{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T18:00:20Z","timestamp":1781200820668,"version":"3.54.1"},"reference-count":48,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>The paper deals with an application of machine learning algorithms to examine the impact of agricultural land-use types on CO\u2082 emissions in Vietnam during the period 1990\u20132019. A four-layer Artificial Neural Network (ANN) model was employed to analyze the relationship between 21 agricultural land-use types (LUTs) and CO\u2082 emissions. The selected LUTs cover major agricultural products, including crops, meat, and vegetables. The results indicate that most agricultural LUTs are positively associated with CO\u2082 emissions, including bananas, dry beans, cabbages, cashew nuts in shell, fresh cassava, raw or retted jute, cauliflowers and broccoli, dry chilies and peppers, raw cinnamon and cinnamon-tree flowers, coconuts in shell, green coffee, groundnuts excluding shelled, fresh hen eggs in shell, watermelons, tea leaves, and sweet potatoes. By contrast, fresh or chilled horse meat, unmanufactured tobacco, soya beans, sesame seed, and rice show negative associations with CO\u2082 emissions. These findings provide empirical evidence to support policymakers in designing targeted strategies for reducing greenhouse gas (GHG) emissions from agricultural production in Vietnam.<\/jats:p>","DOI":"10.1371\/journal.pone.0350920","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T17:26:51Z","timestamp":1781198811000},"page":"e0350920","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["A machine learning algorithm to explore the impact of agricultural land-use types on CO2 emissions in Vietnam in the period 1990\u20132019"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9738-2142","authenticated-orcid":true,"given":"An Thinh","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tuong Anh","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3125-6249","authenticated-orcid":true,"given":"The Hoang","family":"Tran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6448-7505","authenticated-orcid":true,"given":"Xuan Truc","family":"Doan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2375-0176","authenticated-orcid":true,"given":"Ngoc","family":"Trinh Phuong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tuyen Tran","family":"Thi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0756-728X","authenticated-orcid":true,"given":"Ngoc Anh","family":"Le","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thao","family":"Do Thi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3754-6115","authenticated-orcid":true,"given":"Trang","family":"Le Huyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"issue":"1","key":"pone.0350920.ref001","doi-asserted-by":"crossref","first-page":"11665","DOI":"10.1038\/s41598-019-47861-7","article-title":"Climate and Soil Characteristics Determine Where No-Till Management Can Store Carbon in Soils and Mitigate Greenhouse Gas Emissions","volume":"9","author":"SM Ogle","year":"2019","journal-title":"Sci Rep"},{"key":"pone.0350920.ref002","doi-asserted-by":"crossref","DOI":"10.1142\/p970","author":"C Rosenzweig","year":"2015","journal-title":"Handbook of climate change and agroecosystems"},{"issue":"7","key":"pone.0350920.ref003","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1002\/ldr.2475","article-title":"Geomorphological and Hydrological Effects of Subsidence and Land use Change in Industrial and Urban Areas","volume":"27","author":"R Machowski","year":"2016","journal-title":"Land Degrad Dev"},{"issue":"2","key":"pone.0350920.ref004","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1080\/17583004.2014.913867","article-title":"Methods for attributing land-use emissions to products","volume":"5","author":"SJ Davis","year":"2014","journal-title":"Carbon Management"},{"issue":"1","key":"pone.0350920.ref005","doi-asserted-by":"crossref","first-page":"1204809","DOI":"10.1080\/23322039.2016.1204809","article-title":"Dynamic relationship among CO2 emission, agricultural productivity and food security in Nigeria","volume":"4","author":"PE Edoja","year":"2016","journal-title":"Cogent Economics & Finance"},{"issue":"25","key":"pone.0350920.ref006","doi-asserted-by":"crossref","first-page":"24764","DOI":"10.1007\/s11356-018-2523-z","article-title":"Causal relationship between agricultural production and carbon dioxide emissions in selected emerging economies","volume":"25","author":"K Appiah","year":"2018","journal-title":"Environ Sci Pollut Res Int"},{"key":"pone.0350920.ref007","doi-asserted-by":"crossref","DOI":"10.3389\/fsufs.2024.1424173","article-title":"Do agricultural credit, wheat, and rice production impact environmental quality? 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