803 requires disclosure of gross Scopes 1, 2, and 3 emissions under the Corporate Sustainability Reporting Directive (CSRD) and ESRS E1, with first reporting for FY 2024 in 2025, setting a benchmark that many miners and downstream customers are aligning with. Sector guidance from ICMM further standardises Scope 3 accounting for mining and metals.5,6,7 Recent literature highlights two complementary needs for plant-level decision support: integrating LCA with process simulation to address methodological inconsistencies and “black-box” limitations and operationalising the carbon footprint as a practical efficiency metric within standard workflows8. This paper presents a practical LCA application that incorporates a life cycle-based carbon metric – expressed as kg CO₂e per defined output unit – directly into standard mineral processing optimisation studies as an additional metric alongside cost and production KPIs. The framework integrates with standard plant workflows (mass balance, unit models, kWh/t, and recovery) and provides transparent accounting of emission sources relevant to concentrator studies: electricity and fuels, consumables (grinding media, liners, reagents), and maintenance or auxiliary services, parameterised by emission factors and usage rates consistent with ISO 14040/140449,10. Evidence from mining LCA shows that, beyond electricity, consumables and process choices can materially influence the footprint, supporting the inclusion of these sources in routine studies2,11. Rather than creating a separate assessment, the goal is to enhance existing option screening and debottlenecking with comparable carbon results, so evaluations of operational settings and configuration choices consider kg CO₂e with the same rigour as energy and cost. A concise example later in the paper demonstrates the application. Although this paper focuses on the concentrator, the framework is compatible with Mine-to-Mill integration¹² and mine-side options, such as blast design that conditions ROM size, in-pit crushing and conveying (IPCC)¹³ that reduces diesel haulage, or bulk ore sorting that rejects mass prior to grinding¹⁴ – all of which can shift energy use and CO₂-e under the same metric. 2. METHODOLOGICAL FRAMEWORK: PROCESS OPTIMISATION + LCA AS A METAANALYSIS Process Optimisation (PO) and Life Cycle Assessment (LCA) draw on the same operational data but historically have been applied with different depth in mineral processing. PO is widely embedded in plant-level decision making, while LCA has often remained a reporting tool. The framework presented here reinterprets routine PO data under an environmental lens, producing a decision-oriented carbon metric that complements process KPIs without adding modelling. 2.1 Overview of Process Optimisation (PO) PO follows an iterative sequence that includes defining the decision context, collecting and validating operational data, calibrating models, simulating scenarios, and translating results into actionable operational guidance. This approach is unit operation agnostic and applies across comminution and flotation as long as activities remain traceable within defined plant battery limits. Typical PO datasets include throughput and mass balances, grind size, solids content, circulating loads, energy use by stage, reagent additions, grinding media and liner wear, runtime, availability, recovery, and product specifications. Scenario analysis usually compares changes in energy use, recovery, grade, throughput, and wear-related effects to evaluate trade-offs. 2.2 What LCA Adds in an Operational Context When integrated into concentrator optimisation, LCA reframes core operational data—particularly electricity use and key consumables—as sustainability indicators that inform operational decisions rather than broad corporate reporting. System boundaries A gate-to-gate boundary aligned with concentrator battery limits is adopted. Inputs such as electricity, fuels, grinding media, liners, and reagents are represented with cradle-to-gate factors. This
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