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.

翻译：生命周期评估（LCA）在评估产品、技术或服务在整个生命周期中的环境影响方面发挥着关键作用。然而，许多现有的LCA工具和方法缺乏充分的元数据管理，这阻碍了其进一步发展和广泛采用。以清洁能源技术的LCA为例，元数据有助于监控数据及其环境，从而在整个价值链中保持能源资产的完整性和材料来源的可持续性。对元数据进行本体化处理，即建立通用词汇和语言以连接多个数据源，并实施面向AI的数据管理，可以在整个数据生命周期中从不同来源收集和利用高质量数据，从而产生持久、积极且加速的效果。近年来，将本体整合到生命周期评估中已引起了广泛关注。我们综合了现有的LCA本体相关文献，为这一跨学科领域的演变、现状和未来方向提供了见解。我们还提出了一个合适的数据模型框架及其工作流程，以确保与现有本体、实践框架和行业标准保持一致。