Ming Ling , Zihao Feng , Zizhen Chen , Yanping Lan , Xinhong Li , Haotian You , Xiaowen Han , Jianjun Chen
{"title":"黄河源头碳储量变化的驱动效应评估:以 CMIP6 未来发展情景为视角","authors":"Ming Ling , Zihao Feng , Zizhen Chen , Yanping Lan , Xinhong Li , Haotian You , Xiaowen Han , Jianjun Chen","doi":"10.1016/j.ecoinf.2024.102790","DOIUrl":null,"url":null,"abstract":"<div><p>Understanding how future climate scenarios impact land use/cover (LUC) and carbon storage (CS) is crucial for achieving carbon neutrality. However, research often overlooks the spatiotemporal impacts of future climate and socioeconomic changes on CS. This study integrates system dynamic (SD), patch-generating land use simulation (PLUS), the integrated valuation of ecosystem services and tradeoffs (InVEST) model, and the geographical detector to assess the LUC and CS evolution in the source of the Yellow River (SYR) from 2020 to 2060. Utilizing carbon density and LUC data, we explored the influence of natural and socioeconomic factors on CS under five shared socioeconomic pathways and representative concentration pathways (SSP-RCPs) scenarios. Our findings demonstrate that: (1) Ecological land, including woodland, grassland, and wetland, expanded more under SSP126 compared to SSP245, with SSP345, SSP460, and SSP585 showing a trend of degradation tied to deeper economic contribution. (2) By 2060, CS in terrestrial ecosystem under SSP126, SSP245, SSP345, SSP460, and SSP585 were 702.33 × 10<sup>6</sup> t, 700.33 × 10<sup>6</sup> t, 697.22 × 10<sup>6</sup> t, 696.03 × 10<sup>6</sup> t, and 691.21 × 10<sup>6</sup> t, respectively. This represents changes of 3.69 × 10<sup>6</sup> t, 1.69 × 10<sup>6</sup> t, −1.49 × 10<sup>6</sup> t, −2.68 × 10<sup>6</sup> t, and −7.43 × 10<sup>6</sup> t compared to 2020. (3) Soil type predominantly influenced the spatial differentiation of CS, with significant interactions with precipitation. This research provides new insights into land redistribution, economic strategies, and achieving carbon neutrality.</p></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.8000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574954124003327/pdfft?md5=3f165f4f1af317e0955cb37488225858&pid=1-s2.0-S1574954124003327-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Evaluation of driving effects of carbon storage change in the source of the Yellow River: A perspective with CMIP6 future development scenarios\",\"authors\":\"Ming Ling , Zihao Feng , Zizhen Chen , Yanping Lan , Xinhong Li , Haotian You , Xiaowen Han , Jianjun Chen\",\"doi\":\"10.1016/j.ecoinf.2024.102790\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Understanding how future climate scenarios impact land use/cover (LUC) and carbon storage (CS) is crucial for achieving carbon neutrality. However, research often overlooks the spatiotemporal impacts of future climate and socioeconomic changes on CS. This study integrates system dynamic (SD), patch-generating land use simulation (PLUS), the integrated valuation of ecosystem services and tradeoffs (InVEST) model, and the geographical detector to assess the LUC and CS evolution in the source of the Yellow River (SYR) from 2020 to 2060. Utilizing carbon density and LUC data, we explored the influence of natural and socioeconomic factors on CS under five shared socioeconomic pathways and representative concentration pathways (SSP-RCPs) scenarios. Our findings demonstrate that: (1) Ecological land, including woodland, grassland, and wetland, expanded more under SSP126 compared to SSP245, with SSP345, SSP460, and SSP585 showing a trend of degradation tied to deeper economic contribution. (2) By 2060, CS in terrestrial ecosystem under SSP126, SSP245, SSP345, SSP460, and SSP585 were 702.33 × 10<sup>6</sup> t, 700.33 × 10<sup>6</sup> t, 697.22 × 10<sup>6</sup> t, 696.03 × 10<sup>6</sup> t, and 691.21 × 10<sup>6</sup> t, respectively. This represents changes of 3.69 × 10<sup>6</sup> t, 1.69 × 10<sup>6</sup> t, −1.49 × 10<sup>6</sup> t, −2.68 × 10<sup>6</sup> t, and −7.43 × 10<sup>6</sup> t compared to 2020. (3) Soil type predominantly influenced the spatial differentiation of CS, with significant interactions with precipitation. 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Evaluation of driving effects of carbon storage change in the source of the Yellow River: A perspective with CMIP6 future development scenarios
Understanding how future climate scenarios impact land use/cover (LUC) and carbon storage (CS) is crucial for achieving carbon neutrality. However, research often overlooks the spatiotemporal impacts of future climate and socioeconomic changes on CS. This study integrates system dynamic (SD), patch-generating land use simulation (PLUS), the integrated valuation of ecosystem services and tradeoffs (InVEST) model, and the geographical detector to assess the LUC and CS evolution in the source of the Yellow River (SYR) from 2020 to 2060. Utilizing carbon density and LUC data, we explored the influence of natural and socioeconomic factors on CS under five shared socioeconomic pathways and representative concentration pathways (SSP-RCPs) scenarios. Our findings demonstrate that: (1) Ecological land, including woodland, grassland, and wetland, expanded more under SSP126 compared to SSP245, with SSP345, SSP460, and SSP585 showing a trend of degradation tied to deeper economic contribution. (2) By 2060, CS in terrestrial ecosystem under SSP126, SSP245, SSP345, SSP460, and SSP585 were 702.33 × 106 t, 700.33 × 106 t, 697.22 × 106 t, 696.03 × 106 t, and 691.21 × 106 t, respectively. This represents changes of 3.69 × 106 t, 1.69 × 106 t, −1.49 × 106 t, −2.68 × 106 t, and −7.43 × 106 t compared to 2020. (3) Soil type predominantly influenced the spatial differentiation of CS, with significant interactions with precipitation. This research provides new insights into land redistribution, economic strategies, and achieving carbon neutrality.
期刊介绍:
The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change.
The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.