Abiotic resources are extensively used in industrialized societies to deliver multiple services that contribute to human well-being. Their increased extraction and use can potentially reduce their accessibility, increase competition among users, and ultimately lead to a deficit of those services. Life cycle assessment is a relevant tool to assess the potential damages of dissipating natural resources. Building on the general consensus recommending evaluating the damages on the instrumental value of resources to humans in order to assess the consequences of resources dissipation, this research work proposes a novel conceptual framework to assess the potential loss of services provided by abiotic resources, which when facing unmet demand can lead to a deficit to human users and have consequences on human well-being.
Results
A framework is proposed to describe the mechanisms that link human intervention on the resources in the accessible stock to competition among users. Users facing the deficit of resource services are assumed to have to pay to recover the services, using backup technologies. The mechanisms that are proposed to be characterized are dissipation and degradation. Data needed to later operationalize the framework for abiotic resources are identified. It also proposes a framework at the life cycle inventory level to harmonize life cycle inventories with the current impact assessment framework to fully characterize impacts on resource services. It regards ensuring mass balances of elements between inputs and outputs of life cycle inventory datasets as well as including the functionality of resource flows.
Discussion and conclusions
The framework provides recommendations for the development of operational life cycle impact assessment (LCIA) methods for resource services deficit assessment. It establishes the impact pathway to damage on the area of protection “Resource Services”, data needed to feed the model and recommendations to improve the current state of life cycle inventories to be harmonized with the LCIA framework.
For simplification in Fig. 2, the functionality is focused on the service i. However, the subsets could be functional for other services, then that is why the downcycling arrows come back to the reserve base (only the service i is lost but not all the other services).
Additional change of the computational structure of LCA is needed for integrating the feedback loops (as already proposed by Weidema et al. (2018)), due to the use of backup technology (BT) in our framework. However, the implementation of feedback loops requires knowing the inventory of resources used for BTs which is mostly unknown at the time being.
For example, the commodity “motor vehicles, trailers and semi-trailers” in Wieland et al. (2021) is highly aggregated and does not enable the distinction between services of vehicles and trailers, which are different.