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中国土地利用和碳储存的时空空间演变:基于PLUS-INVEST模型和SHAP模型的多场景模拟和驱动因素分析

Haojuan Li¹, Kun Zhang¹, Yongqiang Liu¹

¹College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, China; Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830017, China.

Environmental research

|May 21, 2025

概括

中国中国中国中国.

科学领域:

生态和环境科学生态和环境科学
气候变化研究研究气候变化研究
碳储存的动态碳储存的动态

背景情况:

土地使用/覆盖变化 (LUCC) 和碳储存 (CS) 在生态科学中至关重要.
了解中国的CS变化对于全球气候变化减缓和碳中和目标至关重要.
在不断变化的气候场景下,对LUCC和CS驱动因素的时空分析至关重要.

研究的目的:

分析1990年至2020年中国土地利用和碳储存的时空演变.
预测2030年和2050年在不同CMIP6气候情景 (SSP1-2.6,SSP2-4.5,SSP5-8.5) 下,中国碳储量的未来变化.
量化自然和人为因素对中国生态区碳储存动态的影响.

主要方法:

使用了结合式建模方法,将PLUS和InVEST模型与CMIP6气候预测集成在一起.
分析了1990-2020年的历史数据,并根据三个共享社会经济路径 (SSP) 预测了2030年和2050年的未来变化.
采用随机森林和SHAP方法来确定各种驱动因素对碳储存的影响.

主要成果:

中国的碳储量从1990年至2020年增加,区域差异很大;青海-西藏高原拥有最大份额 (26.96%),而中国南部的份额最低 (4.39%).
在SSP1-2.6下,预计到2030年和2050年,碳储存量将增加. 相反,SSP5-8.5预测下降,而SSP2-4.5则表明相对稳定.
自然和人为因素显著推动了时空空间的CS变化,其影响重量在区域上有差异.

结论:

中国的碳储存表现出积极的趋势,但在区域上不均,突出了需要本地化战略的需要.
未来的碳储存轨迹高度依赖于所选择的气候变化缓解途径 (SSP场景).
这些发现为在中国制定有效的生态保护和碳中和政策提供了关键的科学见解.

关键词:

储存碳的方法储存碳的方法中国中国中国中国.在InVEST模式下进行投资.土地使用类型土地使用类型这就是PLUS模型的特点.这就是 SHAP SHAP 的意思.

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