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一个新的评估框架,以预测在中国的SSP-RCP不同情景下的土地使用和碳储存

Wei Guo¹, Yongjia Teng², Jing Li¹

¹College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China.

The Science of the total environment

|December 6, 2023

概括

实现碳中和需要准确的土地利用和碳储存预测. 我们的新模型整合了全球和本地数据,用于在中国进行精确的LULC和CS评估,帮助气候政策.

科学领域:

环境科学环境科学
气候变化建模模型
生态系统服务生态系统服务

背景情况:

碳中和目标需要改进土地利用和碳储存 (CS) 预测.
全球LULC场景往往缺乏准确的国家/区域CS分析的分辨率.

研究的目的:

开发和应用一个新的框架来评估中国未来的土地利用和土地覆盖 (LULC) 和CS在不同的气候和社会经济路径下.
调查LULC变化的空间异质性,并从空间时间上评估CS.

主要方法:

将土地使用协调 (LUH2) 数据集,补丁生成土地使用模拟 (PLUS) 模型和生态系统服务和权衡 (InVEST) 模型的综合估值集成到 LUH2-PLUS-InVEST (LPI) 框架中.
利用缩小规模的模拟 (500m x 500m) 和对结构LULC预测的校准需求.
量化评估了时空空间的CS,并确定了潜在的生态威胁.

主要成果:

中国未来的LULC和CS是由自然环境,全球排放压力,区域竞争和国家发展模式所塑造的.
该LPI模型成功生成了结构一致的LULC预测,并提供了时空空间的CS评估.
确定了影响土地利用分布和碳捕获能力的关键驱动因素.

结论:

LUH2-PLUS-INVEST (LPI) 模型提供了一个有效的方法来模拟气候社会系统中的LULC和CS反.
调查结果为制定有针对性的战略以实现碳中和目标提供了关键的见解.
强调综合建模对于理解复杂的环境变化的重要性.

关键词:

碳储存是碳的储存方式.生态治理生态治理 LUH2-PLUS-INVEST-INVEST-INVEST-INVEST-INVEST-INVEST.LUH2-PLUS-INVEST-INVEST-INVEST-INVEST.LUH2-PLUS-INVEST-INVEST-INVEST-INVEST-INVEST.LUH2-PLUS-INVEST-INVEST-INVEST.LUH2-PLUS-INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.INVEST.土地使用变化.场景模拟的场景模拟.

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