The market performance of carbon trading in China: A theoretical framework of structure-conduct-performance

Tan, Xueping; Wang, Xinyu

doi:10.1016/j.jclepro.2017.05.019

Cited by 60 publications

(24 citation statements)

References 28 publications

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“…(1) Market factors in the ETS market mainly include carbon price [73], certified emission reduction (CER) price [51,73], futures [41,140], options [141], trading volumes [142], carbon price returns [142] and volatilities [142,143]. On the one hand, existing research has been devoted to capturing the dynamic trends of these market factors in the ETS market [51,64] and their relationships with each other (particularly carbon and CER prices [73]), which provides policy implications for both policy design [109,144] and investment decisions [145].…”

Section: Research Hotspotsmentioning

confidence: 99%

Quantitative models in emission trading system research: A literature review

Tang

Wang

et al. 2020

Renewable and Sustainable Energy Reviews

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Diverse quantitative models have been applied to analyse emission trading system, as the top effective climate change policy. This paper is the first attempt to present a comprehensive literature review on full-scale types of quantitative models in emission trading system research. The models dominating emission trading system-related literature could be categorized as optimization models, simulation models, assessment models, statistical models, artificial intelligences and ensemble models. Using different quantification and solution tools, these models complemented and enriched each other in serving the various agents involved in emission trading system and facilitating their respective emission trading system related works: the government to design emission trading system policies, enterprises to participate in emission trading system and goods markets, third parties to regulate emission trading system and emission trading system markets involving different agents. For each agent, a systematic analysis is provided on research hotspots (the challenges to address), quantitative models (to describe the problems and find the results), main findings (the policy implications from the models) and future research (potential improvements on existing models). Some interesting conclusions are obtained. (1) Generally, China was the largest contributor to emission trading system research using quantitative models (representing 35.71% of the total articles). (2) The research hotspots were decision making by enterprises under an emission trading system (20.92%), spillovers amongst emission trading system and other markets (17.54%) and allowance allocation by the government (12.52%). (3) Popular quantitative models included various optimization models (32.00%) and simulation models (29.64%). Highlights：• A variety of quantitative models have been applied to ETS research.• A comprehensive review on full-scale types of models in ETS research is presented.• Optimization, simulation, assessment, statistical, AI & ensemble models were built.• Models helpfully served the government, enterprises, third parties & market in ETS.• For each agent, hotspots, models, main findings and future research are analysed.

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Section: Research Hotspotsmentioning

confidence: 99%

Quantitative models in emission trading system research: A literature review

Tang

Wang

et al. 2020

Renewable and Sustainable Energy Reviews

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“…Villoria-Saez et al (2017) put forward that a fine of $ 90.22 per ton is the best penalty for achieving maximum carbon reduction. Xueping Tan et al (2017) found that China's Pilot ETS performance is mainly due to the lack of legal binding force, market stagnation, excessive allocation and lack of investment. Xingang Zhao et al (2017) thought that with the increase of trading volume and scale, China's carbon emission market will move from low efficiency to low profitability.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on China's carbon emission trading market

Yin¹,

Zhou²,

Shan³

2018

Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017)

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Since EU ETS established in 2005, the international carbon emission trading markets have been running for 12 years, while China's Pilot ETS just for three years. However, owing to the high level carbon emissions, China's carbon emission trading market has a huge development space. The first part compared current situation between the international and domestic carbon emission trading markets, and indicated some outstanding problems in China. Then this paper studied the inherent relationship between weekly trading volume and return rate in Shanghai ETS, based on the Autoregressive model. Results show that the carbon emission trading volume in Shanghai is fluctuating, and the impact of return rate on the current trading volume is more significant. Finally, this paper gave some suggestions to perfect the carbon emission trading market in China: (i) use financial derivatives to active trading market; (ii) make incentive policies to stimulate transactions; (iii) unify carbon emission trading market regulations.

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“…In addition, by the end of December 2018, the cumulative trading volume pilot exceeded 270 million tons, and the total turnover exceeded 6 billion yuan. Obviously, China is the world's largest energy consumer and greenhouse gas emitter [8]. The ETS has become an extremely significant financial market, and accordingly, it will play an increasingly important role in reducing emissions in the future accordingly.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Zhou

Huo

et al. 2019

Energies

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Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-point symmetric mode decomposition, an extreme learning machine, and a grey wolf optimizer algorithm. Firstly, the extreme-point symmetric mode decomposition is employed to decompose the carbon price into several intrinsic mode functions and one residue. Then, the partial autocorrelation function is utilized to determine the input variables of the intrinsic mode functions, and the residue of the extreme learning machine. In the end, the grey wolf optimizer algorithm is applied to optimize the extreme learning machine, to forecast the carbon price. To illustrate the superiority of the proposed model, the Hubei, Beijing, Shanghai, and Guangdong carbon price series are selected for the predictions. The empirical results confirm that the proposed model is superior to the other benchmark methods. Consequently, the proposed model can be employed as an effective method for carbon price series analysis and forecasting.

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The market performance of carbon trading in China: A theoretical framework of structure-conduct-performance

Cited by 60 publications

References 28 publications

Quantitative models in emission trading system research: A literature review

Quantitative models in emission trading system research: A literature review

Research on China's carbon emission trading market

Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

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