Quantifying environmental impacts on planetary boundaries: A refined and validated impact assessment method

The Planetary Boundaries (PBs) framework defines a safe operating space (SOS) for humanity and is increasingly applied within in Absolute Environmental Sustainability Assessment (AESA) to evaluate the environmental performance of systems against ecological limits. Integrating PBs into AESA requires methods that express environmental impacts using metrics consistent with the PBs' control variables (CVs). However, existing PBs-based impact assessment methods mainly use proxy indicators or outdated CVs inconsistent with the latest advances in the PBs framework. This misalignment, combined with a lack of validation against empirical data, leads to unreliable and potentially incorrect interpretations of AESA results. Herein, we address these gaps by developing a novel PBs-based impact assessment method (PB-IA) that covers seven PBs and nine CVs, aligns with the latest PBs framework, and is validated against empirical data. Key advances include characterisation factors (CFs) for newly proposed CVs: functional integrity, genetic diversity, N and P surplus, as well as updates to existing CFs to improve their temporal and spatial dynamics. PB-IA provides CFs for 554 environmental flows to Exiobase v3.9.6 and is tested and validated through an environmentally extended multi-regional input–output (EE-MRIO) modelling to quantify PBs' CVs results from global anthropogenic activities. PB-IA results for five CVs are largely consistent with the latest PBs assessments and broader literature, with deviations within ±13%, thus providing an early-stage validation. Larger discrepancies for atmospheric aerosols loading, freshwater consumption, and stratospheric ozone depletion are due to differences in data structures and assumptions in Exiobase. Overall, PB-IA provides a scientifically sound and empirically validated methodology for AESA that expresses environmental pressures directly in the units of PBs' CVs. It is readily applicable to EE-MRIO and adaptable to process-based LCA, thereby supporting science-based policy and decision-making across global and sub-global scales. • Develop new characterisation factors (CFs) for Functional Integrity, Genetic Diversity, and N/P surplus. • Provide CFs for 554 flows in Exiobase to enable PBs-based AESA through EE-MRIO modelling. • Validate method against empirical data. • Results show good agreement for 5 control variables. • The method is applicable for EE-MRIO and adaptable to LCA, supporting multi-scale PBs-based AESA.