Characterizing Chemical Toxicity for Life Cycle Assessment Using Machine Learning and Deep Learning Models Based on Environmental Footprint – Methodological Comparison & textile case study

Q: What does this research mean for the field?

Machine learning and deep learning models provide a viable methodological approach to characterize chemical toxicity and address persistent data gaps in Life Cycle Assessment (LCA) and Safe and Sustainable by Design (SSbD) frameworks. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Tianran Ding; Gustavo Larrea-Gallegos; Federico Busio; Antonino Marvuglia; Thomas Schaubroeck

doi:10.1039/d6su00023a

January 1, 2026RSC SustainabilityOpen Access

Characterizing Chemical Toxicity for Life Cycle Assessment Using Machine Learning and Deep Learning Models Based on Environmental Footprint – Methodological Comparison & textile case study

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Key Points

This research aims to improve the assessment of chemical toxicity for Life Cycle Assessment using advanced machine learning techniques.
Methodological comparison of machine learning and deep learning models for toxicity prediction.
Case study on textiles to demonstrate the application of these models in assessing environmental impact.
Analysis of gaps in toxicity data in current Life Cycle Assessment practices.
Machine learning models improved toxicity prediction accuracy compared to traditional methods, with marked enhancements in certain classifications.
Deep learning approaches demonstrated superior performance in handling complex datasets with many variables.
Results indicate significant potential for integrating these models into standard toxicity assessment procedures.

Implication

Compares machine learning and deep learning methods for toxicity assessment in chemicals, suggesting methodological enhancements.

Abstract

The rapid expansion of registered chemicals, coupled with persistent data gaps, poses a major challenge for toxicity assessment in Life Cycle Assessment (LCA) and Safe and Sustainable by Design (SSbD)....

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Cite This Study

Ding et al. (2026) studied this question.

synapsesocial.com/papers/6a250b0e7def13d035e1b0a1 https://doi.org/10.1039/d6su00023a

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Characterizing Chemical Toxicity for Life Cycle Assessment Using Machine Learning and Deep Learning Models Based on Environmental Footprint – Methodological Comparison & textile case study

Key Points

Implication

Abstract

Discussion

Cite This Study

Characterizing Chemical Toxicity for Life Cycle Assessment Using Machine Learning and Deep Learning Models Based on Environmental Footprint – Methodological Comparison & textile case study

Key Points

Implication

Abstract

Discussion

Cite This Study

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