Novel Data Models for Inter-operable LCA Frameworks

TL;DR

This paper reviews the integration of ontologies in life cycle assessment (LCA), proposing a data model framework to improve metadata management, interoperability, and AI integration for environmental impact analysis.

Contribution

It synthesizes existing literature on LCA ontologies and introduces a new data model framework aligned with industry standards and practical workflows.

Findings

Ontologies enhance metadata management in LCA.

Proposed framework aligns with existing standards and improves data interoperability.

Literature review highlights future research directions in LCA ontologies.

Abstract

Life cycle assessment (LCA) plays a critical role in assessing the environmental impacts of a product, technology, or service throughout its entire life cycle. Nonetheless, many existing LCA tools and methods lack adequate metadata management, which can hinder their further development and wide adoption. In the example of LCA for clean energy technologies, metadata helps monitor data and the environment that holds the integrity of the energy assets and sustainability of the materials sources across their entire value chains. Ontologizing metadata, i.e. a common vocabulary and language to connect multiple data sources, as well as implementing AI-aware data management, can have long-lasting, positive, and accelerating effects along with collecting and utilizing quality data from different sources and across the entire data lifecycle. The integration of ontologies in life cycle assessments has garnered significant attention in recent years. We synthesized the existing literature on ontologies for LCAs, providing insights into this interdisciplinary field's evolution, current state, and future directions. We also proposed the framework for a suitable data model and the workflow thereof to warrant the alignment with existing ontologies, practical frameworks, and industry standards.