The application of blockchain and smart contracts in the trusted evidence storage of carbon trading data of listed companies

The increasing need for sustainable practices has encouraged listed companies to participate in carbon trading markets. Traditional centralized systems for managing carbon trading data often face challenges such as limited transparency, poor traceability, and security risks, leading to inefficiencies and compliance issues. This research proposes a blockchain-based framework with smart contracts to provide a secure, decentralized mechanism for recording and verifying carbon trading data. The system ensures tamper-proof logs of emission allowances, trading transactions, and verification events, enabling real-time access for regulatory authorities. Data preprocessing uses Z-score normalization to standardize inputs, while Kernel Principal Component Analysis (KPCA) reduces dimensionality and extracts relevant features. To improve decision-making and cost-efficiency, a Dynamic Cuckoo Search-mutated Locust Swarm Optimization (DCSLSO) algorithm is embedded within the smart contracts to optimize carbon credit allocation and trading strategies. The framework is evaluated through simulations under varying energy demands, carbon prices, and multi-fuel scenarios, using synthetic datasets from energy-intensive industries. The DCSLSO model is implemented using Python and TensorFlow. This research demonstrates that blockchain technology, combined with intelligent smart contracts, can modernize carbon trading for listed companies, fostering transparency, accountability, and long-term economic sustainability in emissions management. This research highlights the potential of combining blockchain technology with intelligent optimization to modernize carbon markets, promoting transparency, accountability, and sustainable economic growth in emissions management.