Preventable Accidents in Indian Coal Mining: A Socio-Technical Alignment Approach to Labour Safety

Preventable Accidents in Indian Coal Mining: A Socio-Technical Alignment Approach to Labour Safety | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Preventable Accidents in Indian Coal Mining: A Socio-Technical Alignment Approach to Labour Safety Aparna Raj C, Prerana Mudaliar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7529003/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Mineral Economics → Version 1 posted You are reading this latest preprint version Abstract With a significant position in the global minerals map, for India, the importance of the triple bottom line of people, planet, and profit in Indian mining is unmissable. Among the various mining activities, coal presents a relevant case as a significant sector for India, with its labourers being exposed to preventable hazards that result in fatalities and chronic injuries despite high mechanisation. This paper intends to develop a conceptual framework to theorise workplace safety in Indian coal mining through the lens of preventable accidents and socio-technical alignment by drawing upon the mining accidents database data from the Ministry of Environment, Forest and Climate Change, and literature on occupational safety. The paper is situated within the broader scholarship on occupational safety and highlights how most fatalities occur in ancillary processes such as haulage and transportation, rather than excavation, underscoring that mechanisation alone has not reduced risks. By advancing the concept of preventable accidents , the paper emphasises that mining fatalities are not naturalised risks inherent to geology or technology but outcomes of systemic failures in organisational routines, regulatory enforcement, and socio-technical integration. The paper foregrounds the concept of preventable accidents to reframe coal mine hazards not as inevitable but as institutionally produced and therefore avoidable. It also develops a theoretical argument that safety outcomes are shaped less by the degree of mechanisation than by the alignment of socio-technical systems, regulatory regimes, and organisational priorities. Through this framing, the paper situates Indian coal mining within global debates on labour precarity, industrial modernisation, and risk governance. The study challenges fatalistic discourses about mining risk and advances a socio-technical alignment model to guide scholarly debates and policy design. The findings have implications beyond coal mining, offering a framework for understanding safety in other high-risk, labour-intensive sectors where mechanisation coexists with organisational and regulatory deficits. Coal mining mining labour preventable accidents Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction With home to over a hundred minerals, India ranks among the top producers of mica, iron ore, bauxite, and coal. Even though the mining sector contributes approximately 2.5 per cent of the country’s gross domestic product, employing over a million workers (Mukherjee, 2024 ), mining practices in India have been mired in controversies. According to the Statistical Review of World Energy 2023 (Energy Institute, 2023 ), India is the second-largest producer, consumer, and coal importer after China, but with the fifth-largest coal reserves after the USA, Russia, Australia, and China. Coal mining remains one of the most hazardous industries worldwide, exposing workers to a broad spectrum of occupational risks, ranging from physical injuries to chronic health conditions. In India, coal mining is important because of its centrality to the country’s energy security, with over 70 per cent of India’s electricity generation being coal-based (Energy Cell Monthly Report, 2023), and the scale of human labour it engages. Despite significant advances in mechanisation and regulatory frameworks, coal mining in India continues to witness persistent workplace accidents. According to the Statistical Analysis & Numbers: Key Evaluation of Trends in Coal Mine Accidents (2016-22), there were more than 600 fatalities in the decade between 2013 and 2022. These incidents not only result in the tragic loss of life and livelihood but also underscore the preventable nature of many mining-related hazards, raising critical questions about organisational accountability, regulatory efficacy, and the socio-economic vulnerabilities of the mining workforce. The irony of modern Indian coal mining lies in its movement toward mechanisation and its simultaneous dependence on labour-intensive practices (Roy & Schaffartzik, 2021 ). Although underground mining has progressively transitioned to mechanised operations, such as longwall and continuous mining methods, the dominance of open-cast mining and associated activities continues to expose workers to severe risks 1 . Accident records suggest that fatalities and injuries are disproportionately concentrated in ancillary processes, particularly during transportation and haulage, rather than in core excavation tasks (Bellanca et al., 2021 ). Such evidence highlights the uneven distribution of risk across mining operations and suggests that mechanisation alone cannot guarantee safety if the systemic organisation of work and enforcement of preventive measures remain weak. The persistence of accidents in Indian coal mines must be viewed in a broader political, economic, and institutional context. Historically, coal mining has been symbolic of extractive labour regimes in the Global South, characterised by informalization, inadequate social protection, and insufficient regulatory mechanisms caught between the imperatives of industrial growth and worker welfare (Lahiri-Dutt, 2018 ). The preventable nature of most reported accidents reflects systemic deficiencies, such as inadequate training, poor safety culture, ineffective monitoring of safety standards, and insufficient investment in worker-centric technologies. Moreover, the disproportionate vulnerability of contract labourers, mostly migrants, who comprise a substantial share of the mining workforce, adds criticality to the debate on labour precarity and occupational health in India’s coal sector (Nayak, 2022 ). Theoretically, coal mine accidents can be conceptualised as random events and patterned outcomes of socio-technical systems. High Reliability Theory (HRT) and Normal Accident Theory (NAT) offer practical but contrasting frameworks for interpreting mining hazards. HRT emphasises that organisations can achieve accident-free operations through rigorous safety culture, training, and redundancy (Weick & Sutcliffe, 2011 ). In contrast, NAT suggests that accidents are inevitable in complex and tightly coupled systems despite preventive efforts (Perrow, 2010 ). However, the Indian coal mining experience complicates this dichotomy of the prevalence of accidents during transportation, which is a relatively less complex subsystem. It suggests that many incidents stem from organisational negligence and weak enforcement rather than systemic inevitability. This positions Indian coal mining as a fertile case for advancing the preventable accidents theory in high-risk industries. Through this study, we intend to make two contributions. Firstly, the paper foregrounds the conceptual category of preventable accidents to examine the persistence of workplace hazards in Indian coal mining. Secondly, it develops a theoretical argument that safety outcomes in mining are determined less by the level of mechanisation than by the alignment of socio-technical systems, regulatory enforcement and organisational priorities. This study contributes to broader debates in management, labour studies, and occupational health about the conditions under which industrial accidents become structurally embedded yet potentially avoidable. We organise the paper in the following order: The literature review studies the extant literature on occupational hazards in coal mining, focusing on mechanisation, labour precarity, and safety management. The subsequent section outlines the methodological orientation, including the database consulted for descriptive analysis. The analysis section then advances the theoretical framework, supported by descriptive evidence from the Indian mining accident data, highlighting the predominance of transportation-related accidents and their preventability. The discussion explores the implications for policy, management practice, and theory-building. Finally, the conclusion sets an agenda for future research on labour issues and industrial safety in the coal sector. Literature Review The study of occupational hazards in coal mining has historically wavered between two perspectives – one that accidents are inevitable consequences of geological or technological risks, and another that they are preventable outcomes of organisational and systemic failures. This review engages with both traditions but develops them further to ground the two contributions of this paper. We foreground the category of preventable accidents to reframe mining hazards as avoidable and institutionally produced and argue that labour safety is determined less by the extent of mechanisation than by the alignment of socio-technical systems, regulatory enforcement, and organisational priorities. The concept of preventable accidents The roots of accident prevention theory can be traced to Heinrich’s Industrial Accident Prevention (1931), which advanced the notion that most industrial accidents are preventable through eliminating unsafe acts and conditions. Although criticised for its behavioural reductionism, Heinrich’s model underscored the preventability of accidents, a theme that subsequent research has developed in more complex ways (Manuele, 2011 ). Reason’s ( 1990 ) Swiss Cheese Model expanded this logic by demonstrating that accidents occur when multiple system defences fail, thereby underscoring that most accidents are systemic failures rather than random events. In mining, Hopkins ( 2005 ) has shown how catastrophic accidents are often preceded by a series of near misses, which, if addressed, could have averted disaster. These studies collectively argue against the naturalisation of industrial accidents and instead locate them within organisational and institutional domains where prevention is possible. Specifically, in Indian coal mining, man accidents are linked to organisational factors such as inadequate supervision, poor training, and cost-cutting measures. Kunar et al. ( 2010 ) further showed that accidents in coal mines are not random but result from demographical characteristics of workers, inadequate support systems and insufficient monitoring. Both studies underscore that fatalities arise from preventable conditions that persist because of weak institutional accountability. More recent global scholarship has reinforced this perspective. Zhang et al. ( 2020 ), analysing Chinese coal mines, identified deficiencies in safety culture as root causes of accidents, emphasising the preventable nature of fatalities even in technologically advanced environments. Similarly, Wu et al. ( 2023 ) highlighted the role of weak supervision in China’s coal sector, showing that preventable hazards persist when regulatory institutions lack authority or capacity. These findings resonate strongly with the Indian case, where accidents disproportionately occur during transportation and haulage operations, indicating subsidiary processes that are highly amenable to prevention through proper training, maintenance, and regulatory enforcement (Maiti & Khanzode, 2009 ). Mechanisation and the persistence of hazards A vast amount of literature has focused on the role of mechanisation in shaping mining safety. Early socio-technical studies, most notably Trist and Bamforth’s ( 1951 ) seminal research on the longwall method of coal-getting, revealed that technological innovations radically reconfigured social organisation in mines, often with unintended safety consequences. This insight laid the foundation for socio-technical systems theory, emphasising that technology and organisation must be optimised for safe and efficient outcomes (Emery & Trist, 1960 ). Mechanisation is often assumed to improve safety by reducing direct human exposure to hazardous tasks. For instance, Australian and U.S. mining studies suggested that mechanised systems reduce fatalities linked to manual handling (Burgess-Limerick, 2011 ; Ruff et al., 2011 ). However, evidence also shows that mechanisation introduces new risks associated with haulage machinery, conveyor systems, and heavy vehicles (Duarte et al., 2021 ). In India, haulage accidents remain a leading cause of fatalities, highlighting that mechanisation has shifted rather than eliminated hazards. Perrow’s ( 2000 ) theory of “normal accidents” provides a conceptual lens for this paradox. He argued that complex, tightly coupled systems are inherently prone to failure, regardless of technological sophistication. In mining, haulage systems, transportation fleets, and ventilation infrastructures exhibit such coupling, creating vulnerabilities where a single failure can cascade into accidents. Weick and Sutcliffe ( 2011 ), through High Reliability Theory, countered that organisations can achieve near-error-free performance in such contexts by institutionalising safety cultures and redundancies. However, empirical studies suggest that neither technological advancement nor safety culture rhetoric has translated into reduced fatalities, precisely because organisational and regulatory misalignments persist (Zhang et al., 2020 ). Subcontracting and precarious labour practices further exacerbate this misalignment. Valluru et al. ( 2017 ) found that subcontracted workers experience different and often poorer safety outcomes than permanent employees, primarily due to weaker training, accountability, and oversight. This aligns with Indian evidence showing that a large proportion of fatalities occur among contract workers, who face systemic vulnerabilities (Nayak, 2022 ). Thus, mechanisation does not automatically translate into improved safety; instead, the alignment of socio-technical, organisational, and regulatory systems determines outcomes. Regulatory Enforcement and Institutional Weakness The role of regulation is central to the persistence of preventable accidents. Comparative studies demonstrate that countries with robust enforcement regimes have reduced mining fatalities (Weeks, 1991 ). In the U.S. and Australia, independent regulators with strong investigative capacities have ensured compliance with safety norms, driving improved labour safety (Gunningham et al., 1998 ). In India, however, regulatory bodies such as the Directorate General of Mines Safety (DGMS) face severe capacity constraints. Sishodiya and Guha ( 2013 ) document how enforcement gaps persist due to understaffing, political pressures, and the dominance of production imperatives. This results in a compliance gap, where safety norms exist on paper but are poorly implemented in practice. Empirical research shows that explosions and gas leaks, though less frequent, are catastrophic when they occur precisely because preventive measures such as ventilation and dust suppression are inadequately enforced (Ray et al., 2022 ; Shrawankar et al., 2022 ). These failures reveal that the issue is not technological insufficiency but institutional weakness. Wu et al. ( 2023 ) and Zhang et al. ( 2020 ) similarly emphasise that in China, safety improvements stagnate when regulatory enforcement is subordinated to production priorities, which is a dynamic directly mirrored in India. Methods and Data This study adopts a conceptual research design aimed at theorising workplace safety in Indian coal mining through the lens of preventable accidents and socio-technical alignment. The methodology is grounded in two interrelated assumptions: (i) that workplace accidents are not naturalised risks but socially constructed outcomes that can, in principle, be prevented (Reason, 1990 ; Hopkins, 2005 ) and (ii) safety outcomes are mediated less by the degree of mechanisation than by the alignment of demographic, organisational, regulatory, and socio-technical dimensions (Trist & Bamforth, 1951 ; Perrow, 2000 ). Our methodological approach combines descriptive analysis of secondary data with conceptual theorisation to offer a generalisable empirical design. We use the Indian mineral accidents data from the Environmental Information, Awareness, Capacity Building and Livelihood Programme database maintained by the Ministry of Environment, Forests & Climate Change and the Indian Institute of Technology Dhanbad. We use coal mining accident data from 2015 to 2025 to understand the causes of accidents at the various mining sites. We also use the data from the Ministry of Coal to map the measured volume of coal available in the different states to understand whether there is a proportional incidence of accidents based on the mining volume. We start the analysis with a descriptive mapping of accident categories in the database, and then we categorise the accidents into roof falls, haulage, machinery failures, explosions, and transportation. The interpretive categorisation is designed to reveal how accident patterns correspond to systemic misalignments in demographic vulnerabilities, organisational priorities, regulatory enforcement, and socio-technical integration. We also draw on socio-technical systems theory and accident prevention literature to conceptually elaborate on how most accidents are preventable. Results To understand the coal mining accidents, we use the data from 2015 to 2025 and classify them into different categories. In Fig. 1 , we plot the total fatalities over the decade. The trend shows a sharp decline after 2015, with a one-third drop in fatalities by 2016. However, the trend constantly fluctuated, with a sudden peak in 2018. Overall, the graph shows a substantial decrease in fatalities, rarely crossing 30 per year since 2019. The decline post-2015 reflects regulatory improvements and technological changes; however, the spikes in 2018 and 2024 highlight that the seriousness of accidents remained recurrent. The low spike between 2020 and 2023 may be due to the reduced activities during the COVID-19 pandemic, though underreporting during this period cannot be ignored. While the trend over the year shows a reduction in the fatalities, mining in India continues to pose a significant threat to the workers, with years marked by accidents serving as a reminder of how brittle safety improvements are. Table 1 Coal mine accidents from 2015 to 2025 Year Drowning Electricity Explosion Fall-related Machinery Roof/Collapse Transportation 2015 5 6 2 14 5 22 44 2016 1 3 4 1 2017 1 5 5 8 9 2018 17 3 1 5 4 11 11 2019 3 1 1 3 5 5 2020 3 3 1 2 3 2021 6 3 22 2022 1 14 2023 1 8 5 2024 9 5 3 17 2 2025 11 1 5 1 Table 1 highlights that the consistent causes of death remain transportation and vehicle-related accidents, and roof/ground collapse, accounting for half of the fatalities. The fatalities caused by roof collapse were high in the early years and notably declined. However, transportation accidents persistently contribute to the majority of underscoring gaps in safety management and vehicle regulations within mines. Operational hazards are routine, as evidenced by the consistent contributions of categories like machinery/equipment accidents and fall-related incidents over the decade. Even though they happen less frequently, explosions, fires, and electricity accidents can be disastrous. Furthermore, the data show that daily operational risks, particularly those related to transportation and mechanisation, continue to dominate the profile of mining accidents, even though some traditional hazards may be decreasing. Figure 2 categorises the proportion of various types of accidents which have occurred over the period. It was found that transport and vehicle-related accidents caused 33 per cent of accidents, and 25 per cent was due to roof/ ground collapse. Fall-related accidents account for 13 per cent, machinery accidents for another 12 per cent and electricity accounts for a smaller share at five per cent. The predominance of transport-related issues showcases the logistical vulnerabilities in the Indian mining industry. Roof collapse remains another critical cause of accidents, highlighting the need for improved infrastructure, support systems, and safety monitoring. The high percentage of falls and equipment incidents further highlights the shortcomings in workplace safety procedures and worker training. Although catastrophic incidents attract public attention, most mining fatalities are caused by routine operating hazards rather than infrequent disasters, as seen by the lower incidence of blasting or electricity-related accidents. This trend supports the idea that habitual negligence and poor enforcement are more of a systemic problem than isolated incidents regarding mining safety in India. The above figure highlights the distribution of mining fatalities across 10 leading states in India between 2015 and 2025. Naturally, the mineral-rich states such as Jharkhand, Odisha, and Chhattisgarh report high accidents because of the large-scale coal and metal ore extraction. Telangana, accounting for the highest number, indicates either an increase in mining intensity or possible deficiencies in operation and safety enforcement. Interestingly, Rajasthan emerged in the top three, suggesting quarry-related accidents contribute more significantly to national accidents than is often recognised. Regional variations in accident frequency are further highlighted by excluding historically mining-heavy states like Goa and Assam from the top fatality rankings, which may indicate better regulation or fewer incidents reported. When taken as a whole, these results show how mining hazards are distributed unevenly throughout India and emphasise the critical need for state-specific safety measures. The analysis across selected states reveals divergence between coal production and accidents (Fig. 3 ). Jharkhand, which accounts for the highest coal reserves, also reports equivalent fatality counts. However, such a proportionality is not visible across all the coal mining states. States like West Bengal, Telangana and Maharashtra exhibit moderate coal reserves but high numbers of reported fatalities. However, the proportion of accidents in states like Assam, Uttar Pradesh and Nagaland, which have very low volumes of coal in mines, is very high, indicating issues underlying flaws in labour safety and regulations rather than the incidence of accidents attributable to production intensity. Discussion India has made significant progress in reducing the mining accidents since 2015, reflecting the improved regulations and safety enforcement. However, critical safety concerns persist. The varying patterns imply that regulatory benefits are not consistently maintained across geographies or types of mining. It is alarming how frequently transportation accidents occur and remain a leading cause of fatality over time. Vehicle accidents in a controlled mine environment should be preventable, unlike collapses, which geological and seasonal factors like monsoons can cause. The persistent occurrence of these incidents suggests that mine traffic regulations are a shortcoming and that safety compliance is not adequately monitored. While the rates have decreased over time, roof and ground collapses are still a serious threat, demonstrating deficiencies in structural evaluations, preventative care, and the application of safety support systems. Deaths related to falls, handling equipment, and working during unsupervised hours (such as after-evening shifts) expose a lack of emergency response facilities, safety nets, and worker training. Rather than resulting from inherent mining risks, these deaths are preventable and reflect systemic negligence. Differences at the state level highlight how mining intensity does not determine safety. The unusually high death toll in some states points to differences in labour conditions, contractor practices, and safety code enforcement. Overall, the results suggest that routine and avoidable operational hazards continue to be a leading cause of death in Indian mines, even though catastrophic events such as fires and explosions are comparatively uncommon, given that they have not been reported. Addressing safety challenges A comprehensive strategy incorporating risk identification, preventive measures, and efficient enforcement must be implemented, aiming for safety in Indian mining operations. Regular risk assessments should be conducted to find possible risks related to coal extraction, especially in locations susceptible to roof collapse, transportation accidents, and fire/gas-related accidents. Clear Safe Operating Procedures (SOPs) must be established to support these assessments, particularly for high-risk operations like the extraction of fiery coal, where there is a risk of combustion and steam buildup. Comprehensive training programs for all labour categories, including contract workers, are crucial. The main training topics should be hazard awareness, proper PPE use, emergency response procedure, and fire safety. Frequent drills would strengthen readiness and promote a strong safety culture throughout mining sites. Another crucial area is workforce management. Strict access controls and the presence of knowledgeable supervisors in high-risk areas can further reduce accidents, while defining roles and responsibilities aids in preventing unauthorised access to hazardous zones. Supervisors and employees must communicate effectively about SOPs and situational awareness to ensure the team stays aware of their surroundings. Additionally, infrastructure upgrades include placements of safety barriers, nets, and fencing around pits and hazardous areas, along with regular inspection, which is required. Strict adherence to operational safety procedures is also required. This includes adopting a thorough work permit system backed by Job Safety Analysis (JSA) before beginning routine or non-routine tasks and rigorously adhering to Lock-Out and Tag-Out (LOTO) procedures during equipment maintenance. Since the risks of insufficient supervision or training are highest in non-routine jobs involving contractual labour, supervision should keep a close eye on these positions. The installation of sensitive Lower Explosive Limit (LEL) detectors to check for gas leaks, the development of dependable alert systems, and on-site medical facilities for prompt injury responses are necessary. When taken as a whole, these measures not only fix structural flaws but also promote a proactive safety culture, lowering the number of avoidable and ongoing deaths in the Indian mining industry. Socio-technical safety alignment to Indian coal mine accidents: A theoretical framework The persistence of fatalities in Indian coal mining, despite decades of mechanisation and regulatory codification, demands a theoretical lens that moves beyond technological determinism. We propose a Socio-Technical Safety Alignment Model (ST-SAM) that conceptualises mining accidents as preventable outcomes of systemic misalignments across four critical dimensions: demographic vulnerabilities, organisational priorities, regulatory enforcement, and socio-technical systems (Fig. 4 ). This framework reframes accidents not as stochastic or inevitable but as socially produced alignment failures. To anchor this framework empirically, we map the theoretical dimensions onto the major categories of accidents like roof falls, haulage accidents, machinery failures, explosions, and transportation-related incidents. By situating accident categories within the ST-SAM, the model not only integrates the empirical realities of Indian coal mining but also demonstrates how each accident category reveals underlying misalignments. We highlight how safety outcomes are mediated by factors such as labour composition, organisational imperatives, enforcement weaknesses, and the socio-technical embedding of mechanisation. We show how each accident category represents preventable misalignments. Demographic vulnerabilities and regulatory gaps Many underground workers are contract labourers recruited from marginalised communities with minimal formal training. Their limited literacy and technical knowledge impede the correct application of roof support systems, such as bolting or props, especially in highly mechanised mines where monitoring systems require careful calibration (Pelders & Nelson, 2019 ). Research has demonstrated that accidents from roof falls, haulage and transportation are rarely the product of unforeseeable natural hazards; instead, they stem from inadequate support systems, poor geological assessments, and failures in supervisory oversight (Zhang et al., 2020 ). These failures are preventable if regulatory bodies strictly enforce roof support standards and companies invest systematically in worker training (Saleh & Cummings, 2011 ; Wu et al., 2023 ). The persistence of roof falls, despite their known causes and available preventive technologies, exemplifies the importance of reclassifying them as preventable accidents. This reclassification underscores institutional responsibility rather than fatalistic acceptance of hazards. Organisational priorities and socio-technical misalignment Coal mine accidents are deeply entwined with organisational priorities and socio-technical misalignments. Mechanisation has intensified haulage operations, with conveyor belts, locomotives, and heavy earth-moving machinery increasingly central to coal extraction. However, the integration of such machinery into organisational routines has been fraught with misalignment. Thompson et al. ( 1998 ) and Pandey and Mishra ( 2023 ) show that haulage accidents often stem from inadequate signalling systems, poor traffic management, and operator fatigue. Organisational pressures to meet production targets exacerbate these risks, as operators are implicitly or explicitly encouraged to work long hours with minimal rest (Erkan et al., 2016 ). Moreover, subcontracting practices diffuse responsibility for maintenance and safety inspections of haulage systems because subcontracted workers frequently operate machinery with insufficient training, increasing the likelihood of accidents (Valluru et al., 2017 ; Valluru et al, 2020 ). Theoretically, haulage accidents exemplify how mechanisation does not inherently improve safety but can generate new vulnerabilities if not aligned with organisational and regulatory systems. Perrow’s ( 2000 ) theory of everyday accidents is instructive because tightly coupled systems, such as haulage machinery in mines, create inevitable failures unless safety is systematically prioritised. The Indian case shows that organisational imperatives, particularly the prioritisation of production over safety, prevent this alignment and perpetuate preventable fatalities. Socio-technical integration and maintenance deficits Machinery-related accidents, such as equipment malfunctions or operator entanglements, highlight the challenges of socio-technical integration. In highly mechanised mines, machinery failures represent one of the most visible categories of preventable accidents. Our data indicate that while mechanisation has reduced some traditional risks, it has simultaneously introduced machinery-specific vulnerabilities. Accidents caused by machinery are often preventable through regular maintenance, adequate training, and the consistent use of protective equipment. However, organisational cost-cutting and inadequate regulatory enforcement compromise these safeguards. Chinniah (2015) mentions that behavioural factors, such as operators bypassing safety procedures to accelerate production, significantly contribute to machinery accidents. These behavioural factors are not individual shortcomings but organisationally induced patterns, shaped by cultures that privilege speed and output over compliance with safety norms. From a socio-technical perspective, machinery failures are emblematic of misalignment between technology and the social systems intended to manage it. Beck’s ( 1992 ) notion of manufactured risks applies directly here, that modernisation introduces new risks that are neither natural nor inevitable but socially produced by adopting technology without corresponding safety infrastructures. Regulatory enforcement and institutional failures Explosions in coal mines, whether due to gas leaks, blasting errors, or coal dust accumulation, remain a significant source of catastrophic accidents. Even though less frequent than roof falls or haulage incidents, explosions cause disproportionately high fatalities when they occur. Explosions underscore the role of regulatory enforcement in shaping safety outcomes. Stringent safety standards exist for ventilation, blasting protocols, and dust suppression, yet implementation is often lax. Sahu & Mishra ( 2023 ) observe that most explosions in Indian mines are preventable with existing technologies and procedures but persist due to weak enforcement and inadequate inspections. In many cases, regulatory agencies lack the personnel or resources to ensure compliance, and political-economic pressures discourage stringent oversight of coal companies tasked with meeting national energy demands. Preventable mine accidents are not technological inevitabilities but institutional failures. They reveal how regulatory enforcement, when subordinated to production imperatives and undermined by compliance gaps, allows preventable hazards to escalate into disasters. Comparative research by Wildfire ( 2012 ) reinforces this point, showing that fatalities can decline in jurisdictions with robust enforcement and independent oversight. Mining accidents as intersections of demography, organisation, and technology Transportation accidents, particularly those involving surface movement of coal and workers, constitute the primary category of fatalities in our dataset. These incidents often occur during the movement of heavy vehicles within mines or coal transport to external facilities. Workers from marginalised groups, frequently employed as drivers or support staff, face precarious working conditions with limited training. Organisational cost-cutting leads to poor vehicle maintenance and inadequate traffic regulation inside mining sites. Regulatory oversight of surface transportation is often weaker than that of underground activities, further compounding risks. Laurence (2005) notes that many accidents occur during shift changes, when many workers are moved simultaneously, overwhelming existing safety protocols and communication gaps. Proper maintenance regimes, traffic management systems, and rigorous training could drastically reduce these accidents. Their persistence underscores the systemic misalignment of social, organisational, and technical factors that ST-SAM seeks to explain. The mapping confirms that fatalities across these categories are not random occurrences but systematically preventable accidents. Each category, when understood through the ST-SAM, demonstrates how safety outcomes depend less on the degree of mechanisation and more on the alignment of demographic, organisational, regulatory, and socio-technical dimensions. Conclusion Our study argues that Indian coal mining accidents, while often normalised as inevitable, are overwhelmingly preventable. By developing the concept of preventable accidents, we reframe mining fatalities as socially produced outcomes of organisational negligence, weak enforcement, and socio-technical misalignments rather than unavoidable risks. The Socio-Technical Safety Alignment Model demonstrates that labour safety depends not merely on mechanisation but on integrating demographic realities, organisational priorities, and regulatory enforcement. Theoretically, the paper contributes to occupational safety scholarship by advancing a middle position between High Reliability Theory and Normal Accident Theory, showing that while complex systems entail risks, institutional failures in even routine subsystems perpetuate hazards. Practically, it urges a shift from technological determinism to systemic reforms that foreground safety culture, regulatory independence, and worker empowerment. Acknowledging the limited data with which the study was done, future research must move toward mixed-methods designs that integrate coal mine accident statistics with ethnographic accounts of miners’ lived experiences and comparative studies across jurisdictions with stronger enforcement regimes. For policymakers, the findings emphasise the urgency of strengthening regulatory capacity, enforcing transparency in accident reporting, and embedding safety within organisational incentives. 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Emerald Group Publishing Limited, pp 309–330 Perrow C (2000) Normal accidents: Living with high risk technologies. Princeton University Press Ray SK, Khan AM, Mohalik NK, Mishra D, Mandal S, Pandey JK (2022) Review of preventive and constructive measures for coal mine explosions: An Indian perspective. Int J Min Sci Technol 32(3):471–485 Reason J (1990) Human error. Cambridge University Press Roy B, Schaffartzik A (2021) Talk renewables, walk coal: The paradox of India's energy transition. Ecol Econ 180:106871 Ruff T, Coleman P, Martini L (2011) Machine-related injuries in the US mining industry and priorities for safety research. Int J Injury Control Saf Promotion 18(1):11–20 Sahu A, Mishra DP (2023) Coal mine explosions in India: management failure, safety lapses and mitigative measures. Extractive Industries Soc 14:101233 Saleh JH, Cummings AM (2011) Safety in the mining industry and the unfinished legacy of mining accidents: Safety levers and defense-in-depth for addressing mining hazards. Saf Sci 49(6):764–777 Shrawankar G, Thakkar L, Mishra R, Randive K (2022) Disasters in Mine: Strategies for Prevention, Management and Control. Medical Geology in Mining: Health Hazards Due to Metal Toxicity. Springer International Publishing, Cham, pp 273–318 Sishodiya PK, Guha R (2013) Safety and health in mining in India. In: Elgstrand K, Vingård E (eds) Occupational safety and health in mining: Anthology on the situation in 16 mining countries. University of Gothenburg, pp 31–41 Statistical Analysis and Numbers Key Evaluation of Trends in Coal Mine Accidents (2016-22), Ministry of Labour and Employment, Government of India. https://www.dgms.gov.in/writereaddata/UploadFile/sanket0404_2024.pdf Thompson RJ, Fourie GA, Visser AT, Smith RAF (1998) Benchmarking haulroad design standards to reduce transportation accidents. Int J Surf Min Reclam Environ 12(4):157–162 Trist EL, Bamforth KW (1951) Some social and psychological consequences of the longwall method of coal-getting: An examination of the psychological situation and defences of a work group in relation to the social structure and technological content of the work system. Hum Relat 4(1):3–38 Valluru CT, Dekker S, Rae A (2017) How and why do subcontractors experience different safety on high-risk work sites? Cogn Technol Work 19(4):785–794 Valluru CT, Rae A, Dekker S (2020) Behind subcontractor risk: A multiple case study analysis of mining and natural resources fatalities. Safety 6(3):40 Weeks JL (1991) Occupational health and safety regulation in the coal mining industry: public health at the workplace. Annu Rev Public Health 12:195–207 Weick KE, Sutcliffe KM (2011) Managing the unexpected: Resilient performance in an age of uncertainty, vol 8. Wiley Wildfire C (2012) Mine safety: Penalty structure and enforcement mechanisms of the Mine Act in the wake of the Upper Big Branch explosion. Administrative Law Rev, 441–472 Wu B, Wang J, Qu B, Qi P, Meng Y (2023) Development, effectiveness, and deficiency of China's coal mine safety supervision system. Resour Policy 82:103524 Zhang J, Fu J, Hao H, Fu G, Nie F, Zhang W (2020) Root causes of coal mine accidents: Characteristics of safety culture deficiencies based on accident statistics. Process Saf Environ Prot 136:78–91 Footnotes Risk Assessments, Online Submission & Monitoring of Environmental & CRZ Clearances, Ministry of Environment, Forest and Climate Change Government of India, https://environmentclearance.nic.in/DownloadPfdFile.aspx?FileName=aXCAorXdOLR7oMCkvQJXYGjrPKdNessRRO5BNurmCIfAcOIhjdZOVCtb8HCivpLH&FilePath=93ZZBm8LWEXfg+HAlQix2fE2t8z/pgnoBhDlYdZCxzVPEh4a7F53Cae7tleKGoXI7f3qHsQ4mEt9+y03oib7i6zg1izg9RsZbVmZBd2bUlrJuQea18z4ICRkFCo2EKv6#:~:text=The%20mining%20activities%20involve%20certain%20types%20of,of%20machinery%2C%20explosion%2C%20to%20name%20a%20few . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7529003","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513853856,"identity":"9d01204c-c073-4aba-a5eb-a3579514cf72","order_by":0,"name":"Aparna Raj C","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDCCAwhmAsMHIMnGTooWxhkgLcwkaGFg5gGTBHTw3T787MHPPXfkzdsPPPxs82ubPB8zA+OHjzm4tUieSzM37Hn2zHDOmYRk6dy+24ZtzAzMkjO34dZicIbBTILnwGGgNxLSmHN7bjMCtbAx8+LVwv5N8s+Bw/Yz+B+kMVv23LYnQguPmTTQlsQZEkBbGH7cTiSoRfIMT7mxzIHDyTMkHiRL9jbcTm5jZmzG6xe+M+zbHr45cNh2Bn9O4ocff27bzm9vPvjhIx4tQMAGpXkSGBjbQAzGBrzqkbSwH2Bg+ENI8SgYBaNgFIxEAAAIrVP0P52Q9gAAAABJRU5ErkJggg==","orcid":"","institution":"Indian Institute of Management Ahmedabad","correspondingAuthor":true,"prefix":"","firstName":"Aparna","middleName":"Raj","lastName":"C","suffix":""},{"id":513853860,"identity":"50cb7a27-1b1c-459f-b747-938645b823f1","order_by":1,"name":"Prerana Mudaliar","email":"","orcid":"","institution":"Indian Institute of Management Ahmedabad","correspondingAuthor":false,"prefix":"","firstName":"Prerana","middleName":"","lastName":"Mudaliar","suffix":""}],"badges":[],"createdAt":"2025-09-03 16:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7529003/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7529003/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13563-025-00592-w","type":"published","date":"2026-01-08T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91304243,"identity":"6702b93a-7636-4ed8-a9d4-ca724bce8255","added_by":"auto","created_at":"2025-09-15 06:22:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23460,"visible":true,"origin":"","legend":"\u003cp\u003eTotal number of fatalities in coal mines over time\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7529003/v1/f1db01bddd40261e2521e056.jpg"},{"id":91305845,"identity":"0e163a7e-c8cd-4fa8-9762-59b4976df8cc","added_by":"auto","created_at":"2025-09-15 06:30:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24641,"visible":true,"origin":"","legend":"\u003cp\u003eCategorisation of coal mine accidents\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7529003/v1/83fcd706cd5b201ad31f79d7.jpg"},{"id":91304246,"identity":"104e2d07-cc78-4bc3-861b-019af7aa9c85","added_by":"auto","created_at":"2025-09-15 06:22:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26273,"visible":true,"origin":"","legend":"\u003cp\u003eCoal production and fatal accidents across states (2015-25)\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7529003/v1/4e8680f5e2ca0e4c7379566b.jpg"},{"id":91305846,"identity":"302769b6-bcfe-449f-96a6-ef0f31733810","added_by":"auto","created_at":"2025-09-15 06:30:17","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22423,"visible":true,"origin":"","legend":"\u003cp\u003eSocio-Technical Safety Alignment Model for coal mine safety\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7529003/v1/a8ceca5cd0b5386630d8efd1.jpg"},{"id":99890920,"identity":"32b0663a-0930-4577-8817-306a627381cc","added_by":"auto","created_at":"2026-01-09 13:41:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":785914,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7529003/v1/bed8d881-66ae-4040-a320-af84a2cac98e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preventable Accidents in Indian Coal Mining: A Socio-Technical Alignment Approach to Labour Safety","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith home to over a hundred minerals, India ranks among the top producers of mica, iron ore, bauxite, and coal. Even though the mining sector contributes approximately 2.5 per cent of the country\u0026rsquo;s gross domestic product, employing over a million workers (Mukherjee, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), mining practices in India have been mired in controversies. According to the \u003cem\u003eStatistical Review of World Energy 2023\u003c/em\u003e (Energy Institute, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), India is the second-largest producer, consumer, and coal importer after China, but with the fifth-largest coal reserves after the USA, Russia, Australia, and China.\u003c/p\u003e\u003cp\u003eCoal mining remains one of the most hazardous industries worldwide, exposing workers to a broad spectrum of occupational risks, ranging from physical injuries to chronic health conditions. In India, coal mining is important because of its centrality to the country\u0026rsquo;s energy security, with over 70 per cent of India\u0026rsquo;s electricity generation being coal-based (Energy Cell Monthly Report, 2023), and the scale of human labour it engages. Despite significant advances in mechanisation and regulatory frameworks, coal mining in India continues to witness persistent workplace accidents. According to the Statistical Analysis \u0026amp; Numbers: Key Evaluation of Trends in Coal Mine Accidents (2016-22), there were more than 600 fatalities in the decade between 2013 and 2022. These incidents not only result in the tragic loss of life and livelihood but also underscore the preventable nature of many mining-related hazards, raising critical questions about organisational accountability, regulatory efficacy, and the socio-economic vulnerabilities of the mining workforce.\u003c/p\u003e\u003cp\u003eThe irony of modern Indian coal mining lies in its movement toward mechanisation and its simultaneous dependence on labour-intensive practices (Roy \u0026amp; Schaffartzik, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although underground mining has progressively transitioned to mechanised operations, such as longwall and continuous mining methods, the dominance of open-cast mining and associated activities continues to expose workers to severe risks\u003csup\u003e1\u003c/sup\u003e. Accident records suggest that fatalities and injuries are disproportionately concentrated in ancillary processes, particularly during transportation and haulage, rather than in core excavation tasks (Bellanca et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such evidence highlights the uneven distribution of risk across mining operations and suggests that mechanisation alone cannot guarantee safety if the systemic organisation of work and enforcement of preventive measures remain weak.\u003c/p\u003e\u003cp\u003eThe persistence of accidents in Indian coal mines must be viewed in a broader political, economic, and institutional context. Historically, coal mining has been symbolic of extractive labour regimes in the Global South, characterised by informalization, inadequate social protection, and insufficient regulatory mechanisms caught between the imperatives of industrial growth and worker welfare (Lahiri-Dutt, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The preventable nature of most reported accidents reflects systemic deficiencies, such as inadequate training, poor safety culture, ineffective monitoring of safety standards, and insufficient investment in worker-centric technologies. Moreover, the disproportionate vulnerability of contract labourers, mostly migrants, who comprise a substantial share of the mining workforce, adds criticality to the debate on labour precarity and occupational health in India\u0026rsquo;s coal sector (Nayak, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTheoretically, coal mine accidents can be conceptualised as random events and patterned outcomes of socio-technical systems. High Reliability Theory (HRT) and Normal Accident Theory (NAT) offer practical but contrasting frameworks for interpreting mining hazards. HRT emphasises that organisations can achieve accident-free operations through rigorous safety culture, training, and redundancy (Weick \u0026amp; Sutcliffe, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In contrast, NAT suggests that accidents are inevitable in complex and tightly coupled systems despite preventive efforts (Perrow, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, the Indian coal mining experience complicates this dichotomy of the prevalence of accidents during transportation, which is a relatively less complex subsystem. It suggests that many incidents stem from organisational negligence and weak enforcement rather than systemic inevitability. This positions Indian coal mining as a fertile case for advancing the \u003cem\u003epreventable accidents\u003c/em\u003e theory in high-risk industries.\u003c/p\u003e\u003cp\u003eThrough this study, we intend to make two contributions. Firstly, the paper foregrounds the conceptual category of \u003cem\u003epreventable accidents\u003c/em\u003e to examine the persistence of workplace hazards in Indian coal mining. Secondly, it develops a theoretical argument that safety outcomes in mining are determined less by the level of mechanisation than by the alignment of socio-technical systems, regulatory enforcement and organisational priorities. This study contributes to broader debates in management, labour studies, and occupational health about the conditions under which industrial accidents become structurally embedded yet potentially avoidable.\u003c/p\u003e\u003cp\u003eWe organise the paper in the following order: The literature review studies the extant literature on occupational hazards in coal mining, focusing on mechanisation, labour precarity, and safety management. The subsequent section outlines the methodological orientation, including the database consulted for descriptive analysis. The analysis section then advances the theoretical framework, supported by descriptive evidence from the Indian mining accident data, highlighting the predominance of transportation-related accidents and their preventability. The discussion explores the implications for policy, management practice, and theory-building. Finally, the conclusion sets an agenda for future research on labour issues and industrial safety in the coal sector.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eThe study of occupational hazards in coal mining has historically wavered between two perspectives \u0026ndash; one that accidents are inevitable consequences of geological or technological risks, and another that they are preventable outcomes of organisational and systemic failures. This review engages with both traditions but develops them further to ground the two contributions of this paper. We foreground the category of \u003cem\u003epreventable accidents\u003c/em\u003e to reframe mining hazards as avoidable and institutionally produced and argue that labour safety is determined less by the extent of mechanisation than by the alignment of socio-technical systems, regulatory enforcement, and organisational priorities.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eThe concept of preventable accidents\u003c/h2\u003e\u003cp\u003eThe roots of accident prevention theory can be traced to Heinrich\u0026rsquo;s \u003cem\u003eIndustrial Accident Prevention\u003c/em\u003e (1931), which advanced the notion that most industrial accidents are preventable through eliminating unsafe acts and conditions. Although criticised for its behavioural reductionism, Heinrich\u0026rsquo;s model underscored the preventability of accidents, a theme that subsequent research has developed in more complex ways (Manuele, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Reason\u0026rsquo;s (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) \u003cem\u003eSwiss Cheese Model\u003c/em\u003e expanded this logic by demonstrating that accidents occur when multiple system defences fail, thereby underscoring that most accidents are systemic failures rather than random events.\u003c/p\u003e\u003cp\u003eIn mining, Hopkins (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) has shown how catastrophic accidents are often preceded by a series of near misses, which, if addressed, could have averted disaster. These studies collectively argue against the naturalisation of industrial accidents and instead locate them within organisational and institutional domains where prevention is possible. Specifically, in Indian coal mining, man accidents are linked to organisational factors such as inadequate supervision, poor training, and cost-cutting measures. Kunar et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) further showed that accidents in coal mines are not random but result from demographical characteristics of workers, inadequate support systems and insufficient monitoring. Both studies underscore that fatalities arise from preventable conditions that persist because of weak institutional accountability.\u003c/p\u003e\u003cp\u003eMore recent global scholarship has reinforced this perspective. Zhang et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), analysing Chinese coal mines, identified deficiencies in safety culture as root causes of accidents, emphasising the preventable nature of fatalities even in technologically advanced environments. Similarly, Wu et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlighted the role of weak supervision in China\u0026rsquo;s coal sector, showing that preventable hazards persist when regulatory institutions lack authority or capacity. These findings resonate strongly with the Indian case, where accidents disproportionately occur during transportation and haulage operations, indicating subsidiary processes that are highly amenable to prevention through proper training, maintenance, and regulatory enforcement (Maiti \u0026amp; Khanzode, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMechanisation and the persistence of hazards\u003c/h3\u003e\n\u003cp\u003eA vast amount of literature has focused on the role of mechanisation in shaping mining safety. Early socio-technical studies, most notably Trist and Bamforth\u0026rsquo;s (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1951\u003c/span\u003e) seminal research on the longwall method of coal-getting, revealed that technological innovations radically reconfigured social organisation in mines, often with unintended safety consequences. This insight laid the foundation for socio-technical systems theory, emphasising that technology and organisation must be optimised for safe and efficient outcomes (Emery \u0026amp; Trist, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1960\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMechanisation is often assumed to improve safety by reducing direct human exposure to hazardous tasks. For instance, Australian and U.S. mining studies suggested that mechanised systems reduce fatalities linked to manual handling (Burgess-Limerick, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ruff et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, evidence also shows that mechanisation introduces new risks associated with haulage machinery, conveyor systems, and heavy vehicles (Duarte et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In India, haulage accidents remain a leading cause of fatalities, highlighting that mechanisation has shifted rather than eliminated hazards.\u003c/p\u003e\u003cp\u003ePerrow\u0026rsquo;s (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) theory of \u0026ldquo;normal accidents\u0026rdquo; provides a conceptual lens for this paradox. He argued that complex, tightly coupled systems are inherently prone to failure, regardless of technological sophistication. In mining, haulage systems, transportation fleets, and ventilation infrastructures exhibit such coupling, creating vulnerabilities where a single failure can cascade into accidents. Weick and Sutcliffe (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), through High Reliability Theory, countered that organisations can achieve near-error-free performance in such contexts by institutionalising safety cultures and redundancies. However, empirical studies suggest that neither technological advancement nor safety culture rhetoric has translated into reduced fatalities, precisely because organisational and regulatory misalignments persist (Zhang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSubcontracting and precarious labour practices further exacerbate this misalignment. Valluru et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found that subcontracted workers experience different and often poorer safety outcomes than permanent employees, primarily due to weaker training, accountability, and oversight. This aligns with Indian evidence showing that a large proportion of fatalities occur among contract workers, who face systemic vulnerabilities (Nayak, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, mechanisation does not automatically translate into improved safety; instead, the alignment of socio-technical, organisational, and regulatory systems determines outcomes.\u003c/p\u003e\n\u003ch3\u003eRegulatory Enforcement and Institutional Weakness\u003c/h3\u003e\n\u003cp\u003eThe role of regulation is central to the persistence of preventable accidents. Comparative studies demonstrate that countries with robust enforcement regimes have reduced mining fatalities (Weeks, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). In the U.S. and Australia, independent regulators with strong investigative capacities have ensured compliance with safety norms, driving improved labour safety (Gunningham et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn India, however, regulatory bodies such as the Directorate General of Mines Safety (DGMS) face severe capacity constraints. Sishodiya and Guha (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) document how enforcement gaps persist due to understaffing, political pressures, and the dominance of production imperatives. This results in a compliance gap, where safety norms exist on paper but are poorly implemented in practice. Empirical research shows that explosions and gas leaks, though less frequent, are catastrophic when they occur precisely because preventive measures such as ventilation and dust suppression are inadequately enforced (Ray et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shrawankar et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These failures reveal that the issue is not technological insufficiency but institutional weakness. Wu et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Zhang et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) similarly emphasise that in China, safety improvements stagnate when regulatory enforcement is subordinated to production priorities, which is a dynamic directly mirrored in India.\u003c/p\u003e"},{"header":"Methods and Data","content":"\u003cp\u003eThis study adopts a conceptual research design aimed at theorising workplace safety in Indian coal mining through the lens of preventable accidents and socio-technical alignment. The methodology is grounded in two interrelated assumptions: (i) that workplace accidents are not naturalised risks but socially constructed outcomes that can, in principle, be prevented (Reason, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Hopkins, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and (ii) safety outcomes are mediated less by the degree of mechanisation than by the alignment of demographic, organisational, regulatory, and socio-technical dimensions (Trist \u0026amp; Bamforth, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1951\u003c/span\u003e; Perrow, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Our methodological approach combines descriptive analysis of secondary data with conceptual theorisation to offer a generalisable empirical design.\u003c/p\u003e\u003cp\u003eWe use the Indian mineral accidents data from the Environmental Information, Awareness, Capacity Building and Livelihood Programme database maintained by the Ministry of Environment, Forests \u0026amp; Climate Change and the Indian Institute of Technology Dhanbad. We use coal mining accident data from 2015 to 2025 to understand the causes of accidents at the various mining sites. We also use the data from the Ministry of Coal to map the measured volume of coal available in the different states to understand whether there is a proportional incidence of accidents based on the mining volume.\u003c/p\u003e\u003cp\u003eWe start the analysis with a descriptive mapping of accident categories in the database, and then we categorise the accidents into roof falls, haulage, machinery failures, explosions, and transportation. The interpretive categorisation is designed to reveal how accident patterns correspond to systemic misalignments in demographic vulnerabilities, organisational priorities, regulatory enforcement, and socio-technical integration. We also draw on socio-technical systems theory and accident prevention literature to conceptually elaborate on how most accidents are preventable.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo understand the coal mining accidents, we use the data from 2015 to 2025 and classify them into different categories. In Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, we plot the total fatalities over the decade. The trend shows a sharp decline after 2015, with a one-third drop in fatalities by 2016. However, the trend constantly fluctuated, with a sudden peak in 2018. Overall, the graph shows a substantial decrease in fatalities, rarely crossing 30 per year since 2019. The decline post-2015 reflects regulatory improvements and technological changes; however, the spikes in 2018 and 2024 highlight that the seriousness of accidents remained recurrent. The low spike between 2020 and 2023 may be due to the reduced activities during the COVID-19 pandemic, though underreporting during this period cannot be ignored. While the trend over the year shows a reduction in the fatalities, mining in India continues to pose a significant threat to the workers, with years marked by accidents serving as a reminder of how brittle safety improvements are.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCoal mine accidents from 2015 to 2025\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDrowning\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eElectricity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExplosion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFall-related\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMachinery\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRoof/Collapse\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTransportation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e highlights that the consistent causes of death remain transportation and vehicle-related accidents, and roof/ground collapse, accounting for half of the fatalities. The fatalities caused by roof collapse were high in the early years and notably declined. However, transportation accidents persistently contribute to the majority of underscoring gaps in safety management and vehicle regulations within mines. Operational hazards are routine, as evidenced by the consistent contributions of categories like machinery/equipment accidents and fall-related incidents over the decade. Even though they happen less frequently, explosions, fires, and electricity accidents can be disastrous. Furthermore, the data show that daily operational risks, particularly those related to transportation and mechanisation, continue to dominate the profile of mining accidents, even though some traditional hazards may be decreasing.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e categorises the proportion of various types of accidents which have occurred over the period. It was found that transport and vehicle-related accidents caused 33 per cent of accidents, and 25 per cent was due to roof/ ground collapse. Fall-related accidents account for 13 per cent, machinery accidents for another 12 per cent and electricity accounts for a smaller share at five per cent. The predominance of transport-related issues showcases the logistical vulnerabilities in the Indian mining industry. Roof collapse remains another critical cause of accidents, highlighting the need for improved infrastructure, support systems, and safety monitoring. The high percentage of falls and equipment incidents further highlights the shortcomings in workplace safety procedures and worker training. Although catastrophic incidents attract public attention, most mining fatalities are caused by routine operating hazards rather than infrequent disasters, as seen by the lower incidence of blasting or electricity-related accidents. This trend supports the idea that habitual negligence and poor enforcement are more of a systemic problem than isolated incidents regarding mining safety in India.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe above figure highlights the distribution of mining fatalities across 10 leading states in India between 2015 and 2025. Naturally, the mineral-rich states such as Jharkhand, Odisha, and Chhattisgarh report high accidents because of the large-scale coal and metal ore extraction. Telangana, accounting for the highest number, indicates either an increase in mining intensity or possible deficiencies in operation and safety enforcement. Interestingly, Rajasthan emerged in the top three, suggesting quarry-related accidents contribute more significantly to national accidents than is often recognised. Regional variations in accident frequency are further highlighted by excluding historically mining-heavy states like Goa and Assam from the top fatality rankings, which may indicate better regulation or fewer incidents reported. When taken as a whole, these results show how mining hazards are distributed unevenly throughout India and emphasise the critical need for state-specific safety measures.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe analysis across selected states reveals divergence between coal production and accidents (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Jharkhand, which accounts for the highest coal reserves, also reports equivalent fatality counts. However, such a proportionality is not visible across all the coal mining states. States like West Bengal, Telangana and Maharashtra exhibit moderate coal reserves but high numbers of reported fatalities. However, the proportion of accidents in states like Assam, Uttar Pradesh and Nagaland, which have very low volumes of coal in mines, is very high, indicating issues underlying flaws in labour safety and regulations rather than the incidence of accidents attributable to production intensity.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIndia has made significant progress in reducing the mining accidents since 2015, reflecting the improved regulations and safety enforcement. However, critical safety concerns persist. The varying patterns imply that regulatory benefits are not consistently maintained across geographies or types of mining.\u003c/p\u003e\u003cp\u003eIt is alarming how frequently transportation accidents occur and remain a leading cause of fatality over time. Vehicle accidents in a controlled mine environment should be preventable, unlike collapses, which geological and seasonal factors like monsoons can cause. The persistent occurrence of these incidents suggests that mine traffic regulations are a shortcoming and that safety compliance is not adequately monitored. While the rates have decreased over time, roof and ground collapses are still a serious threat, demonstrating deficiencies in structural evaluations, preventative care, and the application of safety support systems. Deaths related to falls, handling equipment, and working during unsupervised hours (such as after-evening shifts) expose a lack of emergency response facilities, safety nets, and worker training. Rather than resulting from inherent mining risks, these deaths are preventable and reflect systemic negligence. Differences at the state level highlight how mining intensity does not determine safety. The unusually high death toll in some states points to differences in labour conditions, contractor practices, and safety code enforcement. Overall, the results suggest that routine and avoidable operational hazards continue to be a leading cause of death in Indian mines, even though catastrophic events such as fires and explosions are comparatively uncommon, given that they have not been reported.\u003c/p\u003e\n\u003ch3\u003eAddressing safety challenges\u003c/h3\u003e\n\u003cp\u003eA comprehensive strategy incorporating risk identification, preventive measures, and efficient enforcement must be implemented, aiming for safety in Indian mining operations. Regular risk assessments should be conducted to find possible risks related to coal extraction, especially in locations susceptible to roof collapse, transportation accidents, and fire/gas-related accidents. Clear Safe Operating Procedures (SOPs) must be established to support these assessments, particularly for high-risk operations like the extraction of fiery coal, where there is a risk of combustion and steam buildup. Comprehensive training programs for all labour categories, including contract workers, are crucial. The main training topics should be hazard awareness, proper PPE use, emergency response procedure, and fire safety. Frequent drills would strengthen readiness and promote a strong safety culture throughout mining sites.\u003c/p\u003e\u003cp\u003eAnother crucial area is workforce management. Strict access controls and the presence of knowledgeable supervisors in high-risk areas can further reduce accidents, while defining roles and responsibilities aids in preventing unauthorised access to hazardous zones. Supervisors and employees must communicate effectively about SOPs and situational awareness to ensure the team stays aware of their surroundings. Additionally, infrastructure upgrades include placements of safety barriers, nets, and fencing around pits and hazardous areas, along with regular inspection, which is required.\u003c/p\u003e\u003cp\u003eStrict adherence to operational safety procedures is also required. This includes adopting a thorough work permit system backed by Job Safety Analysis (JSA) before beginning routine or non-routine tasks and rigorously adhering to Lock-Out and Tag-Out (LOTO) procedures during equipment maintenance. Since the risks of insufficient supervision or training are highest in non-routine jobs involving contractual labour, supervision should keep a close eye on these positions. The installation of sensitive Lower Explosive Limit (LEL) detectors to check for gas leaks, the development of dependable alert systems, and on-site medical facilities for prompt injury responses are necessary. When taken as a whole, these measures not only fix structural flaws but also promote a proactive safety culture, lowering the number of avoidable and ongoing deaths in the Indian mining industry.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSocio-technical safety alignment to Indian coal mine accidents: A theoretical framework\u003c/h2\u003e\u003cp\u003eThe persistence of fatalities in Indian coal mining, despite decades of mechanisation and regulatory codification, demands a theoretical lens that moves beyond technological determinism. We propose a Socio-Technical Safety Alignment Model (ST-SAM) that conceptualises mining accidents as preventable outcomes of systemic misalignments across four critical dimensions: demographic vulnerabilities, organisational priorities, regulatory enforcement, and socio-technical systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This framework reframes accidents not as stochastic or inevitable but as socially produced alignment failures. To anchor this framework empirically, we map the theoretical dimensions onto the major categories of accidents like roof falls, haulage accidents, machinery failures, explosions, and transportation-related incidents.\u003c/p\u003e\u003cp\u003eBy situating accident categories within the ST-SAM, the model not only integrates the empirical realities of Indian coal mining but also demonstrates how each accident category reveals underlying misalignments. We highlight how safety outcomes are mediated by factors such as labour composition, organisational imperatives, enforcement weaknesses, and the socio-technical embedding of mechanisation. We show how each accident category represents preventable misalignments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDemographic vulnerabilities and regulatory gaps\u003c/h2\u003e\u003cp\u003eMany underground workers are contract labourers recruited from marginalised communities with minimal formal training. Their limited literacy and technical knowledge impede the correct application of roof support systems, such as bolting or props, especially in highly mechanised mines where monitoring systems require careful calibration (Pelders \u0026amp; Nelson, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Research has demonstrated that accidents from roof falls, haulage and transportation are rarely the product of unforeseeable natural hazards; instead, they stem from inadequate support systems, poor geological assessments, and failures in supervisory oversight (Zhang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These failures are preventable if regulatory bodies strictly enforce roof support standards and companies invest systematically in worker training (Saleh \u0026amp; Cummings, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The persistence of roof falls, despite their known causes and available preventive technologies, exemplifies the importance of reclassifying them as preventable accidents. This reclassification underscores institutional responsibility rather than fatalistic acceptance of hazards.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eOrganisational priorities and socio-technical misalignment\u003c/h2\u003e\u003cp\u003eCoal mine accidents are deeply entwined with organisational priorities and socio-technical misalignments. Mechanisation has intensified haulage operations, with conveyor belts, locomotives, and heavy earth-moving machinery increasingly central to coal extraction. However, the integration of such machinery into organisational routines has been fraught with misalignment. Thompson et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and Pandey and Mishra (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) show that haulage accidents often stem from inadequate signalling systems, poor traffic management, and operator fatigue. Organisational pressures to meet production targets exacerbate these risks, as operators are implicitly or explicitly encouraged to work long hours with minimal rest (Erkan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, subcontracting practices diffuse responsibility for maintenance and safety inspections of haulage systems because subcontracted workers frequently operate machinery with insufficient training, increasing the likelihood of accidents (Valluru et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Valluru et al, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTheoretically, haulage accidents exemplify how mechanisation does not inherently improve safety but can generate new vulnerabilities if not aligned with organisational and regulatory systems. Perrow\u0026rsquo;s (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) theory of everyday accidents is instructive because tightly coupled systems, such as haulage machinery in mines, create inevitable failures unless safety is systematically prioritised. The Indian case shows that organisational imperatives, particularly the prioritisation of production over safety, prevent this alignment and perpetuate preventable fatalities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSocio-technical integration and maintenance deficits\u003c/h2\u003e\u003cp\u003eMachinery-related accidents, such as equipment malfunctions or operator entanglements, highlight the challenges of socio-technical integration. In highly mechanised mines, machinery failures represent one of the most visible categories of preventable accidents. Our data indicate that while mechanisation has reduced some traditional risks, it has simultaneously introduced machinery-specific vulnerabilities. Accidents caused by machinery are often preventable through regular maintenance, adequate training, and the consistent use of protective equipment. However, organisational cost-cutting and inadequate regulatory enforcement compromise these safeguards. Chinniah (2015) mentions that behavioural factors, such as operators bypassing safety procedures to accelerate production, significantly contribute to machinery accidents. These behavioural factors are not individual shortcomings but organisationally induced patterns, shaped by cultures that privilege speed and output over compliance with safety norms.\u003c/p\u003e\u003cp\u003eFrom a socio-technical perspective, machinery failures are emblematic of misalignment between technology and the social systems intended to manage it. Beck\u0026rsquo;s (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) notion of \u003cem\u003emanufactured risks\u003c/em\u003e applies directly here, that modernisation introduces new risks that are neither natural nor inevitable but socially produced by adopting technology without corresponding safety infrastructures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eRegulatory enforcement and institutional failures\u003c/h2\u003e\u003cp\u003eExplosions in coal mines, whether due to gas leaks, blasting errors, or coal dust accumulation, remain a significant source of catastrophic accidents. Even though less frequent than roof falls or haulage incidents, explosions cause disproportionately high fatalities when they occur. Explosions underscore the role of regulatory enforcement in shaping safety outcomes. Stringent safety standards exist for ventilation, blasting protocols, and dust suppression, yet implementation is often lax. Sahu \u0026amp; Mishra (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) observe that most explosions in Indian mines are preventable with existing technologies and procedures but persist due to weak enforcement and inadequate inspections. In many cases, regulatory agencies lack the personnel or resources to ensure compliance, and political-economic pressures discourage stringent oversight of coal companies tasked with meeting national energy demands. Preventable mine accidents are not technological inevitabilities but institutional failures. They reveal how regulatory enforcement, when subordinated to production imperatives and undermined by compliance gaps, allows preventable hazards to escalate into disasters. Comparative research by Wildfire (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reinforces this point, showing that fatalities can decline in jurisdictions with robust enforcement and independent oversight.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eMining accidents as intersections of demography, organisation, and technology\u003c/h2\u003e\u003cp\u003eTransportation accidents, particularly those involving surface movement of coal and workers, constitute the primary category of fatalities in our dataset. These incidents often occur during the movement of heavy vehicles within mines or coal transport to external facilities. Workers from marginalised groups, frequently employed as drivers or support staff, face precarious working conditions with limited training. Organisational cost-cutting leads to poor vehicle maintenance and inadequate traffic regulation inside mining sites. Regulatory oversight of surface transportation is often weaker than that of underground activities, further compounding risks. Laurence (2005) notes that many accidents occur during shift changes, when many workers are moved simultaneously, overwhelming existing safety protocols and communication gaps.\u003c/p\u003e\u003cp\u003eProper maintenance regimes, traffic management systems, and rigorous training could drastically reduce these accidents. Their persistence underscores the systemic misalignment of social, organisational, and technical factors that ST-SAM seeks to explain. The mapping confirms that fatalities across these categories are not random occurrences but systematically preventable accidents. Each category, when understood through the ST-SAM, demonstrates how safety outcomes depend less on the degree of mechanisation and more on the alignment of demographic, organisational, regulatory, and socio-technical dimensions.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study argues that Indian coal mining accidents, while often normalised as inevitable, are overwhelmingly preventable. By developing the concept of preventable accidents, we reframe mining fatalities as socially produced outcomes of organisational negligence, weak enforcement, and socio-technical misalignments rather than unavoidable risks. The Socio-Technical Safety Alignment Model demonstrates that labour safety depends not merely on mechanisation but on integrating demographic realities, organisational priorities, and regulatory enforcement. Theoretically, the paper contributes to occupational safety scholarship by advancing a middle position between High Reliability Theory and Normal Accident Theory, showing that while complex systems entail risks, institutional failures in even routine subsystems perpetuate hazards. Practically, it urges a shift from technological determinism to systemic reforms that foreground safety culture, regulatory independence, and worker empowerment.\u003c/p\u003e\u003cp\u003eAcknowledging the limited data with which the study was done, future research must move toward mixed-methods designs that integrate coal mine accident statistics with ethnographic accounts of miners\u0026rsquo; lived experiences and comparative studies across jurisdictions with stronger enforcement regimes. For policymakers, the findings emphasise the urgency of strengthening regulatory capacity, enforcing transparency in accident reporting, and embedding safety within organisational incentives. Only through such alignment can India\u0026rsquo;s mining sector reconcile its role in national energy security with the dignity, safety, and well-being of its labour force.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.R.C. wrote the manuscript's abstract, introduction, literature review, results, model creation and conclusion.P.M. prepared the data, plotted the figures, worked on the discussion regarding the data, and proofread it.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ehttp://www.ismenvis.nic.in/Database/Mining_Accidents_in_India_24483.aspx\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnand R (2024) Rebuilding safety: Lessons from India's top 10 industrial catastrophes. Anand\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeck U (1992) Risk society: Towards a new modernity. 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Process Saf Environ Prot 136:78\u0026ndash;91\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Risk Assessments, Online Submission \u0026amp; Monitoring of Environmental \u0026amp; CRZ Clearances, Ministry of Environment, Forest and Climate Change Government of India, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://environmentclearance.nic.in/DownloadPfdFile.aspx?FileName=aXCAorXdOLR7oMCkvQJXYGjrPKdNessRRO5BNurmCIfAcOIhjdZOVCtb8HCivpLH\u0026amp;FilePath=93ZZBm8LWEXfg+HAlQix2fE2t8z/pgnoBhDlYdZCxzVPEh4a7F53Cae7tleKGoXI7f3qHsQ4mEt9+y03oib7i6zg1izg9RsZbVmZBd2bUlrJuQea18z4ICRkFCo2EKv6#:~:text=The%20mining%20activities%20involve%20certain%20types%20of,of%20machinery%2C%20explosion%2C%20to%20name%20a%20few\u003c/span\u003e\u003cspan address=\"https://environmentclearance.nic.in/DownloadPfdFile.aspx?FileName=aXCAorXdOLR7oMCkvQJXYGjrPKdNessRRO5BNurmCIfAcOIhjdZOVCtb8HCivpLH\u0026amp;FilePath=93ZZBm8LWEXfg+HAlQix2fE2t8z/pgnoBhDlYdZCxzVPEh4a7F53Cae7tleKGoXI7f3qHsQ4mEt9+y03oib7i6zg1izg9RsZbVmZBd2bUlrJuQea18z4ICRkFCo2EKv6#:~:text=The%20mining%20activities%20involve%20certain%20types%20of,of%20machinery%2C%20explosion%2C%20to%20name%20a%20few\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Coal mining, mining labour, preventable accidents","lastPublishedDoi":"10.21203/rs.3.rs-7529003/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7529003/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith a significant position in the global minerals map, for India, the importance of the triple bottom line of people, planet, and profit in Indian mining is unmissable. Among the various mining activities, coal presents a relevant case as a significant sector for India, with its labourers being exposed to preventable hazards that result in fatalities and chronic injuries despite high mechanisation. This paper intends to develop a conceptual framework to theorise workplace safety in Indian coal mining through the lens of preventable accidents and socio-technical alignment by drawing upon the mining accidents database data from the Ministry of Environment, Forest and Climate Change, and literature on occupational safety. The paper is situated within the broader scholarship on occupational safety and highlights how most fatalities occur in ancillary processes such as haulage and transportation, rather than excavation, underscoring that mechanisation alone has not reduced risks. By advancing the concept of \u003cem\u003epreventable accidents\u003c/em\u003e, the paper emphasises that mining fatalities are not naturalised risks inherent to geology or technology but outcomes of systemic failures in organisational routines, regulatory enforcement, and socio-technical integration. The paper foregrounds the concept of preventable accidents to reframe coal mine hazards not as inevitable but as institutionally produced and therefore avoidable. It also develops a theoretical argument that safety outcomes are shaped less by the degree of mechanisation than by the alignment of socio-technical systems, regulatory regimes, and organisational priorities. Through this framing, the paper situates Indian coal mining within global debates on labour precarity, industrial modernisation, and risk governance. The study challenges fatalistic discourses about mining risk and advances a socio-technical alignment model to guide scholarly debates and policy design. The findings have implications beyond coal mining, offering a framework for understanding safety in other high-risk, labour-intensive sectors where mechanisation coexists with organisational and regulatory deficits.\u003c/p\u003e","manuscriptTitle":"Preventable Accidents in Indian Coal Mining: A Socio-Technical Alignment Approach to Labour Safety","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-15 06:22:12","doi":"10.21203/rs.3.rs-7529003/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c198b041-ca1a-4168-bdfe-ea2260880c01","owner":[],"postedDate":"September 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-09T13:41:14+00:00","versionOfRecord":{"articleIdentity":"rs-7529003","link":"https://doi.org/10.1007/s13563-025-00592-w","journal":{"identity":"mineral-economics","isVorOnly":false,"title":"Mineral Economics"},"publishedOn":"2026-01-08 00:00:00","publishedOnDateReadable":"January 8th, 2026"},"versionCreatedAt":"2025-09-15 06:22:12","video":"","vorDoi":"10.1007/s13563-025-00592-w","vorDoiUrl":"https://doi.org/10.1007/s13563-025-00592-w","workflowStages":[]},"version":"v1","identity":"rs-7529003","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7529003","identity":"rs-7529003","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}