[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119645-en":3,"doc-seo-119645-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},119645,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Predicting Carbon Credit Trading Decisions Using Machine Learning - Bachelor’s Thesis - December 2025","Climate change drives the need for effective emission-reduction strategies, where the Carbon Emission Trading System (ETS) enables enterprise carbon neutrality efforts. Trading decisions are complicated by price volatility and evolving regulations in the carbon credit market, making reliable buy/sell choices difficult. This thesis identifies factors behind BUY/SELL decisions and evaluates machine learning model prediction quality. Using an ~5,000-record carbon trading dataset, the target is relabeled from emission logic and price signals. CRISP-DM supports normalization, RFECV feature selection, and training five classifiers. Tree-based models perform best, with tuned XGBoost achieving leading accuracy, F1-score, and ROC-AUC.","Predicting Carbon Credit Trading Decisions Using Machine Learning  \nTrung Hieu Hua  \nBachelor’s thesis December 2025  \nInformation and Communication Technology  \nTrung Hieu Hua  \nPredicting Carbon Credit Trading Decisions Using Machine Learning  \nJyväskylä: Jamk University of Applied Sciences, December 2025, 47 pages  \nDegree Programme in Information and Communication Technology. Bachelor’s thesis.  \nPermission for open access publication: Yes  \nLanguage of publication: English  \nAbstract  \nClimate change has long been a global threat, this situation necessitated effective emission reduction strategies. The Carbon Emission Trading System (ETS) serves as a core mechanism for enterprises targeting carbon neutrality. Faced with significant challenges from price volatility and changing regulations in the carbon credit market, making the decision to buy or sell becomes more difficult. In this context, machine learning was considered a potential method for identifying market emission factors affecting trading decisions.  \nThis study aims to identify the factors influencing BUY or SELL decisions and evaluate the predictive capability of machine learning models. The analysis used a carbon trading dataset containing approximately 5,000 records. Additionally, The target variable has been relabeled based on emission logic and price signals to reflect market behavior better. Key features related to emissions and price volatility have been constructed. The study applies the CRISP-DM process for data normalization, feature selection using RFECV, and training five classification models. The results indicate that tree-based models deliver the highest performance, XGBoost achieved the best stability after hyperparameter tuning, leading in Accuracy, F1-score, and ROC-AUC metrics.  \nThe results indicated that machine learning models effectively captured the relationship between emission states, price volatility, and trading behaviors. This capability supported reliable BUY and SELL predictions. Although the achieved performance was high, the model's scalability remains limited due to the singlesource dataset. The data used did not fully reflect actual market conditions. In the future, the model could be assessed using a broader range of data and market simulations. This process would help determine its effectiveness in supporting decisions.  \nKeywords/tags (subjects)  \nMachine Learning(ML), Emission Trading System (ETS), Carbon market analytics, CRISP-DM, Carbon credit trading, Target Relabeling, Supervised Learning, Time-series Indicators  \nMiscellaneous (Confidential information)  \nNo confidential information  \nContents  \n1 Introduction ................................................................................................................ 6  \n1.1 Background........................................................................................................................ 6  \n1.2 Motivation ......................................................................................................................... 7  \n1.3 Problem Statement ........................................................................................................... 7  \n1.4 Objectives & Research Questions ..................................................................................... 8  \n1.5 Scope and Limitations ....................................................................................................... 8  \n1.6 Thesis Structure................................................................................................................. 9  \n2 Theoretical Background ............................................................................................... 9  \n2.1 Carbon Credit Trading ....................................................................................................... 9  \n2.2 Machine Learning in Financial Decision Making ............................................................. 10  \n2.3 CRISP-DM Framework ....","cbCaigmbMnAngNVb","https://ap.wps.com/l/cbCaigmbMnAngNVb","pdf",3515661,1,47,"English","en",105,"# 1 Introduction\n## 1.1 Background\n## 1.2 Motivation\n## 1.3 Problem Statement\n## 1.4 Objectives & Research Questions\n## 1.5 Scope and Limitations\n## 1.6 Thesis Structure\n# 2 Theoretical Background\n## 2.1 Carbon Credit Trading\n## 2.2 Machine Learning in Financial Decision Making\n## 2.3 CRISP-DM Framework\n## 2.4 Relevant Studies\n## 2.5 Ethical, Data Reliability, and AI-Assistance Considerations\n# 3 Data and Preprocessing\n## 3.1 Dataset Description\n## 3.2 Feature Overview\n## 3.3 Attribute Significance\n## 3.4 Data Understanding\n## 3.5 Data Preparation","[{\"question\":\"What problem does the thesis address in carbon credit trading?\",\"answer\":\"It addresses how to predict BUY or SELL decisions despite challenges from carbon price volatility and changing market regulations.\"},{\"question\":\"How is the dataset prepared and used for model training?\",\"answer\":\"The study uses a carbon trading dataset of about 5,000 records, normalizes data and constructs emission- and volatility-related features, then performs feature selection using RFECV.\"},{\"question\":\"Which machine learning models perform best and what metrics are used?\",\"answer\":\"Tree-based models achieve the highest performance; after hyperparameter tuning, XGBoost shows the best stability and leads in Accuracy, F1-score, and ROC-AUC.\"}]","Predicting Carbon Credit Trading Decisions Using Machine Learning - Bachelor’s Thesis - December 2025 | PDF",1785725446,118,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"predicting-carbon-credit-trading-decisions-using-machine-learning-bachelors-thesis-december-2025","",{"@graph":36,"@context":86},[37,54,69],{"@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/predicting-carbon-credit-trading-decisions-using-machine-learning-bachelors-thesis-december-2025/119645/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address in carbon credit trading?","Question",{"text":76,"@type":77},"It addresses how to predict BUY or SELL decisions despite challenges from carbon price volatility and changing market regulations.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the dataset prepared and used for model training?",{"text":81,"@type":77},"The study uses a carbon trading dataset of about 5,000 records, normalizes data and constructs emission- and volatility-related features, then performs feature selection using RFECV.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning models perform best and what metrics are used?",{"text":85,"@type":77},"Tree-based models achieve the highest performance; 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