Case studies demonstrate AI's role in improving LCA efficiency for methanol production, suggesting enhanced decision-making capabilities.
Life cycle assessment (LCA) is a globally recognized environmental assessment tool used to quantify ecological indicators and support decarbonization decisions in industry and energy systems. In high-technology production settings, conventional LCA can become a time-consuming effort due to data-intensive applications. The need for accurate life cycle inventory (LCI) data must be assembled across multiple process units and supply-chain tiers. Artificial Intelligence (AI) techniques are increasingly being investigated to strengthen environmental assessment for advanced fuel-production systems. For such applications, the methodological need is not a vague ‘revolution’ of LCA, but more specifically dynamic, data-assimilative, and traceable LCA models that can process higher-frequency operational data and support faster decision-making. In this article, we introduce an AI-enabled LCA concept for handling process efficiency, resource use, and data operations in emerging fuel-production technologies. Case studies featuring two methanol production scenarios were conducted leveraging on technical advances such as the Internet of Things (IoT), blockchain/cloud platforms for tracking real-time LCI database.
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Khoo et al. (2026) studied this question.
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