A non-parametric analysis of the driving factors of China's carbon prices

Forecasting carbon prices in China's pilot carbon market: A multi-source information approach with conditional generative adversarial networks

In recent years, the Chinese government has actively pursued the implementation of its 'dual-carbon' strategy, concurrently establishing a national carbon emissions trading market. Accurate carbon price forecasts have become essential for policymakers and investors involved in related initiatives. Nevertheless, influenced by the interaction of various information sources, carbon trading prices exhibit non-linear and non-stationary characteristics, posing challenges for accurate prediction. Current research, centered around deep learning models, predominantly emphasizes intricate network structures, optimisation algorithms, and data decomposition. However, these models face a developmental bottleneck in extracting carbon price features and efficiently leveraging multi-source information. Consequently, novel ideas and methodologies are imperative. This study focuses on the Hubei and Guangdong regional carbon markets as research subjects. It develops a prediction framework based on a generative adversarial network model to capture the time-series change characteristics of carbon trading prices and the condition matrix. First, a generator prediction model is used to obtain the input matrix features and extract the time series features through a complex network to predict the carbon price data at the next moment using a fully connected layer. Second, a discriminator is utilised to distinguish between the actual values and the predicted values. The generator and the discriminator undergo continuous iterative training and alternate optimisation. This process aims to bring the generated prediction distributions closer to the actual sample data, resulting in more accurate final predictions. The empirical results convincingly show that the proposed model achieves unparalleled forecasting precision in both markets. The proposed model demonstrates the lowest MAE (0.804 and 0.839), lowest MAPE (0.023 and 0.018), lowest RMSE (1.174 and 1.383), and highest R2 (0.971 and 0.989) across both markets, indicating superior predictive accuracy. Additionally, the proposed model consistently outshines traditional forecasting approaches across one-step, five-step, and ten-step forecasts, affirming its robustness and universal applicability in modelling carbon trading price series. The findings suggest that this study can aid policymakers in optimizing the carbon pricing system. Furthermore, it offers a reference for policymakers to comprehensively leverage external factors, such as regulating traditional energy prices, leveraging international carbon market experiences, and monitoring economic dynamics. This comprehensive strategy can streamline the exploration and management of carbon price fluctuations, ultimately strengthening the carbon market's risk control system.

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.

The alternative path for fossil oil: Electric vehicles or hydrogen fuel cell vehicles?

New energy vehicles are accelerating to substitute for internal combustion engine vehicles (ICEVs) and fossil oil. Although most literature acknowledges this trend, few compare two specific substitutable paths in terms of the operation system, namely electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs). This paper makes a comparative analysis of EVs and HFCVs in power sources, fuel storage and transportation, fuel supply infrastructure construction, and the cost and use of vehicles. Our findings indicate that electric passenger vehicles have more advantages in economy, safety, and environmental impact, in comparison with hydrogen fuel cell passenger vehicles. Nevertheless, great efforts should still be made to develop advanced rapid charging technology, shorten charging time, and accelerate charging infrastructure construction. Then, it is just around the corner for EVs to gradually take over from traditional motor vehicles driven by oil. In contrast, popularizing hydrogen fuel cell passenger vehicles faces several insurmountable obstacles in the short run, such as the high hydrogen production price, complicated storage process, and expensive hydrogen refueling station infrastructure. However, hydrogen fuel cell commercial vehicles have unique application scenarios. The dislocation and complementarity principle in different scenarios of EVs and HFCVs is supposed to be firmly grasped.

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.

Assessing the extreme risk spillovers to carbon markets from energy markets: evidence from China

Previous studies point to the impact of energy market on carbon market under normal market conditions, but little is known about the impact under extreme market conditions. Motivated by these concerns, we aim to investigate the extreme risk spillovers to carbon markets from traditional fossil energy and new energy markets in China. After using copula model to obtain nonlinear tail dependence structure between carbon and energy markets, we compute conditional Value-at-Risk (CoVaR) to quantify extreme risk spillovers. The results indicate that (i) the risk spillovers from both traditional fossil energy and new energy markets to carbon markets are obviously larger when extreme events cause large shocks to energy markets; (ii) there are risks in the opposite direction in carbon markets when carbon-intensive energy markets are under extreme market conditions, but the direction of risks in carbon markets is uncertain when low-carbon energy markets are under extreme market conditions; (iii) the extreme risk spillovers to carbon markets from energy markets are regionally heterogeneous not only in magnitude but also in direction; (iv) energy markets prefer to transmit extreme risks to more liquid carbon markets. These new findings have valuable implications for both policymakers and participating enterprises in carbon markets.

Correlation and causality between carbon and energy markets: a complexity perspective

Global warming obliges humans to focus on the relationships between carbon and energy markets. This study considers the relationship between carbon and energy markets from a complexity perspective. Chinese carbon prices and four energy indexes are selected as empirical variables. First, the complexities of carbon and energy markets are measured by multi-scale fuzzy entropy. The critical time delay of the series is then obtained by maximizing the Pearson coefficients between these markets. Further, analysis of the detrended cross-correlation coefficient confirms the existence of time delay. Finally, transfer entropy is applied to investigate the causality pertaining to dynamic complexity. Results of fuzzy entropy analysis reveal that the carbon market has lower complexity than energy markets. Meanwhile, multi-scale results indicate greater complexity on the small time scales in all markets than on the large time scales. The critical time delay is found to be about 50, which maximizes the correlation coefficient. Finally, causality between carbon and energy markets varies. Expectations in the carbon market impact oil gas and coal markets; electricity and new energy affect the carbon market; and cross-causality exists in the relationship between coal and carbon markets. The participants should focus on the information transmission between carbon and energy markets.

A study on the response of carbon emission rights price to energy price macroeconomy and weather conditions

China's carbon emission trading market has gradually attracted worldwide attention. In this paper, a structural VAR model and Shenzhen, a typical city in China, are selected to study the dynamic relationships between China's carbon emission rights price, energy prices, macroeconomic level, and weather conditions. Shanghai crude oil futures, the first crude oil futures contract in China, is used to describe changes in oil market as a substitute for Daqing crude oil price. The results show that the price of carbon emission rights is mainly affected by its own historical price; and the price of carbon emission rights is positively correlated with crude oil price and natural gas price, but negatively correlated with coal price; the change of macroeconomic level will still have a relatively large impact on carbon emission rights price in the current stage of economic development in China, but this impact is not significant; The impact of weather conditions on the price of carbon emission rights is not obvious. It is found that the launch of the national unified carbon market has indeed achieved certain results, but the situation that China's carbon market is still in its infancy has not been changed; further efforts are needed.

How Energy Price Distortions Affect China’s Economic Growth and Carbon Emissions

China’s energy market reform is characterized by “asymmetry” as a result of the transition from a planned economy to a market economy, leading to typical distortions in energy prices. Using panel data from 30 Chinese provinces during 2006–2018, this paper examines the impact of the price distortions of fossil energy sources (coal, oil, and natural gas) as well as renewable energy on China’s economic growth and carbon emissions. The results show that coal price distortion is the highest among the fossil energy sources, followed by oil and natural gas. Renewable energy price is positively distorted. Not all price distortions of energy sources significantly impede economic growth—only those of coal and renewable energy. In contrast, oil and natural gas price distortions promote economic growth. All four energy price distortions contribute significantly to the increase in carbon emissions. Further analysis reveals that regional heterogeneity exists in the impact of energy price distortions on economic growth and carbon emissions. Additionally, this study finds that technological innovation, industrial structure upgrading, the investment effect, the optimization of energy consumption structure, and environmental regulations are important transmission mechanisms of energy price distortions affecting China’s economic growth and carbon emissions. The findings of this study can help us to understand the relationship between energy price distortions and China’s economic growth and carbon emissions, and provide a reference for formulating energy price reform policies that benefit the win-win development of China’s economy and environment.