[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119966-en":3,"doc-seo-119966-105":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},119966,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine learning for gap-filling in greenhouse gas emissions databases","Greenhouse gas (GHG) emissions datasets often contain gaps caused by inconsistent reporting and limited transparency, which undermines evidence-based mitigation planning. This study assesses automated completion using machine learning across three datasets with increasing complexity and 18 gap-filling methods. The results provide guidance for choosing methods: simple interpolation often works best for sparse features or single missing time steps, while learning-based approaches can outperform extrapolation when missing values involve non-reporting emitters. Complex models also yield feature-importance signals to prioritize data collection, and graph-based methods scale well for updates and multimodal sources. ","DOI: 10.1111/jiec.13507  \nMETHODS A RT ICLE  \nMachine learning for gap-filling in greenhouse gas emissions databases  \nLuke Cullen1   Andrea Marinoni1, 2  Jonathan Cullen1   \n1 Department of Engineering, University of Cambridge, Cambridge, UK  \n2 Department of Physics and Technology, UiT the Arctic University of Norway, Tromsø, Norway  \nCorrespondence  \nLuke Cullen, Department of Engineering, University of Cambridge, Cambridge, UK.  \nEmail: [lshc3@cam.ac.uk](lshc3@cam.ac.uk)  \nEditor Managing Review: Deepak Rajagopal  \nFunding information  \nUK Research and Innovation; UKRI Centre for Doctoral Training in Application of Artificial Intelligence to the study of Environmental Risks, Grant/Award Number: EP/S022961/1  \nAbstract  \nGreenhouse gas (GHG) emissions datasets are often incomplete due to inconsistent reporting and poor transparency. Filling the gaps in these datasets allows for more accurate targeting of strategies aiming to accelerate the reduction of GHG emissions. This study evaluates the potential of machine learning methods to automate the completion of GHG datasets. We use three datasets of increasing complexity with 18 different gap-filling methods and provide a guide to which methods are useful in which circumstances. If few dataset features are available, or the gap consists only of a missing time step in a record, then simple interpolation is often the most accurate method and complex models should be avoided. However, if more features are available and the gap involves non-reporting emitters, then machine learning methods can be more accurate than simple extrapolation. Furthermore, the secondary output of feature importance from complex models allows for data collection prioritization to accelerate the improvement of datasets. Graph-based methods are particularly scalable due to the ease of updating predictions given new data and incorporating multimodal data sources. This study can serve as a guide to the community upon which to base evermore integrated frameworks for automated detailed GHG emissions estimations, and implementation guidance is available at [https://hackmd.io/@luke-scot/ML-for-GHG](https://hackmd.io/@luke-scot/ML-for-GHG)database-completion and [https://doi.org/10.5281/zenodo.10463104. This article met](https://doi.org/10.5281/zenodo.10463104. This article met)[ ](https://doi.org/10.5281/zenodo.10463104. This article met)the requirements for a gold-gold JIE data openness badge described at [http://jie.click/](http://jie.click/)[ ](http://jie.click/)[badges.](badges.)  \nKEYWORDS  \nautomation, data completion, data prioritisation, graph representation learning, greenhouse gas emissions, machine learning  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2024 The Author(s). Journal of Industrial Ecology published by Wiley Periodicals LLC on behalf of International Society for Industrial Ecology.  \nCULLEN ET AL.  \n1  INTRODUCTION  \nGreenhouse gas (GHG) emissions datasets are often incomplete both at an individual facility level, for example, ClimateTRACE (2023), and at a national level, for example, UNFCCC (2023). Most countries, and many companies, are accelerating their emissions reduction strategies in line with the Paris climate agreement and net-zero objectives (Arnold & Toledano, 2021; Christiansen et al., 2023; Erb et al., 2022; Rogelj et al., 2016), but incomplete and inaccurate datasets remain a barrier to understanding and, therefore, to effective policy-making(EPA,2022a;IPCC,2021;Marlowe &Clarke, 2022). In regions including the European Union, the United Kingdom, and the United States, large companies are required to use generic emissions intensity factors to convert their facilities’ activity data to GHG emissions. These emissions factors are either provided by the relevant government authority such as DEFRA and the EPA (DEFRA, 2009;E","cbCaimvOER687YHn","https://ap.wps.com/l/cbCaimvOER687YHn","pdf",1426776,1,12,"English","en",105,"# Introduction\n## Dataset incompleteness sources\n## Impacts on emissions reporting and policy decisions","[{\"question\":\"为什么需要对温室气体排放数据库进行补全？\",\"answer\":\"因为数据往往因报告不一致和透明度不足而不完整，导致难以准确识别减排策略的效果，从而影响政策制定。\"},{\"question\":\"何时通常应优先选择简单插值而不是复杂机器学习模型？\",\"answer\":\"当可用数据特征较少，或缺口仅是记录中的单个时间步缺失时，简单插值往往更准确，应避免使用复杂模型。\"},{\"question\":\"图结构方法在补全任务中有哪些优势？\",\"answer\":\"图结构方法具有较强可扩展性，能够在获得新数据后便于更新预测，并能更容易整合多模态数据源。\"}]","Machine learning for gap-filling in greenhouse gas emissions databases | PDF",1785727290,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-gap-filling-in-greenhouse-gas-emissions-databases","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-gap-filling-in-greenhouse-gas-emissions-databases/119966/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"为什么需要对温室气体排放数据库进行补全？","Question",{"text":75,"@type":76},"因为数据往往因报告不一致和透明度不足而不完整，导致难以准确识别减排策略的效果，从而影响政策制定。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"何时通常应优先选择简单插值而不是复杂机器学习模型？",{"text":80,"@type":76},"当可用数据特征较少，或缺口仅是记录中的单个时间步缺失时，简单插值往往更准确，应避免使用复杂模型。",{"name":82,"@type":73,"acceptedAnswer":83},"图结构方法在补全任务中有哪些优势？",{"text":84,"@type":76},"图结构方法具有较强可扩展性，能够在获得新数据后便于更新预测，并能更容易整合多模态数据源。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]