用大模型从论文中挖掘被忽视的气候创新，快且准。
Towards unearthing neglected climate innovations from scientific literature using Large Language Models
- 用大模型评估论文摘要，七维度判断气候潜力与落地性。
- 相比人工，大模型发现更多有潜力但未受关注的解决方案。
- 方法可复用于全球地区，适合政策制定者和科研人员参考。
气候变化带来紧迫的全球威胁，亟需快速识别并部署创新解决方案。我们假设，许多此类方案已存在于科学文献中，却未被充分利用。本研究采用来自 OpenAlex 的精选数据集，利用 GPT4-o 等大语言模型（LLMs），对七类气候相关维度（包括减缓潜力、技术成熟度、部署可行性）评估论文标题与摘要。模型输出与人工评估对比，验证其识别潜在但被忽视的气候创新的能力。结果表明，这些基于 LLMs 的方法能有效补充人类专家，以更高效率、更高速度和更强一致性发现具有潜在影响但尚未被重视的气候解决方案。本研究聚焦英国相关成果，但该流程具备区域无关性。本工作推动了科学文献中被忽视创新的发现，并展示了人工智能在增强气候行动策略中的潜力。
原文摘要 · Abstract (English)
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.
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