Social Responsibility of Science in the Sustainable Development of Mining and Post-Mining Areas

Abstract

Ensuring the long-term sustainability of mining and post-mining practices is crucial for balancing resource extraction with environmental and social responsibilities. This study critically examines the role of science in addressing the complex challenges posed by mining, particularly in the context of the Sustainable Development Goals (SDGs). It identifies key responsibilities for science, including the development of sustainable extraction technologies, innovative land reclamation and ecosystem restoration strategies, and equitable frameworks for resource distribution that prioritize affected communities. The study emphasizes the importance of interdisciplinary approaches, the concept of Responsible Research and Innovation (RRI), and effective knowledge dissemination to minimize adverse impacts while enhancing mining’s contribution to renewable energy transitions. By exploring the interplay between mining, renewable energy, and sustainable development, this study underscores the transformative potential of science to balance humanity’s resource needs with ecological preservation and social equity. The findings offer actionable insights for aligning mining practices with sustainability principles, fostering resilience and equity in mining-impacted regions.

1. Introduction

Science and knowledge stimulate the development of human civilizations. This highlights the importance of science as a framework encompassing the full scope of human actions and decisions. At the current development stage, the consequences of these actions and decisions are already broad enough to affect the future of entire regions, societies, ecosystems, and even the planet. This is why the current era has been termed the Anthropocene [1]—a period, in which changes taking place on Earth are caused to a significant extent by human activity. Research and scientific discoveries have resulted in the development of techniques and technologies that exert influence at local, regional and global scales. Science has improved human health and well-being, yet it has also created technologies that generate new risks and conflicts, giving society the power to transform the world—and to cause large-scale harm [2]. Considering such numerous and serious consequences, science has to be analyzed in terms of responsibility for the results it brings.

This paper analyzes the social responsibility of science in relation to the sustainable development of mining and post-mining areas. The scope of considerations includes the most important real problems that occur and are common to the areas of mining activities. They include the following:

• Technical problems related to deposit extraction and its impact on the rock mass and the land surface;
• Economic issues related to economic profits from mining and related damage to the environment and infrastructure;
• Social issues related to employment in the mining sector and living in mining areas—both in the stage of active mining activity, as well as after its completion (transformation resulting from the closure of mines, post-mining areas).

The aim of this paper is to identify and define the tasks that responsible science must address in a sector that is crucial for material foundations and the further development of human civilisation, namely, mining.

This study applies a qualitative–analytical research design that combines source and method triangulation [3,4] with the logic of an explanatory case study based on analytical replication [5]. The objective was to establish a transparent, traceable chain of evidence linking documented mining activities with their technical, economic, and social consequences, and to interpret these consequences in the framework of the United Nations Sustainable Development Goals (SDGs).

The complex issue of the social responsibility of science in the mining sector was decomposed into three interconnected analytical dimensions: technical, economic, and social (see Section 5.2, Section 5.2.1 and Section 5.2.2). This structure follows the logic of explanatory case study analysis, where the goal is to understand causal mechanisms in real-world systems rather than to produce statistical generalizations [5].

To ensure methodological robustness and reduce the risk of inference bias, the study used triangulation of independent evidence streams [3,4]. The three evidence streams and their analytical functions are summarized in

Table 1. Evidence streams, data sources, and analytical role in the study.

Evidence Stream	Data Source	Function in the Analysis
Empirical field data	peer-reviewed publications of the authors documenting ground deformations, building damage, and mitigation measures in mining areas	basis for the technical dimension
Audited technical and financial	institutional and corporate reports (e.g., RAG—Germany [6]; Coal Authority—UK [7,8]; OSMRE—USA [9]; Codelco—Chile [10]; and Statistics Poland [11])	basis for the economic dimension
International comparative datasets	UNEP IRP [12], World Bank [13,14,15,16], OECD [17,18,19,20], Eurostat [21,22], ILO/ILOSTAT [23,24,25,26], and ICMM [27,28], UNICEF [29], HWR [30]	global contextualization and SDG mapping

.

Triangulation was implemented through pattern matching, that is, by testing whether independent evidence streams converged toward the same analytical relationships [4]. The Sustainable Development Goals (SDGs) were applied as a normative and evaluative framework. Consequences of mining activities identified in the empirical material were examined against SDG targets, but only where correspondence between observed outcomes and SDG criteria was supported by data.

International datasets (UNEP IRP, World Bank, OECD, Eurostat, ILO/ILOSTAT, and ICMM) were used not as sources of case-specific measurements but as reference datasets enabling global comparability, including differences between highly developed countries and emerging economies.

The analytical logic can be expressed as follows:

mining → consequences (technical/economic/social) → correspondence with SDGs

In this stage, the objective was limited to identifying and tracing these relationships; the interpretation of the results lies outside the scope of the methodology section. Interpretation and evaluation of data reliability were supported by the authors’ 28 years of scientific and implementation experience in mining and environmental engineering. This corresponds to Structured Expert Judgement (SEJ) [31] and to the concept of contributory expertise as defined by Collins and Evans [32], where domain expertise refines and validates interpretations within boundaries set by empirical evidence.

Quality assurance and methodological limitations are as follows:

• Triangulation ensured convergence of independent evidence streams and strengthened internal validity.
• Use of SDGs ensured traceability between empirical observations and societal relevance.
• No extrapolation was performed where international datasets lacked sufficient granularity (especially outside OECD jurisdictions).

The methodological focus is on uncovering causal relationships grounded in verifiable empirical evidence and internationally harmonized datasets, ensuring that the conclusions remain analytically robust and reproducible.

Contemporary research increasingly emphasizes corporate social responsibility; however, addressing the social responsibility of science is even more justified, as scientific outcomes influence not only business decisions but also political processes. Science can also provide a shared platform for commitment and cooperation toward sustainable and just development.

Glerup [33] mapped the concept of social responsibility in science and identified four rationalities, each reflecting a different way of defining problems and legitimizing governance mechanisms. Their analysis revealed a spectrum ranging from strict scientific self-governance and autonomy to approaches in which external stakeholders participate in shaping scientific activity.

The debate on the social responsibility of science also includes positions claiming that science inherently carries responsibility, since scientific work ultimately benefits humanity in one form or another [34]. However, historical evidence and ethical evaluation of past events call into question the ability of the scientific community to autonomously ensure that science remains responsible [1,35,36,37].

2. A View on the Social Responsibility of Science

There is a wide discussion on the social responsibility of science and the appropriate management of science to make it responsible [1,33,34,35,36,37,38,39,40,41,42,43,44,45]. It is justified by fears of the danger that new discoveries and technologies may bring.

Despite different approaches within the scientific community [33], the prevailing view today is that shaping science responsibly—for the benefit of society and the environment—requires the involvement of external stakeholders who support ethical assessment and proper research governance.

In this context, the concept of Responsible Research and Innovation (RRI) has gained importance. According to von Schomberg [46], RRI is a transparent and interactive process in which societal actors and innovators become mutually responsive, ensuring the ethical acceptability, sustainability, and societal desirability of research and its outcomes [46].

As the discourse evolved, von Schomberg [46] further emphasized that RRI should be understood as a strategy enabling stakeholders to anticipate the outcomes of research and innovation in response to the ‘grand challenges’ of our time. This requires research and innovation processes to be more adaptive and to incorporate broader foresight and impact assessment—beyond market benefits and risks [47].

A review of the literature on RRI [33,43,47] indicates broad conceptual agreement on the ethical and social responsibility of science. However, translating these principles into practice remains challenging. By analyzing the RRI implementation practice, Schuijff [48] found the following:

• RRI practices would require improving the justification for their use;
• One would expect a broader reflection on the theoretical implications of RRI to be introduced;
• The RRI described in the literature focused on the issue of sharing results, rather than on analyzing the consequences of the research.

Carrier [39], based on their own research, found on the one hand, a friendly approach of the scientific community towards RRI, while on the other hand, some concerns. The reservations of scientists are related to the fear of ignorance and bias of social entities potentially involved in research, loss of scientific autonomy, neglecting the basic research by RRI procedures, difficulties in predicting research results, and their social impact, as well as additional expenses related to involving additional stakeholders. Barriers for RRI vary depending on the research context. Respondents agreed that science should serve society, but expressed concerns that RRI may not always be the most effective way to achieve this. They warned that excessive external influence would harm science or make research more difficult [39]. Concerns formulated in this way provide insight into barriers restricting the implementation of RRI in the scientific community, thus prompting the continuous improvement of methods and the development of acceptable RRI programmes.

The European Union is implementing a new approach to research, development, and innovation activities, taking into account both the effects of this activity and its potential impact on the environment and society, as well as the way of conducting research, development, and innovation activities. This is an approach that highlights the issue of shared responsibility for research and innovation in a much broader way than before and formulates requirements not only for researchers, but also for a wide range of other people and institutions involved in research and innovation processes or those potentially affected by their effects. The approach proposed by the European Commission demands that entities involved in responsible research and innovation (in particular scientists and innovators, but also other stakeholders, e.g., decision-makers) should:

• Seek to obtain appropriate knowledge about ongoing research and innovation activities, take into account the possible consequences of these activities, and be aware of possible scenarios for the development of the situation.
• Take into account ethical and social values important for society, such as prosperity, justice, equality, privacy, independence, security, sustainable development, responsibility, and democracy when designing scientific research, new products, and processes.
• Assess both the results and possible development or use scenarios in the perspective of the above-mentioned values [49]. However, putting these principles into practice is not easy and brings problems.

In 2007, in the USA, a requirement was introduced, the America COMPETES Act (America Creating Opportunities to Meaningfully Promote Excellence in Technology, Education, and Science), which stipulates that each institution applying for financing from the National Science Foundation (NSF) must submit a plan for appropriate training and supervision for responsible conduct of research (RCR). In addition, in 2009, the National Science Foundation (NSF) issued an Implementation Plan requiring Authorized Organizational Representatives of research entities to certify that appropriate plans are in place for the responsible conduct of research at the time of application [50].

Unfortunately, the results of research on the effectiveness of mandatory training for scientists on ethics and social responsibility indicate that such training does not bring the expected results [51]. Scientists and engineers do not consider these trainings to be reasonable and there are even opinions that imposing certain standards may paradoxically lead to the devaluation of ethics by researchers [52]. The papers published by Joyce and co-authors [53] demonstrate that mandatory ethics training in STEM fields is frequently implemented in a perfunctory, compliance-oriented manner that largely functions to ‘check the box’ for funders. They further note that stand-alone online courses exert only limited influence on researchers’ behaviour and may even deter meaningful engagement with the ethical and social dimensions of scientific work.

Nevertheless, it has become obvious that it is impossible to conduct scientific research in isolation from the social aspects of the activity. Modern science has to meet a number of social expectations, which relate not only to the effects of research, but also to the research processes themselves [54]. A significant public financial support of scientific research is also of importance. Such a public investment allows for a reasonable expectation of public participation in decisions shaping scientific policy and determining the directions of research [54,55].

Research conducted by the American Association for the Advancement of Science has allowed us to discern how scientists understand their social responsibility. The research results have revealed a real consistency in scientists’ beliefs about what might be considered their most basic social responsibilities. The authors of the paper clearly indicate that there was a sense of responsibility towards society among the scientists and engineers participating in this study. By providing an answer to question “What are the important social responsibilities of scientists?” the respondents listed the ten most important—in their opinion—social responsibilities, arranged in the following order [56]:

• “1. Mitigate personal biases in your research and when offering expert advice;
• 2. Foster the interests of young generations in science and engineers;
• 3. Take steps to prevent or minimize the risks to society associated with the conduct of your work/research;
• 4. Promote public access to scientific and technical information;
• 5. When it comes to your attention, address any improper use of your research findings or products by others;
• 6. Notify appropriate authorities of suspected or observed research/professional misconduct;
• 7. When deciding on what work/research to pursue, take into account whether its potential effects would benefit or harm society;
• 8. When communicating research findings, acknowledge other relevant research interpretations, whether or not consistent with your own;
• 9. Advocate for publicly funded science and engineering that improves the quality of life for some or all members of society; and
• 10. Communicate your work in a way that makes it understandable to the public” [56].

3. Responsibility in Science vs. Sustainability

While analyzing the understanding of the concept of sustainable development, Salas-Zapata [57] distinguished four types of approaches among scientists, defining sustainability as:

• A set of guiding criteria for human actions;
• The goal of humanity;
• Subject of research;
• An approach to science.

As a result, the scope of sustainable development research ranges from research loosely related to some aspects of sustainable development to the science of sustainable development as a field of research focused on sustainable development issues. While examining the connections between different dimensions of sustainability research and the scientist’s identity, Hakkarainen [58] found that “many sustainability researchers may have a dual identity, for example, as a geographer or sustainability scientist, being committed to promote sustainability transformation, and practising inter- and transdisciplinarity.” At the same time “(…) researcher identities might remain discipline-specific, which could affect how newer approaches within sustainability science (…) that challenge researchers’ roles and identities as objective observers are accepted as a valid way to produce knowledge in wider academia.”

There are many indications that science can and should play a leading role on the path to sustainability. To fulfil this role, it is important that scientists act based on their own sense of responsibility, ethics, and care for others, using a human-centred approach [1]. By considering Etzkowitz’s “Triple Helix” model to be correct [59], as well as the specific roles and relationships of science, politics, industry, and society in an integrated framework of sustainable development, it should be concluded that effective science–policy–society interactions still require refinement and strengthening. More effective integration is still required, firstly within science and secondly between science, politics, society, and industry. Using the Triple Helix of Sustainability as a reference, science—as a key societal institution—should be positioned at the centre of interfaces that enable interactions between:

• The three dimensions of sustainability: environmental, social, and economic;
• The three key actors: science (universities), government, and industry;
• The three research domains: environmental, social, and economic sciences [60].

Considering the above, scientists take on new and additional roles and responsibilities which are not always easy to reconcile. Research conducted by Bulten has shown that although process-oriented or transdisciplinary research approaches are a promising sustainability change strategy for scientists [61], there are contradictory reports “between the roles of traditional researcher and transition participant, traditional researcher and transition leader and between transition leader and self-reflexive participant.” These observations suggest that there is a tension between knowledge and action: it was difficult to combine action-oriented (engaged) roles with knowledge-oriented roles. Therefore, in order for science to play its proper role in the transformation of sustainable development, it is important to appropriately strengthen the competences of researchers in the integration of knowledge and action, as well as the skills of systemic and transdisciplinary activities [62]. In line with the observations presented by Bulten are the conclusions of young sustainability researchers, who strongly feel the contradiction between “sustainability values and current expectations for success in academia: avoiding burnout while securing a position; building networks while avoiding air travel; and expanding concepts of knowledge yet developing expertise and credibility in your field” [62].

4. Transformation of Mining Areas

The transformations of mining areas currently taking place in the Global North are closely related to the energy transformation carried out to meet sustainability ideals [63]. The majority of these changes are related to the shift of energy production from fossil raw materials (hard coal and brown coal) to renewable energy sources and/or nuclear energy. Without assessing the correctness of these transformations and the real convergence of the results with the sustainable development goals (SDGs) [64,65,66,67], this paper will analyze the issues of sustainability of mining areas and the importance of responsible science in this context. Our considerations are based primarily on several decades of experience in scientific work related to the hard coal underground mining in the Polish Upper Silesian Coal Basin and information published by other teams featuring a similar research profile.

The economic transformation towards renewable energy and nuclear power generation has taken place and continues to take place mainly in the areas of the Global North [68,69,70,71,72,73,74]; such transformation has also began in China, which has committed to achieve net zero greenhouse gas emissions by 2060 [75,76]. Obviously, the issue of transformation of mining areas in each country shows a number of very specific features. This results from both strictly objective conditions, such as geological and hydrological conditions, deposit extraction technology and the location of coal mines in relation to buildings, as well as from social, economic, and political conditions. In each area, the size and importance of the problems arising from the transformation varied, but their qualitative nature was the same.

This includes the following:

• Physical and chemical processes related to the mining of deposits in the rock mass;
• Technical consequences of surface deformation for buildings and infrastructure;
• Economic and social effects of transformations on the development of mining and post-mining areas.

A centrally accelerated and insufficiently controlled green energy transition can jeopardize fairness and balance in mining and post-mining regions. Without strategic planning and stakeholder engagement, transformation processes are unlikely to gain social legitimacy [77].

The scientific discussion around a just energy transition focuses primarily on energy and environmental and climate justice [78]. We would like to emphasize that social matters are of special importance here. Unfortunately, based on the facts from the history of energy transformations related to the closure of mines, it can be concluded that there was a serious gap in social and economic sciences, which resulted in the lack of comprehensive models of just and sustainable transformation that could constitute a fair offer for mining areas. Therefore, scientists still face the challenge of developing optimum methods for sustainable and just transformation. In this regard, one should use the already acquired knowledge to:

• Explain the processes underlying the closure of mines;
• Disseminate knowledge among society and decision-makers;
• Forecast problems correctly;
• Develop optimum solutions for problems;
• Engage in implementing solutions in the social–economic and political environment.

5. Major Sustainability Problems in Mining and Post-Mining Areas

5.1. Rock Mass and Land Surface Transformations

The most prominent geotechnical consequence of underground mineral extraction is rock-mass deformation resulting from the creation of mine voids [79,80,81]. In regions with intensive hard-coal mining, secondary land surface subsidence develops gradually. In Poland’s Upper Silesian Coal Basin, depressions of up to ~30 m relative to original terrain have been documented [79]. Deformation continues during mining and after cessation of operations, and its magnitude and rate are determined by geological conditions and mining parameters. Within the influence zone, the land surface undergoes complete geomorphological transformation, and changes to hydrological conditions may lead to local flooding or drying.

The role of science is to forecast these processes in space and time to support safe mine design and protect buildings and infrastructure against mining-induced deformation. This task carries significant social responsibility. A forecast must be based on objective scientific criteria and the best available analytical tools. The most widely applied and validated method for predicting deformation from longwall mining is the Budryk–Knothe model and its later developments [82,83,84]. This is not a strict analytical solution to the governing geomechanical equations; it incorporates empirically derived parameters dependent on rock-mass properties and mining configuration, which must be selected by an expert.

Because deformation forecasts influence mining permits, expert judgement becomes entangled with economic and political expectations. These projects involve multi-million-euro investments, essential raw-material supply, and thousands of jobs. The expert preparing the forecast may therefore face pressure to produce results indicating impacts within acceptable limits. Responsible scientific conduct requires the separation of scientific analysis from its consequences. Forecast parameters must not be adjusted to achieve desired outcomes; only after an objective forecast is completed may broader economic, social, and political implications be evaluated by stakeholders and decision-makers [1].

This illustrates a potential internal conflict: the scientist’s epistemic responsibility may contradict external expectations. Once the forecast exists, the scientist may contribute to interdisciplinary and transdisciplinary analyses, integrating the technical forecast with socioeconomic and environmental considerations [85,86,87]. Such integration must occur after, not during, the scientific process to preserve objectivity and public trust.

5.2. Consequences of Mining-Induced Surface Deformations

5.2.1. Technical Consequences

The most important technical effect of the transformation of the rock mass is the impact on the existing buildings and infrastructure. Many mining areas, especially in Europe, are areas where industry has developed—and, as a result, dense buildings and infrastructure have been created [79,88]. As a result of mining activity, the ground substrate of these structures undergoes continuous time–space deformation. Buildings and linear infrastructure located in mining areas are subjected to multiple cycles of ground movement over their service life.

These repeated deformation cycles lead to accelerated technical degradation of buildings. In accordance with ISO 15686 (Service Life Planning) [89], technical degradation refers to the progressive loss of a building’s performance and functional capacity due to physical, chemical, biological, or mechanical deterioration processes. It is evaluated relative to required performance levels using performance indicators and degradation curves, and is distinct from economic depreciation, as it reflects the physical loss of serviceability rather than market value.

In European mining regions, this phenomenon has particularly severe consequences due to the concentration of residential, industrial, historical, and critical infrastructure in a limited area. Progressive deformation of the substrate results in differential settlements and cyclic strain accumulation in structural elements. Over time, this leads to visible and cumulative damage: cracks, distortion of load-bearing systems, loss of serviceability, and, in extreme cases, structural failures or construction disasters [88,90].

5.2.2. Economic Consequences

Mining-induced damage to buildings and infrastructure entails substantial economic impacts, expressed as both direct and indirect costs. These costs comprise (i) the repair of damaged or destroyed structures, (ii) the reinforcement of existing buildings and (iii) preventive strengthening integrated into newly constructed buildings, as well as (iv) accelerated technical depreciation of assets, and (v) the reduction in property value caused by exposure to mining activity.

Disaggregating these costs into categories (i–v) is analytically demanding, because countries and mining operators use fundamentally different liability and financing architectures. National systems vary in (a) how financial responsibility is allocated between the enterprise and the state, (b) how compensation mechanisms are organized institutionally, and (c) how obligations are disclosed in accounting and financial reporting. In some systems, mining damage costs appear directly as public expenditures; in others, they are recorded as balance-sheet provisions or as operating expenses borne by mining companies. More advanced frameworks use dedicated financial instruments—capitalized funds, guarantee deposits, or hybrid multi-source schemes. Such heterogeneity renders numerical comparison of reported cost data across countries methodologically inappropriate unless the underlying compensation architecture is considered. Therefore, the comparison must be based on functional equivalence of purpose, i.e., on how a given system restores lost value, irrespective of the legal vehicle through which costs are expressed.

To operationalize this approach, this study introduces the concept of an institutional–financial model of mining-damage compensation, defined as the set of legal, economic, and accounting rules that determine (i) who bears liability for restoring damaged assets and (ii) how financial resources are secured and allocated to fulfil that obligation. Comparative analysis of empirical data and regulatory frameworks from nine countries (Poland, Germany, Czechia, the United Kingdom, the United States, China, India, Peru, and Chile) show that these systems fall into five recurrent model types, differentiated by financing structure, degree of state involvement, and long-term economic resilience:

Expenditure-based model. Damage repair and land reclamation are financed through direct public expenditure or dedicated public funds. In the case of legacy or orphaned sites, enterprises are not required to recognize provisions, and liability is assumed by the state, funded indirectly through concession fees or taxation. Expenditure-based arrangements thus mainly apply to legacy or orphaned mines (e.g., Germany—LMBV [91], Peru—Pasivos Ambientales Mineros (PAM), where remediation of high-risk abandoned mines is financed by the state through MINEM/AMSAC when the liable operator is unknown or insolvent [92]). In Germany, this expenditure-based architecture applies exclusively to the remediation of historical mining legacies (Altbergbau) managed by LMBV, whereas the broader German mining sector is governed primarily by enterprise-level balance-sheet provisions; the capitalized RAG-Stiftung applies exclusively to the perpetual obligations of the historical hard-coal sector (Altbergbau) and does not cover other mining industries. In all jurisdictions, expenditure-based remediation is confined to legacy or orphaned liabilities and does not replace provision- or fund-based obligations for active mining operations.

Provision (reserve) model. Compensation obligations are recognized as balance-sheet provisions in accordance with national or international accounting standards [93]. Financial liability is individualized and directly tied to the enterprise’s financial statements (e.g. Poland—Accounting Act 1994 [94,95]; Czechia—Zákon o účetnictví 563/1991 [96]; Germany—HGB §249 [97]; India—Ind AS 37 [98,99]) [100,101,102]. In most jurisdictions, expenditure-based remediation operates alongside provision - and fund-based mechanisms, and therefore applies only to a narrow class of legacy or otherwise ownerless liabilities.

Fund-based model. Dedicated funds or ring-fenced financial guarantees accumulate resources ex ante—financed through extraction charges, enterprise contributions, or public transfers—which is designed to ensures long-term financial security and decouples the creation of the obligation from its settlement (Germany—RAG-Stiftungsgesetz 2008 [103,104,105]; USA—SMCRA 1977: AML Fund (Abandoned Mine Land Fund) non-capitalized federal fund [106,107]; India—Mine Closure Guidelines 2009 and 2025 [108,109,110]; Chile— mandatory financial guarantees under Ley 20.551, 2012 [111,112,113]). In the case of the USA, the AML Fund operates as a revolving earmarked trust rather than a fully endowed capital fund, because annual congressional appropriations and Treasury investment rules prevent the accumulation of a permanent endowment. India appears in both groups because the national system combines enterprise-level balance-sheet provisions (Ind AS 37) with mandatory external mine-closure funds established under the 2009 and 2025 guidelines.

Operational (cash-based) model. Compensation is treated as a current operating expense without valuation of future liabilities and without establishing reserve funds—typical of transitional systems still developing environmental liability accounting [114,115].

Hybrid model. A multi-layer financial architecture combining balance-sheet provisions, public funds, and direct expenditures. Liability is shared between enterprises and the state, with different mechanisms applied to active operations and legacy or orphaned sites, enabling a long-term balance between economic, social, and environmental interests [114,115,116].

In practice, the chosen model determines not only how mining damage costs are recorded, but also whether costs are internalized by the polluter or externalized to the public. This provides the analytical foundation for further examination of the five cost components: (i) structural repairs, (ii) reinforcement of existing buildings, (iii) preventive strengthening in new structures, (iv) accelerated depreciation, and (v) property-value loss.

Table 2. Institutional–financial models of mining damage compensation in selected countries (2024).

Model	Financing Mechanism	System Logic/ Liability Regime	Example Countries and Verified Sources
I. Expenditure-based	Damage repair and land reclamation financed through direct public spending (state budget or public funds). For such legacy or orphaned sites, enterprises are not required to recognize provisions.	Public liability model for the sites covered by the scheme. Financial responsibility for these legacy liabilities rests with the state; enterprises contribute indirectly via concession fees or taxation.	In Peru, the Pasivos Ambientales Mineros (PAM) system covers the inventory and remediation of high-risk abandoned mines, financed by the State through MINEM/AMSAC when the liable operator is unknown or insolvent [117]; In Germany, expenditure-based financing applies solely to historical mining legacies (Altbergbau) managed by LMBV.
II. Provision (reserve-based)	Future compensation obligations recorded as balance-sheet provisions in compliance with national/international accounting standards (IAS/Ind AS/HGB).	Individualized corporate liability. Obligations are capitalized on the enterprise balance sheet; high transparency of financial exposure.	Poland—Accounting Act (provisions for future liabilities, including environ-mental) [94]; Czechia—Zákon o účetnictví (general obligation to recognize provisions for future liabilities [96]); Germany—HGB §249 (requiring provisions for uncertain obligations and certain decommissioning/rehabilitation costs [97]); India—Ind AS 37 (applied by large mining and energy companies to recognize mine-closure and environmental provisions in their financial statements [98]); corporate filings: LW Bogdanka [95], OKD a.s. [118].
III. Fund-based (capitalized)	Establishment of earmarked financial funds accumulating resources ex ante from extraction fees, enterprise contributions, or public transfers.	Pre-funded liability model. Funding is secured before damage occurs; obligation formation is decoupled from cash disbursement.	Germany—RAG-Stiftung Jahresabschluss (a dedicated foundation whose assets and income streams are used to finance the “Ewigkeitslasten” (perpetual obligations) of the former hard-coal sector [6]); USA—SMCRA (Abandoned Mine Land (AML) Fund, a federally administered non-capitalized trust fund financed by per-ton fees on coal production [106,107]); India—(Mine Closure Guidelines and escrow funding, which require operators to deposit closure costs into dedicated escrow accounts during the mine life [108,109,110]); Chile—Ley 20.551 (which mandates financial guarantees for mine closure plans (bonds, letters of credit, etc.) sized to the full closure cost [111]).
IV. Operational (cash-basis)	Compensation recorded as current operating expense; no valuation of long-term liabilities and no dedicated reserves or funds.	Transitional system. Low transparency of future exposure; costs materialize only when paid.	Comparative studies of mine-closure and liability systems show cash-basis practices in parts of Sub-Saharan Africa, Latin America and Southeast Asia, particularly in ASM sectors with weak formal accounting and enforcement: World Bank (2021) [13]
V. Hybrid/multi-layer	Parallel use of provisions, public funds, and direct expenditures; cost burden shared between enterprise and state with different instruments applied to active operations and legacy or orphaned liabilities.	Integrated liability architecture. Balances accounting efficiency with long-term social and environmental sustainability.	Poland—combination of enterprise provisions and substantial public support for mine closure and mining damages under special coal-sector laws and budgetary transfers Statistics Poland [11]; UK—funded through government grant-in-aid, levies and commercial income to manage abandoned coal-mine liabilities and subsidence compensation—Coal Authority Annual Report and Accounts [8]; USA—SMCRA [106] + AMLER/AML programmes, where industry-funded AML fees, federal and state budgets, and company-level provisions coexist in a layered liability architecture—OSMRE FY25 AML Guidance [9]; Chile—Codelco and private mines under Ley 20.551, where company-level provisions and mandatory financial guarantees operate alongside public programmes for legacy mining liabilities - Codelco Sustainability Report [10]; China—a combination of mandatory restoration fees and company-level provisions, together with central and provincial ecological-restoration funds, as reflected in MNR regulatory measures [119], national China Mineral Resources reports [114,115], and financial disclosures of large SOEs (e.g., China Shenhua [116]).

synthesizes the institutional–financial models that govern mining damage compensation and shows how funding mechanisms and liability distribution between the state and mining operators differ depending on legal, accounting, and economic frameworks.

Table 3 provides quantitative evidence supporting these differences by compiling verified financial data on mining-induced damage from selected countries. The dataset separates two analytically distinct financial magnitudes, consistent with the methodology in Appendix A:

• FLOW—realized annual cash outflows (operational or public-budget expenditures for repairs, reclamation, and preventive reinforcement);
• STOCK—balance-sheet-disclosed obligations (provisions, financial guarantees, and security deposits) representing future compensation liabilities.

All values were converted to euros using IMF 2024 [120] annual average exchange rates to ensure cross-country comparability across jurisdictions that differ in currency, accounting practices, and transparency of environmental liability disclosures. The table is based exclusively on audited and publicly verifiable documents—corporate financial statements, government reports, and public-fund reports—avoiding uncertainty associated with secondary or unverifiable estimates.

Table 4 operationalizes the FLOW–STOCK classification, showing that countries with similar levels of mining activity may report non-comparable financial figures due to differences in liability capitalization, disclosure rules, and institutional architecture. Liability allocation and the timing of cost recognition differ fundamentally across systems, with the largest divergence concerning preventive measures (ex ante).

In Poland, Articles 144–152 of the Geological and Mining Law [121] impose full cost internalization on the mining operator, covering not only ex post repair (or compensation where repair is not feasible), but also ex ante preventive reinforcement of newly constructed buildings located within predicted deformation zones. In this model, the polluter finances both the prevention and consequence.

In contrast, in Germany, the United Kingdom, the United States, Australia, and Chile, ex ante instruments (provisions, security deposits, and guarantee funds) primarily secure mine closure and reclamation. Preventive reinforcement of new structures typically remains the responsibility of the private investor or public entity. Mining companies engage financially only after damage occurs (ex post), rather than preventing it.

These differing legal and accounting frameworks generate structurally distinct cost-allocation regimes and economic incentives: full internalization of preventive costs in Poland versus their systematic displacement onto private investors or public entities in most other jurisdiction. As a result, nominal expenditure values are not directly comparable, which justifies using the institutional–financial model that classifies data by system function (FLOW vs. STOCK), rather than nominal amounts.

FLOW values are typically easier to quantify, as they appear in financial statements. In Poland, expenditures on mining-damage repair in 2023 reached PLN 463.6 million (EUR ~99.7 million; IMF 2024) [11,120], including the following:

• PLN 46.6 million (EUR 9.0 million)—residential buildings;
• PLN 41.3 million (EUR 8.1 million)—roads, streets, bridges, and viaducts;
• PLN 36.0 million (EUR 7.5 million)—preventive reinforcement.

Preventive spending represents ~10% of total expenditures, indicating a predominance of reactive over preventive compensation.

However, FLOW values do not capture full financial exposure. Many countries simultaneously apply STOCK mechanisms (provisions, dedicated funds, and security deposits), which represent future liabilities rather than spending already incurred and therefore better reflect long-term financial risk.

Table 3. Audited financial values related to mining-damage liability in selected countries (FLOW vs. STOCK; 2023–2025).

Country	Audited/Reconstructed Financial * (EUR)	Financial Magnitude	Year	Financing/Liability Model	Legally Liable Payer	Cost Scope (i–v)	Audited/Governmental Source
Poland	182 M (FLOW)/ 282 M (STOCK)	Dual FLOW/STOCK system (realized annual spending)	2024	Hybrid (corporate liability + public instruments)	Mining operator (ex ante and ex post)	(i)–(iii)	Statistics Poland (2024) [11]; Geological and Mining Law, Arts. 144–152 [121] and financial statements of mining companies: PGG S.A. [122], JSW S.A. [123], LW Bogdanka S.A. [95] and the Polish state-owned mine restructuring company (SRK S.A.) [124].
Germany	FLOW - not reported; costs internalized in provisions/ 9.76 B (STOCK)	STOCK (balance-sheet provision for perpetual mining obligations—“Ewigkeitslasten”)	2024	Fund–reserve architecture (RAG-Stiftung financing long-term obligations of the former hard coal sector)	RAG-Stiftung (foundation legally responsible for financing and performing perpetual obligations of the former hard-coal sector	(i)–(iii) plus mine-water drainage, geotechnical stabilization of subsided areas, and flood-protection infrastructure	RAG-Stiftung (2024) [6]. Jahresabschluss der RAG-Stiftung.
Czech Rep.	210.6 M (FLOW)/ 318 M (STOCK)	Dual FLOW/STOCK system	2023–2024	Hybrid (corporate provisions + public legacy spending)	Mining enterprise (ex ante) + State (legacy via DIAMO/ MPO)	(i)–(iii) plus mine-water drainage and geotechnical stabilization of post-mining areas	MPO Státní závěrečný účet 2023—Part H [125] and financial statements of mining companies: OKD a.s. [118]; Severní energetická [126]; Severočeské doly [127], Sokolovská uhelná [128], Vršanská uhelná [129].
United Kingdom	64.8 M (FLOW)/ 1.47 B (STOCK)	Dual FLOW (actual public expenditure)/ STOCK system	2024/25	Hybrid (public funding for legacy mines + corporate provisions for active mines)	State (legacy)/operator (active mines)	(i)–(iii) plus mine-water treatment, shaft and adit stabilization, and tip-safety measures	MRA Annual Report and Accounts (2024/25) [8]
USA	1.12 B (FLOW)/15.62 B (STOCK)	FLOW (federal AML/BIL expenditures)	2025	Hybrid (federal AML fund + operator liability for active mines)	AML → state/public; active mines → operator	(i), (iii) plus mine-land reclamation, acid-mine drainage treatment, and stabilization of shafts and openings	OSMRE FY Budget Justification [130]; IIJA AML Guidance [131]; AMLIS Remaining Reclamation Cost [132]; OSMRE AML Payments FY23–25 [133]
China	No auditable aggregate value disclosed (descriptive segment-level indication only; ≈12% reported narratively)	—(Partial disclosure only)	2023	Hybrid (operational + local funds) Enterprise-based (with ad hoc local public support)	Mining enterprise	(i)–(iii) plus land reclamation, tailings-facility safety, and mine-water management	China Shenhua Energy Co. Annual Report [116] (enterprise-level disclosure)
Chile	FLOW—not reported/4.83 B (STOCK—guarantee-based)	STOCK (guarantee-based)+ no audited FLOW available	2022	Hybrid/ fund–assurance basedmodel (mandatory financial guarantee; Ley 20.551)	Mining enterprise + state (post-closure management)	(i)–(iii) plus slope and waste-dump stabilization, tailings-facility safety, and acid-rock drainage control	SERNAGEOMIN (2022) [134,135]; Ley 20.551 [111]
Australia	Public FLOW not applicable/ 7.01 B (STOCK))	STOCK—dominant assurance-based system + public FLOW (not applicable)	2023/24	Assurance–deposit model (ex ante secured liability; no public cash-out)	Mining enterprise (liability secured through rehabilitation deposits and surety instruments)	(iii) plus mine rehabilitation and closure, mine-affected water management, and tailings/waste-rock stability	NSW Resources Regulator Annual Report 2022–23 [136]; Queensland GovernmentAnnual Report [137]; Government of Western Australia [138,139].

* Currency values were converted using the IMF (2024) annual average exchange rates. Unit costs (EUR/t) were calculated only where auditable production–volume data were available Reserve-based or deposit-based figures (Germany, Czechia, and Australia) represent balance-sheet liability valuations, not annual cash expenditures. The scope (i–iii) corresponds to the compensation cost categories used in this study: (i) repair of damaged or destroyed structures, (ii) reinforcement of existing buildings, and (iii) preventive strengthening incorporated into new construction.

Table 3. Audited financial values related to mining-damage liability in selected countries (FLOW vs. STOCK; 2023–2025).

Country	Audited/Reconstructed Financial * (EUR)	Financial Magnitude	Year	Financing/Liability Model	Legally Liable Payer	Cost Scope (i–v)	Audited/Governmental Source
Poland	182 M (FLOW)/ 282 M (STOCK)	Dual FLOW/STOCK system (realized annual spending)	2024	Hybrid (corporate liability + public instruments)	Mining operator (ex ante and ex post)	(i)–(iii)	Statistics Poland (2024) [11]; Geological and Mining Law, Arts. 144–152 [121] and financial statements of mining companies: PGG S.A. [122], JSW S.A. [123], LW Bogdanka S.A. [95] and the Polish state-owned mine restructuring company (SRK S.A.) [124].
Germany	FLOW - not reported; costs internalized in provisions/ 9.76 B (STOCK)	STOCK (balance-sheet provision for perpetual mining obligations—“Ewigkeitslasten”)	2024	Fund–reserve architecture (RAG-Stiftung financing long-term obligations of the former hard coal sector)	RAG-Stiftung (foundation legally responsible for financing and performing perpetual obligations of the former hard-coal sector	(i)–(iii) plus mine-water drainage, geotechnical stabilization of subsided areas, and flood-protection infrastructure	RAG-Stiftung (2024) [6]. Jahresabschluss der RAG-Stiftung.
Czech Rep.	210.6 M (FLOW)/ 318 M (STOCK)	Dual FLOW/STOCK system	2023–2024	Hybrid (corporate provisions + public legacy spending)	Mining enterprise (ex ante) + State (legacy via DIAMO/ MPO)	(i)–(iii) plus mine-water drainage and geotechnical stabilization of post-mining areas	MPO Státní závěrečný účet 2023—Part H [125] and financial statements of mining companies: OKD a.s. [118]; Severní energetická [126]; Severočeské doly [127], Sokolovská uhelná [128], Vršanská uhelná [129].
United Kingdom	64.8 M (FLOW)/ 1.47 B (STOCK)	Dual FLOW (actual public expenditure)/ STOCK system	2024/25	Hybrid (public funding for legacy mines + corporate provisions for active mines)	State (legacy)/operator (active mines)	(i)–(iii) plus mine-water treatment, shaft and adit stabilization, and tip-safety measures	MRA Annual Report and Accounts (2024/25) [8]
USA	1.12 B (FLOW)/15.62 B (STOCK)	FLOW (federal AML/BIL expenditures)	2025	Hybrid (federal AML fund + operator liability for active mines)	AML → state/public; active mines → operator	(i), (iii) plus mine-land reclamation, acid-mine drainage treatment, and stabilization of shafts and openings	OSMRE FY Budget Justification [130]; IIJA AML Guidance [131]; AMLIS Remaining Reclamation Cost [132]; OSMRE AML Payments FY23–25 [133]
China	No auditable aggregate value disclosed (descriptive segment-level indication only; ≈12% reported narratively)	—(Partial disclosure only)	2023	Hybrid (operational + local funds) Enterprise-based (with ad hoc local public support)	Mining enterprise	(i)–(iii) plus land reclamation, tailings-facility safety, and mine-water management	China Shenhua Energy Co. Annual Report [116] (enterprise-level disclosure)
Chile	FLOW—not reported/4.83 B (STOCK—guarantee-based)	STOCK (guarantee-based)+ no audited FLOW available	2022	Hybrid/ fund–assurance basedmodel (mandatory financial guarantee; Ley 20.551)	Mining enterprise + state (post-closure management)	(i)–(iii) plus slope and waste-dump stabilization, tailings-facility safety, and acid-rock drainage control	SERNAGEOMIN (2022) [134,135]; Ley 20.551 [111]
Australia	Public FLOW not applicable/ 7.01 B (STOCK))	STOCK—dominant assurance-based system + public FLOW (not applicable)	2023/24	Assurance–deposit model (ex ante secured liability; no public cash-out)	Mining enterprise (liability secured through rehabilitation deposits and surety instruments)	(iii) plus mine rehabilitation and closure, mine-affected water management, and tailings/waste-rock stability	NSW Resources Regulator Annual Report 2022–23 [136]; Queensland GovernmentAnnual Report [137]; Government of Western Australia [138,139].

* Currency values were converted using the IMF (2024) annual average exchange rates. Unit costs (EUR/t) were calculated only where auditable production–volume data were available Reserve-based or deposit-based figures (Germany, Czechia, and Australia) represent balance-sheet liability valuations, not annual cash expenditures. The scope (i–iii) corresponds to the compensation cost categories used in this study: (i) repair of damaged or destroyed structures, (ii) reinforcement of existing buildings, and (iii) preventive strengthening incorporated into new construction.

Table 4. Classification model for mining damage costs based on visibility and financial treatment (FLOW → STOCK → EXTERNALITIES). The model categorizes mining damage costs by their accounting recognition and degree of internalization: (1) FLOW—realized expenditures (ex post cash outflows); (2) STOCK—disclosed financial obligations (ex ante commitments); (3) EXTERNALITIES—non-internalized social costs. This reflects the progression cash cost → liability exposure → social loss and indicates the extent to which a jurisdiction applies the polluter-pays principle (OECD, 2022) [19]. Note: FLOW = incurred and paid; STOCK = recognized obligation (IAS 37, IFRS liability recognition) [93]; EXTERNALITIES = unrecorded social cost. Where consolidated audited statements are unavailable, national FLOW and STOCK values are reconstructed from governmental budgetary and program-level data. For China, percentage shares reported in enterprise disclosures are descriptive and not auditable financial values.

	FLOW (Realized Cost—Cash Outflow)	STOCK (Recognized Liability—Provisions/Deposits/Funds)	EXTERNALITIES (Unaccounted Social Cost—Not Visible in Accounting)
Accounting status	Recorded in the income statement or public budget as a cash expenditure in the reporting year.	Recorded in the balance sheet as a future financial obligation, in line with IAS 37 [93].	Not recorded in corporate accounts or public budgets; cost remains outside the financial system.
Timing of cost recognition	Ex post—only after damage has occurred and generated an invoice or budget expenditure.	Ex ante—liability is certain or highly probable, although payment will occur later.	Continuous, dispersed over time; no identifiable accounting event.
Cost bearer (default)	Mining operator or public sector (if classified as public expenditure).	Mining operator (reclamation funds, closure funds, security deposits, and provisions).	Property owners, infrastructure users, and local communities (cost shifted).
Economic nature	Realized financial outflow (cash cost).	Capitalized liability exposure (future cost).	Non-internalized socio-economic loss (value loss and functional degradation).
Examples	Repairs, reclamation, and preventive reinforcement of new structures (ex ante).	Closure funds, balance-sheet provisions, rehabilitation deposits, and perpetual funds (e.g., RAG-Stiftung; AML Fund).	Accelerated structural degradation (IV) and real estate devaluation (V).
Data sources/verification	Audited budget and financial reports (Statistics Poland 2024 [69]; OKD 2024 [76]; NSW DCS 2024 [79].	Corporate/fund financial statements (RAG-Stiftung 2024 [6]; AML Fund 2025 [9]; OSMRE 2024 [131]) [78].	Peer-reviewed studies: hedonic pricing models and life-cycle cost analysis [140,141,142,143,144,145].
Use in this study	Comparison of absolute and unit costs (EUR/t of extraction).	Assessment of long-term fiscal and corporate risk (capital at risk).	Quantification of hidden costs that materially affect communities but are not financially reported.

Table 4. Classification model for mining damage costs based on visibility and financial treatment (FLOW → STOCK → EXTERNALITIES). The model categorizes mining damage costs by their accounting recognition and degree of internalization: (1) FLOW—realized expenditures (ex post cash outflows); (2) STOCK—disclosed financial obligations (ex ante commitments); (3) EXTERNALITIES—non-internalized social costs. This reflects the progression cash cost → liability exposure → social loss and indicates the extent to which a jurisdiction applies the polluter-pays principle (OECD, 2022) [19]. Note: FLOW = incurred and paid; STOCK = recognized obligation (IAS 37, IFRS liability recognition) [93]; EXTERNALITIES = unrecorded social cost. Where consolidated audited statements are unavailable, national FLOW and STOCK values are reconstructed from governmental budgetary and program-level data. For China, percentage shares reported in enterprise disclosures are descriptive and not auditable financial values.

	FLOW (Realized Cost—Cash Outflow)	STOCK (Recognized Liability—Provisions/Deposits/Funds)	EXTERNALITIES (Unaccounted Social Cost—Not Visible in Accounting)
Accounting status	Recorded in the income statement or public budget as a cash expenditure in the reporting year.	Recorded in the balance sheet as a future financial obligation, in line with IAS 37 [93].	Not recorded in corporate accounts or public budgets; cost remains outside the financial system.
Timing of cost recognition	Ex post—only after damage has occurred and generated an invoice or budget expenditure.	Ex ante—liability is certain or highly probable, although payment will occur later.	Continuous, dispersed over time; no identifiable accounting event.
Cost bearer (default)	Mining operator or public sector (if classified as public expenditure).	Mining operator (reclamation funds, closure funds, security deposits, and provisions).	Property owners, infrastructure users, and local communities (cost shifted).
Economic nature	Realized financial outflow (cash cost).	Capitalized liability exposure (future cost).	Non-internalized socio-economic loss (value loss and functional degradation).
Examples	Repairs, reclamation, and preventive reinforcement of new structures (ex ante).	Closure funds, balance-sheet provisions, rehabilitation deposits, and perpetual funds (e.g., RAG-Stiftung; AML Fund).	Accelerated structural degradation (IV) and real estate devaluation (V).
Data sources/verification	Audited budget and financial reports (Statistics Poland 2024 [69]; OKD 2024 [76]; NSW DCS 2024 [79].	Corporate/fund financial statements (RAG-Stiftung 2024 [6]; AML Fund 2025 [9]; OSMRE 2024 [131]) [78].	Peer-reviewed studies: hedonic pricing models and life-cycle cost analysis [140,141,142,143,144,145].
Use in this study	Comparison of absolute and unit costs (EUR/t of extraction).	Assessment of long-term fiscal and corporate risk (capital at risk).	Quantification of hidden costs that materially affect communities but are not financially reported.

• STOCK (liability exposure)

In Czechia, audited provisions for mine closure, mining damage and land reclamation—including selected mining-damage obligations reach CZK 7.85 billion (EUR ~318 million, IMF 2024) (OKD [118] p. 92; Severní energetická, 2023 [126], pp. 61–62). In Germany, RAG-Stiftung Jahresabschluss 2024 [6] (p. 28) reports EUR 9.76 billion in balance-sheet provisions for the financing of Ewigkeitslasten—(the perpetual obligations of the former hard-coal sector, comprising long-term mine dewatering, groundwater and gas management, polder and flooding control measures, and geotechnical stabilization associated with historic mining-inducedsubsidence).

In this context, scientific responsibility should include the following:

• Developing and improving methods for predicting mining-induced ground deformation and structural response, as well as effective protection techniques;
• Disseminating knowledge on mining impacts and mitigation strategies to engineers, planners, and financial decision-makers;
• Participating in legislative processes that shape legal and financial liability for mining damage;
• Contributing to public and policy discourse.

Compared with the direct, financially measurable categories (i–iii), the costs of (iv) accelerated structural degradation and (v) loss of real estate value are considerably more difficult to quantify because they do not generate explicit accounting flows. Financial and public accounting systems capture only costs that materialize as expenditures or formally recognized liabilities. They do not record hidden liabilities borne by property owners, building users, or local communities. These unrecorded burdens correspond to:

• (iv) Accelerated technical wear and shortened service life of buildings;
• (v) Permanent loss of market value of real estate located within mining-influence zones.

Unlike repair and reinforcement costs (i–iii), categories (iv–v) do not produce direct financial transfers and therefore remain absent from company financial statements and public budgets. In environmental economics, they constitute negative externalities.

Building life-cycle research shows that operational-phase expenditure and reinvestment caused by degradation dominate total costs. Studies by Biolek, Kishk, Bromilow, and Sobanjo [140,143,144,145] demonstrate that operating-phase costs may account for up to 70% of total life-cycle expenditure [140]. These studies, however, address natural ageing. In mining areas, repeated deformation cycles and dynamic impacts accelerate degradation beyond natural ageing.

A method to quantify accelerated depreciation caused by ground vibrations was proposed by Chrzan [146], who expresses the damage cost as the product of building replacement value and the difference between actual and natural depreciation. Empirical studies confirm the scale of accelerated wear. According to Bryt-Nitarska [147,148], accelerated technical consumption reaches approximately:

• 45% for buildings ~50 years old;
• 50% for buildings ~100 years old;
• 60% for buildings ~130 years old.

• Comparable results were reported by Wodyński [149,150] and Rusek [151,152].

Loss of real estate value constitutes a separate, measurable consequence. In the Upper Silesian Coal Basin, Bryt-Nitarska [153] found that average prices per m² of residential property in mining-impact municipalities were 13% lower, and the lowest observed prices were up to 45% lower, relative to comparable areas without mining influence. International hedonic pricing studies report similar effects:

• Williams [154]: value reduction of 0.34–1.7% at the county level associated with the presence of open-pit mines;
• Malikov [141]: houses located 1 mile closer to a rock mine sell at a 2.3–5.1% discount;
• Kolala [142]: properties ≤2 km from a mine sell at 20–30% lower prices than similar properties ≥6–7 km away.

Accurate valuation of accelerated depreciation and loss of real estate value is essential for determining compensation; thus, it carries a strong social responsibility burden on researchers and engineers.

Categories (iv) and (v) are non-internalized costs: they are not recorded by mining companies nor reflected in public-sector accounting. Consequently, official statistics systematically underestimate the true economic cost of mining.

To address this gap, the present study introduces a cost-classification model based on their visibility in financial reporting:

• FLOW—expenditures already incurred and recorded (ex post), reflected in public budgets or company profit-and-loss accounts;
• STOCK—obligations secured in advance (ex ante), disclosed as provisions, and deposits or dedicated funds;
• EXTERNALITIES—unaccounted social and economic losses borne by property owners and communities (e.g., land-value reduction and accelerated degradation).

This hierarchy reflects the economic sequence cash cost → liability exposure → social loss, consistent with financial-risk analysis and the principle of cost internalization (OECD, 2018 [17]), the entity causing the damage bears its cost.

In Poland, cost internalization is full: mining operators finance both preventive reinforcement (ex ante) and damage repair (ex post). In other jurisdictions, ex ante financing primarily secures mine closure and reclamation, whereas preventive reinforcement remains the burden of the investor, resulting in different risk-allocation structures and incentives.

Table 4 applies the FLOW–STOCK–EXTERNALITIES classification, enabling cross-country comparability regardless of financing architecture or accounting rules. The framework integrates financial data (FLOW/STOCK) with valuation methods for non-internalized losses (EXTERNALITIES), including life-cycle cost analysis, hedonic pricing, and degradation modelling.

The classification serves as an analytical instrument that traces how the financial burden is redistributed over time and across actors: from realized expenditures paid by the mining operator or the public sector (FLOW), through capitalized liabilities disclosed in balance sheets (STOCK), to costs entirely invisible to accounting systems yet absorbed by property owners and local communities (EXTERNALITIES).

Importantly, the absence of categories (iv) and (v) in formal reporting does not indicate the absence of damage, it indicates the absence of cost internalization. When compensation covers only ex post repairs and excludes long-term degradation or property-value loss, the true economic cost of mining remains statistically understated and financially shifted away from the responsible entity.

Thus, the proposed taxonomy provides a basis for quantitative research linking financial accounting data with valuation of asset losses, hedonic property-value modelling, and life-cycle cost analysis (LCCA). By distinguishing between what is paid, what is owed, and what is socially absorbed, the model captures the full economic footprint of mining-induced damage—including the portion that current accounting systems do not recognize.

5.2.3. Social Consequences

The distribution of mining-related benefits and burdens is structurally asymmetric: economic value is captured at the corporate or national level, whereas social and environmental costs are absorbed locally by communities living near extraction sites [155,156]. Mining contributes to GDP, stabilizes regional labour markets, and attracts public and private investment, particularly in mono-industrial regions with limited diversification [157]. However, when preventive and compensatory mechanisms remain weak, geotechnical and landscape transformations—including ground deformation, infrastructure damage, and mining-induced seismicity—lead to a measurable decline in residents’ quality of life [158,159].

• Psychosocial effects of mining-induced seismicity

Underground mining-induced vibrations act as a chronic psychosocial stressor. Research from Polish mining regions shows that even when peak particle velocity (PPV) remains well below structural damage thresholds, residents experience sleep disturbances, anxiety, and diminished sense of safety [146,157]. Survey results indicate that up to 68% of residents report persistent stress linked to recurring seismic events [158]. Recent studies demonstrate that psychosocial responses to mining-induced seismicity are comparable to those observed in natural earthquake zones: it is the loss of perceived safety—rather than deformation magnitude—that primarily drives the reduction in well-being [158,159].

• Health impacts of environmental exposure (Global South)

In low- and middle-income countries, mining-related social consequences include measurable health impacts driven by environmental exposure. In the cobalt mining region of the Democratic Republic of Congo, elevated concentrations of heavy metals have been detected in blood and urine, and children exhibit biomarkers of oxidative stress and DNA damage [160,161]. Analogous pathways occur in artisanal and small-scale gold mining (ASGM) in the Amazon, where mercury used during gold amalgamation bioaccumulates in aquatic food webs; mercury levels in human tissue are significantly higher in mining zones than in control populations [162,163].

Health impacts are commonly quantified using DALY—Disability-Adjusted Life Years, measuring years of healthy life lost due to disease (WHO). To assess broader well-being effects, the literature introduces WELBY—Well-being-Adjusted Life Years, capturing changes in perceived quality of life [164,165].

• Quantifying social impacts: S-LCA

To integrate social impacts into planning and compensation policies, they must be expressed as measurable indicators. Social Life Cycle Assessment (S-LCA) offers a quantitative framework for evaluating effects on community well-being, labour rights, and household economic stability. In ASM gold mining, Springer and D’Eusanio [166,167] identified “social hotspots” across the value chain, including informal labour, income instability, and occupational health and safety risk. In cobalt mining in the Democratic Republic of Congo, Orola [165] quantified household-level impacts: 40–60% of ASM households experience income instability, 20% of workers report excessive working hours, and social impacts expressed as QALY/kg Co range from −0.003 to +0.001 for ASM (depending on Cu/Co allocation), whereas LSM remains near zero with low variability [165]. The negative tail of ASM impacts is primarily driven by child labour, while in LSM the dominant category is contract labour, with impacts around ~0.001 DALY/kg [165]. These results show that the social cost of one kilogram of extracted metal can be quantified and compared across mining models.

The role of science is not limited to generating knowledge; it is to ensure that verified evidence informs planning and regulatory decisions. Research provides the following:

• Empirical identification and quantification of impacts—deformation, seismicity, exposure, and psychosocial effects.
• Analytical tools that support compensation and prevention—from deformation forecasting to S-LCA-based valuation of social consequences.
• Reduction in information asymmetry—open access to data on ground deformation and seismicity, and expert support for local governments in spatial planning.

Through these functions, scientific responsibility means ensuring that extraction decisions account not only for economic efficiency but also for the well-being of communities living in mining areas.

6. The Tasks of Responsible Science in the Context of Sustainable Development Goals (SDGs) for Mining and Post-Mining Areas

We have already observed that while the positive effects of raw material extraction are widespread and clearly visible at the national economic level, the adverse consequences are concentrated locally. Therefore, the sustainable development of mining and post-mining areas is not only about the prudent use of resources with regard to the needs of future generations, but also about ensuring fair compensation for those affected by the negative consequences of mining. When mining companies—and consequently national economies—generate profits from mineral sales, these profits should cover the costs of damage compensation and preventive protection measures. The revenues should compensate the owners of damaged properties and finance the protection of structures and land against further degradation.

The social responsibility of science involves participating in society’s collective effort to achieve the Sustainable Development Goals (SDGs) of the 2030 Agenda. In mining and post-mining regions, implementing these goals entails addressing problems that differ from those encountered in other areas [155]. Specific challenges arise particularly in relation to some of the SDGs:

• Goal 1—elimination of poverty

While mining generates substantial national revenues, the distribution of these benefits is structurally uneven. Economic gains tend to accumulate in state budgets and corporate headquarters, whereas the social and material costs of extraction are borne locally. In this context, scientific responsibility extends beyond improving extraction efficiency. It includes producing evidence that enables equitable redistribution of mining-derived value and protects the economic rights of communities that absorb the social and environmental burden.

In many low- and middle-income economies, artisanal and small-scale mining (ASM) constitutes a critical household livelihood strategy. Hilson [168] shows that ASM is often one of the few available sources of cash income and can serve as a poverty exit pathway, especially where access to formal labour markets is constrained. Short-term earnings from ASM can exceed those from agriculture or informal services, yet the sustainability of these gains depends on institutional factors—market access, capital accumulation, and the degree of sector formalization.

Verbrugge [169] further demonstrates that ASM is both a symptom of structural poverty and a potential driver of socio-economic mobility. Households engaging in ASM frequently improve housing conditions, invest in education, and stabilize consumption. However, these outcomes remain vulnerable to value capture within the mineral supply chain: when intermediaries or external actors extract a disproportionate share of revenue, mining households remain exposed to income volatility despite participating in a high-value sector.

Therefore, aligning mining governance with SDG 1 requires a shift from focusing on national revenue to prioritizing distributional justice. Responsible research contributes to poverty reduction not by increasing extraction, but by providing the following:

• Transparent evidence on how mining-generated value is distributed across actors;
• Analytical tools that support benefit-sharing and compensation mechanisms;
• Data that strengthen the bargaining position of mining regions in national fiscal negotiations.

Mining can reduce poverty—but only under governance frameworks that ensure that value flows not solely to national accounts or corporate shareholders, but also to the communities that incur the social and environmental costs of extraction.

• Goal 3—good health and life quality

Mining inevitably generates environmental degradation, particularly through disturbances to the geological medium and ground stability. However, within the sustainability framework and the SDGs, human health impacts must be prioritized, as in many mining regions the consequences are direct, clinically measurable, and supported by robust epidemiological evidence.

Cobalt mining in the Democratic Republic of Congo, largely performed through artisanal and small-scale mining (ASM), is one of the best-documented examples. Nkulu Banza [160] demonstrated that residents living near ASM sites showed multifold increases in cobalt concentrations in urine and blood, while children exhibited biomarkers of oxidative stress and DNA damage attributable to chronic environmental exposure to heavy metals. Earlier biological monitoring by Nkulu Banza [161] confirmed high levels of cobalt and associated metals in human biological samples from the same region. Critically, both studies show that exposure affects not only miners but the non-mining population living in the vicinity, highlighting community-wide exposure pathways.

Similar mechanisms operate in artisanal and small-scale gold mining (ASGM). In Madre de Dios (Peru), hair mercury biomonitoring revealed that residents of mining zones had significantly higher mercury body burdens than urban controls, with fish consumption and residence location identified as dominant predictors [162]. Complementary research showed that mercury emitted during ASGM is transported by river systems and bioaccumulates in aquatic food webs, generating exposure for populations distant from the mining site [163].

Together, these findings provide unequivocal evidence that health effects occur on a population scale, not solely among workers.

Within the scope of SDG 3—Good Health and Well-Being, socially responsible science must therefore enable the following:

• Identification of exposure pathways;
• Quantification of population-level risk through biomonitoring and epidemiology;
• Development of mitigation strategies and regulatory instruments.

Responsible science in mining regions extends beyond improving extraction technology. It includes knowledge transfer and evidence-based participation in public decision-making. Scientific institutions are expected to support governance processes that ensure not only environmental protection, but also quality of life in mining-affected areas.

Ground deformation, mining-induced seismicity, and infrastructure damage affect well-being in two parallel dimensions: material impacts—physical damage to buildings and infrastructure and psychosocial impacts—chronic stress, loss of perceived safety, and reduced landscape amenity. Repeated shocks, visible cracking of houses, and uncertainty regarding future damage act as persistent stressors. The sense of security—a fundamental human need—is directly undermined by unpredictable ground movements.

Science therefore carries a dual responsibility: to reduce harm (through improved prediction and mitigation of mining impacts) and to reduce fear (through transparent communication of risks, uncertainties, and available protective measures). Trustworthy and accessible scientific communication is itself a form of mitigation.

• Goal 6—clean water

Mining activities—particularly underground extraction—produce significant disturbances in hydrogeological systems. The loss of natural rock-mass continuity alters groundwater flow paths, modifies hydraulic gradients, and increases secondary permeability. Consequently, mining inevitably induces drawdown of groundwater tables, localized dewatering of aquifers, or conversely, rising water levels and surface inundation when post-mining voids fill and overflow. Depending on extraction technology and the geochemical composition of the ore body, mine drainage may also introduce acidic effluents, dissolved heavy metals, and suspended solids into groundwater and surface water systems.

A compelling illustration of these dynamics is the Bayan Obo mining district in Inner Mongolia, China—the world’s largest rare-earth element (REE) extraction complex. Intensive land conversion and open-pit expansion have led to the loss of soils and vegetation and to the degradation of hydrological ecosystem services. Remote-sensing analysis combined with InVEST ecosystem service modelling shows a marked reduction in water-storage and water-regulation capacity, disruption of natural infiltration and runoff patterns, and a decline in overall environmental quality [170]. The strategic relevance of Bayan Obo in global REE supply chains and the scale of the associated environmental transformation are independently corroborated by geological studies [171].

These examples demonstrate that hydrogeological impacts are not incidental side effects of mining—they are systemic. They propagate across spatial and temporal scales, affecting aquifer dynamics, ecosystem water budgets, and regional hydrology long after mining activities cease.

Within this context, the responsibility of science extends beyond documenting degradation. It includes developing predictive and preventive solutions, such as:

• Hydrogeological modelling of mining-induced flow paths;
• Adaptive dewatering and reinjection systems;
• Geochemical control of mine drainage;
• Ecosystem-based restoration measures designed to recover water-retention and purification functions.

In alignment with SDG 6 (Clean Water and Sanitation) and SDG 12 (Responsible Consumption and Production), responsible science must support decision-makers by providing evidence that enables the avoidance, minimization, and long-term mitigation of hydrogeological impacts.

• Goal 7—clean and accessible energy

Mining has remained a fundamental basis for energy generation. According to Statista portal, fossil fuels remain the largest source of electricity in the world. In 2022, coal constituted about 35.8% of world’s energy mix, while the share of natural gas was 22%. Wind and solar energy account for only about 12% of global power generation and their infrastructure is still manufactured using fossil fuel-based materials and energy; tellurium, gallium, and indium are used to manufacture photovoltaic systems, while the rare-earth elements, neodymium and dysprosium, are used to manufacture wind turbines. Cathode raw materials, such as lithium, cobalt, nickel, and manganese, are necessary to manufacture electric vehicle batteries. Forecasts presented by Liang [172] show that “most of the metal demand related to photovoltaics would reach its peak around 2035 and then gradually decline, whereas the demand for rare earth elements (REEs) continue to surge.”

Forecasts presented by Ahmad [173] indicate that renewable energy sources (RES) are expected to become the dominant technology in new power generation installations by 2040. Even under these optimistic scenarios, global demand for natural gas is projected to increase by more than 50% and demand for oil and coal by approximately 15% over the same period. This implies that, despite the rapid expansion of renewable energy systems, the global energy sector will remain structurally dependent on resource extraction—encompassing both fossil fuels and critical minerals required for low-carbon technologies. As a result, the environmental and socio-economic impacts associated with extraction activities will remain a relevant challenge for the foreseeable future.

In this context, the role of science is not to advocate for specific energy pathways or to legitimize continued extraction, but to rigorously disclose the full spectrum of consequences associated with resource exploitation. Guided by truth and scientific integrity, research examines the real impacts of extraction irrespective of whether it supplies conventional energy systems or technologies enabling the renewable transition.

The mandate of science is to generate reliable, measurable, and publicly accessible knowledge on extraction-related impacts. This includes developing methods to identify, monitor, and reduce adverse effects such as land deformation, ecosystem degradation, water contamination, and environmental health burdens. Scientific inquiry also plays a critical role in exposing structural inequalities—particularly those resulting from the geographic displacement of extraction impacts to regions that do not benefit from the economic gains of resource use.

A central responsibility of the scientific community is to build an evidence base that enables the objective understanding of extraction implications across multiple scales: environmental, societal, economic, and within global supply chains. To achieve this, science develops analytical tools such as geotechnical deformation models, contaminant monitoring frameworks, health-risk assessments, and life-cycle approaches (LCA and S-LCA) that quantify environmental and social burdens embedded in resource extraction and processing.

Moreover, scientific responsibility encompasses the development of technical and systemic solutions, including the following:

• Approaches that minimize harm in regions where extraction occurs;
• Low-impact energy generation and storage technologies, designed to reduce material and energy intensity across the entire life cycle (LCA), minimize emissions and waste, and limit ecosystem disturbance during raw-material acquisition, manufacturing, operation, and end-of-life management;
• Systemic methods for managing material and energy flows, aimed at maximizing resource efficiency, reducing material and energy intensity of technologies, and enabling dematerialisation through eco-design that minimizes the dependency on primary resources at the conceptual and infrastructural design stage.

Transparent communication of results is equally essential. Science has an obligation to make data publicly available, especially where extraction generates asymmetric burdens between world regions. This includes cases in which economic and energy benefits are concentrated in the Global North, while environmental, social, and health costs are borne by the Global South. Open dissemination of evidence prevents the externalization of impacts and reveals hidden costs embedded in global supply chains.

Science serves as a custodian of facts. Rather than aligning results with political or economic preferences, it enables informed decision-making by providing what is most valuable: an accurate understanding of reality—even when the evidence is inconvenient for policymakers, investors, or industry.

• Goal 8—economic growth and decent work

The mining sector constitutes a structural pillar of the global material economy, supplying essential inputs to energy systems, construction, infrastructure, and manufacturing. Its macroeconomic relevance is quantified through the Mining Contribution Index (MCI), developed by the International Council on Mining and Metals (ICMM). The index integrates three verifiable economic dimensions:

• The share of mineral exports in total merchandise exports;
• Fiscal revenues from mining (taxes, royalties, and concession fees);
• The sector’s contribution to gross value added (GDP).

In the most recent 7th edition [27], the highest values were recorded for the Democratic Republic of Congo (MCI = 97.0), Mali (MCI = 94.2), and Mongolia (MCI = 92.7), indicating extreme national dependence on mineral extraction as a source of foreign exchange and fiscal stability.

At the system level, the dependence of economic performance on extraction is explained by the United Nations International Resource Panel (UNEP IRP) [12]. The IRP conceptualizes mining as a core component of economic provisioning systems, defined as material supply infrastructures that enable energy production, mobility, construction, and industrial output ([12], pp. 17–19). These provisioning systems determine material demand and embed extraction within the structural mechanics of economic growth.

To test the strength of this relationship, global GDP data from the World Bank were correlated with UNEP IRP data on Global Material Extraction (GME) [12]. When plotted together (Figure 1), the time series demonstrates a persistent and strong coupling between economic expansion and material throughput, revealing that global GDP growth continues to be driven by rising extraction volumes rather than dematerialization or efficiency gains. Although mining contributes to GDP growth, available evidence indicates that this does not translate into proportional improvements in occupational safety. Data compiled by the International Labour Organization (ILO) and Eurostat demonstrate the absence of a global relationship between economic performance and reductions in accident or fatality rates.

According to the ILO, the mining sector accounts for approximately 1% of the global workforce yet contributes to around 8% of all fatal occupational accidents [175]. In the European Union, mining consistently exhibits the highest fatality rate among all NACE economic divisions, reaching 10.2 deaths per 100,000 workers in 2022 (Eurostat, Accidents at work—statistics by economic activity, Table 2 [21]).

For companies belonging to the International Council on Mining and Metals (ICMM)—representing roughly one-third of the global large-scale mining and metals sector—the 2024 fatality frequency rate was 0.015 fatalities per one million hours worked, with 42 fatalities recorded [28]. These comparatively lower values should be interpreted with caution, as ICMM statistics represent operations with formalized safety management systems and mandatory risk reporting, introducing selection bias. They do not capture the safety performance of the majority of mining operations globally.

In particular, ICMM data do not include artisanal and small-scale mining (ASM), which employs at least 45 million people [176] and provides indirect livelihoods to an estimated 134–270 million [14]. The World Bank [14] notes that ASM accident and fatality data are affected by systematic underreporting and limited regulatory oversight, which limits the ability to produce reliable global estimates.

The OECD Due Diligence Guidance for Responsible Supply Chains of Minerals from Conflict-Affected and High-Risk Areas formally classifies ASM as a segment with elevated risk, recommending a structured five-step due-diligence process for operators: establishment of management systems, risk identification, mitigation, third-party auditing, and public reporting [20].

The scientific literature emphasizes the dual nature of ASM. It may function as an income strategy and enable households to improve their economic position [168]. However, the socio-economic outcomes depend primarily on the degree of formalization, access to capital, and market integration [169].

Integrating these datasets leads to a consistent conclusion: mining supports economic growth by supplying material inputs to the global economy, but the occupational risks associated with extraction are not uniformly reduced across countries or mining segments. Formal risk management systems, safety technologies and regulatory oversight are concentrated in large-scale, capital-intensive operations, predominantly in high-income economies, whereas labour-intensive extraction and elevated accident exposure occur mainly in low- and middle-income regions.

In the context of SDG 8 (Decent Work and Economic Growth), improvements in occupational safety require not only technological measures at the mine level (e.g., monitoring, automation and early warning systems), but also institutional instruments such as mandatory disclosure of safety data, supply chain due diligence and enforcement of reporting standards.

• Goal 10—less inequality

In the environment of mining areas, striving to reduce inequalities should be considered both in a regional and global context. In the first case, it is about striving for fair compensation for damage caused by the mining plant operation in their local environment. However, another problem is the broader aspect related to the distribution of raw material mines on Earth and their location in countries with various social–economic–political systems. Responsible science cannot remain silent in the face of the fact that, in general, mining in the developed countries of the Global North is more sustainable and safer for the environment and people employed in mines, while at the same time providing (mostly) decent remuneration conditions. There is still a lot of work to be done to eliminate inequalities in terms of the mineral extraction conditions around the world.

According to a UNICEF report [23,29], 160 million children worldwide are forced to work. More than a million children are used as slave labour in mines and quarries. Much of the minerals extracted by children enter global supply chains, including the automotive, banking, construction, cosmetics, electronics, and jewellery sectors. In some areas, this problem is particularly severe. Research conducted by the International Labour Organization shows that in Burkina Faso and Niger about 30–50 percent of the workforce in gold mines are children [24]. In 2015, it was estimated that 20 percent of miners extracting gold by hand in Mali were children [29]. There are thousands of children employed to extract gold in the Western, Central, and Ashanti Regions of Ghana [30]. Child labour also occurs in mines extracting cobalt and coltan—the minerals used in portable electronic devices and batteries, including electric vehicles. More than 70% of the world’s cobalt supply comes from the Democratic Republic of the Congo (DRC), where children, some as young as seven, work in life-threatening conditions, exposed to violence, extortion, and intimidation [16], while ASM-related risks are documented extensively in the World Bank ASM report [14]. This cobalt has been linked to lithium batteries sold by major international companies. Children also work in mines in situations of debt bondage or human trafficking in countries such as Zambia, Zimbabwe, Nigeria, Ghana, Liberia, Sierra Leone, and the DRC [24]. In India, there are 20,000 children employed to extract mica [177]. Madagascar is the third largest exporter of mica in the world. It is estimated that over 11,000 children are exploited there to extract mica [178].

• Goal 11—sustainable cities and communities

Ensuring that cities and human settlements are safe, resilient, sustainable, and socially inclusive poses unique challenges in mining regions. Mining towns must cope simultaneously with environmental impacts, ground deformation, infrastructure damage, altered hydrogeological conditions, and occupational risks. Therefore, their sustainable development requires scientific and technical foundations capable of addressing the consequences of subsurface geological transformations.

A recent global assessment estimates that areas directly affected by mining currently exceed 57,000 km² worldwide [179]. Notably, 51% of this area is concentrated in only five countries—China, Australia, the United States, Russia, and Chile—while an additional ten countries account for 30%, and all remaining countries represent only 19% [179,180]. The spatial footprint of mining is thus highly concentrated, and so are the social and environmental burdens.

Structural differences between extractive economies further amplify this disparity. In high-income countries, extraction is dominated by large corporations with advanced environmental controls, whereas in low- and middle-income countries, a substantial share consists of artisanal and small-scale mining (ASM). Prior to 1999, small mines producing 15–20% of global non-fuel minerals employed approximately 13 million workers [25]. Today, ASM is defined as extraction conducted manually or with minimal mechanization, often outside formal regulatory frameworks. According to the World Bank [14] and ILO [24] estimates, ASM provides direct employment for more than 45 million people, supports the livelihoods of roughly 225 million household dependents, and shapes the economic well-being of over 315 million individuals worldwide through both direct and indirect pathways. These figures demonstrate that ASM constitutes a core component of local socioeconomic systems, especially in low-income, resource-dependent regions.

Macroeconomic dependency reflects the same pattern. According to the ICMM’s Mining Contribution Index (MCI), mineral exports represent the following:

• >91% of total exports in Botswana and the Democratic Republic of Congo;
• 85.6% in Mongolia;
• 82.6% in Guinea;
• 80.1% in Suriname;
• ≥76% in Burkina Faso and Zambia [27].

Extraction sites attract population flows and industrial activity, producing mono-industrial mining cities whose economic resilience depends on continued mining.

In developed economies, where coal and other extractive industries are being phased out due to decarbonization, the challenge is reversed. Sustainability issues shift from managing extraction impacts to managing the post-mining transition. The dominant risks include the following:

• Loss of employment and regional economic base;
• Accelerated impoverishment and demographic decline;
• Persistence of ground instability and deformation long after closure.

Once mining ceases, the legal entity previously responsible for compensating mining-induced damage often disappears, even though geomechanical processes in the subsurface continue for years or decades.

Countries such as Germany, the United Kingdom, Poland, Romania, the United States, and Australia have already undergone these transformations. Across all cases, mining regions experienced long-term socioeconomic stress, including unemployment, degradation of living standards, and marginalization [181,182,183,184,185,186,187,188,189].

These international experiences demonstrate that sustainable transformation of mining regions is not automatic. It requires strategic planning supported by scientific knowledge across four integrated dimensions:

• Technical—monitoring and mitigating ground deformation, managing hydrological impacts;
• Economic—diversification and reconstruction of regional economies;
• Social—participatory governance, social inclusion and mitigation of marginalization;
• Environmental—land rehabilitation, ecological restoration, and long-term stewardship.

Science therefore plays a critical role not only in documenting mining impacts, but in designing transition pathways that enable mining settlements—both active and post-mining—to evolve into resilient and sustainable communities.

• Goal 12—responsible consumption and production

Providing the patterns of responsible consumption of limited resources of fossil raw materials and responsible production results from taking responsibility for the future generations. The role of science in terms of this is to disseminate knowledge about the planet’s natural resources, the demand for raw materials, the level of their consumption, and forecasts, taking into account the needs of future generations.

• Goal 15—life on land

The challenge to protect, restore, and promote the sustainable use of land ecosystems’ set tasks for science to develop methods of using minerals to minimize the transformation of natural ecosystems. There is also another task—involving the development of methods for restoring areas damaged by mining to a state favourable to human and natural life.

• Goal 17—goal-oriented partnerships

As shown above, mining on a global scale exists in very different conditions: from the industry of large, wealthy mining companies of the Global North to small-scale mines of developing countries, where work takes place in extremely dangerous conditions, workers’ wages are very low, and care for the natural environment practically does not exist. Considering this context, especially strong sounding is the latest SDG from Agenda 2030 to strengthen implementation measures and revitalize the global partnership for sustainable development. Science fulfils its social responsibility when it contributes to sustainable development in mining and post-mining areas. The interdisciplinary roles of science across the life cycle of a mine are illustrated in Figure 2.

7. Conclusions

The existence and development of our civilization is based on natural raw materials extracted using various methods. Mining areas worldwide cover a total of approx. 57,000 sq. km. These areas are characterized by specific problems resulting from mineral deposit extraction. These consequences have technical, economic, social, and environmental dimensions. Natural and technical sciences provide knowledge about natural resources and develop methods of extracting them. These methods are still improved to minimize adverse impact on the rock mass and provide human work safety. However, the scientific achievements are not available and implemented uniformly throughout the world.

By summing up the presented analyses of mining-related technical, economic, and social issues and their positive and negative impacts, we have formulated four major tasks for the responsible implementation of scientific activities in the context of sustainable development of mining areas:

• Reliable research covering all aspects of mineral extraction.
• The primary role of science is striving to know the truth. Therefore, the main task of responsible science is to conduct research to provide the most objective results about the impact of mining on the human environment and nature, in technical, economic, and social dimensions.
• Providing technical solutions.
• Another important role of science is to develop methods of mineral extraction that make this activity safe for people working in the mine and with the least possible impact on the environment. Technical sciences are also responsible for developing methods for protecting buildings and technical infrastructure against the effects of ground deformation, methods of repairing damage, and methods of reclaiming degraded areas, restoring their utility and natural functions.
• Education and disseminating knowledge.
• Acquired knowledge should benefit society. It should support the education of engineers and specialists responsible for exploring and documenting mineral deposits, planning extraction, designing and conducting mining operations, and developing mining-related infrastructure and equipment.
• Social activity and cooperation.

The role of scientists, as those who have the best knowledge and who best understand the mining-related processes and phenomena, is also to influence the political, legal, and economic environment—to shape the extraction of raw materials to make it responsible, sustainable, fair, and ethical.

The analysis presented here synthesizes the challenges of responsible scientific practice in mining-related research and demonstrates how technical, economic, and social dimensions intersect. Mining systems are highly context-dependent—shaped by regional geology, institutional frameworks, and geopolitical conditions—which means that responsibility cannot be reduced to technical compliance alone. The proposed framework is intended to be further developed and validated by other research teams, enabling additional empirical evidence and regional perspectives to strengthen the ongoing discourse. The relationships between science, mining, and society, along with the key tasks of responsible science, are illustrated in Figure 3.

8. Practical Pathways for Implementing Responsible Research and Innovation (RRI)

The social responsibility of science (SRS)—the conceptual foundation of Responsible Research and Innovation—stipulates that scientific activity is accountable not only for producing new knowledge but also for anticipating and evaluating the societal and environmental consequences of its application [35,190]. In this perspective, research must operate transparently, inclusively, and reflexively, producing usable and decision-relevant knowledge for regulatory, planning, and societal processes in mining and post-mining territories.

Scientific responsibility only becomes operative when organizational and governance structures enable systematic interactions between research and decision-making arenas. Saviano [60] demonstrates that responsibility becomes socially consequential when research is embedded within institutional systems that connect three parallel domains—the environmental, social, and economic dimensions of sustainable development—with three categories of actors: science, public administration, and industry. These interdependencies form a governance environment in which research outputs can be translated into decision-making and action.

The operationalization of RRI relies on instruments that act as system interfaces, linking sustainability dimensions and actor groups. The instruments formally integrated into research funding frameworks include the following:

• NSF Broader Impacts (United States) [191]—all funding proposals must demonstrate clear and verifiable societal benefits (National Science Foundation, 2024);
• RRI Toolkit (European Union)—a curated repository of more than 500 tools, checklists, and documented practices facilitating the implementation of RRI [192];
• RRI Monitoring Framework (Horizon Europe) [193]—a formal evaluation system using indicators such as anticipation, inclusion, and responsiveness (European Commission, 2021).

Collaboration frameworks that support RRI contextually, although not mandated by funding agencies, include the following:

• UNESCO Open Science Framework [194]—an international commitment to openness and public accessibility of research results and data;
• Urban/Living Labs (EU innovation policy)—structured environments enabling experimentation and co-creation with stakeholders in real contexts (e.g., climate neutrality and regional transformation) [49];
• Triple Helix model [59]—a model of institutional interaction between science, government and industry, widely adopted in innovation clusters and technology parks.

By using these instruments, implementation pathways were formulated for research addressing mining activities and post-mining land transformation. Their purpose is to reposition science as an active agent of sustainable territorial development rather than a neutral observer. Each pathway defines its institutional basis, objective, and operational actions (see

Table 5. Practical RRI pathways for research on mining and post-mining areas.

RRI Pathway	Institutional Basis/Existing RRI Instrument	Objective (Purpose of the Pathway)	Operational Actions Within Research Practice	External Impact (Effect Beyond Academia)	Related SDGs	Implementation Barriers (Realistic Constraints)
Open Evidence Pathway	UNESCO Recommendation on Open Science (2021) [194]; Horizon Europe RRI Monitoring Framework [193]	Ensure public accessibility and verifiability of data on mining-induced impacts	- Deposit research data in open repositories; - Standardize reporting of deformation, damage and economic consequences.	Scientific evidence becomes usable by municipalities, regulators, affected communities and courts	SDG 12,	Restricted access to corporate/administrative data
Local Impact Labs (co-creation of knowledge with stakeholders)	Living Labs (EU) [49]; Triple Helix collaboration framework [59]	Integrate research with local decision-making in mining regions	- Field studies with municipalities and residents; - Participatory mapping of impacts; - Iterative feedback loops.	Research outputs incorporated into spatial planning and risk-management documents	SDG 11, SDG 9	Asymmetry of knowledge and institutional resistance to participation
Broader Impacts Pathway	NSF Broader Impacts Framework (USA) [191]	Require measurable societal benefits as a condition of research activity	- Define measurable outcomes relevant to communities; - Communicate results to public authorities.	Research results influence administrative and legal decisions	SDG 9	Evaluation of research based only on academic output
Science for Justice Pathway	UNESCO Open Science [194]; African Union STISA-2024 [195] Latin American Open Science Declaration [196]	Reduce inequality by enabling communities to document mining-induced harm	- Provide Community Impact Toolkit (GPS + form + evidence logging); - Strengthen local capacity.	Communities gain agency and evidence for negotiations and compensation	SDG 10, SDG 1	Power imbalance between industry and communities
Responsible Post-Mining Transition Pathway	RRI Toolkit (EU) [192]; LCA and CBA methodological frameworks [17,197,198]	Integrate scientific evidence into post-mining transition planning	- Evaluate multiple redevelopment scenarios (LCA/CBA); - Evidence-based recommendations before decisions.	Decisions on redevelopment are based on evidence, not economic pressure	SDG 11, SDG 12	Short-term economic interests overriding sustainable planning

). Collectively, they represent a shift from research designed solely to produce knowledge toward research that creates conditions for informed action and reduces decision-making uncertainty.

Embedding RRI into mining-related research transforms the function of science. The metric of responsibility is not the volume of publications, but the degree to which research outputs are integrated into decision processes—land-use planning, environmental regulation, compensation procedures, and long-term post-mining redevelopment [35,36,190]. In doing so, science assumes a public purpose, generating reliable, context-ready knowledge that decreases risk and enables decisions aligned with sustainable development goals. Under RRI, science no longer operates outside the system—it becomes an active and constructive component of it.

9. Future Research Directions

The analyses conducted in this study reveal several critical research gaps that require systematic investigation within the fields of mining impact assessments and compensation systems. First, existing reporting frameworks lack quantitative valuation of non-internalized costs (externalities)—particularly (iv) accelerated technical degradation of buildings and (v) permanent loss of property value—which leads to a structural underestimation of the true social burden of mining activities. Second, the absence of explicit differentiation between cost flows, liability stocks, and costs shifted to society prevents cross-jurisdictional comparability and obscures financial responsibility. Third, long-term hydrogeological impacts at the catchment scale remain poorly quantified, despite their relevance to ecosystem services and post-mining land-use. Fourth, psychosocial impacts of induced seismicity are still captured fragmentarily; standardized dose–response relationships linking peak particle velocity (PPV) to well-being loss (WELBY/DALY) have not yet been developed. Fifth, environmental and health inequalities embedded in global supply chains remain insufficiently documented, particularly in the Global South. Finally, post-mining transitions are not currently supported by integrated models capable of linking technical, economic, social, and environmental dimensions.

This paper addresses these gaps by introducing a structured analytical framework that makes them empirically researchable. The FLOW–STOCK–EXTERNALITIES taxonomy, combined with the institutional–financial compensation model, enables the disaggregation of cost categories and reveals social and economic burdens that remain invisible in traditional accounting. By integrating psychosocial impact pathways (S-LCA, PPV → well-being) and hydrogeological components, the study demonstrates how social and environmental effects can be translated into quantifiable and auditable indicators that support evidence-based decision-making.

Future research should focus on the following:

• Developing harmonized datasets and indicators to quantify externalities (LCA/S-LCA, hedonic pricing, and long-term social and hydrogeological monitoring);
• Testing the FLOW–STOCK–EXTERNALITIES framework across different legal and institutional contexts;
• Linking financial-responsibility models with indicators of social resilience and ecosystem-service valuation.

By operationalizing the concept of responsibility into measurable components, the proposed approach shifts the discussion from conceptual diagnosis to an actionable research agenda and enables the empirical testing of real-world responsibility and compensation scenarios in mining regions.