive structural pattern of "remote inflows and proximate outflows," wherein distant systems contributed 85.61% of inflows and adjacent systems received 91.36% of outflows; (4) Among all factors, the consumption level showed the strongest positive influence on embodied carbon flows of CEMRFFLG, with a path coefficient of 0.845. A comparative analysis of direct and embodied carbon flows provides a scientific basis for optimizing embodied carbon flow reduction strategies in Chongqing's CEMRFFLG, offering significant implications for achieving green development of composite ecosystem patterns.

关键词

山水林田湖草复合生态系统 / 隐含碳流 / 元耦合 / MRIO模型 / PLE-SEM模型

Key words

the composite ecosystem of Mountains, Rivers, Forests, Farmlands, Lakes and Grasslands / embodied carbon flows / metacoupling / MRIO model / PLE-SEM model

引用本文

导出引用
张燕, 官冬杰, 周李磊, 和秀娟, 朱旭森. 元耦合下的山水林田湖草复合生态系统隐含碳流耦合研究[J]. 中国环境科学. 2026, 46(2): 1114-1124
ZHANG Yan, GUAN Dong-jie, ZHOU Li-lei, HE Xiu-juan, ZHU Xu-sen. The coupling of embodied carbon flows in the composite ecosystem of Mountains, Rivers, Forests, Farmlands, Lakes, and Grasslands under the metacoupling framework[J]. China Environmental Science. 2026, 46(2): 1114-1124
中图分类号: X171.1   

参考文献

[1] Hou H, Wang J, Yuan M, et al. Estimating the mitigation potential of the Chinese service sector using embodied carbon emissions accounting [J]. Environmental Impact Assessment Review, 2021,86: 106510.
[2] 李峰,胡剑波.中国产业部门隐含碳排放变化的影响因素动态研究——基于细分行业数据的实证分析[J].经济问题, 2021,(11): 77-87. Li F, Hu J B. Dynamic analysis of influential factors on the change of embodied carbon emissions in China's industrial sectors: empirical research based on the industrial data [J]. On Economic Problems, 2021,(11):77-87.
[3] Davis S J, Peters G P, Caldeira K. The supply chain of CO2 emissions [J]. Proceedings of the National Academy of Sciences, 2011,108(45): 18554-18559.
[4] 王少剑,王泽宏,方创琳.中国城市碳排放绩效的演变特征及驱动因素[J].中国科学:地球科学, 2022,52(8):1613-1626. Wang S J, Wang Z H, Fang C L. Evolution characteristics and driving factors of carbon emission performance in Chinese cities [J]. Scientia Sinica Terrae, 2022,52(8):1613-1626.
[5] 钟诗雨,张晓敏,吴佳,等.隐含碳及其测算方法研究综述[J].生态经济, 2023,39(6):32-38. Zhong S Y, Zhang X M, Wu J, et al. Review on the embodied carbon and its accounting methods [J]. Ecological Economy, 2023,39(6): 32-38.
[6] 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[J].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 31provinces [J]. China Environmental Science, 2020,40(12):5540- 5550.
[7] 黄明辉,李巍,陆中桂,等.黄河流域城市群碳足迹及隐含碳转移研究[J].中国环境科学, 2024,44(6):3544-3552. Huang M H, Li W, Lu Z G, et al. Carbon footprint and embodied carbon transfer of the city clusters in the Yellow River basin [J]. China Environmental Science, 2024,44(6):3544-3552.
[8] 李富佳.区际贸易隐含碳排放转移研究进展与展望[J].地理科学进展, 2018,37(10):1303-1313. Li F J. Progress and prospects of research on transfer of carbon emissions embodied in inter-regional trade [J]. Progress in Geography, 2018,37(10):1303-1313.
[9] Zhou L, Chang Q, Guan D, et al. Flow path simulation and diffusion effect evaluation of carbon sequestration services coupled with the SPANs and BBNs models [J]. Ecological Frontiers, 2025,45(3): 610-620.
[10] Friess D A, Phelps J, Garmendia E, et al. Payments for ecosystem services (PES) in the face of external biophysical stressors [J]. Global Environmental Change, 2015,30:31-42.
[11] Fang G, Huang M, Zhang W, et al. Exploring global embodied carbon emissions transfer network—an analysis based on national responsibility [J]. Technological Forecasting and Social Change, 2024, 202:123284.
[12] Kleemann J, Schröter M, Bagstad K J, et al. Quantifying interregional flows of multiple ecosystem services - a case study for Germany [J]. Global Environmental Change, 2020,61:102051.
[13] Liu J. Integration across a metacoupled world [J]. Ecology and Society, 2017,22(4):1708-3087.
[14] Manning N, Li Y, Liu J. Broader applicability of the metacoupling framework than Tobler's first law of geography for global sustainability: a systematic review [J]. Geography and Sustainability, 2022,4(1):6-18.
[15] Xu Z, Chen X, Liu J, et al. Impacts of irrigated agriculture on food-energy-water-CO2 nexus across metacoupled systems [J]. Nature Communications, 2020,11:5837.
[16] Cheng L, Tian J, Xu H, et al. Unveiling the nexus profile of embodied Water-Energy-Carbon-Value flows of the Yellow River Basin in China [J]. Environmental Science & Technology, 2023,57(23):8568- 8577.
[17] Li P, He C, Huang Q, et al. Metacoupling flow of embodied carbon in resource-based cities: a case study of Hohhot-Baotou-Ordos-Yulin urban agglomeration in China [J]. Energy, 2024,313:134041.
[18] 萨娜,赵金羽,寇旭阳,等.“山水林田湖草沙生命共同体”耦合框架、模型与展望[J].生态学报, 2023,43(11):4333-4343. Sa N, Zhao J Y, Kou X Y, et al. Coupling mountains-waters-forests- farmlands-lakes-grasslands-sandlands life community: framework, models and prospect [J]. Acta Ecologica Sinica, 2023,43(11):4333- 4343.
[19] 郭文强,于忠萍,雷明,等.我国碳排放驱动因素分解及脱钩努力效应研究[J].环境科学研究, 2025,38(2):209-219. Guo W Q, Yu Z P, Lei M, et al. Decomposition of carbon emission drivers and decoupling effort effects in China [J]. Research of Environmental Sciences, 2025,38(2):209-219.
[20] 刘贤赵,高长春,张勇,等.中国省域碳强度空间依赖格局及其影响因素的空间异质性研究[J].地理科学, 2018,38(5):681-690. Liu X Z, Gao C C, Zhang Y, et al. Spatial dependence pattern of carbon emission intensity in China's provinces and spatial heterogeneity of its influencing factors [J]. Scientia Geographica Sinica, 2018,38(5):681-690.
[21] Lu Z L, Wang L L, Guo X P, et al. Decoupling effect and influencing factors of carbon emissions in China: based on production, consumption, and income responsibilities [J]. Advances in Climate Change Research, 2024,15(6):1177-1188.
[22] Cui S, Xu P, Wang Y, et al. Influencing mechanisms and decoupling effects of embodied carbon emissions: an analysis based on China's industrial sector [J]. Sustainable Production and Consumption, 2023, 41:320-333.
[23] 周晓艳,张雪莹,吴炫匡,等.中国城市工业贸易隐含碳排放转移网络结构及影响机制[J].中国环境科学, 2025,45(6):3472-3483. Zhou X Y, Zhang X Y, Wu X K, et al. Structure and mechanism of China’s inter-city industrial trade embodied carbon emission transfer network [J]. China Environmental Science, 2025,45(6):3472-3483.
[24] Hong C, Zhao H, Qin Y, et al. Land-use emissions embodied in international trade [J]. Science, 2022,376(6593):597-603.
[25] Zhang Y, Guan D, Zhou L, et al. Coupling process of carbon sink service flow based on metacoupling framework [J]. Scientific Reports, 2025,15(1):6594.
[26] 谭显东,胡兆光.投入产出表外推及主导产业研究[J].华北电力大学学报(自然科学版), 2008,(3):84-89. Tan X D, Hu Z G. Study on extrapolating input-output table and leading industry of China [J]. Journal of North China Electric Power University (Natural Science Edition), 2008,(3):84-89.
[27] 王少剑,王婕妤.区域贸易视角下中国省域隐含土地流动研究[J].地理学报, 2022,77(5):1072-1085. Wang S J, Wang J Y. Embodied land in China's provinces from the perspective of regional trade [J]. Acta Geographica Sinica, 2022,77(5): 1072-1085.
[28] Fu W, Yang S, Hu S, et al. The impact of embodied land flow in interregional trade on carbon emissions in China [J]. Applied Geography, 2023,159:103065.
[29] 王少剑,周诗洁,方创琳.远程关联视角下中国碳储量在建设用地流动中的变化及驱动因素分析[J].科学通报, 2024,69(27):4119-4136. Wang S J, Zhou S J, Fang C L. Changes and driving forces of carbon storage in China's construction land flow from a teleconnection perspective [J]. Science Bulletin, 2024,69(27):4119-4136.
[30] Zhang L, Huang Q, Qiu J, et al. Measuring virtual flows of ecosystem services embedded in traded goods across an urban agglomeration in China [J]. Ecosystem Services, 2024,69:101651.
[31] Liu J. Leveraging the metacoupling framework for sustainability science and global sustainable development [J]. National Science Review, 2023,10(7):nwad090.
[32] Zhang J, He C, Huang Q, et al. Understanding ecosystem service flows through the metacoupling framework [J]. Ecological Indicators, 2023,151:110303.
[33] 吴乐英,赵义义,苗长虹,等.基于多区域投入产出的黄河流域贸易隐含碳排放时空格局及结构分解[J].生态学报, 2024,44(19):8737- 8750. Wu L Y, Zhao Y Y, Miao C H, et al. Spatial-temporal pattern and structural decomposition of trade- embodied carbon emissions in the Yellow River Basin based on multi-regional input-output analysis [J]. Acta Ecologica Sinica, 2024,44(19):8737-8750.
[34] 朱宏城,田甜.微观视角下居民消费碳排放结构及影响因素研究——基于PLS-SEM模型的实证分析[J].干旱区资源与环境, 2022,36(1):59-65. Zhu H C, Tian T. Research on the carbon emission structure of household consumption and its influencing factors from the micro perspective [J]. Journal of Arid Land Resources and Environment, 2022,36(1):59-65.
[35] 戴晓爱,马佳欣,唐艺菱,等.甘肃省植被时空动态变化及其归因分析[J].生态环境学报, 2024,33(8):1163-1173. Dai X A, Ma J X, Tang Y L, et al. Spatio-temporal dynamics and attribution analysis of vegetation in Gansu province [J]. Ecology and Environmental Sciences, 2024,33(8):1163-1173.
[36] 吴启贤,谢新艳,陈赟,等.基于偏最小二乘法结构方程模型的城市轨道交通工程碳排放影响因素分析[J].环境工程, 2023,41(10): 133-140. Wu Q X, Xie X Y, Chen Y, et al. Analysis of factors influencing carbon emissions ofurbanrail transit projects based on partial least squares structural equation modeling [J]. Environmental Engineering, 2023, 41(10):133-140.
[37] Wiedmann T, Lenzen M, Keyßer L T, et al. Scientists' warning on affluence [J]. Nature Communications, 2020,11(1):3107.
[38] 李莹.绿色金融对碳排放的影响研究——基于绿色技术创新的链式中介作用[J].社会科学前沿, 2023,12(10):6100-6115. Li Y. A Study on the impact of green finance on carbon emissions— based on the Chain intermediation of green technology innovation [J]. Advances in Social Sciences, 2023,12(10):6100-6115.
[39] 胡萌,伍雅思,常娇娇.降碳减污协同效应:区域差异与协调路径[J].环境经济研究, 2023,8(4):191-208. Hu Meng, Wu Y S, Chang J J. Synergistic effects of carbon emissions and pollution reduction: regional differences and coordination paths [J]. Journal of Environmental Economics, 2023,8(4):191-208.

基金

国家自然科学基金联合基金项目(U24A20580);国家社科基金一般项目(25BJY215);国家自然科学基金专项项目(42542116);自然资源部国土空间规划监测评估预警重点实验室开放课题(LMEE-KF2024012);重庆自然科学基金创新发展联合基金项目(CSTB2023NSCQ-LZX0009)

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