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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. [ABSTRACT FROM AUTHOR]

Extreme weather is an unexpected shock to the socioeconomic, which is likely to create climate risks in the process of global warming mitigation. The aim of this study is to investigate the impact of extreme weather on prices of China's regional emission allowances, by using the panel data of four representative pilots in China (Beijing, Guangdong, Hubei, and Shanghai) from April 2014 to December 2020. The overall findings reveal that extreme weather, especially extreme heat, has a short-term lagged positive impact on carbon prices. In particular, the specific performance of extreme weather under different conditions is as follows: (i) carbon prices in tertiary-dominated markets are more sensitive to extreme weather, (ii) extreme heat has a positive effect on carbon prices while extreme cold does not, and (iii) the positive impact of extreme weather on carbon market is significantly stronger during compliance periods. This study provides the decision-making basis for emission traders to avoid losses caused by market fluctuations. [ABSTRACT FROM AUTHOR]

Based on the Web of Science database, this paper uses the bibliometric method to analyze the characteristics of the most relevant studies of carbon price in emissions trading scheme. Researchers have shed light on this research field since 1994. The USA and China occupy the leading research position in this field. The most productive journal is Energy Policy and the most productive author is Chevallier J. Chinese Academy of Sciences is the institution with the most publications related to carbon price. Cooperation analysis shows that the cooperation between authors, institutions and countries is constantly growing. We find out that carbon price fluctuation, influencing factors of carbon price, price mechanism of the carbon market, theory analysis of carbon pricing policy and carbon price effects are the main areas of research focus. Also, we detect that different policies, emissions level and energy prices are the main influencing factors on carbon price. [ABSTRACT FROM AUTHOR]

This paper estimates the influences of different industries on the carbon prices and selects the industries with high market power in the carbon market. To realize price discovery in the carbon market and improve the market efficiency, we suggest that industries with higher market power should be included in the market prior to other industries. Using the LASSO regression to analyze the data from the pilot markets of China's ETS and related industrial product outputs, we find that among all related industries, the petrochemical and chemical industries affect the carbon price the most and hence should be included in China's ETS with priority. The power generation industry, though regarded as the major entity of emission control, imposes weaker influences on the carbon price. Industries that may be incorporated in China's ETS later include the transportation equipment manufacturing and the mining and metal industries. [ABSTRACT FROM AUTHOR]

Climate change and carbon emissions are major problems which are attracting worldwide attention. China has had its pilot carbon emission trading markets in seven regions for more than 3 years. What affects carbon emission trading market in China is a big question. More attention is paid to how China promotes the carbon emission trading schemes in the whole country. This paper addresses concerns about the functioning of carbon emission trading schemes in seven pilot regions and takes the weekly data from November 25, 2013, to March 19, 2017. We employ a vector autoregressive model to study how coal price, oil price and stock index have affected the carbon price in China. The results indicate that carbon price is mainly affected by its own historical price; coal price and stock index have negative effects on carbon price, while oil price has a negative effect on carbon price during the first 3 weeks and then has a positive effect on carbon price. More regulatory attention and economic measures are needed to improve market efficiency, and the mechanisms of carbon emission trading schemes should be improved. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As a major carbon-emitting country, China has considerable potential for carbon capture and storage (CCS) applications, but economics is critical for CCS deployment. In this study, the levelized cost of electricity (LCOE) at province level was calculated for operational coal-fired power plants after post-combustion carbon capture (CC) retrofitting. The costs of captured CO2 and avoided CO2 were then compared. The economics of CC-retrofitted coal-fired power plants co-firing with biomass (BECC) were also analyzed. CC retrofitting is economical in North and Northwest China (except Qinghai) where coal prices are low. However, BECC retrofit is more suitable for coal-fired power plants in Central China where biomass prices are low. Therefore, the variation in the resource conditions among provinces should be considered when promoting CC and BECC retrofits. Compared with other power sources, coal-fired power generation becomes much more expensive after retrofits. We calculated the carbon price and capacity subsidy intensity that could support the retrofit of coal units. Effective carbon price should be 142.44–406.06 CNY/t for retrofits, and the capacity subsidy intensity ranges from 175.14–721.52 CNY/kW. The current carbon market and capacity market are not sufficiently developed to support retrofitting. The development of the carbon market with effective carbon price signals and proper power market mechanisms is critical to the rollout of CCS in China. [ABSTRACT FROM AUTHOR]