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The coordination strategies of a two-echelon coal supply chain consisting of a coal enterprise and a downstream enterprise are proposed considering market low-carbon preference and carbon emissions reduction. The cleaning and matching degree (CMD) is introduced to measure the quality of commercial coal which have influence on the production cost and energy consumption of the downstream enterprise. Stackelberg models are established to investigate the coordination strategies using cost-sharing contracts. We use backward induction to solve the models and analyse the optimal decisions in four scenarios. It is revealed that promoting CMD level can extend the market portion and realise more profit. The cost-sharing contract can coordinate the supply chain members, obtain profit Pareto improvement in certain condition of cost-sharing ratio and establish a win-win situation. Additionally, the emission reduction is greater than that under decentralised decision-making. [ABSTRACT FROM AUTHOR]

EU's Carbon Border Adjustment Mechanism (CBAM) has been promulgated. The steel industry is included in CBAM, which will undoubtedly affect the export trade of China's steel manufacturers to the EU. It is necessary to analyse the impact of CBAM on China's steel industry quantitatively. By establishing a system dynamics model that can simulate the operation mechanism of the steel supply chain in China, we have clarified the impacts of CBAM on the China's steel export and carbon reduction. Moreover, we have reasonably set up scenarios that represent the possible decarbonisation paths of China's steel industry and evaluated the implementation effect of each policy in these scenarios. The analysis shows that CBAM will lead to a decrease of 32% in export volume and a loss of 58% in export profits for China's steel exporters. The environmental benefits of CBAM are limited. Due to the low trade exposure, CBAM is unable to promote a large-scale carbon reduction in the China's steel industry. Although CBAM will bring great pressure to steel exporters, a comprehensive policy of adopting more emission reduction measures can greatly improve the carbon reduction efficiency, effectively reduce the negative impact of CBAM, and maintain the stability of steel exports. [ABSTRACT FROM AUTHOR]

Given the importance of crude oil prices in the world economy, accurate price prediction has drawn extensive attention. Nevertheless, because of the complexity of the crude oil market, most traditional forecasting algorithms fail to meet the accuracy requirements. To achieve higher precision, this paper proposes a novel hybrid model for crude oil price forecasting by combining a Hodrick-Prescott filter with X12 methods and adjusting the order used. Application of our model on both West Texas Intermediate and Brent oil prices forecasting demonstrates its accuracy. The results of various forecasting performance evaluation criteria indicate that the model has stronger stability and better accuracy. The mechanism of seasonal and periodic factors is also analyzed, which provides remarkable references to other time-series predictions. Establishing two different types of predictive models that combine multiple knowledge effectively has obvious advantages over other models and provides more reliable cutting-edge information for designing a Chinese energy development strategy. [ABSTRACT FROM AUTHOR]

Hedging is an important measure for investors to resist extreme risks and improve their profits. This paper develops a FIGARCH–EVT–copula–VaR model to derive hedge ratio when hedging crude oil spot and futures markets, overcoming the limitations of static models and simple dynamic models in existing literature. The empirical results indicate that the FIGARCH–EVT–copula–VaR model is superior to the other three commonly used models based on four criteria: mean of returns, variance of returns, ratio of mean to variance of returns, and hedging effectiveness. Comparatively, the new model has superior performance to other three models during the sample period and can be used by investors to obtain excellent hedging effect. [ABSTRACT FROM AUTHOR]

With a number of international cash flows and venture capital pouring into energy markets, the energy price goes up and down sharply. To rise up to the challenge, we must estimate the risk deriving from price volatility accurately. In this paper, we first introduce the energy price risk level index (VaREP), and then set up the energy price risk measurement model based on Copula-VaR. At last we make an empirical analysis about the risk of energy investment portfolios. The result shows that energy price risk measurement model based on Copula-VaR has an advantage over the historical simulation method and weight-equal method because of considering relativity of price variables. When we used the model to analyze the risk of energy investment portfolios, we found that the risk of portfolio is lower than separate investments and the risk-avoiding performance of the optimal investment projects is better than the project of equal weight. [ABSTRACT FROM AUTHOR]