Gönüllü Karbon Açıklaması Tahmininde Finansal Göstergelerin Rolü: Makine Öğrenmesi Yöntemleri ile Karşılaştırmalı Bir Analiz

Abstract

Sanayi 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 işlem gören 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 yapma eğilimlerinin %92’nin üzerinde bir doğruluk oranıyla tahmin edilebildiğini ortaya koymaktadır. 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 kurumsal karbon emisyonlarının tahmininde yatırımcıların risk analizini iyileştirme potansiyeline ve bu bulgunun sürdürülebilir yatırım stratejilerinin hem kuramsal hem de uygulamalı olarak geliştirilmesine katkı sunabileceğine dikkat çekmektedir.

Keywords

• İklim Değişikliği
• Gönüllü Karbon Saydamlığı
• Makine Öğrenmesi
• Açıklanabilir Yapay Zekâ
• Borsa İstanbul

The Role of Financial Indicators in the Prediction of Voluntary Carbon Disclosure: A Comparative Analysis with Machine Learning Methods

Abstract

Since 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.

Keywords

• Climate Change
• Voluntary Carbon Disclosure
• Machine Learning
• Explainable Artificial Intelligence
• Borsa İstanbul

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