Public data openness, element allocation, and urban collaborative innovation

Using double machine learning model and leveraging China’s city-level public data platform rollout as a quasi-natural experiment, this study finds that public data openness significantly enhances urban collaborative innovation, measured by joint patent applications. The effect is primarily driven by optimized allocation of traditional factors and coordinated deployment of new data elements. Results remain robust across specifications. These findings highlight public data’s catalytic role in innovation ecosystems, informing governance frameworks for cross-regional collaboration.