Intelligent data-driven models for accurate multi-factors prediction of carbon credit prices

Alshatri, Najlaa Saad

Intelligent data-driven models for accurate multi-factors prediction of carbon credit prices

Alshatri, Najlaa Saad

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Publication Type:: Thesis
Issue Date:: 2025

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Field	Value	Language
dc.contributor.author	Alshatri, Najlaa Saad
dc.date.accessioned	2026-03-22T23:29:53Z
dc.date.available	2026-03-22T23:29:53Z
dc.date.issued	2025
dc.identifier.uri	http://hdl.handle.net/10453/194407
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	This thesis addresses the challenge of accurately predicting carbon credit prices, which are non-linear, non-stationary, and influenced by multiple correlated external factors such as energy prices, environmental indicators, and economic conditions. Accurate pricing is vital for transparency and effectiveness in carbon markets. A systematic literature review identified research gaps, leading to the development of a Carbon Credit Multi-Factor Prediction (CCMFP) model integrating factor identification and optimized prediction algorithms. The proposed Carbon Credit Multi-Factor Identification (CCMFI) model combines random forest regression with explainable AI to identify the most influential factors among 22 external variables. Feature reduction and extraction techniques, independent component analysis (ICA), nonlinear ICA (NLICA), and principal component analysis (PCA), were then applied, with extracted components used as inputs to SVR and MLP models. Using daily Australian Carbon Credit Units (ACCUs) prices as a case study, experiments evaluated the impact of different factor sets on prediction accuracy. The models achieved an R² of over 97%, with optimal performance from factors including environmental technology patents, CO₂ emissions, renewable energy adoption, global carbon allowances, coal and crude oil prices. These findings enhance market confidence, reduce financial risks, and support global climate change mitigation through effective carbon credit utilization.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/194407/1/thesis.pdf
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	© 2025 Najlaa Saad Alshatri
dc.rights	au.edu.uts.lib/cph
dc.title	Intelligent data-driven models for accurate multi-factors prediction of carbon credit prices	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

This thesis addresses the challenge of accurately predicting carbon credit prices, which are non-linear, non-stationary, and influenced by multiple correlated external factors such as energy prices, environmental indicators, and economic conditions. Accurate pricing is vital for transparency and effectiveness in carbon markets. A systematic literature review identified research gaps, leading to the development of a Carbon Credit Multi-Factor Prediction (CCMFP) model integrating factor identification and optimized prediction algorithms. The proposed Carbon Credit Multi-Factor Identification (CCMFI) model combines random forest regression with explainable AI to identify the most influential factors among 22 external variables. Feature reduction and extraction techniques, independent component analysis (ICA), nonlinear ICA (NLICA), and principal component analysis (PCA), were then applied, with extracted components used as inputs to SVR and MLP models. Using daily Australian Carbon Credit Units (ACCUs) prices as a case study, experiments evaluated the impact of different factor sets on prediction accuracy. The models achieved an R² of over 97%, with optimal performance from factors including environmental technology patents, CO₂ emissions, renewable energy adoption, global carbon allowances, coal and crude oil prices. These findings enhance market confidence, reduce financial risks, and support global climate change mitigation through effective carbon credit utilization.

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http://hdl.handle.net/10453/194407