Spatial optimization of land use and carbon storage prediction in urban agglomerations under climate change: Different scenarios and multiscale perspectives of CMIP6
{"title":"Spatial optimization of land use and carbon storage prediction in urban agglomerations under climate change: Different scenarios and multiscale perspectives of CMIP6","authors":"Hao Wu , Yi Yang , Wen Li","doi":"10.1016/j.scs.2024.105920","DOIUrl":null,"url":null,"abstract":"<div><div>Land use/land cover (LULC) structure optimization can effectively increase carbon storage/carbon sequestration (CS) and help realize carbon neutrality goals<sup>1</sup>. Studying the spatial distributions of LULC and CS under climate change conditions is highly important for realizing sustainable development goals. This study is based on different climate change models, and the coordinated development of economic, water, carbon and ecological sustainability was considered to establish a comprehensive multiscale, multiscenario and multiobjective LULC optimization model. Then, different climate change scenarios were optimized, and regional CS values were predicted. The LULC simulation model provided satisfactory simulation results at different scales. Notably, the average accuracy exceeded 0.92. The optimized land expansion results exhibited heterogeneity. Forestland change accounted for the largest proportion of the total LULC change. After optimization, the CS values under the different scenarios were similar. The northwestern part of the study area served as the main carbon sink area. The aim of this study was to respond to future complex climate change by rationally planning the LULC structure, thus achieving the sustainable development of urban agglomerations.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105920"},"PeriodicalIF":10.5000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670724007443","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
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Abstract
Land use/land cover (LULC) structure optimization can effectively increase carbon storage/carbon sequestration (CS) and help realize carbon neutrality goals1. Studying the spatial distributions of LULC and CS under climate change conditions is highly important for realizing sustainable development goals. This study is based on different climate change models, and the coordinated development of economic, water, carbon and ecological sustainability was considered to establish a comprehensive multiscale, multiscenario and multiobjective LULC optimization model. Then, different climate change scenarios were optimized, and regional CS values were predicted. The LULC simulation model provided satisfactory simulation results at different scales. Notably, the average accuracy exceeded 0.92. The optimized land expansion results exhibited heterogeneity. Forestland change accounted for the largest proportion of the total LULC change. After optimization, the CS values under the different scenarios were similar. The northwestern part of the study area served as the main carbon sink area. The aim of this study was to respond to future complex climate change by rationally planning the LULC structure, thus achieving the sustainable development of urban agglomerations.
期刊介绍：
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;
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