Search

Whether or not China can succeed at the forefront of international development in the coming decades will largely hinge on its ability to adapt to low-carbon economic development and its efforts to promote the Unified National Emission Trading System (UNETS). To understand the evolution of China's carbon market, this paper firstly divides the development of China's regional carbon markets into three phases: pilot construction (phase 1, before 31 May 2015), preparation for the UNETS (phase 2, 1 June 2015–19 December 2017), and formal construction of the UNETS (phase 3, 20 December 2017–present). Then this research reviews the trading performance and employs four robust variance ratio (VR) tests to capture structural changes and examine the efficiency of China's eight regional carbon markets in different periods. Results show that compared to phase 1, smaller price volatility, larger daily trading volume, and higher market liquidity are more frequent at the later stages of majority markets. Despite these improvements, results from the VR tests indicate that the statistic in majority of China's regional carbon markets is insignificant in any given period and, therefore, they are not weak-form efficient. The additional detrended cross-correlation analysis demonstrates that market liquidity affects market efficiency in the Hubei market only, which implies that Hubei, where the market liquidity reaches almost 100% in phase 3, is highly likely to be weak-form efficient shortly. Finally, several recommendations are provided to improve the efficiency and maturity of China's carbon markets, including strengthening legislation, improving the market design, and constructing information platforms. [ABSTRACT FROM AUTHOR]

Emission trading scheme (ETS), the most popular market-based instrument, is widely used to solve carbon emission problems in the world. With the development of carbon market, carbon asset has been a popular financial product to invest and the risk management becomes important for government, regulated enterprises and other investors. As carbon prices have tail characteristics, this paper explores the extremal risks between carbon markets in US, Europe and China using tail dependence correlation coefficients. The empirical analysis demonstrates the tail dependence structure between carbon markets in US. and Europe is the same sign, which indicates that it is unwise to hold these two carbon assets as a portfolio. Moreover, the co-movements between European Union Emission Trade Scheme (EU ETS) and China's carbon markets are partly significant, and the operation mechanisms in China should be improved. In addition, the tail dependence test among the carbon pilots in China shows diversity. Hubei carbon trading pilot, located in central China, has extremal dependence with all other selected pilots for its regulatory program operation. The findings give insight to the carbon market regulars to improve the operation mechanism and are also useful for the investors to manage their portfolios, policymakers to make practically applicable regulations, and relevant organizations to develop procedures. [ABSTRACT FROM AUTHOR]

The forecasting of carbon emissions trading market price is the basis for improving risk management in the carbon trading market and strengthening the enthusiasm of market participants. This paper will apply machine learning methods to forecast the price of China's carbon trading market. First, the daily average transaction prices of the carbon trading market in Hubei and Shenzhen are collected, and these data are preprocessed by PCAF approach to choose the input variables. Second, a prediction model based on Radical Basis Function (RBF) neural network is established and the parameters of the neural network are optimized by Particle Swarm Optimization (PSO). Finally, the PSO-RBF model is validated by the actual data and proved that the PSO-RBF model has better prediction effect than BP and RBF neural network in China's carbon prices prediction. It is indicated that the prediction model has more significant applicability and deserves further popularization. [ABSTRACT FROM AUTHOR]

This paper studies the carbon price fluctuation in China through the adaptive Fourier decomposition (AFD). Apart from the transient time-frequency distribution of the original AFD model, we also reconstruct the mono-components of this model to obtain the components in different time-frequency scales. Our empirical results based on the carbon price in Hubei Province demonstrate that there are three periods when the price fluctuates dramatically, mainly affected by the governmental policies about carbon emission and the development of clean energies, as well as the outbreak of COVID-19. Furthermore, the fluctuations of the price in the three identified periods are reflected in different scales. The comparison of the decomposition results and those of EMD and VMD shows that the AFD performs best in absorbing the price's useful information extracted through all these methods. [ABSTRACT FROM AUTHOR]

This paper analyzes the overall and time-varying efficiency of China's carbon market, which is crucial for carbon risk management and policymaking. Due to the severe illiquidity problems other pilots face, we first select Shenzhen, Guangdong, and Hubei pilots out of the eight carbon pilots for our study based on the statistical analysis. We then test the overall efficiency of the markets using the LM, CD, and Wild-Bootstrap variance ratio test. The sliding window method is combined with the above three variance ratio test methods to validate the time-varying efficiency. The results prove that the overall efficiency of the three carbon pilots has not yet reached a weak form. And we observe that the short-term efficiency is time-varying and may be weak-form. The study further found Hubei to be the most efficient carbon pilot, followed by Guangdong and Shenzhen. Finally, this paper conducts a qualitative analysis on the low efficiency from five aspects namely carbon trading rules, market liquidity, price volatility, trading volume, and types of trading products. Several conclusions and recommendations are proposed for policymakers and investors. • Sliding window method is combined with a series of variance ratio test methods. • The overall and time-varying efficiency of China's carbon market is tested. • China's carbon market has not reached weak-form efficiency. • Hubei is the most efficient carbon pilot. • Reasons for low efficiency are analyzed. [ABSTRACT FROM AUTHOR]

Although China boasts the largest online consumption market, there has been no prior case study addressing the carbon footprint of online consumption. The rapid expansion of online consumption has posed a challenge to China's carbon neutrality pledge. This paper aims to fill this crucial gap by conducting a pioneering study based on survey data collected from 6000 households. To achieve this goal, this study has combined provincial Multi-Regional Input-Output (MRIO) tables with residents' online consumption calculated by the Non-Linear Optimization model, and applied the Environmental Input-Output Life Cycle Assessment (EIO-LCA) model to calculate the carbon footprint of residents' online consumption. Result indicated that the average carbon footprint of residents' online consumption was 0.46 tCO 2 e in 2016, accounting for 12.30% of the total consumption carbon footprint. Notably, high-emission regions include Beijing-Tianjin, Jiangsu-Zhejiang-Shanghai, and Hubei. Moreover, the dominant sources of the online consumption carbon footprint were electricity, food, clothing, and beauty products. Furthermore, we found that the carbon footprint of online consumption tends to increase with income, with differential income elasticities observed for various products and income groups. We also quantified the average expenditure-based life-cycle carbon coefficient for parcel delivery (0.22 kgCO 2 e/yuan) and conducted two scenario analyses to assess its impact. From a policy perspective, our research on measuring the carbon footprint of residents' online consumption can provide valuable insights for designing future policies and decisions related to e-commerce and key sectors along the supply chains. Additionally, it will further encourage Chinese residents to adopt low-carbon lifestyles. • Identifies the expenditure structure of residents' online consumption. • The carbon footprint of Chinese residents' online consumption in 25 regions. • The income elasticity of online consumption carbon footprint. • The last mile emission intensity of e-commerce is quantified. [ABSTRACT FROM AUTHOR]

With the continuous emission of greenhouse gases, the carbon trading market has become a powerful weapon to contain it. It is indispensable to analyze the carbon price of China that acts as the largest emitter of carbon dioxide worldwide. Therefore, this paper proposes an innovative hybrid carbon price forecasting model that incorporates fast ensemble empirical mode decomposition (FEEMD) and extreme learning machine optimized by particle swarm optimization (PSO-ELM) with external and internal influencing factors considered. The original carbon price series are disassembled into several intrinsic mode functions (IMFs) and one residual via FEEMD. The PSO-ELM is then employed to forecast the sub-series. It's remarkable that the inputs of the PSO-ELM model are divided into external and internal influencing factors. Factor analysis is used to extract potential factors from energy prices, macroeconomics and other influencing factors associated with the original carbon price as external influencing factors, and the partial autocorrelation function (PACF) is exploited to select internal influencing factors. A case study in Hubei Province, China shows that the proposed carbon price forecasting model is superior to the contrast models in terms of the smallest prediction error (MAE = 0.1274 yuan, MAPE = 0.8368%) and the strongest stability (RMSE = 0.0116 yuan). And the forecasting results demonstrate that the developed model with external and internal influencing factors considered can highly improve carbon price prediction performance and have potential in a wider range of carbon price forecasting. In addition, accurate carbon price forecasting can help the government realize macro control and the investors fulfill risk minimization. [ABSTRACT FROM AUTHOR]

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. • We propose a novel probability forecast model QTCN for carbon prices. • The impact of economic policy uncertainty and geopolitical risks are considered. • The prediction accuracy of QTCN outperforms other competing models. • QTCN-based trading strategies yield higher profits than a point forecast-based one. [ABSTRACT FROM AUTHOR]

This paper examines the risk spillover effect between the carbon market and the stock market in China and the role of corporate social responsibility (CSR) on this effect. Employing Beijing, Hubei, and Guangdong carbon markets, we apply time-domain and frequency-domain spillover approaches and find that during the Chinese stock market crisis in 2015, risk spillovers from the stock market to the carbon market were more pronounced. Additionally, CSR firms are more dominant as information transmitters than those non-CSR (NCSR) firms in the carbon market. However, plausibly, due to the infancy of carbon trading, our results show that the level of connectedness between the carbon market and the stock market in China is relatively low. [ABSTRACT FROM AUTHOR]

• It evaluates the effect of China's ETS on non-fossil energy development. • It analyzes ETS's effects from energy type, pilot, and pilot-energy type aspects. • The ETS has the most significant promoting effect on hydropower and photovoltaics. • Carbon price plays a positive role in moderating the effect of China's ETS. Emissions Trading Scheme (ETS) can internalize the environmental costs of fossil energy consumption and encourage enterprises to consume non-fossil energy, which is crucial for People's Republic of China (PRC) to achieve the contribution target of the Paris Agreement cost-effectively. However, whether the ETS in PRC can promote non-fossil energy development remains to be answered. This paper uses a difference-in-differences model to evaluate the effect of the ETS on non-fossil energy development in PRC. Using provincial monthly panel data from January 2004 to December 2019, we find that (1) the ETS has significantly promoted non-fossil energy development in PRC, increasing the monthly average share of non-fossil energy power generation in total power generation by 2.326 percentage points, which has been verified by a series of robustness tests. (2) The effect of the ETS on non-fossil energy development is heterogeneous by energy type, pilot region, and pilot-energy type. The ETS has significantly increased the share of hydropower and photovoltaic power generation, and has significantly promoted non-fossil energy development in Guangdong; meanwhile, the ETS has significantly increased the share of hydropower and nuclear power in Guangdong, and the share of wind power and photovoltaic power in Hubei. (3) The higher the carbon price, the greater the role of the ETS in promoting non fossil energy development. [ABSTRACT FROM AUTHOR]

In 2020, China proposed the goal of achieving carbon emission peaks by 2030 and carbon neutrality by 2060. For China, whose energy consumption structure has long been dominated by fossil energy, carbon trading and new energy are crucial for the realization of the emission target. By establishing a connectedness network model, this paper studies the static and dynamic spillovers between the Hubei carbon trading market, new energy stock market, crude oil market, coal market, and natural gas market in China, and draws the following conclusions: (1) the static spillover index of the carbon–energy–stock system is 3.57% and the dynamic spillover index fluctuates between 7.67% and 22.62%, indicating that the spillover effect of the system is low; (2) for the whole system, whether from a static or dynamic perspective, the carbon market always plays the role of net information receiver, while new energy is the net information transmitter; (3) the new energy stock market and the coal market always act as net information transmitters to the carbon market; and (4) the spillover effect of the system is asymmetric, wherein the system is more sensitive to negative information about price returns, and this asymmetry is much greater when the system is active. [ABSTRACT FROM AUTHOR]

China launched seven emissions trading scheme (ETS) pilots in 2011, and a national ETS, at the end of 2017 in an attempt to reduce its greenhouse gas emissions (GHGs) and drive its extensive energy transition in a cost-effective way. The Hubei province pilot ETS has operated since 2014, and an analysis of its effectiveness can provide lessons for the nascent national system. This paper disaggregates Hubei's industrial carbon dioxide (CO 2) emissions from 2005 to 2018 into economic scale, economic structure, energy efficiency, and energy structure effects using the logarithmic mean Divisia index (LMDI). Difference-in-difference (DID) models then assess the implementation effectiveness of this ETS, and its impact on the main influencing factors of the LMDI. Ten cities each in the Hubei and Hunan provinces are selected as treatment and control groups, respectively, in the DID models. Results show that the change in industrial CO 2 emissions is mainly due to the economic scale and energy efficiency effects. After the ETS implementation, the energy structure effect has a limited impact on emissions, while the economic structure effect makes a negligible contribution. Moreover, the implementation of the ETS has had little demonstrable impact on industrial CO 2 emissions, gross domestic product (GDP), energy consumption intensity, and CO 2 emission intensity. This lacking of impact is caused by the GHG monitoring capacity deficits of the local Development and Reform Commission, and irrational quota allocations. Effective supervision and quota allocation should be developed to increase the impacts of the ETS. [ABSTRACT FROM AUTHOR]

China is the world's largest developing country and a carbon dioxide emitter. A functional carbon market can effectively reduce carbon emissions. This paper uses the Markov chain Monte Carlo-stochastic volatility model and the wavelet multi-resolution model to analyze the volatility of price returns and the dynamic characteristics of price fluctuations in the carbon pilot markets in Hubei, Shanghai, and Shenzhen. The price movements in these markets are compared to the emissions trading system of the European Union (EU-ETS). The results show that there is a volatility clustering in the price of carbon trading in Hubei, Shanghai, Shenzhen and the EU-ETS. China's carbon pilot markets have a deficiency in terms of volatility stability, as does the EU-ETS. From a long-term perspective, China's carbon market lacks a detailed development plan, which is vital because the construction of the market system is not yet optimal. From a short-term view, China's carbon market is not active and the participants' attitude toward risk is extremely sensitive. • MCMC-SV and wavelet multi-resolution model applied to carbon price volatility. • Three major Chinese markets are compared to the EU-ETS. • All markets evidence volatility clustering. • China's carbon market lacks a detailed development plan for the long term. • Short-term, China's carbon market must become more active and offer more products. [ABSTRACT FROM AUTHOR]

As an economic means to cope with climate change, carbon trading has become an important measure to reduce carbon emissions in China. It has been over seven years since the initiation of seven pilot carbon markets, but has the implementation achieved the goal of controlling carbon emissions in the region? Are the effects really resulted from the carbon market other than other energy-saving and emission reduction policies? Based on the provincial panel data of China from 2005 to 2016 this paper conducts an empirical study on the emission reduction effects of pilot projects using the Difference-in-differences model (DID). The results show that: (1) The implementation of carbon markets in Beijing, Shanghai and Hubei exhibited a significant inhibitory effect on local carbon emissions, but a promoting effect in Guangdong, and no significant effect was found in Tianjin; (2) In terms of time lag effect, no significant inhibiting effect on the local carbon emissions is found during the first year of opening of Shanghai carbon market but the effect grew significantly from 2015 to 2016; while for Hubei and Beijing, significant effect was shown from the first year of running. Being an experimental program to see whether the carbon trading policy would work in China or not and if it does, under what conditions and settings the policy becomes effective, the 7 pilot markets provide important experiences and lessons to the upcoming nationwide market in China. Based on the empirical result that the carbon trading system in Beijing, Shanghai and Hubei effectively reduced carbon emissions, it is suggested that the national carbon market should actively learn from the experiences of these three pilot markets particularly. [ABSTRACT FROM AUTHOR]