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.

翻译：气候变化构成紧迫的全球性威胁，亟需快速识别并部署创新解决方案。我们假设许多此类解决方案已存在于科学文献中，但尚未得到充分利用。为填补这一空白，本研究采用源自OpenAlex（一个综合性科学论文数据库）的精选数据集。通过使用大型语言模型（如OpenAI的GPT-4o），我们从七个维度评估科学论文的标题-摘要组合，涵盖气候变化减缓潜力、技术发展阶段及部署准备度。随后将语言模型的输出结果与人工评估进行对比，以评估其在识别有前景但被忽视的气候创新方面的有效性。研究结果表明，这些基于大型语言模型的系统能有效增强人类专业知识，以更快的速度、更高的处理量和更好的一致性发掘具有潜在影响力的气候解决方案。本文聚焦于英国本土的解决方案，但该工作流程适用于任何地区。本工作有助于发现科学文献中被忽视的创新，并展示了人工智能在强化气候行动策略方面的潜力。