Using interpretable machine learning to reveal the impact of rapid development in the emerging urban area on carbon storage: Marginal and interaction effects

Using interpretable machine learning to reveal the impact of rapid development in the emerging urban area on carbon storage: Marginal and interaction effects | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 31 August 2025 V1 Latest version Share on Using interpretable machine learning to reveal the impact of rapid development in the emerging urban area on carbon storage: Marginal and interaction effects Authors : Yugang Wang 0009-0000-3006-2681 , Zilin Ye , Xun Gong , Zhixiao Zhang , Fanqiang Meng , Liuhui Zhao , Dongmei Wang , Yuanjun Tong , and Zhengjun Gong [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175665277.72839517/v1 469 views 91 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract With the rapid urbanization of emerging metropolitan areas, accurately assessing carbon storage changes driven by land use transitions is essential for understanding regional carbon cycling and identifying spatial patterns of carbon sources and sinks. This study employs a “past-to-future” analytical framework to explore the spatiotemporal evolution of carbon storage. By integrating interpretable machine learning (IML) methods, we systematically identify main drivers and quantify their marginal effects and interaction mechanisms. This approach overcomes the limitations of traditional models, which often assume linear relationships and overlook complex nonlinear interactions. Our key findings are as follows: (1) From 2000 to 2020, total carbon storage decreased by 6.25 Tg, with woodlands having the highest carbon density and built-up areas the lowest. (2) Under future scenarios SSP126 and SSP245, carbon storage is projected to increase by 52.20 Tg and 39.48 Tg, respectively, mainly due to woodland expansion in eastern hilly regions. In contrast, under SSP585, a decline of 23.15 Tg is expected, primarily caused by urban expansion replacing carbon-rich ecosystems. (3) Digital Elevation Model (DEM), slope (SLOPE), and population (POP) were identified as the most influential variables. DEM and SLOPE show a positive influence on carbon storage, while POP has a negative effect. Meanwhile, these main drivers also exhibit strong interaction effects and temporal variability. This study offers a solid scientific basis for regional land use planning and provides actionable insights for enhancing carbon storage in rapidly urbanizing regions. Supplementary Material File (figure 1.docx) Download 902.80 KB File (figure 2.docx) Download 1.55 MB File (figure 3.docx) Download 559.73 KB File (figure 4.docx) Download 1.26 MB File (figure 5.docx) Download 2.02 MB File (figure 6.docx) Download 466.71 KB File (figure 7.docx) Download 434.04 KB File (figure 8.docx) Download 1.17 MB File (figure 9.docx) Download 1.21 MB File (graphical abstract.docx) Download 1.58 MB File (manuscript.docx) Download 8.55 MB File (table 1.docx) Download 16.89 KB File (table 2.docx) Download 16.72 KB File (table 3.docx) Download 17.47 KB Information & Authors Information Version history V1 Version 1 31 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords carbon storage interpretable machine learning land use shared socio-economic pathways system dynamics Authors Affiliations Yugang Wang 0009-0000-3006-2681 Southwest Jiaotong University View all articles by this author Zilin Ye Southwest Jiaotong University View all articles by this author Xun Gong Southwest Jiaotong University View all articles by this author Zhixiao Zhang Southwest Jiaotong University View all articles by this author Fanqiang Meng Southwest Jiaotong University View all articles by this author Liuhui Zhao Southwest Jiaotong University View all articles by this author Dongmei Wang Southwest Jiaotong University View all articles by this author Yuanjun Tong Southwest Jiaotong University View all articles by this author Zhengjun Gong [email protected] Southwest Jiaotong University View all articles by this author Metrics & Citations Metrics Article Usage 469 views 91 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yugang Wang, Zilin Ye, Xun Gong, et al. Using interpretable machine learning to reveal the impact of rapid development in the emerging urban area on carbon storage: Marginal and interaction effects. Authorea . 31 August 2025. DOI: https://doi.org/10.22541/au.175665277.72839517/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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