The low altitude economy represents an emerging economic form that leverages low-altitude airspace as a key resource for activities such as transportation, logistics, and surveillance. As a strategic emerging industry, it has gained significant attention in recent years due to its potential to drive economic transformation and innovation. In this study, I aim to analyze the spatio-temporal evolution characteristics and influencing factors of low altitude economy development across Chinese provinces from 2011 to 2021. By constructing a comprehensive indicator system and employing advanced statistical methods, I seek to provide insights into regional disparities and the drivers of growth in this sector. The low altitude economy encompasses various dimensions, including technological innovation, industrial development, airspace utilization, policy support, and infrastructure, all of which contribute to its overall development level. Understanding these aspects is crucial for formulating targeted policies and promoting balanced regional development.
To achieve this, I first developed an indicator system based on five dimensions: low-altitude technological innovation, low-altitude industrial development, low-altitude airspace flight, low-altitude policy support, and low-altitude infrastructure. Each dimension includes specific indicators, as summarized in Table 1. For instance, low-altitude technological innovation is measured by patent counts related to drones, helicopters, and eVTOLs, as well as the number of higher education institutions offering relevant programs. Low-altitude industrial development is assessed through the quantity of manufacturing and non-manufacturing enterprises in sectors like drones and helicopters. Data for these indicators were sourced from various databases, including the National Intellectual Property Administration, educational platforms, and statistical yearbooks, ensuring reliability and comprehensiveness.
| Primary Indicator | Secondary Indicator | Unit | Attribute |
|---|---|---|---|
| Low-altitude Technological Innovation | Patents per 10,000 people related to low altitude economy | number/10,000 people | + |
| Low-altitude Technological Innovation | Number of universities offering aviation programs | number | + |
| Low-altitude Industrial Development | Number of non-manufacturing drone enterprises | number | + |
| Low-altitude Industrial Development | Number of non-manufacturing helicopter enterprises | number | + |
| Low-altitude Industrial Development | Number of manufacturing drone enterprises | number | + |
| Low-altitude Industrial Development | Number of manufacturing helicopter enterprises | number | + |
| Low-altitude Airspace Flight | Total length of air mail routes | kilometers | + |
| Low-altitude Airspace Flight | General aviation flight hours | hours | + |
| Low-altitude Policy Support | Investment in general aviation development funds | 10,000 yuan | + |
| Low-altitude Infrastructure | Surveying and mapping benchmark results | points/km² | + |
| Low-altitude Infrastructure | Number of ground observation projects in meteorological observation | number/km² | + |
| Low-altitude Infrastructure | Proportion of employment in air transport industry | % | + |
| Low-altitude Infrastructure | Number of general airports | number/km² | + |
I employed the entropy method to calculate the comprehensive development level of the low altitude economy for each province. The entropy method is a multi-indicator evaluation technique that objectively determines indicator weights based on data dispersion. The calculation involves several steps: first, standardizing the raw data to eliminate dimensional differences; second, computing the entropy value for each indicator; and third, deriving the weights and comprehensive scores. The formula for the entropy value is given by:
$$ E_j = -\frac{1}{\ln n} \sum_{i=1}^{n} p_{ij} \ln p_{ij} $$
where \( E_j \) is the entropy value for indicator \( j \), \( n \) is the number of provinces, and \( p_{ij} \) is the standardized value of indicator \( j \) for province \( i \). The weight \( w_j \) is then calculated as:
$$ w_j = \frac{1 – E_j}{\sum_{j=1}^{m} (1 – E_j)} $$
where \( m \) is the number of indicators. Finally, the comprehensive score for each province is obtained by summing the weighted standardized values. This approach ensures an unbiased assessment of the low altitude economy development level.
To analyze regional disparities, I used the Dagum Gini coefficient, which decomposes the overall Gini coefficient into within-region, between-region, and transvariation density components. The overall Gini coefficient \( G \) is defined as:
$$ G = \frac{\sum_{i=1}^{k} \sum_{h=1}^{k} \sum_{j=1}^{n_i} \sum_{r=1}^{n_h} |y_{ij} – y_{hr}|}{2n^2 \bar{y}} $$
where \( k \) is the number of regions, \( n_i \) and \( n_h \) are the number of provinces in regions \( i \) and \( h \), \( y_{ij} \) and \( y_{hr} \) are the low altitude economy development levels of provinces \( j \) and \( r \) in regions \( i \) and \( h \), and \( \bar{y} \) is the mean development level. The decomposition satisfies \( G = G_w + G_{nb} + G_t \), where \( G_w \) is the within-region Gini coefficient, \( G_{nb} \) is the between-region Gini coefficient, and \( G_t \) is the transvariation density. This decomposition helps identify the sources of inequality in low altitude economy development.
Additionally, I applied kernel density estimation to examine the dynamic evolution of the distribution of low altitude economy development levels. The kernel density estimator is given by:
$$ f(x) = \frac{1}{Nh} \sum_{i=1}^{N} K\left( \frac{X_i – \bar{x}}{h} \right) $$
where \( f(x) \) is the estimated density, \( N \) is the number of observations, \( X_i \) are the observed values, \( \bar{x} \) is the mean, \( h \) is the bandwidth, and \( K(x) \) is the kernel function, often chosen as the Gaussian kernel:
$$ K(x) = \frac{1}{\sqrt{2\pi}} \exp\left(-\frac{x^2}{2}\right) $$
This non-parametric method allows me to visualize the distribution shape and changes over time, providing insights into the convergence or divergence of development levels.
For the influencing factors analysis, I used a panel data model to regress the low altitude economy development level on various economic and social variables. The model is specified as:
$$ y_{it} = \beta_0 + \beta_1 X_{1it} + \beta_2 X_{2it} + \cdots + \beta_k X_{kit} + \varepsilon_{it} $$
where \( y_{it} \) is the development level of province \( i \) in year \( t \), \( X_{jit} \) are the independent variables, \( \beta_j \) are the coefficients, and \( \varepsilon_{it} \) is the error term. The independent variables include economic development level (measured by per capita GDP), industrial structure upgrading (tertiary industry share), human capital (higher education enrollment rate), openness to foreign investment (ratio of foreign trade to GDP), government policy (share of science and technology expenditure in GDP), innovation capability (regional innovation index), and infrastructure (per capita road mileage). These factors are hypothesized to influence the low altitude economy development based on theoretical considerations.
The empirical results reveal that the low altitude economy development level in China showed a steady upward trend from 2011 to 2021. The national average score increased from 0.058 in 2011 to 0.169 in 2021, with an average annual growth rate of 9.21%. This growth underscores the rapid expansion of the low altitude economy as a strategic emerging industry. Regionally, the eastern region exhibited the highest development level and growth rate, followed by the northeastern, central, and western regions. For instance, the eastern region’s average annual growth rate was 14.10%, while the western region’s was only 6.10%, indicating significant regional disparities. The gap between the eastern and western regions widened over time, from 1.98 times in 2011 to 2.62 times in 2021, highlighting the uneven development of the low altitude economy.
Spatially, the development level of the low altitude economy displayed a gradient decreasing pattern from east to west. In 2011, high-level development was concentrated in provinces like Beijing, Shanghai, and Sichuan, while most provinces were at low or medium levels. By 2021, the number of high-level provinces increased to 12, including Jiangsu, Zhejiang, and Guangdong, reflecting the diffusion of low altitude economy activities. The central and western regions predominantly remained at medium or low levels, with provinces like Qinghai and Tibet consistently at the bottom. This spatial evolution suggests that the low altitude economy is more developed in economically advanced regions, which benefit from better infrastructure, innovation capacity, and policy support.
The Dagum Gini coefficient analysis further confirms the regional disparities. The overall Gini coefficient ranged from 0.315 to 0.348 during the study period, indicating a moderate to high level of inequality that increased over time. The decomposition shows that between-region differences were the primary source of overall inequality, with their contribution rising from 51.38% in 2011 to 64.71% in 2021. Within-region differences and transvariation density contributed less, with their shares declining. Specifically, the western region had the highest within-region Gini coefficient, reflecting substantial internal disparities, while the eastern region’s within-region inequality increased over time. The between-region Gini coefficient was largest for the eastern-western pair, emphasizing the stark contrast in low altitude economy development between these regions.
| Year | Overall Gini | Within-Region Gini | Between-Region Gini | Transvariation Density | Within-Region Contribution (%) | Between-Region Contribution (%) | Transvariation Contribution (%) |
|---|---|---|---|---|---|---|---|
| 2011 | 0.315 | 0.084 | 0.162 | 0.070 | 26.54 | 51.38 | 22.09 |
| 2012 | 0.341 | 0.090 | 0.184 | 0.066 | 26.55 | 53.96 | 19.49 |
| 2013 | 0.322 | 0.082 | 0.157 | 0.083 | 25.55 | 48.71 | 25.74 |
| 2014 | 0.316 | 0.082 | 0.162 | 0.072 | 25.99 | 51.21 | 22.80 |
| 2015 | 0.321 | 0.082 | 0.171 | 0.069 | 25.39 | 53.06 | 21.55 |
| 2016 | 0.321 | 0.082 | 0.171 | 0.067 | 25.51 | 53.50 | 21.00 |
| 2017 | 0.348 | 0.088 | 0.197 | 0.063 | 25.36 | 56.50 | 18.14 |
| 2018 | 0.334 | 0.078 | 0.207 | 0.048 | 23.38 | 62.10 | 14.52 |
| 2019 | 0.334 | 0.077 | 0.212 | 0.045 | 22.92 | 63.60 | 13.49 |
| 2020 | 0.343 | 0.077 | 0.225 | 0.040 | 22.54 | 65.67 | 11.80 |
| 2021 | 0.346 | 0.078 | 0.224 | 0.044 | 22.65 | 64.71 | 12.64 |
Kernel density estimates provide further insights into the dynamic evolution of the low altitude economy development distribution. The curves for all regions shifted rightward over time, indicating an overall improvement in development levels. The eastern region showed the largest rightward shift, confirming its leading role in low altitude economy growth, while the western region had the smallest shift. The peaks of the density curves decreased and widened, suggesting increasing internal disparities within each region. Right-tailed distributions were observed, implying that a few provinces achieved high development levels, pulling the overall distribution. For example, in the eastern region, provinces like Beijing and Guangdong had significantly higher scores, whereas in the western region, Sichuan and Shaanxi stood out. This pattern highlights the polarization in low altitude economy development and the need for targeted interventions.
The panel data regression results identify key factors influencing the low altitude economy development level. As shown in Table 3, economic development level, industrial structure upgrading, human capital, openness to foreign investment, government policy, and innovation capability all have statistically significant positive effects. Specifically, a 1% increase in per capita GDP leads to a 0.003 unit increase in the low altitude economy development score, underscoring the importance of economic foundation. Industrial structure upgrading, measured by the tertiary industry share, has a coefficient of 0.350, indicating that shifts toward service-oriented economies bolster the low altitude economy. Human capital, represented by the higher education enrollment rate, contributes positively (coefficient 0.104), as skilled labor is essential for innovation and operation in this sector.
| Variable | Coefficient | P-value |
|---|---|---|
| Constant | 1.115*** | 0.000 |
| Economic Development Level (per capita GDP) | 0.003* | 0.097 |
| Industrial Structure Upgrading (tertiary industry share) | 0.350*** | 0.000 |
| Human Capital (higher education enrollment rate) | 0.104*** | 0.000 |
| Openness to Foreign Investment (trade-to-GDP ratio) | 2.435*** | 0.000 |
| Government Policy (science and technology expenditure share) | 4.122** | 0.026 |
| Innovation Capability (regional innovation index) | 0.397*** | 0.000 |
| Infrastructure (per capita road mileage) | 0.040 | 0.115 |
Openness to foreign investment has a strong positive impact (coefficient 2.435), suggesting that international collaboration and market integration enhance the low altitude economy by facilitating technology transfer and competition. Government policy, measured by science and technology expenditure, is highly significant (coefficient 4.122), emphasizing the role of public investment in fostering innovation and infrastructure. Innovation capability, with a coefficient of 0.397, is a critical driver, as technological advancements are at the core of the low altitude economy. In contrast, general infrastructure (per capita road mileage) is not statistically significant, indicating that traditional transport networks may not directly support low-altitude activities; instead, specialized infrastructure like airports and navigation systems is more relevant.
In conclusion, the low altitude economy in China has experienced significant growth from 2011 to 2021, but with pronounced regional disparities. The eastern region leads in development, while the western region lags behind, and between-region differences are the main source of overall inequality. Factors such as economic development, human capital, openness, and innovation play pivotal roles in driving the low altitude economy. Based on these findings, I recommend focusing on regional pilot programs for the low altitude economy, optimizing policy resource allocation, accelerating the construction of specialized low-altitude infrastructure, and fostering talent development and innovation. For instance, eastern regions should expand low-altitude economic pilots and share experiences, while central and western regions need targeted funding and collaboration with advanced regions to bridge the gap. By addressing these aspects, China can promote a more balanced and sustainable development of the low altitude economy, leveraging it as a catalyst for economic transformation and growth.
To further elaborate, the low altitude economy is not only about airspace utilization but also involves integrating with other sectors like logistics, agriculture, and tourism. For example, drone applications in precision agriculture or aerial surveys can enhance productivity and create new business models. The dynamic evolution of the low altitude economy underscores its potential as a key component of regional development strategies. Policymakers should consider these insights when designing initiatives to harness the full potential of the low altitude economy, ensuring that it contributes to broader economic goals such as innovation-driven growth and regional coordination. As the low altitude economy continues to evolve, ongoing monitoring and adaptive policies will be essential to address emerging challenges and opportunities.
In summary, this study provides a comprehensive analysis of the spatio-temporal evolution and influencing factors of low altitude economy development in China. The methodologies and findings offer a foundation for future research and policy-making, emphasizing the importance of a multi-dimensional approach to understanding and fostering the low altitude economy. By continuously improving data collection and analytical techniques, we can better capture the complexities of this emerging sector and support its sustainable development across different regions.