In recent years, the low altitude economy has emerged as a pivotal driver of economic growth, leveraging airspace below 3000 meters for various aerial activities, including manned and unmanned aircraft operations. This comprehensive economic form encompasses manufacturing, flight operations, infrastructure support, and integrated services, positioning itself as a strategic emerging industry with significant potential. The Chinese government has emphasized the development of the low altitude economy in key policy documents, such as the decision from the Third Plenary Session of the 20th Central Committee, highlighting its role in advancing Chinese modernization. According to reports, China’s low altitude economy scale reached approximately 505.95 billion yuan in 2023, with a growth rate of 33.8%, underscoring its rapid expansion. However, regional disparities and developmental challenges, such as technological immaturity, inadequate infrastructure, and regulatory gaps, necessitate a systematic evaluation of its development level. This study aims to measure the low altitude economy development across 30 provinces in China from 2015 to 2023, analyze regional gaps, and identify influencing factors using advanced methodologies like combined weighting, Dagum Gini coefficient, and Quadratic Assignment Procedure (QAP). By focusing on dimensions like low-altitude manufacturing, flight operations,保障, and integrated services, this research provides insights into achieving balanced regional development and optimizing policy interventions for the low altitude economy.
The concept of the low altitude economy integrates geographical and economic attributes, referring to economic activities conducted within low-altitude airspace, primarily involving general aviation and unmanned aerial vehicles (UAVs). Existing literature has explored various aspects, including the role of financial technology innovation, patient capital, and government guidance in fostering the low altitude economy. For instance, studies have shown that digital infrastructure and tax incentives positively impact the growth of this sector. However, most research has focused on conceptual definitions or isolated factors, with limited attention to comprehensive evaluation systems based on the industrial chain perspective. This study addresses this gap by constructing an evaluation index system that covers the entire upstream and midstream segments of the low altitude economy产业链, including manufacturing, flight operations,保障, and services. The methodology employs a combination of subjective and objective weighting techniques to ensure a balanced assessment, followed by regional disparity analysis and factor identification using relational data approaches. The findings reveal that the low altitude economy in China has experienced steady growth, but with significant regional imbalances, particularly between eastern and western regions. Factors such as digital new infrastructure, human capital, and marketization levels are identified as key drivers of these disparities, providing a foundation for targeted policy recommendations.
Literature Review
The low altitude economy has gained increasing scholarly attention due to its potential as a new economic growth engine. Research in this field can be broadly categorized into conceptual definitions, influencing factors, and impact assessments. In terms of conceptualization, scholars like Liao Xiaohan et al. (2021) emphasize the geographical aspects of low-altitude airspace as a resource, while others like Liu Qiangqiang (2025) define it based on economic activities such as parcel delivery and aerial tourism. Integrated perspectives, as seen in the work of Zhang Jiaxin and Xu Qian (2024), describe the low altitude economy as a comprehensive economic form involving general aviation and UAV industries. These definitions highlight the interdisciplinary nature of the low altitude economy, combining elements of geography, economics, and technology.
Regarding influencing factors, international studies often focus on technological innovations in low-altitude manufacturing, such as airport location factors, electric propulsion systems, and automated piloting systems. In contrast, domestic research in China emphasizes the role of digital new infrastructure, policy support, and tax incentives. For example, digital new quality productivity and digital infrastructure have been shown to positively affect the development of the low altitude economy. Additionally, studies have explored the economic effects of the low altitude economy, such as its impact on modern industrial system construction and new consumption patterns. However, existing evaluations of the low altitude economy development level are limited, often relying on single methods like entropy weighting without considering subjective inputs or regional disparity sources. This study builds on prior work by adopting a combined weighting approach and examining regional gaps through advanced statistical techniques, thereby contributing to a more holistic understanding of the low altitude economy.
Methodology and Data
To measure the development level of China’s low altitude economy, this study utilizes panel data from 30 provinces between 2015 and 2023. The data sources include the China Statistical Yearbook, China Low Altitude Economy Development Research Report, enterprise databases like Qichacha and Qixinbao, and official reports from the Civil Aviation Administration of China. Missing data are supplemented based on annual growth rates to ensure completeness. The evaluation index system is constructed based on the industrial chain of the low altitude economy, covering four primary dimensions: low-altitude manufacturing, low-altitude flight, low-altitude保障, and integrated services. Each dimension is further broken down into specific indicators, as detailed in Table 1.
| Primary Dimension | Secondary Dimension | Tertiary Indicator | Weight | Attribute | |
|---|---|---|---|---|---|
| Low-altitude保障 | Infrastructure Support | Number of General Airports | 0.052 | + | |
| Low-altitude Flight Network Coverage | Number of Flight Service Stations | 0.043 | + | ||
| Coverage Rate of Low-altitude Communication Network | 0.046 | + | |||
| Low-altitude Manufacturing | Innovation Capability | Number of Invention Patents | 0.084 | + | |
| R&D Investment Proportion | 0.073 | + | |||
| Industrial Clustering | Number of “Specialized and New” Enterprises | 0.069 | + | ||
| Number of Industrial Parks | 0.073 | + | Industrial Agglomeration Degree | 0.058 | + |
| Low-altitude Flight | Industrial Supply | Unmanned Aviation Test Zones | 0.067 | + | |
| Number of Enterprises in Low-altitude Economy Chain | 0.075 | + | |||
| Low-altitude Economy Industrial Funds | 0.054 | + | |||
| Market Scale | Distribution of Professional Aviation Institutions | 0.078 | + | ||
| Scale of Low-altitude Economy | 0.055 | + | |||
| Number of Registered General Aircraft | 0.042 | + | |||
| Integrated Services | Accident Incidence | Accidents per Million Flight Hours | 0.021 | + | |
| Emergency Rescue Capability | Assessment of Rescue Plan Completeness and Response Time | 0.022 | + |
The weights for these indicators are determined using a combination of subjective and objective methods. The subjective weighting employs the order relationship analysis, where experts rank indicators based on importance. The relative weight between adjacent indicators is calculated using the formula: $$r_i = \frac{w_{i-1}}{w_i}, \quad (i = n, n-1, \ldots, 2)$$ where \( r_i \) is assigned values like 1.0, 1.2, etc., based on expert judgment. The weight for the least important indicator \( y_n \) is derived as: $$w_n = \left[1 + \sum_{i=2}^n \prod_{k=i}^n r_k\right]^{-1}, \quad w_{i-1} = r_i w_i$$ Objective weighting uses the entropy method, which processes standardized data. For positive indicators, standardization is: $$X_{ij} = \frac{x_{ij} – \min(x_{1j}, x_{2j}, \ldots, x_{nj})}{\max(x_{1j}, x_{2j}, \ldots, x_{nj}) – \min(x_{1j}, x_{2j}, \ldots, x_{nj})}$$ and for negative indicators: $$X_{ij} = \frac{\max(x_{1j}, x_{2j}, \ldots, x_{nj}) – x_{ij}}{\max(x_{1j}, x_{2j}, \ldots, x_{nj}) – \min(x_{1j}, x_{2j}, \ldots, x_{nj})}$$ The proportion of indicator \( j \) for province \( i \) is: $$P_{ij} = \frac{X_{ij}}{\sum_{i=1}^n X_{ij}}$$ The entropy value \( e_j \) is: $$e_j = -(\ln n)^{-1} \sum_{i=1}^n (P_{ij} \ln P_{ij})$$ The redundancy degree \( d_j = 1 – e_j \), and the weight \( w_j = \frac{d_j}{\sum_{j=1}^m d_j} \). The combined weight \( W \) is: $$W = a w_n + (1 – a) w_j, \quad a \in [0,1]$$ with \( a = 0.5 \) assumed for equal importance. The comprehensive index for province \( i \) is: $$I_i = \sum_{j=1}^n X_{ij} W$$
To analyze regional disparities, the Dagum Gini coefficient is applied. The overall Gini coefficient \( G \) is: $$G = \frac{\sum_{j=1}^k \sum_{h=1}^k \sum_{i=1}^{n_j} \sum_{r=1}^{n_h} |y_{ji} – y_{hr}|}{2n^2 \bar{Y}}$$ where \( k \) is the number of regions (east, central, west, northeast), \( n \) is the total number of provinces, \( \bar{Y} \) is the mean of the low altitude economy development index, and \( y_{ji} \) and \( y_{hr} \) are the indices for provinces in regions \( j \) and \( h \). The overall Gini coefficient is decomposed into within-region disparity \( G_w \), between-region disparity \( G_{nb} \), and transvariation density \( G_t \): $$G_{jj} = \frac{1}{2\bar{Y}} \sum_{i=1}^{n_j} \sum_{r=1}^{n_j} |y_{ji} – y_{jr}| / n_j^2, \quad G_w = \sum_{j=1}^k G_{jj} p_j s_j$$ $$G_{jh} = \frac{\sum_{i=1}^{n_j} \sum_{r=1}^{n_h} |y_{ji} – y_{hr}|}{n_j n_h}, \quad G_{nb} = \sum_{j=2}^k \sum_{h=1}^{j-1} G_{jh} (p_j s_h + p_h s_j) D_{jh}$$ $$G_t = \sum_{j=2}^k \sum_{h=1}^{j-1} G_{jh} (p_j s_h + p_h s_j) (1 – D_{jh})$$ where \( p_j = n_j / n \), \( s_j = n_j \bar{Y}_j / (n \bar{Y}) \), and \( D_{jh} \) is the relative impact between regions.
For influencing factors, QAP analysis is used to examine relational data. The model is: $$Y = \beta_0 + \beta_1 X + U$$ where \( Y \) is the matrix of low altitude economy development disparities, \( X \) is the matrix of explanatory variable disparities, and \( U \) is the residual. Independent variables include human capital disparity (measured by average education years), marketization disparity (using marketization index), digital new infrastructure disparity (based on innovation, integration, and information infrastructure), digital government disparity (from government digitalization assessments), and consumption upgrade disparity (share of high-end goods expenditure).
Results and Analysis
The measurement results for the low altitude economy development level from 2015 to 2023 are presented in Table 2. Overall, China’s low altitude economy has shown a steady upward trend, with the comprehensive index increasing from an average of 0.256 in 2015 to 0.303 in 2023. However, the overall level remains relatively low, indicating room for improvement. Regionally, eastern provinces like Guangdong, Jiangsu, and Beijing lead in low altitude economy development, driven by policy support, technological advancements, and industrial clustering. For instance, Guangdong’s index rose from 0.495 to 0.627, benefiting from initiatives like the “Sky City” project and regional cooperation in the Greater Bay Area. Central provinces, such as Anhui and Henan, show moderate development, while western and northeastern provinces lag behind, with indices below 0.3 in many cases. This regional imbalance underscores the need for targeted strategies to promote the low altitude economy across all areas.
| Region | Province | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | Average | Rank |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Eastern | Beijing | 0.417 | 0.432 | 0.428 | 0.458 | 0.485 | 0.496 | 0.504 | 0.541 | 0.552 | 0.479 | 3 |
| Shanghai | 0.415 | 0.420 | 0.411 | 0.436 | 0.448 | 0.435 | 0.415 | 0.418 | 0.424 | 0.425 | 4 | |
| Jiangsu | 0.471 | 0.484 | 0.496 | 0.517 | 0.532 | 0.534 | 0.548 | 0.558 | 0.567 | 0.523 | 2 | |
| Zhejiang | 0.374 | 0.386 | 0.389 | 0.405 | 0.412 | 0.409 | 0.432 | 0.449 | 0.458 | 0.413 | 6 | |
| Guangdong | 0.495 | 0.544 | 0.547 | 0.563 | 0.574 | 0.586 | 0.602 | 0.614 | 0.627 | 0.572 | 1 | |
| Other Eastern | … | … | … | … | … | … | … | … | … | … | … | |
| Central | Anhui | 0.289 | 0.294 | 0.305 | 0.328 | 0.335 | 0.349 | 0.352 | 0.368 | 0.370 | 0.332 | 9 |
| Henan | 0.263 | 0.290 | 0.295 | 0.322 | 0.331 | 0.335 | 0.345 | 0.357 | 0.363 | 0.322 | 12 | |
| Other Central | … | … | … | … | … | … | … | … | … | … | … | |
| Western | Sichuan | 0.289 | 0.294 | 0.305 | 0.328 | 0.335 | 0.349 | 0.352 | 0.368 | 0.370 | 0.332 | 10 |
| Shaanxi | 0.252 | 0.258 | 0.271 | 0.293 | 0.277 | 0.287 | 0.296 | 0.301 | 0.306 | 0.282 | 21 | |
| Other Western | … | … | … | … | … | … | … | … | … | … | … | |
| Northeastern | Liaoning | 0.271 | 0.273 | 0.291 | 0.298 | 0.294 | 0.305 | 0.311 | 0.325 | 0.321 | 0.299 | 18 |
| Other Northeastern | … | … | … | … | … | … | … | … | … | … | … |
Breaking down the dimensions, low-altitude manufacturing shows the highest growth, particularly in eastern regions, due to innovations in UAV and eVTOL production. Low-altitude flight operations also expand, driven by increased industrial supply and market scale, but保障 and integrated services lag, highlighting infrastructure and safety concerns. The regional disparities are further analyzed using the Dagum Gini coefficient, as shown in Table 3. The overall Gini coefficient increases from 0.156 in 2015 to 0.203 in 2023, indicating a widening gap in low altitude economy development. The between-region disparity is the primary source, contributing an average of 74.46% to the total gap, while within-region disparity and transvariation density account for 18.89% and 6.65%, respectively. This suggests that inter-regional differences, such as those between eastern and western China, are the main drivers of imbalance in the low altitude economy.
| Year | Overall G | Within-region (G_w) | Contribution (%) | Between-region (G_nb) | Contribution (%) | Transvariation (G_t) | Contribution (%) |
|---|---|---|---|---|---|---|---|
| 2015 | 0.156 | 0.025 | 16.03 | 0.125 | 80.13 | 0.006 | 3.85 |
| 2016 | 0.174 | 0.032 | 18.39 | 0.132 | 75.86 | 0.010 | 5.75 |
| 2017 | 0.185 | 0.035 | 18.92 | 0.142 | 76.76 | 0.008 | 4.32 |
| 2018 | 0.177 | 0.034 | 19.21 | 0.136 | 76.84 | 0.007 | 3.95 |
| 2019 | 0.198 | 0.035 | 17.68 | 0.140 | 70.71 | 0.023 | 11.62 |
| 2020 | 0.186 | 0.037 | 19.89 | 0.136 | 73.12 | 0.013 | 6.99 |
| 2021 | 0.189 | 0.037 | 19.58 | 0.143 | 75.66 | 0.009 | 4.76 |
| 2022 | 0.194 | 0.038 | 19.59 | 0.138 | 71.13 | 0.018 | 9.28 |
| 2023 | 0.203 | 0.042 | 20.69 | 0.142 | 69.95 | 0.019 | 9.36 |
The QAP analysis identifies key factors influencing these disparities, as summarized in Table 4. Digital new infrastructure disparity has the largest standardized regression coefficient (0.339), indicating it is the most significant driver of low altitude economy development gaps. Marketization disparity (0.166) and digital government disparity (0.114) also play important roles, while human capital disparity (0.030) has a smaller effect. Consumption upgrade disparity is not statistically significant, suggesting that demand-side factors have not yet fully impacted the low altitude economy. These findings highlight the importance of enhancing digital infrastructure, such as 5G-A networks and low-altitude communication systems, to reduce regional imbalances and promote the sustainable development of the low altitude economy.
| Variable | Standardized Coefficient | Significance (p-value) |
|---|---|---|
| Human Capital Disparity | 0.030 | 0.006 |
| Marketization Disparity | 0.166 | 0.000 |
| Digital New Infrastructure Disparity | 0.339 | 0.021 |
| Digital Government Disparity | 0.114 | 0.001 |
| Consumption Upgrade Disparity | 0.041 | 0.198 |
Conclusion and Implications
This study measures the development level of China’s low altitude economy from 2015 to 2023, revealing a steady upward trend but significant regional disparities. The eastern region leads in all dimensions—manufacturing, flight operations,保障, and services—while central, western, and northeastern regions have considerable room for improvement. The overall gap in low altitude economy development is widening, primarily driven by inter-regional differences. Digital new infrastructure is identified as the most critical factor influencing these disparities, followed by marketization and digital government levels.
To enhance the overall development of the low altitude economy, regions should focus on strengthening all dimensions of the industrial chain. In the upstream segment, improving保障 infrastructure, such as general airports and flight service stations, is essential. For midstream segments, boosting low-altitude manufacturing through innovation and industrial clustering, and expanding flight operations via market scale and supply chain development, are crucial. Policies should encourage cross-regional cooperation, such as establishing industrial service platforms and standardizing products and services, to facilitate balanced growth. Additionally, investing in digital new infrastructure, like advanced communication networks, can bridge regional gaps and support the integration of the low altitude economy with other sectors. Long-term strategies should also address human capital development through education and training programs, and stimulate consumption upgrade by exploring new application scenarios, such as “low-altitude + tourism” or “low-altitude + logistics.” By implementing these measures, China can achieve a more equitable and sustainable low altitude economy, contributing to national economic modernization and resilience.