[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125824-en":3,"doc-seo-125824-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125824,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Global meat consumption driver analysis with machine learning methods","The growing global meat consumption creates serious consequences for human health, the environment, and overall food security, making driver identification and evolution prediction essential. This study compares four machine learning methods and selects a random-forest-based model to detect key drivers of global meat consumption. Results indicate per-capita meat consumption is primarily driven by socioeconomic factors such as GDP and urbanization, with declining driver strength from 1990 to 2018.","ORCA – Online Research @  \nCardiff  \nThis is an Open Access document downloaded from ORCA, Cardiff University's institutional repository:[https://orca.cardiff.ac.uk/id/eprint/169713/](https://orca.cardiff.ac.uk/id/eprint/169713/)  \nThis is the author’s version of a work that was submitted to / accepted for publication.  \nCitation for final published version:  \nJia, Junwen, Wu, Fang, Yu, Hao, Chou, Jieming, Han, Qinmei and Cui, Xuefeng 2024. Global meat consumption driver analysis with machine learning methods. Food Security 16 , pp. 829-843.  \n10.1007/s12571-024-01455-y  \nPublishers page: [http://dx.doi.org/10.1007/s12571-024-01455-y](http://dx.doi.org/10.1007/s12571-024-01455-y)  \nPlease note:  \nChanges made as a result of publishing processes such as copy-editing, formatting and page numbers may not be reflected in this version. For the definitive version of this publication, please refer to the published source. You are advised to consult the publisher’s version if you wish to cite this paper.  \nThis version is being made available in accordance with publisher policies. See [http://orca.cf.ac.uk/policies.html](http://orca.cf.ac.uk/policies.html) for usage policies. Copyright and moral rights for publications made  \navailable in ORCA are retained by the copyright holders.  \nGlobal meat consumption driver analysis with machine learning methods  \nJunwen Jia1, 2, Fang Wu 1, Hao Yu 1, 3, Jieming Chou4, Qinmei Han4, 5, Xuefeng Cui1, *  \n1 School of Systems Science, Beijing Normal University, Beijing, 100875, China  \n2 School of Earth and Environmental Sciences, Cardiff University, Cardiff, CF10 3AT, United Kingdom  \n3 Department of Mathematics and Statistics, University of Exeter, Exeter, EX4 4QJ, United Kingdom  \n4 Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China  \n5 National Climate Centre, China Meteorological Administration, Beijing, 100081, China  \n* Corresponding author.  \nE-mail address: [xuefeng.cui@bnu.edu.cn](xuefeng.cui@bnu.edu.cn)  \nAbstract:  \nThe growing global meat consumption has serious consequences on human health, the environment and ultimately impacts global food security. Therefore, identifying the drivers of meat consumption and predicting its evolution is necessary. We compared four machine learning methods in modelling meat consumption, leading to the selection of a random forest-based model to detect main drivers for global meat consumption. Our results show that per capita meat consumption is mainly driven by socioeconomic factors, such as national GDP and urbanization. However, the strength of these drivers declined between 1990 and 2018. Pork, beef, and poultry consumption are mainly driven by socioeconomic factors, whereas mutton consumption appears driven by other factors such as the per capita agricultural land. In this work, the model-agnostic interpretability method is introduced to measure the marginal effect of each driver on meat consumption. We found that there may be insufficient evidence to support the inverted U-shaped relationship between per capita GDP and meat consumption, which is reported in previous studies. Our analysis may provide avenues for predicting meat consumption at the national scale.  \nKey words: Meat consumption, driver analysis, interpretable machine learning, national-level prediction  \n1. Introduction  \nThe world population increased by 145% between 1961 and 2018, from less than  \n3.1 billion to over 7.6 billion. Over the same period of time, worldwide meat consumption and production almost quintupled (4 .6 times and 4.8 times, respectively; FAO, 2021) . This increasing meat consumption has become a major global sustainability issue, with consequences on human health, the environment and global natural resources (Ambikapathi et al., 2018; Micha et al., 2010; Pfeiler and Egloff, 2018a; Tilman and Clark, 2014) .  \nMeat consumption has reached a level such that it negatively affects human health in large parts of medium- and high-income co","cbCailcn3IpT3xtC","https://ap.wps.com/l/cbCailcn3IpT3xtC","pdf",1564125,1,24,"English","en",105,"# Introduction\n## Background and trends in meat consumption\n## Health and environmental impacts\n## Motivation: understanding dietary drivers\n## Related work on drivers and variables","[{\"question\":\"Why is driver analysis of meat consumption important?\",\"answer\":\"Because rising meat consumption affects human health, the environment, and global food security, requiring identification of drivers and prediction of future trends.\"},{\"question\":\"Which machine learning approach was selected in the study?\",\"answer\":\"A random forest-based model, chosen after comparing four machine learning methods for modelling meat consumption.\"},{\"question\":\"What main factors drive per-capita meat consumption, and did their influence change over time?\",\"answer\":\"Per-capita meat consumption is mainly driven by socioeconomic factors like national GDP and urbanization, and the strength of these drivers declined between 1990 and 2018.\"}]","Global meat consumption driver analysis with machine learning methods | 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is driver analysis of meat consumption important?","Question",{"text":75,"@type":76},"Because rising meat consumption affects human health, the environment, and global food security, requiring identification of drivers and prediction of future trends.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning approach was selected in the study?",{"text":80,"@type":76},"A random forest-based model, chosen after comparing four machine learning methods for modelling meat consumption.",{"name":82,"@type":73,"acceptedAnswer":83},"What main factors drive per-capita meat consumption, and did their influence change over time?",{"text":84,"@type":76},"Per-capita meat consumption is mainly driven by socioeconomic factors like national GDP and urbanization, and the strength of these drivers declined between 1990 and 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