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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...

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Autores principales: Cao, Ludan, Kong, Fanbin, Xu, Caiyao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
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.
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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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