Using Life Cycle Assessments and Multi-Criteria Decision Analysis Models for Ex Ante Sustainability Assessment

The minerals industry is faced with choices beginning in the exploration phase and extending over the life cycle of the mine. Firms examine the technical feasibility of alternative actions and typically rank them based on economic performance. Increasingly firms also consider alternativesÆ contribution to sustainable development. There are many visions of what a sustainable future should look like and much debate about what should be sustained. Sustainability is a process, a series of incremental actions, and each alternative course of action has the potential to further progress toward one or more sustainability goal. However, because it is not possible to sustain everything everywhere simultaneously, trade-offs and choices inevitably have to be made.Sustainable decision making before investment (ex ante decisions) should incorporate a broad suite of quantitative and qualitative information. One approach is to utilise an integrated sustainability assessment (ISA) framework that supplements traditional technical and financial tools with other tools such as objectives hierarchies (OH) and life cycle assessments (LCA). OH is a problem framing method that captures stakeholder and decision maker interests, objectives and values. LCA addresses material and energy flows, and environmental impacts. The outputs of these tools can be combined with additional qualitative measures and analysed using multi-criteria decision analysis (MCDA). MCDA models facilitate comparison of different alternatives on the basis of a set of criteria and indicators. Some can incorporate information on risk attitudes and preferences ( multi-attitribute value theory models) and others uncertainty about outcomes (multi-attribute utility models). Studies have been published describing and comparing tools for minerals sector decision analysis. Our paper extends this literature, discussing how a combined OH-LCA-MCDA can support ex ante sustainability assessment by incorporating preferences, risk attitudes, and monetised or non-monetised social and economic variables, in a manner that captures their inherent complexity. We also discuss how such a combined model would support trade-off analysis.