基于PLUS-InVEST模型的黄河流域河南段三生空间演变多情景模拟与碳储量评估
Multi-Scenario Simulation of the Evolution of the Production, Living and Ecological Spaces and Carbon Storage Assessment in the Henan Section of the Yellow River Basin Based on the PLUS-InVEST Model
摘要: 在我国“双碳”战略与黄河流域生态保护和高质量发展战略叠加推进的背景下,三生空间格局转型深刻影响区域碳平衡。以黄河流域河南段为研究区,整合土地利用、自然环境与社会经济等多源数据,耦合PLUS-InVEST模型,开展三生空间演变多情景模拟与碳储量评估,旨在揭示三生空间转型的碳储量效应,为区域低碳发展提供科学支撑。结果显示:1) 2000~2020年研究区生产空间和生态空间缩减、生活空间扩张,空间转移以生产空间转向生活空间为主。2) 近20年,研究区碳储量累计减少9.6 × 106 t,生活空间侵占生产空间是碳储量下降主要因素。3) 多情景模拟显示,耕地保护情景下碳储量最大,为373.21 × 106 t,城镇发展情景下碳储量最小,为366.59 × 106 t。2020~2040年4种发展情景下的碳储量均呈现不同程度的衰减特征。其中,城镇发展情景下碳损失风险最高,碳储量减少量达12.51 × 106 t (降幅3.41%)。耕地保护情景碳损失风险最低,碳储量减少量为5.89 × 106 t (降幅1.58%),但对城镇发展约束较强。可持续发展模式能在保障发展需求的同时最大限度维系碳汇功能(降幅为2.75%),为流域国土空间低碳优化提供了最优路径参考。
Abstract: Against the backdrop of China’s dual carbon goals and the Yellow River Basin’s ecological protection and high-quality development strategy, the transformation of the tripartite spatial structure—production, living, and ecological spaces—has profoundly influenced regional carbon balance. Taking the Henan section of the Yellow River Basin as the study area, this research integrates multi-source data on land use, natural environment, and socioeconomic factors, and couples the PLUS-InVEST model to simulate multiple scenarios of tripartite space evolution and assess carbon storage. The aim is to reveal the impacts of spatial transformation on carbon storage and provide scientific support for low-carbon regional development. Results show: 1) From 2000 to 2020, production and ecological spaces decreased while living space expanded in the study area, with the primary spatial shift being from production to living space. 2) Over the past two decades, total carbon storage in the region declined by 9.6 × 106 tons, primarily due to living space encroaching upon production space. 3) Scenario simulations indicate that carbon storage was the highest under the farmland protection scenario at 373.21 × 106 tons, and the lowest under the urban development scenario at 366.59 × 106 tons. From 2020 to 2040, all four development scenarios showed varying degrees of carbon storage decline. Among them, the urban development scenario carried the highest risk of carbon loss, with a reduction of 12.51 × 106 tons (a 3.41% decrease). The farmland protection scenario had the lowest carbon loss risk, with a reduction of 5.89 × 106 tons (1.58% decrease), although it imposed strong constraints on urban development. The sustainable development scenario achieved the best balance, maintaining carbon sequestration functions to the greatest extent while meeting development needs (a 2.75% decrease), offering an optimal reference for low-carbon optimization of territorial space in the basin.
文章引用:闫怡晨, 郜颖, 刘雨欣, 郝万康, 刘春雨, 张静静. 基于PLUS-InVEST模型的黄河流域河南段三生空间演变多情景模拟与碳储量评估[J]. 地理科学研究, 2026, 15(4): 732-744. https://doi.org/10.12677/gser.2026.154066

参考文献

[1] Niu, J.Y., Jin, G. and Zhang, L. (2023) Territorial Spatial Zoning Based on Suitability Evaluation and Its Impact on Ecosystem Services in Ezhou City. Journal of Geographical Sciences, 33, 2278-2294.
https://doi.org/10.1007/s11442-023-2176-9
[2] Wu, W., Xu, L.Y., Zheng, H.Z. and Zhang, X.R. (2023) How Much Carbon Storage Will the Ecological Space Leave in a Rapid Urbanization Area? Scenario Analysis from Beijing-Tianjin-Hebei Urban Agglomeration. Resources, Conservation and Recycling, 189, Article ID: 106774.
https://doi.org/10.1016/j.resconrec.2022.106774
[3] Li, Q., Pu, Y.C. and Gao, W. (2023) Spatial Correlation Analysis and Prediction of Carbon Stock of “Production-Living-Ecological Spaces” in the Three Northeastern Provinces, China. Heliyon, 9, e18923.
https://doi.org/10.1016/j.heliyon.2023.e18923
[4] Shi, H.Q., Duan, H.E., Li, X.M., Wang, G.G., Chen, A. and Liang, D.R. (2026) Impact of Land Use Change on Carbon Storage Based on the Plus-Invest Model: A Case Study in the Urban Belt along the Yellow River, China. Journal of Arid Land, 18, 452-476.
https://doi.org/10.1016/j.jaridl.2026.03.006
[5] Zou, L.L., Liu, Y.S., Wang, J.Y. and Yang, Y.Y. (2021) An Analysis of Land Use Conflict Potentials Based on Ecological-Production-Living Function in the Southeast Coastal Area of China. Ecological Indicators, 122, Article ID: 107297.
https://doi.org/10.1016/j.ecolind.2020.107297
[6] Ji, Z.X., Liu, C., Xu, Y.Q., Sun, M.X., Wei, H.J., Sun, D.F., et al. (2023) Quantitative Identification and the Evolution Characteristics of Production-Living-Ecological Space in the Mountainous Area: From the Perspective of Multifunctional Land. Journal of Geographical Sciences, 33, 779-800.
https://doi.org/10.1007/s11442-023-2106-x
[7] Song, J.Z., Lei, J. and Wang, P.J. (2025) Evolution of the “Production-Living-Ecological” Space of Urban Trituration and Its Prediction of Carbon Mitigation Potential—The Case of Xi’an. Ecological Indicators, 171, Article ID: 113137.
https://doi.org/10.1016/j.ecolind.2025.113137
[8] 陈美景, 王庆日, 白中科, 等. 碳中和愿景下“三生空间”转型及其碳储量效应——以贵州省为例[J]. 中国土地科学, 2021, 35(11): 101-111.
[9] 陈姜全, 李效顺, 刘希朝, 等. 基于“三生空间”转型的碳收支演变机制与碳补偿研究——以西南地区为例[J]. 长江流域资源与环境, 2025, 34(3): 610-626.
[10] 周宇, 张余, 王琴, 等. 黑龙江省“三生空间”转型及其碳效应[J]. 土壤通报, 2024, 55(4): 901-911.
[11] 曾庆雨, 孙才志. 黄河流域陆地生态系统碳储量测算及其影响因素[J]. 生态学报, 2024, 44(13): 5476-5493.
[12] Ma, Y.M., Wang, J.S., Xin, P.Y., Zhao, Z.X., Zhang, X.X., Yang, Y.X., et al. (2026) Assessing Carbon Storage Dynamics and Policy Impacts: Application of Invest-Plus Framework in the Qinling Mountains, China. Land Use Policy, 164, Article ID: 107947.
https://doi.org/10.1016/j.landusepol.2026.107947
[13] Zhu, Y., Ma, B., Hu, H.B., Ding, D.X., Zhou, H.W., Liu, J.X., et al. (2025) Analysis and Prediction of Spatiotemporal Carbon Storage Changes in the Taihu Lake Basin in Jiangsu Province Based on PLUS and Invest Model. Trees, Forests and People, 21, Article ID: 100916.
https://doi.org/10.1016/j.tfp.2025.100916
[14] 任利敏, 金喜盈, 李娜, 等. 黄河流域河南段碳储量时空演变及驱动因素[J]. 测绘科学, 2026, 51(03): 129-139.
[15] 王文娟, 赵振坤, 赵东方. 1980-2035年黄河流域土地利用和碳储量时空格局演化分析——基于FLUS-InVEST模型[J]. 生态经济, 2025, 41(8): 201-210.
[16] 杨洁. 基于FLUS-InVEST模型的黄河流域土地利用变化模拟与碳储量估算研究[D]: [硕士学位论文]. 开封: 河南大学, 2023.
[17] 张薇, 朱睿, 杨华庆, 等. 基于PLUS-InVEST模型的黄河水源涵养区碳储量时空演变分析及模拟预测[J]. 高原气象, 2025, 44(2): 362-377.
[18] 安文举, 李旭, 郭强, 等. 基于PLUS-InVEST模型预测2033年陕北多沙粗沙区碳储量及经济价值[J]. 应用生态学报, 2026, 37(4): 1165-1174.
[19] 何慧爽, 董鑫鑫, 丁婕妤. 基于PLUS-InVEST模型的黄河流域“三生”空间演变多情景模拟与碳储量评估[J]. 环境科学, 2026, 47(5): 3037-3048.
[20] Cai, J., Chi, H., Lu, N., Bian, J., Chen, H., Yu, J., et al. (2024) Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the Plus-Invest-Geodetector Model. Energies, 17, 5093.
https://doi.org/10.3390/en17205093
[21] 刘晓河, 黄磊, 程爱华. 耦合PLUS-InVEST-Geodetector模型的黄河“几”字弯土地利用与碳储量时空变化及驱动因素分析[J/OL]. 环境科学: 1-17.
https://link.cnki.net/doi/10.13227/j.hjkx.202509302, 2026-04-27.
[22] 毕帆帆, 武志涛, 梁寒雪, 等. 基于PLUS-InVEST-GeoDetector模型的黄河中游碳储量时空变化及驱动因素[J]. 环境科学, 2025, 46(8): 4742-4753.
[23] 丛中笑, 刘金花, 孙增禹, 等. 基于PLUS与InVEST模型的黄河流域九省(区)碳储量变化分析[J]. 人民黄河, 2024, 46(12): 24-30, 36.
[24] 李冰洁, 范志韬, 曲芷程, 等. 基于InVEST-PLUS模型的黄河流域内蒙古段生态系统碳储量评价及预测[J]. 干旱区研究, 2024, 41(7): 1217-1227.
[25] 贾佳, 梁帅, 田世民, 等. 黄河流域河南段生态系统碳储量评估及其影响因素[J]. 人民黄河, 2026, 48(1): 21-25+32.
[26] 夏依宁, 刘鹏翱, 何柯润, 等. 基于土地利用的长株潭都市圈碳储量时空格局与情景模拟[J]. 生态环境学报, 2025, 34(11): 1661-1674.
[27] 杜嘉睿, 宋芊, 高文明. 黄河源区生态系统健康时空演变与多情景模拟研究[J]. 生态环境学报, 2026, 35(4): 551-562.
[28] 梁秋燕, 宋明洁, 张豆, 等. 基于生态安全格局的昆明市2030年和2050年土地利用模拟[J]. 生态环境学报, 2025, 34(9): 1463-1472.
[29] 闵婕, 刘晓煌, 肖粤新, 等. 基于PLUS模型和InVEST模型的新安江流域生态系统碳储量时空变化分析与预测[J]. 现代地质, 2024, 38(3): 574-588.
[30] 曹振江, 张亚丽. 基于InVEST-PLUS模型的河南省土地利用和碳储量时空演变[J]. 环境科学, 2025, 46(11): 7043-7057.
[31] 帕茹克∙吾斯曼江, 艾东, 冀正欣, 等. 人地关系视角下北京市“三生”空间转型及其碳储量效应[J]. 中国环境科学, 2024, 44(5): 2786-2798.