UNCASExt -- A Systematic Computational Framework for Uncertainty Propagation and Scope Consistency in Absolute Environmental Sustainability Assessments (AESA)

2026-06-19

AI Summary

This study addresses critical limitations in current absolute environmental sustainability assessment (AESA) frameworks, where mismatched functional units lead to inconsistent scopes and insufficiently quantified uncertainties, thereby undermining assessment reliability. To resolve this, the authors propose UNCASExt, a novel computational framework that formally harmonizes functional units for both environmental pressures and carrying capacities. UNCASExt ensures scope consistency across three dimensions—impact pathways, accounting perspectives, and activity types—and integrates IPCC AR6 greenhouse gas budget trajectories to enable both retrospective and prospective assessments under static and dynamic carrying capacities. Implemented as the open-source Python toolkit pyaesa and coupled with the EXIOBASE database, the framework employs Monte Carlo uncertainty propagation and multidimensional calibration. Applied to the French electricity sector, it reveals that functional unit mismatches on average underestimate carrying capacity by a factor of 4.6, substantially enhancing the accuracy and comparability of AESA.

Abstract

Absolute environmental sustainability assessment (AESA) has gained increasing attention in environmental research and policymaking. However, its reliability is challenged by several sources of uncertainty that remain insufficiently accounted for, as well as by scope inconsistencies within the absolute sustainability ratio (ASR), which compares estimated environmental burdens with allocated carrying capacities for a given human activity. This work introduces UNCASExt, an extension of the UNCASE framework for systematically propagating uncertainty and ensuring scope consistency in AESA, together with a supporting open-source Python package, pyaesa. At country and sector levels, the computational framework formalizes allocation procedures that match the scope of allocated carrying capacities with that of estimated environmental burdens across three dimensions: impact pathway modeling; production-based versus consumption-based accounting; business-to-consumer versus business-to-business activities. It also incorporates temporal dynamics, supporting both retrospective and prospective assessments with either static steady-state or dynamic carrying capacities, including greenhouse gas budgets from the Intergovernmental Panel on Climate Change Sixth Assessment Report under Shared Socioeconomic Pathway transition scenarios. The framework is applied to a case study of electricity consumption in France over the period 2019 to 2060. The results show that mismatches between the functional units of estimated environmental burdens and allocated carrying capacities can lead to substantial underallocation, with a median factor of 4.6x across all available sector-region pairs in EXIOBASE 3.10.2. Overall, UNCASExt and pyaesa provide a scalable solution to support AESA harmonization and a versatile way forward to bridge the gap between methodological guidelines and practical application.

Problem

• absolute environmental sustainability assessment
• uncertainty propagation
• scope consistency
• absolute sustainability ratio
• environmental burden

Innovation

• uncertainty propagation
• scope consistency
• absolute environmental sustainability assessment
• carrying capacity allocation
• dynamic sustainability assessment