Intelligent Decision Support for Dynamic Carbon Finance Management

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.

Author dc:creator

• Liu, Zhenzhong

Statement dc:rights

• info:eu-repo/semantics/openAccess
• 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.
• © 2025 Charles Z. Liu
• au.edu.uts.lib/cph

Language dc:language.iso

en_US

Handle dc:identifier.uri

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

OAI identifier oai:identifier

oai:opus.lib.uts.edu.au:10453/194963