In recent years, the low altitude economy has emerged as a pivotal sector driving global economic transformation, characterized by the integration of unmanned aerial vehicles (UAVs), advanced air mobility, and related technologies. As a modern industrial paradigm, it encompasses applications in logistics, transportation, agriculture, and tourism, fostering innovation and sustainable growth. I aim to comprehensively measure the development level of the low altitude economy across China’s regions and analyze its spatiotemporal evolution. This study constructs a multidimensional evaluation index system, employs an improved entropy-weighted TOPSIS method for quantification, and utilizes spatial econometric techniques to uncover regional disparities and dynamic patterns. The findings provide insights for policymakers to promote balanced regional development and harness the full potential of the low altitude economy.
The low altitude economy refers to economic activities conducted within airspace below 1000 meters, leveraging advancements in UAV technology, eVTOL (electric vertical take-off and landing) aircraft, and supporting infrastructure. It represents a fusion of traditional industries and cutting-edge technologies, driving modernization through enhanced efficiency, reduced environmental impact, and new business models. Globally, regions like the United States and the European Union have pioneered regulatory frameworks and technological adoption, while China has accelerated efforts through policies such as the “National Comprehensive立体 Transportation Network Plan” and the “General Aviation Equipment Innovation Application Implementation Plan (2024–2030).” Despite growing interest, systematic measurement of the low altitude economy’s development level remains underexplored, particularly in terms of regional variations and temporal shifts. This study addresses this gap by developing a robust framework to assess the low altitude economy’s modernization, focusing on industrial, regional, and socio-environmental dimensions.
Theoretical foundations for this research draw from the “technology-economy” paradigm, which emphasizes how technological innovations, such as those in the low altitude economy, drive industrial transformation and economic modernization. Additionally, the industrial chain value network theory highlights the interconnectedness of stakeholders in the low altitude economy, from manufacturing to service delivery, while the socio-technical systems theory underscores the importance of societal and environmental integration. Based on these frameworks, I conceptualize the evolution of the low altitude economy through stages: emergence of low-altitude enterprises, industrialization, coordinated development, and ultimate modernization. This process involves the transition from technology-driven initiatives to widespread产业化 application, resulting in sustainable socio-economic benefits. To operationalize this, I define the low altitude economy modernization as a comprehensive transformation marked by efficiency, intelligence, and green development, supported by innovation, policy, and market forces.
To measure the development level of the low altitude economy, I constructed an evaluation index system comprising three primary dimensions: industrial development, regional development, and social and environmental development. Each dimension is further divided into secondary and tertiary indicators, as summarized in the table below. The industrial development dimension captures factors like industrial chain foundation, scale, and market demand, reflecting the core drivers of the low altitude economy. The regional development dimension includes regional科技创新, economic水平, and policy environment, highlighting contextual enablers. The social and environmental development dimension assesses sustainability through green indicators and social impact, ensuring that the low altitude economy contributes positively to society.
| Dimension | Primary Indicator | Secondary Indicator | Tertiary Indicator | Unit | Direction |
|---|---|---|---|---|---|
| Industrial Development | Industrial Chain Foundation (x1) | Communication | Internet broadband access ports per 10,000 people | ports/10,000 people | + |
| Communication | Long-distance optical cable length per 10,000 people per unit area | km/10,000 people | + | ||
| Communication | Internet penetration rate | % | + | ||
| Communication | Computers per 100 people | units/100 people | + | ||
| Monitoring and Navigation | Number of high-altitude detection stations | units | + | ||
| Monitoring and Navigation | Number of weather radar observation stations | units | + | ||
| Monitoring and Navigation | Number of agricultural meteorological observation stations | units | + | ||
| Monitoring and Navigation | Number of satellite image reception stations | units | + | ||
| Circulation Capacity | Proportion of transportation fiscal expenditure in total fiscal expenditure | % | + | ||
| Technology Diffusion Efficiency | High-tech industry agglomeration | index | + | ||
| Technology Diffusion Efficiency | Technology market transaction value | 10,000 yuan | + | ||
| Airspace Resource Utilization Efficiency | Low-altitude flight approval efficiency (total航空 mail route length) | km | + | ||
| Airspace Resource Utilization Efficiency | Airspace open area (passenger throughput) | 10,000 people | + | ||
| Industrial Synergy | Industrial-financial synergy (banking institution loan balance as % of GDP) | % | + | ||
| Industrial Synergy | Industry-innovation synergy (R&D external expenditure of industrial enterprises as % of GDP) | % | + | ||
| Industry-University-Research Collaboration | Number of industry-university-research collaboration patents | units | + | ||
| Industry-University-Research Collaboration | Number of industry-university-research collaboration events | times | + | ||
| Industrial Development | Industrial Chain Scale (x2) | Enterprise Input | Number of enterprises with UAV-related business scope | units | + |
| Enterprise Input | Number of enterprises with UAV-related patents | units | + | ||
| Enterprise Input | Number of specialized and sophisticated SMEs with UAV business scope | units | + | ||
| Enterprise Input | Number of specialized and sophisticated SMEs with UAV patents | units | + | ||
| Enterprise Input | Number of high-tech enterprises with UAV business scope | units | + | ||
| Enterprise Input | Number of high-tech enterprises with UAV patents | units | + | ||
| Personnel Scale | Employment in air transport industry | people | + | ||
| Personnel Scale | R&D personnel in electronic and communication equipment manufacturing | people | + | ||
| Personnel Scale | R&D personnel in high-tech industries | people | + | ||
| Industrial Revenue | Main business revenue of electronic and communication equipment manufacturing | 100 million yuan | + | ||
| Industrial Revenue | Main business revenue of high-tech industries | 100 million yuan | + | ||
| Industrial Revenue | Total profit of high-tech industries | 100 million yuan | + | ||
| Industrialization Maturity | Number of certified airports in the year | units | + | ||
| Industrialization Maturity | Actual number of airports | units | + | ||
| Market Demand (x3) | Production Enterprise Count | Number of upstream enterprises in low altitude economy industrial chain | units | + | |
| Production Enterprise Count | Number of midstream enterprises in low altitude economy industrial chain | units | + | ||
| Production Enterprise Count | Number of downstream enterprises in low altitude economy industrial chain | units | + | ||
| Logistics Demand | Freight turnover | 100 million ton-km | + | ||
| Transportation Demand | Length of graded highways | 10,000 km | + | ||
| Transportation Demand | Length of railway routes in operation | 10,000 km | + | ||
| Transportation Demand | Length of expressways | 10,000 km | + | ||
| Regional Development | Regional Science and Technology Innovation (x4) | Education Productivity | Average years of schooling | years | + |
| Education Productivity | Proportion of higher education population | % | + | ||
| Technology Productivity | Number of patent applications (inventions, utility models, designs) | units | + | ||
| Technology Productivity | Industrial innovation expenditure of industrial enterprises | 10,000 yuan | + | ||
| Technology Productivity | Industrial robot installation density | units | + | ||
| Talent Productivity | R&D personnel full-time equivalent | person-years | + | ||
| Talent Productivity | Labor productivity of industrial enterprises (industrial value added per employee) | 10,000 yuan/person | + | ||
| Talent Productivity | Number of scientific papers published by higher education institutions | units | + | ||
| Regional Economic Development Level (x5) | Industrial Structure | Industrial structure高级化 (tertiary industry value added as % of secondary industry) | ratio | + | |
| Industrial Structure | Industrial structure合理化 (calculated using Theil index) | index | – | ||
| Economic Development Potential | Foreign trade dependence (import-export value as % of GDP) | % | + | ||
| Economic Development Potential | Foreign investment dependence (FDI as % of GDP) | % | + | ||
| Policy Environment (x6) | Policy Support Intensity | Number of low altitude economy policy pilots | units | + | |
| Airspace Management Reform Progress | Agricultural aircraft operation area | khm² | + | ||
| Airspace Management Reform Progress | Number of specialized agricultural aircraft | units | + | ||
| Social and Environmental Development | Regional Green Development Environment (x7) | Energy Efficiency | Energy consumption per unit of GDP | 10,000 tons of standard coal/10,000 yuan | – |
| Pollution Emission | SO₂ emissions per unit of industrial value added | tons/10,000 yuan | – | ||
| Environmental Protection | Industrial solid waste generated per unit of industrial value added | tons/10,000 yuan | – | ||
| Environmental Protection | Industrial pollution control investment per 1,000 yuan of industrial value added | yuan/1,000 yuan | – | ||
| Social Impact (x8) | Social Attention | Low altitude economy attention (Baidu search index for relevant terms per capita) | searches/person | + | |
| Industrial Driving Effect | Value added of transport, postal, and storage industries | 100 million yuan | + | ||
| Industrial Driving Effect | Fixed asset investment in logistics industry | 100 million yuan | + |
The industrial structure合理化 indicator is calculated using the Theil index formula:
$$ TL = \sum_{i=1}^{3} \left( \frac{Y_i}{Y} \right) \ln \left( \frac{Y_i / L_i}{Y / L} \right) $$
where \( i \) represents the primary, secondary, and tertiary industries, \( Y \) denotes output value, and \( L \) denotes employment. A lower Theil index indicates a more rational industrial structure, which is crucial for supporting the low altitude economy.
For model specification, I employed an improved entropy-weighted TOPSIS method to assign weights to the indicators and measure the development level of the low altitude economy. Traditional entropy weight methods can overemphasize indicators with high离散度, so I integrated principles from the Analytic Hierarchy Process (AHP) to construct a judgment matrix based on the coefficient of variation. The steps involve normalizing the data, calculating entropy values, determining weights, and evaluating the relative closeness to the ideal solution. The formula for the improved entropy weight is derived as follows: first, normalize each indicator \( x_{ij} \) for province \( i \) and indicator \( j \):
$$ r_{ij} = \frac{x_{ij} – \min(x_j)}{\max(x_j) – \min(x_j)} $$
for positive indicators, and
$$ r_{ij} = \frac{\max(x_j) – x_{ij}}{\max(x_j) – \min(x_j)} $$
for negative indicators. Then, calculate the entropy \( e_j \) for each indicator:
$$ e_j = -k \sum_{i=1}^{n} p_{ij} \ln(p_{ij}) $$
where \( p_{ij} = r_{ij} / \sum_{i=1}^{n} r_{ij} \), and \( k = 1 / \ln(n) \) is a constant. The weight \( w_j \) is then:
$$ w_j = \frac{1 – e_j}{\sum_{j=1}^{m} (1 – e_j)} $$
Finally, the TOPSIS method computes the Euclidean distances to the positive and negative ideal solutions, and the comprehensive score for each province is given by:
$$ C_i = \frac{D_i^-}{D_i^+ + D_i^-} $$
where \( D_i^+ \) and \( D_i^- \) are the distances to the ideal best and worst solutions, respectively. This approach ensures a balanced weighting that reflects the relative importance of each indicator in the context of the low altitude economy.
To analyze regional disparities, I utilized the spatial Gini coefficient, which decomposes overall inequality into within-group, between-group, and super-density components. This method addresses issues of data overlap and spatial correlation, providing a clear picture of how the low altitude economy varies across regions. The Gini coefficient \( G \) is defined as:
$$ G = \frac{\sum_{i=1}^{k} \sum_{j=1}^{k} |y_i – y_j|}{2n^2 \bar{y}} $$
where \( y_i \) and \( y_j \) are the development levels of regions \( i \) and \( j \), \( n \) is the number of regions, and \( \bar{y} \) is the mean development level. Decomposition allows us to isolate contributions from within-region and between-region differences, which is essential for understanding the drivers of inequality in the low altitude economy.
Additionally, I applied the Quadratic Assignment Procedure (QAP) to examine the factors influencing regional differences. QAP is a non-parametric method that assesses correlations between matrices, such as the difference matrices of development levels and indicator matrices. It helps identify key factors like industrial chain scale, market demand, and social impact that contribute to disparities in the low altitude economy. For spatial analysis, I employed spatial kernel density estimation to visualize the distribution and evolution of development levels. The kernel density function for a spatial point \( x \) is given by:
$$ \hat{f}(x) = \frac{1}{nh} \sum_{i=1}^{n} K \left( \frac{x – X_i}{h} \right) $$
where \( K \) is the kernel function, \( h \) is the bandwidth, and \( X_i \) are the observed values. This estimates the probability density of development levels, revealing spatial agglomeration patterns. Furthermore, I used Markov chain analysis to study temporal transitions, dividing development levels into four types (I: low, II: medium-low, III: medium-high, IV: high) based on quartiles. The transition probability matrix \( P \) is constructed, where \( p_{ij} \) represents the probability of moving from state \( i \) to state \( j \) over time. Spatial Markov chains incorporate spatial lags to account for neighborhood effects, enhancing the understanding of path dependence in the low altitude economy.
Data for this study were collected from 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan due to data availability) from 2013 to 2023. Sources include the EPS database, China Statistical Yearbook, China High-tech Industry Statistical Yearbook, China Torch Statistical Yearbook, China Agricultural Machinery Industry Yearbook, and provincial statistical bureaus. Missing values were handled using interpolation methods to ensure consistency. The dataset encompasses a wide range of variables aligned with the index system, enabling a comprehensive assessment of the low altitude economy.
The measurement results reveal significant regional variations in the development level of the low altitude economy across China. Provinces like Guangdong, Jiangsu, Shanghai, Beijing, and Tianjin consistently rank high, reflecting their advanced industrial bases, strong market demand, and supportive policies for the low altitude economy. For instance, Guangdong leads with a comprehensive score of 0.366 in 2023, driven by its robust industrial chain scale and innovation capacity. In contrast, provinces such as Hainan, Jilin, and Heilongjiang exhibit lower scores, indicating that the low altitude economy is still in nascent stages there. The table below summarizes the comprehensive scores and rankings for selected years, highlighting the dynamic nature of development.
| Province | 2013 Score | 2016 Score | 2019 Score | 2022 Score | 2023 Score | Mean Score | Overall Rank |
|---|---|---|---|---|---|---|---|
| Beijing | 0.270 | 0.283 | 0.312 | 0.283 | 0.357 | 0.288 | 2 |
| Tianjin | 0.278 | 0.254 | 0.278 | 0.265 | 0.279 | 0.255 | 4 |
| Hebei | 0.116 | 0.125 | 0.161 | 0.097 | 0.308 | 0.111 | 18 |
| Shanxi | 0.039 | 0.088 | 0.170 | 0.060 | 0.268 | 0.091 | 24 |
| Inner Mongolia | 0.051 | 0.100 | 0.112 | 0.079 | 0.354 | 0.097 | 22 |
| Liaoning | 0.050 | 0.094 | 0.112 | 0.060 | 0.257 | 0.085 | 27 |
| Jilin | 0.053 | 0.088 | 0.063 | 0.063 | 0.246 | 0.076 | 28 |
| Heilongjiang | 0.055 | 0.056 | 0.104 | 0.117 | 0.268 | 0.086 | 26 |
| Shanghai | 0.291 | 0.292 | 0.285 | 0.278 | 0.343 | 0.287 | 3 |
| Jiangsu | 0.281 | 0.153 | 0.159 | 0.220 | 0.303 | 0.210 | 6 |
| Zhejiang | 0.265 | 0.209 | 0.211 | 0.318 | 0.373 | 0.231 | 5 |
| Anhui | 0.060 | 0.091 | 0.178 | 0.142 | 0.259 | 0.125 | 11 |
| Fujian | 0.066 | 0.122 | 0.232 | 0.075 | 0.251 | 0.103 | 20 |
| Jiangxi | 0.050 | 0.063 | 0.069 | 0.093 | 0.266 | 0.094 | 23 |
| Shandong | 0.067 | 0.109 | 0.104 | 0.158 | 0.319 | 0.132 | 10 |
| Henan | 0.050 | 0.115 | 0.075 | 0.108 | 0.292 | 0.115 | 16 |
| Hubei | 0.042 | 0.082 | 0.083 | 0.123 | 0.293 | 0.120 | 13 |
| Hunan | 0.039 | 0.052 | 0.085 | 0.107 | 0.290 | 0.132 | 9 |
| Guangdong | 0.307 | 0.344 | 0.361 | 0.349 | 0.490 | 0.366 | 1 |
| Guangxi | 0.039 | 0.075 | 0.232 | 0.071 | 0.288 | 0.133 | 8 |
| Hainan | 0.061 | 0.064 | 0.118 | 0.050 | 0.202 | 0.070 | 30 |
| Chongqing | 0.037 | 0.065 | 0.054 | 0.071 | 0.229 | 0.070 | 29 |
| Sichuan | 0.071 | 0.104 | 0.098 | 0.150 | 0.312 | 0.119 | 14 |
| Guizhou | 0.061 | 0.077 | 0.121 | 0.071 | 0.267 | 0.087 | 25 |
| Yunnan | 0.066 | 0.088 | 0.278 | 0.107 | 0.305 | 0.121 | 12 |
| Shaanxi | 0.047 | 0.088 | 0.285 | 0.088 | 0.279 | 0.113 | 17 |
| Gansu | 0.046 | 0.076 | 0.335 | 0.064 | 0.257 | 0.100 | 21 |
| Qinghai | 0.050 | 0.047 | 0.275 | 0.051 | 0.264 | 0.109 | 19 |
| Ningxia | 0.025 | 0.276 | 0.275 | 0.046 | 0.195 | 0.115 | 15 |
| Xinjiang | 0.056 | 0.282 | 0.277 | 0.096 | 0.307 | 0.146 | 7 |
Analyzing the trends by region, the eastern region maintains a领先 position with stable growth, supported by strong industrial foundations and innovation ecosystems for the low altitude economy. The central region shows gradual improvement from 2015 onward but experiences a decline after 2020, possibly due to market demand fluctuations and technological constraints. The western region demonstrates growth but with significant volatility, influenced by policy shifts and infrastructure gaps. The northeastern region exhibits the most instability, reflecting challenges in industrial chain development and market adoption of low altitude economy applications. Overall, the national development level of the low altitude economy has risen over time, driven by policy initiatives and technological advancements, yet regional imbalances persist.
The primary indicators also reveal evolving patterns. Industrial chain foundation and scale show steady growth, indicating maturation of the low altitude economy ecosystem. Market demand increases overall but dips in 2020, likely due to the COVID-19 pandemic’s impact on logistics and transportation. Regional science and technology innovation surges after 2018, propelling the modernization of the low altitude economy. Regional economic development and policy environment remain relatively stable, with gradual improvements in supportive measures. Green development and social impact indicators maintain consistent growth, underscoring the increasing emphasis on sustainability in the low altitude economy.
Regional disparity analysis using the Gini coefficient indicates that overall inequality in the low altitude economy development level fluctuates but generally decreases over time, suggesting a trend toward greater regional balance. The Gini coefficient drops to its lowest point in 2023, reflecting reduced disparities. Decomposition of the Gini coefficient shows that between-group differences are the primary source of regional inequality, highlighting the developmental gap between regions like the east and west. Within-group differences diminish over time, indicating internal convergence within regions. The super-density component, which captures overlap between distributions, rises initially but declines after 2020, as some regions accelerate their low altitude economy development.
QAP analysis further elucidates the factors driving these disparities. Industrial chain scale, market demand, and social impact emerge as significant determinants of between-region differences. For instance, industrial chain scale negatively affects disparities between eastern and central regions, as well as eastern and northeastern regions, meaning that larger industrial chains in the east exacerbate gaps. Market demand shows negative effects in multiple regional comparisons, such as between central and western regions, indicating that demand variations hinder convergence. Social impact has mixed effects: it negatively influences east-central disparities but positively affects central-west and central-northeast differences, suggesting that social factors like public attention can either mitigate or amplify inequalities in the low altitude economy.
Spatial kernel density estimation reveals strong agglomeration effects in the distribution of low altitude economy development levels. High-density peaks correspond to provinces with advanced development, such as Guangdong and Shanghai, forming core areas that drive regional growth. The kernel density contours, which plot development levels against their spatial lags, show that most points lie above the 45-degree line, indicating that provinces tend to have higher development levels than their neighbors. This pattern suggests a “core-periphery” structure, where leading provinces exert influence over surrounding areas, yet benefits may not diffuse evenly, perpetuating spatial inequalities in the low altitude economy.
Dynamic kernel density analysis tracks changes over time, illustrating how development levels evolve relative to spatial lags. Nationally, contours parallel to the y-axis and above the diagonal indicate that some provinces experience rapid growth in the low altitude economy while neighbors lag, leading to asynchronous development. In the eastern region, contours resemble the national pattern, reflecting stable and synchronized progress. The western and northeastern regions show contours concentrated above the diagonal, signaling internal disparities and volatile growth. The central region exhibits contours shifted rightward, indicating moderate but steady improvement in the low altitude economy.
Markov chain analysis provides insights into temporal transitions of development levels. Traditional Markov chains for 2013–2016, 2017–2023, and the entire period reveal high diagonal probabilities, indicating strong path dependence. For example, in 2013–2016, type II provinces have a high probability of transitioning to type III (0.667), showing upward mobility potential, while type I provinces rarely jump to type IV. Over the full period, type III and IV provinces have stability probabilities of 51.7% and 68.0%, respectively, forming a high-level club that reinforces their advantage in the low altitude economy. Type I and II provinces exhibit lower stability, with greater chances of upward movement, suggesting that lower-level regions can advance with appropriate support.
Spatial Markov chains incorporate neighborhood effects, revealing that transition probabilities depend on the development level of adjacent provinces. Under type III spatial lag, diagonal values are high, indicating “grade locking” where high-level provinces rarely downgrade. Under type II lag, type I provinces have elevated probabilities of moving to higher levels (e.g., 0.368 to type II and III), demonstrating the辐射 effect of advanced neighbors on the low altitude economy. Under type IV lag, type III provinces show a high risk of downgrading to type I (probability 1.000), highlighting the vulnerability of medium-high regions without strong local foundations. Overall, spatial dependence accentuates path dependence, with high-level regions stabilizing and low-level regions relying on proximity to leaders for advancement in the low altitude economy.
In conclusion, the low altitude economy in China exhibits significant regional disparities, driven by factors like industrial chain scale, market demand, and social impact. The eastern region leads with stable development, while central, western, and northeastern regions face challenges related to volatility and inequality. Spatial agglomeration and path dependence characterize the evolution, with high-level regions maintaining their status and low-level regions depending on辐射 effects for progress. These findings underscore the need for targeted policies to promote balanced development of the low altitude economy.
Implications for policymakers include enhancing support for lagging provinces through tailored industrial policies, technological transfers, and infrastructure investments to strengthen their low altitude economy. Fostering inter-regional collaboration can leverage the strengths of advanced regions to uplift others, reducing disparities in industrial chain scale and market demand. Additionally, emphasizing social and environmental dimensions ensures that the low altitude economy contributes to sustainable development. By addressing these aspects, China can harness the full potential of the low altitude economy as a driver of modernization and economic growth.