Sustainability of Drone-Based Urban Air Mobility: A Systematic Review of Consensus and Controversies

Highlights

What are the main findings?

• Consensus has been reached regarding the challenges of noise, equity, and safety in drone-based Urban Air Mobility (UAM).
• Debates persist concerning drone-based UAM’s potential for emission reduction, commercial viability, and its impact on overall efficiency.

What are the implications of the main findings?

• In-depth research is required for psychoacoustic noise standards, autonomous flight technologies, life cycle assessments (LCA), pricing strategies and ground-access integration issues.
• Evidence-based frameworks integrating LCA and multidimensional social evaluation are critical for sustainable UAM deployment.

Abstract

Drone-based Urban Air Mobility (UAM) shows immense potential in urban logistics and emergency response; however, evidence regarding its systemic sustainability remains fragmented. In a systematic review using the PRISMA methodology, this study analyzes 301 core articles to construct an evaluation framework spanning environmental, economic, social, and systemic effectiveness dimensions. Given technical similarities, electric Vertical Take-off and Landing (eVTOL) findings are integrated to anticipate operational challenges. Results highlight a clear consensus: drone delivery is time-efficient in high-sensitivity scenarios, though noise, equity, and safety remain critical bottlenecks. Meanwhile, deep controversies persist across some dimensions. Environmental benefits are highly context-dependent, contingent on operating models, battery life cycles, and clean energy proportions from a Life Cycle Assessment (LCA) perspective. Economically, a mismatch between high costs and low willingness to pay (WTP) necessitates optimized pricing strategies. Socially, public acceptance is sensitive to the balance between perceived benefits and risks. Furthermore, systemic effectiveness depends on the coupling between vertiports and ground infrastructure. Concluding that sustainable drone-based UAM is a multistakeholder systemic endeavor, we urge future research to prioritize LCA, pricing strategies, public acceptance surveys, and integrated air-ground coordination to resolve controversies and foster sustainable systems.

1. Introduction

In recent years, uncrewed aerial vehicles (UAVs) have demonstrated significant utility in delivery services, with applications spanning logistics, emergency response, and medical transport [1]. Companies such as Meituan have achieved a daily capacity of 12,000 orders through self-developed urban drone delivery systems [2], while SF Express’s subsidiary, Fengyi Technology, utilizes drones for high-efficiency transport with payloads reaching 50 kg [3]. Furthermore, the potential of UAVs for transporting critical medical supplies, such as Automated External Defibrillators (AEDs), has been well-documented [4,5,6].

These applications fall under the broader category of Urban Air Mobility (UAM). Initially proposed by Airbus [7], UAM is defined as an air transportation system operating within and around urban environments [8]. This system primarily comprises electric Vertical Take-off and Landing (eVTOL) aircraft and Uncrewed Aerial Systems (UAS) [9,10], designed to provide safe and efficient transport for passengers and cargo while supporting coordinated crewed or uncrewed operations.

The application scenarios for UAM are extensive, including logistics, emergency response, medical transport, and passenger services [6]. The vision of UAM is to construct a safer, more sustainable, and economical mobility ecosystem [6]. As electric-powered systems, UAM is expected to mitigate urban congestion and environmental pollution by reducing greenhouse gas emissions [4,11]. Consequently, proponents generally regard UAM as a highly energy-efficient mode of transportation [8,12].

A preliminary search of UAM-related literature reveals a distinct multidisciplinary characteristic (Figure 1). According to Web of Science (WoS) data, “Aerospace, Mechanical & Mechanics” (approx. 40.01%) constitutes the core of UAM research, followed by “Energy, Physics & Materials” (17.16%), “Electronics, Telecommunications & Control” (16.09%), “Transportation, Remote Sensing & Planning” (11.21%), “Computer Science, AI & Robotics” (7.83%), and “Environment, Management & Social Sciences” (7.7%). This disciplinary distribution reflects the multi-dimensional nature of UAM sustainability: energy research relates to environmental impact, transport planning to economic viability, and social sciences to public acceptance. Such multidisciplinary synergy aligns with the integrated evaluation logic of environmental, economic, social, and systemic effectiveness [13,14,15]. However, while substantial progress has been made within specific sub-fields, information regarding UAM sustainability remains fragmented and lacks comprehensive integration.

The core focus of this study is drone-based UAM. From an academic perspective, the sustainability of drone-based UAM is far more complex than its initial vision suggests. It extends beyond the simplistic assumptions that electric propulsion guarantees zero emissions or that aerial routing automatically ensures efficiency. In reality, introducing a novel low-altitude transportation network requires a systemic integration into the existing urban fabric [16]. Alongside these systemic hurdles, negative externalities—such as noise annoyance, privacy concerns, and social equity—require rigorous evaluation. Therefore, a systematic literature review is necessary to assess the sustainability performance of drone-based UAM across different dimensions and to verify whether it can truly achieve its low-carbon and high-efficiency objectives.

Notably, drone-based UAM and eVTOLs are the two core carriers of urban air mobility. Some research results of eVTOLs are valuable for drone-based UAM, and we will use them as a reference. Before integrating relevant research results, it is necessary to clarify the boundaries between the two in order to avoid misapplying research conclusions in different operational environments (

Table 1. Boundaries and Reference Values of Drone-based UAM and eVTOL.

Boundaries		Reference Value
Drone-Based UAM	eVTOL
Cargo-focused core scenarios Small payload, unmanned design Ultra-low altitude penetration Point-to-point end delivery capability Mature large-scale commercial operation	Passenger-focused core scenarios Large payload, manned design Fixed routes between urban hubs Limited end-to-end penetration Early pilot stage, no full commercialization	Core carriers of UAM Adopt similar propulsion systems, and face homologous technical bottlenecks, such as battery energy density limits Require new supporting infrastructure Encounter similar cost and commercialization challenges Need full life cycle assessment (LCA) Must comply with noise, safety and privacy rules

).

In terms of core boundaries, drone-based UAM is mainly oriented to urban logistics, medical emergency and other cargo-focused core scenarios, with small payload, unmanned design, lower airworthiness and safety redundancy requirements, ultra-low altitude penetration, and point-to-point end delivery capability, having entered the stage of mature large-scale commercial operation [17,18]; by contrast, eVTOLs focus on urban passenger travel scenarios such as commuting and airport shuttles, which are passenger-focused core scenarios, with large payload, inherent manned attributes, thus facing stricter airworthiness certification and regulatory constraints, operating mainly on fixed routes between urban hubs with limited end-to-end penetration, and are still in the early pilot stage with no full large-scale commercialization [19,20,21].

Despite the above boundaries, the two share significant commonalities aligned with the reference value outlined in this study: both are core carriers of UAM, adopt similar electric vertical take-off and landing propulsion systems, and face homologous technical bottlenecks, such as battery energy density limits [22,23,24]; both require supporting infrastructure, and encounter similar cost and commercialization challenges [25,26,27,28,29]; both operate in urban low-altitude airspace with consistent environmental and social constraints, follow the same underlying logic of low-altitude traffic supervision, and must comply with noise, safety and privacy rules [30,31,32]. Furthermore, their sustainability performance can be evaluated with full life cycle assessment (LCA) [33,34].

While drone-based UAM and eVTOLs differ in core application scenarios, payload scale, airworthiness regulations, operational spatial features, and commercialization maturity, they hold consistent commonalities in propulsion systems, public acceptance formation mechanisms, core commercial logic, and fundamental regulatory logic (Table 1). On this basis, we integrate relevant eVTOL research findings as a complementary reference for drone-based UAM study.

This study aims to address three core research questions:

• What primary dimensions and underlying themes frame the current academic evaluation of drone-based UAM sustainability?
• In which areas does the literature show relative convergence, and where do findings remain conditional or contested?
• What further areas need to be explored to evaluate drone-based UAM sustainability?

The policy implications of these research questions extend beyond academic inquiry. As urban air mobility transitions from technological prototypes to operational systems, its sustainability outcomes will be shaped by the institutional frameworks and decision-making mechanisms that govern its integration. For instance, research on green policy adoption demonstrates that expert involvement and policy entrepreneurs play a critical role in accelerating the implementation of sustainability-oriented initiatives by coupling problem recognition with viable solutions [35]. These insights provide a basis for the analytical approach of this review and the policy implications it can offer, which together seek to identify not only areas of consensus and controversy but also the governance conditions under which sustainable UAM systems may emerge.

The remainder of this paper is organized as follows: Section 2 defines the conceptual scope of “drone-based UAM sustainability” and distinguishes this study from previous reviews. Section 3 details the methodology for article selection and the PRISMA-based review process, including a descriptive analysis of the 301 included articles. Section 4 synthesizes the primary perspectives on drone-based UAM sustainability, identifying areas of consensus and debate. Section 5 discusses these findings, and Section 6 concludes by summarizing the paper’s primary contributions.

2. Conceptual Definition

2.1. Research Scope

The concept of drone-based UAM sustainability discussed in this paper is built upon the framework of sustainable transportation. According to classical definitions, researchers generally categorize sustainable transportation into three core dimensions: environmental, economic, and social [13]. The environmental dimension focuses on the impacts of human activities on local and global environmental changes; the economic dimension addresses the progress of communities toward economic goals such as wealth, employment, and productivity; and the social dimension primarily concerns equity and inclusion in transportation distribution.

Subsequent studies have constructed detailed transport impact matrices. Environmentally, key indicators include air pollution, climate change, noise, habitat degradation, and the depletion of non-renewable resources. Economically, the focus is on infrastructure investment, consumer expenditures, and accident losses. Socially, the emphasis is placed on transportation affordability, impacts on vulnerable groups, human health, and community livability [36]. As research has evolved, scholars have argued that focusing solely on external impacts is insufficient. “Transportation System Effectiveness” must be incorporated as a fourth core dimension, emphasizing that the congestion, operational efficiency and performance of the system itself serve as the technical foundation for long-term sustainability.

This multi-dimensional perspective provides a logical foundation for understanding how transportation systems balance sustainability requirements [14]. To make drone-based UAM sustainability assessment more operational, this study references indicator systems from previous systematic reviews of urban transport sustainability [15,36,37,38] and synthesizes them accordingly (Figure 2).

Within the indicator system of this study, the environmental dimension covers not only traditional metrics—such as air quality, greenhouse gas emissions, noise pollution, energy consumption, and impacts on biological habitats—but also specifically introduces a life-cycle perspective. This approach aims to examine environmental contributions across the entire process, from aircraft manufacturing and operation to end-of-life disposal. The economic dimension encompasses infrastructure investment, operation and maintenance costs, consumer affordability, and the stimulation of employment and productivity. The social dimension focuses on the symbiotic relationship between the drone-based UAM system and urban residents, primarily evaluating transport safety, human health, social equity and justice, public acceptance, and contributions to community livability. To reflect the technical characteristics of drone-based UAM as a novel and complex aviation system, we introduce the dimension of transportation system effectiveness [37], which includes mobility, accessibility, system reliability, operational efficiency, congestion, and airspace capacity and throughput.

In practical terms, sustainability within transportation is a relative concept. Rather than an absolute standard, a system is considered ‘truly sustainable’ only when it represents the optimal choice compared to existing alternatives across weighted evaluation dimensions driven by local policy. Therefore, this review assesses drone-based UAM not against an idealized zero-impact baseline, but by comparing its dimensions of improvement against current alternatives.

2.2. Distinctions from Previous Reviews

Existing review studies have differentiated relatively clearly in both research object and analytical perspective. Rejeb et al., Garg et al., Jazairy et al., and Mohamed and Mohamed all take drone-based last-mile delivery as their central focus [39,40,41,42], yet their emphases differ substantially. Rejeb et al. concentrate on application potential, implementation barriers, and future research directions in supply chain and logistics contexts [39]. Garg et al. synthesize the last-mile delivery literature around three principal lines of inquiry: efficiency, accessibility, and sustainability [40]. Jazairy et al. develop an analytical framework from a logistics management perspective, structured around four stakeholder groups—senders, receivers, regulators, and society [41]. Mohamed and Mohamed, by contrast, organize the field into seven domains: environmental performance, economic impacts, social impacts, policy and regulation, routing and scheduling, charging infrastructure, and energy consumption [42]. In parallel, Kumar et al. narrow the discussion to the environmental implications of drone delivery, systematically reviewing issues such as carbon emissions, energy efficiency, life-cycle effects, and regulatory constraints [43]. Biehle addresses the social sustainability of passenger UAM in Europe, examining affordability, inclusivity, accessibility, user satisfaction, and the quality of public space [44]. Moradi et al. review model types and solution approaches for eVTOL and UAV-assisted last-mile transportation from an operations research perspective [45]. Santos et al. extend the scope to the broader UAM field through indicator identification and thematic mapping [46], whereas Leung and Wen emphasize the bidirectional coupling between drone delivery and socio-environmental constraints and, on that basis, propose a risk–benefit analytical framework [47]. The extant reviews cover multiple dimensions, including last-mile delivery, environmental impacts, social effects, operations research, and broad thematic mapping. However, the field remains largely organized by separate dimensions and discrete topics, with limited cross-dimensional integration, and this is the interdisciplinary and cross-dimensional research that Meta-analysis has been advocating for recently [48].

Against this background, the present study reconstructs the sustainability of drone-based UAM as an overarching analytical problem. Drawing on 301 core publications, it establishes transportation system effectiveness as an independent analytical dimension alongside the environmental, economic, and social dimensions, and incorporates life-cycle emissions, operational reliability, transport accessibility, system capacity, congestion effects, and airspace operational constraints into a unified framework. This structure connects the assessment of external impacts with the internal mechanisms of system operation. In contrast to prior reviews that treat social sustainability, environmental consequences, operations research models, or general thematic mapping as separate analytical tracks, this study places greater emphasis on the integration of cross-dimensional evidence, the identification of contested issues, and the parallel examination of convergent and conflicting findings. Within this framework, time efficiency, noise annoyance, distributive equity, safety risk, emission-reduction conditions, commercial viability, and overall system effectiveness are evaluated comparatively rather than treated as isolated subtopics. The review also incorporates eVTOL research as a comparative reference in order to identify the common constraints and potential governance pathways associated with large-scale drone-based UAM deployment. Accordingly, the objective of this review is not merely to extend the literature within a single thematic domain, but to synthesize areas of consensus and disagreement in the existing literature, and advance a more integrated discussion of the field.

3. Methodology

This study was conducted in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [49]. Following the PRISMA guidelines, we first planned the review by defining the objectives, protocols, and search procedures. Then, we conducted the review by screening the literature and performing descriptive and structural analyses. Finally, we reported the findings by synthesizing them according to the research objectives and presenting them as a systematic review [50,51].

To identify the intersection between UAM and sustainability literature, search keywords were categorized into two groups for Boolean logic combination:

• Subject Terms: Including “Drone,” “UAV,” “UAS,” “Uncrewed Aerial Vehicles,” “Unmanned Aerial Vehicles,” “Urban Air Mobility,” “UAM,” and “eVTOL.” Notably, as drone-related literature in fields such as agriculture, mapping, and photography significantly outweighs that in transportation, specific constraints were applied. These terms were required to appear alongside context keywords such as “Logistics,” “Delivery,” or “Mobility” to exclude papers not centered on UAM sustainability.
• Attribute Terms: Extracted based on sustainability and its four dimensions (Environment, Economy, Society and System Effectiveness). These included “sustainability,” “sustainable,” “Environment,” “Economy,” “Society,” “Effectiveness,” and the names of the 21 sub-dimensions shown in Figure 2.

The search was limited to “peer-reviewed journal articles” published in “English,” with no restriction on the publication date. Inclusion criteria required articles to be available in full text, published in English, and provide insightful conclusions or frameworks relevant to the research objectives. Exclusion criteria targeted articles irrelevant to the research scope, purely theoretical derivations, and technical papers focused on algorithms or modeling that lacked validation in operational scenarios. To ensure a comprehensive search, WoS and Scopus were utilized as the primary databases.

The search was conducted in March 2026, targeting the titles and abstracts of the articles. All retrieved data underwent manual inspection to ensure consistency and accuracy in keyword identification [52]. Two researchers independently screened the titles and abstracts to minimize selection bias; if deemed relevant, the full text was reviewed to determine final inclusion. The full-text screening was conducted to ensure accessibility and to confirm that the records were original research, studies such as reviews were excluded. Two reviewers independently evaluated each paper against the predefined eligibility criteria for inclusion or exclusion. Any discrepancies between the two reviewers were resolved through thorough discussion until a consensus was reached.

To ensure the quality of the articles, we developed a quality assessment form (

Table 2. Quality evaluation standards.

Scenario	Conclusion
Reliability	0 = Purely hypothetical or unverified mathematical derivation; 1 = Cited third-party public data or mature simulation algorithms; 2 = Real-world data.
Completeness	0 = Focuses only on a single flight phase or a single factor; 1 = Covers the entire operational process or multiple core factors; 2 = Covers the full life cycle or multi-party stakeholders.
Temporal Representativeness	0 = Prior to 2020; 1 = 2021–2023; 2 = 2024 to present.
Geographical representative-ness	0 = Isolated case study; 1 = Focused on a specific city; 2 = Includes cross-regional comparisons.
Technological representative-ness	0 = Applies data from traditional fuel helicopters or civil aircraft; 1 = Uses analogous data from general electric aircraft; 2 = Specific to UAM aircraft (UAV/eVTOL) unique attributes.

) based on the Pedigree Matrix, scoring each article across five quality aspects (reliability, completeness, timeliness, geographical and technical representativeness) [53]. The maximum score for each study is 10 points. Study quality is categorized as: excellent (≥9), good (7–8), fair (5–6), poor (3–4), and very poor (≤2).

The initial search yielded 12,530 documents (5924 from WoS and 6606 from Scopus), which was reduced to 907 after de-duplication and preliminary screening of titles. Abstract screening further narrowed it to 337. After a detailed full-text review, 289 articles remained. By incorporating 12 additional papers through citation tracking, a total of 301 core articles were finalized for classification and structural analysis (Figure 3). Among the 301 included articles, 86 were rated as excellent, 146 as good, and 69 as fair. Study quality was not used as a formal mathematical weight due to the diverse methodologies of the included literature. Instead, the Pedigree Matrix scores guided our qualitative synthesis. Specifically, our overarching conclusions and the identified consensus are predominantly grounded in the robust evidence from studies rated as ‘good’ or ‘excellent’. While no studies were excluded entirely based on their scores, those rated as ‘fair’ were interpreted with greater caution and primarily used to provide supplementary background context rather than to drive core findings.

We coded each article based on the framework of 21 sub-dimensions described previously. A qualitative analysis was performed on each study to identify the specific sub-dimensions it addressed. During classification, the primary decision rule was to assign a study to a specific sub-dimension only if its core research objectives, main methodology, or primary findings aligned with the predefined criteria of that sub-dimension.

For example, in a study evaluating UAV routing for emergency blood distribution, the authors explicitly calculated a 58.57% reduction in delivery time, a 96.35% decrease in energy consumption costs, and a 61.20% drop in daily operating expenses compared to traditional ambulances [54]. Based on these findings, this study was simultaneously assigned to three distinct sub-dimensions, all marked with a “positive impact” label: “Energy Consumption”, “Operation & Maintenance Costs”, and “Efficiency”. Following this granular coding, the three sub-dimensions were aggregated into primary dimensions, namely Environmental, Economic, and Systemic Sustainability. Regarding multi-dimensional studies, the categories were not treated as mutually exclusive; instead, studies addressing cross-cutting issues were assigned to multiple primary dimensions simultaneously.

To ensure the reliability of the coding process, two reviewers conducted the assessment independently. The initial percentage agreement of primary dimension level was 81%, indicating a high level of consistency. All remaining discrepancies were resolved through consensus-based discussion. The complete coding matrix for all included studies is available in the Supplementary Material.

4. Results

4.1. Descriptive Analysis

Following the screening process, we analyzed the publication trends of the 301 included articles and provided visual representations. The annual volume of publications has risen steadily since 2016. The social dimension has consistently received significant attention, accounting for 37.53% of the total throughout most of this period. Meanwhile, the research influence of the environmental (18.98%), economic (18.54%), and system effectiveness (24.94%) dimensions has also increased annually (Figure 4). The limited number of articles for 2026 is due to the fact that data collection only covered the first three months of the year.

A Sankey diagram was utilized to further synthesize the internal connections between various themes (Figure 5). The first column of the diagram displays the four primary sustainability dimensions mentioned previously, where the height of each flow represents its relative proportion. The second column presents the 21 sub-dimensions. Regarding sustainability impacts, the primary areas of academic focus are public acceptance (17.57%) and operational efficiency (11.82%).

In the second column of the Sankey diagram, red labels represent articles highlighting the negative impacts of UAM on sustainability within those specific dimensions; green labels indicate those reporting positive impacts, while blue labels signify generally neutral arguments. To provide a systematic synthesis, “Consensus” is defined as areas where an overwhelming majority (>80%) of the literature reports consistent directional impacts, typically visualized as dimensions dominated by a single color in the diagram. Conversely, “Controversy” refers to areas where a substantial portion of studies (typically >20%) presents contradictory evidence, characterized by the coexistence of both red and green labels. Based on these criteria, divergence is clearly observed across six dimensions: climate change, energy consumption, infrastructure investment, operation and maintenance costs, affordability, and congestion. In these areas, the sustainability performance of UAM is highly scenario-dependent and characterized by significant uncertainty.

The third column illustrates the research objects of the included studies. Articles focusing on drones account for 59.09% of the total literature, while eVTOL aircraft represent 40.91%. This indicates that the maturity of drone technology and its applications is significantly higher than that of passenger eVTOLs, having already advanced to the stage of large-scale empirical research. Finally, the fourth column displays their respective application scenarios.

Table 3. Controversial Dimensions.

Controversial Dimensions	Impacts	eVTOL	Drones	UAM
Climate Change	negative	57%	23%	31%
	positive	43%	78%	69%
Energy Consumption	negative	71%	36%	50%
	positive	29%	64%	50%
Infrastructure Investment	negative	59%	56%	40%
	positive	41%	44%	31%
Operation & Maintenance	negative	71%	35%	47%
	positive	29%	65%	53%
Affordability	negative	74%	40%	68%
	positive	26%	60%	32%
Congestion	negative	27%	13%	22%
	positive	73%	88%	78%

provides a detailed breakdown of the dimensions characterized by academic controversy in the second column of the Sankey diagram. While the “UAM” column aligns with the overall distribution shown in Figure 5, the “eVTOL” and “UAV” columns further disaggregate these findings to reveal the distinct performance of each platform within these debated themes. The data reveal a divergence in sustainability perceptions between eVTOL and drone platforms. Regarding climate change, energy consumption, and O&M costs, research on eVTOLs leans toward a cautious or negative outlook, with negative impacts cited in 57% to 71% of the literature. This reflects academic concerns over high energy demands and battery-related environmental footprints. In contrast, drones receive significantly more favorable evaluations in these categories, particularly concerning climate change (78% positive) and congestion mitigation (88% positive). Furthermore, affordability emerges as a primary bottleneck for eVTOLs, with 74% of studies highlighting high commercialization costs, whereas 60% of research views the cost-effectiveness of drone applications optimistically. Figure 6 is a visualization of Table 3.

4.2. Areas of Consensus

4.2.1. Time Efficiency Advantages

The superior speed of drone-based UAM is well-established. By utilizing aerial routes, these systems bypass complex ground-level topography and traffic congestion, enabling significantly faster response times. The high delivery efficiency of UAVs is particularly evident in Medical Services and Emergency Response [55]. Unlike general commercial logistics, these sectors are characterized by extreme time sensitivity, which reduces the relative priority of operational costs. Research indicates that using drones to transport medical supplies—such as blood, AEDs, and vaccines—outperforms conventional ambulances in terms of efficiency [56,57,58,59,60,61]. Taking AEDs as an example, in a case study from Guinea, response times were reduced by 78.8% compared to private vehicles [62]. In France, drones arrived an average of 190 s earlier than Basic Life Support teams (BLSt) [63]. Similarly, in Canada, AED drones consistently reached the scene faster than ambulances, with a time-saving margin ranging from 1.8 to 8.0 min [64]. In search-and-rescue operations across complex terrains, UAVs effectively shorten the interval between patient location and the initiation of treatment [65]. Although the unit operational cost of drone logistics currently exceeds that of traditional road transport, the “life premium” achieved in high-value emergency scenarios far outweighs the incremental logistics costs, the cost was compensated by the shorter travel time, which may be life-saving in an emergency [66,67].

While passenger eVTOLs demonstrate similar time advantages over longer distances [68,69], the flexibility of UAVs in last-mile logistics remains the critical factor for urban operational efficiency, especially compared to the current empirical stage of drone-based UAM.

4.2.2. Noise Pollution

Noise pollution is a widely recognized bottleneck for drone-based UAM. Research into drone noise indicates that flight acoustic profiles possess distinct psychoacoustic characteristics; that is, the degree of annoyance is determined not only by loudness (decibels) but also by subjective factors such as sharpness and tonality [70]. The industry has yet to establish noise standards that incorporate these psychoacoustic dimensions, and future evaluation frameworks must integrate subjective perception metrics into existing decibel-based standards [71,72]. Notably, eVTOL noise exhibits similar psychoacoustic traits, which may hinder the integration of such autonomous aircraft into urban residential communities.

Studies also suggest that ambient background noise exerts a masking effect on noise perception. In quiet areas, noise of the same intensity results in higher levels of annoyance than in noisy environments [73]. Rizzi et al. advocate for planning UAM routes over high-ambient-noise corridors, such as highways, to leverage existing background noise to mask aircraft sounds and reduce the scope of social disturbance [74].

4.2.3. Social Equity Issues

The proliferation of drone-based UAM may exacerbate social inequality. While efficient, drone delivery may place last-mile delivery personnel at risk of unemployment [75]. In discussions regarding social equity, research on eVTOLs offers profound insights applicable to drone-based UAM. Specifically, eVTOL inequality is often manifested through high fares that exclude low-income groups. The high costs associated with hardware, infrastructure investment, and maintenance necessitate high initial pricing [76,77]. Researchers generally anticipate that early adopters will be limited to high-income individuals [78,79,80]. Without regulatory constraints on social equity, UAM resources may skew toward the elite [81], and prohibitive pricing could limit its utility as a public transport tool [76,82]. Furthermore, the development of “UAM-friendly” communities and the selection of vertiport sites could further widen the spatial wealth gap within cities [83,84,85,86].

These findings warn that drone-based UAM may face similar disparities in service distribution. If delivery fees remain high, such convenience will remain inaccessible to the general public. For instance, current Meituan drone stations in Shenzhen are primarily concentrated in core business districts and high-end office buildings. If this infrastructure fails to penetrate older or low-income residential areas, drone delivery risks becoming an elite service, intensifying intra-urban inequality.

4.2.4. Safety Concerns

Drone-based last-mile delivery involves risks such as collisions and falls; some estimates suggest the mortality risk could be over 12 times higher than that of traditional van transport [87]. Furthermore, drones are often unable to operate in extreme weather, which increases economic costs [88]. Range anxiety and payload constraints also limit the scope of application [89]. There remains a lack of public psychological trust in drone safety, with anxiety often outweighing the expectations for convenience [90]. Research indicates that across both UAV and other low-altitude platforms, weather interference and operational risks are the most frequent threats [91]. This technological immaturity makes it difficult for the public to establish sufficient trust in the system’s safety during its early stages [92,93,94].

4.3. Areas of Controversy

4.3.1. Environmental Dimension: Does Drone-Based UAM Enhance Energy Efficiency and Emission Reductions?

The existing literature demonstrates a divided perspective regarding the current energy efficiency of drone-based UAM. Based on the distribution of included studies, current evidence predominantly suggests that net-zero goals have not yet been met due to existing technological constraints. Whether UAM can achieve net-zero goals in the future depends on the decarbonization of the energy mix, breakthroughs in battery technology, and actual operational models. In the literature concerning UAM and greenhouse gas emissions, there is no academic consensus: approximately one-third of the studies are optimistic, while the remainder express skepticism. The following discussion adopts a LCA perspective.

The environmental friendliness of drones is hindered by the high carbon load of manufacturing and supporting infrastructure, significant energy consumption fluctuations due to environmental factors, and challenges in recycling composite materials. Their mitigation potential primarily depends on scientific intermodal coordination and the strategic replacement of traditional vehicles.

From a life-cycle perspective, the environmental benefits of drone-based UAM are hindered by several factors. High carbon loads originate from component manufacturing and the operation of supporting infrastructure, such as smart lockers linked to fossil-heavy grids [26,33]. During operations, battery capacity limits and weather sensitivity significantly impact energy consumption [24,89,95], while the disposal of composite materials presents end-of-life recycling challenges [96]. Therefore, mitigation potential relies heavily on strategic drone-truck tandem models rather than fully replacing established trucking systems [97,98,99].

To provide a complementary perspective on the life cycle of drone-based UAM, the life cycle of eVTOL UAM has also been synthesized. The emission reduction potential of eVTOL aircraft is constrained by intensive battery manufacturing emissions, frequent replacement cycles, the electricity source structure, and lower energy efficiency compared to pure electric vehicles. Its environmental advantage is highly dependent on grid cleanliness and the effective replacement of high-carbon transport modes.

From a life-cycle perspective, the environmental viability of eVTOLs is heavily constrained by battery limitations, energy sources, and operational contexts. Battery production—particularly those using NCM cathodes—is highly carbon-intensive, a burden compounded during the recycling phase; due to rapid degradation from fast charging and high-frequency operations, making batteries the dominant source of life-cycle emissions [20,100]. Operationally, insufficient battery energy density results in excessive vehicle weight, demanding vast amounts of electricity during the energy-intensive takeoff and landing phases [101]. Consequently, if reliant on a fossil-fuel-intensive grid, CO₂ emissions per passenger-kilometer can exceed those of traditional internal combustion engine vehicles [102,103], but these specific comparative conclusions are also highly context-dependent, as actual emissions fluctuate based on variables such as grid carbon intensity, passenger load factors, and mission lengths. Ultimately, evaluating UAM’s mitigation potential requires moving beyond simple cross-dimensional comparisons; its true environmental advantage depends on the prevailing penetration rate of EVs it aims to replace [104,105], alongside the need to offset substantial carbon costs from supporting ground infrastructure and the risk of surging total emissions driven by vertiport access and induced demand [106].

By examining the eVTOL perspective, we further emphasize that assessing the environmental impact of drone-based UAM must transcend the focus on “zero-emission” flight and instead account for total life cycle emissions.

First, the hidden costs of UAV batteries may offset emission reduction benefits. Drawing on data from eVTOL research, carbon emissions during battery manufacturing represent a significant portion of the total environmental burden. For high-frequency drone delivery operations, adopting high-power fast charging to prioritize turnaround speed can significantly shorten battery lifespan. If UAVs require frequent battery replacements similar to eVTOLs, the carbon emissions generated by manufacturing new batteries could likely neutralize the emissions saved by displacing ground-based internal combustion engine vehicles.

Second, the cleanliness of the energy source is a decisive factor. The environmental benefits of drone delivery are highly dependent on the local power grid structure. If the grid remains dominated by fossil fuels, drones essentially shift tailpipe emissions from urban streets to distant power plants.

Finally, the scale effect of drone delivery is a double-edged sword. While dense delivery networks and increased flight frequencies may improve individual delivery efficiency, the construction and maintenance of ancillary infrastructure—such as automated hangars and smart delivery lockers—generate a substantial environmental load. Therefore, achieving true sustainability requires not only extending battery life but also optimizing route planning at a systemic level. This approach is necessary to avoid excessive flights caused by “induced demand” and to ensure that charging infrastructure is deeply integrated with clean energy sources.

4.3.2. Economic Dimension: What Is the Commercial Viability of Drone-Based UAM?

A synthesis of the economic evaluation literature indicates a consensus that early commercialization of drone-based UAM should prioritize scenarios that leverage existing infrastructure. Whether it can evolve into a large-scale industry integrated into daily life depends on user penetration and the alignment between service offerings and market demand. The following section utilizes a SWOT-based framework to analyze four aspects: infrastructure investment advantages, affordability disadvantages, operating cost challenges, and demand expansion opportunities.

• Infrastructure Investment Advantages

The large-scale operation of drone-based UAM necessitates a robust infrastructure network. In the drone sector, integrating existing facilities with a few strategically placed logistics hubs can significantly reduce the average transport distance for last-mile delivery [107]. Similarly, for eVTOL, existing airports and helipads can be retrofitted into vertiports [69,108]. These strategies can substantially lower initial capital expenditure.

• Affordability Disadvantages

Prohibitive pricing remains the primary barrier to the mass adoption of drone-based UAM [94].

Table 4. Key pricing/willingness to pay (WTP) data in different regions.

Scenario	Region	Mode	Key Pricing/WTP Data	Conclusion
Last-mile Logistics	China	Drone	WTP: $1.4 per delivery	Attractive only at this price point compared to ground vehicle delivery; highly price-sensitive [109].
Passenger Service	Tehran, Iran	eVTOL	Key Pricing: $26.4/h	The Value of Time (VOT) for UAM is significantly higher than that of ride-hailing ($5.37) and private cars ($5.25) [110].
Airport Shuttle	South Korea	eVTOL	Mean WTP: $48/trip; Median WTP: $39/trip	Public willingness to pay is generally lower than the fares proposed by institutions [111].
Regional Travel	Illinois, USA	eVTOL	$106–$224 (for a 20-mile trip)	Ride-hailing costs < $45; the price gap compared to UAM is as high as 2–5 times [77].

summarizes existing surveys on pricing and willingness to pay (WTP) for both UAVs and eVTOLs. The eVTOL data highlights significant affordability disadvantages, offering valuable insights for establishing fare structures for drone-based UAM. A clear disparity is observed: on the supply side, operational costs are difficult to compress, rendering fares uncompetitive compared to traditional transportation modes; on the demand side, WTP is generally lower than the commercial pricing projected by institutional assessments. Due to this supply-demand mismatch, drone-based UAM may initially be restricted to premium market segments.

While these empirical survey data are limited in sample size and geographic scope, they consistently reveal a trend of low WTP. Extrapolating from these empirical findings, a conceptual interpretation suggests that the supply-demand mismatch may initially restrict drone-based UAM to premium market segments.

• Operational Cost Challenges

Current research generally acknowledges that drone-based UAM possesses a significant cost advantage in last-mile delivery. Compared to traditional trucks, logistics costs can be reduced by 28% to 93% [112]. However, the primary challenge lies in the need for a comprehensive assessment of battery cycle life, software licensing, regulatory compliance, and investment in ground infrastructure [113]. Furthermore, the economic viability of drones varies significantly across geographic environments; sparsely populated suburban areas often demonstrate higher operational cost-effectiveness than urban centers characterized by high-rise buildings and takeoff/landing restrictions [114].

In the eVTOL sector, operational cost challenges center on the trade-off between flight frequency and load factors [115]. Although the per-trip operating cost of an eVTOL is higher than that of a traditional vehicle, it reduces the generalized travel cost for users through superior commuting efficiency [116].

The operational challenges of eVTOLs provide a strategic reference for drone logistics. This implies that the large-scale commercialization of drone-based UAM necessitates increasing task density to amortize high fixed costs. If high-frequency round-trip operations cannot be maintained, or if infrastructure maintenance costs become excessive, the inherent cost advantages of drones will be negated by hardware expenditures. Consequently, future research should prioritize exploring efficient coupling models between air routes and ground infrastructure to minimize the total operational cost per delivery mission.

• Opportunities in Induced Demand

Research on eVTOLs indicates that UAM, as a novel transportation paradigm, will generate induced demand [117]. Strategic placement of vertiports in areas with high travel demand and severe ground congestion is critical to achieving profitability [118]. Moreover, UAM profitability depends heavily on the precise matching of application scenarios. Research shows that the estimated value of time for business travel is more than 2.5 times that of airport transfers, while the WTP for tourism-related travel is relatively low [119]. Therefore, routes sensitive to time should be prioritized for initial deployment.

Although research on induced demand specifically for UAVs is sparse, insights from eVTOL studies suggest that when drone delivery becomes exceptionally inexpensive and rapid, it may stimulate purchasing behaviors that previously did not exist. Drone-based UAM should utilize strategic station placement and scenario matching to unlock this latent delivery demand.

Regarding infrastructure siting, takeoff and landing facilities should be prioritized in high-density urban areas where ground congestion is severe and time-efficiency is a significant pain point. Such placement highlights the speed advantage of drones through a direct comparison with ground alternatives, thereby inducing users to choose aerial delivery to save time.

In terms of scenario matching, high early-stage operating costs necessitate a focus on time-critical niche markets, such as emergency medicine, urgent documents, or high-premium perishables. Just as the WTP for UAM is significantly higher in business travel than in leisure tourism, drone delivery can only effectively induce latent, fragmented instant-consumption needs when its service scenarios precisely align with the user’s “buying time” motivation. This alignment is essential for achieving cost reductions through economies of scale and establishing a virtuous commercial cycle.

4.3.3. Social Dimension: Factors Influencing Public Acceptance of Drone-Based UAM

Public acceptance is a fundamental pillar of UAM sustainability. To this end, we have systematically reviewed the factors influencing public intention to provide a multi-faceted analysis of this complex issue.

Regarding drones, the public is most concerned with the intended use and safety assurance. Drones utilized for medical assistance and disaster relief receive the highest levels of acceptance [120,121], whereas skepticism remains concerning personal entertainment or high-density commercial flights [120,122]. Factors that enhance acceptance include perceived usefulness, delivery efficiency, environmental benefits [123], and individual familiarity with the technology [124]. Conversely, factors that diminish acceptance primarily include privacy risks, potential safety hazards (such as crash risks), and noise or visual interference [125,126]. Furthermore, increased delivery fees significantly reduce the likelihood of user adoption [127].

Table 5. Factors influencing public acceptance of drones.

Influencing Factors	Sub-Dimensions	Keywords	Frequency
Personal Factors (48)	Demographic & Experience Characteristics	Gender, age, education level, income, residential area, occupation, previous drone operation experience, aviation travel experience	13
	Individual Cognition & Trait Attributes	Knowledge level of drones, public awareness, digital literacy, personal creativity	16
	Individual Psychology & Attitude Orientation	Attitudes towards drone technology, perceived behavioral control, hedonic motivation, technology affinity, technology anxiety, expectation	13
	Individual Trust-related Dimensions	Personal trust propensity, trust in drone technology itself, perceived reliability of drone technology, trust in technical safety and controllability	6
Perceived Benefits (44)	Functional & Economic Benefits	Perceived usefulness, perceived ease of use, convenience, door-to-door delivery, cost savings, service availability in remote/underserved areas	22
	Environmental & Traffic Benefits	Environmental friendliness, carbon emission reduction, traffic congestion reduction, urban road pressure alleviation, environmental sustainability, delivery speed/efficiency	16
	Public Value & Emergency Benefits	medical accessibility improvement, Emergency response capability, contactless delivery safety, disaster relief support, life rescue for time-sensitive goods, social equity & inclusiveness enhancement	6
Perceived Risks (41)	Safety & Physical Environment Risks	Safety risks, performance risk, noise pollution, visual pollution	22
	Privacy & Data Security Risks	Privacy infringement, data leakage risk, tracking and monitoring concerns, personal information security risk	16
	Social & Ethical Risks	Unemployment risk, criminal abuse risk, ethical risk, social inequality exacerbation risk	3
Institutional & External Environmental Factors (24)	Usage Scenarios	Medical/healthcare delivery, civil defense/disaster relief, scientific research, parcel delivery, food delivery, leisure/hobby use	7
	Policy & Regulatory System	Government regulation, policy support, legal framework, industry operation standards, emergency response plan, privacy protection regulations	3
	Infrastructure & Technical Environment	Supporting infrastructure, airspace resource allocation, technology maturity, network coverage, operation guarantee system	2
	Social & Community Environment	Subjective norms, social norms, word of mouth, community engagement, public science education, public communication, social opinion atmosphere	5
	External Subject Trust Dimensions	Trust in government regulators, trust in drone operators/enterprises, perceived effectiveness of regulatory system, trust in institutional guarantee	7

presents the frequency of these influencing factors.

To effectively improve public acceptance, actions should be taken in the following four areas:

• Strengthen Education and Communication: Enhance public awareness of drone benefits through science outreach activities and experimental pilot programs [128,129].
• Refine Regulation and Legislation: Establish transparent regulatory frameworks, clarify privacy protection measures, and define liability allocation to build public trust [126,130], building on this foundation, every effort should be made to take into account the diverse needs of various stakeholders [75].
• Optimize Technology and Infrastructure: Invest in the development of Uncrewed Aircraft System Traffic Management to ensure operations occur within a safe and controllable environment [131].
• Targeted Operational Strategies: Develop differentiated promotion plans based on regional characteristics and demographics [123,132], prioritizing high-utility scenarios such as medical and emergency services.

Public acceptance of eVTOL aircraft is reflected in the trade-off between time efficiency and economic costs. Research indicates that perceived usefulness, travel time, and cost are the most significant factors determining adoption [133,134]. Factors that can enhance acceptance include time efficiency, particularly in scenarios such as airport transfers and business travel where the high time value of UAM makes it highly attractive [119]. At a psychological level, strong utilitarian and hedonistic beliefs promote preference [135,136], while environmental awareness can mitigate negative perceptions regarding high pricing [133]. Conversely, factors that reduce acceptance include safety and privacy concerns [137], negative environmental impacts—especially noise pollution, which directly affects community acceptance and operational density pressure. As air traffic penetration increases, user acceptance may significantly decrease due to a decline in comfort levels [138]. Furthermore, the deployment of accessibility facilities should be considered to accommodate passengers with reduced mobility [139].

Table 6. Factors influencing public acceptance of eVTOL.

Influencing Factors	Sub-Dimensions	Keywords	Frequency
Personal Factors	Demographic & Experience Characteristics	gender, age, education level, income, residential area, occupation, previous eVTOL operation experience, aviation travel experience	19
	Individual Cognition & Trait Attributes	knowledge level of eVTOL, public awareness, digital literacy, personal creativity	7
	Individual Psychology & Attitude Orientation	attitudes towards eVTOL technology, perceived behavioral control, hedonic motivation, technology affinity, technology anxiety, expectation	13
	Individual Trust-related Dimensions	personal trust propensity, trust in eVTOL technology itself, perceived reliability of eVTOL technology, trust in technical safety and controllability	4
Perceived Benefits	Functional & Economic Benefits	perceived usefulness, perceived ease of use, convenience	19
	Environmental & Traffic Benefits	environmental friendliness, carbon emission reduction, traffic congestion reduction, urban road pressure alleviation, environmental sustainability	12
Perceived Risks	Safety & Physical Environment Risks	safety risks, performance risk, noise pollution, visual pollution	15
	Privacy & Data Security Risks	privacy infringement, data leakage risk, tracking and monitoring concerns, personal information security risk	3
Institutional & External Environmental Factors	Usage Scenarios	Travel, business travel, airport shuttle	7
	Policy & Regulatory System	government regulation, policy support, legal framework, industry operation standards, privacy protection regulations	2
	Infrastructure & Technical Environment	supporting infrastructure, airspace resource allocation, technology maturity, network coverage, operation guarantee system	2
	Social & Community Environment	subjective norms, social norms, word of mouth, community engagement, public science education, public communication, social opinion atmosphere	3
	External Subject Trust Dimensions	trust in government regulators, perceived effectiveness of regulatory system, trust in institutional guarantee	2

presents the frequency of these various influencing factors.

To improve public acceptance, the following measures may be implemented:

• Price Strategies: Attract price-sensitive markets by reducing ticket fares or offering differentiated pricing models [27].
• Urban Governance: Establish transparent public communication mechanisms and specialized noise management frameworks, while incorporating vertiport siting into urban governance [72].
• Strategic Deployment: Prioritize high-value specific applications, such as emergency medical services, airport access, and weekly business travel, to establish early trust [119].

Overall, UAVs and eVTOLs share several commonalities regarding public acceptance, yet their respective emphases differ. Regarding similarities, the adoption of both modes is highly dependent on the perceived efficiency by users, while both face challenges stemming from the triad of core risks: safety, noise, and privacy.

In terms of differences, research on eVTOLs highlights extreme public sensitivity to fares and economic affordability, a factor directly tied to their role in passenger transportation. Conversely, the acceptance of drone-based UAM is more contingent on the perceived social utility of its application, the public tends to tolerate life-saving medical UAVs but is significantly less accepting of dense commercial delivery drones operating near residential windows. Furthermore, because UAVs penetrate deeper into residential areas, concerns regarding privacy intrusion are more pronounced than those associated with eVTOLs.

4.3.4. System Effectiveness: Does Drone-Based UAM Enhance Overall Efficiency?

At the application level, the operational effectiveness of drones has been verified across multiple domains. To further enhance their systemic value, future research should focus on optimizing airspace scheduling algorithms, expanding aerial corridor capacity, and reducing system failure rates [140].

A current academic debate centers on whether drone delivery can truly alleviate ground traffic pressure. Drawing from studies on passenger eVTOLs, while aerial transport can divert some long-distance traffic pressure, the demand for “first- and last-mile connectivity” may instead increase ground traffic volume around vertiports, potentially inducing additional vehicle miles traveled [141,142].

This suggests that the systemic effectiveness of drone-based UAM is not equivalent to isolated flight efficiency. If drone delivery sites are disconnected from ground logistics infrastructure (such as courier stations or food delivery hubs), or if ground vehicles are frequently required to transport drones to takeoff points, drone delivery may exacerbate local traffic congestion rather than reduce the number of road-based delivery vehicles. Consequently, the key to enhancing effectiveness lies in achieving “air-ground integrated” digital coordination. Only by integrating drones into existing delivery systems can their potential to replace private freight vehicles be fully realized.

5. Discussion

The sustainable transition of drone-based UAM is not merely a technological evolution but a systemic undertaking involving multiple stakeholders. From an evaluative perspective, social factors occupy a foundational position within the drone-based UAM framework. Environmental noise annoyance is fundamentally a subjective psychoacoustic perception rather than a purely physical acoustic metric; negative feedback from this perception often directly determines the social feasibility of route planning.

Furthermore, pricing mechanisms in the economic dimension are more than commercial strategies; they are directly linked to social equity in resource allocation. To prevent drone-based UAM services from becoming exclusive tools for a minority, establishing multi-tiered pricing strategies is essential. Prioritizing early applications in public service scenarios—such as medical and emergency response—can enhance social compliance and help establish initial public trust in the technology.

This social complexity is mirrored in operational execution. Whether drone delivery alleviates road burdens depends not just on flight speed but on the degree of coordination between vertiports and ground logistics systems. Although aerial flight achieves spatial leaps, cargo accumulation at takeoff points or inefficient ground connectivity can offset time saved in the air, leading to diminished systemic effectiveness. Therefore, future route planning must move beyond optimizing aerial paths and actively coordinate with existing ground logistics infrastructure to avoid the “fast in air, slow on ground” bottleneck.

The foundational role of social factors is further underscored by emerging research on public acceptance. Evidence from urban logistics research indicates that the social benefits of new mobility systems are realized only when effectively integrated into existing transport networks [143]. This insight aligns with UAM deployment, where public acceptance depends on how well aerial services connect with ground infrastructure and everyday travel patterns.

Recent studies have identified multiple determinants of UAM acceptance, including institutional trust as a mediator between perceived safety and behavioral intention, as well as factors such as active experience with drones, noise sensitivity, and privacy concerns [133,144]. Building on these insights, parallel EU funded initiatives have developed integrated assessment frameworks covering noise, visual pollution, privacy, and equity considerations, validated through case studies in cities such as Madrid and Athens [145,146]. The UK public harbors significant concerns that these technologies may exacerbate noise pollution, disrupt wildlife habitats, and lead to disproportionate police surveillance. Consequently, they strongly advocate for independent regulatory oversight during the deployment phase to ensure comprehensive transparency and guarantee affordability for the general populace [147]. Collectively, this evidence suggests that public acceptance should be treated as a design parameter in early-stage infrastructure planning.

We have noticed that drone companies and programs demonstrate strong adaptability in the sustainability of drone-UAM. Environmentally, these projects show significant carbon reduction effects: Manna’s drones emit only 26 g of CO₂ per kilometer, 6 to 8 times lower than small gas-powered vehicles [148], Wing’s large-scale operations in the U.S. can reduce CO₂ emissions by 113,000 tons annually [149], and Zipline’s data in Rwanda shows its carbon emissions are 98% lower than conventional gas vehicles and 94% lower than electric vehicles (EVs) [149]. Economically, Manna’s current delivery price of $4.60, with expectations to drop to $3.50, demonstrates excellent market adaptability and commercial viability [150]. In the social dimension, projects like E-Drone and Future Flight in Place use VR experiences and street-level public education to actively guide the public in understanding and embracing this new mode of transportation [151]. Regarding system effectiveness, to address practical challenges such as medical drones being unable to share helipads with helicopters, the E-Drone project has developed a 4D airspace reservation system. By applying a simple “first-come, first-served” principle, this system effectively eliminates route conflicts within a discretized airspace model, significantly advancing the practical implementation of shared airspace [152].

We argue that future sustainability research should prioritize the following directions:

• Environmentally, studies must transcend single-point operational emission assessments to conduct LCA covering material production, infrastructure construction, and battery recycling.
• Socially, research should shift from physical acoustic metrics to psychoacoustic noise evaluation standards, and from a single noise indicator toward a comprehensive assessment framework that encompasses trust, privacy, equity, and multidimensional perception.
• Economically, pricing strategies balancing commercial viability and social inclusivity should be developed by investigating the demand characteristics and WTP of potential high-frequency users.
• Systemically, future studies should focus on the actual replacement rate of existing ground delivery vehicles through traffic simulation models. Sitting for drone vertiports must be integrated with ground micro-circulation traffic, focusing on the seamless connection between aerial routes and ground interfaces. Sustainable development can only be achieved by ensuring that drones enhance spatial efficiency without triggering local ground congestion.

Translating these research findings into policy practice requires integrated approaches that combine technical innovation with adaptive governance. The present review systematically synthesizes evidence across four sustainability dimensions, contributing to broader evidence-based decision-making that seeks to inform how scientific evidence can shape UAM policy development. This evidence-based approach aligns with frameworks that have proven effective in sustainable urban infrastructure planning, where systematic integration of spatial form indicators and lifecycle assessment has been shown to enhance policy effectiveness [153]. International precedents offer instructive pathways: the City of Madrid’s White Paper on Urban Air Mobility (2025) provides a phased roadmap for embedding social acceptance and equity into infrastructure planning, while a recent survey on UAM-ground integration highlights the need for coordinated infrastructure and regulatory frameworks [141].

Building on the research directions above, three policy priorities emerge. First, UAM infrastructure planning should prioritize coupling with existing ground transport networks to maximize system level benefits. Second, regulatory frameworks should incorporate expert and stakeholder participation at early stages to anticipate social equity trade-offs. Third, performance evaluation must embrace lifecycle thinking that accounts for infrastructure construction, battery production, and end-of-life disposal. Taken together, this evidence-based approach contributes to more robust policy analysis and informed decision-making for the sustainable development of UAM systems.

6. Conclusions

This study defines the research boundaries of drone-based UAM sustainability and synthesizes areas of consensus and controversy. Given the high technical commonality between UAVs and eVTOLs in propulsion systems, operational environments, and regulatory logic, this paper strategically incorporates eVTOL findings as a forward-looking comparison. This cross-perspective approach addresses the current lack of large-scale UAV application cases and provides a reference for anticipating complex challenges as drone logistics become normalized.

Following this logic, a systematic review of 301 core articles was conducted using the PRISMA process. Findings indicate that while time-efficiency advantages have reached industry consensus, subjective noise annoyance and early-stage social inequality remain recognized pain points. The green attributes of drone-based UAM remain contested due to heavy reliance on grid carbon intensity and battery life cycles; similarly, a cautious stance is necessary regarding whether large-scale drone delivery can truly improve systemic effectiveness. Ultimately, the overall sustainability of drone-based UAM is not an absolute state, but relies strictly on its comparative advantage over ground-based alternatives in specific deployment scenarios. This study is subject to several limitations. First, most research focuses on developed urban economies, which may limit the generalizability of the findings to emerging markets. Furthermore, while the PRISMA framework ensured transparent screening of peer-reviewed studies, it does not fully capture a rapidly evolving field like drone-based UAM, where crucial evidence frequently appears in regulatory reports, industry pilots, white papers, and engineering conference outputs. Consequently, our findings should be interpreted as a synthesis of academic literature rather than an exhaustive account of all operational knowledge. We recommend that future review methodologies in this domain extend beyond standard frameworks like PRISMA. To capture a truly comprehensive picture, researchers should establish standardized approaches to systematically integrate industry reports, real-world pilot data, and continuous regulatory updates alongside the traditional academic literature.