Carbon price prediction based on modified wavelet least square support vector machine

Advancing climate-resilient flood mitigation: Utilizing transformer-LSTM for water level forecasting at pumping stations

Proactive management of pumping stations using artificial intelligence (AI) technology is vital for effectively mitigating the impacts of flood events caused by climate change. Accurate water level forecasts are pivotal in advancing the intelligent operation of pumping stations. This study proposed a novel Transformer-LSTM model to offer accurate multi-step-ahead forecasts of the flood storage pond (FSP) and river water levels for the Zhongshan pumping station in Taipei, Taiwan. A total of 19,647 ten-minute-based datasets of pumping operation and storm sewer, FSP, and river water levels were collected between 2014 and 2020 and further divided into training (70 %), validation (10 %), and test (20 %) datasets for model construction. The results demonstrate that the proposed model dramatically outperforms benchmark models by producing more accurate and reliable water level forecasts at 10-minute (T + 1) to 60-minute (T + 6) horizons. The proposed model effectively enhances the connections between input factors through the Transformer module and increases the connectivity across consecutive time series using the LSTM module. This study reveals interconnected dynamics among pumping operation and storm sewer, FSP, and river water levels, enhancing flood management. Understanding these dynamics is crucial for effective execution of management strategies and infrastructure revitalization against climate impacts. The Transformer-LSTM model's forecasts encourage water practices, resilience, and disaster risk reduction for extreme weather events.

Predicting regional carbon price in China based on multi-factor HKELM by combining secondary decomposition and ensemble learning

Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In light of the complex characteristics of the regional carbon price in China, this paper proposes a model to forecast carbon price based on the multi-factor hybrid kernel-based extreme learning machine (HKELM) by combining secondary decomposition and ensemble learning. Variational mode decomposition (VMD) is first used to decompose the carbon price into several modes, and range entropy is then used to reconstruct these modes. The multi-factor HKELM optimized by the sparrow search algorithm is used to forecast the reconstructed subsequences, where the main external factors innovatively selected by maximum information coefficient and historical time-series data on carbon prices are both considered as input variables to the forecasting model. Following this, the improved complete ensemble-based empirical mode decomposition with adaptive noise and range entropy are respectively used to decompose and reconstruct the residual term generated by VMD. Finally, the nonlinear ensemble learning method is introduced to determine the predictions of residual term and final carbon price. In the empirical analysis of Guangzhou market, the root mean square error(RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the model are 0.1716, 0.1218 and 0.0026, respectively. The proposed model outperforms other comparative models in predicting accuracy. The work here extends the research on forecasting theory and methods of predicting the carbon price.

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

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.

Multi-step-ahead and interval carbon price forecasting using transformer-based hybrid model

Accurate and stable carbon price forecasts serve as a reference for assessing the stability of the carbon market and play a vital role in enhancing investment and operational decisions. However, realizing this goal is still a significant challenge, and researchers usually ignore multi-step-ahead and interval forecasting due to the non-linear and non-stationary characteristics of carbon price series and its complex fluctuation features. In this study, a novel hybrid model for accurately predicting carbon prices is proposed. The proposed model combines multi-step-ahead and interval carbon price forecasting based on the Hampel identifier (HI), time-varying filtering-based empirical mode decomposition (TVFEMD), and transformer model. First, HI identifies and corrects outliers in carbon price. Second, TVFEMD decomposes carbon price into several intrinsic mode functions (imfs) to reduce the non-linear and non-stationarity of carbon price to obtain more regular features in series. Next, these imfs are reconstructed by sample entropy (SE). Subsequently, the orthogonal array tuning method is used to optimize the transformer model's hyperparameters to obtain the optimal model structure. Finally, after hyperparameter optimization and quantile loss function, the transformer is used to perform multi-step-ahead and interval forecasting on each part of the reconstruction, and the final prediction result is obtained by summing them up. Five pilot carbon trading markets in China were selected as experimental objects to verify the proposed model's prediction performance. Various benchmark models and evaluation indicators were selected for comparison and analysis. Experimental results show that the proposed HI-TVFEMD-transformer hybrid model achieves an average MAE of 0.6546, 1.3992, 1.6287, and 2.2601 for one-step, three-step, five-step, and ten-step-ahead forecasting, respectively, which significantly outperforms other models. Furthermore, interval forecasts almost always have a PICI above 0.95 at a confidence interval of 0.1, thereby indicating the effectiveness of the hybrid model in describing the uncertainty in the forecasts. Therefore, the proposed hybrid model is a reliable carbon price forecasting tool that can provide a dependable reference for policymakers and investors.

A two-stage interval-valued carbon price forecasting model based on bivariate empirical mode decomposition and error correction

Economic development has brought about global greenhouse gas emissions and, thus, global climate change, a common challenge worldwide and urgently needs to be addressed. Accurate carbon price forecasting plays a pivotal role in providing a reasonable basis for carbon pricing and ensuring the healthy development of carbon markets. Therefore, this paper proposes a two-stage interval-valued carbon price combination forecasting model based on bivariate empirical mode decomposition (BEMD) and error correction. In Stage I, the raw carbon price and multiple influencing factors are decomposed into several interval sub-modes by BEMD. Then, we select artificial intelligence-based multiple neural network methods such as IMLP, LSTM, GRU, and CNN to conduct combination forecasting for interval sub-modes. In Stage II, the error generated in Stage I is calculated, and LSTM is used to predict the error; then, the error forecasting result is added to the first stage result to obtain the error-corrected forecasting result. Taking the carbon trading prices of Hubei, Guangdong, and the national carbon market, China, as the research object, the empirical analysis proves that the combination forecasting of interval sub-modes of Stage I outperforms the single forecasting method. In addition, the error correction technique in Stage II can further improve the forecasting accuracy and stability, which is an effective model for interval-valued carbon price forecasting. This study can help policymakers formulate regulatory policies to reduce carbon emissions and help investors avoid risks.

Examination of the transmission mechanism of energy prices influencing carbon prices: an analysis of mediating effects based on demand heterogeneity

Carbon prices are important for promoting a low-carbon transformation of the economy. The fluctuation of energy prices affects carbon prices through supply and demand chains, thus affecting the achievement of emission reduction targets through carbon pricing tools. Based on daily time series data, a mediating effect model is constructed to study the impact of energy prices on carbon prices. We analyze how energy prices impact carbon prices using four different transmission paths and then test the resulting differences. The main findings are as follows. First, an increase in energy prices significantly negatively affects carbon prices through economic fluctuation, investment demand, speculative demand, and transaction demand. Second, energy price fluctuations mainly affect carbon emission prices through economic fluctuations. The impacts of the remaining transmission paths are in the order of speculative demand, investment demand, and transaction demand. This paper provides theoretical and practical support for reasonably responding to energy price fluctuations and forming effective carbon prices to address climate change.

The role of online news sentiment in carbon price prediction of China's carbon markets

Carbon trading as a vital tool to reduce carbon dioxide emissions has developed rapidly in recent years. Reasonable prediction of the carbon price can improve the risk management in the carbon trading market and make healthy development of the carbon trading market. This paper aims to enhance the predictive performance of carbon price in the China's carbon markets, especially the China's national carbon market, by adding the online news sentiment index which is a kind of unconstructed data, to a deep learning model using traditionally constructed predictors innovatively. Long short-term memory (LSTM) network was applied as the primary model to predict carbon price and random forest as the additional experiment to validate the effectiveness of online news sentiment. The results in the China's national carbon market and Hubei pilot carbon market both proved that the model including the sentiment index performed better than the model does not, and the improvement was significant.

A carbon price hybrid forecasting model based on data multi-scale decomposition and machine learning

Accurate carbon price forecasting is of great significance to the operation of carbon financial markets. However, limited by the non-linearity and non-stationarity of the carbon price, the accurate and reliable predictions are difficult. To address the issue of applicability and accuracy, a novel carbon price hybrid model based on decomposition, entropy, and machine learning methods is proposed, named as CEEMDAN-PE-LSTM-RVM. Adopting the advanced structure (i.e., the prediction under classification), the proposed model owns reliable performance in face of the cases with different complexity. Furthermore, the relationship between the data feature and prediction accuracy is discussed to provide a benchmark for judging the reliability of the prediction, in which the chaos degree is introduced as a feature to characterize carbon price quantitatively. The performance of the proposed model is evaluated through historical data of four representative carbon prices. The results show that the average MAPE and RMSE of the proposed model achieve 1.7027 and 0.7993, respectively, which is significantly greater than others; the proposed model owns great robustness, which is less affected by the complexity of predicted objects. Thus, the proposed model provides a reliable tool for carbon financial markets.

A novel hybrid learning paradigm with feature extraction for carbon price prediction based on Bi-directional long short-term memory network optimized by an improved sparrow search algorithm

An efficient carbon trading market can effectively curb excessive carbon emissions and thus slow down the pace of global warming, which heightens the necessity of improving the accuracy of carbon price forecasting. In order to overcome the weakness of previous prediction model that always trained data in one-way neural networks and propagated the data sequentially, this paper proposes a novel hybrid learning paradigm WPD-ISSA-BiLSTM combining wavelet packet decomposition (WPD), improved sparrow search algorithm (ISSA), and Bi-directional long short-term memory network for deep feature exploration of carbon prices. Firstly, WPD decomposes and reconstructs the original carbon price series into several independent subseries. Then, the input features of the all subseries are filtered with random forest to select the best input features for the prediction model. Finally, a Bi-directional long short-term memory network optimized by the ISSA is employed to deeply delineate the intrinsic evolutionary trends of carbon prices, and the prediction results of all subseries are superimposed on each other to obtain the final carbon price prediction results. The actual carbon emission trading prices are collected as input to the model, and the experimental results show that the RMSE values of the proposed model are 0.2516 and 0.2962 under the mild and severe volatility scenarios, respectively. The proposed model has superiority and robustness compared to the comparison model and several existing models and better understands the intrinsic correlation between historical carbon price data. The results of this study can provide meaningful references for the carbon market development and emission reduction pathways.

Price and scale effects of China's carbon emission trading system pilots on emission reduction

Constructing and adopting a county-level panel dataset containing carbon emission (CO₂) and economic information between 2009 and 2017, this paper employs the continuous difference-in-differences (DID) model and is among the first to conduct an evaluation of the CO₂ reduction effect of China's emission trading scheme (ETS) pilot markets from the dual perspectives of price and scale. The empirical results emerge that the increase of transaction price and the expansion of transaction volume in ETS pilots have a persistent and significant influence on CO₂ reduction. Parallel to this, it is found the rising of transaction price in ETS can be effective on CO₂ reduction by improving the energy structure transition, however, optimization of industrial structure and the development of ICT might be the essential channels driven by the expansion of transaction volume. Last, this paper identifies the synergistic effect on different sorts of contaminants and find it is more substantial to those with the gas state. This paper implies that the policymakers should fully excavate the market-oriented environmental regulation tools from transaction price and volume perspective of views for the well achieving the climate ambitions of carbon peak and neutrality.

Construction of a carbon price benchmark in China-analysis of eight pilot markets

The fluctuation of the carbon price and its related components can effectively reflect the overall economy. This paper explores the fluctuation of the carbon price and its influencing factors. First, the ensemble empirical mode decomposition (EEMD) method is used to decompose the carbon price series of eight pilot projects at multiple timescales. Second, according to the historical trading records in the eight pilot projects, this paper constructs a national carbon price under a variety of scenarios. Finally, based on the average of the eight pilot market daily trading datasets, the national carbon price is constructed, and a short-term prediction is made. The results show that: (1) the pilot projects in Beijing and Hubei are susceptible to short-term external factors, and Beijing's pilot internal market mechanism has a large impact on the carbon price; (2) in most scenarios, the national price fluctuates, with the highest carbon price approaching 70 CNY/tCO2 and the lowest falling below 10 CNY/tCO2; and (3) China's carbon price is still has ample room to rise in the future. This paper provides a theoretical basis and practical guidance for the prediction of carbon prices in China.

An EEMD-CNN-BiLSTM-attention neural network for mixed frequency stock return forecasting

 The regularly issued low frequency data, such as the change of fund position (weekly), and Producer Price Index (monthly), can affect the subsequent trend of stock returns. However, the forecasting effect of low frequency data on high frequency has not been discussed amply. This paper proposes a new mixed frequency neural network that helps to fill this research gap. The original time series is decomposed into several components through ensemble empirical mode decomposition, then the frequency alignment method is applied to integrate the high frequency component with low frequency variable as inputs, and the CNN-BiLSTM-Attention network completes the remaining forecasting work. The empirical results show that compared with other benchmark models, the proposed procedures perform better when predicting the high frequency components and obtain a smaller statistical error in the final ensemble results. The proposed model has great potential for the forecasting of reverse mixed time series.

Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

The carbon market is recognized as the most effective means for reducing global carbon dioxide emissions. Effective carbon price forecasting can help the carbon market to solve environmental problems at a lower economic cost. However, the existing studies focus on the carbon premium explanation from the perspective of return and volatility spillover under the framework of the mean-variance low-order moment. Specifically, the time-varying, high-order moment shock of market asymmetry and extreme policies on carbon price have been ignored. The innovation of this paper is constructing a new hybrid model, NAGARCHSK-GRU, that is consistent with the special characteristics of the carbon market. In the proposed model, the NAGARCHSK model is designed to extract the time-varying, high-order moment parameter characteristics of carbon price, and the multilayer GRU model is used to train the obtained time-varying parameter and improve the forecasting accuracy. The results conclude that the NAGARCHSK-GRU model has better accuracy and robustness for forecasting carbon price. Moreover, the long-term forecasting performance has been proved. This conclusion proves the rationality of incorporating the time-varying impact of asymmetric information and extreme factors into the forecasting model, and contributes to a powerful reference for investors to formulate investment strategies and assist a reduction in carbon emissions.

A building carbon emission prediction model by PSO-SVR method under multi-criteria evaluation

Building carbon emission prediction plays an irreplaceable role in low-carbon economy development, public health protection and environmental sustainability. It is significant to identify influential factors mainly contributed to building emission and predict emission accurately in order to harness the growth from the source. In this paper, 11 influencing factors of building carbon emission are identified and a support vector regression (SVR) prediction model is proposed to forecast building carbon emission considering improvement the prediction accuracy, generalization, and robustness. In the SVR model, parameters are optimized by particle swarm optimization (PSO) algorithm with the aim to improve performance. Cases in Shanghai’s building sector are adopted to demonstrate practical applications of the proposed PSO-SVR prediction model. The results indicate that the presented prediction system has an outstanding performance in forecasting building carbon emission under multi-criteria evaluation. Furthermore, compared to the results from other four prediction models (e.g., linear regression, decision tree), it is shown that PSO-SVR model can achieve higher accuracy (e.g., improvement average of 1.01% R2 under training subset), better generalization (e.g., improvement average of 19.89% R2 under testing subset), and better robustness (e.g., improvement average of 18.93% R2 under different levels of noise intensity).

Modeling the Solubility of Sulfur in Sour Gas Mixtures Using Improved Support Vector Machine Methods

The study of sulfur solubility is of great significance to the safe development of sulfur-containing gas reservoirs. However, due to measurement difficulties, experimental research data on sulfur solubility thus far are limited. Under the research background of small samples and poor information, a weighted least-squares support vector machine (WLSSVM)-based machine learning model suitable for a wide temperature and pressure range is proposed to improve the prediction accuracy of sulfur solubility in sour gas. First, we use the comprehensive gray relational analysis method to extract important factors affecting sulfur solubility as the model input parameters. Then, we use the whale optimization algorithm (WOA) and gray wolf optimizer (GWO) intelligence algorithms to find the optimal solution of the penalty factor and kernel coefficient and bring them into three common kernel functions. The optimal kernel function is calculated, and the final WOA-WLSSVM and GWO-WLSSVM models are established. Finally, four evaluation indicators and an outlier diagnostic method are introduced to test the proposed model's performance. The empirical results show that the WOA-WLSSVM model has better performance and reliability; the average absolute relative deviation is as low as 3.45%, determination coefficient (R 2) is as high as 0.9987, and the prediction accuracy is much higher than that of other models.

A multi-factor integrated model for carbon price forecasting: Market interaction promoting carbon emission reduction

Reasonable carbon price can effectively promote the low-carbon transformation of economy. The future carbon price has an important guiding significance for enterprises and the country. However, the nonlinear and high noise characteristics inherent in carbon price make them challenging to predict accurately. A hybrid decomposition and integration prediction model is proposed using the Hodrick-Prescott filter, an improved grey model and an extreme learning machine to solve this problem. First, a large number of factors that influence carbon price are collected by meta-analysis. The final input is selected through a two-stage feature selection process. Second, the HP filter is used to decompose the input into long-term trends and short-term fluctuations predicted by the improved GM and ELM, respectively. Finally, the two prediction sequences are compared to obtain the final result. European Union Allowances futures price data are applied for empirical analysis. The results show that the prediction performance of this model is better than the other 10 benchmark models, the T-bill, Stoxx50, S&P clean energy index and Brent oil price in the financial and energy markets are helpful in the carbon price's prediction. T-bill affects carbon price frequently, Stoxx50 has a negative correlation with the carbon price in the influence period. Under normal circumstances, the S&P clean energy index is positively correlated with the carbon price. However, when the economic situation is depressed, resulting in a short-term negative correlation between them. In general, carbon market is significantly affected by cross spill over between different markets. The method not only improves the accuracy of carbon price forecast, but also the application of the improved GM explains the reasons for the change of carbon price, which is helpful to promote the realization of carbon neutralization by market-oriented means.

Carbon price prediction for China's ETS pilots using variational mode decomposition and optimized extreme learning machine

With the national goal of "carbon peak by 2030 and carbon neutral by 2060 in China", studies on carbon prices of China's Emissions Trading System (ETS) pilots have shown growing interest in the related fields. Carbon price fluctuations reflect the scarcity of carbon resources, and accurate prediction can improve carbon asset management capabilities. Therefore, in order to clarify the dynamics of carbon markets and assign carbon emissions allocation rationally, we propose a hybrid feature-driven forecasting model with the framework of decomposition-reconstruction-prediction-ensemble. In this paper, the non-stationary, nonlinear and chaotic characteristics of carbon prices in China's ETS pilots have been verified, and then the prediction model is built based on the tested features. Firstly, the original carbon price series are decomposed by Variational Mode Decomposition (VMD), and then reconstructed by Sample Entropy (SE). Next, Extreme Learning Machine (ELM) optimized by Particle Swarm Optimization (PSO) is conducted to predict the subsequences. Lastly, the forecasting series of every subseries are summed to obtain the final results. The empirical results based on carbon prices of China's ETS pilots proved that the proposed model performs more efficiently than the current benchmark models. As carbon prices are expected to increase across all ETS during the post-COVID-19 recovery stage, the new prediction model will be useful for improving the guiding principles of the existing government policies including the likely introductions of Border Carbon Adjustment (BCA) in the EU and the US, and governing the large global public companies to deliver their "net zero" commitments.

Short-term prediction of carbon emissions based on the EEMD-PSOBP model

The recovery of carbon emissions in the past 2 years has alerted us that carbon emissions are a long-term process, and setting short-term emission reduction targets can more effectively curb the rising trend of carbon emissions. Therefore, the research on short-term prediction of carbon emissions is particularly important. In this paper, the idea of "decomposition-prediction" is put forward in the short-term prediction of carbon emissions, and the combined model of "decomposition-prediction" is constructed. The model is composed of ensemble empirical mode decomposition (EEMD) and the backpropagation neural network based on particle swarm optimization (PSOBP). It is also the first time that EEMD has been applied to the field of carbon emission prediction. Firstly, EEMD is used to decompose the daily carbon emission monitoring data into 6 modal functions and one residual sequence, and the partial autocorrelation function (PACF) is used to determine the input of each modal function. Then, PSOBP was used to predict. Finally, adding the prediction results of each sequence to get the final prediction results. To verify the effectiveness and superiority of the EEMD-PSOBP model, 14 comparative models were constructed, and the prediction effect of the models was evaluated by R², RMSE, and MAPE. All the prediction results show that the proposed model has the best prediction performance (R²=0.9507, RMSE=0.3431, MAPE=0.093). Compared with PSOBP, the R² of EEMD-PSOBP was increased by 63.58%, and RMSE and MAPE were decreased by 65.18% and 64.23%, respectively. The accuracy of prediction can be improved significantly by decomposing before predicting. It was also found that EEMD had the highest predictive performance improvement. Therefore, this model will have broad development prospects in the field of short-term carbon emission prediction in the future.

Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm

Effective carbon pricing policies have become an effective tool for many countries to encourage emission reduction. An accurate carbon price prediction model is helpful for the implementation of energy conservation and emission reduction policies and the decision-making of governments and investors. However, it is difficult for a single prediction model to achieve high prediction accuracy because of the high complexity of the carbon price series. Many studies have proved the nonlinear characteristics of carbon trading prices, but there are very few studies on the chaotic nature of carbon price series. As a consequence, this paper proposes an innovative hybrid model for carbon price prediction. A decomposition-reconstruction-prediction-integration scheme is designed to predict carbon prices. Firstly, several intrinsic mode functions (IMFs) and one residue were obtained from the raw data decomposed by ICEEMDAN. Next, the decomposed subsection is reconstructed into a new sequence according to the calculation results by the Lempel-Ziv complexity algorithm. Then, considering the chaotic characteristics of sequence, the input variables of the models are determined through the phase space reconstruction (PSR) algorithm combined with the partial autocorrelation function (PACF). Finally, the Sparrow search algorithm (SSA) is introduced to optimize the extreme learning machine (ELM) model, which is applied in the carbon price prediction for the purpose of verifying the validity of the proposed combination model, which is applied to the pilots of Hubei, Beijing, and Guangdong. The empirical results show that the combination model outperformed the 13 other models in predicting accuracy, speed, and stability. The decomposition-reconstruction-prediction-integration strategy is a method for predicting the carbon price efficiently.