干旱
环境科学
碳纤维
碳循环
平衡(能力)
固碳
环境资源管理
碳核算
自然资源经济学
温室气体
生态学
计算机科学
二氧化碳
经济
生态系统
医学
算法
复合数
物理医学与康复
生物
作者
Jiamin Liu,Xiaolin Pei,Wanyang Zhu,Jizong Jiao
标识
DOI:10.1016/j.jenvman.2023.119016
摘要
The carbon budget has emerged as a central focus in global carbon cycle research. The limited understanding of carbon budget balance dynamics has led to an increasing imbalance between ecological and socio-economic benefits. Building upon a comprehensive analysis of carbon storage and emission in Lanzhou from 2000 to 2020, this study develops a novel deep learning model (CNN-LSTM) to simulate carbon budget under various scenarios from 2030 to 2050. Additionally, scientifically grounded recommendations for carbon compensation are provided. The results demonstrate several key findings: (1) The deep learning model exhibits outstanding performance, with an average overall accuracy exceeding 0.93. The coupled model outperforms individual models, underscoring the significance and necessity of incorporating both temporal and spatial features in land use simulation. (2) Under the ecological protection redline scenario from 2030 to 2050, a noteworthy augmentation in carbon storage and a proficient constraint on carbon emissions are observed. This substantiates the effectiveness of ecological protection interventions. (3) Carbon compensation payment areas are predominantly concentrated in built-up land, with the extent of these areas expanding over time. (4) The disparities in carbon balance effects of forest were more conspicuous than that of built-up land across diverse temporal and scenarios.
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