Intelligent Blockchain for Managing Carbon Credits

Alanazi, Inam A.

Intelligent Blockchain for Managing Carbon Credits

Alanazi, Inam A.

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Publication Type:: Thesis
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Alanazi, Inam A.
dc.date.accessioned	2025-05-07T06:49:56Z
dc.date.available	2025-05-07T06:49:56Z
dc.date.issued	2024
dc.identifier.uri	http://hdl.handle.net/10453/187231
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	The urgency of climate change has spotlighted carbon markets as essential tools for reducing emissions. However, challenges such as double counting, uncertain price predictions, and unreliable market infrastructure hinder their effectiveness. This research explores a novel blockchain-based platform designed to address these issues. By using a transparent, immutable ledger, the platform eliminates double counting, ensuring each carbon credit is uniquely tracked across projects. In testing, it accurately identified 500 unique credits, flagging 50 duplicates to prevent overlap. Additionally, predictive pricing powered by machine learning, including Random Forests and Support Vector Regression, delivered highly accurate carbon price forecasts, with mean absolute errors of 0.0115 and R² scores near 1. The platform’s secure, decentralized marketplace enhances trust and transparency, critical for carbon trading. This study demonstrates that blockchain and advanced algorithms can significantly improve carbon market efficiency and reliability, offering a scalable solution to existing challenges.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2024 Inam A. Alanazi
dc.rights	au.edu.uts.lib/nph
dc.title	Intelligent Blockchain for Managing Carbon Credits	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

The urgency of climate change has spotlighted carbon markets as essential tools for reducing emissions. However, challenges such as double counting, uncertain price predictions, and unreliable market infrastructure hinder their effectiveness. This research explores a novel blockchain-based platform designed to address these issues. By using a transparent, immutable ledger, the platform eliminates double counting, ensuring each carbon credit is uniquely tracked across projects. In testing, it accurately identified 500 unique credits, flagging 50 duplicates to prevent overlap. Additionally, predictive pricing powered by machine learning, including Random Forests and Support Vector Regression, delivered highly accurate carbon price forecasts, with mean absolute errors of 0.0115 and R² scores near 1. The platform’s secure, decentralized marketplace enhances trust and transparency, critical for carbon trading. This study demonstrates that blockchain and advanced algorithms can significantly improve carbon market efficiency and reliability, offering a scalable solution to existing challenges.

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http://hdl.handle.net/10453/187231