Date of Publication :20th November 2023

Abstract: In the growing field of carbon trading the integration of Explainable Artificial Intelligence (XAI) offers a crucial approach, to improving transparency and accountability in decision making processes. This study embarks on a journey to investigate the merging of XAI with carbon trading. It emphasizes the lack of examples and explores hypothetical case studies to shed light on methods and outcomes. Carbon trading plays a role in efforts to combat climate change by allowing permits for carbon dioxide and other greenhouse gas emissions. To ensure effectiveness and fairness it requires transparent decision-making frameworks. XAI, known for its ability to make AI models decision making processes interpretable for users presents a solution to address the complexities and opacities within carbon trading mechanisms. Through speculative case studies and proposed approaches this study aims to uncover paths, challenges and implications of integrating XAI into carbon trading. It lays a foundation for research, in this domain by exploring this innovative intersection that brings together technology and environmental sustainability seamlessly. Ultimately it strives for a future where carbon trading operates transparently equitably and effectively.

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