Global patterns, supply-chain flows, and driving factors of forest loss footprints from 2001 to 2022

Global patterns, supply-chain flows, and driving factors of forest loss footprints from 2001 to 2022 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Global patterns, supply-chain flows, and driving factors of forest loss footprints from 2001 to 2022 Mimi Gong, Zhouyi Liu, Ye Li, Xinkun Wang, Shen Qu, Yinglan A, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8944281/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Monitoring forest loss is critical to keeping global warming below 1.5°C, yet achieving zero deforestation remains elusive. Despite extensive national monitoring and high-resolution earth observation data, deforestation persists due to a limited understanding of the human and economic mechanisms driving forest conversion. Here, we develop a state-of-the-art analytical framework to disentangle these dynamics by tracing forest loss—driven by logging, permanent agriculture, commodity production, and infrastructure expansion—through global supply chains to the consumer side, termed the forest loss footprint. This approach bridges the gap between fine-scale remote sensing data and aggregated economic accounts. We further apply machine learning techniques to identify key socioeconomic drivers behind consumption-induced forest loss. Our results show that 38.15% (13.12 Mha in 2022) of global forest loss is embodied in international trade, with five countries (Brazil, the United States, China, Indonesia, and Russia) accounting for 48.16% of global deforestation. China and Russia exhibit polarizing trends that amplify global forest pressures. Among drivers, the shares of food imports and the urban population emerge as dominant factors explaining cross-national patterns of forest loss. This integrated framework offers a robust, data-driven foundation for advancing global forest conservation and promoting equitable responsibility across international supply chains. Earth and environmental sciences/Environmental social sciences/Sustainability Earth and environmental sciences/Ecology/Forestry forest loss footprint Input-output analysis interpretable machine learning deforestation Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SIExcel1ForestIndatabase.xlsx Inventory of Forest Indicators in Global Databases SupportingInformation.docx Methodological Details and Extended Analyses SIExcel2ForestLossFootprintDataset.xlsx Global Forest Loss Footprint Dataset (2001–2022) Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8944281","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":598022323,"identity":"116eada2-09c7-452a-9ae2-fb3eb72e2baa","order_by":0,"name":"Mimi 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