Carbon Market Price Research Articles

Has the outbreak of COVID-19 changed the carbon market?

This paper infers that the behavior of governments and the public in the face of the pandemic may have reshaped the carbon reduction process and carbon reduction caused by the COVID-19 pandemic can be long-lasting. To test whether the behavioral changes have caused carbon markets to move towards more sustainable goals after the outbreak of COVID-19, this paper examines whether pandemic severity and regulatory policies had a direct and immediate or indirect structural effect on the carbon market. The study uses daily transaction data in the Beijing carbon market from January 22, 2020, to June 4, 2021, to identify factors influencing the carbon market. The empirical results indicate that regulatory policies directly and immediately affected the carbon market. By contrast, changes resulting from the pandemic had a more profound effect and resulted in structural changes to the volatility of carbon market prices and volume. Governments should encourage and provide incentives that promote carbon-free consumption and behavior so that the carbon-reduction effect of the pandemic can persist after the economy has recovered.

Unified carbon emissions and market prices forecasts of the power grid

Carbon emissions and market prices forecasts of the power grid are of great importance for all electricity traders and consumers. Both forecasts enable flexible demand scheduling, ensuring sustainability and cost-efficiency. Many studies show the benefits of using both forecasts independently but not in combination, which remains an unexplored problem. The latest state-of-the-art techniques involve advanced statistical and machine learning algorithms leveraging seasonal patterns and exogenous forecasts. However, most of the reported studies deal only with problems of modeling and feature engineering, neglecting the forecast and model errors, which accumulate within the time-evolving power grid. This research aims to tackle these issues by introducing a versatile framework for short-term probabilistic forecasting of unified carbon emissions and market prices for electricity intra-day market participants. The approach utilizes the Hidden Markov Model for predictive estimation to determine the future energy mix of the country’s power grid. Furthermore, a novel optimization-based strategy, Moving Horizon Predictive Correction, is proposed to enhance the estimated energy mix performance, minimizing forecast and model errors. Subsequently, two separate recurrent neural networks are trained to provide probabilistic forecasts of carbon emissions and market prices, accounting for the stochastic dynamic of the power grid. A comparative analysis examines six case studies from various European countries and compares them with state-of-the-art forecasting methods. The results indicate that the proposed method can improve the qualitative measures by up to 53% for carbon emissions and up to 18% for market prices forecasts. Besides improving traditional point perditions, methods show significant increases in the quality of prediction intervals. Further application of the proposed forecasts is employed for a flexible 4–hour electricity consumption schedule. This showcases the usage of the proposed forecasts to find the best possible trade-off for low carbon emission, cost-effective electricity consumption time slots.

Impacts of Climate Change Risk and Economic Policy Uncertainty on Carbon Prices: Configuration Analysis from a Complex System Perspective

Despite a vast amount of research examining the antecedent variables of carbon prices, the impact of climate change risk on carbon prices has been rarely explored. This study adopted a complex system perspective and applied NCA and fsQCA models to analyze regional climate physical risk (CPR) and climate transition risk (CTR) from a complex system perspective. The results suggested that the primary factors affecting the Hubei carbon price included coal prices, market regulation, and national carbon market prices, and carbon prices could be higher in response to higher levels of economic policy uncertainty (EPU) combined with other factors. By comparing the changes of independent variables before and after the opening of the national carbon market, it was found that lower CPR and CTR were conducive to higher carbon prices; the former has received early attention but the latter has been neglected. The findings from this study provided theoretical and practical insight to inform government regulation of carbon prices and decision-making for carbon market stakeholders. The government should pay attention to the impacts of climate change risks and EPU on carbon prices because these factors could significantly undermine the incentive and economic effectiveness of the carbon market.

Research on the Interrelationship Between Carbon Markets Based on the Method of Wavelet Coherence

In recent years, the excessive use of fossil fuels has led to global warming and a series of environmental crises. Carbon emissions trading, as a market-based mechanism to reduce carbon emissions, has received widespread attention and application. This paper investigates the dynamic dependency and causal interrelationships between Chinese pilot city carbon markets and the European Union Emissions Trading System (EU ETS). Using the wavelet analysis method, this paper empirically examines the data of various cities' carbon markets in China and the EU ETS from June 2014 to February 2023. Significant differences are found in the relationships between Chinese city carbon markets and the EU ETS. The study indicates that, with the exception of Shenzhen, the dynamic interactions between Chinese city carbon markets are weak in the short term but gradually strengthen in the long term. Not all city carbon markets in China have strong correlations, but they are closely related to factors such as local policies, economies, and industrial structures. There is no stable lead-lag relationship among the prices of carbon markets in different Chinese cities. After 2022, there is a trend of positive lag relationship of carbon market prices in Chinese cities (such as Guangdong, Shanghai, Chongqing) with those in the EU ETS. This paper summarizes the relationship between Chinese city carbon markets and the EU ETS and puts forward suggestions for the development of Chinese carbon market construction, including improving institutional systems, strengthening market supervision, promoting technological innovation, and implementing policies to stabilize price fluctuations.

Multisource Data-Driven Carbon Price Composite Forecasting Model: Based on Feature Selection and Multiscale Prediction Strategy

Accurate prediction of carbon trading prices is crucial for guiding investors to make informed decisions and assisting governments in formulating scientific carbon trading policies. This paper introduces a multisource data-driven carbon price composite forecasting model, aimed at enhancing prediction accuracy through advanced data processing and analysis methods. The model initially employs a secondary decomposition strategy, including Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) methods, to decompose the original data series into three sub-sequences of different frequencies. Subsequently, it utilizes the Partial Autocorrelation Function (PACF) and Random Forest algorithm for feature selection to determine the input variables for different frequency sequences and conducts in-depth analysis and selection of influencing factors, including unstructured data. Furthermore, the model employs a multiscale forecasting strategy, combining Particle Swarm Optimization (PSO) enhanced Bidirectional Long Short-Term Memory (BiLSTM) and Extreme Gradient Boosting (XGBoost) models, along with the Autoregressive Integrated Moving Average (ARIMA) method, to predict based on the characteristics of different frequency components. Finally, the forecasts are integrated using PSO-BiLSTM to form a comprehensive forecast. Notably, given the high correlation between the trend series and influencing factor variables, the model jointly predicts them. A case study based on the Guangdong carbon market in China demonstrates that the proposed composite forecasting model outperforms other benchmark models, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values of 0.4009, 0.2699, and 0.5183%, respectively. This forecasting model provides an effective tool for predicting and analyzing carbon price fluctuations, offering new insights for precise carbon market price predictions.

Framework for multivariate carbon price forecasting: A novel hybrid model

The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization algorithms, model combination and evaluation indicators. First, this study collects and organizes the historical carbon price series of Hubei and Shanghai as well as the influencing factors in five dimensions including structured and unstructured data, totaling twenty variables. Second, data dimensionality reduction is performed and input variables are obtained using the least absolute shrinkage and selection operator, followed by the introduction of nine advanced deep learning models to predict carbon price and compare the prediction effects. Then, through the combination of models, three models with the best performance are combined with Pelican optimization algorithm to construct a hybrid forecasting model. Finally, the experimental results show that the developed forecasting model outperforms other comparation models in terms of prediction accuracy, stability and statistical hypothesis testing, and exhibits excellent prediction performance. Furthermore, this study also applies the developed model to European carbon market price prediction and uses the Hubei carbon market as an example for quantitative trading simulation, and the empirical results further verify its robust prediction performance and investment application value. In conclusion, the proposed hybrid prediction model can not only provide high-precision carbon market price prediction for the government and corporate decision makers, but also help investors optimize their trading strategies and improve their returns.

Impact of carbon market prices on oil market fear across quantiles

ABSTRACT This paper uses quantile regression to investigate how carbon market prices affect oil market fear. Our results show a significant and strong negative effect of changes in carbon market prices on changes in oil market fear at high quantiles. Notably, this negative effect is driven by the decrease rather than the increase in carbon market prices. We also observe that the COVID-19 pandemic enhances the negative impact of the decreased carbon market prices at high quantiles.

Investigating the impact of the future carbon market on the profitability of carbon capture, utilization, and storage (CCUS) projects; the case of oil fields in southern Iraq

Carbon capture utilization and storage for enhanced oil recovery (CCUS-EOR) is considered a promising solution to meet future global energy needs while significantly reducing carbon dioxide emissions. However, the future growth of CCUS-EOR is uncertain due to the global economic recession and low oil prices. This research analyzes the impact of uncertain oil and carbon market prices on the economic value of a CCUS-EOR project which contributes to appropriate investment and future policy decisions. To explore the potential trade-offs between available carbon dioxide (CO2) quantities for trading or injection for enhanced oil recovery, the market's trading mechanism is analyzed by predicting future oil and carbon prices. The forecasted market prices are then used in an optimization model to estimate the optimal cumulative cash flow from implementing a proposed CCUS-EOR project to capture CO2 from the Hartha power plant located in the southern Basra Province of Iraq, considering three different oil fields of Rumaila, Nassriya, and Noor. The results revealed that the profitability of the proposed CCUS-EOR project is affected by both oil and carbon market prices, and a higher carbon price is needed to compensate for the financial setbacks, resulting from reduced oil prices. The research also features the strategic decision-making of oil fields across different sizes in response to various price scenarios. The medium-sized fields consistently prioritize carbon injection for enhanced oil recovery (EOR) over market trading. In contrast, small and giant fields adapt their approach based on the prevailing market price dynamics.

Analysis of China's carbon market price fluctuation and international carbon credit financing mechanism using random forest model.

This study aims to investigate the price changes in the carbon trading market and the development of international carbon credits in-depth. To achieve this goal, operational principles of the international carbon credit financing mechanism are considered, and time series models were employed to forecast carbon trading prices. Specifically, an ARIMA(1,1,1)-GARCH(1,1) model, which combines the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models, is established. Additionally, a multivariate dynamic regression Autoregressive Integrated Moving Average with Exogenous Inputs (ARIMAX) model is utilized. In tandem with the modeling, a data index system is developed, encompassing various factors that influence carbon market trading prices. The random forest algorithm is then applied for feature selection, effectively identifying features with high scores and eliminating low-score features. The research findings reveal that the ARIMAX Least Absolute Shrinkage and Selection Operator (LASSO) model exhibits high forecasting accuracy for time series data. The model's Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error are reported as 0.022, 0.1344, and 0.1543, respectively, approaching zero and surpassing other evaluation models in predictive accuracy. The goodness of fit for the national carbon market price forecasting model is calculated as 0.9567, indicating that the selected features strongly explain the trading prices of the carbon emission rights market. This study introduces innovation by conducting a comprehensive analysis of multi-dimensional data and leveraging the random forest model to explore non-linear relationships among data. This approach offers a novel solution for investigating the complex relationship between the carbon market and the carbon credit financing mechanism.

Investigating Drivers Impacting Carbon Stock and Carbon Offset in a Large-Scale Rubber Plantation in the Middle South of Thailand.

A large-scale rubber plantation in Southern Thailand is expected to capture a significant amount of carbon dioxide from emissions through carbon sinks in the vegetation and soil. The goal of this research is to create a carbon offset assessment for rubber plantations lasting for 30 years using a voluntary market contract approach. To evaluate the area of large-scale rubber plantations, this study evaluated major growing regions in five provinces in the middle-south region of Thailand (Nakhon Si Thammarat, Phatthalung, Songkhla, Satun and Trang) using an integrated RS-GIS technique that incorporated biomass allometric equations, soil series databases, and object-based classification. The classification of rubber plantation areas and the mapping of rubber stand ages were conducted to estimate the above-ground biomass of the rubber tree. Texture analysis was used in the rubber classification process, and normalised difference vegetation index (NDVI) was combined with texture analysis to separate vegetation areas from other land cover. Four groups of varying ages (1-6, 7-13, 14-20 and 21-30 years old) were evaluated for their capacity to generate carbon offsets. The equations of voluntary market contract revenue according to the contract method of the CCX were applied for this case study. This evaluation was used to estimate their annual value, total and net incomes in the carbon market price regarding the RGGI Allowance (RGA). Carbon offset income was then used to estimate the potential income (over a 30-year period) of the life of the contract. The results showed that the carbon stock potential of rubber plantations depended on the age of the trees and the soil carbon stock. The total carbon stock in the rubber plantations varied from 249.73 to 301.48 Mg C/ha (or equivalently 916.49 to 1,106.44 Mg CO2e/ha). Furthermore, the potential net income of the contract was estimated to be between USD5,378.32 and USD5,930.38 Mg CO2e/ha over a 30-year period according to the voluntary market contract revenue. These results suggest that the large agricultural land plot policy could create opportunities for carbon offsetting. The policy of large-scale rubber areas could be used as a tool and mechanism for farmers who are considering participating in carbon-crediting mechanisms. Then, farmers could use voluntary market contracts as a guide and foundation for their decision-making. The carbon offset credit strategy could assist Thailand in achieving its climate goals of transitioning to a low-carbon agriculture sector.

국제 자발적 탄소시장 크레딧 가격 결정요인분석

This study analyzes the correlation between voluntary carbon market credit prices and three key sets of variables. Prior research on compliance carbon markets has identified these factors as a significant influence on emission price factors. This study employs the Autoregressive Distributed Lag (ARDL) model to test these relationships and empirically examines short and long term correlations by conducting cointegration tests between each variable and the emission credit price. The results of the study are as follows:First, electricity prices exhibit a positive relationship with credit prices in the short term but a negative relationship in the long term. Second, compared to regulatory carbon market prices, credit prices are influenced by temperature variables, reflecting local weather conditions. Third, the energy variable emerges as the most crucial factor impacting prices in both compliance emissions trading systems and voluntary carbon credits. This implies that companies aiming for carbon neutrality through voluntary carbon market credits must develop comprehensive strategies that consider various external circumstances.

Forecasting carbon price using signal processing technology and extreme gradient boosting optimized by the whale optimization algorithm

AbstractPredicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices.

A hybrid forecasting approach for China's national carbon emission allowance prices with balanced accuracy and interpretability

A significant milestone in China's carbon market was reached with the official launch and operation of the National Carbon Emission Trading Market. The accurate prediction of the carbon price in this market is crucial for the government to formulate scientific policies regarding the carbon market and for companies to participate effectively. Nevertheless, it remains challenging to accurately predict price fluctuations in the carbon market because of the volatility and instability caused by several complex factors. This paper proposes a new carbon price forecasting framework that considers the potential factors influencing national carbon prices, including data decomposition and reconstruction techniques, feature selection techniques, machine learning forecasting techniques for intelligent optimisation, and research on model interpretability. This comprehensive framework aims to improve the accuracy and understandability of carbon price projections to respond better to the complexity and uncertainty of carbon markets. The results indicate that (1) the hybrid forecasting framework is highly accurate in forecasting national carbon market prices and far superior to other comparative models; (2) the factors driving national carbon prices vary according to the time scale. High-frequency series are sensitive to short-term economic and energy market indicators. Medium- and low-frequency series are more susceptible to financial markets and long-term economic conditions than high-frequency series. This study provides insights into the factors affecting China's national carbon market price and serves as a reference for companies and governments to develop carbon price forecasting tools.

A Deep Neural Network-Based Assistive Decision Method for Financial Risk Prediction in Carbon Trading Market

The price of carbon emission rights in the market fluctuates greatly due to various factors from economy, finance, and climate. For an enterprise that needs to conduct carbon trading activities, it is extremely necessary to fully grasp the price of carbon trading in the future period. Thus in this paper, a deep neural network-based assistive decision model for financial risk prediction in carbon trading market is proposed for this purpose. Specifically, the dynamic risk spillover effects of domestic and international carbon trading markets are studied, and a frontier time-varying model is utilized to measure the risk spillover effects. Then, the deep neural network is used for quantitative research and to construct an intelligent decision scheme that outputs financial risk prediction results. Four perspectives, energy price, climate environment, carbon market price, and macroeconomics, are selected as the input to analyze the influence factors of carbon emission rights price. Finally, several linear regression models are adopted as the baseline methods for comparison, and experimental results show that the proposed method can achieve better prediction performance compared with baseline methods.

Network evolutionary game analysis of green credit: A perspective of carbon emissions trading

AbstractAs an effective measure for the local government to promote reduction of corporate carbon emissions and sustainable development of social economy, the implementation of green finance policies in enterprises and financial institutions has encountered severe strikes from the low returns of low carbon production and low interest rates of green credit business. Under the premise of liberalizing carbon emissions trading, a triple‐level network evolutionary game consisting of a local government, banks, and enterprises is constructed to analyze response behavior and strategy selection mechanism to green credit. The impacts of management tools are investigated in terms of setting carbon market price, limiting the financing upper bound of misbehaving enterprises, limiting the emissions quota of enterprises for fraudulent green credit, and rewarding clean project development on the decisions of individuals in the network. Results show the following: First, increasing the trading price of carbon emissions has a catalytic effect on enhancing the activeness of banks and enterprises to follow green finance policies, but the autonomous regulation ability of which is limited. Second, reducing the funding ceiling for emitting emissions illegally ambiguates enterprises' preferences for production strategies and discourages them from undertaking low‐carbon projects. Third, excessive restrictions on the share of emissions may evoke regulatory inaction by the local government, consequently leading to instability in the evolution network. Fourth, granting reward for clean production is instrumental for the network to promote green finance policies, but exorbitant subsidies increase the fiscal burden of the local government with no significant effect on the node selection of environment‐friendly strategies.

Carbon market price prediction based on sequence decomposition-reconstruction-dimensionality reduction and improved deep learning model

Identifying carbon market price signals and accurately predicting carbon prices is of great significance in the development of carbon markets and carbon trading management. In this study, a fast ensemble empirical mode decomposition (FEEMD) method was used to analyze the fluctuation patterns of carbon market prices and obtain their characteristic components. Then the characteristic components of carbon prices were reconstructed using sample entropy theory (SRSE), and a partial autocorrelation analysis (PACF) technique was used to screen the data points exerting large impacts on carbon market prices. The FEEMD-SRSE-PACF-ILSTM hybrid model constructed. The main innovation points are as follows: The FEEMD is proposed to solves the common modal aliasing problem during carbon price decomposition and improves the efficiency of the carbon price sequence decomposition; the sample entropy theory and partial autocorrelation analysis methods are applied to reconstruct the characteristic components of carbon prices; the LSTM neural network model was optimized by the grey wolf algorithm to enhance its stability and adaptability. The analyzed price changes of large regional carbon markets in China, the European Union, and the United States can be effectively used to predict prices in the carbon market and guide its participants towards low-carbon investment and trading.

Information gap decision theory-based optimization of joint decision making for power producers participating in carbon and electricity markets

Carbon markets have been established in many countries and regions with the goal of promoting global carbon neutrality. The position of power producers as the dominant carbon emitters necessitates that they engage in both electricity and carbon markets. However, most studies have considered only short-term electricity markets and unrealistically static carbon markets, and the speculative behavior of power producers in the carbon market remains poorly considered. The present study addresses these issues by proposing an optimized joint decision model based on information gap decision theory to facilitate the participation of power producers in annual and monthly electricity markets, and monthly carbon markets, where uncertainties in the prices of electricity and carbon quotas, and the speculative behavior of power producers in the carbon market are explicitly considered to ensure that the revenues of market participants do not fall below a predetermined minimum acceptable value. The results of simulations based on the rules and actual market data obtained for electricity and carbon markets in a specific province of China demonstrate that the proposed model provides power producers with trading solutions to meet different expected revenue targets, and thereby assists them as much as possible in counteracting the risks to profit associated with fluctuations in electricity and carbon market prices.

Production Strategy and Technology Innovation under Different Carbon Emission Polices

Given that the production process in the manufacturing industry is a significant contributor to carbon emissions, many governments have implemented carbon emission-related policies to incentivize manufacturers to optimize their production process or invest in green production technology, with the aim of reducing their carbon footprint. Therefore, it is necessary to determine the effectiveness of carbon policies considering manufacturers always tend to prioritize economic profits in their actual operations. We compare the effects of three different carbon emission policies—carbon tax, cap-and-trade, and intensity target—on manufacturers’ production strategies and technology innovation investments, which include production and carbon emission quantities, price of goods and emission permit, and technological innovation of the production improvement and the emission abatement. The study results show that the intensity target policy increases the carbon market price and decreases the carbon emission rate. For the manufacturers with higher (lower) initial emission intensity, intensity target policy induces them to decrease (increase) production quantities and carbon emission quantities. For the technology innovation choice, cap-and-trade induces more incentive on production improvement technology innovation for manufacturers with lower initial emission intensity and carbon emission abatement technology innovation for manufacturers with higher level initial emission intensity.

Probabilistic carbon price prediction with quantile temporal convolutional network considering uncertain factors

Accurate carbon price projections can serve as valuable investment guides and risk warnings for carbon trading participants. However, the escalation of uncertain factors has brought numerous new hurdles to existing carbon price forecast methods. In this paper, we develop a novel probabilistic forecast model called quantile temporal convolutional network (QTCN) that can precisely describe the uncertain fluctuation of carbon prices. We also investigate the impact of external factors on carbon market prices, including energy prices, economic status, international carbon markets, environmental conditions, public concerns, and especially uncertain factors. Taking China's Hubei carbon emissions exchange as a study case, we verify that our QTCN outperforms other classical benchmark models in terms of prediction errors and actual trading returns. Our findings suggest that coal prices and EU carbon prices have the most significant effect on Hubei carbon price forecasting, while air quality index appears to be the least important. Besides, we demonstrate the great contribution of geopolitical risk and economic policy uncertainty to carbon price projections. The effect of these uncertainties is more pronounced when the carbon price is at a high quantile level. This research can offer valuable guidelines for carbon market risk management and provide new insight into carbon price formation mechanisms in the era of global conflict.

Effects of personal carbon trading scheme on consumers' new energy vehicles replacement decision: An economic trade-off analysis

Carbon emissions from private transportation and resident travel are exhibiting a fast upward trend that requires immediate action. Personal carbon trading (PCT) in private transportation sector is considered to be an effective market tool based on the “cap and trade” principle to guide residents to carry out low-carbon travel behaviour. This study proposed a replacement decision model under the PCT scheme and applied the theories of life cycle cost (LCC) to address the issue of new energy vehicles (NEVs) replacement decision. The results of the numerical analysis and mathematical model solution demonstrate that the adoption of a PCT scheme can accelerate the conversion of consumers to adopting new energy vehicles. When the carbon market price fluctuates erratically, consumers are unlikely to decide to replace their fuel vehicles. The process of consumers' decision making to switch to NEVs will accelerate through the increase in gasoline prices. Conversely, the increase in market discount rates will make the replacement decision of consumers more uncertain and discourage them from making early replacement decisions. The gradual withdrawal of financial subsidies will have a certain delaying effect on consumers' decision-making behaviours. Moreover, the results of sensitivity analysis support the robustness of our research findings. Finally, we summarize the conclusions, implications and limitations.