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.
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Alanazi, I., AL-Doghman, F., Alsubhi, A., Hussain, F. (2024). Carbon Credits Price Prediction Model (CCPPM). In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-031-57870-0_13
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DOI: https://doi.org/10.1007/978-3-031-57870-0_13
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