Enhancing Transparency and Fraud Detection in Carbon Credit Markets Through Blockchain-Based Visualization Techniques
Yun‐Cheng Tsai
Abstract
Net-zero emission targets require transparent and efficient carbon credit trading systems. This paper introduces a blockchain-based data visualization framework to enhance decision-making in the production and logistics sectors by simplifying blockchain transaction records and identifying potential arbitrage activities. The framework integrates real-time decision support tools, enabling production system managers to monitor carbon offset activities, detect fraudulent behaviors, and streamline operations. This research provides actionable insights into supply chain emissions management and operational risk reduction by leveraging advanced visualization techniques. The proposed approach offers innovative solutions to address the complexities of blockchain-based carbon trading, emphasizing transparency and sustainability. Our analysis demonstrates the effectiveness of these techniques in mitigating fraud and supporting compliance with international carbon trading standards. The findings contribute to integrating advanced technologies into sustainable production systems, offering practical implications for achieving global climate change mitigation goals and fostering a more efficient and secure carbon credit market.