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

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Publicado en:The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2025), p. 226-231
Autor principal: Agrawal, Swati
Otros Autores: Senthilkumar, R, Hassan, Muntather M, Otamirzaev Muzaffar Bakhodir Ugli, Manikandan, S, Thella Preethi Priyanka, Pallavi, M O
Publicado:
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Materias:
Robotics
Climate change
Carbon offsets
Emissions trading
IoT devices
Fraud
Satellite imagery
Environmental impact
Blockchain
Climate change mitigation
Monitoring
Tracking
Real time
Carbon
Carbon footprint
Sustainable practices
Economic
Environmental
Acceso en línea:Citation/Abstract
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Resumen:Conference Title: 2025 International Conference on Intelligent Systems and Pioneering Innovations in Robotics and Electric Mobility (INSPIRE)Conference Start Date: 2025 Nov. 20Conference End Date: 2025 Nov. 21Conference Location: Mangalore, IndiaThe 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.
DOI:10.1109/INSPIRE67328.2025.11300592
Fuente:Science Database