世界科技研究与发展 ›› 2025, Vol. 47 ›› Issue (1): 111-128.doi: 10.16507/j.issn.1006-6055.2024.07.004 cstr: 32308.14.1006-6055.2024.07.004

• 科技管理与政策 • 上一篇    下一篇

基于深度学习和灰色关联分析的我国氢能政策协同测度研究

连迎洁1,2,3 孙玉玲1,2 罗家棠1,2   

  1. 1.中国科学院文献情报中心;2.中国科学院大学经济与管理学院信息资源管理系;3.中国三峡新能源(集团)股份有限公司建设管理分公司
  • 出版日期:2025-03-06 发布日期:2025-03-06
  • 基金资助:
    中国科学院发展规划局文献情报能力建设专项“支撑院科技规划与布局的全球科技态势战略研判”(E1290423),中国科学院战略研究与决策支持系统建设专项“‘双碳’行动计划战略研究”(GHJ-ZLZX-2023-06)

Research on Coordinated Measurement of China's Hydrogen Energy Policy Based on Deep Learning and Grey Relational Analysis

LIAN Yingjie1,2,3 SUN Yuling1,2 LUO Jiatang1,2   

  1. 1.National Science Library,Chinese Academy of Sciences; 2.Department of Information Resources Management,School of Economics and Management,University of Chinese Academy of Sciences; 3.China Three Gorges Renewables (Group) Co.,Ltd.Construction Management Branch
  • Online:2025-03-06 Published:2025-03-06

摘要: 政策协同是保障和促进产业良性健康发展的重要基础。本文以我国868份氢能相关政策为研究对象,设计包含时间、空间两维度,政策主体、工具和主题三要素的政策协同测度框架,利用深度学习和灰色关联分析法量化测度我国氢能政策的协同情况。研究表明:时间维度上,我国氢能政策主体合作网络规模日益扩大,协同度持续提升;工具协同度略有降低,偏好使用试点示范和法规标准子工具,而较少使用政府采购和税收优惠子工具;主题协同度处于中上水平且不断提升,但制氢、储氢、运氢和加氢环节的政策支持仍有待提升。从空间维度看,中央政府的主体协同度明显高于其他氢能产业聚集区,主题协同度处于中下水平,明显低于其他氢能产业聚集区;中央及各氢能产业聚集区政策工具使用情况相近,工具协同度相差不大。未来应从建立跨区域政策协同框架与沟通平台、优化政策工具组合、促进氢能上游及中游环节发展等方面进行协同优化。

关键词: 氢能, 政策协同测度, 深度学习, 政策工具, 政策主题

Abstract: Policy coordination is a crucial foundation for ensuring and promoting the healthy and sustainable development of industries.This paper takes 868 hydrogen energy-related policies in China as the research object,designing a policy coordination measurement framework that includes two dimensions-time and space-and three elements:policy actors,instruments,and themes.Using deep learning and grey relational analysis,the study quantifies the coordination of China’s hydrogen energy policies.The findings indicate that,in the time dimension,the network of cooperation among policy actors in China’s hydrogen energy sector is expanding,and the degree of coordination continues to improve.However,the coordination of policy instruments has slightly decreased,with a preference for pilot demonstrations and regulatory standards over tools like government procurement and tax incentives.The coordination of policy themes is at an upper-middle level and continues to improve,though policy support for hydrogen production,storage,transportation,and refueling still needs enhancement.In the spatial dimension,the coordination of actors at the central government level is significantly higher than in other hydrogen energy industrial clusters,while the coordination of themes is at a lower-middle level,notably below that of other clusters.The use of policy instruments by the central government and hydrogen energy industrial clusters is similar,with little difference in instrument coordination.Moving forward,efforts should focus on establishing cross-regional policy coordination frameworks and communication platforms,optimizing the combination of policy instruments,and promoting the development of upstream and midstream segments of the hydrogen energy industry for coordinated optimization.

Key words: Hydrogen, Policy Synergy Measurement, Deep Learning, Policy Instruments, Policy Themes