The future industry is driven by breakthrough technological innovation as its core driving force, and its development combines multiple characteristics such as strategic leadership, industrial disruption, and technological route uncertainty. The layout of such industries not only helps to grasp the initiative of the global technological revolution, but also serves as a key path to reconstruct the competitive advantage of the industry. With the deepening development of artificial intelligence, big data, and cloud computing technology, computing power resources are gradually replacing traditional production factors and becoming an important driving force for future industrial innovation. This article calculates the future industrial innovation network based on cooperative patents in 285 cities from 2011 to 2021, and measures urban computing infrastructure through the construction of supercomputing centers. The impact mechanism and effect of supercomputing center construction on urban future industrial joint innovation are tested through a difference in differences model. The following conclusions are reached: (1) The construction of supercomputing centers can positively promote the enhancement of the strength of future industrial innovation networks, and this conclusion has been tested through robustness methods such as adjusting regression samples, adjusting model settings, using dual machine learning models, and endogeneity tests, indicating that the empowerment of computing infrastructure can promote the development of future industrial innovation networks. At the same time, computing infrastructure mainly strengthens the external innovation network connection of cities, and its innovation network reconstruction function is more reflected in the external connection level that breaks through administrative boundaries, but it has limited innovation synergy effects among innovation subjects within cities. (2) The computing infrastructure mainly affects the future development of industrial innovation networks through mechanisms such as knowledge innovation linkage, innovation factor adsorption, and innovation boundary expansion. Computing infrastructure helps to strengthen knowledge cooperation links in the innovation chain, promote the flow and aggregation of urban innovation resources, expand technological boundaries, and thus promote the improvement of future industrial innovation network levels. It is worth noting that the policy combination formed by the construction of computing infrastructure, the optimization of innovation ecology policies, and the smooth flow of data elements can generate positive synergies, further promoting the development of future industrial innovation networks. (3) Heterogeneity testing reveals that in the central and western regions, regions with higher levels of market integration and information costs, the construction of computing infrastructure has a more significant impact on the strength of future industrial innovation network connections. The construction of computing infrastructure has positively promoted the centrality of innovation network nodes, while its negative impact is close to centrality, indicating that it is promoting the formation of an unbalanced development pattern in the future industrial innovation network of cities. The results of the spatial double difference model show that the construction of supercomputing centers promotes the enhancement of collaborative innovation capabilities in neighboring cities through spatial spillover effects, and the spillover effects become stronger with increasing distance. Based on the above conclusions, this article has the following policy implications. The first is that the government should optimize the layout of computing infrastructure and promote the deepening of urban innovation networks. The second is that the government should form a diversified policy combination, creating a synergistic effect between computing power and innovation ecology. Thirdly, the government needs to promote the construction of computing infrastructure according to local conditions and narrow the regional innovation gap.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |