Artificial Intelligence in Carbon Trading: Enhancing Market Efficiency and Risk Management

Abstract

Carbon trading is a market-based technique to decrease greenhouse gas (GHG) emissions through the sale and purchase of carbon offsets. Incorporating artificial intelligence (AI) into carbon trading can alter the industry by improving information processing, statistical modeling, and trade automation. This paper presents an extensive structure for AI-driven carbon trading that considers critical aspects such as carbon trading volume and pricing to maximize productivity and sustainability. The study assesses numerous AI and machine learning (ML) theories, including their use in cost prediction, real-time market forecasting, and financial risk assessment. The main results show that AI integration increases market transparency, lowers fraud, and promotes informed decision-making, all of which helps to establish an environmentally friendly, effective, and adaptable carbon market. Furthermore, this work underscores the role of AI in advancing carbon-neutral economies by fostering innovation in emissions monitoring and reporting. These advancements highlight AI's critical contribution to achieving global climate objectives and addressing the urgent challenges posed by climate change.