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Blockchain-Driven Carbon Credit Trading for Real-Time Climate Change Mitigation and Monitoring

Swati AgrawalKalinga University,Department of Civil Engineering,Raipur,IndiaSenthilkumar RVimal Jyothi Engineering College,Department of Electrical and Electronics Engineering,Kannur,Kerala,IndiaMuntather M. HassanCollege of Technical Engineering, The Islamic University,Department of Computer Techniques Engineering,Najaf,IraqOtamirzaev Muzaffar Bakhodir UgliFaculty of Humanities & Pedagogy, Turan International University,Namangan,UzbekistanS. ManikandanKarpagam Institute of Technology,Department of Electrical and Electronics EngineeringThella Preethi PriyankaSaveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,Department of Computer Science and Engineering,Chennai,Tamilandu,IndiaM.O. PallaviSapthagiri NPS University,Department of Artificial Intelligence & Data Science,Bangalore,India
2025
ABI

Аннотация

The growing urgency of climate change necessitates innovative solutions for transparent and efficient carbon credit trading. Since no carbon credit exchanges are yet available, this research presents a blockchain-based carbon credit trading framework combined with real-time monitoring in AI and tracking of emissions using IoT. Smart contracts are used in the system to automate compliance validation and ensure fraud-proof transactions. NFTs, i.e., tokenised carbon credits, are created whilst carbon credits are made immutable and dynamic, reflecting real-time environmental impact. Using AI-driven models, IoT sensor data, and satellite imagery, an accurate carbon footprint is calculated, and decentralised governance via DAOs (Decentralised Autonomous Organisations) ensures stakeholder transparency. It enables automated carbon tracing to align with the optimisation of future carbon offset trades that promote sustainable practices and penalise excessive emissions. In this approach, reporting delays are avoided, global standardisation is achieved, and double counting is prevented in carbon markets. The system's efficiency in real-time carbon tracking, automated transactions, and regulatory compliance auditing is also illustrated through a prototype implementation. It also compares favourably with traditional systems, offering improved security, reduced costs, and greater scalability. The future would include predictive AI for proactive carbon mitigation, as well as cross-border interoperability for global carbon markets. This research offers an innovative and revolutionary solution to build an ecosystem for a decentralised, tamper-resistant, and efficient carbon credit market.

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