This study aims to understand the patterns that characterize the impact of artificial intelligence (AI) policies on urban innovation performance, and reveal how these patterns vary across different regions, thereby helping AI policy-making and promoting the urban innovation.
This research focuses on how AI policies influence innovation using the city as unit of analysis. AI policy and patent data were collected from 156 Chinese cities over a decade. Coding and machine learning methods were applied to extract policy features, including three types of policy instruments, policy continuity, policy intensity, and policy count. The fuzzy set Qualitative Comparative Analysis (fsQCA) method is used to identify patterns that explain how AI policies influence urban innovation performance and to further explore regional differences.
Comparing four models for extracting policy instruments, ERNIE 3.0 has been proven to be the most accurate and effective model. Three patterns are found using fsQCA: the environment-safeguard, demand-pull, and supply-environment-demand triple-drive patterns. Moreover, these patterns reflect the development distinction of the eastern, middle, and western cities, respectively. Hence, governments should focus on the intricate interplay and synergistic application of multiple policy levers, and enhance creativity in policy formulation based on their specific developmental characteristics.
This research analyzed the patterns that AI policies influence urban innovation from the national and regional perspective. Automated methods were introduced for policy feature extraction, particularly in identifying policy instruments, thereby significantly cutting down on labor and enhancing the efficiency of data analysis. Besides, this research highlights the interplay among various factors, utilizing fsQCA to reveal the collaborative dynamics at work, which compensates for the deficiency of independent assumptions in regression analysis, and analyze the synergistic effects of different factors from a systematic perspective.
