Integrating Artificial Intelligence (AI) into carbon accounting presents a transformative approach to Environmental, Social, and Governance (ESG) leadership, improving transparency, accuracy, and regulatory compliance. Traditional carbon accounting frameworks are often limited by inefficiencies, inconsistencies, and reliance on self-reported data, leading to inaccuracies in sustainability disclosures. This research explores how AI-driven technologies, including machine learning, predictive analytics, blockchain, and the Internet of Things (IoT), enhance carbon footprint assessments, mitigate risks of greenwashing, and optimize emissions reporting in emerging markets. A mixed-methods approach is employed, incorporating qualitative insights from expert interviews and surveys, alongside quantitative AI-driven data analytics. Findings highlight that AI-powered ESG reporting enables real-time emissions monitoring, improves risk management, and fosters corporate sustainability strategies. However, challenges such as high implementation costs, ethical concerns, algorithmic bias, and regulatory fragmentation must be addressed. Future research should focus on enhancing AI model accuracy, developing explainable AI (XAI) frameworks, and expanding AI adoption across various industries beyond high-carbon sectors. Policy recommendations emphasize the need for regulatory support, incentivization of AI adoption, and interdisciplinary collaboration between AI developers, sustainability experts, and policymakers. This study contributes to existing knowledge by providing actionable insights into AI’s role in ESG performance, advancing sustainable business practices, and supporting global climate initiatives. By leveraging AI-driven solutions, businesses and regulators can enhance sustainability leadership, ensuring long-term environmental responsibility and compliance with international climate standards.
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