Yigang Wei , Entong Gao , Xiaowei Fu , Yingbo Li , Zhiwen Wang , Haoxiang Tang
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引用次数: 0
Abstract
Against net-zero targets, policymakers view biomass as indispensable, but systematic foresight on its technology pathways and innovation niches is still scarce. Employing machine learning techniques, this study analyzes 26,865 patents (1970–2022) using topic modeling, document embeddings (Doc2Vec), social-network analysis, and deep neural network to identify technological themes, emerging innovations, and strategic patenting directions. Our analysis identifies 17 core technological themes, pinpointing five significant emerging areas: solid fuel production technologies, biomass drying technologies, biomass fermentation technologies, reactor design, and waste treatment technologies. The study introduces an innovative integrated analytical framework combining patent data analytics and policy semantics, alongside a dynamic four-stage technological lifecycle model, significantly enhancing the accuracy of technological forecasting. These concrete findings offer strategic guidance for policymakers and industry stakeholders, fostering targeted innovations and sustainable biomass energy development.
期刊介绍:
The aim of World Patent Information is to provide a worldwide forum for the exchange of information between people working professionally in the field of Industrial Property information and documentation and to promote the widest possible use of the associated literature. Regular features include: papers concerned with all aspects of Industrial Property information and documentation; new regulations pertinent to Industrial Property information and documentation; short reports on relevant meetings and conferences; bibliographies, together with book and literature reviews.