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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
Summary
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.
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.
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