A new life cycle impact assessment methodology for assessing the impact of abiotic resource use on future resource accessibility

Abstract

Purpose: Abiotic resource is included as an impact category in life cycle impact assessment (LCIA). The most widely accepted LCIA method is abiotic resource depletion potential (ADP). However, numerous studies have illustrated the limitations of the ADP method, such as the neglect of resources that can be recycled. This paper aims to develop a comprehensive and objective method for assessing the impact of resource use on future generations, which can be used at different stages of the life cycle. Methods: Based on the above research objectives, this paper proposes a new method, the abiotic resource expected dissipation potential (AEDP) method, for assessing the impacts of current resource use on the abiotic resource accessibility. The method is divided into four impact categories based on different endpoints of the dissipative flow and replaces the resource extraction rate with the global annual dissipation rate and adds anthropogenic stocks to the total reserves, resulting in the characterization factor AEDPs. Finally, the four impact categories are weighted to obtain a final impact score for resource use. Results: Results of the new method are presented as a multi-dimensional reflection of natural reserves, dissipation rates, and extraction rates of resources. The comparison between AEDPs and ADPs revealed differences between them, but they were not significant. A higher power of the total reserves in the AEDP formula can overemphasize the effect of natural reserves on the characterization factor. Furthermore, other natural reserve data was used as alternative indicators in the sensitivity analysis. Conclusion: The new assessment method enables the future impacts of abiotic resource use to be more accurately assessed. It can be used at any life cycle stage to support relevant stakeholder decision-making. However, a broader database is required to be developed to calculate more characterization factors. Moreover, the over-dominance of reserve data in the characterization factors overshadows the influence of other dimensions. Consequently, further research is necessary to improve the operability and plausibility of this method.