From Risk Perception to Sustainable Governance: A Stakeholder-Centric Approach in Urban Infrastructure Development

Abstract

Amid rapid urbanization, this study investigates public safety risks in Shanghai’s urban construction projects through a stakeholder-centric framework, aiming to advance sustainable urban development. By classifying stakeholders into core, potential, and peripheral groups, the analysis employs mixed methods—qualitative case studies and the analytic hierarchy process (AHP)—to identify systemic risks spanning policy cognition gaps, compensation inequities, and environmental adaptation challenges. The findings reveal critical tensions between infrastructure-driven imperatives and community welfare, highlighting governance blind spots that threaten urban sustainability. This study proposes adaptive strategies to harmonize technical efficiency with social equity, including transparent policymaking, equitable compensation models, and cross-sector collaboration. These pathways prioritize long-term ecological and social resilience, offering a scalable governance framework to mitigate risks while fostering inclusive urban transitions. By bridging stakeholder interests with sustainable development goals, this study advances a replicable framework for megacities like Shanghai, balancing infrastructural expansion with equitable urban transitions while safeguarding social stability and ecological resilience.

1. Introduction

The growing concentration of economic resources and populations in urban centers has heightened risks related to public safety and natural disasters. Since the introduction of China’s Holistic View of National Security in 2014, the research popularity of risk governance has been increasing consecutively. The current research on urban risk governance mainly focuses on designing disaster prevention plans, governance models, and specific governance strategies for the public safety risk governance of Chinese cities from the theoretical perspective of resilient cities and the construction of smart cities. For example, Fang et al.’s (2023) study proposed that an intelligent emergency system built through the Internet of Things, the Internet, and computers would be conducive to constructing an urban safety operation framework and response strategies from the three aspects of disaster prevention, mitigation, and relief, thereby improving the level of the city’s emergency system in responding to risks and disasters [1]. Marshall and Rowberry (2013) systematically categorize resilience indicators from western scholarship and analyze global case studies, demonstrating that quantitative assessment is essential to identify urban resilience determinants for risk governance [2]. Concurrently, recent studies critique traditional risk governance models, proposing data-driven frameworks and intelligent governance systems to address emerging disaster causation mechanisms [3,4]. The above research indicates that public safety is a prerequisite for the construction of a livable city [5]. However, with the development of information socialization, the traditional power-concentrated management model adopted by governments is increasingly struggling to satisfy the complex needs of risk governance [6]. Governments have noticed the importance of public participation in risk governance decision-making and increasingly regard the public as “partners” in urban issues, focusing more on promoting the substantive participation of the public in urban public safety [7]. Therefore, the specific interests and demands of the general public in urban sustainable development are becoming increasingly important for risk governance. It can be said that the survival and safety of urban residents are their core interests and demands regarding urban development. As the core leaders of urban governance, the functional allocation, value orientation, and public service capacity of governments play a decisive role in urban sustainable development and the well-being of the people, all of which are reliant on a relatively complete governance system. Therefore, urban public safety is the core interest of both the government and the general public, and both the government and the public are stakeholders in urban development. This study considers how stakeholder-centric frameworks can systematically reconcile technical efficiency with community welfare in megacity infrastructure projects and why this balance is pivotal for sustainable development. We choose Shanghai as the empirical focus due to its dual identity as China’s economic powerhouse and a policy laboratory for megacity governance. As the one of global financial hubs, it faces acute pressures from rapid urbanization, including land use conflicts, infrastructure overload, and socioeconomic disparities. The city’s proactive stance in piloting integrated land–transportation projects—coupled with its transparent governance frameworks—provides a unique opportunity to study stakeholder dynamics in high-stakes urban renewal contexts.

Stakeholder theory came from western countries, and the concrete term “stakeholder” was officially introduced in the 1960s by the Stanford Research Institute. Since then, stakeholder theory has primarily served as an economic theory for the understanding and governance of businesses; meanwhile, it has been widely applied in the realm of risk evaluation and management. For instance, from a stakeholder perspective, value-centered structured decision-making methods have been used to include the public and assessment experts in environmental risk management decisions [8]. Alternatively, it has been found that disparities in identity, environment, and experience will affect the evaluation outcomes based on the public’s participation in environmental impact evaluations from different stakeholders [9]. Additionally, more comprehensive risk content regarding projects has been obtained from various stakeholders through modeling based on specific cases [10]. Furthermore, a stakeholder-centric case study of Seoul has revealed the feasibility of urban land use and transportation integration policies and how they can bring positive social outcomes in rapidly growing metropolises [11].

The Chinese domestic research on stakeholders began in the 1990s, initially used for research related to corporate responsibility and institutional management. Since the 21st century, domestic applications of stakeholder theory have extended to the fields of social governance and management decision-making. For example, Qingchun’s research has proposed a stakeholder analysis model for public crises and coordination strategies among stakeholders through empirical analysis [12]. Additionally, stakeholder theory has been applied to the study of urban village redevelopment, suggesting that the multi-dimensional interests of stakeholders should be considered in a dynamically balanced manner [13]. Furthermore, the analytical method of stakeholder theory is applied to address the current state of smart city research domestically and internationally, focusing, for example, on the issues in China’s smart city construction by constructing a 5P model for smart city value creation based on stakeholders [14]. Additionally, from a stakeholder theory perspective, an analysis of the factors affecting the quality of rural public safety services has been conducted [15]. Although domestic scholars have widely applied stakeholder theory to the levels of social governance and management decision-making, the focus has mainly been on the construction of analytical models, the theoretical interpretation of governance issues, and accountability for emergencies or crises. Few scholars have explored the categories of interest demands that trigger urban public safety risks and the types and governance of urban public safety risks based on stakeholder demand perception from the perspective of stakeholder theory.

Construction projects based on land use and transportation integration are essential for sustainable urban development. Specifically, the prevention of safety risks in urban engineering projects aimed at land resource utilization does not stem from the interest demands of a single social entity but from the collection of interest demands of all related social entities, involving the rights and responsibilities among multiple stakeholders. Tracing its causes and reasons, the governance of public safety risks in urban engineering projects not only needs to consider the actual difficulties and interest demands of stakeholders but also requires stakeholders to achieve a balance between collective and individual interests under the reality of limited urban resources. The government, as the main owner of urban resources, is both the direct promoter of urban engineering projects and the main manager of projects’ public safety risks. The general public is the direct recipient of public safety risks in urban engineering projects and also the main beneficiary of their governance. Public safety in urban engineering projects concerns the core safety risk demands of both the government and the public, although the specific content of these demands may differ. Therefore, research on the governance of public safety risks in urban engineering projects needs to clarify the core stakeholders and their main interest demands, while also identifying and preventing potential public safety risks in urban engineering projects.

In this study, we aim to identify and evaluate public safety risks in urban construction projects, particularly in land use and transportation integration efforts in Shanghai, from a stakeholder perspective. The research begins by classifying stakeholders into core, potential, and peripheral groups, systematically analyzing their demands and risk perceptions. Using a combination of qualitative case studies, expert opinions, and the analytic hierarchy process (AHP), key risk factors are identified, including policy cognition, expected returns, policy implementation, and environmental adaptability. The study focuses on Shanghai as a case study, leveraging its unique urban development context to provide practical insights. Empirical data are collected through stakeholder interviews, project documentation, and public records, ensuring a comprehensive analysis of the risk dynamics. The marginal contributions of this study are twofold: (1) unlike traditional risk assessments, this research introduces a stakeholder-based framework for the identification and categorization of risks in urban construction projects, particularly in land use and transportation integration scenarios; (2) by integrating qualitative case studies with a quantitative AHP analysis, this study offers a comprehensive and practical approach to risk governance, filling a gap in research on the efficacy of stakeholder-focused risk management in urban construction projects. Additionally, this study emphasizes the importance of addressing disparities in risk perception among different stakeholder groups, providing actionable recommendations for policy and governance. The findings reveal significant disparities in risk perception and interest conflicts among stakeholders, particularly between core stakeholders (e.g., government functional departments and direct stakeholder owners in the community) and potential stakeholders (e.g., merchants and other residents). Key risk factors, such as policy implementation and environmental adaptability, are identified as critical to project success. Based on these insights, this study proposes governance pathways, including enhancing policy transparency, formulating fair compensation schemes, strengthening interdepartmental coordination, and developing robust emergency plans. The study concludes that stakeholder-oriented risk identification and governance can significantly improve the ability of urban construction projects and government functional departments to manage social stability risks, ensuring project success and safeguarding urban public safety. These findings provide valuable theoretical and practical contributions to the field of urban development and public safety management.

2. Conceptual Framework

The stakeholder classification operationalizes this study’s core research question—how to balance technical efficiency and social equity in risk governance—by hierarchizing actors based on their risk cognition priorities. Rooted in modern risk society and stakeholder governance principles, the framework distinguishes core stakeholders whose divergent priorities directly shape governance legitimacy, beginning with peripheral actors with an indirect influence. This hierarchy aligns with the analytic hierarchy process (AHP), seeking to quantify power asymmetries in urban decision-making, addressing both classical economics’ “rational man” assumption and the multi-dimensional realities of stakeholder behavior. While classical models emphasize economic rationality, empirical evidence reveals that stakeholders’ decisions are driven by a complex interplay among economic security, social accomplishment, and risk perceptions, shaped by cognitive biases, information access, and socioeconomic contexts [16]. By integrating these dynamics into the AHP framework, this study systematically classifies stakeholders’ risk cognition hierarchies, enabling policymakers to reconcile technical imperatives with community-driven welfare demands through adaptive governance mechanisms, as well as offering a replicable model to balance urban development imperatives with community welfare.

2.1. Classification of Stakeholders in Urban Engineering Construction Projects

Stakeholders are broadly defined as “groups or individuals who can affect the success or failure of an organization’s objectives, or are deeply affected by the organization’s objectives” [17]. In the context of urban construction projects, stakeholders are indeed considered in a broad sense. Some scholars have classified stakeholders into direct and indirect based on whether they have a specific transactional relationship with the construction project [18]. Others have categorized stakeholders into supportive, marginal, mixed, and antagonistic types, based on factors that affect their potential for threat or cooperation [19]. Additionally, scholars also have proposed assessing potential stakeholders based on three dimensions: legitimacy, power, and urgency [20]. Building on this foundation, this study measures the differences in group risk perception under the context of hyper-urbanization based on the classification perspective of stakeholders. Overall, as research into stakeholder classification deepens, it is becoming evident that stakeholders are in constant flux in response to actual changes in societal objectives. Within specific societal objectives, stakeholders’ risk perceptions exhibit inter-subjective differences based on varying social contexts and interests. By reviewing the relevant literature and considering the relevance, importance, and rationality of urban development decision-making in relation to public safety, as well as the closeness, impact, and urgency of urban construction projects’ objectives, stakeholders can be categorized into core stakeholders (risk bearers, evaluators, and managers), peripheral stakeholders (communicators), and potential stakeholders (bystanders).

In order to facilitate subsequent analysis and further demonstrate the research findings, this study presents a case study analysis of a road planning and construction project carried out in a new developing area in Shanghai City, China, a metropolis, with the aim of integrating land use and transportation. The case focuses on a road planning and construction project in the central business district (CBD) of a new developing area in Shanghai, where the Y residential area, completed in 2000, is ideally located, with comprehensive amenities such as subways, hospitals, supermarkets, and schools. The Y residential area consists of a total of nine buildings, among which three buildings with 65 households are facing relocation due to the central road planning of the new developing area in Shanghai. This study conducted on-site research, household surveys, and in-depth interviews with the stakeholders of the project, concluding that, in the road planning and construction in the CBD area, the core stakeholders include the government functional departments responsible for organizing the project and the homeowners of the Y residential area who are directly affected by the project. Peripheral stakeholders include public service departments and third-party inspection agencies, while potential stakeholders include other building owners in residential areas and nearby businesses indirectly affected by the project. Different stakeholder groups have different interests and requirements for urban construction projects. The stakeholders are categorized in

Table 1. The classification of stakeholders.

Stakeholder Classification	Stakeholder	Abbreviation	Representative
Core stakeholders	Government Functional Departments	GFD	Bureau of Housing Security and Real Estate Administration; Sub-District Administrative Office
	Direct Stakeholder Owners of Community Y	DSYO	Owners of demolition buildings
Potential stakeholders	Merchants within the Planning Scope	MPS	Snack bar; garage
	Indirect Stakeholder Owners of Community Y	ISYO	Owners of non-demolition buildings
Peripheral stakeholders	Public Service Departments	PSD	12345 Hotline
	Third-Party Inspection Department	TID	Building Design Institute

.

2.2. Demand-Oriented Stakeholder Analysis

GFD not only frame the public in an institution-led manner but also encourage target individuals to develop their behavior patterns and value concepts towards the standards that they expect. In the specific case of the land expropriation project in Shanghai, the GFD invited the Architectural Design Institute to inspect the project and issue a feasibility report, providing a detailed explanation of the demolition of buildings in the Y residential area. Figure 1 shows the demolition plan diagram for Building 3 in Community Y.

In urban engineering construction projects, the core stakeholders include GFD and the direct stakeholder owners of Community Y (DSYO) who are directly affected, with significant differences in their demands. GFD, based on the national new urbanization policy, are committed to promoting the road planning of the new city in Shanghai, aiming to create a transportation hub, optimize the spatial layout, and promote economic transformation, so as to enhance the city’s competitiveness. However, in this process, the macro development goals of the local government have come into conflict with the individual rights and interests of the DSYO. The 65 households to be relocated believe that the project is “high-risk and low-return”, worrying that relocation will lead to a reduction in sources of income, the interruption of education and medical welfare, and the disintegration of social relations. Therefore, they strongly oppose relocation, emphasizing that living in peace and contentment should take priority over urban construction.

The potential stakeholders include the merchants within the planning scope (MPS) and indirect stakeholder owners of Community Y (ISYO). The MPS do not question the rationality of the project but require the government to compensate for the business losses caused by relocation. The ISYO are concerned that the project will damage the integrity of the community, leading to the devaluation of their houses and environmental safety risks (such as noise, engineering accidents), and they claim that they should enjoy the same compensation as the relocated households.

The peripheral stakeholders involve the public service departments (PSD) and the third-party inspection department (TID). The PSD, such as the 12,345 hotline, have received a large number of complaints from owners, focusing on issues such as the government’s tough attitude, unfair compensation, and unreasonable resettlement plans. As the TID, the Architectural Design Institute of Shanghai has argued for the scientific nature of the demolition plan through feasibility reports, indirectly providing professional endorsement for government decisions and influencing public opinion. The relationships among the three types of stakeholders are shown in Figure 2.

Overall, the core contradiction of the project lies in the balance between public interest and individual rights and interests: The government promotes the project in the name of development, but the overexercising of its power and the neglect of residents’ demands have increased the social risks. Although the interests of the potential and peripheral groups are not in direct conflict, they reflect the chain of economic, environmental, and social problems caused by the project. To resolve these contradictions, it is necessary to enhance transparency, ensure fair compensation, and reduce the impacts on people’s livelihood rights and interests caused by the imbalance of power.

3. Research Design

In November 2020, the Department of Housing and Urban–Rural Development formulated the “Implementation of Urban Renewal Actions”, which clearly stipulates the need to significantly enhance the scientific, refined, and intelligent governance of cities and to effectively improve the risk prevention and control capabilities of metropolises. However, “any risk issues involving complex technology or professional knowledge may lead to excessive reactions or other irrational attitudes and behaviors among the general public due to their lack of professional knowledge” [21]. Therefore, identifying various risk factors that may affect social stability through risk surveys, comprehensive analyses, and systematic classification is an essential part of urban public safety risk management. Continuous assessment will provide valuable feedback on whether the proposed policies are successfully bridging the awareness–adoption gap [22]. In this section, the analytic hierarchy process (AHP) is applied to systematically assess stakeholder risk perceptions in Shanghai’s urban construction projects through expert scoring, hierarchical model development, and rigorous consistency validation.

3.1. Statistical and Measurement Methods for Risk Indicators

The analytical hierarchy process (AHP), a globally accepted method, has become a major tool among the MCDM techniques [23]. The AHP, as a systematic analysis method, can effectively handle complex systems with multiple indicators and is compatible with risk analysis theory from the perspective of stakeholders. The current AHP has been applied to risk assessment in natural disaster fields such as regional flooding [24], forest fires [25], and earthquake vulnerability [26], as well as in artificial fields such as tunnel engineering safety [27], multi-unit residential safety [28], and public transportation services [29]. The AHP combines qualitative and quantitative analysis; thus, it can identify the key risk factors affecting a project, providing specific technical support for risk management research in urban engineering construction projects. In this paper, through the AHP, weights are assigned to the risk factors of concern to different stakeholders, thereby more scientifically assessing the project’s risks.

In this research, expert scoring is utilized to quantify stakeholder risk perceptions through the analytic hierarchy process (AHP), ensuring methodological rigor in prioritizing urban governance challenges. To ensure methodological rigor, six experts were systematically selected through a stratified process: three academic scholars specializing in urban governance (with ≥10 years of research experience and peer-reviewed publications in risk assessment) and three senior practitioners from Shanghai’s Housing Security Bureau and Urban Planning Institute (each overseeing ≥8 land use integration projects since 2018). It is important to clarify that the sample size of 6 experts, while relatively small, is not uncommon in the literature on expert surveys [30]. Using between 4 and 20 experts is common in such studies [31].

Potential biases inherent in the expert-driven AHP were mitigated through anonymized scoring protocols to reduce conformity pressures, the validation of the consensus strength via Kendall’s W coefficient (W = 0.82, p < 0.01), and triangulation with stakeholder survey data. This hybrid approach balanced technical expertise with empirical validation, addressing both institutional priorities and community-driven concerns while maintaining alignment with established methodologies for small-N expert panels in urban governance research.

3.2. Construction of an Evaluation System

The analytic hierarchy process (AHP) is a multi-criteria decision-making method that can effectively handle indicators that are not easily quantified. It can decompose the research question into a hierarchy, express and process subjective judgments in numerical form at each level, and also indicate whether the researcher’s subjective judgments are consistent. Based on the medium- and high-risk-level indicators identified in the risk assessment of Shanghai’s urban construction projects, a hierarchy for stakeholder risk assessment was established. Further analysis and verification were carried out for the identified medium- and high-risk factors. The specific analytical structure of the hierarchical model is shown in Figure 3.

As shown in Figure 3, the objective decision layer represents the risk level required by stakeholders. The intermediate layer consists of four risk-level indicators derived from the previous analysis. The alternative layer is divided into six categories of stakeholders: core stakeholders such as GFD and DSYO, potential stakeholders such as MPS, and peripheral stakeholders such as PSD and TID. This model structure is based on the identification of risk-level requirements for stakeholders in Shanghai’s urban construction projects. It aims to analyze the relative importance of the four risk-level indicators in the intermediate layer to the six categories of stakeholders in the Shanghai project.

Firstly, we calculate the matrix indicator weights based on risk metrics. Using a 1–9 scale, we provide a reasonable quantitative description for each pairwise comparison of indicators in the intermediate layer. Based on the comparison criteria for risk factors and expert scoring, we conduct pairwise quantitative comparisons between indicators. This process enables us to construct a weight judgment matrix for the target layer and intermediate indicator layer of Project A, as shown in

Table 2. Weighting judgement matrix for the medium-level risk indicators.

	Policy Perception	Expected Return	Policy Implementation	Environmental Adaption
Policy perception	1	1/5	1/2	2
Expected return	5	1	4	6
Policy implementation	2	1/4	1	3
Environmental adaption	1/2	1/6	1/3	1

.

Secondly, by using the column normalization method, the relative weights of the compared indicators in Table 2 are calculated for the indicator matrix. First, it is necessary to calculate the vector M, where M = (m1, m2, m3, …, mn)^T, $m_i=\sqrt[n]{\left(\prod _{j=1}^n{b}_{ij}\right)}$, i = 1, 2, …, n. The weights are then calculated by normalization, ω = (ω1, ω2, …, ωn)^T, to derive the relative importance of the quantitative values of the risk indicators as ωi in

Table 3. Summary of medium-risk-level indicator weights.

	Policy Perception	Expected Return	Policy Implementation	Environmental Adaption	ωi	Aωi
Policy perception	0.12	0.12	0.09	0.17	0.13	0.48
Expected return	0.59	0.62	0.69	0.5	0.60	2.53
Policy implementation	0.24	0.15	0.17	0.25	0.20	0.85
Environmental adaption	0.06	0.10	0.06	0.08	0.08	0.315

. A represents the entire matrix of indicators, and Aωi is the matrix combination weight. The weights are calculated by the formula ${\omega }_i={\displaystyle \frac{m_i}{\sum _{i=1}^n{m}_i}}$.

As can be seen from Table 3, the four risk factor indicators are ranked from large to small according to the weight value ω as the expected return, policy implementation, policy cognition, and environmental adaptation, which is in line with the previous results regarding the identification of the risk levels of the engineering and construction projects in Shanghai.

Then, by using the formula of ${\mathsf{\lambda }}_{max}={\sum }_{i=1}^n{\displaystyle \frac{\left[\mathrm{A}\mathrm{W}\right]}{n{w}_i}}$, the maximum eigenvalue of the aforementioned risk indicator matrix is calculated. This value is substituted into the consistency check formula to determine if the matrix is valid, resulting in a consistency index (CI) value of 0.0083. By consulting the average random consistency index values in

Table 4. Randomized consistent RI values.

N Step	3	4	5	6	7	8	9	10	11	12	13	14	15	16
RI Value	0.52	0.89	1.12	1.26	1.36	1.41	1.46	1.49	1.52	1.54	1.56	1.58	1.59	1.59
N Step	17	18	19	20	21	22	23	24	25	26	27	28	29	30
RI Value	1.61	1.61	1.62	1.63	1.64	1.64	1.65	1.65	1.66	1.66	1.66	1.67	1.67	1.67

, the random consistency index (RI) for a fourth-order matrix is found to be 0.89.

According to the consistency ratio formula, it is obtained that CR = 0.0093 < 0.1; therefore, the matrix of stakeholder risk-level indicators passes the consistency test. Similarly, for the program-level indicators, the six-level hierarchical matrices for the intermediate levels B1–B4 are constructed separately and the combined weights are calculated for consistency testing, as shown in

Table 5. Summary of primary risk-level indicator weights.

Stakeholders	GFD	DSYO	MPS	ISYO	PSD	TID	ωi	Aωi
B1 Policy perception
GFD	0.437	0.584	0.333	0.294	0.453	0.409	0.44	2.894
DSYO	0.048	0.065	0.238	0.294	0.075	0.068	0.12	0.779
MPS	0.062	0.013	0.047	0.059	0.056	0.045	0.04	0.459
ISYO	0.087	0.012	0.048	0.058	0.075	0.068	0.05	0.341
PSD	0.218	0.194	0.190	0.176	0.226	0.272	0.21	1.379
TID	0.146	0.129	0.143	0.117	0.113	0.136	0.13	0.861
B2 Expected return
GFD	0.169	0.133	0.129	0.146	0.181	0.218	0.161	1.060
DSYO	0.507	0.530	0.645	0.585	0.364	0.273	0.497	3.278
MPS	0.225	0.106	0.129	0.146	0.182	0.218	0.169	1.026
ISYO	0.056	0.059	0.032	0.049	0.091	0.218	0.079	0.527
PSD	0.028	0.066	0.032	0.037	0.045	0.018	0.036	0.38
TID	0.014	0.106	0.032	0.037	0.136	0.054	0.056	0.366
B3 Policy implementation
GFD	0.465	0.586	0.511	0.379	0.258	0.263	0.428	2.824
DSYO	0.155	0.195	0.306	0.315	0.193	0.210	0.238	1.571
MPS	0.093	0.065	0.102	0.189	0.322	0.211	0.154	1.018
ISYO	0.077	0.039	0.034	0.126	0.129	0.157	0.075	0.493
PSD	0.116	0.065	0.020	0.063	0.064	0.105	0.061	0.399
TID	0.093	0.048	0.025	0.031	0.032	0.052	0.043	0.280
B4 Environmental adaption
GFD	0.045	0.052	0.042	0.017	0.121	0.111	0.428	0.389
DSYO	0.409	0.469	0.506	0.357	0.424	0.444	0.238	2.811
MPS	0.272	0.235	0.253	0.446	0.182	0.167	0.154	1.842
ISYO	0.227	0.117	0.051	0.178	0.121	0.112	0.075	0.796
PSD	0.023	0.067	0.063	0.089	0.120	0.110	0.061	0.374
TID	0.022	0.058	0.084	0.044	0.060	0.055	0.043	0.314

.

The consistency test of the B1, B2, B3, and B4 matrices, respectively, yielded CRB1 = 0.0852, CRB2 = 0.0940, CRB3 = 0.0950, CRB4 = 0.0849, and the consistency ratios of the four matrices are less than 0.1, indicating that the hierarchical analysis model based on the risk levels of stakeholders’ claims is valid.

3.3. Risk Level Scheme and Indicator Analysis

Using a 1–9 quantification scale, the indicators at the scheme level were weighted against the stakeholders’ risk levels at the target level.

Table 6. Summary of scheme-level indicator weights.

Ordinal Value	Scheme Level	ωi
1	Direct Stakeholder Owners of Community Y (DSYO)	0.3948
2	Government Functional Departments (GFD)	0.1921
3	Merchants within the Planning Scope (MPS)	0.1596
4	Indirect Stakeholder Owners of Community Y (ISYO)	0.1279
5	Public Service Departments (PSD)	0.0640
6	Third-Party Inspection Department (TID)	0.0616

presents the resulting ranking.

As can be seen from Table 6, the DSYO and the GFD have the highest weights in the stakeholder risk-level decision-making. Next are the MSP, as well as the ISYO. Meanwhile, the PSD and the TID have the lowest weights. This indicates that the ranking and classification of core stakeholders, potential stakeholders, and peripheral stakeholders presented earlier are reasonable.

4. Results

This section bridges the methodological framework of stakeholder risk cognition with empirical validation in urban governance contexts. Drawing on hierarchical risk classification models and multi-criteria expert evaluations, the analysis demonstrates how systematically quantified risk indicators both confirm theoretical predictions and expose governance paradoxes. Key findings reveal fundamental tensions between technical risk assessments and socio-political imperatives, while establishing consensus thresholds for actionable risk prioritization. These insights advance the transition from static risk perception models to adaptive governance strategies, providing empirical grounding for the reconciliation of metropolitan development objectives with multi-dimensional stakeholder claims in complex renewal contexts.

4.1. Risk Indicators Calculation

This study employs the PESTEL analysis method, which is based on the reasonable, necessary, and feasible interests and needs of different stakeholders. It integrates six factors that influence urban construction projects: political, economic, cultural, technological, environmental, and legal. Through questionnaires and semi-structured interviews on the road planning and construction project in Shanghai’s new urban area, this study has identified the risk points of Shanghai’s urban construction projects and summarized them. The results are shown in

Table 7. Statistics of risk factors.

Targets (Stakeholder Risk Classification)	Primary Indicators (Actual Entity)	Secondary Indicators (Risk Factors)	Evaluation Interests and Claims
Core stakeholders	DFD	Legislation	Elements of decision-making
		Policy implementation	Motivations for decision-making
		Sustainable development	Urban development
	DSYO	Expected returns	Financial compensation
		Environmental adaptation	Resettlement
		Policy perception	Information disclosure
Potential stakeholders	MPS	Adjustment of interests	Project compensation
		Social stability	Rent-seeking
	ISYO	Social safety	Residential environment
		Social tension	Resident attitudes
		Quality of life	Noise nuisance
Peripheral stakeholders	PSD	Governance	Legitimate basis
		Social security	Relocation
	TID	Social equality	Interests’ allocation
		Technical feasibility	Design of engineering projects

.

To further enhance the precision and effectiveness of risk identification, this project invited six experts, as mentioned above, to conduct a secondary screening of the preliminary aggregated risk factors of the new city road planning and construction project in Shanghai.

Firstly, a 1–9 scale method is used to provide a quantitative description of the comparison between risk indicators. The comparison quantification values between indicators are assigned scores of 1, 3, 5, 7, and 9, respectively, representing very unreasonable, unreasonable, neutral, reasonable, and very reasonable. The arithmetic means of the scores given to each level of indicator are used to represent the “degree of consensus” among the experts, and the coefficient of variation of the scores given to each indicator is used to represent the “degree of coordination” among the experts. Let Xij represent the score given by the i expert to the j indicator, with a total of n experts. The steps for the calculation of the expert opinion coordination degree are as follows:

$$M_j={\displaystyle \frac{1}{n}}{\sum }_{i=1}^n{X}_{ij}$$

(1)

$$S_j=\sqrt{{\displaystyle \frac{1}{n-1}}{\sum }_{i=1}^n{\left(X_{ij}-M_j\right)}^2}$$

(2)

$$V_j={\displaystyle \frac{S_j}{M_j}}$$

(3)

M_j indicator is the average rating of the j indicator marked by n experts; Sj refers to the standard deviation of the scores given by n experts for the j indicator; V_j refers to the coefficient of variation of the evaluations of the j indicator by all experts. After the experts have scored, the average values of each level of indicator and the degree of coordination among expert opinions are as follows in

Table 8. Rationalization results for primary risk indicators.

Primary Risk Indicator	Core Stakeholders		Potential Stakeholders		Peripheral Stakeholders
	GFD	DSYO	MPS	ISYO	PSD	TID
$V_j$	0.09	0.00	0.13	0.14	0.12	0.12
$M_j$	8.67	9.00	8.00	8.00	8.33	8.33

and

Table 9. Rationalization results for secondary risk indicators.

Evaluation	Secondary Risk Indicators	${\mathit{M}}_{\mathit{j}}$	${\mathit{V}}_{\mathit{j}}$
Elements of decision-making	Legislation	8.20	0.13
Motivations for decision-making	Policy implementation	9.00	0.00
Urban development	Sustainable development	9.00	0.00
Financial compensation	Expected returns	9.00	0.00
Resettlement	Environmental adaptation	7.67	0.13
Information disclosure	Policy perception	7.00	0.00
Project compensation	Adjustment of interests	7.67	0.15
Rent-seeking	Social stability	8.67	0.09
Residential environment	Social safety	8.33	0.12
Resident attitudes	Social tension	7.33	0.11
Noise nuisance	Resident attitudes	9.00	0.00
Legitimate basis	Governance	8.00	0.20
Relocation	Social security	8.60	0.10
Interests’ allocation	Social equality	9.00	0.00
Design of engineering projects	Technical feasibility	9.00	0.00

.

Using the arithmetic mean M_j = 6.00 as the critical value for the concentration of opinions, M_j > 6.00 indicates a high degree of concentration among the experts’ opinions; when the coefficient of variation V_j < 0.25, it suggests that the experts’ opinions on its rationality are relatively consistent. According to Table 8 and Table 9, it can be seen that the experts generally agree on the rationality of the primary and secondary risk indicators.

4.2. Assessment of the Risk Level

This study continues to employ the risk matrix method to assess the risk levels of the preliminary aggregated risk factors presented in Table 4. Initially, the risk probability intervals are quantified and assigned values from 1 to 5, corresponding to the risk occurrence probability boundaries of extremely low (0–10%), low (10–25%), medium (25–50%), high (50–75%), and extremely high (90–100%).

Based on the principle of equal weighting, the arithmetic mean score of each assessment expert is denoted by P, which represents the average value of the probability of occurrence of the risk, and is calculated by the formula

$$P_{f0}={\displaystyle \frac{a_1+a_2+\dots +a_n}{n}}$$

(4)

where “a” represents the assessment score and “n” represents the number of assessment experts. The expert assessment results regarding the probability of occurrence of risk factors can be derived through calculation.

Then, the 1–5 grading is assigned to the risk affectivity, which corresponds to the hazard level of urban public safety, divided into very low (0, 1%), low (1%, 5%), medium (1%, 5%), high (10–15%), and very high (15–20%), respectively. Moreover, based on the principle of the equal weighting of the assessment experts, according to the above formula used to calculate the average score, the results $C_{f0}$ of the risk hazard severity assessment based on the identification of stakeholder risk factors can be obtained in the same way. Based on the formula $R_{f0}=P_{f0}\times C_{f0}$, the results of the risk level comparison are as shown in

Table 10. Risk level comparison.

$\mathbf{Risk}\mathbf{Level}{\mathit{R}}_{\mathit{f}0}$			$\mathbf{Risk}\mathbf{Probability}{\mathit{P}}_{\mathit{f}0}$
			Very Low	Low	Medium	High	Very High
			1	2	3	4	5
Risk hazard severity $C_{f0}$	Very low	1	1	2	3	4	5
	Low	2	2	4	6	8	10
	Medium	3	3	6	9	12	15
	High	4	4	8	12	16	20
	Very High	5	5	10	15	20	25

.

In the risk level comparison in Table 10, the value interval (1–4) belongs to a low risk level, the interval (5–12) belongs to a medium risk level, and interval (15–25) belongs to a high risk level. According to the risk level formula $P_{f0}$ mentioned above, we can calculate the risk factor levels of the relevant interest claims in the road planning and construction project in Shanghai. Then, based on the risk level control allocation in Table 10, we can obtain the specific risk factors for the Shanghai project, as shown in

Table 11. Risk factor level assignment for engineering projects in Shanghai.

Risk Factor	${\mathit{P}}_{\mathit{f}0}$	${\mathit{C}}_{\mathit{f}0}$	${\mathit{R}}_{\mathit{f}0}$
Legislation	0.8	1.4	1.12
Policy implantation	2.5	3.4	8.50
Sustainable development	0.6	1.2	0.72
Expected return	3.9	3.8	14.82
Environmental adaptation	2.1	2.9	6.09
Policy perception	2.7	2.4	6.48
Adjustment of interests	1.2	2.4	2.88
Social stability	1.1	3.2	3.52
Social safety	1.5	2.3	3.45
Social tension	3.2	1.5	4.80
Quality of life	2.2	2.1	4.62
Governance	1.3	1.2	1.56
Social security	1.2	1.1	1.32
Social equality	1.8	2.5	4.5
Technical feasibility	1.5	1.8	2.7

.

Based on the summary of the risk content obtained from the stakeholder surveys, and in accordance with the risk probability and risk impact levels, the specific average value assignment is calculated by the project evaluation experts, as shown in Table 11. Policy implementation, expected returns, environmental adaptation, and environmental adaptation all belong to the medium risk level, while other factors belong to the low risk level. The following section will further assess and validate the existence of medium–high-level risk factor items, namely policy perception, expected benefits, policy implementation, and environmental adaptation, in specific urban engineering construction projects in Shanghai. These risk factors have been identified as requiring further investigation due to their potential to impact the success of such projects.

5. Discussion

Building on the empirical findings from the stakeholder risk prioritization and quantified indicators, this section translates the risk hierarchies into actionable governance strategies. The analysis bridges technical risk assessments with socio-political dynamics, offering an adaptive framework to reconcile urban development imperatives with community welfare in complex renewal contexts.

5.1. Risk Factor Anlaysis

In the studied case, urban construction projects involve urban traffic renovation and land use development, which can lead to numerous conflicts of interest and risks, thus affecting the smooth progress of the project and urban sustainable development. The analysis combined with the risk indicator layer is as follows.

5.1.1. Risks of Policy Perception

“Risk perception” refers to an individual’s awareness and understanding of various objective risks present in the external environment, emphasizing the impact of experience gained from intuitive judgment and subjective feelings on individual cognition [32]. Wide divergence in the level of perceived risk between experts and the public has been established [33]. Since urban road planning and construction projects involve one of the core stakeholders being the local residents, their subjective understanding of specific government policies is limited, and it is necessary to protect their immediate actual interests, they may hold a negative attitude towards specific policies, even to the point of protesting and resisting the implementation of related urban construction projects.

The land planning and construction project in Shanghai serves the planning and construction of its new city’s central business district, which is beneficial in allowing Shanghai’s new city to contribute to the integrated development of the Yangtze River Delta. However, from the perspective of the homeowners in Community Y, their community has an excellent geographical location, the buildings are not old, the infrastructure is intact, and the living conditions are superior, so they believe that urban development plans unrelated to the community’s residents are not important. As core stakeholders, DSYO, who are the direct bearers of the risks, have differences in policy risk perception compared to the GFD, which are also core stakeholders, as well as the experts and TID, who are potential stakeholders. Therefore, the relevant residents resist the project’s implementation because their own interests are not met. All of this requires the GFD to actively communicate with the public, provide detailed policy explanations and ideological work, actively help the public to solve problems within their capabilities, and resolve policy risk perceptions from the public level.

5.1.2. Risks of Expected Return

Land conversion increases in response to cost sharing, rental payments, and technical assistance, which reduces uncertainty about land benefits. When the correlation between benefits is nonpositive, risk aversion leads the land user to diversify their land among uses [34]. In specific research cases, based on field surveys and the collation of relevant materials, it has been found that the compensation demands put forward by the homeowners of Community Y are much higher than the standards provided in the draft opinion solicitation for the construction project by the GFD of Shanghai. The current compensation plan offered by the government cannot meet the interests of the homeowners in Community Y, and there is a discrepancy between their compensation demands and expected returns and the compensation plan that the government can provide, which may lead to direct conflicts between the stakeholders. The difference between the psychological expectation of benefits and the actual compensation standards that can be obtained may lead the directly affected groups in construction projects to take different approaches to achieve their demands, thereby bringing more specific engineering risks, administrative litigation risks, etc.

According to the statistical results, more than half of the homeowners in Community Y believe that the compensation standards are not reasonable. Based on specific research findings, the demands for returns and compensation risks from homeowners involve several major risk issues, including insufficient subsidies for housing areas, doubts about the fairness of housing value assessment, low price subsidy coefficients, insufficient monetary compensation, low transition fees and rental subsidies, and a lack of compensation for ground parking spaces. Therefore, the GFD must consider the project mechanisms, compensation standards, the acceptance levels of the affected, and the pressure on collaborative departments’ execution efficiency in the areas where urban construction projects are carried out, while also carefully considering the multi-dimensional, multi-level operation models of urban construction projects required by the regional administrative system. The smooth implementation of urban construction projects in the new city of Shanghai requires the administrative efficiency and supporting work of various GFD to be in place.

5.1.3. Risks of Policy Implementation

“Policy implementation is an important link in achieving policy goals, and the essence of policy implementation is a process of interest games between subjects considering gains and losses” [35]. The specific value goals and standards guided and motivated by the government “should be identifiable by the majority of social members; otherwise, this social incentive mechanism will fail due to rejection by the majority of society” [36]. In the case study of Shanghai, the negative attitude of the homeowners in Community Y, as core stakeholders, can lead to a certain degree of policy implementation risk.

In the specific implementation of the project, some stakeholders may refuse to relocate. Alternatively, due to resistance actions during the project, they may disturb the normal lives of other potential stakeholders in the vicinity. This reminds the GFD, which are also core stakeholders, to carefully control the project links, transform and improve the policy implementation model, and actively explore communication channels and risk response methods between them and the direct audience. Against the background of risk prediction, whether grassroots government departments have a good response mechanism has become a key factor in the prevention and control of public safety and social security risks in the process of urban construction projects. In addition, policy implementation must be reasonable and grounded to avoid the possibility of conflicts between different stakeholders.

5.1.4. Risks of Environmental Adaptation

“Social adaptation” is a process involving the active and passive adjustment of actors to changes in the surrounding environment, including both objective and subjective levels, and can be operationalized into at least three dimensions: economic survival, social interaction, and psychological identification [37]. In the specific case of the land planning construction project in Shanghai, the homeowners in Community Y, as core stakeholders, have many concerns about the resettlement issues after the project and believe that the project poses specific environmental adaptation risks to their later lives.

According to the statistical results, all homeowners in Community Y believe that the implementation of the project will disrupt the current living status of the local residents and there will be varying degrees of resettlement issues. Moreover, for a considerable period in the future, homeowners will be forced to leave their current places of residence, causing inner unease and worry. After the implementation of this project, the transition period for relocation may cause temporary discomfort in the lives of a few people: for example, the elderly among the core stakeholders may feel reluctant to leave their hometown, and the direct audience, including minors, may be deeply affected by issues such as childcare and school enrollment due to housing relocation. At the same time, potential stakeholders living in the surrounding areas of the land expropriation region will bear a series of living environment risks brought by the construction project, facing short-term declines in quality of life, noisy living environments, a decline in air quality, and other adaptive issues.

5.2. Implications and Recomendations

The proposed governance strategies inherently involve trade-offs between competing stakeholder priorities. The high-risk rating of compensation equity demands reflects a structural tension between displaced residents’ livelihood claims and municipal fiscal constraints. While policy transparency is critical for trust building, strict adherence risks prolonging decision cycles, as seen in the medium–high policy implementation risk ($R_{f0}$ = 8.50). Similarly, the prioritization of technical efficiency (GFD’s ω_i = 0.428 in B3) conflicts with community-driven environmental adaptation concerns (DSYO’s $R_{f0}$ = 6.09). These tensions validate the framework’s adaptive calibration mechanism: through expert consensus thresholds (CV < 0.25 in Table 8 and Table 9), stakeholders accept moderated compromises when procedural fairness aligns with their risk cognition hierarchies. For instance, the hybrid weighting of compensation equity (ω_i = 0.497) and policy efficiency (ω_i = 0.428) demonstrates how divergent priorities can coexist within polycentric governance systems. Drawing on the findings from stakeholder demand analysis and risk identification presented in Section 5.2.1 to Section 5.2.4, the risk governance and prevention strategies for urban construction projects can be outlined as follows.

5.2.1. Breaking Down Information Barriers in Policy Cognition

Information disclosure is an important measure in dealing with urban public safety risks, especially playing an essential role in responding to emergencies. When confronting urban public safety risks, the governance should recognize that risks are inherently a collection of uncertainties, complexities, and changes in information, and fully considering the interests and public opinion feedback of the general public is beneficial in enabling decision-makers to fully control urban public safety risks. As core stakeholders, the general public often has a policy cognition information gap with the government regarding the future development of the city, and various functional departments are required to facilitate upward communication in the government system. Cognitive differences and the extension chain of complex information may form information barriers between the general public and the government. To break the information silos and delays, the government and related functional departments need to strengthen their direct communication and exchange with the general public, proactively disclose project-related information, truthfully inform them about the progress of the project, and establish transparent and smooth channels for information disclosure and communication.

5.2.2. Clarifying Fair and Comprehensive Compensation

Compensation is the necessary cost for the government to acquire private immovable property. In urban construction projects, the core interests of the general public are subordinated to urban construction and development, thereby creating certain life risks and giving them the right to demand fair compensation from the government. The core issue of project implementation is indeed a matter of interest; in the specific procedures of urban construction projects, on the one hand, compensation must be adequate for the directly affected groups, and, on the other hand, it is necessary to guard against the excessive demands of direct stakeholders to avoid potential urban public safety governance risks arising from compensation issues. The weighing of interests requires the sequential consideration of “appropriateness”, “necessity”, and “balance”, establishing the correct scope and standards for compensation. In a buyer’s monopoly market, the government, as a core stakeholder, has the power to price land and is in a relatively proactive position compared to the general public. The key to the smooth implementation of urban construction projects lies in finding a balance of interest between the government and the general public, while also considering the actual interests of other potential stakeholders within the project, reaching a consensus on the value of interest in urban construction projects among various stakeholders.

5.2.3. Implementing Coordinated and Orderly Departmental Supervision

Urban construction projects are complex tasks involving a wide range of stakeholders. Clarifying the responsibilities and rights of relevant personnel, making various contingency plans, and effectively preventing and resolving public safety risks are necessary prerequisites for GFD to undertake construction projects. Decision-makers in relevant departments need to make comprehensive arrangements for the entire construction project, including making work arrangements for urban construction projects and assigning specific tasks to each functional department, establishing a responsibility list system, and ensuring that all departments can perform their duties, obey unified dispatch, and effectively control the project, truly achieving the “full-process” management of urban public safety risks in the specific implementation of urban construction projects. At the same time, in line with the spirit of higher-level government supervision over lower-level governments and the same-level government supervision over its functional departments established by the constitution and organizational laws, strict hierarchical supervision should be established, and a sound internal government supervision mechanism should be developed to fundamentally regulate the administrative supervision and management behavior of urban construction projects. It should play an external supervisory role, especially by strengthening the supervision of law enforcement activities by state power organizations and judicial authorities, in order to promote the smooth implementation of urban construction projects.

5.2.4. Implementing Robust and Feasible Emergency Plans

In order to better meet the need for comprehensive emergency management, systematic and flat emergency management systems can be built through the use of computer technology such as cloud computing and artificial intelligence [38]. Emergency plans help to identify construction project risk hazards, predict projects that may lead to emergencies, and respond to and deal with emergencies in a timely manner to prevent the expansion or escalation of such emergencies and minimize the potential damage that they may cause. For urban construction projects, their primary purpose is to meet the public interest needs of urban development, so, in the specific execution process, it is inevitable that the individual interest demands of the general public will be directly affected by the project. Thus, there is a public safety risk of developing sudden mass incidents during the specific process of urban construction projects. Therefore, the GFD responsible for the implementation of urban construction projects should prepare project emergency plans in advance to ensure the availability of emergency supplies in the event of sudden mass incidents and prevent adverse consequences of urban public safety events triggered by construction projects. At the same time, in the supervision and implementation of the project, as a core stakeholder, the government can construct a horizontal multi-departmental supervision mechanism, implement mutual supervision among departments related to urban construction project work, or accept public supervision through the establishment of dedicated lines, etc., to maintain a transparent and fair government image during the urban construction project process and avoid reducing the justice of the project and weakening the authority of the government due to issues with execution methods.

Based on the various demands of stakeholders, conducting urban public safety risk identification can enhance the social stability risk management capabilities of urban construction project units and government departments, facilitate communication and consultation between project units and government departments, enable them to better assume urban public safety governance responsibilities, effectively protect the fundamental interests of the general public, and continuously improve the prevention and resolution capabilities of social stability risks and the maintenance of urban public safety.

6. Conclusions

This study examines the governance of public safety risks in urban infrastructure development through a stakeholder-centric perspective. Against the backdrop of increasingly complex urban risks, conventional governance models fail to reconcile conflicts between technical requirements and socio-political realities. A stakeholder-oriented risk analysis framework emerges as a critical mechanism to bridge this gap, necessitating the integration of multi-dimensional risk perceptions among diverse urban actors. This approach systematically addresses the limitations of technocratic governance paradigms while advancing collaborative strategies for sustainable urban development. The analytical framework innovatively classifies stakeholders into three tiers—core, potential, and peripheral—based on their legitimacy, influence, and urgency in decision-making processes. Through a mixed-methods approach combining qualitative case analysis and quantitative hierarchical modeling, this study systematically maps divergent risk priorities and governance demands across these groups. This methodology advances the risk governance discourse by reconciling institutional objectives with community welfare imperatives, offering a replicable model to balance urban development efficiency and social equity in complex metropolitan contexts.

This study further identifies four interconnected risk dimensions: policy perception gaps, compensation expectation disparities, policy implementation legitimacy deficits, and environmental adaptation anxieties. The analysis reveals fundamental asymmetries in risk prioritization between institutional actors—who emphasize technical efficiency and policy compliance—and community stakeholders—who prioritize livelihood security and spatial justice. By integrating the analytic hierarchy process (AHP) with the risk assessment methodology, this study quantifies these divergences, demonstrating how technical governance models inadequately address socio-political imperatives. The proposed adaptive governance framework bridges this gap through dual-track strategies. Empirical validation via expert consensus analysis confirms the framework’s robustness in hierarchically prioritizing risks while exposing systemic vulnerabilities in polycentric decision-making systems. These insights advance the urban sustainability discourse by operationalizing stakeholder alignment as a critical governance lever, offering scalable solutions to balance metropolitan development imperatives with community welfare in rapidly urbanizing contexts. This work contributes both theoretically—through its integration of risk perception and institutional analysis paradigms—and practically—by providing policymakers with actionable tools to transform risk conflicts into collaborative solutions.

This study also exhibits three primary limitations that warrant attention in future research. First, the geographical specificity of the case study restricts the direct generalizability of the findings to other megacities, particularly those in the Global South, with divergent governance structures or urbanization trajectories. However, the stakeholder-centric governance challenges identified in Shanghai reflect broader urbanization dilemmas across Chinese megacities. Beijing struggles in reconciling technical planning and political imperatives, highlighting systemic tensions between technical rationality and institutional rigidity [39]. Guangzhou’s micro-redevelopment model [40], which prioritizes heritage conservation over profit-driven demolition, contrasts with Shanghai’s compensation equity debates, yet both underscore the need to recalibrate stakeholder power dynamics. Meanwhile, Shenzhen’s data-driven livability assessments [41] parallel Shanghai’s environmental adaptation anxieties. These cases collectively affirm that stakeholder risk cognition alignment, and not uniform institutional pathways, drives sustainable urban transitions. Second, while the hybrid model effectively quantifies stakeholder risk perceptions, its reliance on expert scoring introduces inherent subjectivity biases, especially in translating qualitative community concerns into hierarchical metrics. The current AHP framework employs the equal weighting of expert opinions; future studies could enhance the robustness testing through sensitivity analyses, as advocated for in multi-criteria decision-making. Third, the cross-sectional design prioritizes immediate risk identification over longitudinal analysis, thereby overlooking the dynamic evolution of stakeholder interactions and risk perceptions across project lifecycles. Additionally, the framework’s stakeholder classification, though theoretically robust, may underrepresent informal sector actors or transient populations, whose risk narratives could further complicate the urban governance dynamics. Finally, the proposed governance innovations remain conceptual and require empirical validation through pilot implementation to assess their scalability and adaptability. These limitations highlight opportunities for future studies to expand the geographical coverage, integrate real-time data analytics, and adopt participatory action research methods to strengthen the framework’s practical relevance.

Nowadays, land use optimization is still a promising approach to achieving urban sustainability [42]. However, the interactive relationship between transportation and land use has become more difficult to understand and predict, due to the economic boom and the corresponding fast-paced proliferation of private transportation and land development activities [43]. The study of land use transition has generally become an important breakthrough point to deeply understand the human–land interaction and reveal major socioeconomic development issues and related environmental effects [44]. Functional differences in land use produce different travel needs and have different impacts on traffic [45]. Risk prevention is crucial in promoting sustainable development, enhancing economic vitality, and ensuring stability [46]. Therefore, the current main task is to develop an intensive, efficient, and ecological strategy, considering rural–urban integration and harmonious urbanization, to pursue a harmonious relationship between urbanization and sustainable land use [47].