Prediction of Carbon Emissions Trading Price in Fujian Province: Based on BP Neural Network Model

Du, Yi; Chen, Keren; Chen, Simin; Yin, Kai

doi:10.3389/fenrg.2022.939602

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…Du et al used a BP model to analyze the influencing elements on carbon prices. According to the results, the BP model displayed satisfactory results in predicting carbon prices and examining their determinants in Fujian province [33].…”

Section: Introductionmentioning

confidence: 94%

A Multi-Strategy Integration Prediction Model for Carbon Price

Dong

Yang

et al. 2023

Energies

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Carbon price fluctuations significantly impact the development of industries, energy, agriculture, and stock investments. The carbon price possesses the features of nonlinearity, non-stationarity, and high complexity as a time series. To overcome the negative impact of these characteristics on prediction and to improve the prediction accuracy of carbon price series, a combination prediction model named Lp-CNN-LSTM, which utilizes both convolutional neural networks and long short-term memory networks, has been proposed. Strategy one involved establishing distinct models of CNN-LSTM and LSTM to analyze high-frequency and low-frequency carbon price sequences; the combination of output was integrated to predict carbon prices more precisely. Strategy two comprehensively considered the economic and technical indicators of carbon price sequences based on the Pearson correlation coefficient, while the Multi-CNN-LSTM model selected explanatory variables that strongly correlated with carbon prices. Finally, a predictive model for a combination of carbon prices was developed using Lp-norm. The empirical study focused on China’s major carbon markets, including Hubei, Guangdong, and Shanghai. According to the error indicators, the performance of the Lp-CNN-LSTM model was superior to individual strategy prediction models. The Lp-CNN-LSTM model has excellent accuracy, superiority, and robustness in predicting carbon prices, which can provide a necessary basis for revising carbon pricing strategies, regulating carbon trading markets, and making investment decisions.

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Section: Introductionmentioning

confidence: 94%

A Multi-Strategy Integration Prediction Model for Carbon Price

Dong

Yang

et al. 2023

Energies

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“…Foreign carbon markets influence China’s carbon prices 11 . On the one hand, Chinese carbon markets are still in the development stage.…”

Section: Data Descriptionmentioning

confidence: 99%

“…Nevertheless, once the two are connected, the issue of speculation is expected to escalate. Hence, foreign carbon prices will have a dual effect: they will inform the establishment of carbon prices in China and potentially drive up the carbon price in the country through speculative activities 11 .…”

Section: Data Descriptionmentioning

confidence: 99%

“…The existing literature primarily utilizes carbon price time series data for the modeling process. However, the dynamics of carbon trading prices are influenced by various factors, including energy factors, macroeconomic factors, and industry structures 11 . In general, since external factors can be analyzed, carbon price forecasting built upon multiple influencing factors is important for carbon market research.…”

Section: Introductionmentioning

confidence: 99%

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Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm

Feng,

Duan,

Wang

et al. 2023

Sci Rep

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It is essential to predict carbon prices precisely in order to reduce CO2 emissions and mitigate global warming. As a solution to the limitations of a single machine learning model that has insufficient forecasting capability in the carbon price prediction problem, a carbon price prediction model (GWO–XGBOOST–CEEMDAN) based on the combination of grey wolf optimizer (GWO), extreme gradient boosting (XGBOOST), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is put forward in this paper. First, a random forest (RF) method is employed to screen the primary carbon price indicators and determine the main influencing factors. Second, the GWO–XGBOOST model is established, and the GWO algorithm is utilized to optimize the XGBOOST model parameters. Finally, the residual series of the GWO–XGBOOST model are decomposed and corrected using the CEEMDAN method to produce the GWO–XGBOOST–CEEMDAN model. Three carbon emission trading markets, Guangdong, Hubei, and Fujian, were experimentally predicted to verify the model’s validity. Based on the experimental results, it has been demonstrated that the proposed hybrid model has enhanced prediction precision compared to the comparison model, providing an effective experimental method for the prediction of future carbon prices.

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“…Many scholars have done research on carbon trading price, by traditional statistical models, machine learning models and so on. Zhong-fang Gao [2] proposed to use the GARCH Family Models to predict the price of carbon trading and compared multiple GARCH models to select the most suitable GARCH prediction model for different carbon markets to get more accurate prediction results; Zhao et al [3] found that the regression model using the Euro Stoxx 50 index as an explanatory variable could improve the prediction ability of carbon trading price; Chevallier [4] used a nonparametric model to predict the European Environment Exchange carbon spot price and the European Climate Exchange carbon futures price, the results showed that the prediction accuracy of the nonparametric model was higher than that of the autoregressive (AR) model; Shu-hang Guo [5] established BP neural network model to learn carbon trading price and compared it with the multivariate model, which verified the better prediction effect of BP model in carbon trading price prediction; Jia-yu Liu [6] selected support vector machine (SVM) model and random forest model for modeling prediction, and gave relevant suggestions from macro and micro perspectives.…”

Section: Introductionmentioning

confidence: 99%

Prediction of carbon trading price based on ARIMA-BP-SSALSTM dynamic weighted combination model

Cuan

Lü

Liu

2023

Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022)

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A single prediction model has its own advantages and disadvantages in different aspects. In order to improve the accuracy of carbon trading price prediction, this paper proposes ARIMA-BP-SSALSTM dynamic weighted combination model that combines ARIMA model, BP model and LSTM model optimized by Sparrow Search Algorithm (SSALSTM). Based on information entropy theory, the greater the information entropy, the more the information provided and the greater the weight. This model uses information entropy to assign dynamic weight and weights to obtain the combined prediction value. The empirical results show that the ARIMA-BP-SSALSTM dynamic weighted combination model is more accurate than the ARIMA model, BP model and SSALSTM model.

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Prediction of Carbon Emissions Trading Price in Fujian Province: Based on BP Neural Network Model

Cited by 9 publications

References 29 publications

A Multi-Strategy Integration Prediction Model for Carbon Price

A Multi-Strategy Integration Prediction Model for Carbon Price

Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm

Prediction of carbon trading price based on ARIMA-BP-SSALSTM dynamic weighted combination model

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