Efficient ML technique in blockchain-based solution in carbon credit for mitigating greenwashing

The rapid growth of carbon credit markets, driven by global efforts to mitigate climate change, highlights the critical need for transparency and accountability—particularly in forest-based carbon offset projects. Forest ecosystems play a vital role in carbon sequestration; however, these projects are increasingly vulnerable to greenwashing, where organizations exaggerate or misrepresent their environmental impact to appear more sustainable than they are. This literature review explores the integration of blockchain technology and machine learning (ML) to enhance verification processes and reduce fraudulent practices in forest carbon credits. Blockchain’s decentralized, immutable ledger offers a transparent and tamper-proof system for recording carbon credit transactions, ensuring traceability and reducing the risk of manipulation. Smart contracts embedded within blockchain networks can automate verification and compliance processes, enhancing efficiency while minimizing the need for human oversight. However, while blockchain ensures transparency, it lacks real-time anomaly detection capabilities. ML algorithms, particularly supervised models such as Random Forest, XGBoost, and Neural Networks, are well-suited for detecting fraudulent patterns and verifying the authenticity of forest carbon credit transactions. These algorithms can process large datasets, including satellite imagery and corporate disclosures, to identify discrepancies and improve the accuracy of carbon sequestration claims. This review also examines key performance metrics such as accuracy, precision, recall, and processing time to evaluate the efficiency of various ML algorithms for real-time fraud detection. The findings suggest that integrating ML and blockchain technologies, combined with satellite data, can significantly strengthen transparency and verification in forest carbon credit markets. By enhancing verification mechanisms, this interdisciplinary approach helps mitigate greenwashing and fosters a more credible and transparent carbon credit market. It supports global sustainability efforts by ensuring that carbon sequestration claims from forest-based projects are both accurate and verifiable.

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