Carbon Credits Price Prediction Model (CCPPM)

Alanazi, I; AL-Doghman, F; Alsubhi, A; Hussain, F

Carbon Credits Price Prediction Model (CCPPM)

Alanazi, I AL-Doghman, F Alsubhi, A Hussain, F

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Lecture Notes on Data Engineering and Communications Technologies, 2024, 201, pp. 143-150
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Alanazi, I
dc.contributor.author	AL-Doghman, F
dc.contributor.author	Alsubhi, A
dc.contributor.author	Hussain, F https://orcid.org/0000-0003-1513-8072
dc.date.accessioned	2024-08-22T04:54:38Z
dc.date.available	2024-08-22T04:54:38Z
dc.date.issued	2024-01-01
dc.identifier.citation	Lecture Notes on Data Engineering and Communications Technologies, 2024, 201, pp. 143-150
dc.identifier.isbn	9783031578694
dc.identifier.uri	http://hdl.handle.net/10453/180538
dc.description.abstract	The valuation of carbon credits is a multifaceted task and is influenced by a wide range of factors encompassing economic activity, energy prices, weather conditions, policy adjustments, and market expectations. The accurate prediction of carbon credit prices is essential for traders, investors, regulators, and policymakers. To address the various facets of the carbon price prediction challenge, this paper contributes to this evolving field by proposing a solution that employs a machine learning methodology to enhance the accuracy and reliability of carbon credit price predictions. The proposed model is strong, precise and has the potential to guide decision making in the carbon market domain, with its proven accuracy and reliability highlighting its advantages as a valuable tool for stakeholders dealing with the intricacies of the carbon credit landscape.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Lecture Notes on Data Engineering and Communications Technologies
dc.relation.ispartofseries	Lecture Notes on Data Engineering and Communications Technologies
dc.relation.isbasedon	10.1007/978-3-031-57870-0_13
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.rights	This version of the book chapter has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms to use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at https://link.springer.com/chapter/10.1007/978-3-031-57870-0_13
dc.title	Carbon Credits Price Prediction Model (CCPPM)
dc.type	Chapter
utslib.citation.volume	201
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-22T04:54:37Z
pubs.publication-status	Published
pubs.volume	201

Abstract:

The valuation of carbon credits is a multifaceted task and is influenced by a wide range of factors encompassing economic activity, energy prices, weather conditions, policy adjustments, and market expectations. The accurate prediction of carbon credit prices is essential for traders, investors, regulators, and policymakers. To address the various facets of the carbon price prediction challenge, this paper contributes to this evolving field by proposing a solution that employs a machine learning methodology to enhance the accuracy and reliability of carbon credit price predictions. The proposed model is strong, precise and has the potential to guide decision making in the carbon market domain, with its proven accuracy and reliability highlighting its advantages as a valuable tool for stakeholders dealing with the intricacies of the carbon credit landscape.

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

http://hdl.handle.net/10453/180538