The “15th Five-Year Plan” of China proposes to “promote the healthy and orderly development of the low-altitude economy.” As a prime example of this new economic frontier and a key driver of new-quality productive forces, the unmanned drone industry in China has experienced rapid growth. It has initially established a full-chain competitive edge spanning from “technology to market.” On the technological front, China accounts for approximately 70% of global patent applications related to unmanned drone technology, solidifying its position as the world’s largest source of innovation in this field. Regarding market presence, the number of registered unmanned drones and users witnessed a year-on-year increase of over 98% by the end of 2024, demonstrating immense application potential in sectors like logistics, surveying, and civil-military integration.
However, beneath this progress, two critical contradictions are becoming apparent. The first is the disconnect between technological innovation and industrial fundamentals. Dependence on imported core components, lagging standard systems, and an imperfect industry-university-research collaborative innovation ecosystem pose significant “bottleneck” risks to technological security. The second contradiction lies in the imbalance between market expansion and safety governance. Frequent incidents of unauthorized flights (“black flights”) disrupting airspace, infrastructure collisions, and crashes causing injuries highlight substantial regulatory shortcomings.
In response, the national strategy of “Ensuring both Development and Safety” has been emphasized. Policies such as the “Interim Regulations on the Flight Management of Unmanned Aircraft (2023)” and the “Implementation Plan for Innovation and Application of General Aviation Equipment (2024-2030)” have been promulgated. These aim to construct a cohesive Development-Safety policy mix to accelerate the shaping of a comprehensive industrial advantage characterized by technological controllability, orderly markets, and完备的监管. While existing scholarship has explored unmanned drone policies and competitive advantages, two main gaps persist. First, policy studies often treat “development” and “safety” policies in isolation, focusing on single-policy net effects rather than adopting an integrative perspective to examine the structure and configurational effects of a policy mix. Second, research on competitive advantage frequently concentrates on dimensions like market or military potential, seldom incorporating safety governance, and offers limited insight into the driving mechanisms behind the unmanned drone industry’s advantage.
The Theory of Comprehensive Advantage, a strategic management framework concerning the formation and growth of competitive advantage, provides a useful lens to systematically deconstruct industrial competitiveness in the digital-intelligent era and reveal its underlying drivers. To deeply explore the contemporary内涵 of unmanned drone industry competitiveness and the driving effects of the Development-Safety policy mix, this analysis constructs a research framework based on the Theory of Comprehensive Advantage and Policy Mix Theory. It employs a configurational perspective to investigate how different combinations of policy tools, within specific regional contexts, drive or inhibit the formation of a comprehensive advantage in the unmanned drone industry across 30 provincial-level regions in China.
Theoretical Foundation and Analytical Framework
The Comprehensive Advantage of the Unmanned Drone Industry
The unmanned drone industry, an emerging pillar sector integrating R&D, manufacturing, application, and services centered on unmanned aerial vehicles, is characterized by high innovation risk, broad applicability, and strong radiating effects. Drawing from the Theory of Comprehensive Advantage, the comprehensive advantage of the unmanned drone industry is defined as a synthesized competitive edge integrating “innovation advantage, market advantage, safety advantage, and ecological advantage.” This is formed under the leadership of strategic resources accumulated by the industry over the long term, supported by the “twin wheels” of core capabilities: innovation development capacity and safety governance capacity.
This内涵 fully reflects the vision of the new era to “Ensure both Development and Safety.” Innovation and market advantages,立足 on the technological and application ends respectively, reflect the industry’s high-quality development level. Safety advantage, as a systematic integration of technological security, operational safety, and public safety, reflects the achievements in constructing high-level safety. Ecological advantage embodies the synergy between development and safety. Only when these two aspects resonate can mutually empowering innovation and governance ecosystems be built, providing optimal resource allocation and adaptive mechanisms to support the industry’s healthy development.
The Development-Safety Policy Mix
Development and safety are the “twin wings” of industrial comprehensive advantage, with industrial policies serving as the engine and balancer. Drawing on Policy Mix Theory and employing a “Strategy-Instruments-Context” analytical framework, the Development-Safety policy mix is defined as a policy system where, under the guidance of national strategy, local governments flexibly combine “development-oriented” and “safety-oriented” policy instruments in line with local contexts to drive the growth of industrial comprehensive advantage. It consists of three modules:
1. The “Ensuring both Development and Safety” Policy Strategy: This serves as the policy vision and action framework for systematically advancing high-quality development and high-level safety, providing overall direction and basic principles for local policy design from the central level.
2. The Development-Safety Policy Instrument Mix: This constitutes the implementation means and portfolio of measures. To accurately classify instrument types, a method combining topic modeling (LDA) with theoretical归纳 is adopted. Six policy instrument types are identified, as summarized in Table 1.
| Policy Dimension | Instrument Type | Typical Policy Measures |
|---|---|---|
| Development-Oriented | Innovation-Driven Policy | R&D subsidies, tax incentives, talent cultivation, industry-university-research collaboration, “challenge and tournament” mechanisms. |
| Application Expansion Policy | Commercialization funds, qualification certification, operational subsidies, government procurement, pilot demonstration projects. | |
| Ecological Optimization Policy | Investment and financing guidance, industrial park construction, service platforms, infrastructure (e.g., vertiports, charging stations), event support. | |
| Safety-Oriented | Science & Technology Safety Policy | Patent navigation, standard development, intellectual property services, countermeasures to entity lists, supply chain strengthening actions. |
| Operational Safety Policy | Negative list management, business environment optimization, network security监管, data governance, supplier vetting. | |
| Public Safety Policy | Safety education, risk monitoring, hazard排查, emergency预案 and drills, rescue team building. |
3. The Implementation Context: This is the spatial arena where policies actually function. It requires local governments to customize the combination of various policy instruments to achieve synergistic effects between the instruments and regional factor endowments.
Configurational Effects of the Development-Safety Policy Mix
The six types of policy instruments exhibit “multiple conjunctural” configurational effects on the industry’s comprehensive advantage. This manifests in two primary ways. First, complex interrelationships exist among the instruments. They may generate complementary effects (1+1>2) or substitution effects depending on contextual suitability. Furthermore, leverage policies and bottleneck policies, as two special types, exert multiplier effects and bottleneck effects respectively, determining the upper and lower limits of policy effectiveness. The superposition of these four types of policy effects forms a “joint effect” acting upon the comprehensive advantage. Second, influenced by the implementation context, the “optimal combination” of policy instruments varies across different regions, leading to equifinal configurations.
Research Framework
The integrated analytical framework is presented below. The policy strategy directs the growth trajectory of the industrial comprehensive advantage and guides policy instrument design. “Development-oriented” and “Safety-oriented” policy instruments are customized and combined within specific implementation contexts, influencing the unmanned drone industry’s comprehensive advantage through configurational effects.
Research Design and Measurement
Method and Sample
Fuzzy-set Qualitative Comparative Analysis (fsQCA) is employed for this study. This method is suitable because: (1) It applies systems thinking to identify sets of antecedent conditions for an outcome, capturing configurational effects of policy mixes rather than single-policy net effects. (2) fsQCA adheres to the principle of equifinality, able to identify different policy configurations applicable to diverse contexts. (3) It allows for in-depth case analysis to explain the “why” behind the identified effects.
The sample comprises 30 provincial-level administrative regions in China. This provides sufficient sample size for QCA, significant inter-provincial policy variation for deriving diverse configurations, and data availability from government websites and statistical yearbooks.
Variable Measurement and Calibration
Antecedent Variables (Policy Instruments): Measured using policy text quantification. Policy measures were extracted from 131 relevant policy documents (2010-2023) and coded into the six instrument types. The intensity of each policy instrument i in year t (TPit) was quantified by aggregating scores from three dimensions: promulgation form (PFit), intervention type (PTit), and policy goal clarity (PGit).
$$ TP_{it} = PF_{it} \times PT_{it} \times PG_{it} $$
The cumulative policy intensity index for instrument i in a province (STPi) is then calculated, considering policy persistence.
$$ STP_i = \sum_{t=2010}^{2023} TP_{it} $$
Outcome Variable (Comprehensive Advantage): A measurement system was constructed across four dimensions: Innovation Advantage, Market Advantage, Safety Advantage, and Ecological Advantage. Data for 2024 was collected, normalized, weighted using the entropy method (weights: 0.342, 0.287, 0.204, 0.167 respectively), and aggregated via linear weighting.
$$ \text{Comprehensive Advantage Index} = \sum_{j=1}^{4} w_j \cdot I_j $$
Where \( w_j \) is the weight and \( I_j \) is the normalized index for dimension \( j \).
Calibration: All variables were calibrated into fuzzy sets using the direct method. The anchors (fully in, crossover, fully out) were set at the 75th percentile, median, and 25th percentile of the raw data distribution, respectively, as detailed in Table 2.
| Variable Type | Variable Name | Fully In | Crossover | Fully Out |
|---|---|---|---|---|
| Antecedent Variables (Policy Intensity) | Innovation-Driven Policy | 269 | 115 | 62 |
| Application Expansion Policy | 196 | 99.5 | 49.5 | |
| Ecological Optimization Policy | 223 | 106 | 56.5 | |
| Science & Tech Safety Policy | 184.5 | 77 | 43.5 | |
| Operational Safety Policy | 291 | 162 | 65 | |
| Public Safety Policy | 163.5 | 86.5 | 49 | |
| Outcome Variable | Comprehensive Advantage | 0.674 | 0.479 | 0.382 |
Empirical Analysis and Discussion of Results
Analysis of Necessary Conditions
Necessity analysis was conducted for individual policy instruments and their negations. The consistency and coverage scores for all conditions were below the 0.90 threshold, confirming that no single policy instrument is necessary or sufficient for achieving (or not achieving) a high level of comprehensive advantage. This preliminary finding supports the core premise of configurational analysis—that outcomes are driven by combinations of conditions.
Analysis of Sufficient Configurations
Using fsQCA software with a consistency threshold of 0.80 and a frequency threshold of 1, sufficient configurations for both high and non-high levels of comprehensive advantage were identified. The intermediate solutions are reported, with core and peripheral conditions distinguished with reference to the parsimonious solution. The results are presented in Table 3.
| Antecedent Conditions | High Comprehensive Advantage | Non-High Comprehensive Advantage | ||||
|---|---|---|---|---|---|---|
| C1 | C2 | C3 | NC1 | NC2a | NC2b | |
| Innovation-Driven Policy | ● | ● | ● | ⊗ | ⊗ | |
| Application Expansion Policy | ● | ● | ||||
| Ecological Optimization Policy | ● | ⊗ | ● | ⊗ | ⊗ | ⊗ |
| Science & Tech Safety Policy | ● | ● | ⊗ | ⊗ | ||
| Operational Safety Policy | ● | ● | ● | ⊗ | ⊗ | |
| Public Safety Policy | ● | ● | ⊗ | ⊗ | ||
| Consistency | 0.912 | 0.875 | 0.863 | 0.918 | 0.844 | 0.906 |
| Raw Coverage | 0.301 | 0.243 | 0.210 | 0.285 | 0.216 | 0.129 |
| Unique Coverage | 0.115 | 0.084 | 0.078 | 0.101 | 0.091 | 0.062 |
| Overall Solution Consistency | 0.883 | 0.875 | ||||
| Overall Solution Coverage | 0.691 | 0.558 | ||||
| Configuration Label | Tech-Anchor Safety-Core | Scenario-Expansion Operation-Escort | Eco-Synergy Multi-Governance | Innovation Ecology Imbalance | Safety Governance Gap | |
Note: ● = core condition present; ● = peripheral condition present; ⊗ = core condition absent; ⊗ = peripheral condition absent; blank = condition irrelevant (could be either present or absent).
Configurations for High Comprehensive Advantage
Three distinct policy configurations lead to a high level of comprehensive advantage for the unmanned drone industry.
1. Technology-Anchor & Safety-Core Mode (C1): This configuration centers on the complementary presence of Innovation-Driven Policy and Science & Technology Safety Policy as core conditions, supplemented by Ecological Optimization and Operational Safety as peripheral conditions. It is typical for provinces with strong R&D bases and existing航空 clusters (e.g., Shanghai, Hubei, Shaanxi). The policy logic focuses on building a robust technological foundation through collaborative innovation while actively safeguarding the security and controllability of the core technology supply chain. This mode effectively cultivates both resource-based (technological) and institutional-based (safety standards) dominant advantages, leading to high performance in innovation and safety dimensions.
2. Scenario-Expansion & Operation-Escort Mode (C2): This path is defined by the core presence of Innovation-Driven, Application Expansion, and Operational Safety policies, coupled with the peripheral presence of Public Safety policy and the absence of Ecological Optimization policy. It fits regions with dynamic private capital and diverse application demand (e.g., Zhejiang, Sichuan). The strategy aggressively promotes unmanned drone use in various pilot scenarios (logistics, emergency response) through subsidies and procurement, while simultaneously establishing a robust flight supervision system (e.g., real-time monitoring networks) to “escort” these large-scale operations. This creates a distinctive advantage rooted in market application innovation and operational safety assurance.
3. Ecology-Synergy & Multi-Governance Mode (C3): This integrated configuration requires the core presence of Innovation-Driven, Ecological Optimization, Science & Technology Safety, and Public Safety policies, with Operational Safety as a peripheral condition. It is exemplified by leading industrial hubs with mature clusters (e.g., Guangdong, Jiangsu). The policy mix fosters a synergistic industrial ecosystem through financial funds, testing centers, and exhibition platforms, while implementing multi-dimensional governance covering technological autonomy, flight operations, and public safety incidents. This results in a balanced and leading comprehensive advantage across all four dimensions.
Configurations for Non-High Comprehensive Advantage
Two primary patterns explain the absence of high comprehensive advantage, both stemming from inappropriate policy mixes.
1. Innovation Ecology Imbalance Mode (NC1, NC2a): This pattern is characterized by the absence of Innovation-Driven, Ecological Optimization, and Science & Technology Safety policies, potentially with only Application Expansion policy present. It is observed in provinces like Fujian and Gansu. The policy effort is skewed towards stimulating demand (e.g., promoting unmanned drone tourism) without providing balanced support for the supply side (technology R&D) and environmental side (industrial infrastructure, safety resilience). This creates an “unbalanced”动力 structure, failing to build a stable and well-functioning innovation ecosystem for the unmanned drone industry.
2. Safety Governance Gap Mode (NC2b): This pattern features the comprehensive absence of all three safety-oriented policies: Science & Technology Safety, Operational Safety, and Public Safety. Provinces like Heilongjiang fall into this category. Despite having plans for unmanned drone industry development, the lack of concomitant institutional建设 for airspace management and flight监管 leads to frequent safety incidents, eroding public trust and fundamentally hindering the growth of a sustainable comprehensive advantage. This highlights a failure to effectively implement the “Ensuring both Development and Safety” strategy at the local level.
Analysis of Policy Mix Effects
The configurational analysis reveals several key policy mix effects that drive the formation of the unmanned drone industry’s comprehensive advantage.
1. Complementary Effect: The co-occurrence of Innovation-Driven Policy and Operational Safety Policy as core or important conditions in all high-advantage configurations (C1, C2, C3) indicates a strong complementary effect. Innovation-Driven policy cultivates resource-based dominant advantages (e.g., core technologies), while Operational Safety policy builds institutional-based dominant advantages (e.g., flight rules). Together, they synergistically foster the “twin-wheel” core capabilities of innovation development capacity and safety governance capacity, creating a high-resilience industrial ecology.
2. Substitution Effect: A substitution relationship is observed between the “Ecological Optimization – Science & Technology Safety” policy pair and the Application Expansion policy. Configuration C1 and C3 rely on the former pair, while C2 relies on the latter in their absence. This effect is highly context-dependent. The choice between perfecting the innovation/safety ecosystem versus aggressively pushing application scenarios depends on the region’s specific endowments—its existing industrial base, capital availability, and regulatory readiness. There is no one-size-fits-all approach for the unmanned drone sector.
3. Multiplier Effect and Bottleneck Effect: Innovation-Driven Policy acts as a leverage policy, present as a core condition in all high-advantage paths. Its presence maximizes the撬动 of resources and capability building, exerting a multiplier effect on the overall outcome. Conversely, Science & Technology Safety Policy exhibits characteristics of a bottleneck policy. Its absence is a core condition in all non-high-advantage configurations (NC1, NC2a). Neglecting this dimension creates a critical bottleneck that constrains the growth of the unmanned drone industry’s comprehensive advantage, regardless of other policy efforts. This underscores the strategic foresight of “Ensuring both Development and Safety”—innovation is the engine, but technological security is the ballast.
Conclusions and Implications
Main Conclusions
This study investigates how the Development-Safety policy mix shapes the comprehensive advantage of the unmanned drone industry from a configurational perspective. The main conclusions are as follows:
First, the Development-Safety policy mix is a system composed of the overarching “Ensuring both Development and Safety” strategy, a portfolio of six categorized policy instruments (three development-oriented, three safety-oriented), and the implementation contexts that necessitate customization.
Second, the comprehensive advantage of the unmanned drone industry is a synthesized competitive edge encompassing innovation, market, safety, and ecological advantages. It is formed through the synergistic drive of the policy mix, which cultivates resource-based and institutional-based dominant advantages to support the dual core capabilities of innovation and safety governance.
Third, the policy mix exerts “multiple conjunctural” effects, leading to different effective configurations across diverse regional contexts. Three successful paths were identified: Technology-Anchor & Safety-Core, Scenario-Expansion & Operation-Escort, and Ecology-Synergy & Multi-Governance. Failure often stems from an imbalanced policy mix causing innovation ecology imbalance or a critical safety governance gap. The analysis reveals significant complementary effects (between innovation and operational safety policies), context-dependent substitution effects, and identifies Innovation-Driven policy as a leverage (multiplier) instrument and Science & Technology Safety policy as a potential bottleneck instrument.
Theoretical and Practical Implications
Theoretically, this study contributes by integrating the “development” and “safety” perspectives into a unified policy mix framework, moving beyond isolated analyses. It enriches the conceptualization and measurement of industrial comprehensive advantage in the new era, explicitly incorporating safety and ecological dimensions crucial for the unmanned drone sector. By adopting a configurational effects perspective, it reveals the complex causal mechanisms behind industrial policy outcomes, complementing traditional net-effect studies.
Practically, the findings offer guidance for policymakers and industry stakeholders. The central government should reinforce top-level design, ensuring the upcoming “15th Five-Year Plan” for the unmanned drone industry integrates innovation ecosystem development with a safety-backed governance ecosystem. Establishing cross-departmental coordination mechanisms is vital. Local governments must tailor their policy mixes to local conditions. They should choose a configuration mode aligned with their endowments, strengthen leverage policies like innovation support, and decisively address potential bottlenecks, particularly in science and technology safety. Learning from advanced regions is recommended. Unmanned drone enterprises should proactively utilize policy support, aligning their R&D and business strategies with both developmental incentives and safety governance requirements to achieve sustainable, ecologically-sound growth.