Botes, A, Sheridan, C & Steyn, C 2025, 'A probabilistic framework for mine closure financial liability assessment', in S Knutsson, AB Fourie & M Tibbett (eds), Mine Closure 2025: Proceedings of the 18th International Conference on Mine Closure, Australian Centre for Geomechanics, Perth, pp. 1-14, https://doi.org/10.36487/ACG_repo/2515_29
(https://papers.acg.uwa.edu.au/p/2515_29_Botes/)
Abstract: Mine closure remains a critical challenge for the global mining industry, particularly in ensuring adequate financial provisions to mitigate environmental risks and long-term liabilities. Traditional cost estimation models rely on deterministic approaches that often underestimate closure liabilities by failing to account for uncertainties related to future environmental conditions, regulatory changes, and economic variability.
This paper introduces a probabilistic framework for assessing mine closure liability, which integrates an uncertainty-based cost estimation model. Unlike conventional models that provide single-point cost estimates based on itemised engineering breakdowns, this framework employs a Monte Carlo simulation approach to generate a probabilistic range of potential closure costs. The model incorporates multiple closure options per action/component, each assigned a probability of occurrence. Additionally, quantities and unit rates are represented as probability distributions rather than fixed values, ensuring a comprehensive assessment of variability and risk.
The model dynamically refines closure cost projections using an interactive database integrating satellite imagery, computer vision, historical knowledge, artificial intelligence, and statistical modelling. This framework enhances accuracy and facilitates data-driven decision-making by applying the Pareto principle to action/components that are key cost drivers, such as water treatment and material movement. The output from the framework generates a probabilistic cost distribution, accounting for uncertainties and enabling the determination of confidence intervals for closure costs. This approach improves transparency, supports risk-adjusted financial assessments, and ensures a robust evaluation of closure liabilities.
The proposed probabilistic model offers stakeholders, regulators, and financial auditors a comprehensive, data-driven assessment of mine closure costs. It facilitates rigorous financial provisioning by aligning closure liabilities with site-specific risk profiles. Ultimately, this framework enhances regulatory compliance and promotes financial transparency. Integrating risk-adjusted planning and informed decision-making supports more sustainable approaches to post-mining land rehabilitation. Doing so helps ensure that environmental restoration is adequately funded and that mine closure processes align with long-term, sustainable land use goals.

Keywords: mine closure, financial provisioning, probabilistic cost estimation, Monte Carlo simulation, environmental risk management, closure liabilities, unknown-unknowns