The Taskforce on Nature-related Financial Disclosures (TNFD) has established a global framework to assist companies in understanding their dependencies on natural and identifying risks posed by environmental changes. However, a methodology for integrating the nature dependency into conventional analytical approaches has yet to be developed. Life Cycle Assessment (LCA), a commonly and widely used method for evaluating environmental impacts along life cycle and value chains, offers a potential foundation for addressing this gap. This study proposes a framework that integrates nature dependency into the LCA process, explicitly linking ecosystem services to the resilience of value chains under environmental shifts. The proposed conceptual framework leverages the IPBES and integrates it into all four phases of the existing LCA. In the initial phase—goal and scope definition—unit process is defined according to geographic location rather than solely on technical criteria. System boundaries are thus delineated by grouping unit process based on their locations, further distinguishing them as foreground or background systems depending on the availability of specific location data. During the inventory phase, the study expands elementary flows to include nature-dependency flows regarding ecosystem services and employs the concept of materiality to identify material nature dependencies. The third phase, called nature dependency assessment, quantifies characterization as the relative change of nature dependency between specific time periods, similar to conventional impact assessment. Characterization factors were primarily derived from the literature, and three scenarios—SSP1-2.6, SSP3-6.0, and SSP5-8.5—were developed to address uncertainty. A case study focusing on the Taiwanese economy was conducted to identify adaptation hotspots. The contribution of this research lies in the integration of the IPBES framework throughout all LCA phases, positioning LCA as a strategic tool for advancing climate resilience and promoting sustainable development at scale.
Huang et al. (Thu,) studied this question.