[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126374-en":3,"doc-seo-126374-105":31,"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":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},126374,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","THE ROLE OF FINANCIAL INDICATORS IN THE PREDICTION OF VOLUNTARY CARBON DISCLOSURE - A COMPARATIVE ANALYSIS WITH MACHINE LEARNING METHODS","Study examines financial determinants behind corporate carbon risk awareness, proxied by willingness to respond to the CDP survey, for firms listed on Borsa Istanbul from 2016 to 2023. Using nonlinear ensemble machine learning models, Random Forest and XGBoost achieve prediction accuracy above 92% based on financial indicators. Explainable AI analysis shows that ratios such as equity-to-total-debt, fixed assets-to-equity, and long-term-debt-to-total-debt substantially improve XGBoost model interpretability, supporting theory and practice in sustainable investing.","THE ROLE OF FINANCIAL INDICATORS IN THE PREDICTION OF VOLUNTARY CARBON DISCLOSURE: A COMPARATIVE ANALYSIS WITH MACHINE LEARNING METHODS  \nGönüllü Karbon Açıklaması Tahmininde Finansal Göstergelerin Rolü: Makine Öğrenmesi Yöntemleri ile Karşılaştırmalı Bir Analiz  \nYunus Emre AKDOĞAN*  \nKeywords:  \nClimate Change, Voluntary Carbon Disclosure, Machine Learning, Explainable Artificial Intelligence, Borsa İstanbul.  \nJEL Codes:  \nQ54, C45, C53, C55, G30, G32 .  \nAnahtar Kelimeler:  \nİklim Değişikliği, Gönüllü Karbon Saydamlığı, Makine Öğrenmesi, Açıklanabilir Yapay Zekâ, Borsa İstanbul.  \nJEL Kodları: Q54, C45, C53, C55, G30, G32 .  \nAbstract  \nSince the Industrial Revolution, carbon dioxide emissions and deforestation have been considered the primary causes of climate change. Many countries are developing policies to reduce greenhouse gas emissions and are encouraging firms to disclose and reduce their carbon emissions. This study aims to identify the potential financial determinants of carbon risk awareness, as measured by the willingness to respond to the CDP (Carbon Disclosure Project) survey, among firms listed on the Borsa Istanbul between 2016 and 2023, using machine learning methods. The findings reveal that whether firms will make voluntary carbon disclosures can be predicted with an accuracy rate exceeding 92% using nonlinear, ensemble learning-based Random Forest and XGBoost algorithms in models based on financial indicators. Furthermore, analyses conducted with explainable artificial intelligence tools indicate that specific financial ratios, such as the ratio of equity to total debt, the ratio of fixed assets to equity, and the ratio of long-term debt to total debt, significantly enhance the model's explainability within the XGBoost algorithm. Finally, the study highlights the potential of machine learning algorithms to improve investors' risk analysis in predicting corporate carbon emissions and demonstrates that this finding contributes to both the theoretical and practical development of sustainable investment strategies.  \nÖz  \nSanayi Devriminden bu yana atmosferdeki karbondioksit emisyonları ve ormansızlaşmanın iklim değişikliğinin başlıca nedenleri olduğu düşünülmektedir. Birçok ülke sera gazı emisyonlarını azaltmak için politikalar geliştirmekte ve firmaları karbon emisyonlarını açıklamaya ve azaltmaya teşvik etmektedir. Bu çalışma, makine öğrenmesi yöntemlerini kullanarak, 2016-2023 yılları arasında Borsa İstanbul'da işlemgören firmaların CDP (Carbon Disclosure Project) anketine yanıt verme istekliliği ile ölçülen karbon riski farkındalığının potansiyel finansal belirleyicilerini ortaya çıkarmayı amaçlamaktadır. Çalışmanın bulguları, firmaların finansal göstergelerine dayalı modeller aracılığıyla, doğrusal olmayan, topluluk öğrenmesi tabanlı Rastgele Orman ve XGBoost algoritmaları kullanılarak, gönüllü karbon açıklaması yapmaeğilimlerinin %92’nin üzerinde bir doğruluk oranıyla tahmin edilebildiğini ortayakoymaktadır. Ayrıca açıklanabilir yapay zekâ araçları kullanılarak yapılan analizler,özkaynakların toplam borçlara oranı, duran varlıkların özkaynaklara oranı ve uzun vadeli borçların toplam borçlara oranı gibi belirli finansal oranların, XGBoost algoritmasında modelin açıklayıcılığına önemli düzeyde katkı sağladığını göstermektedir. Son olarak, çalışma, makine öğrenmesi algoritmalarının kurumsalkarbon emisyonlarının tahmininde yatırımcıların risk analizini iyileştirme potansiyeline ve bu bulgunun sürdürülebilir yatırım stratejilerinin hem kuramsal hem de uygulamalı olarak geliştirilmesine katkı sunabileceğine dikkat çekmektedir.  \n* Assist. Prof. Dr., Yozgat Bozok University, Faculty of Economics and Administrative Sciences, Department of Business Administration, Türkiye, [emre.akdogan@bozok.edu.tr](emre.akdogan@bozok.edu.tr)  \nReceived Date (Makale Geliş Tarihi): 05.03.2025 Accepted Date (Makale Kabul Tarihi): 04.07.2025  \nThis article is licensed under Creative Commons Attribution 4.0 International License.","cbCaiq7Ipt37HGS2","https://ap.wps.com/l/cbCaiq7Ipt37HGS2","pdf",1001121,4,1,22,"English","en",105,"# Introduction\n## Climate change context\n## Carbon emissions, institutions, and policies\n# Study design and objective\n# Machine learning prediction approach\n## Random Forest\n## XGBoost\n# Explainable AI analysis\n## Key financial ratios\n# Findings and implications\n## Investor risk analysis and sustainable investment strategies","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To identify the financial determinants of carbon risk awareness, measured by willingness to respond to the CDP survey, for Borsa Istanbul firms between 2016 and 2023.\"},{\"question\":\"Which machine learning models are used, and how accurate are they?\",\"answer\":\"The study uses Random Forest and XGBoost ensemble methods, achieving prediction accuracy exceeding 92% using financial indicator-based models.\"},{\"question\":\"Which financial ratios improve explainability in the XGBoost model?\",\"answer\":\"Explainable AI tools indicate that the equity-to-total-debt ratio, fixed assets-to-equity ratio, and long-term-debt-to-total-debt ratio significantly enhance XGBoost interpretability.\"}]","THE ROLE OF FINANCIAL INDICATORS IN THE PREDICTION OF VOLUNTARY CARBON DISCLOSURE - A COMPARATIVE ANALYSIS WITH MACHINE LEARNING METHODS | PDF",1785904731,55,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"the-role-of-financial-indicators-in-the-prediction-of-voluntary-carbon-disclosure-a-comparative-analysis-with-machine-learning-methods","",{"@graph":37,"@context":86},[38,54,69],{"@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":20},"https://docshare.wps.com/document/the-role-of-financial-indicators-in-the-prediction-of-voluntary-carbon-disclosure-a-comparative-analysis-with-machine-learning-methods/126374/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",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 is the main goal of the study?","Question",{"text":76,"@type":77},"To identify the financial determinants of carbon risk awareness, measured by willingness to respond to the CDP survey, for Borsa Istanbul firms between 2016 and 2023.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models are used, and how accurate are they?",{"text":81,"@type":77},"The study uses Random Forest and XGBoost ensemble methods, achieving prediction accuracy exceeding 92% using financial indicator-based models.",{"name":83,"@type":74,"acceptedAnswer":84},"Which financial ratios improve explainability in the XGBoost model?",{"text":85,"@type":77},"Explainable AI tools indicate that the equity-to-total-debt ratio, fixed assets-to-equity ratio, and long-term-debt-to-total-debt ratio significantly enhance XGBoost interpretability.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]