A Proxy Method to Bridge LCA Data Gaps Using Automated Material Classification and Probabilistic Under-Specification.

Area of Science:

• Environmental Science
• Materials Science
• Computational Chemistry

Background:

• Life cycle assessments (LCAs) are crucial for evaluating material production's environmental impact.
• Data gaps in life cycle inventory (LCI) present a significant challenge for LCA practitioners, particularly during early design stages.
• Current methods for filling LCI data gaps are time-consuming and require specialized expertise.

Purpose of the Study:

• To develop a time-efficient methodology for filling material LCI data gaps.
• To leverage automatic material classification and probabilistic under-specification for data gap imputation.
• To enhance the accuracy and applicability of LCAs in early design phases.

Main Methods:

• Utilized open-source software (ClassyFire) to classify material LCI data into the ChemOnt chemical taxonomy.
• Generated proxy environmental impact distributions based on classified LCI data.
• Applied automatic material classification to input materials with data gaps, enabling selection of proxy values from distributions.

Main Results:

• The proposed method efficiently fills material LCI data gaps using chemical structure-based classification.
• Chemical classification provides a more granular taxonomy than industrial classification, enhancing proxy data utility.
• The approach successfully applied to data from Federal LCA Commons and Ecoinvent databases, demonstrating broad applicability.

Conclusions:

• Chemical structure-based classification offers a more refined approach to environmental impact assessment in LCAs.
• The developed method significantly reduces the time and expertise needed to address LCI data gaps.
• This facilitates more robust environmental impact comparisons between design alternatives early in the development process.