What question did this study set out to answer?

The review aims to clarify the appropriate use of attributional and consequential life cycle assessments while integrating machine learning for improved sustainability insights.

May 4, 2026Open Access

Attributional and Consequential Life Cycle Assessments: A Practice-Oriented Framework Integrating System Boundaries and Machine Learning

Puntos clave

The review aims to clarify the appropriate use of attributional and consequential life cycle assessments while integrating machine learning for improved sustainability insights.
Mapped common system boundaries for products and transport fuels like well-to-wheel and well-to-wake.
Outlined life-cycle sustainability assessment pathways linked with environmental techno-economic analysis.
Surveyed machine learning applications for inventory gap-filling, impact characterization, and forecasting marginal emission factors.
Identified fit-for-purpose patterns across various sectors, enhancing consistent interpretation of comparative results.
Demonstrated benefits of time-resolved background data for more accurate assessments.
Strengthened decision relevance, reproducibility, and durability of LCA across various fields.

Resumen

Life cycle assessment (LCA) is critical for credible climate decisions, although practice varies contextually. This review explains when to use attributional (ALCA) versus consequential (CLCA) LCAs, consolidating guidance from ISO 14040/14044 and related standards. We map common system boundaries for products and transport fuels (e.g., well-to-wheel and well-to-wake) to interpret comparative results consistently. Additionally, we outline life-cycle sustainability assessment (LCSA) pathways and their integration with environmental techno-economic analysis (e-TEA) to link environmental impacts with cost and performance for design and scale-up. Cross-sector syntheses (transport, electricity, industry, agriculture, commercial, buildings) reveal fit-for-purpose patterns and the benefits of time-resolved background data. A practice-oriented section surveys machine-learning uses inventory gap-filling and impact characterization support for ALCA; forecasting marginal emission factors and other decision-responsive signals for CLCA; and probabilistic uncertainty analysis with global sensitivity methods. Collectively, these steps translate into practically implementing our findings, strengthening the decision relevance, reproducibility, and durability of LCA across policy, corporate disclosure, and engineering design.

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