Multi-scenario simulation of carbon budget balance in arid and semi-arid regions.
J Environ Manage
; 346: 119016, 2023 Nov 15.
Article
em En
| MEDLINE
| ID: mdl-37738724
ABSTRACT
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Carbono
/
Florestas
Tipo de estudo:
Health_economic_evaluation
/
Prognostic_studies
País/Região como assunto:
Asia
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article