Intelligent Decision Support for Dynamic Carbon Finance Management

Liu, Zhenzhong

Intelligent Decision Support for Dynamic Carbon Finance Management

Liu, Zhenzhong

Permalink

Publication Type:: Thesis
Issue Date:: 2025

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download thesisAdobe PDF (9.29 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Liu, Zhenzhong
dc.date.accessioned	2026-05-13T04:30:31Z
dc.date.available	2026-05-13T04:30:31Z
dc.date.issued	2025
dc.identifier.uri	http://hdl.handle.net/10453/194963
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	The growing urgency of climate change mitigation has positioned carbon finance as a key mechanism within sustainable environmental economics. However, the dynamic and cross disciplinary nature of carbon credits spanning environmental, economic, social, and financial domains poses significant challenges for effective decision making. This doctoral research proposes an intelligent knowledge assembly based decision support system for the dynamic management of carbon finance. The study addresses critical challenges including heterogeneous and sparse data integration, dynamic interactions between environmental and market factors, decision bias, and limited computing resources. By leveraging machine learning and intelligent computing, the proposed system integrates diverse data and expert knowledge into a unified and adaptive decision making framework. The research further explores strategies for supporting decisions under data scarcity and computational constraints while ensuring consistency and objectivity across stakeholder perspectives. The results demonstrate that intelligent knowledge assembly combined with high performance computing can effectively support dynamic carbon finance management. This work provides a technical foundation for improving carbon credit decision making and contributes to the sustainable development of environmental economic systems.	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/10453/194963/1/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 Charles Z. Liu
dc.rights	au.edu.uts.lib/cph
dc.title	Intelligent Decision Support for Dynamic Carbon Finance Management	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

The growing urgency of climate change mitigation has positioned carbon finance as a key mechanism within sustainable environmental economics. However, the dynamic and cross disciplinary nature of carbon credits spanning environmental, economic, social, and financial domains poses significant challenges for effective decision making. This doctoral research proposes an intelligent knowledge assembly based decision support system for the dynamic management of carbon finance. The study addresses critical challenges including heterogeneous and sparse data integration, dynamic interactions between environmental and market factors, decision bias, and limited computing resources. By leveraging machine learning and intelligent computing, the proposed system integrates diverse data and expert knowledge into a unified and adaptive decision making framework. The research further explores strategies for supporting decisions under data scarcity and computational constraints while ensuring consistency and objectivity across stakeholder perspectives. The results demonstrate that intelligent knowledge assembly combined with high performance computing can effectively support dynamic carbon finance management. This work provides a technical foundation for improving carbon credit decision making and contributes to the sustainable development of environmental economic systems.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/194963