Governance quality systems and operations of sustainable smart cities

The new approaches are planning and managing urbanisation in cities and play a vital role in the 21st century (Zhu, 2025; OECD, 2020). Therefore, smart cities utilise technology to enhance the efficiency of urban services and improve the quality of life for the population. Hence, good governance, having technology with a broad vision in urban development, is what smart cities depend on for their foundation (Aisyah et al., 2024). Likewise, sustainability is the key factor with an important reference for modern cities: environment, economy and society. This leads to sustainability in the urban governance context, which states that development occupies present needs without compromising future generations (Joss et al., 2019). Meanwhile, smart cities offer platforms for upgrading the levels of their infrastructures based on data; hence, they have tools that measure, monitor and advocate sustainability. In this level, governance adaptations should be implemented for favourable social and sustainable outcomes. This situation builds healthy, robust and resilient urban areas to counter the many challenges of the modern age, such as climate change, resource scarcity and social inequality (Tan and Taeihagh, 2020). Therefore, Data-driven governance enhances a new frontier, employing real-time data analytics to inform strong decisions and policies for building urban areas as sustainable cities. Additionally, transparency for citizens through information sharing would then be promoted by the ability to control environmental indicators and optimise resources (Meijer and Bolívar, 2016).

The sustainability and smart city projects are explored, while the current study seeks to inform the design of participatory governance models in which citizens and other stakeholders can be made to participate in smart city policy. The various perspectives will strengthen the level of technological development in terms of community needs and values, thus adding to the legitimacy and sustainability of urban development programs. From a theoretical perspective, studies on governance, sustainability and smart cities shape the development of integrated frameworks drawing on urban planning, public tasks and information technology. This perspective explained how governance mechanisms can utilise technological innovations to reach sustainability goals. Meanwhile, in empirically view, this study enhances evidence-based assessments of smart city initiatives and their effects on promoting sustainable outcomes. The current study uses methodological methods for studying complex urban systems by identifying interactions among governance processes, technological infrastructures and sustainability indicators in smart cities through mixed-method study designs.

The governance and operations of sustainable smart cities can be understood in terms of systems-oriented and socio-technical theories. Systems Theory and Socio-Technical Systems Theories (Cardenas and Kozine, 2025; Ropohl, 1999) view cities as interconnected and adaptive networks where technical infrastructures and social institutions interact. The foundation of smart urban management rests on the coordination, integration, and balance between technological efficiency and citizen well-being. Indeed, the Cybernetic and Control Theory (François, 1999). Systemics also delivers the fundamental tenets underlying feedback mechanisms as well as the regulation of real-time data, which underpin evidence-based and adaptive governance.

In parallel, there are Transition Management and Good Governance Theories (Meadowcroft, 2009; Rotberg, 2014). These have expounded how cities prospectively get towards sustainability on the basis of efficient institutions, open policies and sound reforms. The quality of governance, participation and regulations, which play a role in promoting sustainable and digitalising transformations, is brought to bear in this argument by these theories of governance. In the practical application of these theories to industrial engineering, operations are optimised to a level such that all technological transitions are seamlessly contending with the social and environmental goals.

Governance is widely recognised as a key aspect of sustainable smart cities; however, the literature presents the following considerable gap: governance-quality indicators, such as regulatory quality, rule of law and government effectiveness, have not been systematically and empirically integrated into models of smart city-performance and sustainability. Existing studies and indices have tended to focus on the technological and structural dimensions, whereas the governance dimension has generally been neglected or treated very descriptively. This gap presents challenges to making such comparisons across countries and creates obstacles to understanding how governance quality conditions the sustainability impacts of smart city development. The integration of this perspective is of utmost importance for the purpose of theory development, refining measurement frameworks and informing evidence-based policy design for sustainable smart cities.

The current study has integrated governance quality indicators such as regulatory quality, rule of law and government effectiveness into analysing smart city performance and sustainability outcomes. Therefore, this study affords a governance-setting framework that explicitly outlines how institutional quality conditions the effectiveness of smart city structures and technologies in delivering sustainable urban development. Hence, the main contribution of this study is to internationally recognised governance measures with smart city rating indices and their structural and technological pillars, thus providing an understanding of why cities with the same technological capacities achieve such diverse sustainability outcomes. This contribution strengthens not only the theoretical framework of smart city outcomes but also leads policymakers in highlighting governance reforms as a precondition for inclusive, resilient and sustainable smart cities.

Following the introduction, the current study is structured as follows: Section 2 presents the background and literature review, Section 3 explains the methodology, Section 4 presents the results, Section 5 discusses those results and Section 6 concludes with policy implications.

The earlier focus of smart cities on technological adoption without caring to create an entire discourse around the importance of governance quality in bringing on a sustainable urban transformation, the role that governance quality plays in securing the whole transformation (Tan and Taeihagh, 2020). An area of a kind of shift that has emerged in theory is in the conceptualisation of making the case for the importance of governance quality, as it draws attention away from merely the earlier focus of adoption of technologies and digital infrastructures towards the acknowledgment that sustainability in this space is limited by governance systems that articulate policy, regulate technological integration and ensure institutional effectiveness. Governance quality is also multifaceted, as regulated quality, adherence to the rule of law and effective government constitute the widely recognised dimensions of the Worldwide Governance Indicators (WGI, 2025) on governance quality. These last indicators depict a government's ability to design and enforce well-sounded policies and an efficient delivery of public services, through that emerging societal complexity that cities have to negotiate in their real-time socio-technical interactions (World Bank, 2025; Kaufmann et al., as reported in WGI). Regulatory quality is intended to guarantee that policies and regulations promote innovation and competition, while the rule of law is the source of legal certainty and accountability for data governance and citizen protections. Only government effectiveness incorporates public sector competence in the implementation of strategic urban initiatives, which is a prerequisite to alignment of technological investments with sustainability objectives.

Cities are often rated and assessed with the use of rating smart city indices such as the IMD Smart City Index, which determines such aspects as technological adoption, quality of life, infrastructure, governance and environmental performance in relation to each other and serves as a basis for benchmarking urban-to-urban performance (IMD Smart City Index, 2025). These indices indicate that cities endowed with strong governance frameworks tend to do better in integrating their structural content, such as institutional arrangements, public services, planning systems, into technological components such as IoT, AI and data analytics that allow real-time decision-making and operational efficiency (IMD Smart City Index, 2025; research on smart cities and what put them at the core or criteria of an index). This leads to building a structural dimension that involves how organisational processes and regulatory frameworks shape cross-sector adaptive governance, a necessary condition for sustainable smart city development.

The smart cities enhance governance sustainability and are not hampered solely by technological deployment but by interactively creating a governance structure and a technological system. Therefore, smart governance includes digital platforms and data-driven decision-making, along with institutional capacity and mechanisms for citizen engagement that promote accountability, transparency, and participatory planning (Almulhim and Yigitcanlar, 2025). Furthermore, the continuing problems created by digital divides and privacy, institutional inertia and poor, incoherent regulatory frameworks, especially where these advancements must be accompanied by governance reforms to realise sustainability objectives (Smart City Governance in Developing Countries, 2020; Almulhim and Yigitcanlar, 2025). Finally, an integrative system that systematically accommodates governance quality indicators, alongside smart city indices, technology and structural readiness, can offer better explanatory power in understanding sustainability outcomes across smart cities and create more comfortable policy interventions.

The smart cities have transitioned from being led by technology towards a multifaceted governance orientation that considers sustainability, quality of institutions, and value for people (Almulhim and Yigitcanlar, 2025). In the earlier studies, the focus was on the systems of information and communications technology (ICT) towards greater urban efficiency, international competitiveness and productive service delivery (Hollands, 2008; Nam and Pardo, 2011). From this view, cities charge that such a narrow concern upon ultimate objectives of smart city governance obstructs their realisation within the governance structure, the whole regulatory framework, and institutional capacity in actualising sustainable outcomes of smart cities (Kitchin, 2015; Meijer and Bolívar, 2016; Almulhim and Yigitcanlar, 2025).

While later literature enhances governance quality, regulatory quality, rule of law and government effectiveness, there is a basic limitation for sustainable smart city performance. Therefore, regulatory quality reflects the ability of institutions to design and implement policies that promote an environment for innovation, where competition and private sector contributions are encouraged in carrying out smart city initiatives (Kaufmann et al., 2011; Bashir et al., 2025). Meanwhile, weak regulatory indicators tend to fragment smart city projects, foster limited interoperability and poorly align with sustainability goals (Udoh and Reyes, 2025; Yigitcanlar et al., 2018). Furthermore, the rule of law plays a vital role in enhancing data protection and cybersecurity, transparency and accountability, all ensuring citizen trustworthiness and long-term sustainability in digitally enabled cities (Kitchin, 2016). Lastly, government efficiency directs the execution and expansion of endeavours in smart infrastructures, digital platforms and sustainability concerns (Mora et al., 2017).

Most empirical smart city studies thus far have continued to rely heavily on smart city indices, such as IMD Smart City Index and Cities in Motion Index, which emphasised technological readiness, infrastructural capability, human capital and quality-of-life indicators (Giffinger et al., 2007; IMD Smart City Index analysis, 2024; IMD, 2023). While these indices undoubtedly serve the purpose of a guide for the benchmarking of smart urbanity, their common criticism is centred on methodological fragmentation, restricted comparability across regions and not having sufficiently integrated formal governance quality indicators (Albino et al., 2015; Mora et al., 2019). Furthermore, when governance is factored into this evaluative process, it is often treated, regressively, as a sub-dimension, lessening its perception-based measure rather than considering it as a structural and institutional determinant with recognised governance metrics internationally.

On the other hand, structural and technological dimensions of smart cities, such as digital infrastructure, sensor networks, data platforms and integration of urban services, are largely acknowledged to enable sustainability transitions (Batty et al., 2012). However, technological development is in itself unable to secure environmental or social sustainability. Thus, this leads to a dependence on the interplay between technology and the governance systems that shape policy coherence, institutional coordination and stakeholder engagement (Meijer and Bolívar, 2016). The cities with strong governance levels exhibit a higher tendency for smart technology to be in harmony with sustainability objectives, while those with weak institutions suffer from digital platforms, social exclusion and policy ineffectiveness. The most relevant theory to our topic is social-ecological systems (Lebel et al., 2006), which explains the interweaving of human societies and ecological systems. The social-ecological systems focus on sustainable development through adaptive systems of governance that can learn from environmental feedback and raise consciousness within society.

Therefore, governance has become one of the main pillars in steering smart city initiatives towards sustainability within urban studies. For example, Bolivar (2016) indicated that governance with emerging technologies for smart city projects should be more inclusive and fairer. Therefore, sustainability in smart cities refers to the environmental aspect and economic viability, and enhances this perspective giving paramount importance to social justice. Additionally, a smart city enhances an environment that enriches its citizens' quality of life by sustainable economic development and social inclusion (Caragliu et al., 2011). Meanwhile, Castelnovo et al. (2016) refer to the need for an integrated method for governance in smart cities, which brings together technological, human and institutional dimensions to achieve sustainability objectives. Recently, Zhang and Wang (2024), Worrakittimalee et al. (2024) and Meijer and Bolívar (2016) analysed the smart urban governance by identifying governance capacity as one of the limitations of smart city success. They indicated that institutions with effective performance, participatory processes and inter-agency coordination are necessary to promote smart technologies and urban transformation. Therefore, governance is represented as the “glue” that binds technology to the genuine needs of the citizens. According to Tan and Taeihagh (2020), different forms of regulatory reform, capacity enhancement and stakeholder engagement are key factors when considering the successful implementation of smart cities. Governance is thus represented as a digital inclusion and fair access to urban innovation. Moreover, Joss et al. (2019) argue that alignment of smart city objectives with sustainability and climate resilience relies heavily on governance mechanisms, particularly in institution building and policy integration. The good governance enhanced a transformational process rather than providing short-term benefits related to new technologies (Atar et al., 2025). Aisyah et al. (2024) provide that strong local governance positively contributes to the quality of life and sustainability of smart city transformation through innovative policies, transparency and environmental impacts.

According to recent literature, such as Manoharan et al. (2023), factors include, for example, a lack of labour motivation, poor performance evaluation practices and poor communication between parties. This suggests that governments need to have policies for construction enterprises to solve financial procedures, communication strategies, and resource management (Paglieri et al., 2025). Hermawan et al. (2024) indicated that sustainable manufacturing plays a significant role in SMEs' environmental performance and regulations; meanwhile, the government regulation and eco-innovation indicators have a positive and significant effect on environmental performance. Furthermore, Quiroz-Flores et al. (2024) explained how the Internet of Things and Blockchain promote more transparency, process optimisation and waste reduction, which reflects on improving sustainability. Meanwhile, Manoharan et al. (2024) supported site supervisors' cognitive domains in construction planning and materials were identified as the top-ranking competencies in the list, along with labour management and labour performance assessment.

Moreover, according to Kirchherr et al. (2017), frameworks mentioned that the Circular Economy reflects efficiency, waste minimisation and closed-loop resource utilisation. They can be equally combined with Complexity and Network Governance and Performance Measurement theories to form tools that can be of direct application to the designing and measuring of urban systems towards effectiveness, resilience and sustainability. All put together, this ultimately defines smart cities as socio-technical systems with multiple levels where productivity improvement and quality of urban life come from the convergence of engineering optimisation, institutional governance and sustainability principles. According to OECD (2020), inclusive and effective governance is a must for fair digital innovation. It recommends that the local government strengthen its capacity towards transparency, multi-level coordination and citizen-centred policies if the smart city is to benefit all. Based on the literature review, the hypotheses are as follows:

This study aims to investigate whether governance quality systems (GQS) and operational of sustainable smart cities (OSSC) can inspire the top 20 smart cities in countries. The study sample covered the period of 2021–2025. The study limitation is that no data from countries before 2021 for smart cities. The dataset included 100 observations for the study of 20 smart countries. The data was collected from World Development Indicators (WDI, 2025), Worldwide Governance Indicators (WGI, 2025), Human Development Reports (UNDP, 2025), and IMD Smart City Index from 2021–2025. The panel data technique used in this study to test the main model, which consisted of the governance quality systems (GQS) proxy, measured the independent variable, split into three independent variables of GQS, i.e. regulatory quality (RQ), rule of law (RL) and government effectiveness (GE). Three variables, i.e. measure the dependent variable of smart cities (OSSC), smart rating index (SC), structure (ST) and technology (TE). Three control variables will be used: inflation, calculated as the log of the consumer price index (% of annual) (CPI); GDP growth rate (annual% %) (GDP) and human development index (HDI). Additionally, the study used diagnostic tests to check the appropriate models and variables for more robustness. The study model was organised according to the following equation of FGLS:

Where GQS_i,t is the $IVi,t$ Independent variable proxy, measured by GE, RL, and RQ. OSSC is the dependent variable and is measured by SC, ST, and TE. The $controlvariablesi,t$ Are GDP _i,t, CPI _i,t and HDI _i,t, while ε (_i,t) is the error term. β₀ stands in as the intercept, then there's β₁, which marks the impact of the independent variable, while β₂ plays a similar role for the control variable. The ranked top 20 smart cities in 2025 (In Order) and methodology measures are presented in Appendix A.

The independent variables connected by governance quality systems are the institutional bases through which governments design, implement and enforce public policies. Regulatory quality, rule of law and government effectiveness are among the most widely accepted measurements of governance quality, capturing together the capacity, credibility and legitimacy of public institutions. These indicators are distinct, interrelated and jointly shape the overall performance of governance systems and associated developmental outcomes. Therefore, the regulatory quality is the ability of governments to formulate and implement policies, and high regulatory quality reduces uncertainty and transaction costs and increases investment through cohesive, transparent and predictable regulations. While regulatory quality cannot operate without depending on the rule of law that guards the enforcement of regulations, property rights and the enforcement of contract obligations. Furthermore, the rule of law depends upon government effectiveness, for effective adjudication and law enforcement are the processes that translate legal frameworks into practice.

Finally, government effectiveness captures the quality of public service delivery, the independence of institutions from political interference, and the credibility of policy implementation. This leads to an operational channel through which regulatory levels are translated into positive outcomes. High levels of government effectiveness improve regulatory quality by encouraging coordination of policy action and implementation capacity, and concurrently strengthen the rule of law through smooth administration, monitoring and enforcement. Meanwhile, weak government effectiveness, on the other hand, undermines regulatory quality and rule of law, producing inconsistent policies, weak enforcement and erosion of public trust. Therefore, these three dimensions of governance build up to constitute a reinforcing system. The regulatory quality provides the policy architecture, the rule of law guarantees legal certainty and accountability, and the government effectiveness provides the execution of their institutional performance. At the same time, smart rating indices and structural and technological facets constitute the relationships that define smart cities, showcasing the integrated nature of contemporary smart urban advancement. Various indices are mostly dependent on and shaped by the structural aspects of a city: physical infrastructure, institutional arrangement, human capital and urban systems to enhance coordination in service delivery. The other dimension is the technology, which acts as the operational enabler that converts these structures through the application of digital connectivity, data platforms, sensors, artificial intelligence and intelligent urban services. If anything, advanced technologies provide efficiency and innovation, and the smart city's performance will be achieved mainly under the auspices of much stronger structures like regulatory frameworks, organised capacity and sector-wide integration. Thereby, smart rating outcomes are not purely driven by technology but rather by the synergistic interaction between technological capabilities and structural readiness. This means cities achieve higher smart city performance when the deployment of technology is aligned with coherent urban structures and long-term strategic planning.

Diagnosis tests have found the presence of heteroscedasticity and serial correlation according to Breusch–Pagan/White tests for heteroscedasticity and Durbin–Watson/Wooldridge tests for autocorrelation. This calls for the FGLS estimator (Hansen, 2007; Bickel and Levina, 2008) to improve the efficiency and robustness of estimates, following the examination of the residuals from OLS. The model corrects for heteroscedasticity, which occurs when the variance of errors differs between countries, and for serial correlation, which arises when errors are correlated across countries over time. However, FGLS cannot solve all possible endogeneity issues relating to the sustainability of governance and smart cities. Therefore, for endogeneity, unobserved heterogeneity, and dynamic effects, a one-step system such as the Generalised Method of Moments (GMM) (Hansen et al., 1996) is an effective remedy. This estimator models a dynamic panel with lagged dependent variable characteristics, to settle the endogeneity in this sense, since, while measurement errors and country-specific effects are also solved via differencing or the system equations, using instrumental variables (lags of variables) for consistent estimation. In addition to these, one carries out diagnostic tests, ensuring the absence of second-order autocorrelation: Hansen or Sargan tests test instrument validity, while Arellano–Bond tests verify autocorrelation. Therefore, the study rewrote equation (1) of the one-step system of GMM as follows:

Where OSSC _{it -1} is the lagged dependent variable capturing the dynamic nature of OSSC, V_it is the country-specific fixed effect, and ε (i,t) is the error term.

Table 1 presents empirical analysis of the descriptive statistics of both models integrated for the study, including nine variables and 100 observations in the top 20 smart cities in countries from 2021 to 2025. The SD, mean, median, skewness, kurtosis and Jarque–Bera were obtained. Besides, Figures 1 and 2 present trends of the variables; the smart rating improved slightly during the study, while the structure pillar of developing the infrastructure of cities is more than that of the technology pillar. The other variables show that the RL, GE, HDI and RQ are horizontal lines and constant, with high GDP trends for the first year 2021, then down to 2023, then up and down again in 2025. Finally, CPI had increased from 2021 to 2022, then it decreased in 2024, after which it has shown to develop horizontally in terms of the year 2025.

The significance levels and probabilities of the variables are represented in Table 2, where the correlation results are stated. The outcomes have been in line with those of the FGLS and the system GMM estimators. The coefficient values of most variables did not exceed 55%, which gives a strong coefficient indication and no multicollinearity between the predictor and the response variables.

VIF test robustness has been presented in Table 3. Each variable's VIF value is less than 5, and the average is 2.98. These results reflect the absence of multicollinearity among two or more independent variables in the regression model. Hence, it proves that there was no multicollinearity detected and that the models of the study were fit and appropriate. The tolerance equals 1/VIF, which must be more than 0.1; thus, no multicollinearity. This result describes how much variance of a regression coefficient in the study is inflated by multicollinearity.

The Breusch–Pagan/Cook–Weisberg heteroscedasticity test and the Wooldridge test for autocorrelation are reported in Table 4. The results showed that neither test was significant, indicating that there were no heteroscedasticity or serial correlation assumptions violated in either model.

These panels include the LLC unit root tests and the IPC unit root tests, thereby allowing for the determination of the stationarity of the variables. In conclusion, all variables were found to be I (0) integrated according to Table 5. Hence, the above variables are considered stationary even at the level.

The assumptions of homoskedasticity, no serial correlation, and no unobserved heterogeneity across panels are often violated in panel data, as shown in Table 4, and consequently, the study employed FGLS to correct for heteroscedasticity, serial correlation and cross-sectional dependence. FGLS is beneficial for detecting panel-specific heteroskedasticity or AR (1) autocorrelation. The FGLS results for three models are presented in Table 6. The study relies on the FGLS results, which indicate that Model 1 revealed significant positive relationships between RL, GE and SC. Specifically, a 1% increase in RQ, GE led to increases of 1.58 and 1.32% in SC, respectively. Additionally, there was a significant positive relationship between RQ, RL, GE, and ST. Specifically, a 1% increase in RQ, RL, and GE resulted in a 1.66%, 1.95% and 2.25% increase in ST in Model 2. Additionally, Model 3 showed significant positive relationships between GE and TE. Specifically, a 1% increase in GE led to an increase of 0.768% in TE. However, the control variables showed that significant positive relationships were observed between HDI and ST in Models 2 and 3. Meanwhile, a significant negative relationship between GDP and SC, ST and TE was noted in Models 1, 2, and 3. Furthermore, a significant negative relationship was found between CPI and ST in Model 2. Finally, the diagnostic test results for the FGLS estimator revealed that the panel data exhibited no autocorrelation and were homoscedastic.

Table 7 presents the one-step system, Generalised Method of Moments (GMM), which was used to address the endogeneity problem of the panel data. The current results are consistent with previous tests of Feasible Generalised Least Squares (FGLS), promoting the robustness and consistency of the findings. The results indicate that Model 1 revealed significant positive relationships between RL, GE, and SC. Specifically, a 1% increase in RL, GE led to increases of 0.907 and 1.14% in SC, respectively. Additionally, there was a significant positive relationship between RQ, GE and ST. Specifically, a 1% increase in RQ and GE resulted in a 1.51 and 1.70% increase in ST in Model 2. Additionally, Model 3 showed significant positive relationships between GE and TE. Specifically, a 1% increase in GE led to an increase of 0.985% in TE. Meanwhile, the control variables showed that significant positive relationships were observed between HDI and ST in Models 1, 2, and 3. Meanwhile, a significant negative relationship between GDP and SC was noted in Model 1. Furthermore, a significant negative relationship was found between CPI and ST in Model 2. All current results were robust, and the endogeneity tests indicated that the AR (1), AR (2), Sargan and Hansen test results had a significance level of more than 5%, indicating that the study models were appropriate and well-fitted.

The sustainability governance and smart technologies will be facilitated by creating resilient urban systems to resist environmental, social and economic shocks. This is consistent with Zhang and Wang (2024) and Worrakittimalee et al. (2024), who supported that smart governance measures, such as digital participatory and interagency coordination in Thailand, are pillars for implementing smart city development initiatives. Furthermore, the main results revealed a significant positive relationship between the sustainability of governance, measured by government effectiveness, and smart cities rating, infrastructure and technology. Meanwhile, there is a significant positive relationship between the sustainability of governance, as measured by the rule of law, and the smart cities rating, particularly in terms of infrastructure. Finally, a significant positive relationship was found between the sustainability of governance, measured by regulatory quality and the smart cities rating. The control variables have made an impact and showed a positive and significant correlation between HDI and the development of smart cities regarding infrastructure and technology, suggesting that countries with higher HDI have better access to quality education, healthcare, and incomes, creating a conducive environment for innovation. Meanwhile, there is a significantly negative relationship between GDP and smart city development, as measured through ratings, infrastructure and technology, indicating “no” automatic translation of economic growth to smarter urban development. Additionally, high-GDP countries may prioritise consumption-driven growth without corresponding investments in sustainable urban planning and technological innovation. Further, these differences in resource allocation or governance inefficiencies could lead to imbalanced development where economic surpluses are not reinvested in modernising urban systems, negatively affecting overall smart city ratings and technological progress. Finally, a significant negative relationship is asserted for the Consumer Price Index (CPI) and smart city infrastructure development, suggesting that inflationary pressures may erode the actual purchasing power of governments or citizens, thereby hindering the possibility of funding large-scale, long-term infrastructure projects. Thus, this inflation-induced fiscal burden can compromise the crucial quality and, hence, infrastructure development needed for smart city progress. These current results are consistent with studies that help establish a nexus between a higher Human Development Index (HDI) and the effective implementation of smart city infrastructures and technologies. According to Xholo et al. (2025), human capital development is enhanced through education and digitalisation, which are fundamentally important to the smart city process. Chen and Cheng (2022) opined that investments in smart cities have little or even negative economic impacts if not strategically managed, amid rather high infrastructure costs that often outweigh benefits. Furthermore, in China, Ye et al. (2021) reported that smart city programs have not improved the quality of foreign direct investment, suggesting that GDP or other economic growth metrics may not indicate the effectiveness of smart city developments.

This study highlighted that integrated effective governance structures with smart technologies have produced sustainable urban outcomes, turning governance, sustainability and smart cities into interdependent components. Hence, governance plays a vital role in planning, implementing, and controlling smart city initiatives alongside the alignment with sustainability limits. However, smart technologies contribute to improving sustainability by promoting resource efficiency, reducing emissions, and providing better service provision in urban areas. Therefore, technologies would not be useful without sustainability and appropriate governance. The main contribution of this study is to lead the governance to meet the challenges of innovative complexity due to technology and ensure that smart development is contributing positively to sustainable environmental, social and economic conditions. The economic policies are important in the long-term impact of smart city initiatives in the appropriate utilisation of public funds and equity in the distribution of the economic benefits across society. This situation required policy reforms to facilitate the hassle-free incorporation of these areas. The top 20 cities must adopt participatory models of governance whereby citizens are involved throughout the smart city project conceptualisation processes, along with government officials. Additionally, engagement of different stakeholders makes the processes more responsive to community demands while increasing public trust in governance processes. Thus, a well-defined policy implication is necessary, stating the sustainability principles to be integrated into smart city planning and growth.

The empirical findings of governance sustainability and smart city outcomes were positive, presenting an attractive contribution and policy direction of the governance–smart city nexus. These results strongly support the institutional theory and the new public governance-type framework, which maintains that sustainable growth outcomes in complex socio-technical systems depend on the quality, stability and effectiveness of relevant governing institutions rather than on the adoption of technology. However, the government's effectiveness has a positive correlation with smart city ratings, infrastructure and technology, highlighting the administrative capacity, policy credibility and public sector competence in ensuring the successful planning, implementation and scaling of smart city initiatives. Thus, effectively, smart governments coordinate across sectors, make decisions on resource allocation wisely and embed digital technologies into urban infrastructures, thus resulting in augmenting delivery services with sustainability in the longer term. Additionally, the significant relation between the rule of law and smart city rating, mostly through infrastructure, underlines the importance of legal certainty, accountability and enforcement mechanisms to increase the development of smart cities. Meanwhile, the theoretical implication in institutional economics highlighted that secure property rights, enforceable contracts and predictable legal systems minimise uncertainty and therefore investment risks. Hence, the operationalising of smart cities leads findings to imply that cities with a strict rule of law are more likely to attract long-term investments into smart infrastructures and sustainable urban systems that would allow for technological innovation.

Additionally, the positive behaviour between regulatory quality and smart city ratings adds to the theoretical construct that high regulatory frameworks are key in directing technological innovation towards sustainability objectives. Therefore, high regulatory quality enables governments to produce coherent policies to increase compatibility, competition and innovation, while at the same time minimising the associated risks of market failure and cybersecurity externalities. This enhances the bureaucratic fragmentation polices, increases transparency, and raises regulatory predictability, which impact the successful functioning of smart cities, even under conditions of little technological impact. The main recommendations for policymaking are as follows. First, policy makers should formulate governance reforms at the top 20 of smart cities development, leading investments in digital infrastructure and technology, which offer good returns when undergirded by a credible public administration, strong legal institutions and quality regulation. Second, the top 20 smart cities strategies should be framed within larger governance sustainability levels, emphasising institutional resilience, legal accountability and regulatory coherence. Finally, international agencies and urban policymakers should develop fresh evaluative benchmarking tools that provide for the inclusion of governance quality indicators into smart city assessment to enforce much more inclusive, resilient and sustainable smart city pathways.

A limitation of this study is that the smart cities' pillars were not available before 2021, and the author(s) estimated the governance index, GDP, HDI and CPI measures for the year 2025 by calculating the average of the years from 2021 to 2024. Future studies may delve deeper into smart cities and incorporate additional social variables to explore the relationship between governance and the development of smart cities.

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