Towards Sustainable Urban Mobility: An ESG-Based Decision Framework for Urban Air Integration

Abstract

Urban Air Mobility (UAM) has emerged as a promising solution to alleviate urban congestion and support low-carbon transportation by utilizing low-altitude airspace. However, its large-scale deployment requires governance mechanisms that simultaneously address environmental impacts, social acceptance, and institutional coordination. Existing studies have not yet provided an operational Environmental, Social, and Governance (ESG)-based decision framework for UAM governance. This study develops and empirically validates an ESG-oriented governance model for UAM integration into urban development. A mixed-method approach was adopted, including literature and policy analysis to identify 22 execution-level factors, a questionnaire survey of industry practitioners and experts (N = 307), and the Analytic Hierarchy Process (AHP) combined with expert consultation to determine priority weights. The results show that the Governance dimension has the highest importance (38.72%), followed by Social (32.15%) and Environmental (29.13%). Laws and regulations, standard certification, and digital management constitute the core institutional foundations for UAM deployment. Privacy protection and social acceptance are the dominant social concerns, while noise pollution represents the most critical environmental constraint. Across all dimensions, standard certification, privacy, noise control, management framework, and digital management are the highest-weighted factors. The proposed framework provides a practical ESG-based decision tool to support policy prioritization and sustainable UAM implementation in rapidly urbanizing regions.

1. Introduction

Urban transportation constitutes the foundation of socio-economic activities and plays a critical role in shaping urban productivity, accessibility, and spatial organization [1]. With the acceleration of global urbanization and the increasing emphasis on low-carbon and sustainable development, many cities are experiencing growing pressure from traffic congestion, declining travel efficiency, and limited land availability [2]. The imbalance between continuously increasing travel demand and the finite capacity of ground transportation systems has therefore become a major constraint on sustainable urban development [3]. As surface transportation infrastructure approaches its operational limits, the exploration of three-dimensional transportation systems has become an important strategy for expanding mobility capacity while improving the efficiency of urban spatial resource utilization [4].

Urban Air Mobility (UAM), which utilizes low-altitude airspace as an additional layer of urban transportation, has attracted increasing attention as a potential solution to relieve congestion, improve travel efficiency, and support low-carbon mobility [5]. The rapid development of enabling technologies, including electric vertical take-off and landing aircraft, autonomous navigation, intelligent air traffic management, and advanced communication networks, has significantly enhanced the technical feasibility of UAM operations [6]. At the global level, major aerospace organizations and technology companies, such as NASA, Airbus, and Boeing, as well as leading Chinese enterprises including Ehang and Xpeng AeroHT, have intensified investment and pilot testing activities [7]. Market forecasts indicate that the global UAM industry could reach a value of approximately USD 1.5 trillion by 2040, with annual passenger volumes expected to exceed 740 million by 2030 [8]. In China, the Civil Aviation Administration of China has issued operational approvals for manned electric vertical take-off and landing aircraft [9]. Demonstration projects in cities such as Shenzhen, Guangzhou, and Hefei have clearly demonstrated that UAM is gradually entering an early stage of commercialization within the broader development of the low-altitude economy [10].

Although technological progress has accelerated the development of UAM, its large-scale integration into urban systems involves far more than technical feasibility. UAM represents a complex socio-technical transition that requires coordinated responses to environmental risks, public acceptance, safety concerns, infrastructure compatibility, and institutional readiness [11]. In this context, the Environmental, Social, and Governance framework has emerged as an important analytical perspective for evaluating the long-term sustainability of infrastructure systems and urban innovation initiatives [12]. From an environmental perspective, UAM deployment may generate impacts related to noise exposure, visual disturbance, energy consumption, and life-cycle emissions [13]. From a social perspective, issues such as safety perception, privacy protection, public acceptance, and equitable access require careful consideration [14]. From a governance perspective, effective implementation depends on regulatory clarity, cross-sector coordination, technical standardization, and digital management capacity [15]. Integrating ESG principles into UAM governance is therefore essential for ensuring that technological innovation contributes to sustainable and socially acceptable urban development.

Despite the rapid growth of research interest, significant gaps remain in the systematic application of ESG perspectives to UAM governance. First, within the environmental dimension, existing studies often emphasize the potential carbon reduction benefits of electric vertical take-off and landing aircraft but provide limited assessment of broader environmental externalities. Issues such as low-altitude noise, visual impacts, land-use conflicts, and the life-cycle environmental burden associated with battery production and disposal have received comparatively less attention. Second, research on the social dimension remains fragmented. Current studies have not sufficiently examined how equitable access to UAM services can be ensured across different social groups, nor have they fully addressed privacy risks arising from aerial monitoring, data collection, and location tracking. Third, within the governance dimension, much of the existing literature focuses on technical feasibility analysis or macro-level policy discussion, while lacking structured frameworks that integrate ESG criteria into operational decision-making. This limitation is particularly evident in the context of China’s rapidly developing low-altitude economy and urban renewal initiatives, where institutional coordination mechanisms and regulatory systems are still evolving. In addition, most existing studies rely primarily on qualitative analysis or case-based discussion. Quantitative research that systematically evaluates and prioritizes ESG-related governance factors remains limited.

Against this background, an ESG-based perspective provides an appropriate analytical basis for examining UAM governance in an integrated manner. UAM deployment is shaped not only by technical feasibility, but also by environmental constraints, societal acceptance, and institutional preparedness. An ESG framework is therefore useful for organizing these interrelated issues into a coherent evaluative structure and for supporting the identification of governance priorities during the early stage of implementation. To address the identified research gaps, this study develops a hierarchical governance framework that integrates ESG principles into the evaluation of UAM deployment within urban development. A mixed-method research design is employed, combining a systematic literature review, a questionnaire survey with 307 valid responses, semi-structured expert consultation, and the Analytic Hierarchy Process to identify and quantitatively prioritize key factors influencing sustainable UAM implementation. By linking ESG dimensions with operational decision variables, the study provides a structured basis for understanding governance priorities during the early stage of UAM development. Accordingly, this study addresses the following research questions:

• What are the key factors influencing sustainable UAM implementation under an ESG-oriented governance framework?
• How can a hierarchical multi-criteria decision model be constructed to determine the relative priorities among governance, social, and environmental dimensions?
• What empirical insights can be derived from China’s emerging low-altitude economy context for sustainable UAM governance?

2. Literature Review

2.1. Systematic Literature Review for UAM Factor Screening

To develop a scientifically grounded and operational ESG-integrated governance framework for Urban Air Mobility, a systematic literature review was conducted to identify the key factors influencing UAM implementation. The review aimed to capture major research themes, analyze the frequency of factor occurrence, and provide an empirical basis for subsequent hierarchical structuring using the Analytic Hierarchy Process. The relevant literature was retrieved from international databases, including Web of Science and Scopus, as well as the Chinese National Knowledge Infrastructure database, in order to ensure comprehensive coverage of both global and regional studies [16]. The search keywords included “Urban Air Mobility”, “Advanced Air Mobility”, “Urban Air Transportation”, and “ESG”. The search period was limited to publications from 2019 to 2025 to reflect recent technological developments and policy progress. Literature types were restricted to peer-reviewed journal articles, review papers, academic theses, and authoritative industry or policy reports, as these sources provide systematic insights into research trends and implementation challenges.

The screening criteria were established to ensure both relevance and analytical value [17]. First, the selected studies had to be directly related to UAM development and its environmental, social, or governance implications. Second, the literature needed to be published in peer-reviewed academic journals or issued by authoritative industry or policy institutions. Third, the studies were required to provide empirical evidence, conceptual analysis, or structured discussion of key influencing factors. Fourth, the literature had to present clear factor identification logic or framework construction relevant to UAM implementation. After initial retrieval and duplicate removal, 128 publications were obtained. Following abstract screening and full-text assessment, 98 studies that did not meet these criteria were excluded. Finally, 30 publications were retained as the core evidence base for factor extraction and subsequent framework development.

2.1.1. Temporal and Geographical Distribution of UAM Research

The temporal and geographical distribution of the selected studies is illustrated in Figure 1. As shown in Figure 1a, academic attention to Urban Air Mobility has increased substantially over the past several years. The number of publications exhibits a clear upward trend from 2019 to 2024, with a pronounced peak in 2024, which accounts for 38% of the selected studies. This pattern indicates that UAM has recently become a major research focus, driven by rapid technological progress, expanding pilot applications, and growing demand for sustainable urban mobility solutions [18,19,20]. The relatively lower number of publications in 2025 reflects the fact that the literature search covered only studies available up to the middle of the year rather than the full annual output, as UAM research has shifted to infrastructure optimization and multi-modal integration [21,22].

Figure 1b presents the geographical distribution of the selected studies using a proportional structure. The results show that UAM research is highly concentrated in technologically advanced economies. The United States contributes the largest share (26.7%), followed by China (20.0%), the United Kingdom (16.7%), and Germany (10.0%), while other countries collectively account for 26.6%. These regions have taken leading roles in technological development, regulatory experimentation, and industrial deployment of UAM. Representative initiatives include the Federal Aviation Administration’s Concept of Operations in the United States, low-altitude economy pilot programs in China, and the European Union Aviation Safety Agency’s risk assessment framework [23,24]. The concentration pattern suggests that the development and research intensity of UAM are closely associated with national innovation capacity, urbanization level, and the availability of institutional and policy support.

2.1.2. Frequency Analysis of Key UAM Factors

Through thematic coding and content analysis of the 30 selected studies, 22 execution-level factors influencing the integration of UAM into urban development were identified. The frequency of occurrence of each factor was calculated to reflect the level of attention it has received in existing academic and policy discussions. Although frequency does not directly indicate practical importance, it provides a useful indication of dominant research concerns and supports the identification and categorization of factors for subsequent framework development. The identified factors and their frequency of occurrence are summarized in

Table 1. Frequency of key factors identified from the literature.

No.	Factor	Description	Frequency
1	Infrastructure development	Includes vertiport construction, hub layout, land-use planning, capacity design, and spatial configuration for UAM operations.	22
2	Digital management	Enables real-time traffic monitoring, intelligent route optimization, and digital approval systems for low-altitude operations.	21
3	Legal regulations	Addresses regulatory gaps, as existing aviation frameworks are not fully applicable to low-altitude and high-frequency UAM operations.	17
4	Social acceptance	Public perception and concerns regarding safety, economic feasibility, and environmental impacts of UAM.	15
5	Flight safety	Ensures reliable communication, obstacle avoidance, and redundant safety systems in complex urban environments.	13
6	Management framework	Establishes cross-departmental coordination and integrated regulatory mechanisms for UAM governance.	11
7	Technical challenges	Addresses eVTOL performance limitations, including safety, endurance, and system integration.	10
8	Green energy utilization	Improves environmental performance through advancements in battery technology and energy systems.	9
9	Airspace structure design	Develops dynamic airspace planning and management for efficient low-altitude operations.	7
10	Noise pollution	Low-altitude operational noise that may affect urban residents and constrain large-scale deployment.	7
11	Standard certification	Establishes airworthiness, safety, communication, and operational standards for UAM systems.	6
12	Cost	High infrastructure investment and operational expenses that may hinder commercialization.	6
13	Dispatch optimization	Improves traffic efficiency and safety through advanced scheduling and operational coordination.	6
14	Airspace allocation efficiency	Enhances utilization through differentiated zoning and resource management.	6
15	Talent requirements	Requires new technical workforce development and retraining for related industries.	5
16	Privacy protection	Addresses data security and cybersecurity risks related to location tracking and aerial data collection.	4
17	Market positioning	Defines service demand, operational scenarios, and business models for UAM applications.	2
18	Visual impact	Potential changes to urban landscape and skyline that may influence public acceptance.	2
19	Emissions reduction potential	Contribution of electric operations to carbon reduction in urban transport.	2
20	Conceptual design	Integrates UAM into urban planning and long-term development strategies.	1
21	Facility assurance	Provides charging, maintenance, and operational support infrastructure.	1
22	Land utilization	Ensures spatial compatibility and integration with existing urban facilities and transport hubs.	1

, which serves as the basis for factor screening rather than for weighting.

The results indicate that infrastructure and governance issues receive the greatest attention. Infrastructure Development ranks first with 22 mentions, highlighting the widely recognized importance of vertiport construction, spatial layout, land-use planning, and capacity design as the physical foundation for large-scale UAM deployment [25]. Digital Management follows with 21 mentions, reflecting the need for real-time traffic monitoring, intelligent routing, and integrated digital platforms to support high-density low-altitude operations [26]. Legal Regulations, appearing 17 times, further emphasizes institutional challenges, as existing aviation frameworks are not fully adapted to low-altitude and high-frequency urban operations and therefore require dedicated regulatory systems [27]. Social Acceptance (15 mentions), Flight Safety (13 mentions), and Management Framework (11 mentions) also indicate that public trust, operational reliability, and cross-sector coordination are essential conditions for the societal integration of UAM. The distribution of factor frequencies suggests that UAM implementation represents a systemic transition rather than a purely technological innovation, requiring coordinated development of infrastructure, institutional capacity, operational safety, and social legitimacy. Although several factors appear less frequently, they remain important for ensuring the completeness and long-term applicability of the framework. Conceptual Design, Facility Assurance and other elements cover early planning, infrastructure compatibility and system resilience, and are retained to reflect key considerations in existing studies.

2.2. Factor Categorization and Framework Construction

2.2.1. Categorization Logic of Key Factors

Based on the functional requirements of UAM operations, urban development characteristics, and classifications reported in existing studies, the 22 execution-level factors were grouped into five decision-layer dimensions: Infrastructure, Social Safety, Environmental Impact, Operations and Organization, and Traffic Management. Previous studies have emphasized that the successful implementation of UAM requires coordinated development of physical facilities, public acceptance, environmental management, institutional support, and operational control [28]. This categorization therefore reflects the key conditions required for integrating UAM into urban systems from physical, social, environmental, institutional, and operational perspectives.

(1) Infrastructure

This dimension includes infrastructure development, technical challenges, green energy utilization, standard certification, cost, conceptual design, and facility assurance [29]. Volakakis et al. (2025) [30] highlighted that the availability and spatial compatibility of vertiports and supporting facilities represent fundamental constraints for large-scale UAM deployment. Site selection and layout planning must consider operational safety, land-use efficiency, and demand distribution, while integration with existing transport hubs can improve accessibility [31]. In addition, the high investment and maintenance costs of infrastructure remain major barriers. Li et al. (2025) [32] further emphasized the need for unified technical standards for facility construction, safety requirements and communication protocols to support coordinated development and risk management.

(2) Social Safety

The social safety dimension consists of social acceptance, flight safety, and privacy protection. Tedeschi et al. (2024) [33] noted that public concerns regarding operational safety and system reliability significantly influence the willingness to adopt UAM services. As a new transportation mode operating in densely populated areas, UAM must ensure reliable communication, system redundancy, and stable performance under complex urban conditions [34]. In addition, large-scale operations will generate substantial volumes of flight and user data, raising concerns related to data security and privacy protection. Kim et al. (2025) [35] argued that strengthening regulatory oversight and improving public awareness are essential for enhancing social trust and long-term acceptance.

(3) Environmental Impact

This dimension includes noise pollution, visual impact, emissions reduction potential, and land-use compatibility [36]. Park et al. (2026) [37] identified low-altitude operational noise as one of the most critical environmental constraints affecting public acceptance. Although electric vertical take-off and landing aircraft may reduce operational emissions, Li et al. (2025) [38] pointed out that the overall environmental benefits depend on battery production, energy sources, and operational intensity. In addition, visual disturbance and potential changes to urban landscapes may generate community opposition. Chen (2026) [39] emphasized that UAM facility planning should be coordinated with environmental protection objectives and urban spatial constraints.

(4) Operations and Organization

This dimension includes legal regulations, management frameworks, talent requirements, and market positioning [40]. Aldemir (2024) [41] highlighted that existing aviation regulations are not fully adapted to low-altitude urban operations, indicating the need for dedicated regulatory systems and clear institutional responsibilities. Effective governance also requires cross-departmental coordination mechanisms to support planning, approval, and supervision processes [42]. In addition, workforce training and certification systems are necessary to support emerging technical and operational roles, while appropriate market positioning is important for sustainable industry development [43].

(5) Traffic Management

The traffic management dimension focuses on operational efficiency and airspace utilization, including digital management, dispatch optimization, airspace structure design, and low-altitude airspace resource allocation [44]. Husemann et al. (2023) [45] emphasized that intelligent traffic management systems should integrate flight planning, airspace monitoring, and meteorological data analysis to support high-density operations. The development of three-dimensional air corridors and dynamic airspace allocation mechanisms is also essential for improving resource efficiency and emergency response capability [46]. Furthermore, Song et al. (2021) [47] noted that advanced communication, navigation, and surveillance technologies enable real-time information exchange and risk control, thereby enhancing operational safety and system reliability.

2.2.2. Integration of AHP and ESG Framework

The Analytic Hierarchy Process (AHP) is a multi-criteria decision-making method that integrates qualitative judgment with quantitative analysis to support complex problems involving multiple interrelated factors [48]. The method decomposes a complex system into a hierarchical structure consisting of objective, criterion, and indicator levels, allowing the relative importance of different elements to be systematically evaluated [49]. Through pairwise comparisons and expert judgments, element weights are derived, converting qualitative assessments into quantitative results.

The AHP was employed to construct a hierarchical decision framework for ESG-oriented integration of UAM into urban development. Based on the factors identified through systematic literature review and categorization, a three-level structure was established, including a goal layer, a decision layer, and an execution layer, as illustrated in Figure 2. The goal layer defines the overall objective of sustainable UAM integration under the ESG framework. The decision layer consists of five governance dimensions derived from the literature: Infrastructure, Social Safety, Environmental Impact, Operations and Organization, and Traffic Management. The execution layer includes the 22 specific factors associated with these dimensions, which serve as operational variables for pairwise comparison and weight calculation. To ensure alignment with the ESG framework, the decision-layer dimensions were mapped onto the Environmental, Social, and Governance components. Environmental Impact corresponded to the environmental dimension, while Social Safety corresponded to the social dimension. Infrastructure, Operations and Organization, and Traffic Management were classified as governance-related dimensions, since governance in this study denotes the institutional capacity to regulate, coordinate, provide, and control the conditions necessary for UAM implementation. Infrastructure is associated with governance functions related to facility provision, standard setting, and spatial coordination. Operations and Organization concerns regulatory design, institutional responsibilities, and organizational coordination. Traffic Management pertains to the rule-based control of low-altitude operations through monitoring, allocation, and digital supervision of airspace resources.

3. Materials and Methods

3.1. Data Collection

3.1.1. Questionnaire Survey

A questionnaire survey was conducted to obtain empirical data for evaluating the relevance of the identified UAM factors within the ESG framework and to support the subsequent AHP analysis. Questionnaire-based methods are widely used to capture expert perceptions and to provide standardized quantitative data for multi-criteria decision studies [50]. The instrument was developed based on the factor system derived from the literature review and hierarchical framework construction. Respondents were asked to assess the importance and applicability of the identified factors in relation to the Environmental, Social, and Governance dimensions using a five-point Likert scale [51]. This design enabled the systematic quantification of expert judgments and provided an empirical basis for examining the alignment between UAM operational factors and ESG-oriented governance requirements.

The questionnaire consisted of three sections. The first section introduced the research purpose and provided definitions of key concepts to ensure a consistent understanding of UAM and ESG among respondents. The second section collected demographic information, including work experience, educational background, professional position, and familiarity with UAM and ESG. The third section contained the core evaluation items corresponding to the identified factors. The survey was administered online through the Questionnaire Star platform and targeted professionals with practical or research experience in UAM-related industries or ESG-related fields. To ensure adequate statistical reliability, a minimum sample size of 300 was established. A total of 307 valid questionnaires were retained after screening for incomplete or inconsistent responses. The final dataset was used for subsequent statistical analysis, whereas the AHP weighting was based on expert judgment. The questionnaire is provided in Appendix A.1.

3.1.2. Semi-Structured Interviews

Semi-structured interviews were conducted to complement the questionnaire survey and to provide qualitative input for the refinement of the proposed ESG-based UAM governance framework. Semi-structured interviews are widely used in engineering management and sustainability research to obtain expert judgments while allowing flexible discussion of practical issues [52]. In this study, the interview protocol was developed based on the preliminary hierarchical framework, with the objective of examining the feasibility, completeness, and practical relevance of the identified dimensions and factors in the context of urban UAM development.

The interviews were conducted with seven experts from industry, government, academia, and ESG-related fields. Participants were invited to comment on the appropriateness of factor classification, the applicability of the framework, and potential areas for adjustment. The interview data were used to assess the coherence of the proposed hierarchical structure, refine the categorization and wording of the execution-layer factors, and verify the practical relevance of the decision-layer dimensions. The interviews did not serve as a direct basis for pairwise scoring in the AHP procedure; instead, they provided qualitative support for framework refinement and for interpreting the weighting results, particularly the prominence of regulatory coordination, management framework, and digital control in the early-stage UAM context. The participants have 7–15 years of professional experience in areas including UAM engineering, aviation regulation, urban planning, digital management, infrastructure operation, and sustainability assessment. Detailed profiles of the interview participants are provided in

Table A4. Profile of interview participants.

Expert ID	Sector	Organization Type	Position	Experience (Years)	Expertise Area
E1	UAM Industry	eVTOL Manufacturer	Senior Engineer	8	Aircraft systems
E2	Aviation Authority	Government Agency	Policy Analyst	12	Airspace regulation
E3	Urban Planning	Municipal Government	Urban Planner	10	Urban transport planning
E4	ESG Consulting	Consulting Firm	ESG Consultant	7	Sustainability assessment
E5	Academia	University	Associate Professor	15	Transportation systems
E6	Digital Technology	Technology Company	Project Manager	9	Smart mobility systems
E7	Infrastructure	Airport Operator	Operations Manager	11	Infrastructure planning

.

3.1.3. Sample Characteristics

After data screening, a total of 307 valid responses were obtained, and the demographic characteristics of the respondents are presented in

Table A5. Demographic characteristics of the respondents (N = 307).

Variable	Category	Number	Percentage (%)
Working experience	≤5 years	183	59.61
	6–10 years	121	39.41
	>10 years	3	0.98
Education level	Doctoral degree	9	2.93
	Postgraduate	85	27.69
	Bachelor/Junior college	211	68.73
	High school or below	2	0.65
Position	General employee	285	92.83
	Department head	17	5.54
	Senior manager	3	0.98
	Other	2	0.65
Professional background	ESG-related	164	53.42
	UAM-related	102	33.22
	Other related fields	41	13.36
Familiarity with UAM	First time / unfamiliar	206	67.10
	≤1 year	97	31.60
	1–2 years	2	0.65
	>2 years	2	0.65
Familiarity with ESG	First time	7	2.28
	0–1 years	43	14.01
	1–2 years	71	23.13
	2–3 years	114	37.13
	3–4 years	52	16.94
	4–5 years	17	5.54
	>5 years	3	0.98
	Total	307	100.00

. In terms of professional experience in ESG- or UAM-related fields, most respondents had less than ten years of working experience. Individuals with five years or less accounted for 59.61% of the sample, while those with 6–10 years represented 39.41%, and only 0.98% reported more than ten years of experience. Regarding educational background, 68.73% of the respondents held a bachelor’s degree or junior college qualification, followed by 27.69% with postgraduate degrees and 2.93% with doctoral degrees, whereas only 0.65% had a high school education or below. In terms of organizational position, the majority of respondents were general employees (92.83%), with 5.54% serving as department heads and 0.98% as senior managers. With respect to professional background, 53.42% of the respondents were engaged in ESG-related work, 33.22% were involved in UAM-related fields, and 13.36% belonged to other related areas. Regarding domain familiarity, 67.10% of the respondents reported that they were unfamiliar with UAM or had encountered the concept for the first time, while only a small proportion had more than one year of exposure. Familiarity with ESG showed a broader distribution, with the largest proportion of respondents reporting 2–3 years of experience (37.13%), followed by 1–2 years (23.13%) and 3–4 years (16.94%).

3.2. Analytical Methods

To operationalize the proposed ESG-oriented UAM governance framework and support subsequent multi-criteria evaluation, the collected questionnaire data were analyzed through a structured procedure consisting of descriptive statistical analysis, reliability testing, and Analytic Hierarchy Process modeling. Descriptive statistics were used to summarize response characteristics and examine the distribution patterns of factor evaluations under the Environmental, Social, and Governance dimensions [53]. Reliability analysis was conducted to assess the internal consistency of the measurement scales [54]. Based on validated data, the Analytic Hierarchy Process was applied to derive priority weights for both decision-layer dimensions and execution-layer factors. This sequential analytical design ensured the statistical validity of the data and provided a quantitative basis for governance prioritization. Statistical processing was performed using SPSS 26.0.

3.2.1. Reliability Analysis

To evaluate the internal consistency of the questionnaire measurement items, reliability analysis was conducted using Cronbach’s alpha coefficient, which is widely used to assess the degree to which multiple items jointly measure a consistent construct [55]. Cronbach’s alpha ranges from 0 to 1, with higher values indicating stronger internal consistency. In applied research, a coefficient of 0.70 or above is generally considered acceptable for internal consistency reliability, while values above 0.80 indicate good reliability [56]. The Cronbach’s alpha coefficient is calculated as

$$\alpha ={\displaystyle \frac{k}{k-1}}\left(1-{\displaystyle \frac{\sum {\sigma }_i^2}{{\sigma }^2}}\right)$$

(1)

where $k$ denotes the number of items in the scale, $\sum {\sigma }_i^2$ represents the sum of the variances of individual items, and ${\sigma }^2$ is the variance of the total score formed by aggregating all items. The coefficient reflects the average inter-item covariance and provides a lower-bound estimate of reliability under classical test theory.

3.2.2. Descriptive Statistical Analysis

Descriptive statistical analysis was employed to examine the overall characteristics of the sample and to provide an initial quantitative profile of respondents’ evaluations of UAM-related factors. Frequency and percentage analysis were used to describe the structural composition of the sample in terms of working experience, education level, professional position, industry background, and domain familiarity. Such analysis helps identify the representativeness and structural distribution of the respondent group, which is a common practice in empirical studies involving survey-based data collection.

For the measurement items constructed based on the five-point Likert scale, the mean and standard deviation were calculated for each execution-layer factor across the Environmental, Social, and Governance dimensions. The mean value reflects the overall perceived relevance or importance of each factor, enabling horizontal comparison and subsequent ranking [57]. The standard deviation indicates the dispersion of responses and the degree of consensus among respondents. Lower dispersion suggests higher agreement, while higher dispersion indicates heterogeneous perceptions [58]. This descriptive analysis provides a statistical foundation for understanding factor distribution characteristics and supports subsequent hierarchical weighting analysis.

3.2.3. Analytic Hierarchy Process

The Analytic Hierarchy Process (AHP) was adopted to determine the relative importance of governance dimensions and operational factors within the proposed ESG-oriented UAM framework. The pairwise comparisons were completed by the expert participants. AHP is a structured multi-criteria decision-making method that decomposes complex decision problems into hierarchical levels and derives priority weights through systematic pairwise comparisons [59]. Owing to its capability to integrate expert judgment with quantitative analysis, AHP has been widely applied in sustainability assessment and infrastructure governance research [60].

(1) Hierarchical structure

The AHP model was constructed as a three-level hierarchy. The goal layer represents the overall objective of achieving sustainable UAM integration within an ESG-oriented urban development context. The decision layer consists of five governance dimensions, including Infrastructure, Social Safety, Environmental Impact, Operations and Organization, and Traffic Management. The execution layer comprises 22 specific factors categorized under the five decision-layer dimensions. This hierarchical structure enables systematic evaluation from the overall governance objective to specific operational indicators.

(2) Pairwise comparison and judgment matrix

For each level of the hierarchy, elements were compared pairwise with respect to their relative importance to the higher-level criterion [61]. The comparison results were organized into a judgment matrix:

$$\mathbf{A}={\left[a_{ij}\right]}_{n\times n}$$

(2)

where $a_{ij}$ represents the relative importance of element $i$ compared with element $j$, satisfying $a_{ji}=1/a_{ij}$ and $a_{ii}=1$. The comparisons followed Saaty’s fundamental nine-point scale, in which values of 1, 3, 5, 7, and 9 represent equal, moderate, strong, very strong, and extreme importance, respectively.

(3) Weight derivation

The priority weights were calculated using the summation–product procedure [62]. First, the judgment matrix was column-normalized to obtain the normalized matrix:

$$b_{ij}={\displaystyle \frac{a_{ij}}{{\displaystyle {\sum }_{i=1}^n}{a}_{ij}}}, i,j=1,\dots ,n$$

(3)

Then, the feature vector was obtained by summing each row of the normalized matrix:

$$v_i={\displaystyle \sum _{j=1}^n}{b}_{ij}, i=1,\dots ,n$$

(4)

Finally, the weight vector was calculated by normalizing the feature vector:

$$w_i={\displaystyle \frac{v_i}{{\displaystyle {\sum }_{i=1}^n}{v}_i}}, i=1,\dots ,n$$

(5)

where $\mathbf{w}$ represents the relative importance weights of the compared elements.

(4) Consistency test

To ensure the logical consistency of expert judgments, a consistency test was performed for each judgment matrix. The weighted sum vector was first calculated as $\mathbf{Aw}$, and the maximum eigenvalue was estimated as

$$\lambda max={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n}{\displaystyle \frac{(\mathbf{Aw}{)}_i}{w_i}}$$

(6)

The consistency index (CI) and consistency ratio (CR) were then calculated as:

$$CI={\displaystyle \frac{\lambda \max -n}{n-1}}$$

(7)

$$CR={\displaystyle \frac{CI}{RI}}$$

(8)

where RI is the random consistency index corresponding to matrix order n. A judgment matrix was considered to have acceptable consistency when CR < 0.10. Matrices not satisfying this criterion were subject to further examination and revision prior to final weight calculation.

(5) Weight calculation procedure

Following the above procedure, AHP was first applied to determine the relative importance of the five decision-layer dimensions and subsequently to calculate the weights of the execution-layer factors within each dimension [63]. The resulting feature vectors and normalized weights provide the quantitative basis for the subsequent analysis of governance priorities and the aggregation of results across the Environmental, Social, and Governance dimensions.

4. Results

4.1. Reliability Analysis Results

To assess the internal consistency reliability of the questionnaire scale, Cronbach’s alpha coefficients were calculated for the Environmental (E), Social (S), and Governance (G) rating sets across the five decision-layer dimensions. The results are presented in

Table 2. Cronbach’s Alpha Coefficients by Decision Layer.

Decision Layer	Cronbach’s Alpha
	Environmental	Social	Governance
Infrastructure	0.930	0.939	0.947
Social Safety	0.872	0.868	0.871
Environmental Impact	0.848	0.858	0.872
Operations & Organization	0.880	0.907	0.898
Traffic Management	0.925	0.929	0.922

. All coefficients exceeded the commonly accepted threshold of 0.70, indicating acceptable to high levels of internal consistency for all dimensions and hierarchical groupings [64]. The alpha values ranged from 0.848 to 0.947, suggesting that the measurement items within each decision-layer dimension exhibit a stable and coherent response structure.

The reliability results are summarized in Table 2, which reports Cronbach’s alpha coefficients for the Environmental, Social, and Governance rating sets across the five decision-layer dimensions. A comparison across decision layers shows that the Infrastructure dimension demonstrates the highest internal consistency, with the maximum coefficient observed under the Governance ratings (α = 0.947). The Traffic Management dimension also presents consistently high reliability across the three ESG rating sets, with coefficients between 0.922 and 0.929. In contrast, the lowest coefficient was identified in the Environmental Impact dimension under the Environmental ratings (α = 0.848); however, this value remains within the range of good internal consistency. The relatively narrow variation in alpha values across dimensions indicates a stable level of internal consistency reliability in the questionnaire-based measurement framework.

4.2. Descriptive Statistics and Ranking Analysis

4.2.1. Descriptive Statistics

Descriptive statistical analysis was conducted to examine the distribution characteristics of the 22 execution-layer factors under the Environmental, Social, and Governance dimensions. Mean values and standard deviations were calculated to assess the perceived relevance of each factor and the level of response dispersion. As shown in

Table A6. Descriptive Statistics of Execution-Layer Factors.

Execution Layer	No.	Environmental Mean	SD	Social Mean	SD	Governance Mean	SD
Conceptual Design	I1	3.396	0.577	3.126	1.344	3.312	0.685
Technical Challenges	I2	4.000	0.648	2.823	0.846	3.993	1.282
Standard Certification	I3	4.629	0.639	2.797	0.822	4.323	1.480
Cost	I4	3.021	0.308	2.897	0.835	3.528	1.081
Green Energy Utilization	I5	4.035	0.703	3.957	0.694	4.180	1.505
Infrastructure Development	I6	4.125	0.628	3.972	1.315	4.201	1.141
Facility Assurance	I7	2.929	0.890	2.823	1.373	3.348	1.558
Social Acceptance	SS1	3.935	1.113	4.241	1.112	4.022	1.078
Privacy	SS2	2.663	1.174	4.589	0.698	4.033	1.069
Flight Safety	SS3	3.248	1.565	3.801	1.455	3.205	0.554
Noise Pollution	EI1	4.272	0.372	4.121	1.143	3.919	0.648
Visual Pollution	EI2	4.194	0.452	4.101	1.141	3.868	0.698
Emissions Reduction Contribution	EI3	3.855	0.458	2.738	0.894	4.062	0.445
Land Utilization	EI4	2.773	0.855	3.429	1.066	3.282	0.890
Market Positioning	OO1	3.033	0.669	3.585	0.929	3.433	0.982
Talent Requirements	OO2	2.780	0.836	3.911	0.685	3.773	0.855
Legal Regulations	OO3	3.979	0.725	3.490	0.703	4.467	0.383
Management Framework	OO4	3.467	0.812	3.975	0.693	3.955	0.856
Airspace Structure Design	TM1	2.053	1.379	2.718	1.078	1.972	0.710
Digital Management	TM2	3.603	0.927	3.676	0.923	4.241	1.147
Low-Altitude Airspace Resource Allocation Efficiency	TM3	3.230	1.151	2.430	1.154	3.267	1.147
Dispatch Optimization	TM4	2.298	0.850	3.709	1.161	3.862	0.858

, the mean scores ranged from 1.972 to 4.629, indicating substantial variation in the relative importance assigned to different factors across the three ESG dimensions. Most standard deviation values were within the range of 0.6–1.2, suggesting an acceptable level of dispersion and a generally consistent evaluation pattern among respondents.

At the decision-layer level, the Environmental Impact dimension showed relatively higher mean scores, with most values exceeding 3.85, as shown in Table A6. Within this dimension, Noise Pollution (EI1) showed consistently high mean values across the Environmental (4.272), Social (4.121), and Governance (3.919) dimensions, whereas Land Utilization (EI4) recorded comparatively lower values, particularly in the Environmental dimension (2.773). In the Operations and Organization dimension, scores were relatively balanced, mostly ranging from 3.4 to 4.0 with low dispersion. Notably, Legal Regulations (OO3) achieved the highest mean score in the Governance dimension (4.467) and showed a high level of agreement among respondents. In contrast, the Traffic Management dimension presented greater variability and generally lower mean values. Airspace Structure Design (TM1) received the lowest evaluations across all three ESG dimensions, while Digital Management (TM2) maintained relatively high mean scores across the Environmental (3.603), Social (3.676), and Governance (4.241) dimensions.

4.2.2. Ranking Analysis

To further examine the relative importance of execution-layer factors, mean scores under the Environmental, Social, and Governance dimensions were ranked. The highest-ranked factors for each dimension are reported in

Table 3. Top-Ranked Execution-Layer Factors under ESG Dimensions.

Rank	Environmental	Mean	Social	Mean	Governance	Mean
1	Standard Certification	4.629	Privacy	4.589	Legal Regulations	4.467
2	Noise Pollution	4.272	Social Acceptance	4.241	Standard Certification	4.323
3	Visual Pollution	4.194	Noise Pollution	4.121	Digital Management	4.241
4	Infrastructure Development	4.125	Visual Pollution	4.101	Infrastructure Development	4.201
5	Green Energy Utilization	4.035	Management Framework	3.975	Green Energy Utilization	4.180

, while the complete ranking results are provided in

Table A7. Full ranking of execution-layer factors based on mean scores.

Rank	Environmental	Mean	Social	Mean	Governance	Mean
1	Standard Certification	4.629	Privacy	4.589	Legal Regulations	4.467
2	Noise Pollution	4.272	Social Acceptance	4.241	Standard Certification	4.323
3	Visual Pollution	4.194	Noise Pollution	4.121	Digital Management	4.241
4	Infrastructure Development	4.125	Visual Pollution	4.101	Infrastructure Development	4.201
5	Green Energy Utilization	4.035	Management Framework	3.975	Green Energy Utilization	4.180
6	Technical Challenges	4.000	Infrastructure Development	3.972	Emissions Reduction Contribution	4.062
7	Legal Regulations	3.979	Green Energy Utilization	3.957	Privacy	4.033
8	Social Acceptance	3.935	Talent Requirements	3.911	Social Acceptance	4.022
9	Emissions Reduction Contribution	3.855	Flight Safety	3.801	Technical Challenges	3.993
10	Digital Management	3.603	Dispatch Optimization	3.709	Management Framework	3.955
11	Management Framework	3.467	Digital Management	3.676	Noise Pollution	3.919
12	Conceptual Design	3.396	Market Positioning	3.585	Visual Pollution	3.868
13	Flight Safety	3.248	Legal Regulations	3.490	Dispatch Optimization	3.862
14	Low-Altitude Airspace Resource Allocation Efficiency	3.230	Land Utilization	3.429	Talent Requirements	3.773
15	Market Positioning	3.033	Conceptual Design	3.126	Cost	3.528
16	Cost	3.021	Cost	2.897	Market Positioning	3.433
17	Facility Assurance	2.929	Technical Challenges	2.823	Facility Assurance	3.348
18	Talent Requirements	2.780	Facility Assurance	2.823	Conceptual Design	3.312
19	Land Utilization	2.773	Standard Certification	2.797	Land Utilization	3.282
20	Privacy	2.663	Emissions Reduction Contribution	2.738	Low-Altitude Airspace Resource Allocation Efficiency	3.267
21	Dispatch Optimization	2.298	Airspace Structure Design	2.718	Flight Safety	3.205
22	Airspace Structure Design	2.053	Low-Altitude Airspace Resource Allocation Efficiency	2.430	Airspace Structure Design	1.972

in Appendix A. The ranking results indicate that factors related to regulation, risk control, and system management received higher evaluations, whereas airspace planning and operational optimization factors were consistently ranked at lower levels.

A cross-dimensional comparison shows that several factors maintained relatively high rankings across multiple ESG dimensions. Standard Certification achieved the highest score in the Environmental dimension (4.629) and remained among the top positions in Governance (4.323). Noise Pollution and Visual Pollution were highly ranked in both the Environmental and Social dimensions, while Infrastructure Development and Green Energy Utilization also received consistently high evaluations across dimensions. In contrast, Airspace Structure Design, Low-Altitude Airspace Resource Allocation Efficiency, and Dispatch Optimization were repeatedly positioned in the lower ranks, with mean values generally below 3.0. Distinct evaluation patterns were observed among the three ESG dimensions. In the Environmental dimension, physical externalities and infrastructure-related factors were dominant, with Noise Pollution (4.272), Visual Pollution (4.194), and Infrastructure Development (4.125) forming the core high-score group. The Social dimension was characterized by a clear leading factor, as Privacy (4.589) exceeded the second-ranked factor by a noticeable margin, followed by Social Acceptance (4.241) and environmental disturbance factors. In the Governance dimension, institutional and management elements were most prominent, with Legal Regulations (4.467), Standard Certification (4.323), and Digital Management (4.241) forming the highest-ranked group. Compared with the other two dimensions, governance-related factors showed a generally higher overall evaluation level, with most mean values exceeding 3.2.

4.3. AHP Weighting Results

This section presents the weighting results derived from the Analytic Hierarchy Process (AHP), including the relative importance of the decision-layer dimensions, the execution-layer factors, and the aggregated priorities of the Environmental, Social, and Governance dimensions. All pairwise comparison matrices satisfied the AHP consistency requirement (CR < 0.10), indicating acceptable logical consistency in the expert pairwise judgments.

4.3.1. Decision-Layer Results

The weights of the five decision-layer dimensions are shown in

Table 4. Decision-layer weights.

Dimension	Feature Vector	Weight (%)
Infrastructure	0.322	6.445
Social Safety	0.350	6.999
Environmental Impact	0.893	17.859
Operations & Organization	2.062	41.245
Traffic Management	1.373	27.452
Maximum eigenvalue	λmax = 5.223	-
Consistency index	CI = 0.056	-
Consistency ratio	CR = 0.050	-

. For the judgment matrix (n = 5), the maximum eigenvalue was calculated as λ_max = 5.223, resulting in a consistency index of CI = 0.056 and a consistency ratio of CR = 0.050, which meets the acceptable threshold. Among the five dimensions, Operations and Organization received the highest weight (41.245%), followed by Traffic Management (27.452%) and Environmental Impact (17.859%). In contrast, Social Safety (6.999%) and Infrastructure (6.445%) were assigned relatively lower weights. The results indicate that governance-related functions associated with institutional coordination and operational management occupy a dominant position within the overall decision structure.

4.3.2. Execution-Layer Results

Pairwise comparisons were conducted for the execution-layer factors within each decision dimension, and the resulting weights are presented in

Table 5. Execution-layer weights.

Decision Layer	Execution Factor	Weight (%)
Infrastructure	Conceptual Design	4.50
	Technical Challenges	16.20
	Standard Certification	31.07
	Cost	12.18
	Green Energy Utilization	7.54
	Infrastructure Development	19.46
	Facility Assurance	9.05
Social Safety	Social Acceptance	38.10
	Privacy	46.18
	Flight Safety	15.72
Environmental Impact	Noise Pollution	54.05
	Visual Pollution	13.77
	Emissions Reduction Contribution	26.19
	Land Utilization	5.99
Operations & Organization	Market Positioning	18.25
	Talent Requirements	23.45
	Legal Regulations	27.10
	Management Framework	31.20
Traffic Management	Airspace Structure Design	8.03
	Digital Management	50.53
	Low-altitude Airspace Resource Allocation Efficiency	29.41
	Dispatch Optimization	12.03

. Within the Infrastructure dimension, Standard Certification (31.07%) and Infrastructure Development (19.46%) were identified as the most influential factors, followed by Technical Challenges (16.20%) and Cost (12.18%), while Conceptual Design received the lowest weight (4.50%). For the Social Safety dimension, Privacy (46.18%) and Social Acceptance (38.10%) account for the majority of the total weight, whereas Flight Safety contributes a comparatively smaller proportion (15.72%). In the Environmental Impact dimension, Noise Pollution shows a dominant weight (54.05%), substantially exceeding Emissions Reduction Contribution (26.19%), Visual Pollution (13.77%), and Land Utilization (5.99%).

Within the Operations and Organization dimension, the weight distribution is relatively balanced, with Management Framework (31.20%) and Legal Regulations (27.10%) ranking highest, followed by Talent Requirements (23.45%) and Market Positioning (18.25%). For Traffic Management, Digital Management occupies a leading position (50.53%), followed by Low-altitude Airspace Resource Allocation Efficiency (29.41%), while Dispatch Optimization (12.03%) and Airspace Structure Design (8.03%) received lower weights. Across the five decision dimensions, the results indicate that the importance structure is concentrated in a limited number of key factors within each dimension.

4.3.3. ESG Aggregation Results

To clarify the overall orientation of the proposed framework, the execution-layer weights were aggregated according to their classification under the Environmental, Social, and Governance categories, as shown in

Table 6. Cumulative weights of ESG dimensions.

ESG Dimension	Cumulative Weight (%)
Governance (G)	38.72
Social (S)	32.15
Environmental (E)	29.13

. The Governance dimension accounts for the largest proportion (38.72%), followed by the Social dimension (32.15%) and the Environmental dimension (29.13%), indicating a relatively balanced distribution across the three ESG dimensions, with governance ranked slightly higher than the other two dimensions. The Governance weight is primarily driven by the high-priority factors Legal Regulations, Management Framework, Digital Management, and Standard Certification. The Social dimension is largely shaped by Privacy and Social Acceptance, while the Environmental dimension is dominated by Noise Pollution, with Emissions Reduction Contribution providing a secondary contribution. Overall, the aggregated results reveal that institutional and operational mechanisms occupy a structurally central position within the hierarchy, whereas social and environmental factors remain closely integrated but comparatively less dominant components of the ESG framework.

5. Discussion

5.1. Interpretation of Key Findings

The results indicate a governance-dominated priority structure for UAM development, with the Governance dimension receiving the highest cumulative weight, followed by the Social and Environmental dimensions. This pattern is also reflected in the descriptive results, in which legal regulations, standard certification, and digital management all received relatively high mean scores, particularly under governance-related evaluations. Unlike conventional transport systems, UAM operates within an emerging low-altitude mobility regime in which regulatory responsibilities, operational rules, and inter-agency coordination mechanisms are still evolving. The prominence of these governance-related factors in both analyses suggests that stakeholders regard institutional clarity, standardization, and operational coordination as central conditions for early-stage implementation.

At the decision-layer level, the dominance of Operations and Organization and Traffic Management further highlights the importance of institutional readiness and operational controllability. UAM operations involve high-frequency take-off and landing activities in densely populated urban environments, which increase the complexity of airspace allocation, traffic control, and safety supervision. These operational requirements depend heavily on real-time information exchange, digital monitoring, and cross-sector coordination. This result is consistent with the descriptive ranking, in which Legal Regulations and Digital Management were among the most highly evaluated governance-related factors. Compared with these system-integration challenges, physical infrastructure expansion appears to be a less immediate constraint at the present stage of development. Although Infrastructure Development received relatively high descriptive scores, the Infrastructure dimension was assigned a comparatively low decision-layer weight. This suggests that stakeholders do not discount the importance of infrastructure itself; rather, they appear to view governance capacity and operational coordination as more immediate constraints than the physical provision of facilities.

The execution-layer results show differentiated priority patterns across ESG dimensions. Within the Social dimension, Privacy received the highest weight, exceeding both Social Acceptance and Flight Safety. This pattern is also supported by the descriptive analysis, in which Privacy recorded the highest mean score under the social evaluation. This finding reflects the data-intensive nature of UAM operations, which rely on continuous location tracking, aerial observation, and platform-based information processing. In high-density urban settings, such data generation raises concerns regarding personal privacy, data ownership, and cybersecurity risks. These concerns may directly influence public trust and willingness to accept new mobility services. Within the Environmental dimension, Noise Pollution received a substantially higher weight than Emissions Reduction Contribution. A similar tendency is evident in the descriptive results, where Noise Pollution ranked prominently across evaluations, indicating that immediate and perceptible environmental disturbance is regarded as more salient than broader system-level environmental benefits. Low-altitude flight operations expose residential areas to frequent acoustic disturbance, whereas the environmental benefits of electrification depend on energy structure, operational scale, and life-cycle performance, which may reduce their perceived urgency.

Across the five decision dimensions, several factors, including Standard Certification, Digital Management, and Management Framework, consistently ranked among the highest positions. These factors represent key mechanisms for ensuring system reliability, operational interoperability, and regulatory enforcement. The overall priority order of Governance, followed by Social and then Environmental considerations, reflects the transitional characteristics of an emerging system. During the early stage of development, uncertainty related to regulation, operational risk, and public trust tends to dominate decision-making. Environmental performance remains important, but it is more likely to become a strategic concern after the institutional framework and operational conditions have been established. These results further clarify how sustainable UAM implementation can be examined through an ESG-oriented governance framework. The identified factors reveal the main environmental, social, and governance-related conditions shaping UAM deployment, while the AHP results show how these factors can be structured and prioritized within a hierarchical decision model. In China’s emerging low-altitude economy, the prominence of institutional readiness, regulatory coordination, digital management, privacy protection, and noise control indicates that early-stage UAM governance should be understood as a multidimensional challenge involving institutional capacity, social trust, and environmental risk management, rather than as a purely technological or infrastructure issue.

5.2. Comparison with the Existing Literature

Existing research on Urban Air Mobility has largely been dominated by a technology-centered perspective, with emphasis on aircraft performance, infrastructure configuration, and system-level environmental benefits. Many studies highlight the potential of electric vertical take-off and landing aircraft to reduce emissions and improve transport efficiency, while infrastructure-related work focuses on vertiport location, network design, and operational feasibility [65]. In contrast to this emphasis on physical and technical readiness, the results of the present study indicate that institutional capacity and operational governance constitute more critical constraints during the early stage of UAM development. The high weights assigned to Operations and Organization and Traffic Management suggest that regulatory clarity, cross-agency coordination, and digital operational control represent the primary conditions for implementation, whereas physical infrastructure expansion plays a comparatively supporting role. This finding provides quantitative evidence that complements previous studies, which have typically discussed regulatory issues in qualitative or conceptual terms [66].

With respect to the social dimension, prior studies generally identify public acceptance and safety perception as the key social barriers to UAM deployment [67]. The empirical results of this study reveal a more differentiated structure, in which privacy protection receives the highest weight and exceeds both social acceptance and flight safety. This pattern reflects the operational characteristics of UAM, which involve continuous low-altitude movement, aerial observation, and extensive data transmission. Compared with traditional transport systems, concerns related to personal information exposure and spatial surveillance may therefore play a more immediate role in shaping stakeholder perceptions. By quantifying the relative importance of privacy within the broader social framework, the study extends existing research that has addressed privacy risks only as a secondary issue.

For environmental assessment, the current literature tends to focus on life-cycle emissions and energy efficiency as the primary indicators of sustainability [68,69]. However, the results show that noise pollution is evaluated as substantially more important than emission reduction benefits. This difference suggests that stakeholders prioritize direct and locally perceived environmental disturbances over long-term or system-level environmental gains. The finding refines the prevailing assumption that carbon reduction constitutes the dominant environmental advantage of UAM. More broadly, the relative weight distribution across the three ESG dimensions indicates that governance readiness and social risk management have greater immediate significance than environmental performance. By integrating these dimensions within a unified analytical framework, the study shifts the evaluation logic from technology-driven feasibility to risk-oriented governance conditions, thereby providing a more comprehensive perspective for understanding the early-stage dynamics of UAM implementation.

5.3. Policy and Management Implications

The findings provide a structured basis for identifying priority governance actions to support the sustainable integration of Urban Air Mobility within an ESG-oriented framework. Given the dominant role of the governance dimension, regulatory authorities should establish a coordinated institutional environment for UAM development. Key priorities include the formulation of dedicated regulatory frameworks for low-altitude operations, the development of unified standards for airworthiness, safety, noise, and data management, and the establishment of cross-sector coordination mechanisms among aviation authorities, urban planners, environmental agencies, and digital infrastructure providers. At the same time, the high importance assigned to privacy and social acceptance indicates that public trust should be treated as a critical precondition for large-scale deployment. Strict data protection and cybersecurity mechanisms are required to address concerns related to location tracking, aerial imaging, and information security. In addition, transparent communication, pilot demonstration programs, and participatory planning processes can improve public understanding and reduce uncertainty regarding emerging low-altitude mobility services.

From an environmental and operational perspective, management efforts should focus on mitigating local externalities and improving resource efficiency in urban contexts. Noise control should be incorporated into aircraft design requirements, operational scheduling, and flight path planning in order to minimize disturbance in densely populated areas. The spatial deployment of vertiports should prioritize integration with existing transportation hubs and urban infrastructure to reduce additional land consumption and avoid conflicts with urban land-use planning. Furthermore, the promotion of renewable energy for charging systems and the adoption of energy-efficient operational strategies can enhance the overall environmental performance of UAM. Together, these measures support the alignment of UAM deployment with broader urban sustainability objectives while improving the feasibility of long-term system operation.

6. Conclusions

Urban Air Mobility (UAM) is increasingly viewed as a potential solution to urban congestion and low-carbon transportation; however, its large-scale implementation depends on governance mechanisms capable of addressing environmental impacts, social acceptance, and institutional readiness in an integrated manner. This study developed an ESG-oriented governance framework for UAM integration into urban development and quantitatively evaluated the relative importance of key factors using a mixed-method approach that combined a systematic literature review, questionnaire survey (N = 307), expert consultation, and the Analytic Hierarchy Process. Through the identification and prioritization of ESG-related factors, the study clarifies the main conditions shaping sustainable UAM implementation and demonstrates how these conditions can be organized into a hierarchical decision-making structure. The results indicate that the Governance dimension received the highest cumulative weight in the early stage of UAM development, accounting for 38.72% of the total, followed by the Social dimension (32.15%) and the Environmental dimension (29.13%). At the decision-layer level, Operations and Organization and Traffic Management were identified as the most influential dimensions, while at the execution level, Standard Certification, Privacy, Noise Control, Management Framework, and Digital Management emerged as the most critical factors within the overall ESG structure. In the context of China’s emerging low-altitude economy, these findings suggest that institutional capacity, regulatory clarity, digital governance infrastructure, privacy protection, and noise mitigation represent important enabling conditions for early-stage sustainable UAM deployment, while social trust and environmental performance remain integral components of the broader implementation context. By introducing an ESG-based evaluation structure and a hierarchical multi-criteria decision model, this study provides an exploratory empirical perspective on governance priorities in this emerging context. Rather than constituting a universally generalizable or fully validated model, the proposed framework should be understood as a context-sensitive decision-support reference for the early stage of UAM implementation.

Despite these contributions, several limitations should be acknowledged. A limitation of the study lies in the composition of the questionnaire sample. Given the heterogeneous levels of familiarity with UAM among respondents, and the mismatch between the technical complexity of UAM governance and the level of domain expertise represented in part of the sample, the survey results are more appropriately interpreted as indicating broader perception patterns than as expert-level assessments of governance priority. The empirical analysis relies on cross-sectional questionnaire data reflecting expert perceptions rather than observed operational performance, and the AHP weighting process inevitably involves a degree of subjectivity. In addition, the study focuses primarily on the early-stage governance context of China’s low-altitude economy, which may limit the direct generalizability of the findings to other regional contexts. Future research could further validate and refine the proposed framework by incorporating longitudinal evidence from pilot projects or operational records, conducting comparative studies across different cities or countries, and integrating additional analytical approaches to explore the interactions among governance, social, and environmental factors over time.