基于InVEST-PLUS-GeoDectetor模型的环杭州湾碳储量时空演变与多情景预测

孙严超, 蔡俊, 王子豪, 田凤雅, 李天龙

南京林业大学学报(自然科学版) ›› 2025, Vol. 49 ›› Issue (5) : 19-28.

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南京林业大学学报(自然科学版) ›› 2025, Vol. 49 ›› Issue (5) : 19-28. DOI: 10.12302/j.issn.1000-2006.202312009
专题报道:土壤碳汇与养分元素循环利用研究(执行主编 张金池 薛建辉 阮宏华)

基于InVEST-PLUS-GeoDectetor模型的环杭州湾碳储量时空演变与多情景预测

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Spatio-temporal evolution and multi-scenario prediction of carbon storage around Hangzhou Bay based on InVEST-PLUS-GeoDectetor model

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摘要

【目的】探究环杭州湾土地利用变化和碳储量时空分异特征,为未来区域碳平衡、内部土地利用结构优化和社会经济可持续发展提供思路。【方法】以环杭州湾为例,基于InVEST和PLUS模型,评估1980—2020年土地利用对碳储量变化和生态系统碳储量服务脆弱性的影响,并利用地理探测模型(GeoDectetor)分析碳储量分异变化的驱动因素,预测2030年多情景下土地利用格局和碳储量变化。【结果】①环杭州湾主要地类为耕地和林地,其土地利用强度近40年间提高了10.35,处于中等发展水平,呈逐年上升的趋势。同时,研究区碳储量总体呈现先增后减的趋势,1980—2000年整体表现为碳汇,而在2000—2020年表现为碳源。②单因子探测得出碳储量分异变化主要受坡度等自然因素影响,双因子交互探测得出碳储量的变化是诸多因素共同作用的结果。自然因素与人为因素共同制约植被覆盖度的增长,最终影响区域碳储量分布情况。③2030年除生态保护情景下碳储量有所增加,自然发展和经济增长情景下碳储量均不同程度减少,表明未来在生态保护情景下,生态用地的有效保护对碳储量的增加较自然发展情景和经济增长情景更有利。【结论】未来生态保护情景下的环杭州湾土地利用能综合统筹各地类功能,减缓碳储量损失,对区域国土空间优化具有重要意义。

Abstract

【Objective】Against the backdrop of the carbon peaking and carbon neutrality goals, this study delves into the spatial-temporal differentiation characteristics of land-use changes and carbon storage. The aim of this study is to offer insights for future regional carbon balance, the optimization of the internal land-use structure, and the sustainable social-economic development around Hangzhou Bay.【Method】Taking the Hangzhou Bay area as a case study, by leveraging the InVEST and PLUS models, this paper assessed the impacts of land use on the alterations in carbon storage and the vulnerability of ecosystem carbon storage services from 1980 to 2020. The GeoDectetor model was utilized to analyze the driving factors behind the differentiation and changes in carbon storage, and forecasted the land-use patterns and changes in carbon storage under multiple scenarios in 2030.【Result】(1) Cultivated land and forest land were the predominant land types around Hangzhou Bay. Over the past nearly 40 years, the overall land-use intensity increased by 10.35 units. The land usage remained at a medium-development level and demonstrated a year-on-year upward trend. Meanwhile, the total carbon storage in the study area generally exhibited a pattern of first increasing and then decreasing. From 1980 to 2000, it was predominantly a carbon sink, while from 2000 to 2020, it turned into a carbon source. (2) The single factor detection revealed that the differentiation and changes in carbon storage were mainly and significantly influenced by natural factors such as slope. The two-factor interaction detection indicated that the changes in carbon storage resulted from the combined effects of multiple factors. Besides being affected by natural factors, human factors also impeded the growth of vegetation coverage, ultimately influencing the distribution of regional carbon storage. (3) Under the three scenarios in 2030, with the exception of an increase under future ecological protection scenario, the other two scenarios showed varying degrees of decline. This implied that in the future, under the ecological protection scenario, the effective conservation of ecological land had a substantially higher impact on the stability of carbon storage compared to the natural development scenario and the economic growth scenario.【Conclusion】The land use around Hangzhou Bay under future ecological protection scenario can comprehensively coordinate the functions of diverse land types, decelerate the loss of carbon storage, and is of great significance for the optimization of the regional territorial space.

关键词

碳储量 / 土地利用 / 多情景预测 / 地理探测器 / 环杭州湾

Key words

carbon storage / land use / multi-scenario forecast / GeoDetector / around Hangzhou Bay

引用本文

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孙严超, 蔡俊, 王子豪, . 基于InVEST-PLUS-GeoDectetor模型的环杭州湾碳储量时空演变与多情景预测[J]. 南京林业大学学报(自然科学版). 2025, 49(5): 19-28 https://doi.org/10.12302/j.issn.1000-2006.202312009
SUN Yanchao, CAI Jun, WANG Zihao, et al. Spatio-temporal evolution and multi-scenario prediction of carbon storage around Hangzhou Bay based on InVEST-PLUS-GeoDectetor model[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2025, 49(5): 19-28 https://doi.org/10.12302/j.issn.1000-2006.202312009
中图分类号: X171.1;S717   

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基金

国家社会科学基金项目(71203054)
安徽农业大学2021年安徽乡村振兴战略研究中心项目(校科字〔2021〕10号)
河北省科学院科技计划(23A14)

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