Clustering of technologies in CE.

Technological innovation serves as the catalyst for the shift towards circular practices. Technologies not only address technical challenges, facilitating the transition to a more circular economy, but they also enhance business efficiency and profitability. Furthermore, they promote inclusivity and create job opportunities, ultimately yielding positive societal impacts. The research in this area tends to focus on digital technologies, neglecting other technological areas. Moreover, it heavily relies on literature reviews and expert opinions, potentially introducing biases. In this article we investigate the technological landscape of the circular economy through Natural Language Processing (NLP), examining key technologies used in this sector and the primary challenges in managing these technologies. The methodology is applied to more than 45,000 scientific publications and aims to extract technologies in the text of scientific articles with NLP. The findings of our analysis reveal a strong emphasis on emerging digital, life cycle assessment and biomaterials technologies. Furthermore, we identified seven distinct technological domains within the CE field. Finally, we provide advantages and problems arising in the adoption and implementation of these technologies in an industrial context.

技术创新是推动循环实践转型的核心催化剂。各类技术不仅可破解技术瓶颈，助力向更高水平的循环经济转型，还能提升企业运营效率与盈利能力；此外，技术还可推动社会包容、创造就业岗位，最终带来积极的社会效应。当前该领域的研究多聚焦于数字技术，却忽视了其他技术领域；且此类研究高度依赖文献综述与专家主观判断，可能引入研究偏倚。本文借助自然语言处理（Natural Language Processing, NLP）技术，剖析循环经济领域的技术格局，梳理该领域的核心应用技术，并剖析管理此类技术的核心挑战。本研究方法覆盖超过45000篇学术文献，旨在通过NLP技术从学术论文文本中提取相关技术信息。分析结果显示，当前研究高度关注新兴数字技术、生命周期评价技术与生物材料技术。此外，本研究在循环经济领域识别出七个独立的技术领域。最后，本文还探讨了在产业场景中应用与落地此类技术所带来的优势与现存问题。