Abstract: New quality productive forces represent a pioneering pathway of advanced high-quality development. Driven by technological innovation, they embody the productive capacity arising from critical and disruptive technological breakthroughs and serve as a vital means for achieving high-quality development. Recently, artificial intelligence (AI) has emerged as a transformative technology reshaping economic and social operations, as well as driving productivity leaps, assuming an increasingly prominent role in international competition and national development. As an institutional framework, AI policy holds substantial potential to guide and promote AI-enabled development of new quality productive forces. However, the mechanisms through which AI policy empowers new quality productive forces, as well as the contingent conditions governing this empowerment effect, remain under-researched through systematic empirical analysis. Existing evidence on this relationship has relied predominantly on theoretical discussions, while the limited quantitative literature focuses primarily on the firm level, leaving a gap in city-level empirical analysis. Given that cities serve as critical hubs for innovation clustering and industrial transformation, such analysis carries greater policy relevance. Furthermore, prior studies have emphasized the technology itself rather than policy effects. Drawing on an analytical framework encompassing technological innovation, production-factor allocation, and industrial upgrading, this study exploits the gradual implementation of China’s National New Generation Artificial Intelligence Innovation and Development Pilot Zones since 2019 as a quasi-experiment to quantitatively examine the impact of AI policy on multiple dimensions of new quality productive forces, including new workers, new means of production, new objects of labor, new technologies, new production organization, and new data factors. Results from a staggered difference-in-differences (DiD) estimation indicate that AI policy has increased new quality productive forces by at least 5.84%. Mechanism analysis reveals that the policy enhances new quality productive forces through stimulating technological innovation, optimizing production-factor allocation, and catalyzing industrial upgrading. Heterogeneity tests further indicate that the policy’s effect on new quality productive forces is more pronounced in cities with higher marketization levels and stronger technological capacity. This study offers insights into how AI policy can enhance high-quality development and foster the advancement of new quality productive forces. Sustained, AI policy-driven progress in new quality productive forces requires prioritizing technological innovation as the core driver, optimizing production-factor allocation as a supporting pillar, and advancing industrial upgrading as the implementation pathway, while concurrently improving cities’ marketization levels and technological capacity. Policy implementation should first establish a multi-tiered innovation ecosystem, such as creating an integrated support mechanism spanning basic research, applied research and commercialization. Second, efforts should continue toward building a unified national market for data factors, promoting institutional innovation in data ownership confirmation, circulation, trading, and regulation. Third, policies should foster coordinated development between traditional and emerging industries—on one hand, advancing the intelligent transformation of traditional industries, and on the other, cultivating strategic emerging industries and promoting the development of new AI-driven models. Finally, AI policy should enhance targeting precision and equity through guiding market entry cities with lower level of marketization while reducing algorithm adoption barriers in technologically disadvantaged areas via national big data hub clusters and cross-regional scheduling networks.

摘要： 理解人工智能政策如何促进新质生产力是加快技术服务于高质量发展的前提。基于技术创新、要素配置和产业升级分析框架，以2019年国家新一代人工智能创新发展试验区渐进推行作为准实验，定量考察人工智能政策对包含新劳动者、新劳动资料、新劳动对象、新技术新生产组织及新数据要素的多维新质生产力的影响。交叠双重差分估计结果表明，人工智能政策至少促进了5.84%的新质生产力水平提升。人工智能政策通过技术创新赋能、要素配置优化和产业升级催化促进新质生产力。异质性结果显示，人工智能政策的赋能效应在市场化程度更高和技术能力更强的城市更显著。结论表明，持续推动人工智能政策赋能新质生产力需要以技术创新赋能为核心，以要素配置优化为支撑，以产业升级催化为路径，改善城市市场化程度和技术能力条件。