Coal Market Research Articles

An Economic Analysis of Coal Pricing in Mongolia

Coal is a critical export for Mongolia, playing a significant role in the nation’s economy by contributing to GDP and export revenues. This paper provides an economic analysis of coal pricing in Mongolia, examining the primary factors that drive price fluctuations in both domestic and international markets. Key areas of focus include global demand, particularly from neighboring China, transportation costs, government policies, currency exchange rates, and environmental regulations. The paper employs a mixed-method approach, analyzing historical price data alongside policy reviews to identify the challenges and opportunities Mongolia faces in stabilizing coal prices. Global coal demand, especially from China, has a profound effect on Mongolian coal prices, while infrastructure limitations and transportation costs further exacerbate price volatility. Additionally, Mongolia’s coal sector is influenced by international competition, particularly from established exporters like Australia and Indonesia, which benefit from more developed supply chains. This research aims to provide valuable insights for policymakers and stakeholders by offering recommendations on how Mongolia can enhance price stability and strengthen its position in the global coal market. By analyzing the economic forces shaping Mongolian coal prices, the study contributes to broader discussions on resource management and economic sustainability in coal-dependent economies. The findings will aid policymakers in developing strategies that address Mongolia's infrastructure challenges and ensure the long-term viability of its coal industry in the face of evolving global energy dynamics.

Extreme weather, policy uncertainty, and risk spillovers between energy, financial, and carbon markets

Extreme weather, as well as policy uncertainties associated with climate change, have fueled global energy market turbulence and cross-market risk contagion, which have had serious effects on the economy. Using time-frequency network analysis based on quantile vector autoregression (QVAR), this study reveals the risk spillovers between China's energy, financial, and carbon markets in different time and frequency domains. The effects of extreme weather and policy uncertainty on market risk spillovers are also examined. The results show that: (1) Risk spillovers vary significantly in different time and frequency domains, and extreme risks tend to occur in the tails of the shock distribution, and market risk spillovers occur mainly in the long term; (2) Extreme weather and policy uncertainty significantly affect energy, financial, and carbon markets, altering their net spillover levels. Among which the coal market is the most volatile and highly sensitive to changes in external conditions; (3) Risk spillovers exhibit distinct time-varying characteristics, with extreme weather, climate policy uncertainty, and trade policy uncertainty exacerbating market risk spillovers. These findings could serve as a reference for monitoring market risk spillovers, facilitating energy transition, and achieving dual‑carbon targets.

Global Market for Metallurgical Coal

Global Market for Metallurgical Coal

Enhancing the role of dynamic capabilities of the coal companies under the conditions of sharp price fluctuations at the international markets

In recent years, international coal markets have been facing a trend of a growing number of sharp price fluctuations during a year. This can be regarded as the evidence for the increasing intensity of market competition as well as for the growing number and significance of the external environment factors affecting the world coal market. Actually, the coal market players have to operate in the conditions of unstable prices and external limitations that in their turn require searching for new sales markets, changing logistics routes, product lines, production and technological processes, etc. It results in the increasing significance of dynamic capabilities of coal companies – organizational capabilities on changing the portfolio of resource and capabilities in response to the changing competition at the market. In the conditions of growing volatility of coal markets the adaptation capabilities of a company become a basis for its economic effectiveness and competitiveness as well as for the viability as a whole as it allows to accelerate the process of making decisions about the necessary transformations and evaluate the risks, and are crucial for the implementation of new projects. Any activities on the evaluation and development of dynamic capabilities are possible only inside a company. This task should be solved at the strategic level of decision-making alongside with identifying the company’s individual priorities for development of organizational capabilities lying in the basis of dynamic capabilities of a company.The article deal with the author’s definition of the basic categories of dynamic capabilities implemented for the coal industry. The definition presents the priorities for operation in the current conditions and the capability to transform and accept new business-models, develop analytical capabilities and skills of acquisition and alliances with other companies.

Research on Decision Optimization and the Risk Measurement of the Power Generation Side Based on Quantile Data-Driven IGDT

In an environment marked by dual carbon goals and substantial fluctuations in coal market prices, coal power generation enterprises face an urgent imperative to make scientifically informed decisions regarding production management amidst significant market uncertainties. To tackle this challenge, this paper proposes a methodology for optimizing electricity generation side market decisions and assessing risks using quantile data-driven information-gap decision theory (QDD-IGDT). Initially, a dual-layer decision optimization model for electricity production is formulated, taking into account coal procurement and blending processes. This model optimizes the selection of spot coal and long-term contract coal prices and simplifies the dual-layer structure into an equivalent single-layer model using the McCormick envelope and Karush–Kuhn–Tucker (KKT) conditions. Subsequently, a quantile dataset is generated utilizing a short-term coal price interval prediction model based on the quantile regression neural network (QRNN). Interval constraints on expected costs are introduced to develop an uncertainty decision risk measurement model grounded in QDD-IGDT, quantifying decision risks arising from coal market uncertainties to bolster decision robustness. Lastly, case simulations are executed by using real production data from a power generation enterprise, and the dual-layer decision optimization model is solved by employing the McCormick–KKT–Gurobi approach. Additionally, decision risks associated with coal market uncertainties are assessed through a one-dimensional search under interval constraints on expected cost volatility. The findings demonstrate the effectiveness of the proposed research methodology in cost optimization within the context of coal market uncertainties, underscoring its validity and economic efficiency.

Assessing Contagion Risks of Carbon Reduction Policies on the Stock Returns: Evidence from China’s Energy Markets

ABSTRACT This study investigates the impacts of China’s carbon mitigation policies spanning from 2011 to 2023 on the stock returns of seven distinct nonrenewable and renewable energy markets. We apply contagion tests, examining variations in linear, asymmetric, and external dependence to identify the transmission of policy shocks from the coal market to energy markets. Among the analyzed carbon reduction policies, both cap-and-trade schemes and coal capacity cut policies present stronger evidence of policy contagion between the coal and energy markets compared to national development plans. Additionally, nonrenewable energy markets demonstrate more pronounced evidence of policy contagion than renewable energy markets during the implementation of carbon reduction plans.

Coal price shock propagation through sectoral financial interconnectedness in China's stock market: Quantile coherency network modelling and shock decomposition analysis

The long and continuing coal-dominated energy structure in China makes it important to investigate the impact of coal price shocks on China's financial markets. This study identifies whether volatilities in coal market may propagate between sectoral equity markets through the heterogeneous connectedness between these markets, and even further contribute to larger scale overall instabilities. We first apply the cross-spectral quantile coherency (QC) to identify the time-frequency interconnectedness among returns of 28 sectors in China's equity market. A spatial autoregressive (SAR) framework based on the QC network is further utilized to identify the indirect effect propagating through the heterogeneous interconnectedness between 28 sectoral equity markets. The empirical results indicate significant risk contagion effects during market turmoil, while strong risk absorbing effects are confirmed for the tranquil case. The significantly varying sectoral interconnectedness along with the corresponding heterogeneous pattern of shock propagation under various market specifications may provide evidence for the spillover effects to be the key mechanism and the sectoral interconnectedness as an important channel for coal price shock propagation, which is essential to the effectiveness of portfolio diversification and financial stabilizing policy.

The time‐varying volatility spillover effects between China's coal and metal market

AbstractThis study employs a time‐varying parameter vector autoregression methodology with the Diebold and Yilmaz spillover index to scrutinize the temporal fluctuations in volatility spillovers between the Chinese coal and metal markets. The analysis is conducted from the dual perspectives of security indices and futures prices. The findings reveal a robust correlation between the coal and metal markets, with the coal market serving as a primary conduit for volatility spillover into the metal market. Furthermore, this study investigates the time‐specific impacts of coal decommissioning policies, the COVID‐19 pandemic, and the coal supply crisis on the coal–metal market volatility spillovers. The findings indicate that these three unique shocks significantly increase the overall risk spillover index between the coal and metal markets. Moreover, during these exceptional events, the extent or role of risk spillover in the coal–metal market undergoes varying degrees of change. On the basis of these findings, this article presents pertinent policy recommendations.

Scenario Planning Approach of Coal Market Sales on PT ABCoal Towards Energy Transitions Era

Nowadays, energy transition is one of the major events faced by coal producers, particularly Indonesian coal producers. PT ABCoal is one of the largest coal producers in Indonesia. Based on the company’s 5-year plan, PT ABCoal is expected to increase its production volume from 25 to 30 million metric tons to around 35 to 45 million metric tons. Within the export market, most of the sales (around 50%) were sold to the Chinese market through off-takers. Meanwhile, the 25% for the domestic market is obliged by the Indonesian government regulation and is not negotiable. This research aimed to understand the future condition of the coal market within the next five years and what scenario would likely happen in the next five years. Afterward, it would try to provide the coal sales strategy that will fit into those scenarios using explorative scenario planning with expert interviews to obtain the perspective and knowledge from an interviewee who has expertise in this field. The obtained answers are then mapped into the PESTLE analysis framework to obtain more understanding and collect thorough driving forces data. The critical impact and high uncertainty were determined through qualitative-quantitative analysis from interview results using content analysis. Based on the analysis in this research, it is concluded that the top driving forces that are being used for scenario elements are coal price and government regulation. This research creates four scenarios that are called business as usual, finding a new home, home sweet home, and more is better. Each scenario needs a different approach to coal sales strategy to optimize its revenue and ensure the sustainability of its selling operation. The coal sales strategy optimization for each scenario is based on domestic and export composition, preferable domestic industry target composition, and developing-country export composition.

Coal crisis in China: development, mechanism, causes and possible consequences

This article analyzes the dynamics of coal prices and systemic changes in the coal market in the PRC, revealing the internal Chinese mechanism of the coal crisis that broke out in the PRC in 2020. The data studied and the conclusions obtained in the article refute the widely held view in expert circles, according to which the cause of the coal crisis in China was the rejection of Australian coal. The study was carried out by analyzing a data array on the dynamics of changes in the price of coal for different types of coal and for different regions and provinces of China. In particular, the article is based on data on the dynamics of coal prices in Shaanxi and Hubei.
 The results of this research provide useful insight into both the nature of the coal crisis and the patterns of the system of formation of coal prices in the PRC, partially touching on the issue of the integrity of the Chinese coal market. The statistical data referring to the coal prices is viewed through the prism of the concept of a “multi-track” pricing system put forward by M. V. Karpov and, in general, confirms fruitfulness and significance of his theory. Also, the article, based on data from the Hong Kong Stock Exchange, shows the commercial practical significance of the findings and the importance of having up-to-date information data on dynamics of coal prices in China.

Spillover Analysis of Carbon Market, Crude Oil Market, and Coal Market based on BEKK-GARCH

Global warming has become an urgent issue for all mankind, and climate change caused by carbon dioxide emissions is a growing concern. China, as a major global energy consumer, has set "carbon neutrality" as its vision for 2035. This study aims to explore the impact of carbon neutral policy on the volatility of the energy market, and through a comprehensive analysis of the historical data of the carbon market, crude oil market, and power coal market returns, we aim to reveal the volatility spillover effect among markets. In this study, we determine the distribution trend of yield receipts as t-distribution by performing statistical tests, ADF analysis on the yield historical data of the three markets. Under this premise, ARCH model, GARCH model and EGARCH model are further used to carry out the preliminary validation of the yield series of each market, and under the premise of ensuring the existence of volatility benefits in the data of each market, BEKK-GARCH model is used to carry out the analysis of multi-market spillover benefits, and the significance of the results is ensured by the WALD test. Finally, the results of the study are linked to actual policies and market operations to provide relevant recommendations for governments, investors, enterprises and carbon trading platforms. This study provides in-depth market perspectives and policy support for addressing the challenge of climate warming, and provides strong data support and decision-making reference for achieving the goal of carbon neutrality.

Examining the linkage between economic policy uncertainty, coal price, and carbon pricing in China: Evidence from pilot carbon markets

Economic policies affect companies’ production decisions. And the energy consumption volume is an intuitive reflection of the enterprise’s production decisions. In China, coal is the main source of carbon emissions and the most important energy source. Therefore, the coal market and the uncertainty of economic policies are both directly tied to the carbon market. This study explores both the direct impact of economic policy uncertainty and coal price on carbon prices as well as the indirect impact of economic policy uncertainty on carbon prices through coal prices by utilizing the DCC-GARCH model and the NARDL model. The findings indicate that the dynamic correlations between coal prices and the CEPU are always negative and that those between the price of carbon and the CEPU vary by area. Meanwhile, the dynamic correlations between coal and carbon prices are only positive in Shenzhen and Beijing. Both coal prices and economic policy uncertainty produce asymmetrical impacts on carbon prices. Some policy implications are provided for developing the carbon markets in light of the results drawn from the study.

Пространственная организация рынков продукции топливно-энергетического комплекса России

The article analyzes the markets of fuel and energy complex products in Russia, i.e. of oil, natural gas, coal, electricity, and heating energy. Among the emphasized features of the markets of fuel and energy complex products are the manifold of regulatory influences, the dependence of geographical boundaries of the markets on the development of the corresponding infrastructure, the presence of vertical and horizontal integration, the high dependence of the national markets of the complex products on the corresponding world markets. The combination of the described features on each of the markets of fuel and energy complex products forms a variety of conditions for their functioning within the boundaries of the national economy, which sets the direction of commodity flows and determines the development of the relevant transport infrastructure, which, in turn, are manifested in the spatial organization of product markets (their geographical segmentation). The research algorithm consisted of the analysis of sectoral markets based on the following parameters: properties of energy as a commodity, resource base, characteristics of producers, consumption structure, infrastructure development, and institutional conditions. Based on the combination of parameters, segments were identified for each of the fuel and energy complex product markets. The oil, natural gas, coal and heating market have two segments each: western and eastern. In the coal market, the eastern segment itself has two segments: eastern coastal and eastern mainland. The electricity market has three segments: western, eastern and northeastern. The analysis allows us to identify the western and eastern segments in each of the fuel and energy complex product markets, which are diametrically different in their characteristics. The stability of the distribution of the country’s territories by segments suggests the presence of invariants determined by the parameters of physical space, the effect of which is weakly susceptible to regulatory influences

Quantifying the Resilience of Coal Energy Supply in China Toward Carbon Neutrality.

Facing the challenge of achieving the goal of carbon neutrality, China is decoupling the currently close dependence of its economy on coal use. The energy supply and demand decarbonization has substantial influence on the resilience of the coal supply. However, a general understanding of the precise impact of energy decarbonization on the resilience of the coal energy supply is still lacking. Here, from the perspective of network science, we propose a theoretical framework to explore the resilience of the coal market of China. We show that the processes of increasing the connectivity and the competition between the coal enterprises, which are widely believed to improve the resilience of the coal market, can undermine the sustainability of the coal supply. Moreover, our results reveal that the policy of closing small-sized coal mines may not only reduce the safety accidents in the coal production but also improve the resilience of the coal market network. Using our model, we also suggest a few practical policies for minimizing the systemic risk of the coal energy supply.

Techno-economic assessment for metallurgical coals: a ‘value-in-use’ approach

The optimization of coke cost is the most significant cost-controlling factor for hot metal production. Therefore, worldwide cokemakers always have extra pressure from management to produce superior quality coke with inferior raw materials and optimal costs. Coke quality is crucial in blast furnace operation regarding cost and productivity. The quality of coke is significantly influenced by the quality of different categories, viz., prime hard coking coal, hard coking coal, soft coking coal and semi-soft coking coal of individual coals used in the coal blend. The impacts of these coals directly influence coke properties because all coals have an inherent characteristic with different coking potentials in terms of value-in-use (VIU). Also, the technological and techno-commercial change of the metallurgical coal market of today differs from past decades. Likewise, the future metallurgical coal market will vary from today’s market. Therefore, a process for assessing/evaluating the coking potential of metallurgical coal shall be used for appropriate value-in-use to produce quality coke with the optimum cost. The composite coking potential methodology served the value-in-use purpose and was successfully implemented. The paper describes the significance of the value-in-use of metallurgical coal to evaluate the most economically favourable technique for producing the desired coke quality.

Valuation and Financial Performance Analysis of Pt. Indika Energy

According to a coal market update study by the International Energy Agency, coal prices have been highly volatile during the previous five years, significantly influencing Indonesian coal companies’ operations and substantially impacting income and stock values. Furthermore, the increased global attention to addressing climate change is anticipated to lead to stricter enforcement of carbon emissions laws by 2030. PT. Indika Energy is an energy company that highly relies on coal prices, accounting for 75% to 88% of its revenue. Indika has stated four strategies, two of which are Efficiency and Synergy Optimization, and Prudent management. However, the result of profitability performance is not aligned with the company strategy. This research proposes to evaluate the comprehensive financial performance, assess the intrinsic value using the DCF-FCFF approach, and estimate PT’s price-to-earnings (P/E) ratio and Market to Book (M/B) ratio. Indika Energy. This study uses several ratios, including liquidity, profitability, activity, and leverage ratio. The study revealed that the company demonstrates the worst liquidity performance and is the least profitable among its peers. However, the company has managed to maintain the most significant leverage ratio. According to the DCF valuation, the company appears to be undervalued, with an intrinsic value of IDR 3,247, while the market value is IDR 1,540. The company is also undervalued based on its relative valuation metrics. It has a price-to-earnings ratio (PER) of 1.06 and a price-to-book value (PBV) of 0.45, compared to the industry average PER of 5.15 and PBV of 5.27. Hence, the company should minimize its reliance on subcontractors by optimizing its cost structure.

Перспективное освоение угольных запасов Арктики на основе пространственной организации коммуникаций

In the current conditions of economic instability, the problem of developing the regional space of the Arctic zone, where huge reserves of hydrocarbons and other minerals are concentrated, which are the main driver of the future development of the Arctic economy, is becoming increasingly important. The development of the Arctic region was strongly influenced by the international environment, which, on the one hand, poses a threat to the Arctic mineral projects, and on the other hand, opens up new opportunities for their successful implementation. Against this background, the task of the Arctic coal industry within the framework of spatial and regional development is the territorial and production optimization of production and transportation of coal products. The purpose of the study is to analyze current trends in the development of the coal mining market in the new geo-economic and political conditions, which results in the proposed approaches to the spatial organization of the transport communications system, allowing maximizing the economic and export potential of the prospective development of Arctic coal reserves. It has been established that the current situation greatly affects the energy market. Russian coal market is being transformed under the influence of geo-economic transformations, the formation of new coal mining centers is shifting eastwards closer to the Asia-Pacific region, which will become the main center of coal trade in the near future. The study concludes that the efficiency of the development of Arctic coal reserves directly depends on the spatial organization of a complex system of integrated production and transport corridors (IPTC) that combine the production and logistics chain of mining and transportation of coal products into a single holistically integrated space of sea, coastal and land communications. On the basis of the analysis of the Taimyr coal basin, a priority level for the prospective development of mineral resources has been determined, based on a combination of factors that condition the economic and export potential of coal deposits.

Price trends on the international steam coal market in 2000–2020

Price trends on the international steam coal market in 2000–2020

A hybrid causal machine learning to reveal driving factors responsible coal market: Case of the Chinese industry

To achieve carbon neutrality, China must grasp the intricate factors driving coal market, as it addresses coal consumption and reduces the carbon footprint. While previous studies have identified certain factors, they have largely overlooked the impact of the overall industry sector on the coal market. In this paper, a hybrid causal machine learning model was proposed that combine with least absolute shrinkage and selection operator (LASSO), multiple linear regression (MLR), peter and clark momentary conditional independence (PCMCI) and interpretable machine learning. The approach employed a complex network perspective to identify 10 key factors in China's coal market from three dimensions: industrial sector, foreign trade and energy market. The hierarchical pattern of these factors is classified into two levels: direct factors, primarily encompassing crude oil futures, exchange rates and architectural decoration, and intermediary factors, mainly including the chemical industry and building material. Interestingly, this paper reaffirms the roles of crude oil futures and exchange rates in the coal market, as identified in prior studies. Additionally, it uncovers new influential factors primarily driven by the chemical industry and architectural decoration, previously unexplored. Furthermore, this paper found a significant contemporaneous relationship between crude oil futures and coal price index (0.151) as well as coal consumption (0.102), and the strong relationship between coal consumption and the Indonesian Rupiah to Chinese Yuan exchange rate (0.063) was also confirmed. Among the emerging dominant factors, the coal price index and the chemical industry (0.145), along with coal consumption and building materials (0.250), also exhibit a strong contemporaneous relationship. These findings highlight the importance of considering the entire industry sector while analyzing the coal market and promoting a cleaner transformation of the industry in China, and the paradigm presented not only applicable to the coal market, but can be extended to other industries.

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.