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The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.

The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.

The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.

Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.

The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.

Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.

Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts.

Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence.

Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods.

This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.

The coal-fired power plants have been confronted with new operation challenge since the unified carbon trading market was launched in China. To make the optimal decision for the carbon emissions and power production has already been an important subject for the plants. Most of the previous studies only considered the market prices of electricity and coal to optimize the generation plan. However, with the opening of the carbon trading market, carbon emission has become a restrictive factor for power generation. By introducing the carbon-reduction target in the production decision, this study aims to achieve both the environmental and economic benefits for the coal-fired power plants to positively deal with the operational pressure.

A dynamic optimization approach with both long- and short-term decisions was proposed in this study to control the carbon emissions and power production. First, the operation rules of carbon, electricity and coal markets are analyzed, and a two-step decision-making algorithm for annual and weekly production is presented. Second, a production profit model based on engineering constraints is established, and a greedy heuristics algorithm is applied in the Gurobi solver to obtain the amounts of weekly carbon emission, power generation and coal purchasing. Finally, an example analysis is carried out with five generators of a coal-fired power plant for illustration.

The results show that the joint information of the multiple markets of carbon, electricity and coal determines the real profitability of power production, which can assist the plants to optimize their production and increase the profits. The case analyses demonstrate that the carbon emission is reduced by 2.89% according to the authors’ method, while the annual profit is improved by 1.55%.

As an important power producer and high carbon emitter, coal-fired power plants should actively participate in the carbon market. Rather than trade blindly at the end of the agreement period, they should deeply associate the prices of carbon, electricity and coal together and realize optimal management of carbon emission and production decision efficiently.

This paper offers an effective method for the coal-fired power plant, which is struggling to survive, to manage its carbon emission and power production optimally.

Sustainability is a major challenge for India’s (Bharat’s) coal mining industry. The government has prioritized sustainable growth in the coal mining industry. It is putting forth multifaceted economic, environmental and social efforts to accomplish the Sustainable Development Goals (SDGs). This research aims to identify the factors for sustainable improvements in coal mining operations. Secondly, this study examines the intensity of causal relations among the factors. Thirdly, this study examines whether causal relations exist among the factors to be considered for sustainable improvement in coal mining operations. Lastly, the study aims to understand how the factors ensure sustainable improvement in coal mining operations.

An integrated three-phase methodology was applied to identify the critical factors related to coal mining and explore the contextual relationships among the identified factors. Fifteen critical factors were selected based on the Delphi technique. Subsequently, the fifteen factors were analyzed to determine the contextual and causal relationships using the total interpretive structural modelling (TISM) and DEMATEL methods.

The study identified “Extraction of Coal and Overburden” as the leading factor for sustainable improvement in coal mining operations, because it directly or indirectly influences the overall mining operation, environmental impact and resource utilization. Hence, strict control measures are necessary in “Extraction of Coal and Overburden” to ensure sustainable coal mining. Conversely, “Health Impact” is the lagging factor as it has very low or no impact on the system. Therefore, it requires fewer control mechanisms. Nevertheless, control measures for the remaining factors must be decided on a priority basis.

The proposed structural model can serve as a framework for enhancing sustainability in India’s (Bharat’s) coal mining operations. This framework can also be applied to other developing nations with similar sustainability concerns, providing valuable guidance for sustainable operations.

The current study highlights the significance of logical links and dependencies between several parameters essential to coal mining sustainability. Furthermore, it leads to the development of a well-defined control sequence that identifies the causal linkages between numerous components needed to achieve real progress towards sustainability.

In response to the navigation challenges faced by coal mine tunnel inspection robots in semistructured underground intersection environments, many current studies rely on structured map-based planning algorithms and trajectory tracking techniques. However, this approach is highly dependent on the accuracy of the global map, which can lead to deviations from the predetermined route or collisions with obstacles. To improve the environmental adaptability and navigation precision of the robot, this paper aims to propose an adaptive navigation system based on a two-dimensional (2D) LiDAR.

Leveraging the geometric features of coal mine tunnel environments, the clustering and fitting algorithms are used to construct a geometric model within the navigation system. This not only reduces the complexity of the navigation system but also optimizes local positioning. By constructing a local potential field, there is no need for path-fitting planning, thus enhancing the robot’s adaptability in intersection environments. The feasibility of the algorithm principles is validated through MATLAB and robot operating system simulations in this paper.

The experiments demonstrate that this method enables autonomous driving and optimized positioning capabilities in harsh environments, with high real-time performance and environmental adaptability, achieving a positioning error rate of less than 3%.

This paper presents an adaptive navigation system for a coal mine tunnel inspection robot using a 2D LiDAR sensor. The system improves robot attitude estimation and motion control accuracy to ensure safe and reliable navigation, especially at tunnel intersections.

With the rapid low-carbon transformation in China, the industrial approach and labor structure of mining enterprises are undergoing constant changes, leading to an increasing psychological dilemma faced by coal miners. This study aims to reveal the relationship and mechanism of factors influencing the psychological dilemma of miners, and to provide optimal intervention strategies for the safety and sustainable development of employees and enterprises.

To effectively address the complex issue of the psychological dilemma faced by miners, this study identifies and constructs five-dimensional elements, comprising 20 indicators, that influence psychological dilemmas. The relational mechanism of action of factors influencing psychological dilemma was then elucidated using an integration of interpretive structural modeling and cross-impact matrix multiplication.

Industry dilemma perception is a “direct” factor with dependent attributes. The perceptions of management response and relationship dilemmas are “root” factors with driving attributes. Change adaptation dilemma perception is a “susceptibility” factor with linkage attributes. Work dilemma perception is a “blunt” factor with both dependent and autonomous attributes.

The aforementioned findings offer a critical theoretical and practical foundation for developing systematic and cascading intervention strategies to address the psychological dilemma mining enterprises face, which contributes to advancing a high-quality coal industry and efficient energy development.

The purpose of this paper is to present a case study the application of lean six sigma combined with mining transportation overall vehicle effectiveness (MTOVE) to improve mining transportation performance. MTOVE is a newly developed model to measure the overall effectiveness of mining transportation.

The method used is case study combines the MTOVE and LSS methodologies. Data were collected from the hauling operation during a three-month period. Various lean six-sigma tools, such as the Pareto chart, ANOVA, two sample t-tests, one sample t-test, cause-and-effect analysis and time study, have been used.

The case study resulted in improvement of vehicle overall effectiveness; a 35% increase in MTOVE value, a 17% improvement in productivity and a 9% increment in truck utilization. Statistical tests confirmed the significance of reducing the mean and variation in the hauling process cycle time, which led to productivity improvement.

This study provides practitioners with additional quantitative evidence of the potential benefits of LSS methods in the coal mining industry.

This paper practically and unquestionably has contributed to the LSS body of knowledge focused on the mining sector, which is recently still far behind the manufacturing sector. The study has demonstrated that some challenges in the mining environment can be solved through the effective implementation of LSS tools. Hence, this paper could be used as a reference for both researchers and practitioners.

The study contributes in the field of LSS spread in mining industries using a case study. This study shows practical evidence of improving overall vehicle effectiveness using LSS. Practitioners can refer to this study to understand the benefits of LSS in mining sector. Since the mining industry should also adopt the LSS principle into the mining business process due to its ability to improve business performance (Valente et al., 2020; Tupamahu et al., 2019; Zanon et al., 2021).

There has been little scientific study of the LSS implementation in the mining industry. This research provides detailed evidence of LSS implementation in the mining sector. The main contribution is the implementation framework, which shows the combination of newly developed indicators (MTOVE and LSS) to enhance hauling operation effectiveness. This paper demonstrates how LSS tools and methods can be applied in the mining transportation industry.

Just transition is a fundamental concept for supply chain management but neither discipline pays attention to the other and little is known about how supply chains can be orchestrated as socioecological systems to manage these transitions. Building from a wide range of just transition examples, this paper explores just transition to understand how to move beyond instrumental supply chain practices to supply chains functioning in harmony with the planet and its people.

Building from a systematic review of 72 papers, the paper identifies just transition examples while interpreting them through the theoretical lens of supply chain management, providing valuable insights to help research and practice understand how to achieve low-carbon economies through supply chain management in environmentally and socially just ways.

The paper defines, elaborates, and extends the just transition construct by developing a transition taxonomy with two key dimensions. The purpose dimension (profit or shared outcomes) and the governance dimension (government-/industry-led versus civil society-involved), generating four transition archetypes. Most transitions projects are framed around the Euro- and US-centric, capitalist standards of development, leading to coloniality as well as economic and cultural depletion of communities. Framing just transition in accordance with context-specific plural values, the paper provides an alternative perspective to the extractive transition concept. This can guide supply chain management to decarbonise economies and societies by considering the rights of nature, communities and individuals.

Introducing just transition into the supply chain management domain, this paper unifies the various conceptualisations of just transition into a holistic understanding, providing a new foundation for supply chain management research.

This study aims to explore the diverse sources of electricity generation (coal, natural gas, oil and hydroelectricity) and their respective associations with economic growth and environmental quality.

This study uses static panel data analysis with a random effects model for six selected ASEAN countries (Indonesia, Malaysia, Filipina, Thailand, Vietnam and Myanmar) from 1994 to 2014.

This study reveals that economic growth in six selected ASEAN countries is enhanced by electricity generation from all sources, while the contribution of electricity production from hydroelectricity remains the largest and strongest. There is no environmental impact of electricity production from hydroelectric, whereas fossil fuel-based electricity production emits carbon dioxide, with coal sources being the largest contributor, followed by natural gas and oil.

Based on the results, these six ASEAN countries should invest more in hydropower projects, reduce the coal mix in power generation and promote clean coal technology to improve economic efficiency and environmental sustainability.

To the best of the authors’ knowledge, no research has examined the relationship between electricity production, environmental quality and economic growth in Southeast Asian nations. Therefore, the outcome of this study is expected to provide insightful results to supplement the framing and implementation of national and collective regional strategies for sustainable electricity generation in ASEAN countries.

The paper explores the roles of involved organizations in the designation of Batu Arang in Gombak, Selangor, Malaysia, as the Coal Mining and Geological Heritage Site, the heritage significance selected as the elements of its designation and the challenges faced by the involved stakeholders during the designation process.

The primary research material is derived from the documentation review on the final draft of the Special Area Plan (RKK) of Batu Arang and the final draft of the Geopark Management Plan, field investigation on the heritage significances and interviews with the involved organizations: the Selayang Municipal Council (MPS), the State of Selangor Malay Custom and Heritage Corporation (PADAT) and the Village Community Management Council (MPKK) of Batu Arang.

This paper found that Batu Arang has the potential to be developed as an important heritage mining site and heritage tourism destination; however, many of the heritage significances are being demolished, invaded and abandoned due to human aggression or effects of nature. In addition, it reveals the roles of each involved organization, issues that occurred and challenges faced by the organizations during the designation process as a heritage site, namely in terms of management, property ownership and promotion.

The paper outlines that a heritage mining site like Batu Arang should be considered an important heritage as well as other heritage sites in Malaysia, and issues and challenges faced during the designation process should be discussed critically to ensure that these heritage significances will not be neglected and abandoned.