The synergy between central and local IP finance policies is important for the implementation of central policies and the optimal policy effect,so that IP finance policies can become a powerful aid in regional innovation-driven development.In this paper,we use social network analysis,LDA topic mining and semantic similarity calculation in natural language processing to empirically demonstrate the current situation of central-territory policy synergy from two perspectives:subject synergy and theme synergy,taking Beijing,Tianjin and Hebei as an example.The results show that Beijing has the most active local policy subjects and the highest policy subject diffusion in terms of subject synergy.Among the policy-making subjects,the central government has more types of IP financial policy participants,while the policy-making subjects of local governments are more homogeneous,almost exclusively the government and its government departments.In terms of thematic synergy,the policy themes in Tianjin are relatively concentrated,while those in Beijing and Hebei are richer.Among them,Tianjin's policy themes are the most similar to the central government's policies,Hebei province is the second,and Beijing's policy themes are richer in local characteristics.
policy synergyintellectual property financecentral and localBeijing-Tianjin-Hebei region
NETL
NSTL
万方数据
