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Because sustainability remains a wicked problem, more sophisticated tools need to be applied to identify better solutions in a more efficient manner and align with the 11th, 12th, and 13th sustainable development goals: sustainable cities and communities, responsible consumption and production, and climate action. To ease the burdens of conducting sustainability studies, especially life cycle assessments (LCA), practitioners may consider integrating large language models (LLM) into LCAs. This emerging application may offer some advantages due to the capability of these models to generate and process text quickly and efficiently, decreasing the time it takes to complete an LCA and increasing the accessibility of LCAs. In this perspective, we assess the ability of LLMs to complete LCA tasks and encourage the LCA community to study the potential strategies for enhancing the integration of LLMs in LCA methodologies and collaborate to develop standards for responsible use. Because of these advantages, LLMs show promise for life cycle inventory data collection and interpreting the life cycle impact assessment. Challenges arise primarily from the inclusion of hallucinations in the content generated by the LLM, which can be mitigated if the LCA practitioner uses prompt engineering techniques. Moreover, the risk that models cannot take responsibility for generated content can be ameliorated by having the LCA practitioner carefully review the LLM output and take responsibility for decisions made based on the generated content. So long as appropriate steps are taken to overcome the challenges and risks of using of LLMs for LCA, the opportunities presented by integrating the generative AI models can streamline the LCA process and result in significant benefits for the LCA practitioner. [Display omitted] • Large language models have yet to be integrated into life cycle assessment tasks. • Large language models may offer significant time savings for life cycle assessments. • Life cycle inventory data can be acquired and processed by large language models. • Hallucinations in model output require careful reviewing of generated content. • Large language models cannot be held accountable for generated content. [ABSTRACT FROM AUTHOR]

• Review of emerging generative artificial intelligence models like large language models. • Trends of GenAI applications in systems design, optimization, and control. • Potential improvements of PSE methods using emerging GenAI. • Challenges like data needs, and ensuring trust in GenAI applications within PSE. This review article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE. [ABSTRACT FROM AUTHOR]