[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126052-en":3,"doc-seo-126052-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126052,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Exploring the implementation feasibility of the sol-char sanitation system using machine learning and life cycle assessment","Global sanitation gaps persist: in 2022, 1.5 billion people lacked access to safe facilities, driving health risks and environmental damage. The study introduces the Sol-Char sanitation system and builds a machine learning model to assess implementation viability across 76 high–open defecation countries. A Random Forest model selects 42 suitable countries and supports solar technology suitability prediction. Ex-ante life cycle assessment compares scenarios, showing Scenario 1 yields the lowest emissions and scenario-specific logistics and material optimization reduce impacts.","Exploring the implementation feasibility of the sol-char sanitation system using machine learning and life cycle assessment  \nCitation for published version (APA):  \nLian, J. Z. , Sai, N. , Campos, L. C. , Fisher, R. P. , Linden, K. G. , & Cucurachi, S. (2024) . Exploring the implementation feasibility of the sol-char sanitation system using machine learning and life cycle assessment. Resources, Conservation and Recycling, 209, Article 107784. [https://doi.org/10.1016/j.resconrec.2024.107784](https://doi.org/10.1016/j.resconrec.2024.107784)  \nDocument license:  \nCC BY  \nDOI:  \n10.1016/j.resconrec.2024.107784  \nDocument status and date:  \nPublished: 01/10/2024  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 01. Feb. 2025  \nResources, Conservation & Recycling 209 (2024) 107784  \nContents lists available at ScienceDirect  \nResources, Conservation & Recycling  \njournal [homepage:](homepage: www.sciencedirect.com/journal/resources-conservation-and-recycling)[ www.sciencedirect.com/journal/resources-conservation-and-recycling](homepage: www.sciencedirect.com/journal/resources-conservation-and-recycling)  \n| Exploring the implementation feasibility of the sol-char sanitation system using machine learning and life cycle assessment |  |  |  |\n| --- | --- | --- | --- |\n| Justin Z. Liana , Nan Saib , Luiza C. Campos c , Richard P. Fisher d , Karl G. Linden d, * , Stefano Cucurachia, *\u003Cbr>a Leiden University, Institute of Environmental Science – Industrial Ecology, Van Steenisgebouw, Einsteinweg 2 2333 CC Leiden, The Netherlands\u003Cbr>b Eindhoven University of Technology, Industrial Engineering and Innovation Sciences, Information Systems IE&IS, P.O. Box 513, Atlas 5600 MB Eindhoven, The Netherlands\u003Cbr>c Centre for Urban Sustainability and Resilience, Civil Environmental & Geomatic Engineering, University College London, Gower Street, London WC1E 6BT, UK d Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, 4001 Discovery Drive, Boulder, Colorado 80303, USA |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Sol-Char Sanitation System\u003Cbr>Machine Learning\u003Cbr>Life Cycle Assessment\u003Cbr>Human waste & resource mana","cbCaiimxAO9wBg0N","https://ap.wps.com/l/cbCaiimxAO9wBg0N","pdf",7623328,7,1,12,"English","en",105,"# Introduction\n# Study objectives and approach\n## Machine learning feasibility modeling\n## Solar technology suitability prediction\n## Ex-ante life cycle assessment and scenarios\n## Emissions comparison and contribution analysis","[{\"question\":\"What problem does the Sol-Char sanitation system aim to address?\",\"answer\":\"The system targets the lack of safe sanitation access, which in 2022 contributed to major health risks and environmental degradation for billions of people.\"},{\"question\":\"How does the research use machine learning to evaluate implementation feasibility?\",\"answer\":\"It develops a Random Forest model to identify suitable locations among 76 countries, considering factors such as solar energy availability and economic feasibility.\"},{\"question\":\"What do the life cycle assessment results show across different scenarios?\",\"answer\":\"The baseline scenario produces the least emissions, while the scenario including international transportation has the highest emissions; a localized scenario falls between them, highlighting the importance of materials, design, and logistics optimization.\"}]","Exploring the implementation feasibility of the sol-char sanitation system using machine learning and life cycle assessment | PDF",1785902794,30,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"exploring-the-implementation-feasibility-of-the-sol-char-sanitation-system-using-machine-learning-and-life-cycle-assessment","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/exploring-the-implementation-feasibility-of-the-sol-char-sanitation-system-using-machine-learning-and-life-cycle-assessment/126052/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What problem does the Sol-Char sanitation system aim to address?","Question",{"text":77,"@type":78},"The system targets the lack of safe sanitation access, which in 2022 contributed to major health risks and environmental degradation for billions of people.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the research use machine learning to evaluate implementation feasibility?",{"text":82,"@type":78},"It develops a Random Forest model to identify suitable locations among 76 countries, considering factors such as solar energy availability and economic feasibility.",{"name":84,"@type":75,"acceptedAnswer":85},"What do the life cycle assessment results show across different scenarios?",{"text":86,"@type":78},"The baseline scenario produces the least emissions, while the scenario including international transportation has the highest emissions; 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