[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119819-en":3,"doc-seo-119819-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119819,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Prediction of CO2 Emissions Using Machine Learning","Carbon dioxide (CO2) emissions are a critical factor driving global climate change, primarily due to fuel combustion, leading to global warming. Consequently, environmental concerns have gained significant attention worldwide. This research paper explores the implementation of four distinct prediction models: Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN), to forecast CO2 trapping efficiency. The models analyze the relationships between CO2 emissions, energy consumption, and Gross Domestic Product (GDP). The Machine Learning (ML) methodologies employed in this study demonstrated robust performance, with the SVM and CNN models achieving high accuracy, as indicated by the Mean Absolute Percentage Error (MAPE). The findings derived from this study offer a valuable basis for decision support systems, aiming to develop effective policies for global CO2 emission reduction strategies. The authors acknowledge the financial support provided by Suan Sunandha Rajabhat University for the realization of this research.","LOW CARBON DEVELOPMENT AND BIOECONOMY 129  \n[https://doi.org/10.7250/CONECT.2023.099](https://doi.org/10.7250/CONECT.2023.099)  \nPREDICTION OF CO2 EMISSIONS USING MACHINE LEARNING  \nKanyarat BUSSABAN1*, Kunyanuth KULARBPHETTONG2,  \nChongrag BOONSENG3  \n1,2 Faculty of Science and Technology, Suan Sunandha Rajabhat University, Bangkok, Thailand  \n3 Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand  \n* [Corresponding author.](Corresponding author. E-mail address: kanyarat.bu@ssru.ac.th)[ E-mail address: kanyarat.bu@ssru.ac.th](Corresponding author. E-mail address: kanyarat.bu@ssru.ac.th)  \nAbstract – Carbon dioxide (CO2) is one of the important issues concerning human evolution that drives global climate change. It is emitted from the combustion of fuels causing global warming. The global community has gradually turned to pay more attention to environmental issues. This paper implements four prediction models using Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF) and Convolutional Neural Network (CNN, or ConvNet)  \nto predict CO2 trapping efficiency among CO2 emissions, energy use, and GDP. The Machine Learning (ML) approaches used in this study have shown good performance with SVM and CNN models with MAPE. The result can be a significant model for the decision support system to improve a suitable policy for global CO2 emission reduction.  \nKeywords – Carbon dioxide (CO2); Convolutional Neural Network; Forecast; Multiple Linear Regression; Random Forest; Support Vector Machine  \nAcknowledgement  \nThe authors express their sincere appreciation to Suan Sunandha Rajabhat University for financial support of the study.","cbCaijjqRLSh1SUB","https://ap.wps.com/l/cbCaijjqRLSh1SUB","pdf",235742,1,"English","en",105,"# Prediction of CO2 Emissions Using Machine Learning\n## Abstract\n## Keywords\n## Acknowledgement","[{\"question\":\"What are the main issues addressed in this research?\",\"answer\":\"This research addresses the critical issue of carbon dioxide (CO2) emissions, which contribute to global climate change and global warming. It aims to predict CO2 trapping efficiency using various machine learning models.\"},{\"question\":\"Which machine learning models were used in this study?\",\"answer\":\"The study implemented four prediction models: Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN).\"},{\"question\":\"What were the key findings of the study regarding the prediction models?\",\"answer\":\"The study found that SVM and CNN models showed good performance in predicting CO2 trapping efficiency, as measured by MAPE. This suggests their potential utility in decision support systems for CO2 emission reduction policies.\"}]","Prediction of CO2 Emissions Using Machine Learning | PDF",1785726484,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"prediction-of-co2-emissions-using-machine-learning","",{"@graph":35,"@context":84},[36,52,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/prediction-of-co2-emissions-using-machine-learning/119819/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What are the main issues addressed in this research?","Question",{"text":74,"@type":75},"This research addresses the critical issue of carbon dioxide (CO2) emissions, which contribute to global climate change and global warming. It aims to predict CO2 trapping efficiency using various machine learning models.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning models were used in this study?",{"text":79,"@type":75},"The study implemented four prediction models: Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN).",{"name":81,"@type":72,"acceptedAnswer":82},"What were the key findings of the study regarding the prediction models?",{"text":83,"@type":75},"The study found that SVM and CNN models showed good performance in predicting CO2 trapping efficiency, as measured by MAPE. This suggests their potential utility in decision support systems for CO2 emission reduction policies.","https://schema.org",{"og:url":50,"og:type":86,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":88,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]