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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Framework for multivariate carbon price forecasting: A novel hybrid model.

Xuankai Zhang1, Ying Zong1, Pei Du1

  • 1School of Business, Jiangnan University, Wuxi, 214122, China.

Journal of Environmental Management
|September 1, 2024
PubMed
Summary

This study introduces a novel hybrid model for accurate carbon price forecasting, combining deep learning and optimization algorithms. The model demonstrates superior prediction accuracy and stability for carbon markets.

Keywords:
Carbon price predictionDeep learningHybrid forecasting modelIntelligent optimization algorithm

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Area of Science:

  • Environmental Economics
  • Computational Finance
  • Data Science

Background:

  • Carbon price forecasting is challenging due to volatility and non-linearity.
  • Accurate prediction is crucial for market stability and investment strategies.

Purpose of the Study:

  • To develop a hybrid multivariate carbon price forecasting model.
  • To enhance prediction accuracy and stability in carbon markets.

Main Methods:

  • Feature selection using the least absolute shrinkage and selection operator (LASSO).
  • Application of nine advanced deep learning models.
  • Hybridization of top-performing models with the Pelican optimization algorithm.

Main Results:

  • The proposed hybrid model significantly outperforms existing models in prediction accuracy and stability.
  • The model demonstrates robust performance in European carbon market price prediction.
  • Quantitative trading simulations in the Hubei carbon market validate its investment value.

Conclusions:

  • The hybrid model offers high-precision carbon price prediction for decision-makers.
  • It provides a valuable tool for investors to optimize trading strategies and returns.