Coal Price Fluctuations Research Articles

Joint Optimization of Inventory and Schedule for Coal Heavy Rail Considering Production–Transportation–Sales Collaboration: A Spatio-Temporal-Mode Network Approach

China’s coal industry has long faced challenges due to imbalance between supply and demand divisions and the discrepancies between supply and demand prices. Research on optimizing coal transfer amid fluctuating coal prices is crucial for enhancing coal resource allocation, ensuring a balanced and stable supply of coal resources, and promoting the sustainable development of the coal industry. This paper focuses on optimizing coal transfer schemes within a comprehensive transportation system, considering fixed transportation routes and opening times of railroad transportation and waterway transportation, with road transportation serving as a supplementary network. A three-dimensional spatio-temporal network model is established to represent the spatial and temporal paths and transportation modes of coal logistics. The study uses the coal transportation network from Inner Mongolia and Shaanxi to East China as a case study, analyzing factors such as coal price fluctuation, transportation rates, and the ratio of self-produced to purchased coal at the production end via sensitivity analysis. The findings indicate that the proposed model can effectively guide transportation practices under the integrated production, transportation, and sales framework in the energy industry. Additionally, it shows that enterprises can achieve higher profits and maintain revenue and supply stability despite coal price fluctuations, aligning with the coal industry’s transformation and development in the current context.

Mid-long term boundary dynamic optimization of open-pit coal mine considering coal price fluctuation.

The delineation of the open-pit mining boundary, particularly in the context of medium to long-term planning, forms the foundation of mining design. However, due to the non-linear and dynamic nature of the economic and technical parameters influencing boundary delineation, determining the optimal mining boundary can be exceedingly challenging. Currently, most boundary optimization methods assume that block parameters remain fixed, which results in enterprises assuming a certain level of risk when facing changes in internal and external conditions. In this regard, this paper introduces the concept of "achievement degree" to reflect the risk associated with the results of boundary design. Using coal prices as an example, this article applies the predicted coal price curve to boundary optimization adjustments by specifying the "achievement degree" requirements for various time periods, thereby facilitating risk-controlled and economically optimal boundary decisions. Taking the illustrative case of an idealized small-scale inclined coal seam open-pit mine, adjustments to the boundary closely track variations in coal prices, further enhancing returns. The results demonstrate that the method proposed in this paper can increase overall revenue by approximately 51.15% within the forecast period, while effectively managing risks.

Energy price bubbles and extreme price movements: Evidence from China's coal market

This study investigates the factors behind the extreme price movements in China's coal market, with a particular focus on the impact of climate risk and energy transition in recent years. The Generalized Sup Augmented Dickey-Fuller (GSADF) method is employed to detect coal price bubbles, and a dynamic model averaging (DMA) approach is then used to analyze the causes of these price bubbles. The findings reveal that price bubbles in the Chinese coal market are mainly triggered by fluctuations in international energy prices. The extreme prices are rooted in supply-demand imbalances resulting from energy transition, economic development, and geopolitical conflicts. Policies aimed at adjusting coal supplies can effectively mitigate abnormal coal price fluctuations in China, while normal coal price fluctuations are significantly influenced by changes in energy demand driven by macroeconomic development. During the green transition towards renewable energy, the current high prices of fossil energy present challenges to China's energy supply security but also offer opportunities for the development of the renewable energy market. However, energy transition has facilitated the spread of price bubbles across coal, natural gas, and renewable energy markets, potentially leading to contagion effects.

A coal-based polygeneration system of synthetic natural gas, methanol and ethylene glycol: Process modeling and techno-economic evaluation

Coal to synthetic natural gas(CtSNG) industry is an effective way to alleviate the contradiction between supply and demand of natural gas in China. However, the product structure of the traditional CtSNG process is single and the carbon utilization rate is low, so there may be potential risks of product and coal price fluctuations. In order to address these issues, a coal-based polygeneration process of SNG, methanol and ethylene glycol(CtSMEG) was proposed in this study and compared with the CtSNG process. The entire CtSMEG system was first modelled and simulated by Aspen Plus, then the detailed techno-economic analysis was performed and the carbon balance and exergy balance were established. In addition, its carbon utilization efficiency, exergy efficiency and CO2 emissions were compared to that of the traditional CtSNG process. The results showed that the carbon utilization efficiency and exergy efficiency of the CtSMEG system were increased by 35.3 % and 27.3 % respectively, and the CO2 emissions and wastewater discharge were reduced by 0.613 kg/kg-production and 1719.3 kg/h. Although the total capital investment of the CtSMEG system was higher than that of the CtSNG process, the net present value was better than that of the CtSNG process. According to these findings, the CtSMEG system would be an effective way to overcome the problems of the current CtSNG process.

Boundary optimization of inclined coal seam open-pit mine based on the ISSA–LSSVR coal price prediction method

As an important link in the complex system engineering project of open pit mining, the quality of the boundary determines the performance of the project to a large extent. However, changes in economic indicators may raise doubts about the optimality of mining boundaries. In this article, a coal price time series forecasting model that considers some amount of redundancy is proposed, which combines an improved sparrow search algorithm (ISSA) and a least squares support vector regression machine regression (LSSVR) algorithm. The optimal values of the penalty factor and kernel function parameter of the LSSVR model are selected by ISSA, which improves the prediction accuracy and generalization performance of the forecasting model. A multistep decision optimization method under fluctuating coal price conditions is proposed, and the model prediction results are applied to the boundary optimization design process. Using the widely applied block model as the basis, a set of optimal production nested pits is obtained, allowing the realm design results to fit the coal price fluctuation trend and further enhance enterprise efficiency. The applicability and effectiveness of this method were verified by taking an ideal two-dimensional model and an inclined coal seam open-pit coal mine in Xinjiang as an example.

Dirty versus renewable energy consumption in China: a comparative analysis between conventional and non-conventional approaches.

This study uses two empirical approaches to explore the asymmetric effects of oil and coal prices on renewable energy consumption (REC) in China from 1970 to 2019. As a conventional approach, we used the nonlinear autoregressive distributed lags (NARDL) model, while machine learning was used as a non-conventional approach. The empirical findings of the NARDL indicate that oil and coal price fluctuations have a significant effect on REC for both the short and long term. The results of the non-conventional approaches based on machine learning indicated that the SVM model was more efficient than the KNN model in terms of accuracy, performance, and convergence. Referring to the SVM model findings, the results show that an increase in the coal price has a higher ability to predict REC than the oil price. As a robustness check, we also find that an increase in Brent prices significantly decreases REC. The findings of this study support the view that there is a substitution effect from oil to coal before initiating the use of renewable energy in China.

Designing and analysis of index-based long-term electricity market contract considering recent surge of coal price in China

Under the background that the spot market has not yet been fully established, the risk of coal price fluctuation lacks an effective mechanism to transmit to users. During the period of high coal prices, coal-fired units suffered serious losses and the willingness to generate electricity declined extremely, which was not conducive to the stable growth of the national economy. To solve the above problems, after investigating domestic and foreign solutions, this paper conceived a designing idea for the index-based long-term electricity market contract suitable for the current electricity market. Based on the actual data from a province in China, the specific parameters of index-based contract were designed and the profit of units were analyzed. The results show that the profit fluctuations of the coal-fired units will be significantly reduced after the trading contract adopted. In the end, the key issues of trading contract and the impact on market entities were discussed.

The Impact Effect of Coal Price Fluctuations on China’s Agricultural Product Price

Few studies have used China’s latest economic data to verify the interaction between coal price fluctuation and vegetable price fluctuation. Therefore, the sharing of existing knowledge in the academic community is mainly reflected in this paper, which explores the influence between coal prices and agricultural product prices for the first time. Further, it supplements the verification of the effective parameters of vegetable price fluctuation in academia. The current study investigates the relationship between coal prices (thermal coal price) and agricultural product prices (vegetable prices) in China from 2016 to 2021. It uses separate time-series models to verify the effect of China’s coal price fluctuation on the price of agricultural products and explores the effect of the coal price on the vegetables’ price trend. The results confirm that the thermal coal price significantly impacts and positively affects vegetable prices. There is also a linkage between the price of coal and the security of agricultural products. It might mainly be due to coal usage in various stages of the growing, storage, transportation, and distribution of agricultural products. Higher coal prices may lead to higher agricultural prices, threatening China’s coal-dominant energy structure. These higher coal prices will endanger domestic energy security and agricultural security. Finally, this study also suggests ways to manage the effect of increased coal prices on agricultural product prices and then puts forward policy suggestions.

Comprehensive competitiveness assessment of four coal-to-liquid routes and conventional oil refining route in China

Four coal-to-liquid (CTL) routes, called direct coal liquefaction (DCL), indirect coal liquefaction (ICL), coal-based methanol to gasoline (CBMTG), and coal tar hydrogenation (CTH), has been fully industrialized in China to relieve the excessive dependence on external oil. However, the knowledge and understanding of the strengths and weaknesses of the four CTL routes and the conventional oil refining (OR) route are insufficient. This study conducted a comprehensive competitiveness assessment regarding technology, environment, and economy to compare the four CTL routes and the OR route based on a life-cycle perspective. The results that the CTH route has the best comprehensive competitiveness and is the only CTL route that performs better than the OR route, thanks to its excellent technical and environmental competitiveness and considerable economic performance. However, the other three CTL routes face several challenges such as high energy and water consumption and intensive carbon emissions. Additionally, a sensitivity analysis was carried out to analyze the impact of variations in coal and oil prices, which shows that the CTH route has a strong ability to maintain a relative economic advantage over the OR route when coal and oil price fluctuations and hence is promising to be an effective alternative.

Analysis on the Mutation and Time-Varying Characteristics of Coal Price System Evolution from the Perspective of Finance

Coal is essential to ensure China’s energy security. The sudden or gradual change of coal price reflects the degree of disequilibrium or expected disequilibrium of coal supply and demand, which will not be conducive to energy security. Therefore, it is important to analyze the change points of coal price and explore the reason of the price fluctuation. This paper analyses the coal price from January 2008 to June 2019 as the perspective of the financial market. Firstly, the PPM-DBSCAN model is used to identify the mutation point of coal price fluctuation. Secondly, path analysis is used to extract the core driving factors that affect coal price. Thirdly, the authors construct a time-varying and time-lag effect analysis model for structural changes of coal price based on the TVP-VAR model. The results show that there are 11 mutation points of coal price fluctuation. Financial market factors, coal supply and demand and alternative factors are the reasons of coal price mutation. The authors find that the imbalance of coal supply and demand in traditional view cannot fully explain the fluctuation of coal price. The impact of the financial market and non-thermal power generation have more influence on the coal price.

Will Coal Price Fluctuations Affect Renewable Energy Substitution and Carbon Emission? A Computable General Equilibrium-Based Study of China

Changes in the energy price system will determine the direction of evolution of the energy industry structure. As a country where coal is the dominant energy source, what is the effect of coal price fluctuations on China’s industry development costs and energy consumption structure? To investigate this problem, this paper utilized an economy–energy–environment computable general equilibrium model. In this study, four aspects were analyzed: Energy supply side, proportion of renewable energy consumption, macroeconomy, and changes in CO2 emissions. The results of this study show that an increase of 10%–20% in coal prices contributes to a shift into using renewable energy, which leads to energy saving and emission reduction. Renewable energy and clean energy rose by 0.57%–4.47% in the energy structure, but this has a certain negative impact on the macroeconomy. The gross domestic product (GDP) fell by 0.07%–0.18%. As a result, the decline in coal prices became an obstacle to renewable energy substitution and energy conservation. In addition, we put forward policy suggestions according to the results in energy, economic, and environmental effects.

The effect of international energy market shocks on coal price of China based on the fuzzy integrated vector auto regressive and error correction model

From the perspective of external market shocks, this paper proposed fuzzy integrated vector auto regression (FVAR) model that determines the long-term basis and short-term basis interactions of China’s coal price with international energy prices. The proposed FVAR preform coal price fluctuation based on long-term and short term span in six stages including unit root testing, Johansen cointegration test, vector auto regression (VAR) model construction, fuzzification of VAR model, vector error correction (VEC) model and an impulse response function(IRF). It is observed that there is a steady long-term stability and equilibrium bond between the China’s domestic coal price, international coal price and the international crude (unrefined) oil price. The international coal and international crude oil price have an opposite effect on China’s domestic coal price. In addition, the former has a stronger fuzzy price discovery function on China’s domestic coal market than the latter. In the short term, China’s domestic coal price is more complex to instability reactions and is affected by market expectations. The international energy market is more effective than domestic coal market, and there is a relatively stable price adjustment mechanism between the two, with the international coal price playing a leading role in the fuzzy guidance of China’s coal price. Therefore, in reference to international energy pricing models, the paper proposes a fuzzy pricing model for a coal futures index based on the coal futures trading price and supplemented by the premium and discount agreed to by both trading parties.

Research on influencing factors of coal price based on improved principal component analysis and grey correlation method

As an important basic energy in China, the price fluctuation of coal is related to the operation of China’s coal market and the whole energy market and coal price analysis has always been the focus of the industry. By establishing an improved principal component-grey correlation analysis (PCA-GRA) model, the influencing factors of coal price were analyzed and tested one by one, and the influence degree of the influencing factors such as total coal production, total coal consumption, coal import and export volume, and world coal price on China’s coal price was studied. The results show that coal price fluctuation is greatly influenced by coal energy storage level and market environment level. In order to promote the development of coal industry, some suggestions are put forward.

Multistep-ahead forecasting of coal prices using a hybrid deep learning model

An accurate forecasting model for the future coal price fluctuations provides critical information and early warning for government and policymakers to provide a stable supply of energy which considered the main concern for China's policymakers. This paper proposes a deep learning model for accurately forecasting monthly coal price fluctuations at different horizons. The proposed LSTM–DNN model combines long-short term memory (LSTM) and deep neural network (DNN). To demonstrate how LSTM–DNN model leads to improvements in predictive accuracy. We compared the results of the LSTM–DNN model to other competitive models, which include multilayer perceptron neural network (MLP) and support vector machine (SVM). Experimental results show the superiority of the hybrid LSTM–DNN model over the other competitive models and its capability to forecast multiple steps accurately and flexible. Therefore, the proposed approach represents an effective and promising technique for the long-term future prediction of coal price fluctuations.

Can energy-price regulations smooth price fluctuations? Evidence from China’s coal sector

Due to the dominance of coal in China’s energy mix, coal prices have always been a challenging part of pricing reform. The recent frequent government interventions raise the key research questions: what is the actual impact of price policies on coal price fluctuations, and how can forward-looking pricing policies be made. By proposing a novel classification of coal pricing policies and introducing an expectation and forward-looking coefficient, the paper examines the relationship between coal price fluctuations and pricing policies using the generalized method of moments (GMM) method. It shows that the lagging coal price and coal demand play a positive role in regulating coal prices, while coal supply and marketization have significantly negative effects on coal price fluctuations. The heterogeneous impacts of price policies are due to differences in market players’ expectations, policy instruments and the methods of policy release. In addition, China’s coal pricing policy portfolio from 2013 to 2016 exerted synergy effects on the restraint of coal price fluctuations. As the forward-looking coefficient was considerably low, the government's intervention behaviors were obviously biased towards ex post facto responses. The paper suggests short run and long run policies to advance marketization of coal prices amid the energy transition.

Research on optimization of hedging ratio of thermal coal futures in thermal power enterprises based on Delphi method

The enthusiasm of coal-fired thermal power enterprises to participate in coal futures hedging to avoid the risk of coal price fluctuations is increasing. The key to hedge operation is to determine the total coal inventory required by the power generation company according to the amount of electricity generated. Participating in the hedging of coal futures not only plays a role in avoiding the risk of price fluctuation, but also reduces the occupation of funds and improves the utilization ratio of enterprises’ funds. When the total stock of the power generation enterprise has been determined, the optimal ratio of coal futures hedging is calculated scientifically, and then the virtual stock position of coal is calculated, and the actual stock and virtual stock of the coal are reasonably determined. This paper constructs the optimization model of coal hedging ratio of coal-fired thermal power generation enterprises, and innovatively puts forward the Delphi method to determine the three coefficients in the hedging ratio model based on the practical experience.

Study on the coordinated development of economy, environment and resource in coal-based areas in Shanxi Province in China: Based on the multi-objective optimization model

Coal-based areas face resource depletion, environmental pollution, ecological damage, and other coordinated economic and social pressures. Based on the theory of policy network and the interest interaction in the policy network of the stakeholders, the paper puts forward the development goals of economic growth maximization, environmental pollution minimization and coal resource consumption minimization. Utilizing the data of the input-output table in Shanxi in 2007 and the China Environment Statistics Yearbook in 2008 and the multi-objective optimization model, this paper designs and assesses the green development policies effects, taking Shanxi province as the case. The results are that: (1) Three stakeholders of coordinated development in coal-based regions are the government, coal enterprises and the residents of the mine area. (2) The common goals of the three stakeholders are the maximization of industry output value, the minimization of pollutant discharge, and the minimization of coal consumption. (3) The research shows the coal industry will still be the main industry in Shanxi in 2015–2020. According to the conclusions, the paper puts forward to pay attention to the government interests, coal enterprises and residents of the mining area. Furthermore, coal exploitation and utilization should be planned scientifically, the trend of the coal market and price fluctuations should be analyzed, and the appropriate tax rate should be formulated.

Analysis of the affecting factors of coal price in China based on VAR model

Coal has the main position in China’s energy structure; coal prices have an important impact on China’s economy. The price of coal is directly measurable variables which reflect the change and development of the coal industry; coal price fluctuations will not only affect the coal industry itself, but also relates to the development of the national economy and social stability. This paper takes the analysis on the influence factors of coal prices, using the monthly data of coal price and the influence factors of 2012–2016 years, to study the influence factors of coal price in China by the unit root test, co-integration test and VAR model and the establishment of the impulse response function.

Coal price fluctuations in China: Economic effects and policy implications

The price of coal fluctuated widely in the past few years in China. It is widely believed that rising coal prices can push up the inflation; this affected the determination of the policy of controlling coal price. This paper examines whether the rising coal price determines inflation in China. Bootstrap correlation test indicates that the Consumer price index (CPI) is not always positively correlated to the coal price in preceding months. The Bootstrap Granger test shows that coal price Granger-causes the CPI with 4–6 lags. However, many regression coefficients in the Granger test are negative and significant, which is consistent with the result of the bootstrap correlation test and contradicts the cost-push theory. Based on the Vector autoregression (VAR) model, bootstrap impulse response analysis reveals that coal price affects the CPI, but the directions of the effects vary and cancel each other out over a long period. Therefore, we find no evidence that shows a rise in coal price leads to cost-push inflation. Electricity price regulations, the price filter effect in the industry chain, and the Fisher currency trading equation theory can be used to explain this phenomenon. Finally, we conclude that both coal prices and the CPI are caused by inflation (in addition to market liquidity) instead of being the cause.

Coal price fluctuation mechanism in China based on system dynamics model

We analyze and identify the factors that influence coal price fluctuations and construct a system dynamics model of these factors. The simulation results show that the trend in China’s future coal prices first declines and then rises, that the trends of coal supply and consumption in China are quite similar, both exhibiting a rising tendency, and that the gap between supply and demand is small and that the market is essentially in equilibrium. However, the increase in the coal industry profit margins also entails an increase in coal prices, and the magnitude of coal price increases also gradually grows. Greater adjustment coefficient factors lead to higher simulated coal price values, and the magnitude of increase also tends to rise. On the one hand, the greater the coal industry policy coefficient is, the closer the simulated coal price is to the value in the real scenario. On the other hand, the smaller the coal industry policy coefficient is, the greater the extent to which the coal price deviates from the real simulated value.