AI in Carbon Sink Trading: Using Attack Trees to Assess Low- to Medium-Risk Scenarios

As global climate change intensifies, carbon emission trading systems have become vital tools to reduce greenhouse gas emissions through market-based mechanisms. Carbon sink trading incentivizes emission reduction and fosters global cooperation. Recently, artificial intelligence (AI) has been widely applied in these systems to enhance efficiency, transparency, and intelligence in data analysis, market forecasting, and trading optimization. However, AI integration also introduces security risks such as data tampering, algorithmic manipulation, and system intrusions, threatening market stability, and fairness. To address these challenges, this paper adopts the attack tree method to systematically assess security risks in AI-driven carbon sink trading. The attack tree provides a structured framework to visualize potential attack paths and identify threat sources. By combining attack tree modeling with risk assessment theory, this study identifies key risk scenarios-data theft, system manipulation, market manipulation, and service disruption-and quantitatively evaluates their likelihood, potential impact, and overall threat level. Based on the analysis, corresponding protection strategies are proposed for each attack path, offering practical security measures for regulators, AI developers, and trading platform operators. The proposed framework enhances risk identification and management for AI systems in carbon markets, providing a scientific basis for targeted mitigation. Ultimately, this contributes to improving the security and stability of carbon trading systems and supports the advancement of global climate governance.