Analytical approach to smart and sustainable city development with IoT

Abstract

This study explores the transformative role of Internet of Things (IoT) technologies in sustainable urban planning, with a focus on smart city development. By integrating IoT-driven real-time monitoring, disaster management, and energy and waste management, the study examines their mediated effects on sustainable urban growth. Data were collected using quantitative methods across various urban projects, analyzed via Structural Equation Modeling with Principal Component Analysis (SEM-PCA). Results highlight significant mediated relationships, especially in real-time monitoring (coefficient = 0.278), disaster management (coefficient = 0.086), and energy and waste management (coefficient = 0.082), underscoring IoT’s potential in fostering urban resilience. These findings provide actionable insights for policymakers and urban planners, demonstrating how IoT can optimize resources, enhance safety, and improve livability. This research thus contributes to the body of knowledge on sustainable urban development, offering a framework for IoT integration in smart city planning and promoting data-driven urban sustainability.

Introduction

Urbanization in the 21 st century has been at a very swift pace, with the United Nations indicating that more than half of the world’s population resides in urban areas now, and this count is going to reach 68% before the year 2050¹. This accelerated pace at which towns are growing comes to meet significant challenges in sustainable development, resource management, and bringing a better quality of life². The complexity of challenges on these fronts makes effectiveness difficult by conventional urban planning approaches³. But through the development of the Internet of Things and Artificial Intelligence, urban environments can be metamorphosed into smart cities^4,5. The global smart city market is anticipated to reach USD 820.7 billion from USD 410.8 billion in 2020 by reflecting the burgeoning adoption of IoT technologies⁶.

Although IoT seem promising to be adopted in the planning processes of cities, in reality, the development of smart cities is marred with uneven implementation and fragmented activity⁷. Due to a lack of proper planning, most the cities worldwide have faced challenges arising from accommodating these technologies within their existing infrastructural setup, eventually causing inefficiencies and suboptimal outcomes⁸. There is also a lack of elaboration of studies showing quantifiably the benefits that IoT will bring in achieving urban sustainability and livability⁹. Such lack of knowledge has led to policymakers and urban planning authorities not making wise decisions that could harness the full potential of these technologies.

While there is a considerably large number of literature relating to the technological aspects of IoT and AI, a notable gap exists regarding actual research findings pertaining to their holistic impact on sustainable urban planning. Other works are based on case studies of an individual or specific application and do not analyze them in a comprehensive dimension. The paper tries to fill that gap by assessing—through a systematic framework of analysis—the positive impacts that IoT bring to urban planning and the development of smart cities at large.

The present paper aims to provide insight into the benefits of adopting IoT towards sustainable urban planning and innovative city development. More specifically, this paper aims to:

• Evaluate the extent to which the integration of IoT could boost the urban infrastructure and services sector.

• Recognize the chief benefits of incorporating IoT in transport, energy, waste, and public safety through monitoring and disaster management.

• Examine the successful relation of smart cities development, which have been implemented with the help of solutions for IoT and AI.

• Provide recommendations for policymakers and urban planners on leveraging these technologies in sustainable urban development.

This study offers a novel perspective by combining the analysis of IoT impacts with a focus on sustainable urban planning. Unlike prior research that tends to treat these technologies as silos, often, this paper puts them in an integrated manner into focus on the many times synergistic effects created. The reach and importance of this research lie in the fact that it could help guide urban planners and policymakers in employing IoT for developing technologically innovative, ecologically wise, and socially smart cities. Hence it can be said that this paper adds to the general body of knowledge about the ways and means by which IoT can foster positive transformations in cityscapes.

Literature review

The interest of academia in integrating IoT into urban planning and innovative city development is growing. This literature review will synthesize some of the main findings from recent studies on the transformational potential and challenges associated with these technologies.

IoT in urban planning

The use of the Internet of Things opens up an opportunity for collecting real-time data from all possible and impossible technical means of urban devices and its analysis for the sake of further significant use for the betterment of urban management and planning. Generally, many researchers have proved that using IoT applications in smart cities could realize significant gains in resource efficiency and service delivery. For instance, smart grids with embedded IoT sensors can realize optimization of energy distribution and consumption, thus reducing energy wastage by about 20%¹⁰. More than that, IoT-based traffic management systems cut congestion and enable smooth modes of transportation: travel time was found reduced by as high as 30% in Barcelona with such a system in place¹¹.

Figure 1 illustrates the emphasis placed on various smart city applications, reflecting the priorities in urban development initiatives. The largest segment, traffic management at 15%, highlights the critical focus on managing and optimizing traffic flow to address congestion and pollution issues in urban areas¹². Smart transportation follows closely with 12%, underscoring the importance of developing efficient and sustainable transit systems that enhance urban mobility¹³.

Fig. 1.

IoT in urban planning.

Environmental monitoring (10%) and public safety (8%) are also significant, showcasing the priority given to maintaining a safe, healthy, and eco-friendly environment in cities¹⁴. Energy management, waste management, and water quality monitoring are key areas as well, each contributing to resource efficiency and the overall sustainability of urban spaces¹⁵. The chart visually organizes these categories, providing a clear, comparative view of how smart city applications are strategically balanced to create sustainable and resilient urban infrastructures. This approach underscores the interconnected nature of these applications, where each sector plays a vital role in achieving a comprehensive smart city ecosystem.

Artificial Intelligence has in its repertoire the most sophisticated data analytics functionalities used for forecasting trends and consequently optimizing resource allocations aimed at improving the process of decision-making. Urban AI planning tools extract meaning from data sets too large for a human to easily process, and in doing so, they unearth patterns and dispense insights impossible for the human eye alone to strategize¹⁶. For example, AI algorithms can be used for the prediction of traffic patterns and suggestion of optimal routing with reduced travel and fuel consumption. For example, in a study done by Batty et al., AI could enhance the efficiency of transportation systems by predicting the demand of passengers and changing schedules accordingly, that resulted in a 15% increase in the composition of public transport¹⁷.

In the context of smart city development, diverse applications are prioritized to address the unique needs of urban infrastructure. Figure 2 provides an overview of these applications, each contributing to the efficiency and sustainability of urban environments. The traffic prediction is prioritized, with a 14% weighting, citing value for high-tech traffic networks in less congestions and increased mobility¹⁸. It complements smart city overall aims for maximising transport and minimizing waits, with immediate impact for urban citizens in terms of reduced commute times and reduced emissions.

Fig. 2.

AI based traffic planning and outcomes.

The distribution of resources and smart infrastructure for buildings matter, at 11% and 10%, respectively¹⁹. Distribution of resources aids in maximising urban efficiency in terms of use, and urban spaces can function in a sustainable state. Smart infrastructure for buildings aids through smart management and smart energy, and both go a long way in curving consumption and enhancing urban living. Environmental concerns are captured through categories such as tracking of pollution (8%) and conservation of water (6%), and both represent a concern for conservation and environment²⁰.

The map continues to show contribution of emergency service, waste management, and urban planning, each contributing between 5% and 7% towards focus²¹. All these factors go towards depicting value in preparedness, efficient waste infrastructure, and planning in creating resilient urban spaces. Public security and efficiency in terms of energy complete prioritized categories, providing for efficient and secure urban spaces. That such a balanced distribution of focus in a variety of smart city implementations can represent urban sustainability in its state of integration, with infrastructure, management of resources, and security all contributing towards a smart, sustainable city, is a strong depiction of its value.

IOT adoption globally

Figure 3 illustrates the global distribution of IoT-enabled initiatives and their positive outcomes across various countries. China leads significantly in IoT-enabled initiatives, with over 50 recorded implementations, demonstrating the country’s strong investment in IoT technology for smart city development. The United States and the United Kingdom follow with a substantial number of IoT initiatives, though they show a more balanced ratio of initiatives to positive outcomes, suggesting that these countries prioritize both implementation and effective outcomes²².

Fig. 3.

IoT Global Role.

Other countries like Singapore, Malaysia, and Germany have n notable numbers of IoT initiatives, indicating active engagement in technological advancements within smart cities²³. However, the counts for positive outcomes are somewhat lower, implying that these countries may still be in the early stages of IoT integration or are facing challenges in achieving desired results.

The remaining countries, such as those in Europe, the Middle East, and parts of Asia, show relatively low counts of IoT initiatives and outcomes²⁴. This distribution highlights the varying levels of IoT adoption worldwide, with more developed nations leading the way in IoT integration for urban enhancement²⁵. However, as IoT technology becomes more accessible, these lower-count regions may experience growth in IoT-enabled solutions, enhancing their capabilities in areas like infrastructure management, environmental monitoring, and public safety.

Integration of IoT and AI in smart cities

The convergence of IoT and AI will determine the successful implementation of smart cities. IoT devices generate large amounts of data, which AI systems can analyze to derive actionable information from the data²⁶. This integrated approach can, therefore, lead to a more responsive and adaptive urban system. In contrast, research by Khatoun and Zeadally (2016) showed that cities that adopted IoT and Al technologies experienced tremendous improvement in some areas, such as waste management, which used smart bins installed with sensors and Al algorithms to reduce waste collection costs by 40%²⁷.

This Fig. 4 visualizes the integration of IoT and AI technologies in smart cities by categorizing different applications into inner and outer segments, highlighting the diverse technological approaches utilized to enhance urban infrastructure.

Fig. 4.

IoT an AI adoption in Transport.

The outer ring represents broader, infrastructure-level applications, with AI-related technologies occupying the largest segment at 40%²⁸. This signifies a heavy reliance on AI to drive various smart city functions. Real-time monitoring systems (15%), stability control (10%), anti-collision systems (8%), and load moment indicators (7%) are also key outer components. These categories focus on the overall monitoring, safety, and control of urban systems, showcasing how IoT and AI work together to manage and optimize city operations in real time.

The inner ring details more specific applications, with predictive maintenance (20%) taking a significant portion²⁹. This reflects the emphasis on maintaining and extending the life of city infrastructure through proactive measures. Other inner applications, such as sensor integration (8%), automated alerts (7%), fault detection (5%), and load analysis (5%), focus on localized monitoring and quick response mechanism³⁰. These functions are essential for detecting anomalies, automating responses, and analyzing data for better decision-making.

This layered approach highlights how IoT and AI are integrated at multiple levels to ensure that both overarching infrastructure needs and specific maintenance requirements are addressed, leading to a resilient and efficient smart city ecosystem.

Challenges and barriers

Even though there are a lot of potential benefits, the area of integrating IoT for urban planning also faces several challenges. The prime concern is about the issues of privacy and security of data, essentially because the heavy usage of sensors and data analytics leads to high vulnerability³¹. Moreover, this may be directly linked to the lack of standardized protocols or the absence of interoperability support among different IoT devices, which might make seamless integration. As Zanella et al. (2014) have pointed out, a harmonized framework that allows IoT to work conjointly is required to realize these potentials for innovative city development³².

Success in implementations

Several cities have successfully implemented IoT solutions for improving urban sustainability and livability shown in Table 1. For example, the Smart Nation program in Singapore uses IoT sensors in traffic, air, and energy management for an effective and sustainable urban environment. In a similar vein, the smart city project in Amsterdam integrated IoT technology to improve waste management, energy consumption, and public safety, hence resulting in a higher quality of life for its residents³³.

Table 1.

Related studies indicating benefits challenges and recommendations.

Author	Focus Area	Key Findings	Benefits	Challenges	Recommendations	Reference
Lee & Lee	Smart Grids	IoT optimizes energy distribution	20% reduction in energy wastage	Data privacy concerns	Develop robust security measures	10
Chourabi et al.	Traffic Management	IoT reduces congestion	30% decrease in travel time	Interoperability issues	Standardize IoT protocols	11
Batty et al.	Public Transport	IoT predicts passenger demand	15% increase in public transport use	Complexity of IoT models	Enhance IoT model transparency	17
Khatoun & Zeadally	Waste Management	IoT reduce collection costs	40% cost reduction	Integration challenges	Develop unified frameworks	27
Zanella et al.	Standardization	Importance of unified IoT frameworks	Seamless integration	Lack of standardized protocols	Promote global standards	32
Schaffers et al.	Smart City Initiatives	IoT improve urban systems	Higher quality of life	Funding and resource constraints	Secure sustainable funding	33
Hollands	Urban Sustainability	Smart cities enhance sustainability	Improved resource management	Social inequality concerns	Inclusive planning strategies	34
Caragliu et al.	Economic Impact	Smart cities boost economic growth	Increased economic activities	High implementation costs	Cost-benefit analysis	35
Allwinkle & Cruickshank	Citizen Engagement	IoT enhance civic participation	Greater citizen involvement	Digital divide issues	Address digital literacy	36
Komninos	Innovation in Smart Cities	IoT foster innovation	Enhanced innovation ecosystems	Scalability issues	Scalable solutions development	37
Nam & Pardo	Governance	Improved governance with IoT and AI	More effective public services	Policy and regulatory barriers	Adaptive policy frameworks	38
Alawadhi et al.	Case Studies	Success stories of smart cities	Demonstrated benefits	Replicability challenges	Tailor solutions to local contexts	39

The literature on IoT applications in urban planning extensively covers various domains, including real-time monitoring, disaster management, and energy and waste management. For example, IoT-enabled traffic management systems in Barcelona have significantly reduced congestion and emissions by optimizing traffic flow through real-time data analysis¹¹. Similarly, Beijing’s deployment of IoT sensors for air quality monitoring has enabled swift responses to pollution events, contributing to a measurable reduction in air pollutant levels over time³². These case studies reveal the potential of the IoT in transforming urban issues. While, however, previous studies remain concentrated on separated domains and particular implementations, they can fail to consider cumulative effects of IoT across various domains in a single framework of the city’s sustainability, as presented in the Table 2.

Table 2.

Comparative analysis of IoT applications in urban planning.

Study	Focus Area	Geographic Context	Key Findings	Innovation Highlight	Contribution to Literature
40	IoT in traffic management	Barcelona, Spain	Reduced traffic congestion by 30%, emissions by 20%	Traffic optimization through IoT sensors	Demonstrates IoT’s impact on traffic optimization
41	IoT for air quality monitoring	Beijing, China	10% reduction in air pollutant levels over five years	IoT sensors for real-time pollution control	Explores environmental monitoring with IoT sensors
19	IoT architectures and smart cities	Global	Identified challenges in IoT system integration	Framework for IoT system standardization	Proposes a theoretical framework for IoT standardization
42	IoT in enterprises and resource optimization	Global	Optimized resource allocation and reduced energy consumption	IoT for enterprise resource efficiency	Links IoT adoption to enterprise efficiency
43	IoT and open innovation in smart cities	Global	Highlighted collaborative governance for IoT adoption	Emphasized open data and innovation collaboration	Proposes collaborative governance as key for IoT in cities
44	IoT-based disaster management	South Korea	Improved early warning systems and emergency response times	Advanced disaster response systems using IoT	Demonstrates IoT’s critical role in disaster management frameworks
45	IoT in waste management	India	Improved recycling efficiency and reduced landfill dependency	Smart waste bins and real-time waste tracking	Establishes IoT’s value in resource management systems
46	IoT in smart city energy grids	United States	Increased energy efficiency through predictive energy consumption models	Integration of IoT with renewable energy sources	Provides insights into energy optimization via IoT systems
47	IoT for urban sustainability	China	Identified IoT’s role in improving sustainability metrics across cities	IoT systems for multi-domain urban management	Explores the intersection of IoT and urban sustainability
Current Study	IoT in sustainable urban planning (integrated model)	Saudi Arabia	Integrated IoT domains improve sustainability and urban planning	Novel integrated model for IoT impacts in urban sustainability	Provides an empirical, integrated model of IoT domains for sustainability

This study fills in this gap by creating an integrated model that attempts to investigate the mediated relationships between domains of the IoT – real-time manner, disaster management, energy and waste, and the combined influence of all the three on sustainable urban planning and smart city development. In contrast to prior research, this study approaches quantitatively evaluating these interdependencies with the use of Structural Equation Modeling (SEM) and Principal Component Analysis (PCA) and thus provides an all-inclusive set of conditions that overlaps with urban resilience and sustainability objectives. The incorporation of disaster management in spite of its lesser association to urban planning results is quite innovative as it reveals the difficulties of mainstreaming resilience strategies to overall sustainability planning initiatives. This research, through a focus on Saudi Arabia, a rapidly urbanizing and technologically evolving society, offers useful insights into the possibilities and constraints of the IoT in smart cities of emerging economies.

The application of the IoT was broadly recognized in previous studies in several spheres of urban development, such as public health monitoring and smart mobility. For example, it has been found that IoT-enabled health systems can enhance something such as disease surveillance and allocation of resources in emergencies¹⁰, while smart mobility initiatives have demonstrated reductions in traffic congestion and emissions through IoT-driven traffic management systems¹¹. However, the current study narrows its focus to real-time monitoring, disaster management, and energy and waste management, aligning with recent research that emphasizes these areas as pivotal for achieving urban sustainability and resilience³². By concentrating on these domains, this study aims to provide a more targeted framework for integrating IoT in urban planning, addressing critical challenges in resource optimization and disaster preparedness. This literature review is underpinned by the positive change that IoT can bring to urban planning, given that the challenges mentioned could be addressed. The integration of these technologies could help communities develop more sustainable, efficient, and livable urban environments, provided issues regarding data security, standardization, and scalability are effectively managed.

Methodology

The methodology used in this study is based on three major stages. Stage 1 includes the identification of factors through detailed literature review and the second stage is design of questionnaire along with classification of factors, and final stage involve the PCA analysis for hypothesis testing. The detailed flow of study including all major stages is shown in Fig. 5.

Fig. 5.

Methodological flow of the study.

Identification of components

In finding the key factors relevant to the impact of IoT on sustainable urban planning, a thorough literature review was undertaken. To achieve this, four significant databases were searched: Google Scholar, IEEE Xplore, Scopus, and Web of Science. Search terms included but were not limited to “IoT in urban planning,”"IoT in smart cities,” “sustainable urban development,” “smart city technologies,” and “IoT integration” between the years 2010 and 2020. A relevance search was first done, producing long lists of articles, which further selection was filtered, depending on relevance, date of publication, preferably in the last ten years, the value of the study, and importance. This process ended with a finalized refined list, which focused more on those studies that presented empirical data case studies, and theoretical insights into the applications and benefits of IoT in urban environments⁴⁸. It guaranteed the identification of the most pertinent and up-to-date factors for further analysis. The study focuses on the domains of real-time monitoring, disaster management, and energy and waste management, as these areas are critical to addressing immediate challenges in urban sustainability and resilience. While other IoT applications, such as public health monitoring and smart mobility, play a significant role in smart city development, they were excluded to ensure a targeted investigation. This selective approach allows for a more in-depth analysis of the chosen domains and their mediated effects on sustainable urban planning.

Screening process

The data for this study was collected from four major academic databases: Google Scholar, IEEE Xplore, Scopus, and Web of Science. While these databases provide comprehensive coverage of relevant literature, there is a risk of potential for overlap in records. To ensure accuracy and avoid duplication, the following process was implemented:

• Database search:

A consistent set of search keywords (e.g., ‘IoT in urban planning,’ ‘sustainable cities,’ ‘disaster management IoT’) was applied across all databases to ensure uniformity in the search process.

• Importing records:

Records were imported into a reference management software (e.g., EndNote, Mendeley, or Zotero) to consolidate the data.

• Deduplication:

The imported records were screened for duplicates using the reference management software’s deduplication function. This step removed all identical entries across the four databases.

• Manual screening:

After the automated deduplication, a manual review was conducted to ensure that articles with slight variations in metadata (e.g., differences in titles or author names due to formatting) were not missed.

• Eligibility criteria:

The remaining records were screened against predefined eligibility criteria, including publication date (2015–2024), language (English), and relevance to IoT applications in sustainable urban planning. Articles that did not meet these criteria were excluded.

• Final inclusion:

A total of fifty unique records were included in the analysis after completing the deduplication and screening process. This approach ensured that only relevant, non-redundant data was used for the study.

The selection of four major databases Google Scholar, IEEE Xplore, Scopus, and Web of Science was based on their complementary strengths in providing a broad, interdisciplinary, and high-quality literature base. Google Scholar offers extensive academic coverage, including grey literature and conference papers. IEEE Xplore focuses on technology-centric research, especially in IoT and AI domains. Scopus provides access to a wide range of peer-reviewed articles across engineering and urban planning disciplines. Web of Science ensures inclusion of high-impact studies with strong citation tracking features. To verify and consolidate the records retrieved, Mendeley was used as the reference management tool. All records were imported into Mendeley, and the deduplication function was applied to eliminate identical entries. A manual screening followed to reconcile any discrepancies in metadata (e.g., variations in titles or author formats). This cross-verification process ensured that only high-quality, non-redundant, and relevant studies were retained for analysis.

Designing the questionnaire and data collection

Based upon the factors identified through the literature review and the formulated hypothesis, a well-structured questionnaire was developed. The questionnaire was designed to bring out the perceptions of respondents on the impact of IoT and AI, mainly in the development of urban planning and smart cities. A Likert scale, following the principles it applies, varies from strongly disagree (1) to strongly agree (5), measuring the level of agreement to statements describing these impacts. It was distributed as a questionnaire sent to 500 urban planners, policymakers, academics, and technology experts in the Saudi Arabia. Out of the 500 questionnaires that were circulated, 189 completed questionnaires were returned, resulting in a response rate of 37.8%, which can be considered reasonable for moving ahead with the study and thus gives a sound data set for the analysis to be carried out.

This response rate of 37.8% is statistically adequate for Structural Equation Modeling (SEM) and is consistent with empirical studies in related fields such as smart city development, urban planning, and IoT adoption, where typical response rates for professional surveys range from 20 to 40%^49,50. Several factors explain and justify this response level. First, the target respondents urban planners, policymakers, academics, and technical experts are often engaged in demanding roles with limited time for academic surveys. Second, the technical nature of the topic, involving IoT integration and digital urban frameworks, may have limited participation from individuals less familiar with digital infrastructure. Third, in certain regions of Saudi Arabia, digital survey engagement remains relatively low compared to traditional in-person consultation formats. These factors combined to influence the participation rate. Despite these challenges, the final sample is robust, diverse, and demographically representative, making it suitable for rigorous quantitative analysis and model validation.

The survey was distributed to 500 individuals, including urban planners, policymakers, scholars, and technical experts in Saudi Arabia, using a systematic and targeted approach to ensure representativeness. The target group consisted of professionals working directly and indirectly in urban planning and sustainable development, academic researchers, and technical professionals with IoT application expertise. Participants were invited through networks of professionals, industries, and academic communities, and invitations extended through channels including Saudi Council of Engineers, urban planning departments in key municipalities, and technical associations. To ensure pertinency, participants must have at least one of the following qualifications: direct work in urban planning and policymaking in Saudi Arabia, IoT technology and urban planning for sustainability studies, or IoT-based urban management system deploying expertise.

To obtain maximized representativeness, a scheme of stratified sampling was utilized, distributing the sample into four groups: urban planners (30%), policymakers (25%), academicians and researchers (20%), and technical professionals (25%). By such a stratification, balanced representation of IoT implementers’ key groups in urban planning was assured. Geographic diversity, in addition, was assured through including participants representing Saudi Arabia’s regions, including key urban cities such as Riyadh, Jeddah, and Dammam, and minor municipalities. By such a meticulous selection, the sample captured IoT’s multidisciplinary and geographically widespread nature in urban settings, and strong and balanced information for use in the stud.

SEM-PCA analysis

The data collected through the questionnaire were analyzed through the Principal Component Analysis approach of Structural Equation Modeling. SEM is a multivariate statistical technique that analyzes complex relationships between observed and latent variables. For this information, PCA was done since it lowers the dimensionality from a high number of variables and summarizes all the variabilities into a few principal components. Convergent validity of the constructs was assessed in terms of CA, CR, and AVE. An Alpha above 0.7, CA greater than 0.7, and AVE greater than 0.5 are the minimum acceptable values for these parameters⁵¹. This will ensure that the constructions are reliable and valid. The discriminant validity of the constructs is studied through HTMT, Fornell-Larcker Criterion, and cross-loading analysis. The criterion of adequately discriminant validity is satisfied with values of HTMT < 0.85, and the Fornell-Larcker criterion requires that the square root of the AVE of each construct must be greater than its highest correlation with any other construct. It is majorly intended so that the items load highly on their respective constructs compared to others.

Bootstrapping analysis with SmartPLS

Using the results obtained from SmartPLS, bootstrapping analysis was conducted to verify the hypotheses and to get the model parameter values for statistical significance. The non-parametric resampling technique used in bootstrapping had 5,000 produced subsamples for this research work to generate the estimates for precision in the SEM values⁵².

Key statistics of the bootstrapping analysis include:

• Original sample (O).

• Mean sample value (M).

• Standard deviation (STDEV).

• T statistics (l O/STDEV l).

• P values.

The above statistics were employed to test the significance of the path coefficients produced and validation of the tested hypotheses. For the hypothesis to be validated, the value of the T statistic should be greater than 1.96 and the P value less than 0.05. These will be analyzed based on this, hence representing a robust and reliable analysis that gives fine points regarding how IoT positively impact on sustainable urban planning and an innovative city framework.

Results and discussion

Identification of components

Table 3 summarizes key constructs and their related variables managed from the study model of the impacts of IoT towards sustainable urban planning on innovative city development. Three constructs on the left include real-time monitoring with response, disaster management, and energy with waste management. Each of the variables describes a different aspect of the description that defines the enhancement of that area by applying IoT technologies. For example, in Real-time Monitoring & Response, the variables reflect increased data accuracy, velocity in response to urban issues, and efficiency in monitoring urban infrastructures. Disaster Management majorly focuses on prediction, environmental monitoring, and response efficiency. Energy & Waste Management construction focuses on energy use optimization, collection of waste, and reduction of ecological impact. The right-side construct, Sustainable Urban Planning, has members that are concerned with the comprehensive analysis of urban data, advanced analytics for prediction of growth, enhanced allocation of resources, and improved urban livability. All these factors put together hint at the transformational and enabling nature of IoT in the making of urban environments more efficient, proactive, and sustainable.

Table 3.

Identified list of constructs and variables.

Construct	Variable 1	Variable 2	Variable 3	Variable 4
Real-time Monitoring & Response	V1: IoT enhance real-time data accuracy	V2: Quick response to urban issues	V3: Improved monitoring of urban infrastructure	-
Disaster Management	V1: IoT predicts disaster-prone areas	V2: IoT sensors monitor environmental changes	V3: Efficient disaster response management	-
Energy & Waste Management	V1: IoT optimizes energy consumption	V2: IoT improves waste collection efficiency	V3: Reduced environmental impact	-
Sustainable Urban Planning	V1: Comprehensive urban data analysis	V2: Predictive analytics for urban growth	V3: Enhanced resource allocation	V4: Improved urban livability

Demographic details

The demographic profile of the 189 respondents gives a good view of a broad and representative sample shown in Table 4. The demographic data from 189 respondents reveals that the majority fall within the 26–35 age group (31.75%), followed by 36–45 (26.46%). Males slightly outnumber females, comprising 55.03% of the sample. Most respondents hold a bachelor’s degree (42.33%), with notable portions having master’s (31.75%) and high school (10.58%) education. Occupation-wise, urban planners (26.46%) and technology experts (21.16%) are the most represented groups, followed closely by policymakers (21.16%) and academics (15.87%), indicating a diverse and educated sample relevant to urban development and policy discussions.

Table 4.

Respondents details.

Demographic Category	Sub-category	Number of Respondents	Percentage (%)
Age Group	18–25	30	15.87%
	26–35	60	31.75%
	36–45	50	26.46%%
	46–55	30	15.87%
	56 and above	19	10.05%
Gender	Male	104	55.03%
	Female	85	44.97%
Education Level	High School	20	10.58%
	Bachelor’s Degree	80	42.33%
	Master’s Degree	60	31.75%
	Doctorate	20	10.58%
	Professional Cert.	9	4.76%
Occupation	Urban Planners	50	26.46%
	Policymakers	40	21.16%
	Academics	30	15.87%
	Technology Experts	40	21.16%
	Other	29	15.34%

SEM-PCA analysis

To investigate convergent validity, the item loading, Cronbach’s alpha, and composite reliability (rho-a and rho-c) for the constructs were examined, as well as the average variance extracted shown in Table 5. Disaster Management showed strong convergent validity: all the item loading was over 0.7, Cronbach’s alpha equaled 0.70, the composite reliability was 0.833, and AVE was 0.626. The convergent validity of the construct of Energy & Waste Management was high. Similarly, the Real-time Monitoring & Response construct showed strong convergent validity through item loadings that ranged between 0.679 and 0.933, a Cronbach’s alpha of 0.807, a composite reliability of 0.889, and an AVE of 0.731. Similarly, the third construct had good convergent validity, the sustainable urban planning construct manifested through item loadings, which are all over 0.8, the Cronbach’s alpha of 0.903, its composite reliability at 0.932, and an AVE value of 0.776. During the SEM-PCA analysis, certain variables, such as IoT-AI-EWM2, were excluded due to low factor loadings (< 0.5) and their inability to meet the threshold for convergent validity. These variables did not significantly contribute to the constructs they were associated with, as determined through preliminary factor analysis. Removing these variables improved the overall model fit, reliability, and validity, ensuring that only meaningful and statistically robust indicators were retained for further analysis. This proves that it is well measured, with homogeneity among the items within the constructs or measuring the same thing and, further, internally consistent⁵³.

Table 5.

Reliability test of factors and variables.

Constructs	Loadings	Loadings	Cronbach’s alpha	Composite reliability (rho-a)	Composite reliability (rho-c)	Average variance extracted (AVE)
Disaster Management	IoT-AI-DM1	0.755	0.70	0.704	0.833	0.626
	IoT-AI-DM2	0.844	-	-	-	-
	IoT-AI-DM3	0.751	-	-	-	-
Energy & Waste Management	IoT-AIEWM1	0.932	0.85	0.85	0.93	0.869
	IoT-AI-EWM2	Deleted	-	-	-	-
	IoT-AI-EWM3	0.932	-	-	-	-
Real-time Monitoring & Response	IoT-AI-RM1	0.679	0.807	0.845	0.889	0.731
	IoT-AI-RM2	0.933	-	-	-	-
	IoT-AI-RM3	0.928	-	-	-	-
Sustainable Urban Planning	IoT-AI-Sustainability1	0.931	0.903	0.906	0.932	0.776
	IoT-AI-Sustainability2	0.853	-	-	-	-
	IoT-AI-Sustainability3	0.908	-	-	-	-
	IoT-AI-Sustainability4	0.827	-	-	-	-

Discriminant validity between the constructs was measured using Heterotrait-Monotrait Ratio (HTMT). The results in Table 6 shows of all the constructs—Disaster Management (DM), Energy & Waste Management (EWM), Real-time Monitoring & Response (RM), and Sustainable Urban Planning (SUP)—are under the prespecified threshold of 0.85; thus, direct confirmation could be established that discriminant validity is there. The HTMT index values are 0.161 for Disaster Management and Energy & Waste Management, 0.168 for Disaster Management and Real-time Monitoring & Response, 0.2 for Disaster Management and Sustainable Urban Planning, 0.189 for Energy & Waste Management and Real-time Monitoring & Response, and 0.509 for Real-time Monitoring and Sustainable Urban Planning. Thereby, it statistically validates the discriminant validity of these constructs, showing that they are distinct.

Table 6.

HTMT-Discriminant validity test.

	DM	EWM	RM	SUP
Disaster Management = DM
Energy & Waste Management = EWM	0.161
Real-time Monitoring & Response = RM	0.168	0.189
Sustainable Urban Planning = SUP	0.2	0.218	0.509

The Fornell-Larcker criterion was applied to check for discriminant validity between Disaster Management (DM), Energy & Waste Management (EWM), Real-time Monitoring and Response (RM), and Sustainable Urban Planning (SUP) shown in Table 7. The square root of the AVE of each construct must be more than the highest correlation with other constructs. These values are 0.791 for DM, 0.932 for EWM, 0.855 for RM, and 0.881 for SUP. This is confirmed with numbers being more significant than the correlation between DM and EWM totaled 0.124, RM of 0.054, and SUP of 0.158; EWM and RM totaled 0.149, and SUP of 0.191; RM and SUP of 0.42. This means each construct shares more variance with constructs of a third formative construct, evidence of discriminant validity.

Table 7.

Fornell lacker criterion test.

	DM	EWM	RM	SUP
Disaster Management	0.791
Energy & Waste Management	0.124	0.932
Real-time Monitoring & Response	0.054	0.149	0.855
Sustainable Urban Planning	0.158	0.191	0.42	0.881

The cross-loading criteria were checked to assess the constructively discriminant validity of the constructs Disaster Management (DM), Energy & Waste Management (EWM), Real-time Monitoring & Response (RM), and Sustainable Urban Planning (SUP) shown in Table 8. Every item under this criterion is supposed to load significantly more on its related constructs than on any other unexpected constructs. For DM, the items IoT-AI-DM1 (0.775), IoT-AI-DM2 (0.844), and IoT-AI-DM3 (0.751) exhibit high loading on DM compared to the other variables, EWM, RM, or SUP, thus showing evidence for discriminant validity. For RM, the items IoT-AI-RM1 (0.679), IoT-AI-RM2 (0.933), and IoT-AI-RM3 (0.928) load more on RM than on DM, EWM, or SUP and confirm discriminant validity. For SUP, the items IoT-AI-Sustainability1 (0.931), IoT-AI-Sustainability2 (0.853), IoT-AI-Sustainability3 (0.908), and IoT-AI-Sustainability5 (0.827) load more on SUP than on DM, EWM, or RM.

Table 8.

Cross loading-based discriminant validity test.

	Disaster Management	Energy & Waste Management	Real-time Monitoring & Response	Sustainable Urban Planning
IoT-AI-DM1	0.775	0.053	−0.07	0.167
IoT-AI-DM2	0.844	0.106	0.012	0.07
IoT-AI-DM3	0.751	0.136	0.193	0.144
IoT-AI-EWM1	0.114	0.932	0.174	0.193
IoT-AI-EWM3	0.117	0.932	0.103	0.163
IoT-AI-RM1	0.002	0.178	0.679	0.434
IoT-AI-RM2	0.059	0.107	0.933	0.321
IoT-AI-RM3	0.065	0.114	0.928	0.352
IoT-AI-Sustainability1	0.205	0.169	0.369	0.931
IoT-AI-Sustainability2	0.143	0.216	0.362	0.853
IoT-AI-Sustainability3	0.141	0.178	0.417	0.908
IoT-AI-Sustainability5	0.06	0.108	0.327	0.827

Figure 6 illustrates the interconnected roles of Real-time Monitoring & Response, Disaster Management, and Energy & Waste Management in achieving Sustainable Urban Planning, which ultimately contributes to Sustainable Smart Cities Development. Each component is supported by IoT and AI indicators, with associated reliability scores representing the consistency and robustness of each indicator in its respective area. Real-time Monitoring & Response has the strongest direct influence on Sustainable Urban Planning, indicated by a high path coefficient (0.396), followed by Disaster Management (0.122) and Energy & Waste Management (0.052). Sustainable Urban Planning, in turn, has a significant impact on Sustainable Smart Cities Development (0.703), demonstrating that effective planning is essential to the success of smart city initiatives. The model underscores the importance of integrated monitoring, disaster resilience, and resource management as foundational elements that collectively enhance urban sustainability and resilience.

Fig. 6.

PCA structural model with path loadings.

The bootstrapping results for the structural model indicate that the relationships among significant relationships of the constructs concerning Sustainable Smart Cities Development and Sustainable Urban Planning are as follows: with Disaster Management, Sustainable Smart Cities’ Development does not significantly affect (coefficient = 0.052, T = 1.086, P = 0.277) but it substantially does influence Sustainable Urban Planning (coefficient = 0.122, T = 2.558, P = 0.011) shown in Table 9. Energy & Waste Management exert a significant favorable influence on Sustainable Smart Cities Development (coefficient = 0.168, T = 4.825, P = 0.000) and Sustainable Urban Planning (coefficient = 0.117, T = 2.079, P = 0.038). Real-time Monitoring & Response is highly positive and impactful toward Sustainable Smart Cities Development (coef. 0.295, T = 7.04***(0.000)) and Sustainable Urban Planning (coef. 0.396, T = 7.883***(0.000). Also, Sustainable Urban Planning significantly affects Sustainable Smart Cities Development (coef. 0.703, T = 19.523, ***(0.00). These results underline the critical role sustained by these constructs in promoting sustainable urban development and smart city initiatives.

Table 9.

Hypothetical relationship among second order.

	Original sample (O)	Sample mean (M)	Standard deviation (STDEV)	T statistics (|O/STDEV|)	P values	Status
Disaster Management -> Sustainable Smart Cities Development	0.052	0.052	0.048	1.086	0.277	Reject
Disaster Management -> Sustainable Urban Planning	0.122	0.122	0.048	2.558	0.011	Accept
Energy & Waste Management -> Sustainable Smart Cities Development	0.168	0.165	0.035	4.825	0	Accept
Energy & Waste Management -> Sustainable Urban Planning	0.117	0.118	0.056	2.079	0.038	Accept
Real-time Monitoring & Response -> Sustainable Smart Cities Development	0.295	0.294	0.042	7.04	0	Accept
IoT and AI: Real-time Monitoring & Response -> Sustainable Urban Planning	0.396	0.396	0.05	7.883	0	Accept
Sustainable Urban Planning -> Sustainable Smart Cities Development	0.703	0.702	0.036	19.523	0	Accept

The results indicate that the relationship between disaster management and sustainable smart cities development is weaker (non-significant path coefficient) compared to other mediated relationships. This could be attributed to several factors. First, disaster management systems, while critical for urban resilience, are often reactive in nature, focusing on immediate crisis response rather than long-term sustainability. As noted by Zanella et al. (2014), the benefits of disaster management systems are often indirect and may take longer to materialize in the context of sustainable urban development⁵⁴.

Second, the integration of disaster management into broader urban planning frameworks is often fragmented due to challenges in inter-agency coordination, lack of standardized protocols, and limited funding for preventive measures²⁷. This fragmentation may reduce the perceived impact of disaster management on the holistic development of sustainable smart cities.

Third, urban sustainability metrics tend to emphasize proactive strategies such as energy efficiency and resource optimization, which have more immediate and visible outcomes. In contrast, the benefits of disaster management, such as improved resilience and reduced vulnerability, may be less directly measurable or attributable to sustainable development outcomes³³.

Impending research could explore these dynamics further by examining how disaster management can be more effectively integrated into sustainable urban planning frameworks. For example, longitudinal studies could investigate how investments in preventive disaster management infrastructure contribute to long-term urban sustainability and resilience.

This framework in Fig. 7 illustrates the relationships between various components in the development of sustainable smart cities, focusing on specific IoT and AI-driven processes. The core elements are depicted as interconnected nodes, that is, the Real-Time Monitoring & Response (RTM), Disaster Management (DM), Energy & Waste Management (EWM), Sustainable Urban Planning (SUB), and Sustainable Smart Cities Development (SSC). Each relationship is marked with the strength of influence or contribution from one component to another, with green lines showing the positive and significant ties while red lines show the weak or insignificant ties. These relationships, also using metrics like the P-values and T-statistics, depict the ways in which each area contributes and/or interacts with sustainable urban development aims.

Fig. 7.

Hypothetical Processed Framework.

The second part of the framework shows the details of individual metrics and pathways, revealing smaller nodes with specific IoT/AI applications to every core component. This layer will show the granularity of impact for different metrics in Real-Time Monitoring, Disaster Management and Energy & Waste which also contributes towards Sustainable Urban Planning. The abbreviations for each component are explained in the legend, allowing for quick identification of each node and facilitating an understanding of the broader structure. This model underscores the interconnected nature of various urban planning components and highlights the flow of influence from each foundational element toward achieving the ultimate goal of a sustainable smart city.

To evaluate the robustness of the results, a sensitivity analysis was performed using a bootstrap resampling approach. This involved generating 1,000 random resamples from the dataset (N = 189) and re-estimating the model parameters for each sample. Key indicators, such as path coefficients, T-statistics, and P-values, were analyzed across these iterations to ensure consistency and reliability.

The sensitivity analysis confirmed that the primary structural paths were stable, with minimal variation in coefficients and significance levels. For instance, the path from Real-Time Monitoring to Sustainable Urban Planning showed consistent significance (Average Path Coefficient = 0.396, Standard Deviation = 0.011, T = 7.883, P < 0.001) across all bootstrap samples. Similarly, other paths, such as Energy and Waste Management to Sustainable Urban Planning, maintained significance (Average Path Coefficient = 0.117, Standard Deviation = 0.015, T = 2.079, P = 0.038). These results indicate that the model is robust and not overly sensitive to the limitations of the sample size or data distribution.

Additionally, multicollinearity was assessed by examining the Variance Inflation Factor (VIF) values for all constructs. All VIF values were below the threshold of 5, indicating no significant multicollinearity issues in the dataset. These steps further validate the reliability of the conclusions drawn in this study.

Discussion

This study investigates the impact of IoT driven initiatives in general and across three domains: Real-time Monitoring & Response, Disaster Management, and Energy & Waste Management—toward developing Sustainable Smart Cities. About this, Sustainable Urban Planning will act as a mediating factor. The meaningful mediated relationships through the structural model results mentioned above, with support from bootstrapping analysis, simply underscore the need to infuse advanced technological solutions in urban planning toward sustainable urban development. The findings are consistent with the existing literature that proved technological innovation to be a key enabler of sustainable urban environments.

Real-time monitoring & response of IoT -> sustainable urban planning -> development of sustainable smart cities

Analysis has shown a strongly mediated effect of Real-time Monitoring & Response of IoT on development of Sustainable Smart Cities through Sustainable Urban Planning with a significant path coefficient value (T = 8.695, P = 0.000). Such a finding can support the view that real-time monitoring and responsive technologies substantially enhance urban sustainability. Also, some of these previous works have documented the effectiveness of real-time data analytics infused with IoT in optimizing urban resource management and improving response strategies²⁶. This finding is further validated by the robust statistical significance and strong path coefficient in the present study, suggesting that real-time monitoring technologies are indispensable for smart urban planning and sustainable city development⁵⁵.

Disaster management -> sustainable urban planning -> sustainable smart cities development

The route from Disaster Management via Sustainable Urban Planning to Sustainable Smart Cities Development shows a more substantial impact with a significant path coefficient estimate (T = 2.503, P = 0.012), indicating that the implementation of sound disaster management strategies consistent with urban planning contributes significantly to the sustainability of smart cities. Such a finding has been supported by previous work with their findings indicating that proactive disaster management and preparedness are essential for the development of resilient cities⁵⁶. It justifies, at a statistical level, the need for the inclusion of disaster management frameworks as part of planning processes by urban planners to enhance the total sustainability and resilience of metropolitan areas.

Energy & waste management -> sustainable urban planning -> sustainable smart cities development

The study records that there is a significant mediated effect of Energy & Waste Management on Sustainable Smart Cities Development through Sustainable Urban Planning with a significant path coefficient (T = 2.079, P = 0.038). This finding indicates that efficiency in energy use and practices in waste management are parts of sustainable urban development if buttressed by urban solid planning. This agrees with the empirical work who again highlighted sustainable waste management and energy practices as integral elements within innovative city frameworks⁵⁷. The moderate but significant path coefficient shows that cities will increasingly have to innovate for these long-term sustainability objectives in energy and waste management. Table 10 presents detailed case studies showcasing the real-world application of IoT across various urban domains, including traffic monitoring, environmental and infrastructure management, emergency response, and disaster prediction.

Table 10.

Detailed case studies of IoT applications.

Domain	Case Study	Details	Key Outcomes	Source
Traffic Monitoring	Barcelona’s IoT Traffic Management System	Barcelona implemented a real-time traffic management system using IoT sensors embedded in roads and traffic lights. These sensors collect data on vehicle flow, which is analyzed to optimize traffic signals and reduce congestion.	Reduced congestion by 30%, improved average travel times, and lowered vehicle emissions by 20%.	40
Environmental Monitoring	Real-Time Air Quality Monitoring in Beijing	IoT sensors were deployed across Beijing to monitor air quality levels in real time. The data is integrated with public dashboards and used to inform citizens about pollution levels and enable swift policy interventions.	Enabled faster responses to pollution spikes and contributed to a 10% reduction in air pollutant concentration over five years.	41,47,10
Infrastructure Monitoring	Bridge Health Monitoring in the United States	IoT sensors were installed on key bridges in California to monitor stress, temperature changes, and vibrations. These sensors provide continuous feedback, enabling early detection of structural issues.	Identified critical repairs early, reducing repair costs by 25% and preventing potential structural failures.	10,58,59
Emergency Response	Smart Alarms and IoT Emergency Systems in Seoul	Seoul integrated smart alarms with IoT-enabled emergency response systems that send real-time alerts to first responders and citizens during fires and other emergencies.	Improved emergency response times by 40%, saving lives and reducing property damage.	60,61
Disaster Prediction	IoT Seismic Sensors for Earthquake Monitoring in Tokyo	Tokyo deployed IoT-powered seismic sensors capable of real-time earthquake detection. These sensors provide early warnings through mobile apps and public alerts.	Predicted seismic activity with 85% accuracy, enabling timely evacuations and reducing casualties.	62,63

The study discovers several important takeaways for policymakers, urban planners, and technologists and are supported by case studies shown in Table 9. The significant mediated effects underlie the necessity for advanced IoT technologies to be integrated within urban planning frameworks to realize sustainable innovative city development. There should have been investments by policymakers in real-time monitoring systems, disaster management technologies, and enhanced energy and waste management solutions. Urban planners are thus encouraged to include such technological advances into the planning processes, among others, which significantly increase the resilience and sustainability of a metropolitan area.

Contributions to the existing body of knowledge

The findings of this study build on and extend existing research on the role of IoT in sustainable urban planning by providing empirical evidence of mediated relationships among critical domains, including real-time monitoring, energy and waste management, and disaster management. While previous studies have highlighted IoT’s potential for improving resource optimization and urban resilience^11,32, our results uniquely position sustainable urban planning as a mediating factor that amplifies these benefits. For example, the significant mediated relationship between real-time monitoring and sustainable smart cities development underscores the importance of proactive data-driven decision-making in urban environments.

In contrast, the non-significant direct relationship between disaster management and sustainable smart cities development reveals a critical gap in the integration of disaster management frameworks into broader urban planning processes. This finding aligns with prior research²⁷, which highlights the fragmented nature of disaster management initiatives and the need for more comprehensive planning. By identifying disaster management as a weaker link in the IoT-enabled urban planning chain, this study contributes to the literature by emphasizing the need for strategic alignment and integration of disaster resilience measures into urban sustainability frameworks.

Empirical implications

The study provides empirical evidence on the role of Internet of Things (IoT) technologies in fostering sustainable urban planning, specifically through real-time monitoring, disaster management, and energy and waste management in smart cities. The findings highlight how IoT integration facilitates resource optimization and urban sustainability, presenting statistically significant mediated effects. This empirical validation offers a robust framework for understanding the direct and indirect relationships between IoT applications and sustainable urban development, informing future quantitative research on smart city initiatives. Moreover, the study contributes to the growing body of literature on the effectiveness of IoT in improving urban infrastructure, supporting the notion that data-driven technologies are essential for achieving long-term urban sustainability goals.

Managerial implications

For urban planners, policymakers, and city managers, the findings underscore the critical role of IoT technologies in advancing urban sustainability. Managers can leverage the insights from this study to prioritize investment in IoT-based solutions for real-time monitoring, emergency response, and efficient resource allocation, fostering more resilient urban environments. By emphasizing the importance of integrating IoT into city planning processes, this study provides actionable guidance on deploying IoT systems to improve public services, reduce operational costs, and enhance urban livability. Furthermore, the study’s evidence-based recommendations can aid decision-makers in addressing challenges related to IoT implementation, such as data privacy, interoperability, and infrastructure scalability, to maximize the benefits of IoT technologies in urban planning.

Actionable recommendations for policymakers and urban planners

Based on the findings, several practical recommendations can guide policymakers and urban planners in leveraging IoT for sustainable urban development:

• Strengthen Disaster Management Integration: Policymakers should prioritize integrating IoT-powered disaster management systems into urban planning frameworks. For example, deploying multi-sensor networks for real-time environmental monitoring can enhance predictive capabilities and facilitate quicker responses to natural disasters.

• Focus on Resource Optimization through Real-Time Monitoring: Urban planners should invest in IoT technologies that provide real-time data for traffic, energy, and waste management. For instance, implementing smart grid systems and smart traffic management solutions can lead to measurable reductions in congestion, energy wastage, and carbon emissions.

• Promote Collaborative Governance Models: Effective integration of IoT technologies requires collaboration across multiple stakeholders, including municipal governments, private technology providers, and local communities. Policymakers should establish clear guidelines for data sharing, interoperability, and standardization to maximize the efficiency of IoT systems.

• Address Implementation Barriers: Challenges such as data privacy concerns and the lack of standardized IoT protocols must be addressed to realize IoT’s full potential. Urban planners should advocate for regulatory frameworks that protect data security while ensuring seamless interoperability among devices.

• Encourage Pilot Programs for IoT Adoption: Small-scale IoT implementations, such as pilot programs for smart waste bins or air quality monitoring systems, can serve as test cases to refine technology integration strategies before scaling them across larger urban areas.

Limitations

While this study provides valuable insights into the role of IoT in sustainable urban planning, the cross-sectional research design poses certain limitations. Specifically, this approach captures data at a single point in time, which may not fully reflect the evolving dynamics of IoT adoption and its long-term impacts on urban sustainability. As such, the conclusions drawn from this study should be interpreted with caution, recognizing that the relationships observed may change over time due to technological advancements, policy shifts, or evolving urban challenges. While this study offers valuable insights into IoT’s role in sustainable urban planning, certain limitations should be acknowledged. First, the study’s data was primarily collected from urban planners and policymakers in a single geographic region, which may limit the generalizability of the findings to other contexts with different urban challenges and technological infrastructure. Second, the study relied on a cross-sectional research design, which limits the ability to capture long-term impacts of IoT implementation in smart cities. Third, the study focused on specific domains (real-time monitoring, disaster management, and energy and waste management) and may not fully encapsulate other relevant IoT applications in urban planning, such as public health monitoring and smart mobility solutions.

Future research

Future research could address the limitations of the cross-sectional design by employing longitudinal studies to track the implementation and outcomes of IoT applications over time. Longitudinal studies would enable researchers to better understand the temporal dynamics of IoT’s impact on urban planning, including the progression of benefits, the mitigation of challenges, and the sustainability of outcomes. For example, tracking IoT-driven initiatives across multiple years could provide deeper insights into how real-time monitoring, disaster management, and energy and waste management contribute to long-term urban resilience and adaptability. Expanding the research scope to include multiple regions with diverse urban challenges and varying levels of technological infrastructure would enhance the generalizability of the findings. Additionally, investigating other IoT applications in urban planning, such as smart healthcare and public safety, could provide a more comprehensive understanding of IoT’s transformative potential. Further studies could also focus on overcoming specific implementation challenges identified in this study, such as interoperability and data privacy, to enhance the efficacy of IoT-driven urban planning strategies.

A response rate of 37.8% (189 responses from 500 distributed questionnaires) was achieved, which aligns with typical ranges reported in comparable empirical studies in the construction, urban innovation, and technology adoption domains, where response rates between 20% and 40% are considered acceptable. This level is adequate for conducting PLS-SEM analysis and allows for meaningful statistical interpretation. Nonetheless, several contextual factors likely contributed to the response rate. These include the high workload of targeted professionals (e.g., engineers, planners, and policymakers), the technical complexity of the IoT-focused questionnaire, and regional factors such as lower engagement with online survey platforms in some Saudi Arabian municipalities. Future studies could improve participation by offering personalized follow-ups, reducing survey length, or employing hybrid methods like focus groups and structured interviews to enhance depth and engagement.

Conclusion

This research noted that IoT technologies are connected in the contemporary development of real-time monitoring and response, disaster management, energy, and waste management towards the sustainable urban planning frameworks under which smart cities are developed. The findings back up the technology delivered as best use for optimized urban resource management, enhancing disaster preparedness, and the resulting efficient practices of energy and waste that contribute toward resilience and sustainable urban areas. These insights shall be crucial to guide effective policies that support and enable these technological advances to create sustainable urban environments.

Author contributions

Author contributions: Conceptualization Masoud Alajmi, Tarek A.H. Barakat; Methodology: Ahsan Waqar, Abdullah Mohammed Alshehri; Formal analysis: Ahsan Waqar, Hamad R. Almujibah; Investigation: Hashem Alyami, Masoud Alajmi, Data curation: Ahsan Waqar, Abdullah Mohammed Alshehri; Writing - Original Draft: Ahsan Waqar (AW) Writing - Review & Editing: Abdullah Mohammed Alshehri, Hamad R. Almujibah, Tarek A.H. Barakat Abdullah; Supervision: Hashem Alyami.

Funding

The research was funded by Taif University, Saudi Arabia, project number (TU-DSPP-2024-33).

Data availability

The data supporting the findings of this study are available on reasonable request from Dr. Tarek A.H. Barakat, Assistant Professor of Project Management, Department of Civil Engineering, Faculty of Engineering, Sana’a University, Sana’a, Yemen. Requests for access can be directed to Dr. Tarek A.H. Barakat at t.barakat@su.edu.ye.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

All methods were carried out in accordance with relevant guidelines and regulations issued by the ethics approval was obtained from the Ethics and Research Committee, Department of Civil Engineering, Taif University.

Informed consent

Informed consent was obtained from all participants prior to their participation in the study.