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SemaNet: Bridging Words and Numbers for Predicting Missing Environmental Data in Life Cycle Assessment.

Bin Chen1, Hong Chen2, Zhishan Quan3

  • 1School of Electrical and Electronic Engineering, University of Sheffield, Sheffield S1 4DT, U.K.

Environmental Science & Technology
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces SemaNet, a novel method using language models to predict missing environmental data for Life Cycle Assessment (LCA). It accurately fills data gaps, even with 100% missing information, improving sustainability assessments.

Keywords:
data efficiencylife cycle assessmentlife cycle inventoryneural network methodsemantic learning

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Area of Science:

  • Environmental Science
  • Computer Science
  • Data Science

Background:

  • Life Cycle Assessment (LCA) is crucial for environmental impact evaluation.
  • Missing Life Cycle Inventory (LCI) data severely limits LCA's effectiveness.
  • Current LCI data completion methods struggle with data scarcity.

Purpose of the Study:

  • To develop a novel framework for LCI data completion.
  • To leverage qualitative process descriptions for quantitative environmental flow prediction.
  • To overcome limitations of existing numerical correlation-based methods.

Main Methods:

  • Proposed a semantic-based neural network framework, SemaNet.
  • Utilized pretrained language models to bridge qualitative descriptions and quantitative environmental flows.
  • Implemented semantic filtering to reduce computational requirements.

Main Results:

  • SemaNet achieved superior performance in predicting missing LCI values.
  • The method demonstrated high accuracy even with 100% missing numerical data.
  • Reduced computational requirements by 99% compared to existing approaches.

Conclusions:

  • SemaNet offers a paradigm shift in LCI data completion.
  • Significantly reduces data collection efforts and time for LCA practitioners.
  • Enables reliable and faster environmental impact and sustainability assessments across industries.