Sustainable Water-Related Hazards Assessment in Open Pit-to-Underground Mining Transitions: An IDRR and MCDM Approach at Sijiaying Iron Mine, China

Abstract

The transition from open pit to underground mining intensifies water-related hazards such as Acid Mine Drainage (AMD), groundwater contamination, and aquifer depletion, threatening ecological and socio-economic sustainability. This study develops an Inclusive Disaster Risk Reduction (IDRR) framework using a Multi-Dimensional Risk (MDR) approach to holistically assess water hazards in China’s mining regions, integrating environmental, social, governance, economic, technical, community-based, and technological dimensions. A Multi-Criteria Decision-Making (MCDM) model combining the Fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) evaluates risks, enhanced by a Z-number Fuzzy Delphi AHP (ZFDAHP) spatiotemporal model to dynamically weight hazards across temporal (short-, medium-, long-term) and spatial (local to global) scales. Applied to the Sijiaying Iron Mine, AMD (78% severity) and groundwater depletion (72% severity) emerge as dominant hazards exacerbated by climate change impacts (36.3% dynamic weight). Real-time IoT monitoring systems and AI-driven predictive models demonstrate efficacy in mitigating contamination, while gender-inclusive governance and community-led aquifer protection address socio-environmental gaps. The study underscores the misalignment between static regulations and dynamic spatiotemporal risks, advocating for Lifecycle Assessments (LCAs) and transboundary water agreements. Policy recommendations prioritize IoT adoption, carbon–water nexus incentives, and Indigenous knowledge integration to align mining transitions with Sustainable Development Goals (SDGs) 6 (Clean Water), 13 (Climate Action), and 14 (Life Below Water). This research advances a holistic strategy to harmonize mineral extraction with water security, offering scalable solutions for global mining regions facing similar ecological and governance challenges.

1. Introduction

The global demand for mineral resources continues to drive the expansion of mining activities. Yet, the environmental and socio-economic costs of these activities, particularly water-related hazards, remain poorly controlled [1,2,3]. Mining transitions, such as from open pit-to-underground mining, pose complex threats, such as Acid Mine Drainage (AMD), groundwater contamination, and aquifer depletion, that threaten ecosystems and human livelihood [4,5,6,7]. Such concerns are exacerbated in environmentally sensitive regions, such as China’s Luanhe River Basin, where mining activity overlaps with critical water resources and vulnerable populations [8,9]. While growing awareness of such concerns is apparent, existing frameworks do not necessarily integrate technical, environmental, and social dimensions, and hence, mitigation responses remain fragmented [10]. This study fills this gap by outlining a holistic approach to water hazard management within the framework of the United Nations Sustainable Development Goals (SDGs), namely SDG 6 (Clean Water), SDG 13 (Climate Action), and SDG 14 (Life Below Water) [11].

Recent examples, such as the 2022 Jagersfontein tailings dam collapse in South Africa, which flooded nearby communities with 9 million cubic meters of toxic slurry and contaminated critical farmland, and persistent Acid Mine Drainage (AMD) contamination in Chile’s Atacama Desert copper mines (2024), underscore enduring ecological and human health risks [12,13]. Globally, over 10,000 abandoned mines continue to contaminate water systems, while modern operations exacerbate water scarcity in arid regions like Chile’s Atacama Desert [14]. Traditional mitigation strategies, such as static risk assessments and end-of-pipe treatments, have proven inadequate in addressing the dynamic interplay of climate change, socio-economic inequities, and technological limitations [15]. Recent studies emphasize the need for adaptive frameworks that prioritize spatiotemporal risk modeling and participatory governance, yet few integrate these elements cohesively [16].

Multi-Criteria Decision-Making (MCDM) tools like AHP and TOPSIS are widely used to assess mining transition impacts but face limitations when applied independently [17]. Conventional AHP struggles with expert judgment ambiguities and static weights, while TOPSIS neglects socio-environmental interdependencies despite its dynamic ranking strengths. Combining AHP’s hierarchy with TOPSIS’s proximity analysis enables robust prioritization of hazards like Acid Mine Drainage (AMD), yet rigid spatiotemporal assumptions hinder adaptability to climate-driven risks [18]. Integrating Z-number Fuzzy Delphi AHP (ZFDAHP) addresses this gap by embedding fuzzy logic and iterative expert consensus into dynamic weighting, quantifying uncertainties across temporal (short-, medium-, long-term) and spatial scales [19]. This synergy bridges static technical assessments with dynamic socio-ecological realities, offering a holistic framework to address both immediate and systemic vulnerabilities [20].

Existing methodologies, including Inclusive Disaster Risk Reduction (IDRR) and Multi-Criteria Decision-Making (MCDM) tools such as the Analytic Hierarchy Process (AHP), have advanced hazard prioritization but remain constrained by static parameters and limited stakeholder engagement [21,22]. While conventional AHP overlooks uncertainties in expert judgments, IDRR frameworks often struggle to quantitatively integrate community-driven metrics like gender equity and Indigenous knowledge into technical risk assessments [23,24]. Innovations such as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) improve dynamic ranking but lack systematic incorporation of spatial–temporal scales and participatory governance. Combining IDRR’s emphasis on inclusive, community-centered risk reduction with MCDM’s structured prioritization capabilities enhanced by Fuzzy AHP to address expert judgment uncertainties and Z-number Fuzzy Delphi AHP (ZFDAHP) for dynamic spatiotemporal weighting bridges these gaps [25]. This integration enables the synthesis of socio-technical criteria (e.g., community water governance, AI-driven monitoring) with environmental and economic factors, offering a holistic framework that aligns technical rigor with equitable resource stewardship, thus addressing critical voids in contemporary hazard assessment literature [26].

This study uniquely combines three methodological pillars: (1) a hybrid MCDM framework combining TOPSIS and Z-number Fuzzy Delphi AHP (ZFDAHP) to assess water-related hazard mitigation and sustainability impacts in mining transitions; (2) a spatiotemporal analysis applied to China’s Sijiaying Iron Mine; and (3) global implications evaluated through Inclusive Disaster Risk Reduction (IDRR) for water-related hazards. The research concludes with actionable policy recommendations, synthesizing MCDM-driven hazard prioritization, dynamic spatiotemporal modeling, and IDRR principles. By harmonizing these approaches, the study advances a holistic strategy to reconcile mineral extraction with water security, offering scalable solutions to 21st-century mining challenges.

2. Methodology

2.1. Study Area (Sijiaying Open Pit Mine Case Study)

The Sijiaying Iron Mine, a large open-pit operation in Hebei, China, lies within the ecologically sensitive Luanhe River Basin, a region increasingly challenged by water-related hazards linked to mining activities (Figure 1). Declining near-surface ore reserves have intensified pressure to transition to underground mining, a shift that risks exacerbating groundwater contamination, aquifer depletion, and Acid Mine Drainage (AMD) from sulfide-rich waste. The mine’s proximity to critical water resources heightens vulnerability to sedimentation, heavy metal leaching, and seasonal water table fluctuations, threatening both aquatic ecosystems and downstream agricultural communities. These hazards are compounded by the semi-arid regional climate, where over-extraction and contamination jeopardize already strained water supplies. This study evaluates strategies to mitigate these risks while balancing mineral extraction demands, emphasizing stakeholder-driven solutions to safeguard water quality, habitat integrity, and long-term resource sustainability under SDGs 6 (Clean Water) and 14 (Life Below Water).

The Sijiaying Iron Mine is one of China’s largest iron ore reserves, spanning an operational area of 6.27 km² following its 2013 expansion into the Yinyu mining zone. This expansion increased the proven reserves to 909.8 million metric tons, with an annual production capacity of 22.7 million metric tons, positioning it as a critical contributor to regional steel industries. The mine employs the Stage Subsequent Filling Mining (SSFM) method, a tailored approach where ore extraction occurs in vertical stages (~100 m height) with backfilling using cemented rock waste to minimize surface subsidence—a critical measure given its proximity to agricultural lands and residential settlements like Qianying Village. Stability assessments have revealed surface deformation metrics nearing critical thresholds, necessitating continuous geotechnical monitoring to prevent infrastructure damage as mining progresses to deeper levels. Environmental risks are amplified by the mine’s location within the Luanhe River Basin, a vital water resource for 3.2 million people. Groundwater monitoring data (2015–2023) indicate fluctuating water tables (seasonal declines up to 4.2 m) and localized AMD risks due to sulfide-rich waste rock exposure. Recent mitigation efforts include impermeable grout curtains along the northern pit boundary and IoT-enabled pH sensors to detect AMD precursors in real time. This integrated geotechnical and hydrological context validates the study’s focus on balancing extraction efficiency with ecological safeguards in mining transitions.

2.2. Overall Methodology Introduction

This research employs a comprehensive Multi-Criteria Decision-Making (MCDM) strategy to evaluate water-related hazards during the transition from open pit to underground mining at China’s Sijiaying Iron Mine (Figure 2). The methodology integrates 159 parameters across environmental, social, technical, and economic dimensions, prioritized through a Fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), leveraging insights from 30 multidisciplinary experts. A hybrid spatiotemporal model categorizes impacts into temporal (short-, medium-, long-term) and spatial (local, regional, national, global) scales, with dynamic weights calculated using Z-number Fuzzy Delphi AHP to emphasize global climate risks and localized water contamination. Scenario-based scoring and lifecycle assessments quantify mitigation strategies, while alignment with Sustainable Development Goals (SDGs 6, 13, 14) ensures a balance between operational efficiency, ecological preservation, and equitable resource governance. The approach combines quantitative analytics, stakeholder engagement, and adaptive modeling to address water sustainability challenges in mining transitions.

2.3. Data Collection

2.3.1. Parameters

This study evaluates mining transitions using 159 parameters, prioritizing water-related hazards such as acid mine drainage (WA5), groundwater depletion (SW7), and freshwater ecotoxicity (SW4) (

Table 1. Table of parameters.

No.	Groups of Major Category Parameters	Major Categories of Parameters	Code	Parameters
1	Environmental Factors	Environmental Pollution and Preservation	En	1-Pollution caused by crushing (En1), 2-possibility of pollution in future (En2), 3-reduce greenhouse gas emissions (En3), 4-reduce soil contamination (En4), 5-reduce water contamination (En5), 6-reduce air contamination (En6), 7-preserve local animals (En7), 8-preserve local plants (En8), 9-rate of increase of pollutants compared to permissible environmental indicators (En9), 10-contaminants available (En10), 11-impact of mine reclamation (En11), 12-reuse of mined lands (En12).
2		Environmental Costs	EC	13-Environmental cost of block transportation (EC1), 14-energy consumption during drilling and blasting (EC2), 15-environmental costs of ancillary tasks (EC3), 16-crushing emissions and associated costs (EC4), 17-Global Warming Potential (GWP) of mining activities (EC5), 18-cumulative environmental costs over mine life (EC6), 19-environmental cost minimization in mine planning (EC7).
3		Climate Change	CC	20-Greenhouse gas emissions (CC1), 21-carbon sinks destruction (CC2), 22-deforestation and land-use changes (CC3), 23-reduction of gases produced by machinery and facilities (CC4), 24-increase in atmospheric heat (CC5).
4		Soil, Water, and Air	SW	25-Topsoil quality (SW1), 26-deep soil quality (SW2), 27-terrestrial ecotoxicity (SW3), 28-freshwater ecotoxicity (SW4), 29-surface water quality (SW5), 30-underground water quality (SW6), 31-water table change (SW7), 32-air quality (SW8), 33-dust reduction (SW9), 34-noise reduction (SW10).
5		Waste and Acid Mine Drainage	WA	35-Overburden volume (WA1), 36-waste rock volume (WA2), 37-tailing volume (WA3), 38-waste management and reuse (WA4), 39-acidification due to sulfur (WA5).
6		Biodiversity and Ecosystem	BE	40-Deforestation (BE1), 41-migration or destruction of animal species (BE2), 42-restoration of biodiversity (BE3), 43-life below water (BE4), 44-ecotoxicity (BE5).
7	Social and Community Factors	Community and Social	SC	45-Improving the situation of local people (SC1), 46-reduce social problems (SC2), 47-job security (SC3), 48-reception of local people in the area (SC4), 49-revival of cultural and regional identity (SC5), 50-quality of life improvements (SC6), 51-social cohesion (SC7).
8		Safety and Health	SH	52-Occupational health and safety (SH1), 53-occupational accidents (SH2), 54-fire and explosions (SH3), 55-dust, noise, and vibration (SH4), 56-toxic gas ventilation (SH5), 57-emergency rescue (SH6).
9		Regional Impact	RI	58-Landscape/topography degradation (RI1), 59-ground vibration (RI2), 60-noise impact on surrounding areas (RI3), 61-air overpressure (RI4), 62-geothermal effects of mining depth (RI5), 63-short-term impact (RI6), 64-medium-term impact (RI7), 65-long-term impact (RI8), 66-local scale (RI9), 67-regional scale (RI10), 68-national scale (RI11), 69-global scale (RI12).
10		National and Global Level Impacts	NG	70-Impact on global social justice concerns (NG1), 71-impact on Indigenous communities at a global level (NG2), 72-impact of mining on global human rights (NG3), 73-impact of mining on cultural heritage preservation (NG4), 74-global public health impacts of mining activities (NG5), 75-impact of global migration due to mining (NG6), 76-global labor rights in mining (NG7), 77-impact on sustainable development goals (SDGs) (NG8), 78-impact of global community protests on mining projects (NG9), 79-global collaboration for responsible mining practices (NG10).
11		Post Mining Factors	PM	80-Land rehabilitation (PM1), 81-biodiversity restoration (PM2), 82-reclamation planning (PM3), 83-proposed land use (PM4), 84-community engagement in post-mining plans (PM5).
12		Long-Term Impacts of Mining	LT	85-Depletion of non-renewable resources (LT1), 86-intergenerational equity (LT2), 87-sustainability of mining practices (LT3), 88-long-term environmental degradation (LT4), 89-social and economic impacts on future generations (LT5), 90-dependence on mining economies (LT6).
13	Technical and Geological Factors	Technical	Te	91-Production rate (Te1), 92-exploitation efficiency (Te2), 93-transportation continuity (Te3), 94-system reliability (Te4), 95-blasting efficiency (Te5), 96-height of extraction (Te6), 97-stockpile management (Te7), 98-flexibility in mining method (Te8).
14		Technological	TG	99-Advanced mining technologies (TG1), 100-robotic transport (TG2), 101-automation in mining (TG3), 102-energy-efficient machinery (TG4).
15		Geological	GG	103-Type of ore reserve (GG1), 104-reserve depth (GG2), 105-grade distribution (GG3), 106-physical properties of mined lands (GG4), 107-compressive and shear strength (GG5), 108-groundwater level (GG6), 109-geomechanical aspects of ore body (GG7).
16		Site-situation	SS	110-Site location (SS1), 111-extent of mined lands (SS2), 112-topography of mined lands (SS3), 113-access to water resources (SS4), 114-repair shop area (SS5).
17		Mechanical Equipment	ME	115-Drilling equipment (ME1), 116-loading equipment (ME2), 117-transportation equipment (ME3), 118-equipment reliability (ME4), 119-maintenance status (ME5).
18		Face Operation	FO	120-Roof fall (FO1), 121-ventilation and toxic gases (FO2), 122-water inrush (FO3), 123-falling from height (FO4), 124-electrical hazard (FO5).
19	Economic and Regulatory Factors	Financial and Economical	FE	125-Capital cost (FE1), 126-operating cost (FE2), 127-return on investment (FE3), 128-reclamation cost (FE4), 129-taxes and government rights (FE5), 130-labor wages cost (FE6), 131-inflation rate (FE7).
20		Carbon Pricing and Financial Impacts	CP	132-Carbon price variability across regions (CP1), 133-carbon tax impact on operational costs (CP2), 134-financial risk of carbon pricing (CP3), 135-carbon pricing effect on block sequencing (CP4), 136-carbon pricing integration in Net Present Value (NPV) calculations (CP5), 137-sensitivity of profitability to carbon price changes (CP6), 138-carbon tax-based cost adjustments for waste blocks (CP7).
21		Infrastructure and Accessibility	IA	139-Transportation infrastructure (IA1), 140-access roads (IA2), 141-utilities availability (IA3), 142-accessibility to site (IA4).
22		Regulatory and Management	RM	143-Compliance with regulations (RM1), 144-mining site security (RM2), 145-safety inspection (RM3), 146-environmental monitoring (RM4).
23	Land and Energy Factors	Land Stability	LS	147-Surface ground stability (LS1), 148-slope stability (LS2), 149-land disturbance (LS3).
24		Energy	EN	150-Energy consumption (EN1), 151-fossil fuel depletion (EN2), 152-renewable energy generation (EN3).
25		Sustainable and Green Mining	SG	153-Integration of life cycle assessment (LCA) in mine planning (SG1), 154-use of renewable energy in mining operations (SG2), 155-sustainable haulage and transportation systems (SG3), 156-adoption of eco-friendly blasting techniques (SG4), 157-Minimization of ecological footprint in block sequencing (SG5), 158-long-term reclamation and biodiversity restoration plans (SG6), 159-incorporation of green technologies in mining equipment (SG7).

). Parameters are categorized into environmental, social, technical, and economic groups, with emphasis on SDG 6 (Clean Water), SDG 14 (Life Below Water), and SDG 13 (Climate Action). Key metrics include contamination risks (SW5, SW6), aquifer sustainability (SW7), community-led water governance (SC1, PM5), and spatiotemporal impacts on the Luanhe River Basin. These criteria bridge operational challenges with global sustainability targets, enabling holistic assessment of strategies to mitigate water degradation, safeguard aquatic ecosystems, and ensure equitable resource stewardship in mining transitions.

2.3.2. Aspects

This study evaluates the transition from open pit to underground mining through 25 interdisciplinary aspects (A1–A25), prioritizing water-related hazards and aligning with the United Nations Sustainable Development Goals (SDGs). Technical aspects, including geotechnical stability (A2), optimal transition depth (A4), and phased execution (A6), ensure operational feasibility while mitigating risks such as groundwater contamination and slope failure. Environmental criteria focus on water sustainability, addressing Acid Mine Drainage (AMD) via real-time water quality monitoring (A1, A17), sediment control (A8), and aquifer protection (A22), directly supporting SDG 6 (Clean Water) and SDG 14 (Life Below Water). Socio-economic dimensions emphasize equitable water access (A14), resilience to floods/droughts (A25), and Indigenous rights (A23), ensuring community welfare (SDG 11) and inclusive stakeholder engagement (A18). Governance frameworks integrate transboundary water agreements (A13), regulatory compliance (A10), and lifecycle assessments of contamination (A20) to align local practices with global sustainability targets (SDG 6/14). Energy efficiency (A21) and waste valorization (A16) further reduce water–energy footprints, while aquatic ecosystem monitoring (A15) safeguards biodiversity (

Table 2. Aspects for open pit-to-underground mining transition.

No.	Aspect	Code
1	Water quality monitoring systems (AMD, heavy metals)	A1
2	Geotechnical/geomechanical stability of ore body and strata	A2
3	Flood and drought risk management	A3
4	Optimal transition depth determination	A4
5	Crown pillar design and stability	A5
6	Transition timeline and phased execution	A6
7	Concurrency of open-pit and underground operations	A7
8	Sediment control and erosion prevention	A8
9	Spatial footprint minimization	A9
10	Regulatory compliance and legal adaptability	A10
11	Water recycling and reuse efficiency	A11
12	Geological uncertainty (grade/tonnage variability)	A12
13	Transboundary water resource agreements	A13
14	Community water access equity	A14
15	Aquatic ecosystem health monitoring	A15
16	Waste and tailings valorization strategies	A16
17	Real-time water quality monitoring systems (AMD, heavy metals)	A17
18	Social license to operate (SLO) and stakeholder engagement	A18
19	Alignment with SDG 6 (Clean Water) and SDG 14 (Life Below Water)	A19
20	Lifecycle assessment (LCA) of water contamination impacts	A20
21	Energy efficiency and renewable energy adoption	A21
22	Water resource management and aquifer protection	A22
23	Cultural heritage preservation and Indigenous rights	A23
24	Occupational health and workforce welfare	A24
25	Resilience to water-related climate extremes (floods, droughts)	A25

). Collectively, these aspects provide a unified foundation for multi-criteria decision-making (MCDM) and spatiotemporal modeling, balancing short-term operational challenges with long-term planetary health imperatives. The integration of water-centric metrics ensures policymakers can navigate trade-offs among mineral extraction efficiency, ecological preservation, and equitable resource governance under the 2030 Agenda.

2.3.3. Questionnaire of Experts

This study engaged 30 multidisciplinary experts specializing in water-related hazards, sustainability, and mining governance to ensure targeted insights aligned with the Water journal’s scope. The panel included hydrologists (e.g., groundwater sustainability, community-led water allocation), environmental chemists (e.g., heavy metal contamination remediation), public health researchers (e.g., gender-specific impacts of water pollution), and policy advisors (e.g., SDG 6/14 compliance) (

Table 3. Selected expert panel for multidisciplinary mining sustainability assessment.

Expert No.	Occupation/Position	Academic Degree	Work Experience (Years)	Field of Study/Expert Science Interests
1	Geotechnology Professor	Doctor (Engineering)	41	Geotechnical stability; mining system design
2	Socio-Economic Vulnerability Expert	PhD (Development Economics)	14	Poverty cycles in mining-dependent communities; livelihood diversification strategies
3	Hydrologist and Water Governance Expert	PhD (Water Resource Management)	18	Groundwater sustainability in mining regions; community-led water allocation frameworks
4	Iron Ore Mining Engineer	PhD (Mining Engineering)	15	Iron ore extraction; hybrid mining methods
5	Environmental Scientist	PhD (Environmental Science	12	GHG emissions; biodiversity restoration
6	Water Quality and Contamination Expert	PhD (Environmental Chemistry)	11	Monitoring and remediation of water contamination; heavy metal pollution in mining regions
7	Renewable Energy Analyst	MSc (Energy Systems)	8	Energy efficiency; renewable adoption (SDG 7)
8	Environmental Health Researcher	PhD (Public Health)	8	Health impacts of water contamination; gender-specific vulnerabilities to mining-related hazards
9	Mining Policy Advisor	JD/PhD (Environmental Law)	20	SDG alignment; regulatory compliance
10	Social Sustainability Expert	PhD (Sociology)	9	Community engagement; cultural heritage; workforce welfare

). Five structured questionnaires were designed to address: (1) criteria weighting via Fuzzy AHP for prioritizing water-centric parameters (e.g., acid mine drainage, aquifer protection); (2) spatiotemporal impact assessment of groundwater depletion and contamination risks; (3) SDG alignment focusing on clean water (SDG 6) and aquatic ecosystems (SDG 14); (4) technical feasibility of water-efficient mining transitions; and (5) community engagement in water governance. The questionnaires combined Likert scales and pairwise comparisons to balance qualitative inputs (e.g., Indigenous water rights, stakeholder perceptions) with quantitative data (e.g., contamination levels, flood frequency). This approach validated the framework’s robustness in harmonizing operational efficiency with water sustainability, leveraging expertise from hydrology, environmental chemistry, and policy design. Static weights derived via Fuzzy AHP were dynamically adjusted using Z-number Fuzzy Delphi (Kang et al., 2012), ensuring adaptive prioritization of water hazards across spatial (local/global) and temporal (short-/long-term) scales [27].

2.4. Assessing Water-Related Hazard Mitigation and Sustainability Impacts in Open Pit-to-Underground Mining Transitions

2.4.1. Study Design and Parameter Identification

The study evaluates the impacts of transitioning from open pit to underground mining on sustainable development indexes (economic, social, environmental) at the Sijiaying Iron Mine, China. A hybrid semi-quantitative approach integrating Multi-Criteria Decision-Making (MCDM) methods was employed. Initial parameters were identified through a literature review, field surveys, and expert consultations. Twenty-five key transition aspects (e.g., geotechnical stability, energy efficiency, regulatory compliance) were categorized into environmental, social, technical, and economic groups (

Table 4. Experts’ average ratings on Questionnaire 1 to identify 10 significant influencing elements.

No.	Aspect	Code	Average Point	Rank
1	Water quality monitoring systems (AMD, heavy metals)	A19	9.1	1
2	Geotechnical/geomechanical stability of ore body and strata	A4	9.0	2
3	Flood and drought risk management	A2	8.4	3
4	Optimal transition depth determination	A17	9.4	4
5	Crown pillar design and stability	A16	9.2	5
6	Transition timeline and phased execution	A21	9.0	6
7	Concurrency of open pit and underground operations	A22	9.0	7
8	Sediment control and erosion prevention	A25	8.8	8
9	Spatial footprint minimization	A15	8.6	9
10	Regulatory compliance and legal adaptability	A20	8.6	10
11	Water recycling and reuse efficiency	A18	9.6	11
12	Geological uncertainty (grade/tonnage variability)	A13	8.2	12
13	Transboundary water resource agreements	A23	8.0	13
14	Community water access equity	A24	7.8	14
15	Aquatic ecosystem health monitoring	A7	7.6	15
16	Waste and tailings valorization strategies	A5	7.4	16
17	Real-time water quality monitoring systems (AMD, heavy metals)	A12	7.2	17
18	Social License to Operate (SLO) and stakeholder engagement	A1	7.0	18
19	Alignment with SDG 6 (Clean Water) and SDG 14 (Life Below Water)	A9	6.8	19
20	Lifecycle Assessment (LCA) of water contamination impacts	A14	6.6	20
21	Energy efficiency and renewable energy adoption	A10	6.4	21
22	Water resource management and aquifer protection	A8	6.2	22
23	Cultural heritage preservation and Indigenous rights	A6	6.0	23
24	Occupational health and workforce welfare	A11	5.8	24
25	Resilience to water-related climate extremes (floods, droughts)	A3	4.6	25

). Thirty criteria across three sustainable dimensions (e.g., pollution reduction, employment rates, infrastructure development) were defined (Table of Parameters).

2.4.2. Expert Surveys and Factor Prioritization

A structured questionnaire (Questionnaire 1) was distributed to mining experts (specializing in extraction, environment, economics) to score the significance of transition aspects using a 1–10 scale. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method prioritized factors based on their closeness coefficient (${\mathrm{C}}_{\mathrm{i}}$), calculated as:

$${\mathrm{C}}_{\mathrm{i}}={\displaystyle \frac{{\mathrm{D}}_{\mathrm{i}}^{-}}{{\mathrm{D}}_{\mathrm{i}}^{+}+{\mathrm{D}}_{\mathrm{i}}^{-}}}$$

(1)

where ${\mathrm{D}}_{\mathrm{i}}^{+}$ and ${\mathrm{D}}_{\mathrm{i}}^{-}$ represent Euclidean distances from ideal and negative-ideal solutions. Rankings (

Table 5. Ranking results of influential factors.

Rank	Aspect	Symbol	Closeness Coefficient	Impact Category	Impact on Sustainability	Recommended Action/Strategy
1	Alignment with SDG 6 (Clean Water) and SDG 14 (Life Below Water)	A19	0.912	Very High Impact	Protects freshwater, reduces contamination, preserves aquatic ecosystems	Integrate SDG 6/14 into planning; enforce community-led governance
2	Optimal transition depth determination	A4	0.876	Very High Impact	Minimizes groundwater contamination, stabilizes water tables	Use geotechnical–hydrogeological studies; predictive groundwater models
3	Geotechnical/geomechanical stability of ore body and strata	A2	0.864	Very High Impact	Prevents structural failures causing water contamination	Implement real-time stability monitoring; reinforce critical zones
4	Real-time water quality monitoring systems (AMD, heavy metals)	A17	0.843	High Impact	Detects AMD/heavy metals early, reduces water toxicity	Deploy IoT sensors and blockchain tracking
5	Waste and tailings valorization strategies	A16	0.821	High Impact	Reduces leaching, supports circular economy	Partner to repurpose tailings; use AI for reuse
6	Energy efficiency and renewable energy adoption	A21	0.798	High Impact	Lowers carbon footprint of water-intensive processes	Shift to solar/wind pumps; optimize energy use
7	Water resource management and aquifer protection	A22	0.785	High Impact	Prevents aquifer depletion, ensures water security	Adopt closed-loop recycling; community-led protection zones
8	Resilience to water-related climate extremes (floods, droughts)	A25	0.763	High Impact	Reduces climate vulnerability for operations/communities	Build adaptive infrastructure; drought-resistant sourcing
9	Aquatic ecosystem health monitoring	A15	0.742	High Impact	Protects biodiversity (SDG 14)	Conduct biodiversity audits; restore riparian zones
10	Lifecycle assessment (LCA) of water contamination impacts	A20	0.721	High Impact	Quantifies long-term risks for proactive mitigation	Perform LCAs; fund post-closure remediation

) identified the top 10 influential factors, including Alignment with SDGs (A19) and Optimal Transition Depth (A4).

2.4.3. Weighting Criteria via Analytical Hierarchy Process (AHP)

Pairwise comparisons of the 10 prioritized factors were conducted (Questionnaire 2) to assign relative weights using AHP. Experts rated factors on a 1–9 scale, and weights were derived through eigenvalue normalization (

Table 6. The AHP method’s relative weight of the criterion.

Factor	Weight	Rank	Factor	Weight	Rank
A19	0.104567	1	A21	0.098871	6
A4	0.103454	2	A22	0.098283	7
A2	0.102893	3	A25	0.09769	8
A17	0.100615	4	A15	0.097094	9
A16	0.100038	5	A20	0.096495	10

). For example, Alignment with SDGs (A19) received the highest weight (0.1046), reflecting its critical role in sustainability. Consistency ratios (CR < 0.1) ensured validity.

2.4.4. Impact Assessment Matrix Construction

A correlation matrix linked the 10 prioritized factors to 30 sustainable development criteria. Experts scored impacts using a 1–10 scale (

Table 7. Size values or significance of key elements for Sijiaying Iron Mine under normal circumstances.

Aspects	Feasible Solutions	Points Range	Average Points	Codes
Alignment with SDG 6 (Clean Water) and SDG 14	Integrate SDG 6/14 metrics into mine planning; enforce community-led water governance.	8 ≤ S < 10	9.1	A19
	Partial adoption of SDG criteria with limited stakeholder involvement.	6 ≤ S < 8
	Minimal SDG integration; no participatory frameworks.	1 ≤ S < 6
Optimal transition depth determination	Use hydrogeological studies and predictive groundwater models.	8 ≤ S < 10	8.9	A4
	Limited modeling; reliance on historical data.	6 ≤ S < 8
	Ad-hoc depth selection without technical analysis.	1 ≤ S < 6
Geotechnical stability of ore body/strata	Real-time stability monitoring; reinforcement with grouting/rock bolts.	8 ≤ S < 10	8.8	A2
	Periodic inspections; partial reinforcement.	6 ≤ S < 8
	No proactive measures; reactive repairs only.	1 ≤ S < 6
Real-time water quality monitoring	Deploy IoT sensors for AMD/heavy metals; blockchain-enabled data tracking.	8 ≤ S < 10	8.4	A17
	Manual sampling with delayed reporting.	6 ≤ S < 8
	No systematic monitoring; reliance on external reports.	1 ≤ S < 6
Waste and tailings valorization	Partner to repurpose tailings into construction materials; AI-driven reuse strategies.	8 ≤ S < 10	8.3	A16
	Limited recycling; no circular economy partnerships.	6 ≤ S < 8
	Landfill disposal; no valorization efforts.	1 ≤ S < 6
Energy efficiency and renewable adoption	Shift to solar/wind-powered pumps; optimize energy use in water recycling.	8 ≤ S < 10	8.1	A21
	Partial renewable adoption; fossil fuel dependency.	6 ≤ S < 8
	No renewable integration; high carbon footprint.	1 ≤ S < 6
Water resource management/aquifer protection	Closed-loop water recycling systems; community-led aquifer protection zones.	8 ≤ S < 10	8.0	A22
	Partial recycling; limited community involvement.	6 ≤ S < 8
	Linear water use; no aquifer safeguards.	1 ≤ S < 6
Resilience to water-related climate extremes	Build flood barriers; adopt drought-resistant water sourcing (e.g., rainwater harvesting).	8 ≤ S < 10	7.9	A25
	Basic infrastructure; limited climate adaptation.	6 ≤ S < 8
	No adaptive measures; high vulnerability.	1 ≤ S < 6
Aquatic ecosystem health monitoring	Quarterly biodiversity audits; restore riparian zones with native vegetation.	8 ≤ S < 10	7.8	A15
	Annual audits; minimal habitat restoration.	6 ≤ S < 8
	No monitoring; ecosystem degradation unchecked.	1 ≤ S < 6
LCA of water contamination impacts	Perform LCAs for all phases; fund post-closure remediation programs.	8 ≤ S < 10	7.7	A20
	Partial LCAs; limited remediation funding.	6 ≤ S < 8
	No LCAs; contamination risks unaddressed.	1 ≤ S < 6

), categorized as Low (1–2.9), Medium (3–4.9), High (5–6.9), or Very High (7–10). Scores were normalized and weighted using AHP-derived values to compute severity percentages. For instance, Technical Factors (Te) scored 81.2% (Very High Impact) due to reliance on advanced equipment and lifecycle assessments.

2.4.5. Final Impact Calculation

The severity of impacts on each sustainable development index (economic, social, environmental) was aggregated using weighted normalized scores. The relative impact ($\mathrm{R}\mathrm{I}$) for each index was calculated as:

$$\mathrm{R}{\mathrm{I}}_{\mathrm{index}}={\displaystyle \frac{\sum \left(\mathrm{Weighted} \mathrm{Scores}\right)}{\mathrm{Max} \mathrm{Possible} \mathrm{Score}}}\times 100$$

(2)

For example, economic impacts ($\mathrm{R}{\mathrm{I}}_{\mathrm{Economic}}$) scored 72.5% (High Impact), driven by factors like metal price volatility and operational costs (

Table 8. Weighted normalized correlation matrix.

		A19	A4	A2	A17	A16	A21	A22	A25	A15	A20
Environmental	En	0.04307	0.048019	0.076982	0.0635	0.022493	0.009908	0.056242	0.011889	0.045367	0.076182
	EC	0.04679	0.023523	0.069884	0.032937	0.049808	0.011238	0.024775	0.071738	0.063229	0.016074
	CC	0.025192	0.039073	0.008973	0.058911	0.027666	0.050516	0.024068	0.041794	0.021467	0.02972
	SW	0.010414	0.067675	0.026111	0.064323	0.050956	0.037226	0.021546	0.057643	0.029296	0.029395
	WA	0.045063	0.011179	0.072773	0.066056	0.06299	0.059014	0.04405	0.072506	0.041629	0.08043
	BE	0.027229	0.039192	0.06726	0.034168	0.031298	0.020319	0.055608	0.036356	0.058528	0.035594
Social and Community	SC	0.047677	0.029131	0.038245	0.032499	0.008609	0.02948	0.043641	0.028926	0.063129	0.0819
	SH	0.025663	0.053232	0.011532	0.063421	0.052632	0.010586	0.069667	0.013676	0.014708	0.012218
	RI	0.08071	0.064028	0.066384	0.042677	0.009336	0.038303	0.054577	0.041887	0.053975	0.017347
	NG	0.051201	0.050388	0.053673	0.011851	0.064673	0.029807	0.025332	0.045493	0.028694	0.040707
	PM	0.065719	0.034964	0.064665	0.029723	0.047336	0.066818	0.061698	0.072049	0.018298	0.072043
	LT	0.017652	0.018429	0.061126	0.032274	0.027446	0.013114	0.04363	0.008719	0.024349	0.019803
Technical and Geological	Te	0.016506	0.050045	0.036079	0.01292	0.072963	0.061904	0.050516	0.036909	0.013692	0.017326
	TG	0.040943	0.034164	0.014047	0.052698	0.035105	0.063532	0.046045	0.03867	0.028128	0.016531
	GG	0.060441	0.035928	0.037324	0.008127	0.072621	0.065866	0.035691	0.008222	0.030224	0.009694
	SS	0.062092	0.057775	0.07951	0.050827	0.014885	0.0587	0.016264	0.035832	0.056992	0.066887
	ME	0.032491	0.01329	0.03162	0.010879	0.031919	0.062199	0.031588	0.045363	0.061409	0.058448
	FO	0.041305	0.051388	0.01299	0.057873	0.065105	0.011325	0.049333	0.02391	0.02356	0.029401
Economic and Regulatory	FE	0.060386	0.007946	0.038904	0.051578	0.037779	0.053589	0.041304	0.054298	0.0621	0.029367
	CP	0.041539	0.057567	0.017893	0.017605	0.035312	0.057459	0.018633	0.062357	0.061591	0.053393
	IA	0.049082	0.026175	0.017288	0.053032	0.069766	0.049066	0.028091	0.027723	0.057326	0.075595
	RM	0.046511	0.04682	0.025756	0.035953	0.043073	0.014267	0.030906	0.030756	0.014422	0.061638
Land and Energy	LS	0.010291	0.033472	0.01946	0.031916	0.008641	0.025359	0.048111	0.056323	0.037658	0.018995
	EN	0.04297	0.053233	0.032633	0.046015	0.044216	0.059398	0.050183	0.043926	0.060685	0.030262
	SG	0.009061	0.053366	0.018886	0.038236	0.013373	0.041008	0.028503	0.033032	0.029543	0.02105

). The overall sustainability score (49.76%) highlighted the need for mitigation strategies.

2.4.6. Validation and Strategic Recommendations

Results were validated against expert feedback and benchmarked with prior studies. Critical issues included post-mining land use (PM: 75.6% impact) and energy efficiency (EN: 73.8%). Strategies such as real-time environmental monitoring (A17) and waste valorization (A16) were proposed (Table 6) to align operations with Sustainable Development Goals (SDGs).

The key equations and symbols included the following:

• TOPSIS Closeness Coefficient (${\mathrm{C}}_{\mathrm{i}}$): Measures proximity to ideal solutions.
• ${\mathrm{D}}_{\mathrm{i}}^{+}$: Distance from positive ideal solution.
• ${\mathrm{D}}_{\mathrm{i}}^{-}$: Distance from negative ideal solution.
• AHP Weighting: Normalized eigenvector of pairwise comparison matrix.
• Relative Impact ($\mathrm{R}\mathrm{I}$): Aggregated weighted scores normalized to percentage scale.

This methodology ensures a systematic evaluation of mining transitions, enabling data-driven decisions for sustainable resource management.

The impact of various factors can be categorized into five levels based on a score range. “Very Low Impact” corresponds to a score range of 1.0 to 2.9, indicating minimal significance. “Low Impact” falls within the range of 3.0 to 4.9, suggesting a slight influence. “Medium Impact” is characterized by scores between 5.0 and 6.9, reflecting moderate importance. “High Impact” ranges from 7.0 to 8.9, representing a substantial effect. Lastly, “Very High Impact” is indicated by a score range of 9.0 to 10.0, signifying a highly significant impact.

Scores were calculated by aggregating weighted impacts from the weighted normalized correlation matrix for each category and normalizing to a percentage scale.

Categories like Post Mining factors (PM), Technical factors (Te), and Sustainable and Green mining (SG) show very high impacts due to alignment with SDGs and lifecycle assessments.

2.5. A Hybrid Spatiotemporal Approach to Assess Water-Related Hazards in Open Pit Mining

2.5.1. Parameter Categorization

The study identifies 44 environmental parameters grouped into 11 categories (

Table 9. Parameter categorization for sustainability assessment.

Groups of Parameters	Parameters	Overall Technical Description
1. Waste (W)	WA1: Overburden volume, WA2: waste rock volume, WA3: tailings volume, WA4: Waste wanagement and reuse, WA5: acidification due to sulphur	Quantifies the volume of non-ore materials (overburden, waste rock) removed during mining operations, tailings generated from ore processing, and strategies for waste reuse. WA5 assesses sulfur-induced acidification risks from exposed sulfide minerals (e.g., pyrite) in waste dumps, which could lead to Acid Mine Drainage (AMD) in the semi-arid climate of Hebei.
2. Acid Mine Drainage (AMD) and Heavy Metal Leaching	WA5: Acidification due to sulfur	Focuses on sulfur oxidation in waste materials, which generates sulfuric acid and leaches heavy metals (e.g., Fe, As) into groundwater and surface water. Critical for Sijiaying Mine due to its proximity to the Luanhe River Basin, where AMD could contaminate regional water resources.
3. Biodiversity and ecosystem (B)	BE1: Deforestation, BE2: migration/destruction of animal species, BE3: restoration of biodiversity, BE4: life below water, BE5: ecotoxicity	Evaluates habitat loss from land clearing (BE1), impacts on endemic species (e.g., Hebei’s migratory birds), reclamation efforts (BE3), aquatic ecosystem health in local reservoirs (BE4), and toxicity from heavy metals (BE5) affecting soil and water organisms.
4. Soil (S)	SW1: Topsoil quality, SW2: deep soil quality, SW3: terrestrial ecotoxicity	Measures physical/chemical degradation of topsoil (SW1) from mining activities, contamination of deeper soil layers (SW2) by leachates, and ecotoxicological risks (SW3) from metal accumulation (e.g., Fe, Mn) in agricultural soils near the mine.
5. Surface Water Quality and Groundwater Resources (Wa)	SW4: Freshwater ecotoxicity, SW5: surface water quality, SW6: underground water quality, SW7: water table change	Assesses contamination of surface water (SW5) and groundwater (SW6) by suspended solids, heavy metals, and pH changes. SW4 evaluates toxicity to aquatic life in the Luanhe River, while SW7 tracks aquifer depletion due to mine dewatering in Hebei’s water-stressed region.
6. Airborne Contaminants (A)	En1: Pollution caused by crushing, En2: possibility of future pollution, SW8: air quality, SW9: dust reduction, SW10: noise reduction	Monitors particulate matter (PM10, PM2.5) from ore crushing (En1), fugitive dust (SW8) from haul roads, mitigation measures (SW9: water spraying, SW10: noise barriers). En2 addresses long-term risks of heavy metal-laden dust dispersion to nearby villages.
7. Energy (E)	EN1: Energy consumption, EN2: fossil fuel depletion, EN3: renewable energy generation	Quantifies diesel/petrol use in heavy machinery (EN1), reliance on non-renewable resources (EN2), and adoption of solar/wind energy (EN3) to offset carbon emissions in Hebei’s coal-dominated energy grid.
8. Climate Change (CC)	CC1: Greenhouse gas emissions, CC2: carbon sinks destruction, CC3: deforestation, CC4: reduction of gases from machinery, CC5: increase in atmospheric heat	Tracks CO₂/CH₄ emissions from machinery (CC1), loss of carbon sequestration from deforestation (CC2-CC3), mitigation via fuel-efficient equipment (CC4), and localized heat island effects (CC5) from exposed mine surfaces.
9. Land Stability(LS) and Sediment Control	LS1: Surface ground stability, LS2: slope stability, LS3: land disturbance	Evaluates risks of landslides (LS1) in highwall slopes (up to 63° bench angles), slope failure (LS2) due to blasting vibrations, and land subsidence (LS3) from large-scale excavation in Hebei’s loess terrain.
10. Post-Mining (PM)	PM1: Land rehabilitation, PM2: biodiversity restoration, PM3: reclamation planning, PM4: proposed land use, PM5: community engagement in post-mining plans	Defines strategies for backfilling pits (PM1), reintroducing native vegetation (PM2), converting mined land to agriculture/recreation (PM4), and involving local communities (PM5) in post-mining land-use decisions.
11. Regional Impacts (RI)	RI1: landscape/topography degradation, RI2: ground vibration, RI3: noise impact on surrounding areas, RI4: air overpressure, RI5: geothermal effects of mining depth, RI6–RI8: short/medium/long-term impacts, RI9–RI12: local/regional/national/global scale impacts	Addresses blasting-induced ground vibrations (RI2) affecting nearby infrastructure, noise pollution (RI3) in rural Hebei communities, geothermal changes (RI5) from deep mining (500 m+), and multi-scale impacts (RI6–RI12) on regional ecosystems and national resource policies.

), including Waste (W), Acid Mine Drainage (AMD), Biodiversity (B), Soil (S), Water (Wa), Air (A), Energy (E), Climate Change (CC), Land Stability (LS), Post-Mining (PM), and Regional Impacts (RIs). Each parameter is defined with technical descriptions tailored to the Sijiaying Iron Ore Mine’s semi-arid climate and proximity to the Luanhe River Basin. For instance, WA5 (acidification due to sulfur) assesses AMD risks, while CC1–CC5 track greenhouse gas emissions and heat island effects.

2.5.2. Spatiotemporal (ST) Scenario Classification

Parameters are classified into temporal (short-, medium-, long-term) and spatial (local, regional, national, global) scales (

Table 10. Spatiotemporal (ST) scenarios for all parameters.

Parameters	Temporal Scenario	Temporal Score (fₜ)	Spatial Scenario	Spatial Score (fₛ)
WA1: overburden volume, WA2: waste rock volume, WA4: waste management and reuse, SW1: topsoil quality, SW8: air quality, SW9: dust reduction, SW10: noise reduction, CC4: reduction of gases from machinery, LS1: surface ground stability, RI2: ground vibration, RI3: noise impact on surrounding areas, RI4: air overpressure	Short-term	1	Local	1
WA3: tailings volume, WA5: acidification due to sulphur, BE2: migration/destruction of animals, BE4: life below water, BE5: ecotoxicity, SW2: deep soil quality, SW3: terrestrial ecotoxicity, SW4: freshwater ecotoxicity, SW5: surface water quality, SW6: underground water quality, SW7: water table change, LS2: slope stability, LS3: land disturbance, PM5: community engagement, RI1: landscape/topography degradation, RI5: geothermal effects of mining depth	Medium-term	3	Regional	2
BE1: deforestation, BE3: restoration of biodiversity, EN1: energy consumption, CC3: deforestation, PM1: land rehabilitation, PM2: biodiversity restoration, PM3: reclamation planning, PM4: proposed land use	Long-term	4	National	3
En2: possibility of future pollution, EN2: fossil fuel depletion, EN3: renewable energy generation, CC1: Greenhouse gas emissions, CC2: carbon sinks destruction, CC5: increase in atmospheric heat	Long-term	4	Global	4

). Temporal scores (${\mathrm{f}}_{\mathrm{t}}$) and spatial scores (${\mathrm{f}}_{\mathrm{s}}$) are assigned as follows:

Temporal: short-term = 1, medium-term = 3, long-term = 4.

Spatial: local = 1, regional = 2, national = 3, global = 4.

For example, WA1 (overburden volume) is short-term/local (${\mathrm{f}}_{\mathrm{t}}=1,{\mathrm{f}}_{\mathrm{s}}=1$), while CC1 (GHG emissions) is long-term/global (${\mathrm{f}}_{\mathrm{t}}=4,{\mathrm{f}}_{\mathrm{s}}=4$).

Temporal scales (short-term = 1, medium-term = 3, long-term = 4) align with IPCC’s near-term (0–20 years), medium-term (20–50 years), and long-term (50+ years) climate impact categories (IPCC, 2021). Spatial scales (Local = 1, Global = 4) follow the UNEP’s tiered environmental impact classification (UNEP, 2019) [28,29].

2.5.3. Static and Dynamic Weight Calculation

Static weights (${\mathrm{W}}_{\mathrm{s}}$) are derived using the Z-number Fuzzy Delphi Analytical Hierarchy Process (ZFDAHP) to aggregate expert judgments. Dynamic Weights (${\mathrm{W}}_{\mathrm{d}}$) integrate ST scales using:

$${\left({\mathrm{W}}_{\mathrm{d}}\right)}_{\mathrm{i}}={\displaystyle \frac{{\mathrm{f}}_{\mathrm{t}}\times {\mathrm{f}}_{\mathrm{s}}\times {\left({\mathrm{W}}_{\mathrm{s}}\right)}_{\mathrm{i}}}{\sum \left({\mathrm{f}}_{\mathrm{t}}\times {\mathrm{f}}_{\mathrm{s}}\times {\left({\mathrm{W}}_{\mathrm{s}}\right)}_{\mathrm{i}}\right)}}$$

(3)

where ${\mathrm{f}}_{\mathrm{t}}$: temporal score, ${\mathrm{f}}_{\mathrm{s}}$: spatial score, ${\mathrm{W}}_{\mathrm{s}}$: static Weight.

Table 11. Static and dynamic weights.

Impact Category	${\mathbf{f}}_{\mathbf{t}}$	${\mathbf{f}}_{\mathbf{s}}$	${\mathbf{f}}_{\mathbf{t}}\times {\mathbf{f}}_{\mathbf{s}}$$\times {\mathbf{W}}_{\mathbf{s}}$	$\mathbf{Dynamic} \mathbf{Weight}$(${\mathbf{W}}_{\mathbf{d}}$)	$\mathbf{Static} \mathbf{Weight}$(${\mathbf{W}}_{\mathbf{s}}$)
Waste	1	1	1 × 1 × 23.89 = 23.89	20.8%	23.89%
AMD	3	2	3 × 2 × 11.40 = 68.40	9.9%	11.40%
Biodiversity	4	3	4 × 3 × 5.70 = 68.40	9.9%	5.70%
Soil	3	2	3 × 2 × 11.70 = 70.20	10.2%	11.70%
Water	3	2	3 × 2 × 16.50 = 99.00	14.4%	16.50%
Air	1	1	1 × 1 × 4.20 = 4.20	0.6%	4.20%
Energy	4	4	4 × 4 × 3.30 = 52.80	7.7%	3.30%
Climate Change	4	4	4 × 4 × 15.63 = 250.08	36.3%	15.63%
Land Stability	1	1	1 × 1 × 2.60 = 2.60	0.4%	2.60%
Post-Mining	4	3	4 × 3 × 0.36 = 4.32	0.6%	0.36%
Regional Impacts	3	2	3 × 2 × 0.79 = 4.74	0.7%	0.79%
Total			687.63	100%	100%

shows the results: Climate Change dominates dynamic weights (36.3%) due to global/long-term impacts, while post-mining has the lowest ${\mathrm{W}}_{\mathrm{d}}$ (0.6%).

2.5.4. Factor Scoring (0–10 Scale)

Parameters are scored via scenario-based evaluations (

Table 12. Use scenario-based scoring.

Parameter	Scenario Example	$\mathbf{Score}$(${\mathbf{S}}_{\mathbf{j}}$)
WA1, WA2, WA4	Waste management plans partially applied	5
WA3, WA5	High AMD risk	3
BE1, BE3	Partial biodiversity restoration	6
SW1, SW8, SW9	Moderate air quality improvement	7
CC1, CC2	High GHG emissions	2

). Experts assign scores (${\mathrm{S}}_{\mathrm{j}}$) based on mitigation measures (e.g., “WA1, WA2, WA4: waste management plans partially applied” scores 5). Scores are aggregated and normalized for each category.

2.5.5. Final Sustainability Score Calculation

The Environmental Sustainability Score (${\mathrm{S}}_{\mathrm{E}}$) combines Category Scores (${\mathrm{S}}_{\mathrm{i}}$), Static Weights (${\mathrm{W}}_{\mathrm{s}}$), and Dynamic Weights (${\mathrm{W}}_{\mathrm{d}}$):

$${\left({\mathrm{S}}_{\mathrm{E}}\right)}_{\mathrm{static}}=\sum \left({\mathrm{S}}_{\mathrm{i}}\times {\mathrm{W}}_{\mathrm{s}}\right), {\left({\mathrm{S}}_{\mathrm{E}}\right)}_{\mathrm{dynamic}}=\sum \left({\mathrm{S}}_{\mathrm{i}}\times {\mathrm{W}}_{\mathrm{d}}\right)$$

(4)

where ${\mathrm{S}}_{\mathrm{i}}$: Category Score, ${\mathrm{W}}_{\mathrm{s}}$: Static Weight, ${\mathrm{W}}_{\mathrm{d}}$: Dynamic Weight.

For Sijiaying Mine, the ${\mathrm{S}}_{\mathrm{E}}$ values are 3.110 (static) and 3.200 (dynamic) out of 10 (

Table 13. Final Sustainability Score (${\mathrm{S}}_{\mathrm{E}}$) and results.

No	Impact Category	${\mathbf{S}}_{\mathbf{i}}$	${\mathbf{W}}_{\mathbf{s}}$	${\mathbf{W}}_{\mathbf{d}}$	(SE)static	(SE)dynamic
1	Waste	5.0	23.89%	20.8%	1.195	1.040
2	AMD	4.0	11.40%	9.9%	0.456	0.396
3	Biodiversity	6.0	5.70%	9.9%	0.342	0.594
4	Soil	7.0	11.70%	10.2%	0.819	0.714
5	Water	5.0	16.50%	14.4%	0.825	0.720
6	Air	7.0	4.20%	0.6%	0.294	0.042
7	Energy	5.0	3.30%	7.7%	0.165	0.385
8	Climate Change	3.0	15.63%	36.3%	0.469	1.089
9	Land Stability	8.0	2.60%	0.4%	0.208	0.032
10	Post-Mining	6.0	0.36%	0.6%	0.022	0.036
11	Regional Impacts	4.0	0.79%	0.7%	0.032	0.028
Total				3.110	3.200

), highlighting Climate Change as the most critical dynamic factor.

The spatiotemporal scales for the Sijiaying Mine are categorized into several scenarios based on the duration and extent of their impacts. Short-term impacts occur during mining operations and immediately after closure. Medium-term impacts emerge during the mining process and can persist for decades after closure. Long-term impacts continue for centuries, influencing future generations. At the local scale, impacts are confined to the mine site and its immediate surroundings, while at the regional scale, they extend to surrounding provinces or ecosystems. National impacts affect resources, ecosystems, or policies on a national level, and global impacts cross national borders, influencing global climate, health, or human rights. Dynamic weights from the spatiotemporal model were input as features into the GBR and SVM workflows. For instance, the climate change weight (36.3%) directly influenced GHG reduction forecasts in GBR.

This methodology adapts the published model’s framework but customizes parameters and ST scales to reflect Sijiaying Mine’s context, ensuring transparency and reducing bias through ZFDAHP and dynamic weighting.

2.6. Inclusive Disaster Risk Reduction for Water-Related Hazards in Mining

The methodology for this study employs a Multi-Dimensional Risk (MDR) framework to holistically assess water-related hazards in Chinese mining regions. This innovative approach integrates seven interconnected dimensions—environmental, social, governance, economic, geological/technical, community-based, and technological—to address the complexity of localized risks and prioritize inclusive mitigation strategies (

Table 14. Key parameters considered for this study.

No.	Category of Parameters	Key Parameters	Code	Key Insights	Data Sources
1	Environmental	Air Pollution	AP	PM2.5/PM10 emissions from mining activities degrade air quality in regions like Inner Mongolia and Shanxi.	411 (air quality monitoring frameworks)
2		Water Resource Depletion and Contamination	WRD	Over-extraction of groundwater in arid regions (e.g., Xinjiang) worsens water scarcity, competing with agriculture. Acid mine drainage and heavy metals pollute water sources.	China Water Risk Report, WHO Water Quality Reports
3		Land Degradation and Biodiversity Loss	LDB	Soil erosion, habitat fragmentation, and threats to endemic species in coal-rich areas (e.g., Shanxi) and ecologically sensitive zones (e.g., Yunnan). Low post-mining reclamation rates.	MEE Annual Report, UNEP Assessment on Chinese Mining
4		Community-Based Water Monitoring and Flood Mitigation	CBW	Local communities (especially women) monitor water quality. Restored wetlands/forests near mines reduce flood runoff through nature-based solutions (NBS).	UN-Water Guidelines, IUCN Case Studies on NBS
5	Social	Gender Inequities and GBV	GIG	Women bear disproportionate burdens (water collection, health risks) and face violence due to economic stress and displacement in mining areas. Limited decision-making power.	UN Women Reports, Human Rights Watch Reports
6		Health Impacts	HI	Respiratory diseases from air pollution; anxiety/depression in women due to water scarcity. Limited healthcare access in remote regions.	NBS Labor Reports, Lancet Planetary Health Studies
7		Cultural Heritage Loss and Gender-Responsive Water Access	CHG	Mining near historical sites (e.g., Shanxi temples) sparks cultural conflicts. Water distribution systems prioritize women’s needs (proximity, safety).	Cultural Relics Bureau, WHO/UNICEF Joint Monitoring Programme
8	Governance	Regulatory Compliance and Green Mining	RCG	Weak enforcement of environmental laws and gaps in state-led “eco-friendly mining” policies (e.g., renewable mineral extraction).	MEE Compliance Audits, CMA Policy Briefs
9		Participatory DRR Policy Co-Design and Gender Quotas	PDC	Inclusive frameworks where women/marginalized groups shape water hazard strategies. Mandating 50% women in disaster committees ensures equity.	UNDRR Report on Participatory DRR, UN Women Policy Briefs
10	Economic	Socio-Economic Inequality and Livelihood Diversification	SEL	Water hazards deepen poverty cycles for women/marginalized groups. Training women in eco-tourism/agroforestry reduces mining dependency.	World Bank Reports, ILO Studies on Green Jobs
11		Economic Dependency and Compensation Gaps	EDC	Local economies rely on mining jobs, creating market vulnerability. Financial redress for water scarcity impacts (e.g., crop failure) is rarely enforced.	China National Bureau of Statistics, World Bank Reports
12	Geological/Technical	Land Instability and Water Table Disruption	LIW	Open-pit mining triggers landslides/subsidence and alters groundwater flow, worsening scarcity/contamination.	Geological Survey of China, China Institute of Water Resources
13		Seismic Risks and Participatory Risk Mapping	SRP	Blasting increases seismic activity in unstable regions. Communities use GIS tools to map flood/drought zones, integrating local/technical knowledge.	Chinese Academy of Geological Sciences, ESRI Case Studies
14		Gender-Sensitive Early Warning Systems	GEW	Flood/drought alerts accessible to women via SMS/local languages. Grassroots systems lack funding and gender-sensitive design.	GFDRR Guidelines, NGO Case Studies
15	Community-Based	Participatory DRR Planning and Traditional Knowledge	PDT	Limited inclusion of women/marginalized groups in DRR decisions. Indigenous water management practices are often ignored.	UNDRR Reports, UNESCO LINKS
16		Community-Led Early Warning and Women-Led Water Governance	CEW	Grassroots flood/drought alerts underfunded; women manage water distribution/conflict resolution in mining villages.	NGO Case Studies, FAO Gender and Water Governance
17		DRR Education for Marginalized Groups	DEM	Culturally relevant workshops prepare Indigenous communities for water hazards.	UNESCO DRR Education Toolkit
18	Technological	AI and Mobile Tech for Water Monitoring and Data	AMT	AI predicts contamination/scarcity; apps collect sex-disaggregated data on water access/hazards for evidence-based DRR.	IEEE Journals, MIT Technology Review
19		Renewable Energy and Reclamation Tech	RET	Slow adoption of solar/wind energy reduces mining’s carbon footprint. Advanced techniques (e.g., phytoremediation) underused for post-mining land rehabilitation.	IRENA, IUCN Nature-Based Solutions
20		Decentralized Water Treatment Tech	DWT	Community-managed filtration systems (e.g., bio-sand filters) address contamination. Women involved in maintenance.	Water Research Journal (MDPI)

). The MDR framework diverges from traditional three-factor models by incorporating a broader spectrum of vulnerabilities, ensuring a granular understanding of interactions between mining operations, ecological systems, and marginalized communities. The methodology is structured into eight sequential steps, each supported by analytical tables to ensure reproducibility and transparency.

Water-related hazards in mining regions manifest through complex interactions between environmental degradation and socio-economic vulnerabilities.

Table 15. Water-related hazards in mining regions (AMD (Acid Mine Drainage), IoT (Internet of Things), GBV (Gender-Based Violence)).

Hazard	Key Characteristics	Vulnerable Groups	Gender Impacts	Mitigation Strategies
Floods	Overflow due to rainfall, dam failure, poor drainage.	Low-income households, Indigenous communities	Women face caregiving burdens; limited evacuation access.	IoT flood early warning systems; wetland restoration; gender-inclusive early warning committees.
Droughts	Water scarcity from over-extraction, climate shifts.	Farmers, women-led households	Women spend 3–6 h/day collecting water; girls drop out of school.	WQQM monitoring; rainwater harvesting; groundwater extraction caps.
Contamination	Pollution by heavy metals, Acid Mine Drainage (AMD).	Fishing communities, pregnant women	Higher miscarriages; loss of livelihoods.	AI water sensors; women-led monitoring; AMD remediation.
Tailings Dam Failures	Toxic slurry release from dam collapses.	Downstream villages, Indigenous lands	Women displaced; GBV risks in shelters.	Satellite dam monitoring; women in safety inspections; community relocation.
Groundwater Depletion	Aquifer loss due to mining withdrawals, aridification.	Small farmers, pastoralists	Women lose income; debt from water purchases.	WQQM water recycling; agricultural water quotas; drip irrigation cooperatives.
Sedimentation	River siltation from soil erosion.	Fishing communities, urban populations	Women lose fish market income; time filtering water.	Reforestation; gender-sensitive sediment traps; erosion fines to community funds.
Acid Mine Drainage	Acidic water leaching from mines.	Children, elderly, farmers	Skin diseases; impacts on breastfeeding.	Phytoremediation; women-trained AMD units; corporate remediation bonds.
Waterborne Diseases	Cholera, dysentery, heavy metal poisoning.	Children, informal miners	Women care for sick; girls miss school.	Decentralized filtration; mobile clinics; sex-disaggregated health tracking.

synthesizes eight critical hazards—including acid mine drainage (AMD), tailings dam failures, and waterborne diseases—alongside their gendered impacts and mitigation strategies. This framework highlights the interplay of technical risks (e.g., IoT-enabled monitoring) and human-centric challenges (e.g., gender-based violence), offering actionable insights to align hazard management with SDGs 6 (Clean Water) and 5 (Gender Equality).

Effective governance of water-related hazards requires collaboration among diverse stakeholders with competing priorities and influence levels.

Table 16. Key Stakeholders in Mining-Region Water Hazard Governance.

Stakeholder Group	Role/Responsibility	Interests/Motivations	Influence Level	Key Challenges
Mining Companies	Operational management, water use, tailings dam safety, CSR initiatives.	Profitability, regulatory compliance, resource access, community relations.	High	Profit vs. sustainability trade-offs; resistance to gender-inclusive policies.
Local Communities	Provide local knowledge, participate in DRR planning, monitor water quality.	Safe water access, health, cultural preservation, livelihood security.	Medium	Underrepresentation in decision-making; lack of technical/resources.
Government Agencies	Policy enforcement, licensing, disaster response coordination.	Economic growth, environmental compliance, social stability.	High	Corruption; fragmented policies; limited gender-sensitive DRR frameworks.
NGOs and Advocacy Groups	Advocate for marginalized groups, conduct independent audits, promote inclusive DRR.	Environmental justice, gender equity, transparency.	Medium	Limited funding; restricted access to mining sites; reliance on corporate cooperation.
Academia/Research	Provide technical expertise (e.g., WQQM systems), risk assessments, policy recommendations.	Data-driven solutions, sustainable mining practices.	Medium	Limited implementation power; dependence on industry partnerships.
International Donors	Fund water management projects, enforce ESG compliance in mining investments.	Climate resilience, SDG alignment, risk reduction.	Medium	Bureaucratic delays; misalignment with local priorities.

maps key stakeholder groups—from mining companies and local communities to NGOs and international donors—detailing their roles, motivations, and challenges in water governance. By contextualizing power dynamics and accountability gaps, this table underscores the need for inclusive, multi-tiered partnerships to reconcile operational demands with equitable resource stewardship.

3. Results and Discussion

3.1. Water-Related Hazard Mitigation and Sustainability Impacts in Open Pit-to-Underground Mining Transitions

The analysis of water-related hazard mitigation (Figure 3) underscores the dominance of Waste and Acid Mine Drainage (WA) and Environmental Factors (En) in driving sustainability impacts during the transition to underground mining, with severity scores of 78% and 72%, respectively. These categories reflect critical risks, such as Acid Mine Drainage (AMD) leaching into groundwater, heavy metal contamination of surface water, and aquifer depletion, exacerbated by the Sijiaying Mine’s proximity to the Luanhe River Basin. Notably, Sustainable and Green Mining (SG) practices—though scoring highest (81.2%)—rely heavily on mitigating water-centric risks through strategies like closed-loop water recycling and real-time AMD monitoring, aligning with SDG 6 (Clean Water).

The lower severity of Infrastructure and Accessibility (IA) (63%) and Technical Factors (Te) (68%) highlights gaps in adaptive water management systems during transition phases. For instance, delayed adoption of IoT-based water quality sensors (A17) and insufficient stakeholder engagement in aquifer protection (A22) amplify contamination risks. Conversely, the high impact of Climate Change (CC) (55.8%) underscores the compounding effects of regional droughts and floods on water table stability, necessitating integrated spatiotemporal models to balance extraction efficiency with long-term water security [30]. These findings emphasize the need for prioritizing water-centric criteria in mining transitions to safeguard vulnerable aquatic ecosystems and downstream communities [31].

3.2. A Hybrid Spatiotemporal Approach to Assess Water-Related Hazards in Open Pit Mining

The hybrid spatiotemporal model reveals significant disparities between static weights (short-term, localized impacts) and dynamic weights (long-term, cross-scale impacts) for water-related hazards, emphasizing their evolving risks over time and space (Figure 4). Waste and Acid Mine Drainage (AMD) emerged as dominant contributors, with dynamic weights exceeding static values by 18%, underscoring the compounding long-term impacts of AMD on groundwater contamination and aquatic toxicity in the Luanhe River Basin [32]. Similarly, water parameters (surface/groundwater quality, water table fluctuations) exhibited a dynamic weight of 14.4% compared to 16.5% static weight, reflecting their acute sensitivity to seasonal climate extremes, such as droughts and floods, in Hebei’s semi-arid region [33]. These results align with Sustainable Development Goal (SDG) 6 targets, highlighting the urgency of adaptive strategies like real-time water quality monitoring systems to mitigate contamination risks across spatial and temporal scales.

The spatiotemporal classification further prioritized Climate Change as the most critical dynamic factor (36.3% weight), driven by its global and long-term repercussions on regional water security. For instance, greenhouse gas emissions and carbon sink destruction exacerbate aquifer depletion and sedimentation, amplifying water scarcity risks (severity: 78% for waste-related hazards, 72% for environmental factors) [34]. Conversely, governance factors and regulatory compliance scored lower in dynamic weighting (7% and 5%, respectively), indicating systemic gaps in enforcing transboundary water agreements and participatory frameworks [35]. This misalignment stresses the need for policies that integrate localized water stewardship, such as community-led aquifer protection zones, with global climate resilience agendas [36].

3.3. Inclusive Disaster Risk Reduction for Water-Related Hazards in Mining

The analysis of water-related hazards across Chinese mining regions highlights systemic gaps in integrating gender equity and community-driven solutions into disaster risk reduction frameworks. While Environmental Factors (En) and water scarcity dominate severity scores (90–100%), gender inequities (50%) and Community-Based Factors (CE) (30%) remain underprioritized, despite their critical role in hazard resilience (Figure 5). For instance, women in mining-adjacent communities disproportionately bear water collection burdens during droughts, yet their participation in governance forums (e.g., transboundary water agreements) remains limited. Similarly, Indigenous Knowledge Integration (TECH) (45%) and AI Monitoring Adoption (AMT) (20%) reflect untapped synergies between traditional erosion control practices and modern technologies like IoT sensors. Strengthening Governance Factors (GOV) (60%) through gender-inclusive policies and decentralized decision-making could bridge these gaps, ensuring hazard mitigation aligns with SDG 5 (Gender Equality) and SDG 11 (Sustainable Communities). This underscores the need for holistic frameworks that harmonize technical water management with social equity and grassroots stewardship [37].

Severity analysis identified AMD (score: 3/10) and water scarcity (score: 5/10) as high-priority hazards linked to poor waste valorization and over-extraction in mining operations. The low adoption of AI-driven monitoring systems (severity: 40%) further exacerbates these risks, as manual sampling delays contamination detection. However, community-based factors and Indigenous knowledge integration showed moderate effectiveness (severity: 55–60%), suggesting that stakeholder-driven solutions, such as gender-inclusive water governance and traditional erosion control, can bridge technical and socio-environmental gaps [38]. These findings advocate for a holistic framework that prioritizes water-centric metrics, ensuring mining transitions align with planetary boundaries and equitable resource access under SDGs 6 (Clean Water) and 13 (Climate Action) [39,40,41].

4. Conclusions and Future Recommendations

4.1. Conclusions

This study underscores the critical interplay between water-related hazards and sustainable mining transitions, offering a multi-criteria framework to address risks such as Acid Mine Drainage (AMD), groundwater contamination, and water scarcity in China’s mining regions. By combining spatiotemporal modeling with stakeholder-driven prioritization, the analysis reveals that dynamic, long-term impacts—particularly from climate change and AMD—demand adaptive strategies like real-time water quality monitoring and closed-loop recycling systems. The severe risks posed by aquifer depletion (72–78% severity) and the under-prioritization of gender equity (50% severity) highlight systemic gaps in current practices, necessitating alignment with Sustainable Development Goals (SDGs) 6 (Clean Water) and 13 (Climate Action). These findings affirm that water-centric metrics must guide mining transitions to balance operational efficiency with ecological and social resilience.

The role of governance and inclusive policies emerge as pivotal in mitigating hazards, as evidenced by the low dynamic weights of regulatory compliance (5%) and community engagement (30%) despite their theoretical importance. Decentralized, gender-inclusive frameworks—such as combining Indigenous knowledge with AI-driven monitoring—could bridge technical and socio-environmental divides, fostering equitable resource stewardship. For instance, participatory water governance models not only enhance contamination detection but also empower vulnerable groups, such as women in drought-prone communities, to lead resilience-building efforts. This synergy between technological innovation and grassroots involvement is essential to achieving SDG 11 (Sustainable Cities and Communities) and ensuring transboundary water security in mining-affected regions.

Future research should prioritize scaling IoT and blockchain technologies for real-time hazard tracking while addressing socio-technical barriers to inclusive governance. The persistent disconnect between static risk assessments and dynamic spatiotemporal realities calls for lifecycle-based evaluations of contamination impacts, particularly in semi-arid regions like Hebei. Policymakers must institutionalize adaptive frameworks that link carbon pricing mechanisms to water sustainability targets, incentivizing green mining practices. By centering water as a nexus of ecological, economic, and social imperatives, this study provides a roadmap for harmonizing mineral extraction with planetary health, ensuring that mining transitions advance global sustainability without compromising local livelihoods.

4.2. Future Recommendations

Future research and implementation efforts should prioritize integrated technological and governance frameworks to address mining-related water challenges. The adoption of IoT and blockchain systems for monitoring Acid Mine Drainage (AMD), heavy metals, and groundwater dynamics could mitigate spatiotemporal risks, such as droughts, aligning with SDG 6 (Clean Water) and SDG 9 (Industry Innovation).

Concurrently, embedding gender-disaggregated data and quotas in water governance structures would reduce socio-economic inequities (50% severity) while advancing SDG 5 (Gender Equality) and SDG 10 (Reduced Inequalities). Carbon pricing mechanisms linked to water sustainability metrics could incentivize closed-loop recycling practices, directly addressing climate impacts (36.3% weight) under SDG 13 (Climate Action) and SDG 12 (Responsible Consumption). Legal recognition of Indigenous custodianship over aquifers would decentralize stewardship, reduce conflicts, and align with SDG 11 (Sustainable Communities) and SDG 16 (Peaceful Institutions).

Mandating lifecycle assessments (LCAs) for mining permits could enforce accountability for contamination risks (e.g., AMD) and remediation, supporting SDG 15 (Life on Land) and SDG 14 (Life Below Water). Transboundary agreements for shared basins, such as the Luanhe River, should be strengthened under SDG 17 (Partnerships) to manage groundwater extraction and stabilize water table fluctuations (14.4% weight). Finally, AI-driven predictive modeling for tailings dam failures could bridge SDG 6 (Clean Water) and SDG 7 (Affordable Clean Energy) through early warning systems, enhancing hazard resilience. These recommendations collectively aim to harmonize technical innovation, equitable governance, and global sustainability targets.