干旱和半干旱地区碳预算平衡的多场景模拟
Jiamin Liu1, Xiutong Pei1, Wanyang Zhu1
1College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China; The Key Laboratory of Western China's Environmental Systems, Ministry of Education (MOE), Lanzhou, 730000, China.
Journal of environmental management
|September 22, 2023
概括
这项研究引入了一个深度学习模型来模拟碳预算,表明生态保护有效地提高了碳储存和限制排放. 碳补偿工作集中在城市地区,森林的影响比建筑土地更大.
科学领域:
- 环境科学 环境科学
- 气候变化研究 气候变化研究
- 生态建模 生态建模
背景情况:
- 全球碳循环研究强调了碳预算的重要性.
- 缺乏对碳预算动态的理解,导致生态和社会经济利益之间的不平衡.
- 以前的土地使用模拟经常忽视了时间和空间特征的整合.
研究的目的:
- 开发和验证一种新的深度学习模型 (CNN-LSTM) 来模拟碳预算.
- 分析从2000年到2020年在兰州的碳储存和排放,以及从2030年到2050年的项目场景.
- 为碳补偿战略提供科学基础的建议.
主要方法:
- 对兰州 (2000-2020年) 的碳储存和排放数据进行了全面分析.
- 开发和应用结合的卷积神经网络-长期短期记忆 (CNN-LSTM) 深度学习模型.
- 在各种未来情景 (2030-2050年) 下模拟碳预算,包括生态保护红线情景.
主要成果:
- 通过整合时间和空间数据,CNN-LSTM模型表现出高精度 (平均整体精度>0.93),表现优于单个模型.
- 生态保护红线情景显示,碳储存和碳排放的有效控制显著增加,验证了干预的有效性.
- 碳补偿支付主要分配给建筑用地,随着时间的推移,面积不断扩大;森林表现出比建筑用地更明显的碳平衡效应.
结论:
- 开发的深度学习模型是模拟碳预算和土地使用动态的强大工具.
- 生态保护措施在增强碳捕获和减轻排放方面是有效的.
- 有针对性的碳补偿策略,考虑到土地类型的影响差异,对于有效的碳管理至关重要.
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