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Exploring ecosystem carbon storage change and scenario simulation in the Qiantang River source region of China
To explore the impact of land-use change on carbon storage, this study coupled the InVEST model and the FLUS model to analyse the spatial and temporal characteristics of carbon storage in the Qiantang River source region from 2000 to 2030. The carbon storage in the study area is evaluated which decl...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450464/ https://www.ncbi.nlm.nih.gov/pubmed/36062714 http://dx.doi.org/10.1177/00368504221113186 |
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author | Cao, Ludan Kong, Fanbin Xu, Caiyao |
author_facet | Cao, Ludan Kong, Fanbin Xu, Caiyao |
author_sort | Cao, Ludan |
collection | PubMed |
description | To explore the impact of land-use change on carbon storage, this study coupled the InVEST model and the FLUS model to analyse the spatial and temporal characteristics of carbon storage in the Qiantang River source region from 2000 to 2030. The carbon storage in the study area is evaluated which declined rapidly from 166.22 × 10(6) t in 2000 to 164.41 × 10(6) t in 2020, and the spatial distribution of carbon storage could be characterized by “the northwest and the southwest of region with higher, the east and the centre of the region with lower”. The carbon storage was simulated based on the historical trend development scenario, the food security scenario, and the ecological protection scenario. The carbon storage with the food security scenario could achieve 162.74 × 10(6) t in 2030. The carbon storage with the ecological protection scenario had an increase of 62.60 t/km(2) compared to the historical natural tendency development. Interestingly, the food security scenario had the smallest carbon loss value which is about $1.39 × 10(9), and its net carbon storage value was the largest which is about $3.71 × 10(9). The results of this study could provide a scientific reference for the conservation of carbon storage and land use management for climate change and sustainable development. This paper also can lay the foundation for subsequent further studies such as artificial intelligence. |
format | Online Article Text |
id | pubmed-10450464 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-104504642023-08-26 Exploring ecosystem carbon storage change and scenario simulation in the Qiantang River source region of China Cao, Ludan Kong, Fanbin Xu, Caiyao Sci Prog Applying Artificial Intelligence Techniques to Encourage Economic Growth and Maintain Sustainable Societies To explore the impact of land-use change on carbon storage, this study coupled the InVEST model and the FLUS model to analyse the spatial and temporal characteristics of carbon storage in the Qiantang River source region from 2000 to 2030. The carbon storage in the study area is evaluated which declined rapidly from 166.22 × 10(6) t in 2000 to 164.41 × 10(6) t in 2020, and the spatial distribution of carbon storage could be characterized by “the northwest and the southwest of region with higher, the east and the centre of the region with lower”. The carbon storage was simulated based on the historical trend development scenario, the food security scenario, and the ecological protection scenario. The carbon storage with the food security scenario could achieve 162.74 × 10(6) t in 2030. The carbon storage with the ecological protection scenario had an increase of 62.60 t/km(2) compared to the historical natural tendency development. Interestingly, the food security scenario had the smallest carbon loss value which is about $1.39 × 10(9), and its net carbon storage value was the largest which is about $3.71 × 10(9). The results of this study could provide a scientific reference for the conservation of carbon storage and land use management for climate change and sustainable development. This paper also can lay the foundation for subsequent further studies such as artificial intelligence. SAGE Publications 2022-09-04 /pmc/articles/PMC10450464/ /pubmed/36062714 http://dx.doi.org/10.1177/00368504221113186 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Applying Artificial Intelligence Techniques to Encourage Economic Growth and Maintain Sustainable Societies Cao, Ludan Kong, Fanbin Xu, Caiyao Exploring ecosystem carbon storage change and scenario simulation in the Qiantang River source region of China |
title | Exploring ecosystem carbon storage change and scenario simulation in the Qiantang River source region of China |
title_full | Exploring ecosystem carbon storage change and scenario simulation in the Qiantang River source region of China |
title_fullStr | Exploring ecosystem carbon storage change and scenario simulation in the Qiantang River source region of China |
title_full_unstemmed | Exploring ecosystem carbon storage change and scenario simulation in the Qiantang River source region of China |
title_short | Exploring ecosystem carbon storage change and scenario simulation in the Qiantang River source region of China |
title_sort | exploring ecosystem carbon storage change and scenario simulation in the qiantang river source region of china |
topic | Applying Artificial Intelligence Techniques to Encourage Economic Growth and Maintain Sustainable Societies |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450464/ https://www.ncbi.nlm.nih.gov/pubmed/36062714 http://dx.doi.org/10.1177/00368504221113186 |
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