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周源
2023-05-12 12:02
  • 周源
  • 周源 - 副教授-清华大学-公共管理学院-个人资料

近期热点

资料介绍

个人简历


周源,清华大学长聘副教授、博士生导师、特别研究员;入选国家级人才计划青年学者;清华大学中国工程科技发展战略研究院副院长,清华大学国家治理与全球治理研究院兼职研究员,国家社科重大专项首席专家。在新加坡南洋理工大学获学士和硕士学位,在英国剑桥大学获博士学位(科技管理)。\r
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2010年,加入清华大学公共管理学院任教至今。获清华大学先进工作者、清华大学精品课程、清华大学年度优秀教学奖、清华大学评教前5%等教学荣誉或奖项。主持国家社科基金、国家自科基金、国家高端智库、教育部人文社科基金、国家科技重大专项子课题、国家发改委规划研究课题等国家重大、重点、面上等课题20余项。已在《Engineering》(IF=12.834)、《Industrial & Corporate Change》、《Science and Public Policy》、《R&D Management》、《Technological Forecasting & Social Change》、《IEEE Transactions on Engineering Management》、《Nano Energy》、《Energy Policy》、《中国软科学》等国内外期刊上发表论文共60余篇,其中SSCI检索的国际英文期刊论文30余篇,入选ESI前1%高被引论文4篇。另发表《创新与战略路线图:理论、方法及应用》、《战略性新兴产业:政策与治理创新研究》等著作4本,并获得北京市哲学社科优秀成果奖、摩根大通金融年度书籍等荣誉。\r
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担任《Technovation》(IF=11.373)、《IEEE Transactions on Engineering Management》(IF=8.702)等国际权威SSCI期刊的副主编。担任中国工程院院刊《Engineering》编委(工程管理领域),担任《Technological Forecasting & Social Change》(IF=10.884)、《R&D Management》、《Scientometrics》等国际权威SSCI期刊编委或客座主编。担任Globelics全球经济与创新研究网络理事会执行委员。

研究领域


"公共政策、创新管理、创新政策"

近期论文


主要英文期刊论文\r
Hain, D., Jurowetzki, R., Lee, S., & Zhou, Y. (2023). Machine learning and artificial intelligence for science, technology, innovation mapping and forecasting: Review, synthesis, and applications. Scientometrics, 1-8.\r
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Meissner, D.*, Zhou, Y., Fischer, B., & Vonortas, N. (2022). A multilayered perspective on entrepreneurial universities: looking into the dynamics of joint university-industry labs. Technological Forecasting and Social Change, 178. DOI: 10.1016/j.techfore.2022.121573. (SSCI, SPPM A-class journal, IF=8.593)\r
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Dai, P., Zhang, S., Gong, Y., Zhou, Y., & Hou, H.. (2022). A crowd-sourced valuation of recreational ecosystem services using mobile signal data applied to a restored wetland in china. Ecological Economics, 192. (SSCI, SPPM A-class journal)\r
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Chen, J., Di Minin, A., Minshall, T., Su, Y. S., Xue, L., & Zhou, Y. (2021). Introduction to the Special Issue on the New Silk Road of Innovation: R&D Networks, Knowledge Diffusions, and Open Innovation. R&D Management, 51(3), 243-246. (SSCI, SPPM A-class journal)\r
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Zhou, Y., Dong, F. , Liu, Y.*, & Ran, L. . (2021). A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of cnc machine tool. Scientometrics. 126, pages 969–994. DOI: 10.1007/s11192-020-03797-8. (SSCI, SPPM A-class journal, IF=2.77)\r
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Zhou, Y. , Li, Z. , Liu, Y. , & Deng, F. . (2020). Network proximity and communities in innovation clusters across knowledge, business, and geography: evidence from china. IEEE Transactions on Engineering Management, PP(99), 1-10. DOI: 10.1109/TEM.2020.3032160. (SSCI, ABS3, IF=5.839)\r
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Liu, H., Chen, Z., Tang, J., Zhou, Y.*, Liu, S. (2020). Mapping the Technology Evolution Path: A Novel Model for Dynamic Topic Detection and Tracking. Scientometrics. DOI: 10.1007/s11192-020-03700-5. (SSCI, SPPM A-class journal)\r
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Zhou, Y., Miao, Z., Urban, F.* (2020). China’s leadership in the hydropower sector: identifying green windows of opportunity for technological catch-up. Industrial & Corporate Change. Volume 29, Issue 5, 1319–1343, https://doi.org/10.1093/icc/dtaa039. (SSCI, SPPM A-class journal)\r
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Zhou, Y., Zhou, R. , Chen, L.*, Y Zhao, & Zhang, Q. (2020). Environmental policy mixes and green industrial development: an empirical study of the chinese textile industry from 1998 to 2012. IEEE Transactions on Engineering Management, PP(99), 1-13. doi: 10.1109/TEM.2020.3009282. (SSCI, ABS3, IF=5.839)\r
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Xu, G., Zhou, Y.*, & H Ji. (2020). How can government promote technology diffusion in manufacturing paradigm shift? evidence from china. IEEE Transactions on Engineering Management, PP(99), 1-13. doi: 10.1109/TEM.2020.2981147. (SSCI, ABS3, IF=5.839)\r
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Xu, G., Hu, W., Qiao, Y., & Zhou, Y.*. (2020). Mapping an innovation ecosystem using network clustering and community identification: a multi-layered framework. Scientometrics. 124:2057–2081. DOI: 10.1007/s11192-020-03543-0. (SSCI, SPPM A-class journal, IF=2.77)\r
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Zhou, Y., Dong, F., Liu, Y*. et al. (2020). Forecasting emerging technologies using data augmentation and deep learning. Scientometrics. 123, 1–29 https://doi.org/10.1007/s11192-020-03351-6. (SSCI, SPPM A-class journal, IF=2.77)\r
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Li, X., M. Fan, Y. Zhou*, J. Fu, F. Yuan and L. Huang (2020). \

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