Towards unearthing neglected climate innovations from scientific literature using Large Language Models
This addresses the need for rapid discovery of climate solutions for global climate action, though it is incremental as it applies existing LLM methods to a new domain.
The study tackled the problem of identifying underutilized climate innovations in scientific literature by using Large Language Models (LLMs) to evaluate papers on dimensions like mitigation potential and deployment readiness, finding that LLMs can effectively augment human expertise with greater speed and consistency.
Climate change poses an urgent global threat, needing the rapid identification and deployment of innovative solutions. We hypothesise that many of these solutions already exist within scientific literature but remain underutilised. To address this gap, this study employs a curated dataset sourced from OpenAlex, a comprehensive repository of scientific papers. Utilising Large Language Models (LLMs), such as GPT4-o from OpenAI, we evaluate title-abstract pairs from scientific papers on seven dimensions, covering climate change mitigation potential, stage of technological development, and readiness for deployment. The outputs of the language models are then compared with human evaluations to assess their effectiveness in identifying promising yet overlooked climate innovations. Our findings suggest that these LLM-based models can effectively augment human expertise, uncovering climate solutions that are potentially impactful but with far greater speed, throughput and consistency. Here, we focused on UK-based solutions, but the workflow is region-agnostic. This work contributes to the discovery of neglected innovations in scientific literature and demonstrates the potential of AI in enhancing climate action strategies.