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Record W4409321797 · doi:10.1016/j.ecolind.2025.113458

Complementary strengths of water footprint and life cycle assessments in analyzing global freshwater appropriation and its local impacts – Recommendations from an Interdisciplinary discussion series

2025· article· en· W4409321797 on OpenAlexaff
Markus Berger, P.W. Gerbens-Leenes, Fatemeh Karandish, Maite M. Aldaya, Anne‐Marie Boulay, Rick J. Hogeboom, Andreas Link, Alessandro Manzardo, Oleksandr Mialyk, Masaharu Motoshita, Montserrat Núñez, Stephan Pfister, Ralph K. Rosenbaum, Laura Scherer, Han Su, Lara Wöhler

Bibliographic record

VenueEcological Indicators · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsPolytechnique Montréal
FundersAgència de Gestió d'Ajuts Universitaris i de RecercaEuropean Research CouncilEdema-Steernberg FoundationGeneralitat de CatalunyaIndiana Retired Teachers AssociationEuropean CommissionMinisterio de Ciencia e InnovaciónEuropean Social FundCentres de Recerca de Catalunya
KeywordsAppropriationEnvironmental scienceFootprintEnvironmental resource managementWater cycleSeries (stratigraphy)Ecological footprintEnvironmental planningGeographyEcologySustainabilityGeologyBiologyArchaeology

Abstract

fetched live from OpenAlex

• Assessing water use along supply chains is key in addressing global water challenges. • Water Footprint and Life Cycle Assessment are not in competition with each other. • The methods have strengths and weaknesses in relation to different questions. • Recommendations for application-dependent uses are provided. Considering globally increasing water challenges, the analysis of water use along supply chains is of great relevance and can be tackled by mainly two methodological approaches: Water Footprint Assessment (WFA) and Life Cycle Assessment (LCA). While sharing the same goal of promoting sustainable water use, both methods developed in different contexts and scientific communities. This has led to heated debates on methodological presuppositions that at times has become unconstructive. To build mutual understanding and enable a fruitful cooperation, researchers from both communities have exchanged over the course of two years. This paper summarizes the outcomes of this discussion series by providing i) a description of the development of both approaches and their ways of assessing freshwater consumption and pollution, ii) an application in a case study, and iii) an analysis of strengths and weaknesses in relation to questions decision-makers may have. Our analysis revealed that WFA’s strength lies in its ability to measure freshwater appropriation, water-use efficiency, water scarcity and total pollution levels. This makes WFA particularly useful for crop selection as well as agricultural and river basin water management. With its focus on assessing impacts, LCA is strong in quantifying potential consequences of water use for humans and ecosystems. This makes it particularly useful for assessing complex supply chains and for analysing water-related impacts in combination with other environmental aspects. Rather than being in competition with each other, we emphasize the individual and complementary strengths of both approaches and their joint efforts in addressing the world’s pressing water challenges.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.225
metaresearch head score (Gemma)0.164
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.225
Threshold uncertainty score0.956

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2250.164
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0100.010
Science and technology studies0.0050.013
Scholarly communication0.0210.051
Open science0.0110.017
Research integrity0.0240.024
Insufficient payload (model declined to judge)0.0090.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.316
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2025
Admission routes1
Has abstractyes

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