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Point and interval forecasting for carbon trading price: a case of 8 carbon trading markets in China

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Abstract

Carbon trading price (CTP) prediction accuracy is critical for both market participants and policymakers. As things stand, most previous studies have only focused on one or a few carbon trading markets, implying that the models’ universality is insufficient to be validated. By employing a case study of all carbon trading markets in China, this study proposes a hybrid point and interval CTP forecasting model. First, the Pearson correlation method is used to identify the key influencing factors of CTP. The original CTP data is then decomposed into multiple series using complete ensemble empirical mode decomposition with adaptive noise. Following that, the sample entropy method is used to reconstruct the series to reduce computational time and avoid overdecomposition. Following that, a long short-term memory method optimized by the Adam algorithm is established to achieve the point forecasting of CTP. Finally, the kernel density estimation method is used to predict CTP intervals. On the one hand, the results demonstrate the proposed model’s validity and superiority. The interval prediction model, on the other hand, reflects the uncertainty of market participants’ behavior, which is more practical in the operation of carbon trading markets.

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Abbreviations

CTP:

Carbon trading price

EU ETS:

European Union emissions trading system

PACF:

Partial autocorrelation function

mRMR:

Max-relevance min-redundancy

GRA:

Gray relation analysis

KICA:

Kernel independent component analysis

LASSO:

Least absolute shrinkage and selection operator

EMD:

Empirical mode decomposition

EEMD:

Ensemble empirical mode decomposition

CEEMD:

Complete ensemble empirical mode decomposition

CEEMDAN:

Complete ensemble empirical mode decomposition with adaptive noise

ICEEMDAN:

Improved complete ensemble empirical mode decomposition with adaptive noise

MOEMD:

Empirical mode decomposition algorithm for mean value optimization

MEEMD:

Modified ensemble empirical mode decomposition

ESMD:

Extreme-point symmetric model decomposition

VMD:

Variational model decomposition

MRSVD:

Multi-resolution singular value decomposition

CCI:

Comprehensive contribution index

ApEn:

Approximate entropy

SE:

Sample entropy

RF:

Random forest

CIM:

Cointegration model

GARCH:

Generalized autoregressive conditional heteroskedasticity

CPN:

Carbon price network

LSSVR:

Least squares support vector regression

ELM:

Extreme learning machine

KELM:

Kernel-based extreme learning machine

WRELM:

Weighted regularized extreme learning machine

KNEA:

Kernel-based nonlinear extension of the Arps decline model

ANFIS:

Adaptive network–based fuzzy inference system

CNN:

Convolution neural network

DBN:

Deep belief network

RNN:

Recurrent neural network

GNN:

Gray neural network

RBFNN:

Radial basis function neural network

NARNN:

Nonlinear autoregressive neural network

LSTM:

Long short-term memory

PSO:

Particle swarm optimization

CKA:

Kidney algorithm with scaling factor and cooperation factor

ACA:

Ant colony algorithm

GWO:

Gray wolf optimizer

AWOA:

Adaptive whale optimization algorithm

IWOA:

Improved whale optimization algorithm

MOGOA:

Multiobjective grasshopper optimization algorithm

MOCSCA:

Multiobjective chaotic sine cosine algorithm

SGD:

Stochastic gradient descent

AdaGrad:

Adaptive gradient algorithm

RMSprop:

Root mean square prop

KDE:

Kernel density estimation

NDE:

Normal distribution estimation

IMF:

Intrinsic mode function

MB:

Memory blocks

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

RMSE:

Root mean square error

U1:

Theil U statistic 1

PICP:

Prediction interval coverage probability

PINAW:

Prediction interval normalized average width

PIECE:

Prediction interval comprehensive evaluation criteria

AQI:

Air quality index

EUAP:

European Union carbon emission allowances price

BOP:

Brent oil price

NGP:

Nature gas price

ER (EUR–CNY):

Exchange rate between EUR and CNY

CEA:

Carbon emission allowances

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Acknowledgements

The authors are grateful to the editor and anonymous reviewers for their work.

Availability of data and materials

The [carbon price and influencing factors] data used to support the findings of this study are included in Supplementary information. For more details, please consult the corresponding author on reasonable request.

Funding

This study is supported by the Natural Science Foundation of China under Grant No. 71973043.

Author information

Authors and Affiliations

  1. School of Economics and Management, North China Electric Power University, Beijing, 102206, China

    Yihang Zhao, Huiru Zhao & Sen Guo

  2. Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Changping, Beijing, 102206, China

    Yihang Zhao, Huiru Zhao & Sen Guo

  3. Department of Economic Management, North China Electric Power University, Baoding, 071003, Hebei Province, China

    Bingkang Li

  4. State Grid Chaoyang Electric Power Company, Beijing, 100031, China

    Boxiang Wu

Authors
  1. Yihang Zhao
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  2. Huiru Zhao
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  3. Bingkang Li
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  4. Boxiang Wu
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  5. Sen Guo
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Contributions

Yihang Zhao: writing, visualization, software. Huiru Zhao: conceptualization, reviewing. Bingkang Li: data curation, supervision. Boxiang Wu: data curation. Sen Guo: reviewing and editing.

Corresponding author

Correspondence to Sen Guo.

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Responsible Editor: Nicholas Apergis

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Highlights

• A hybrid point and interval forecasting model for carbon trading price is proposed.

• The efficacy is tested in all the carbon trading markets in China.

• The proposed model can greatly enhance the prediction performance.

• The interval prediction for carbon trading price has more practical application value.

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Zhao, Y., Zhao, H., Li, B. et al. Point and interval forecasting for carbon trading price: a case of 8 carbon trading markets in China. Environ Sci Pollut Res 30, 49075–49096 (2023). https://doi.org/10.1007/s11356-023-25151-0

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  • DOI: https://doi.org/10.1007/s11356-023-25151-0

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