In recent years, the low-altitude economy has emerged as a strategic and rapidly developing sector prioritized by national industrial policies. It serves as a vital platform for cultivating new productive forces and advancing high-quality economic growth. By restructuring airspace resource utilization and promoting the integration of transportation and communication networks, the low-altitude economy provides new spatial capacity and growth momentum. The 2024 Report on the Work of the Government of China officially incorporated the “low-altitude economy” into the national strategic plan for the first time. Several provinces and municipalities subsequently issued specialized development programs, signifying that the low-altitude economy has entered a new stage of industrialization and institutionalization.

The low-altitude economy generally refers to economic activities conducted within airspace below 3000 meters above ground level, centered on UAVs and other aerial platforms for commercial and public services. Rooted in UAV technology, this emerging economic form integrates multiple digital technologies such as communication, navigation, AI, big data, and the Internet of Things (IoT), thereby driving profound transformations in sectors including urban mobility, logistics, emergency rescue, agricultural protection, and cultural tourism. Compared with ground transportation networks, low-altitude airspace offers greater spatial freedom and lower congestion costs, enabling multi-UAV collaboration, route optimization, and real-time communication. Consequently, it demonstrates enormous potential for improving resource allocation and spatial efficiency.

Globally, the low-altitude economy has become a new technological and industrial frontier. Countries such as the United States, Germany, and Japan have already established relatively mature regulatory and market systems. For example, the U.S. Urban Air Mobility (UAM) program is advancing the commercialization of “air taxis” and “aerial delivery” services ([1]). These experiences suggest that the development of the low-altitude economy depends not only on technological innovation but also on the deep integration of AI, communication networks, and data systems. Against this backdrop, the rise of AI provides new momentum for the low-altitude economy.

With its capabilities in high-precision perception, intelligent decision-making, dynamic learning, and autonomous control, AI offers critical support for low-altitude economic operations. On one hand, AI algorithms significantly enhance UAVs’ autonomous flight performance, path planning, and obstacle avoidance, enabling safe operations in complex environments. On the other hand, AI-driven visual recognition, semantic analysis, and edge computing improve the intelligent scheduling and resource allocation of communication networks in low-altitude airspace, thereby increasing overall efficiency and safety. Moreover, the widespread application of AI accelerates the digital transformation of traditional industries, fostering new industrial systems characterized by automation and intelligence, and driving the formation of new productive forces.

The integration of AI and the low-altitude economy not only represents technological empowerment but also generates notable economic and social externalities. AI-driven low-altitude platforms can perform high-risk tasks—such as border patrols, forest fire monitoring, emergency rescues, and geological disaster surveillance—without increasing human risk ([10]). Through real-time data collection, intelligent analysis, and dynamic response, these systems enhance public safety while reducing human and temporal costs. Furthermore, AI-enabled low-altitude logistics systems are reshaping supply chain management and urban delivery, providing new pathways for green and low-carbon transformation.

Therefore, the deep integration of AI and the low-altitude economy signifies a new phase of technological revolution and industrial transformation, holding substantial strategic importance for building a modern industrial system and advancing digital China. However, academic research on the relationship between AI and the low-altitude economy remains limited. Existing studies primarily focus on industrial chain development, regulatory frameworks, and their connection to new productive forces ([7]; [4]), or on AI’s effects on industrial upgrading and economic growth ([9]; [3]). Yet, few have examined how AI empowers the low-altitude economy through technological innovation pathways and mediating mechanisms such as industrial structural transformation. Particularly in China—where institutional environments and regional disparities are pronounced—variations in AI development, industrial transformation speed, and spatial distribution of low-altitude activities provide unique theoretical and practical contexts for empirical inquiry.

Using panel data for 30 Chinese provinces from 2012 to 2023, this study constructs a two-way fixed effects model to systematically investigate the impact of AI on the development of the low-altitude economy from three perspectives: overall effect, mechanism effect, and heterogeneity characteristics. The empirical results show that: 1) AI significantly promotes the development of the low-altitude economy, and the conclusion remains robust after controlling for various economic and structural factors. Specifically, a one-unit increase in AI level raises the low-altitude economy index by approximately 0.042 units, suggesting that AI effectively empowers this emerging economy through resource optimization, operational efficiency, and scenario expansion. 2) Mechanism analysis indicates that AI promotes the low-altitude economy mainly through improving digital infrastructure and enhancing R&D investment. Internet penetration plays a significant positive moderating role, confirming the “amplifier effect” of digital infrastructure; R&D input strengthens AI’s enabling impact by enhancing innovation capacity and technology commercialization. 3) Heterogeneity analysis reveals that the enabling effect of AI is more prominent in coastal provinces, implying that economic foundation, absorptive capacity, and openness amplify AI’s impact. Temporally, the effect was stronger during 2012-2016, highlighting the role of market-driven and innovation-led mechanisms before formal policy interventions.

This paper contributes to the literature in three main ways. First, it extends the theoretical perspective on AI’s role in enabling the low-altitude economy. Prior research has mainly addressed engineering or policy aspects, lacking systematic analysis from the perspective of digitalization, intelligence, and general-purpose technology diffusion. By conceptualizing AI as a core general-purpose technology driving the evolution of the low-altitude economy, this paper constructs a logical framework linking “AI development—digital infrastructure—technological innovation—low-altitude economic growth”, revealing the internal mechanism of AI empowerment and offering new insights into how technology diffusion fosters emerging industries. Second, it enriches empirical evidence on AI’s economic effects and mechanism testing. Unlike studies focusing solely on macroeconomic growth or industrial upgrading, this research centers on the low-altitude economy, identifying two specific mediating pathways—digital infrastructure and innovation investment—and empirically validating their regional and temporal heterogeneity. Third, it deepens the policy understanding of how digital technologies drive new productive forces. The results show that AI’s enabling effect on the low-altitude economy manifests not only as efficiency improvement but also as structural and institutional spillovers. These findings provide quantitative evidence for policymaking in airspace governance, digital infrastructure development, and AI industry deployment, offering practical implications for building an intelligent low-altitude industrial system and accelerating the cultivation of new productive forces.

In the ongoing wave of technological revolution and industrial transformation, Artificial Intelligence (AI), as a general-purpose technology, has become a crucial driving force of global economic growth and industrial upgrading ([2]). The defining characteristics of GPTs—broad cross-industry applicability, persistent knowledge spillovers, and strong diffusion capacity—enable AI to continuously stimulate the economic system by optimizing factor allocation, enhancing technological innovation, and reshaping industrial structures ([9]). Existing research shows that the deep integration of AI with emerging digital technologies such as the Internet of Things (IoT), big data, and cloud computing significantly improves operational resilience and resource allocation efficiency in industrial production ([6]). Moreover, AI enhances firms’ productivity and economic growth by alleviating financing constraints, strengthening innovation capability, and optimizing market decision-making processes. As a key technological engine for the formation of new productive forces, AI is rapidly penetrating emerging domains including transportation, energy, manufacturing, finance, and, more recently, the low-altitude economy, thereby serving as a catalyst for scientific and industrial transformation.

Within the field of the low-altitude economy, AI’s empowering role is particularly pronounced ([8]). The development of the low-altitude economy results from the joint action of multiple elements—airspace, equipment, scenarios, technology, and policy—among which digitalization and intelligence are the core conditions for achieving high-quality and sustainable growth. With its advanced capabilities in perception, decision-making, deep learning, and autonomous control, AI provides essential technical support for low-altitude economic operations. For example, AI algorithms significantly enhance UAVs’ autonomous flight, path planning, and obstacle avoidance performance, strengthening the refined scheduling of airspace resources and the coordinated operation of multiple UAVs. At the same time, AI applications in image recognition, semantic analysis, and edge computing enable the intelligent optimization of communication and navigation systems, improving operational safety and systemic efficiency.

From a theoretical perspective, AI empowers the low-altitude economy through multiple mechanisms. First, the application and diffusion of AI promote the development of digital infrastructure such as information and communication systems, cloud computing platforms, and data-sharing networks, thereby reinforcing the technological foundation necessary for low-altitude economic activities. Digital infrastructure not only serves as a prerequisite for AI deployment but also acts as an “amplifier” of its enabling effects. As connectivity, data transmission, and intelligent scheduling capabilities improve, the efficiency of data collection, airspace management, and task allocation in low-altitude activities increases significantly ([9]). Such upgrades enhance resource allocation efficiency and provide a solid foundation for intelligent airspace management and the construction of Unmanned Traffic Management (UTM) systems.

Second, AI stimulates R&D investment and innovation output, strengthening the technological supply capacity and industrial upgrading potential of the low-altitude economy. The widespread adoption of AI reshapes innovation resource allocation and enhances R&D efficiency, enabling enterprises to accelerate their transformation from “manufacturing-driven” to “innovation-driven” development models ([7]). In the context of the low-altitude economy, this mechanism manifests in the iterative upgrading of key components—such as navigation chips, intelligent sensors, and power systems—as well as the innovation and diffusion of new service models like low-altitude logistics, emergency response, and aerial tourism. Increased R&D investment not only enhances firms’ independent innovation capabilities but also strengthens the empowering effect of AI through technology commercialization and spillover effects.

From an industrial ecosystem perspective, AI forms a positive feedback cycle of “technological empowerment-value creation-reinforcement”. Through data-driven processes, knowledge sharing, and intelligent collaboration, AI enables dynamic coordination among multiple stakeholders—UAV manufacturers, service operators, infrastructure providers, financial institutions, and regulatory bodies ([10]). This multi-layered and platform-based innovation ecosystem facilitates optimal resource allocation and institutional innovation, driving the transformation of the low-altitude economy from policy-driven growth to technology-driven development. Accordingly, the following hypothesis is proposed:

H1:The development of artificial intelligence has a significant positive impact on the growth of the low-altitude economy.

Based on panel data from 30 Chinese provinces between 2012 and 2023, this study empirically examines the enabling effects of Artificial Intelligence (AI) on the development of the Low-Altitude Economy (LAE) and explores its underlying mechanisms. The main findings are as follows: 1) AI significantly promotes the development of the low-altitude economy. This conclusion remains robust after controlling for innovation capacity, excluding municipalities, and applying robust standard errors, confirming the stability of the results. 2) Digital infrastructure and technological innovation serve as key mediating mechanisms. AI drives the low-altitude economy by enhancing digital infrastructure—such as network connectivity and information transmission—and by stimulating R&D investment that strengthens innovation capability and technology commercialization. 3) Significant regional and temporal heterogeneity exists in AI’s enabling effect. The effect is stronger in coastal provinces with higher economic development, openness, and absorptive capacity, and is more pronounced during the early market-driven stage (2012-2016) than in the later policy-driven stage (2017-2023).

Building on these findings, the following policy implications are proposed: 1) Accelerate the construction of intelligent digital infrastructure. Governments should strengthen the integration of air-ground networks and low-altitude communication systems to provide a technological foundation for AI-driven applications in the low-altitude economy. 2) Enhance R&D investment and innovation incentives. Firms should be encouraged to engage in joint research on AI and low-altitude applications, promoting technology transfer and commercialization of scientific achievements. 3) Optimize regional coordination and industrial layout through concrete institutional mechanisms. Policymakers should establish inter-provincial data-sharing protocols for Unmanned Traffic Management (UTM) systems to enable seamless cross-regional operations of low-altitude aircraft. Coastal provinces with advanced AI capabilities—such as Guangdong, Jiangsu, and Zhejiang—could serve as technology transfer hubs, with dedicated programs to license AI-driven flight control algorithms, intelligent scheduling software, and predictive maintenance systems to inland counterparts at preferential rates. Provincial governments should consider joint venture frameworks that pair coastal UAV manufacturers with inland logistics operators, combining technological expertise with market access. Additionally, the establishment of regional low-altitude economy demonstration zones spanning coastal-inland boundaries would facilitate knowledge spillovers and standardize regulatory approaches across jurisdictions.

It should be acknowledged that industrial robot density, while a widely used and validated proxy for AI development in the empirical literature, primarily captures AI applications in manufacturing and industrial automation. This measure may not fully represent AI’s role in the service-oriented and knowledge-intensive aspects of the low-altitude economy, such as intelligent airspace management, real-time route optimization, and AI-driven communication systems. Future research could complement this measure with indicators of AI adoption in service sectors, such as the deployment of machine learning algorithms in logistics platforms or the penetration of autonomous navigation systems in commercial UAV operations, pending data availability.

*Corresponding author.