# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 1 Introduction and Motivation  
Over the past few years, the growing awareness of climate change and environmental deterioration has underscored the critical need for adopting sustainable practices in various industries. Addressing these challenges requires a fundamental shift towards sustainable practices across all industries. This imperative is underscored by global initiatives such as the Sustainable Development Goals (SDGs) of the United Nations, particularly SDG 9 (Industry, Innovation, and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). In alignment with these global objectives, the European Union has launched the Green Deal, an ambitious roadmap that aims to make Europe the first climate-neutral continent by 2050. The Green Deal sets interim targets, including a reduction in greenhouse gas emissions by at least 55% below 1990 levels by 2030 [1]. Achieving these targets necessitates transformative changes in production, consumption, and waste management practices, demanding innovative solutions that can be integrated into existing industrial processes. To understand the environmental impacts of the proposed changes and achieve these ambitious targets, we first require comprehensive environmental assessments. These assessments enable us to quantify the effects of different practices and policies on greenhouse gas emissions. Once we understand the environmental impacts, we can identify possible changes that can contribute to emission reductions. Lastly, optimization becomes essential; by leveraging knowledge support systems, we can make informed decisions to implement the best strategies for achieving sustainabilitygoals. Life cycle assessment (LCA) is an essential method for assessing the environmental effects linked to each phase of a product's lifespan. However, several literature reviews have highlighted inconsistencies and challenges in current LCA practices, such as data quality issues and a lack of transparency. For example, a review of published LCAs on waste management shows inconsistencies between studies [2], and a review of LCAs of salmonid aquaculture production systems revealed poor data quality, complicating the comparison of LCA results. A review of LCAs for 3D printing (3 DP) highlighted a lack of transparency and robustness in their methods, often because the authors are not experts in performing LCAs [3]. These challenges indicate the need for more streamlined and integrated approaches to LCA that can handle heterogeneous data sources and incorporate early-stage design decisions. To address these challenges, this article aims to achieve the following research objectives. 1. Develop a comprehensive methodology that uses knowledge graphs (KGs) to integrate, enrich, and analyze heterogeneous data sources, including domain expertise, databases, and language models, to support LCA. 2. Enable the incorporation of early-stage design decisions into the LCA process by modeling the entire product life cycle within a KG, thereby highlighting dependencies and influences across different phases. 3. Facilitate the LCA of products that are traditionally difficult to analyze due to data scarcity or complexity by utilizing language models to estimate missing data and incorporating them into the KG. 4. Demonstrate how the constructed KG can support analytical applications to provide actionable insights for decision-making. To achieve these objectives, we developed a methodology that leverages KGs as the central tool. KGs offer structured and interconnected representations of data, making them powerful facilitators. In the context of LCA and product development, KGs enable the seamless integration of information across different phases, from material selection and design to manufacturing and end-of-life considerations. By capturing the relationships and dependencies between various components and processes, KGs allow algorithms to analyze environmental impacts holistically.