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This study aims to explore the key drivers of voluntary carbon disclosures among French firms, highlighting environmental, social and governance (ESG) components and sustainable investments as crucial factors.

This study uses H2O AutoML, an advanced machine-learning framework, to examine CO2 emissions disclosure among 77 French non-financial companies listed on the SBF 120 index from 2017–2021. The research rigorously assesses CO2 disclosures using the Carbon Disclosure Project Index criteria, enhancing the precision of the findings. This approach paves the way for innovative advancements in ESG research by integrating cutting-edge computational tools with conventional environmental reporting metrics.

Social performance and sustainable investments are the most significant predictors of CO2 disclosure, outweighing traditional financial metrics such as the Book-to-Market ratio and direct environmental factors. Variable importance analysis and heatmaps underscore the critical role of social factors in shaping corporate transparency regarding carbon emissions.

This research offers valuable insights for companies and policymakers aiming to enhance environmental transparency and reporting, underscoring the significance of integrating ESG factors. Carbon emissions disclosure plays a critical role in promoting sustainability, ensuring regulatory compliance, attracting investors and strengthening risk management practices.

This research presents an innovative methodology for analyzing CO2 emissions disclosure using advanced machine learning within the H2O AutoML framework. It emphasizes the importance of model diversity and variable consideration to comprehensively understand environmental disclosure, potentially guiding policymakers and businesses in improving transparency and sustainability practices.

This study aims to identify the key determinants of carbon emissions disclosure from an environmental, social and governance (ESG) perspective, offering insights into how these factors influence corporate transparency and sustainability practices.

This study uses H2O Automated machine learning (AutoML), a sophisticated machine learning framework, to analyze CO₂ emissions disclosure among 77 French nonfinancial companies listed on the SBF 120 index between 2017 and 2021. This investigation robustly evaluates CO₂ emission disclosures based on the Carbon Disclosure Project Index criteria. This approach enhances the accuracy of the findings and pioneers a new path in ESG research, blending sophisticated computational tools with traditional environmental reporting metrics.

The study shows an optimal balance between model complexity and accuracy, with social factors and the book market being more influential in CO₂ disclosure than direct environmental factors. The heatmap analysis revealed the significance of these variables in predicting CO₂ disclosures.

This research provides insights for firms and policymakers to improve environmental transparency and reporting, emphasizing the importance of considering ESG aspects. Carbon emissions disclosure is crucial for sustainability, ensuring regulatory compliance, attracting investors and improving risk management.

This research introduces a cutting-edge methodology for analyzing CO₂ emissions disclosure, applying the H2O AutoML framework specifically to French nonfinancial companies listed on the SBF 120 index. This unique application within the French regulatory context, combined with a focus on ESG factors, sets this study apart from previous research. By emphasizing model diversity and the integration of multiple advanced algorithms, the approach provides a more nuanced understanding of environmental disclosure, offering novel insights that can guide policymakers and businesses in enhancing transparency and sustainability practices.