Towards integrating the ecosystem services cascade framework within the Life Cycle Assessment (LCA) cause-effect methodology.

The assessment of ecosystem services (ES) is covered in a fragmented manner by environmental decision support tools that provide information about the potential environmental impacts of supply chains and their products, such as the well-known Life Cycle Assessment (LCA) methodology. Within the flagship project of the Life Cycle Initiative (hosted by UN Environment), aiming at global guidance for life cycle impact assessment (LCIA) indicators, a dedicated subtask force was constituted to consolidate the evaluation of ES in LCA. As one of the outcomes of this subtask force, this paper describes the progress towards consensus building in the LCA domain concerning the assessment of anthropogenic impacts on ecosystems and their associated services for human well-being. To this end, the traditional LCIA structure, which represents the cause-effect chain from stressor to impacts and damages, is re-casted and expanded using the lens of the ES 'cascade model'. This links changes in ecosystem structure and function to changes in human well-being, while LCIA links the effect of changes on ecosystems due to human impacts (e.g. land use change, eutrophication, freshwater depletion) to the increase or decrease in the quality and/or quantity of supplied ES. The proposed cascade modelling framework complements traditional LCIA with information about the externalities associated with the supply and demand of ES, for which the overall cost-benefit result might be either negative (i.e. detrimental impact on the ES provision) or positive (i.e. increase of ES provision). In so doing, the framework introduces into traditional LCIA the notion of "benefit" (in the form of ES supply flows and ecosystems' capacity to generate services) which balances the quantified environmental intervention flows and related impacts (in the form of ES demands) that are typically considered in LCA. Recommendations are eventually provided to further address current gaps in the analysis of ES within the LCA methodology.