The integration of the digital economy and the real economy, often referred to as digital-real integration, is a critical driver for high-quality economic development and the construction of a modern industrial system. This integration aims to empower traditional industries through digital technologies, promoting intelligent, digital, and green development, thereby enhancing overall economic efficiency and quality. However, the process faces challenges such as inadequate digital platform construction, insufficient application scenario expansion, and imbalances in internal factor allocation. These issues highlight the need for new productive forces, such as the low altitude economy, to lead the breakthrough in digital-real integration. The low altitude economy, as an emerging economic form, provides new application scenarios and practical pathways for this integration, injecting new momentum into economic development. This study employs panel data from 30 regions in China from 2017 to 2024, utilizing a difference-in-differences (DID) model in a quasi-natural experiment setting to explore the impact of the low altitude economy on digital-real integration and its underlying mechanisms, with a focus on technological innovation pathways.
The low altitude economy encompasses various economic activities conducted in low-altitude airspace, including drone logistics, agricultural monitoring, emergency rescue, and urban air mobility. By integrating with digital technologies, it drives the digital transformation and upgrading of related industries, optimizing industrial structure, improving operational efficiency, and fostering innovation-driven development. The policy evolution of the low altitude economy can be divided into four stages: initial exploration (2010-2015), pilot promotion (2016-2020), policy deepening (2021-2023), and comprehensive advancement (2024-present). Key policy documents, such as the “Opinions on Deepening the Reform of Low-Altitude Airspace Management” (2010) and the “National Comprehensive Three-Dimensional Transportation Network Plan Outline” (2021), have progressively elevated the low altitude economy to a national strategy, providing a robust policy framework for its development.
To measure the level of digital-real integration, an evaluation index system is constructed from five dimensions: digital foundation, digital application, digital input, real economy structure, and real economy enterprises. The entropy method is used to assign weights and calculate the comprehensive scores for the digital economy and real economy subsystems. The integration level (DRE) is derived using the coupling coordination degree model, expressed as:
$$ DRE = \sqrt{DE \times TRE} \times \left( \frac{DE + TRE}{2} \right) $$
where DE represents the digital economy development level, and TRE represents the real economy development level. A DRE value closer to 1 indicates a higher level of integration.
The baseline regression model is specified as a DID model:
$$ DRE_{it} = \alpha + \beta LAE_{it} + \gamma Control_{it} + \mu_i + \delta_t + \epsilon_{it} $$
where DREit is the digital-real integration level for region i in year t, LAEit is the policy variable (1 for pilot regions post-2021, 0 otherwise), Controlit is a vector of control variables, μi represents region fixed effects, δt represents time fixed effects, and εit is the error term. Control variables include economic development level (Gdp), industrial structure level (IND), digital infrastructure level (INF), human capital level (HUM), foreign direct investment (FDI), logistics efficiency (LOG), population size (POP), marketization level (MAR), and government support (GOV).
Data sources include macroeconomic statistics from the China Statistical Yearbook, technological innovation indicators from the National Intellectual Property Office, and policy data from official government websites. The sample consists of 30 provinces from 2017 to 2024, with 15 pilot regions designated as the treatment group and the remaining 15 as the control group. Descriptive statistics for the variables are presented in Table 1.
| Variable Category | Variable Name (Symbol) | Number of Observations | Mean | Standard Deviation | Minimum | Maximum |
|---|---|---|---|---|---|---|
| Dependent Variable | Digital-Real Integration (DRE) | 2640 | 0.653 | 0.121 | 0.300 | 0.901 |
| Core Explanatory Variable | Low Altitude Economy Policy (LAE) | 2640 | 0.500 | 0.250 | 0 | 1.000 |
| Mediating Variable | Technological Innovation Level (TIL) | 2640 | 0.451 | 0.154 | 0.200 | 0.801 |
| Control Variables | Economic Development Level (Gdp) | 2640 | 4.312 | 3.103 | 0.361 | 12.912 |
| Industrial Structure Level (IND) | 2640 | 0.403 | 0.082 | 0.251 | 0.602 | |
| Digital Infrastructure Level (INF) | 2640 | 5.457 | 1.457 | 2.100 | 9.900 | |
| Human Capital Level (HUM) | 2640 | 49.987 | 9.987 | 20.123 | 79.876 | |
| Foreign Direct Investment (FDI) | 2640 | 0.303 | 0.053 | 0.150 | 0.500 | |
| Logistics Efficiency (LOG) | 2640 | 0.754 | 0.097 | 0.550 | 0.954 | |
| Population Size (POP) | 2640 | 0.457 | 0.147 | 0.201 | 0.798 | |
| Marketization Level (MAR) | 2640 | 0.652 | 0.126 | 0.400 | 0.900 | |
| Government Support (GOV) | 2640 | 0.251 | 0.051 | 0.150 | 0.350 |
The baseline regression results demonstrate that the low altitude economy policy significantly promotes digital-real integration. As shown in Table 2, the coefficient for LAE is positive and statistically significant across various model specifications, confirming Hypothesis H1. For instance, in the full model with controls and fixed effects, the coefficient is 0.271, significant at the 1% level, indicating that the policy implementation enhances integration by approximately 0.271 units.
| Variable | DRE (1) | DRE (2) | DRE (3) | DRE (4) |
|---|---|---|---|---|
| LAE | 0.180*** (4.225) | 0.245*** (3.664) | 0.164*** (6.266) | 0.271*** (3.403) |
| Gdp | 0.166* (1.849) | 0.177*** (2.961) | ||
| IND | 0.159* (1.771) | 0.177*** (2.226) | ||
| INF | 0.101** (2.115) | 0.093* (1.802) | ||
| HUM | 0.819* (1.732) | 0.752* (1.899) | ||
| FDI | -0.062 (-1.345) | 0.059** (2.101) | ||
| LOG | 0.091** (2.350) | 0.085*** (3.413) | ||
| POP | 0.133*** (2.996) | -0.222 (-0.245) | ||
| MAR | 0.238** (2.357) | 0.234*** (3.432) | ||
| GOV | 0.134*** (3.265) | 0.131*** (4.221) | ||
| Constant | 0.225*** (3.516) | 0.346*** (4.992) | 0.128*** (3.229) | 0.113* (1.782) |
| Region FE | No | No | Yes | Yes |
| Time FE | No | No | Yes | Yes |
| Observations | 2640 | 2640 | 2640 | 2640 |
| R-squared | 0.687 | 0.517 | 0.761 | 0.821 |
Robustness checks include parallel trend tests, placebo tests, PSM-DID, exclusion of policy interference, addition of control variables, policy timing changes, and winsorization. The parallel trend test confirms that the treatment and control groups had similar trends in digital-real integration before policy implementation, with a significant divergence post-2021. Placebo tests involving 1000 random samplings show that the estimated coefficients for pseudo-policies are centered around zero, while the actual policy coefficient is significantly positive, validating the robustness of the results. The PSM-DID approach, which matches regions based on propensity scores, yields a coefficient of 0.132 for LAE, significant at the 5% level, consistent with the baseline findings. Additional robustness tests, such as excluding regions affected by the “Internet Plus” policy, adding digital financial inclusion and green innovation controls, altering policy timing, and winsorizing variables, all support the conclusion that the low altitude economy policy positively influences digital-real integration.
The mediating effect of technological innovation is tested using a two-step approach. The first step examines the impact of the low altitude economy policy on technological innovation level (TIL), measured by the number of digital technology patents:
$$ TIL_{it} = \alpha_1 + \beta_1 LAE_{it} + \gamma_1 Control_{it} + \mu_i + \delta_t + \epsilon_{1it} $$
The second step assesses the effect of TIL on digital-real integration:
$$ DRE_{it} = \alpha_2 + \beta_2 TIL_{it} + \gamma_2 Control_{it} + \mu_i + \delta_t + \epsilon_{2it} $$
The results, presented in Table 3, show that LAE significantly increases TIL (coefficient 0.136, significant at 1%), and TIL positively affects DRE (coefficient 0.049, significant at 1%). This confirms Hypothesis H2, indicating that technological innovation serves as a key mediator in the relationship between the low altitude economy and digital-real integration. The low altitude economy drives innovation through core technology breakthroughs, technology fusion applications, and talent aggregation, thereby enhancing integration.
| Variable | Step 1: TIL | Step 2: DRE |
|---|---|---|
| LAE | 0.136*** (4.654) | |
| TIL | 0.049*** (5.005) | |
| Control Variables | Yes | Yes |
| Constant | 0.346*** (2.735) | 0.322** (2.236) |
| Region FE | Yes | Yes |
| Time FE | Yes | Yes |
| Observations | 2640 | 2640 |
| R-squared | 0.468 | 0.522 |
Heterogeneity analysis explores variations in the policy effect based on economic development level, application scenario richness, and airspace resource availability. Regions are grouped using GDP rankings for economic development, the number of low-altitude economy-related enterprises for application scenarios, and low-altitude airspace openness and utilization rates for airspace resources. The results, summarized in Table 4, reveal that the promoting effect of the low altitude economy on digital-real integration is more pronounced in regions with medium and low economic development levels, diverse application scenarios, and limited airspace resources. In high-development regions, the coefficient is insignificant (0.139), while in medium and low-development regions, it is significant (0.225 and 0.212, respectively). For application scenarios, regions with diverse scenarios show a highly significant coefficient (0.466), whereas those with单一 scenarios are insignificant (0.124). Similarly, airspace-limited regions exhibit a significant effect (0.331), while airspace-rich regions do not (0.159). These findings suggest that policy resources, market demand, and innovation incentives in less developed or resource-constrained areas amplify the impact of the low altitude economy.
| Variable | High Economic Development | Medium Economic Development | Low Economic Development | Diverse Application Scenarios | Single Application Scenarios | Airspace-Rich | Airspace-Limited |
|---|---|---|---|---|---|---|---|
| LAE | 0.139 (1.364) | 0.225** (2.146) | 0.212** (2.302) | 0.466*** (3.253) | 0.124 (1.186) | 0.159 (0.970) | 0.331** (2.482) |
| Control Variables | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Constant | 0.152* (1.735) | 0.636*** (3.735) | 0.426*** (4.735) | 0.236** (2.335) | 0.518*** (5.754) | 0.336*** (3.732) | 0.626*** (4.737) |
| Region FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Time FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 940 | 970 | 730 | 1200 | 1440 | 1460 | 1180 |
| R-squared | 0.495 | 0.527 | 0.273 | 0.603 | 0.475 | 0.378 | 0.673 |
In conclusion, the low altitude economy significantly promotes digital-real integration through direct effects and the mediating role of technological innovation. The development of the low altitude economy fosters integration by enhancing digital application scenarios, optimizing resource allocation, and driving industrial synergy. Technological innovation, measured by digital technology patents, acts as a critical pathway, facilitating core technology breakthroughs, cross-domain applications, and talent accumulation. Heterogeneity analysis underscores the importance of regional characteristics, with stronger effects observed in areas with medium or low economic development, diverse application scenarios, and limited airspace resources. These regions benefit from policy incentives, market potential, and innovation drivers that amplify the impact of the low altitude economy.
To maximize the promoting effect of the low altitude economy on digital-real integration, several recommendations are proposed. First, establish a national digital platform for the low altitude economy to integrate flight data, airspace resources, and infrastructure, enabling smart management and efficient resource allocation. This platform should leverage big data, AI, and 5G technologies to create a “digital brain” for real-time monitoring and response. Second, expand application scenarios through policy guidance and funding, developing diverse uses such as drone logistics, low-altitude tourism, and agricultural monitoring. A scenario inventory can facilitate targeted expansion and synergy. Third, strengthen infrastructure by building an integrated system of facilities, air networks, route networks, and service networks, including universal airports, charging stations, and low-altitude communication systems. This foundation supports the safe and efficient operation of the low altitude economy. Fourth, enhance innovation capacity by increasing R&D investment in core technologies like flight control systems and propulsion, fostering industry-academia-research collaboration, and establishing innovation centers. This will reduce external dependencies and drive high-end, intelligent development. By implementing these measures, the low altitude economy can fully unleash its potential as a new productive force, advancing the deep integration of the digital and real economies and contributing to high-quality economic development.