This blog summarizes research trends based on published paper abstracts. Specific numbers or findings may contain inaccuracies. For scholarly rigor, always consult the original papers cited in each post.
The smart city has become the dominant paradigm for urban development in the 21st century. From Singapore's sensor-laden streetscapes to Jakarta's traffic management systems to Delhi's integrated command centers, cities across the Global South are investing heavily in IoT infrastructure, data platforms, and AI-powered governance tools. The promise is compelling: real-time data will enable efficient resource allocation, predictive analytics will anticipate infrastructure failures, and digital platforms will connect citizens to government services seamlessly.
The sociological question, however, is not whether smart cities can be built, but for whom they are built. A growing body of critical research—particularly from Southeast Asia, where smart city investment has been substantial—suggests that the distribution of smart city benefits maps closely onto existing socioeconomic hierarchies. The digitally connected, formally employed, property-owning urban middle class experiences the smart city as a genuine improvement in quality of life. The informal sector worker, the migrant without documentation, the elderly person without a smartphone experiences it differently—or does not experience it at all.
The Southeast Asian Evidence
Chen (2025) provides a systematic review of smart city development and social equity across Southeast Asia, examining case studies from Singapore, Kuala Lumpur, Bangkok, Jakarta, Manila, and Ho Chi Minh City. Despite significant digital infrastructure investments across the region, the research reveals that smart city initiatives have not systematically reduced urban inequality.
The review identifies a consistent pattern: smart city investments concentrate in central business districts and affluent residential areas, while peripheral and informal settlements—where the majority of urban poor reside—receive minimal infrastructure. The "smart" in smart city, in practice, means "smart for those who are already served by urban infrastructure." The digital divide is not merely a matter of access to devices; it is a spatial divide that mirrors and reinforces existing patterns of urban segregation.
Several structural mechanisms drive this pattern. First, smart city projects typically require formal property ownership or registered addresses for service delivery—requirements that exclude residents of informal settlements. Second, the data platforms that power smart city services rely on digital identities and smartphone access, creating barriers for populations that interact with the city through informal, cash-based, and face-to-face channels. Third, the governance models that manage smart city projects tend to privilege technical expertise over community knowledge, marginalizing the voices of those who understand urban informality from lived experience.
India's Smart City Mission: The Implementation Gap
Baboo (2025) provides a detailed case study of Delhi's experience under India's Smart City Mission (SCM), launched in 2015 as one of the world's largest urban transformation programs. The study evaluates the gap between policy ambition and implementation reality.
India's SCM was designed to enhance citizens' quality of life through technology-driven governance, sustainable infrastructure, and citizen-centered development. But the Delhi case reveals significant implementation challenges: coordination failures between municipal, state, and national agencies; procurement processes that favor large technology vendors over local solutions; and citizen participation mechanisms that exist on paper but function poorly in practice.
The governance dimension is particularly instructive. Smart city projects require integration across traditionally siloed government departments—transport, water, electricity, waste management, public safety—that have different data systems, reporting structures, and institutional cultures. The technology for integration exists; the institutional capacity for it often does not.
The Governance Paradox
Herawati, Dwimawanti, and Maesaroh (2025) examine the relationship between governance models and smart city outcomes in Indonesia. Their framework analyzes six dimensions of smart city development: smart economy, smart mobility, smart environment, smart people, smart living, and smart governance.
The study argues that agile and dynamic governance approaches—characterized by adaptive decision-making, cross-sectoral collaboration, and iterative implementation—are better suited to smart city development than traditional hierarchical governance. But they also identify a paradox: the governance structures that smart cities require (agile, data-driven, cross-sectoral) are precisely the structures that most Indonesian municipal governments currently lack. The technology arrives faster than the institutional capacity to use it effectively.
Smart Surveillance: The Security-Liberty Trade-off
Kristiono (2025) examines a dimension of smart cities that urban planners often treat as unproblematic: the integration of surveillance technologies into urban public safety. The study investigates how IoT-based surveillance, real-time data platforms, intelligent traffic systems, and predictive analytics are being deployed to enhance policing in smart city contexts.
The study frames these technologies as tools for improving police performance and public trust. But the sociological literature on surveillance suggests a more complex picture. Predictive policing systems, facial recognition networks, and real-time monitoring infrastructure raise questions that the smart city discourse rarely addresses:
- Who is watched? Surveillance infrastructure tends to concentrate in areas associated with crime—which, in most cities, means areas associated with poverty, racial minorities, and informal economic activity. Smart surveillance can become a mechanism for over-policing already-marginalized communities.
- Who decides what counts as suspicious? Algorithmic threat detection systems encode assumptions about normality and deviance that reflect the training data's biases. A person "loitering" in a business district may trigger an alert that the same behavior in a residential area would not.
- What accountability mechanisms exist? Real-time surveillance generates continuous data streams that are rarely subject to democratic oversight. The data may be accessed by multiple agencies, retained indefinitely, and used for purposes beyond the original mandate.
The Inequality Reproduction Mechanism
Daimah (2025) offers a critical perspective on smart city practices in Indonesia that connects the dots between infrastructure investment, governance capacity, and social outcomes. Despite increasing investments in smart infrastructure, significant disparities in socioeconomic outcomes remain prevalent.
The paper identifies what might be called the "smart city inequality trap": investments in digital infrastructure require complementary investments in digital literacy, institutional capacity, and social inclusion to produce equitable outcomes. When the digital infrastructure arrives without these complementary investments—which is the typical pattern in resource-constrained municipalities—the benefits accrue to populations that already possess the skills, devices, and institutional connections to engage with digital services.
Claims and Evidence
<
| Claim | Evidence | Verdict |
|---|
| Smart city investments reduce urban inequality | Chen (2025): investments concentrate in already-served areas; inequality not systematically reduced | ❌ Refuted |
| Smart city governance requires institutional transformation, not just technology | Baboo (2025), Herawati et al. (2025): governance capacity gaps limit smart city effectiveness | ✅ Supported |
| Smart surveillance improves public safety without civil liberties trade-offs | Kristiono (2025): frames surveillance positively but does not address over-policing or accountability | ⚠️ Uncertain |
| Indonesia's smart city initiatives address socioeconomic disparities | Daimah (2025): disparities persist despite infrastructure investment | ❌ Refuted |
Open Questions
Can smart cities be designed "from the bottom up"? Most smart city initiatives are top-down, designed by technology vendors and urban planners. What would a smart city designed by informal sector workers, street vendors, and migrant communities look like?Is the smart city concept inherently biased toward formal economic activity? If smart city services require digital identity, formal address, and smartphone access, are they structurally incapable of serving informal populations—not because of design failure, but because of conceptual assumptions?How should smart city surveillance be governed? The absence of surveillance governance in most smart city frameworks is a gap that becomes more consequential as the technology becomes more capable. What democratic accountability mechanisms are appropriate for real-time urban surveillance?What can Southeast Asian smart city experiences teach other regions? The concentration of smart city investment in Southeast Asia provides a natural experiment. What transferable lessons emerge from Singapore's comprehensive approach versus Indonesia's decentralized experiments?Implications
The research reviewed here suggests that "smart" is not a synonym for "equitable." Smart city technologies can improve urban efficiency, and in many cases they genuinely do. But efficiency gains are not the same as equity gains, and the conflation of the two serves the interests of technology vendors and municipal leaders more than it serves the interests of urban populations who are excluded from the smart city's digital infrastructure.
The path toward equitable smart cities requires starting not from the technology but from the social question: what do different urban populations need, and how can technology serve those needs? This inversion—from technology-first to need-first—has implications for procurement, governance, participation, and evaluation. It is easier to deploy sensors than to listen to communities, but listening is what equity requires.
The smart city has become the dominant paradigm for urban development in the 21st century. From Singapore's sensor-laden streetscapes to Jakarta's traffic management systems to Delhi's integrated command centers, cities across the Global South are investing heavily in IoT infrastructure, data platforms, and AI-powered governance tools. The promise is compelling: real-time data will enable efficient resource allocation, predictive analytics will anticipate infrastructure failures, and digital platforms will connect citizens to government services seamlessly.
The sociological question, however, is not whether smart cities can be built, but for whom they are built. A growing body of critical research—particularly from Southeast Asia, where smart city investment has been substantial—suggests that the distribution of smart city benefits maps closely onto existing socioeconomic hierarchies. The digitally connected, formally employed, property-owning urban middle class experiences the smart city as a genuine improvement in quality of life. The informal sector worker, the migrant without documentation, the elderly person without a smartphone experiences it differently—or does not experience it at all.
The Southeast Asian Evidence
Chen (2025) provides a systematic review of smart city development and social equity across Southeast Asia, examining case studies from Singapore, Kuala Lumpur, Bangkok, Jakarta, Manila, and Ho Chi Minh City. Despite significant digital infrastructure investments across the region, the research reveals that smart city initiatives have not systematically reduced urban inequality.
The review identifies a consistent pattern: smart city investments concentrate in central business districts and affluent residential areas, while peripheral and informal settlements—where the majority of urban poor reside—receive minimal infrastructure. The "smart" in smart city, in practice, means "smart for those who are already served by urban infrastructure." The digital divide is not merely a matter of access to devices; it is a spatial divide that mirrors and reinforces existing patterns of urban segregation.
Several structural mechanisms drive this pattern. First, smart city projects typically require formal property ownership or registered addresses for service delivery—requirements that exclude residents of informal settlements. Second, the data platforms that power smart city services rely on digital identities and smartphone access, creating barriers for populations that interact with the city through informal, cash-based, and face-to-face channels. Third, the governance models that manage smart city projects tend to privilege technical expertise over community knowledge, marginalizing the voices of those who understand urban informality from lived experience.
India's Smart City Mission: The Implementation Gap
Baboo (2025) provides a detailed case study of Delhi's experience under India's Smart City Mission (SCM), launched in 2015 as one of the world's largest urban transformation programs. The study evaluates the gap between policy ambition and implementation reality.
India's SCM was designed to enhance citizens' quality of life through technology-driven governance, sustainable infrastructure, and citizen-centered development. But the Delhi case reveals significant implementation challenges: coordination failures between municipal, state, and national agencies; procurement processes that favor large technology vendors over local solutions; and citizen participation mechanisms that exist on paper but function poorly in practice.
The governance dimension is particularly instructive. Smart city projects require integration across traditionally siloed government departments—transport, water, electricity, waste management, public safety—that have different data systems, reporting structures, and institutional cultures. The technology for integration exists; the institutional capacity for it often does not.
The Governance Paradox
Herawati, Dwimawanti, and Maesaroh (2025) examine the relationship between governance models and smart city outcomes in Indonesia. Their framework analyzes six dimensions of smart city development: smart economy, smart mobility, smart environment, smart people, smart living, and smart governance.
The study argues that agile and dynamic governance approaches—characterized by adaptive decision-making, cross-sectoral collaboration, and iterative implementation—are better suited to smart city development than traditional hierarchical governance. But they also identify a paradox: the governance structures that smart cities require (agile, data-driven, cross-sectoral) are precisely the structures that most Indonesian municipal governments currently lack. The technology arrives faster than the institutional capacity to use it effectively.
Smart Surveillance: The Security-Liberty Trade-off
Kristiono (2025) examines a dimension of smart cities that urban planners often treat as unproblematic: the integration of surveillance technologies into urban public safety. The study investigates how IoT-based surveillance, real-time data platforms, intelligent traffic systems, and predictive analytics are being deployed to enhance policing in smart city contexts.
The study frames these technologies as tools for improving police performance and public trust. But the sociological literature on surveillance suggests a more complex picture. Predictive policing systems, facial recognition networks, and real-time monitoring infrastructure raise questions that the smart city discourse rarely addresses:
- Who is watched? Surveillance infrastructure tends to concentrate in areas associated with crime—which, in most cities, means areas associated with poverty, racial minorities, and informal economic activity. Smart surveillance can become a mechanism for over-policing already-marginalized communities.
- Who decides what counts as suspicious? Algorithmic threat detection systems encode assumptions about normality and deviance that reflect the training data's biases. A person "loitering" in a business district may trigger an alert that the same behavior in a residential area would not.
- What accountability mechanisms exist? Real-time surveillance generates continuous data streams that are rarely subject to democratic oversight. The data may be accessed by multiple agencies, retained indefinitely, and used for purposes beyond the original mandate.
The Inequality Reproduction Mechanism
Daimah (2025) offers a critical perspective on smart city practices in Indonesia that connects the dots between infrastructure investment, governance capacity, and social outcomes. Despite increasing investments in smart infrastructure, significant disparities in socioeconomic outcomes remain prevalent.
The paper identifies what might be called the "smart city inequality trap": investments in digital infrastructure require complementary investments in digital literacy, institutional capacity, and social inclusion to produce equitable outcomes. When the digital infrastructure arrives without these complementary investments—which is the typical pattern in resource-constrained municipalities—the benefits accrue to populations that already possess the skills, devices, and institutional connections to engage with digital services.
Claims and Evidence
<
| Smart city investments reduce urban inequality | Chen (2025): investments concentrate in already-served areas; inequality not systematically reduced | ❌ Refuted |
| Smart city governance requires institutional transformation, not just technology | Baboo (2025), Herawati et al. (2025): governance capacity gaps limit smart city effectiveness | ✅ Supported |
| Smart surveillance improves public safety without civil liberties trade-offs | Kristiono (2025): frames surveillance positively but does not address over-policing or accountability | ⚠️ Uncertain |
| Indonesia's smart city initiatives address socioeconomic disparities | Daimah (2025): disparities persist despite infrastructure investment | ❌ Refuted |
Open Questions
Can smart cities be designed "from the bottom up"? Most smart city initiatives are top-down, designed by technology vendors and urban planners. What would a smart city designed by informal sector workers, street vendors, and migrant communities look like?Is the smart city concept inherently biased toward formal economic activity? If smart city services require digital identity, formal address, and smartphone access, are they structurally incapable of serving informal populations—not because of design failure, but because of conceptual assumptions?How should smart city surveillance be governed? The absence of surveillance governance in most smart city frameworks is a gap that becomes more consequential as the technology becomes more capable. What democratic accountability mechanisms are appropriate for real-time urban surveillance?What can Southeast Asian smart city experiences teach other regions? The concentration of smart city investment in Southeast Asia provides a natural experiment. What transferable lessons emerge from Singapore's comprehensive approach versus Indonesia's decentralized experiments?Implications
The research reviewed here suggests that "smart" is not a synonym for "equitable." Smart city technologies can improve urban efficiency, and in many cases they genuinely do. But efficiency gains are not the same as equity gains, and the conflation of the two serves the interests of technology vendors and municipal leaders more than it serves the interests of urban populations who are excluded from the smart city's digital infrastructure.
The path toward equitable smart cities requires starting not from the technology but from the social question: what do different urban populations need, and how can technology serve those needs? This inversion—from technology-first to need-first—has implications for procurement, governance, participation, and evaluation. It is easier to deploy sensors than to listen to communities, but listening is what equity requires.
References (5)
[1] Chen, L. (2025). Smart Cities and Social Equity: A Review of Digital Urban Governance in Southeast Asia. International Journal of Smart Sustainable Architecture & Technology, 1(1), 001.
[2] Daimah, D. (2025). Smart Cities and Socioeconomic Inequality: A Critical Perspective on Smart City Practices in Indonesia. Injurity, 4(2), 1420.
[3] Baboo, P. (2025). Bridging Policy and Practice: Evaluating Urban Governance and Implementation Gaps in India's Smart City Mission — A Case Study of Delhi.
[4] Herawati, A.R., Dwimawanti, I.H., & Maesaroh, M. (2025). Agile and Dynamic Governance: Driving Smart City Innovations in Indonesia. KnE Social Sciences, 10(4), 18045.
[5] Kristiono, C. (2025). Integrating Smart City Technologies to Enhance Police Performance and Urban Public Safety. Puruhita, 7(2), 37896.