ArticleEnvironmental science and ecotechnology2026
Cross-sector deep learning scales life cycle assessment using unified textual descriptions.
Article in Environmental science and ecotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Life cycle assessment (LCA) is a foundational framework for quantifying global environmental impacts to guide decarbonization and circular economy transitions. Yet, conventional inventory compilation remains severely bottlenecked by data scarcity and intensive manual curation, with existing machine learning solutions typically confined to isolated industrial sectors rather than offering a scalable way to overcome these limitations. This fragmentation prevents pan-industrial scaling, thereby masking hidden environmental risks and hindering rapid, data-driven sustainability interventions. Here we develop LCA-TextNet, a generalizable deep learning framework that bypasses domain-specific feature engineering by predicting 25 life cycle impact indicators across 20 distinct industrial sectors directly from knowledge-based textual descriptions. Leveraging a Transformer-based architecture trained on over 16,000 datasets, the model maps high-dimensional text embeddings into uniform semantic spaces, achieving high accuracy (
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