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A dual decomposition integration and error correction model for carbon price prediction.

Yanan Li1, Xinsheng Zhang1, Minghu Wang1

  • 1School of Management, Xi'an University of Architecture and Technology, Xi'an, 710055, China.

Journal of Environmental Management
|January 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced carbon price prediction model using dual decomposition and error correction. The model significantly improves prediction accuracy for carbon markets, benefiting policymakers and stakeholders.

Keywords:
Carbon price predictionDecompose integrationError correctionFuzzy entropyLong short-term memory networkVariational mode decomposition

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Area of Science:

  • Environmental Economics
  • Computational Finance
  • Time Series Analysis

Background:

  • Carbon price prediction is vital for market stability and policy but is challenging due to complex, nonlinear market dynamics.
  • Existing models often struggle with the inherent instability and multifactorial influences on carbon prices.
  • Accurate forecasting is essential for effective climate change mitigation strategies and carbon market operations.

Purpose of the Study:

  • To develop and validate a novel carbon price prediction model that enhances accuracy by integrating dual decomposition and error correction techniques.
  • To address the limitations of existing models in capturing the complex dynamics of carbon price fluctuations.
  • To provide a more reliable tool for stakeholders involved in carbon markets and climate policy.

Main Methods:

  • Decomposition of carbon price series into intrinsic mode functions (IMFs) using variational mode decomposition optimized by sparrow search algorithm (SVMD).
  • Classification of IMFs by complexity using fuzzy entropy, followed by prediction using whale optimization algorithm-optimized long short-term memory networks (WLSTM) for complex IMFs and extreme learning machines (ELM) for simpler IMFs.
  • Error correction through ensemble empirical mode decomposition (EEMD) of prediction errors, followed by reconstruction of initial and error predictions.

Main Results:

  • The proposed model demonstrated superior performance compared to 15 benchmark models across key metrics (RMSE, MAE, MAPE, R²).
  • Average improvements in performance indicators reached at least 19.89% (RMSE), 25.11% (MAE), 25.01% (MAPE), and 0.79% (R²).
  • Validation using real data from Chinese carbon exchanges confirmed the model's effectiveness and robustness.

Conclusions:

  • The integrated dual decomposition and error correction model offers a significant advancement in carbon price prediction accuracy.
  • The methodology effectively handles the nonlinearity and instability inherent in carbon market data.
  • The findings provide a valuable tool for enhancing decision-making in carbon markets and supporting climate policy.