Tech-driven smart cities must shift to governance-led models

Researchers are urging a reset in how smart cities are understood and governed, arguing that urban digital transformation has moved too far toward infrastructure, automation and optimization without enough attention to public value, inclusion and long-term resilience. A new research published in Encyclopedia says the next phase of smart city development will depend less on how many advanced tools cities deploy and more on whether those tools are embedded in systems that protect human well-being, environmental goals and institutional trust.

The study, titled “Human-Centric, Sustainable and Resilient Smart Cities in Industry 5.0,” presents a broad conceptual framework for understanding how cities should move beyond earlier technology-led smart city models and instead organize digital transformation around the three core Industry 5.0 values of human-centricity, sustainability and resilience.

Cities face growing pressure to modernize public services with artificial intelligence, connected sensors, immersive digital systems and automated infrastructure while also responding to climate stress, social inequality, cybersecurity threats and declining public trust in opaque digital governance. The authors argue that these pressures cannot be solved by adding more technology alone. The claim that smart cities must be treated as socio-technical ecosystems where governance, accountability, participation and long-term impact matter as much as technical performance.

The paper rejects the older assumption that urban innovation can be measured mainly through efficiency, automation and data integration. Early smart city models, often shaped by Industry 4.0 thinking, focused heavily on sensing, connectivity and analytics for operational gains. The study says that approach improved performance in some areas but also produced technocentric bias, weak participation, privacy concerns and legitimacy gaps. That has driven a broader shift toward what the paper identifies as a more citizen-oriented and value-driven smart city model.

Industry 5.0 is a policy and conceptual turning point. According to them, the emerging model does not discard digital technology, but recasts it as a means rather than an end. Artificial intelligence, the Internet of Things, blockchain, robotics, extended reality, digital twins and advanced communications all remain important, but their relevance now depends on whether they improve quality of life, support ecological stewardship and strengthen a city’s ability to withstand disruption.

A shift from technology inventories to urban capabilities

The study states that the smart city debate has remained too fragmented. Existing work often isolates technologies on one side and values like ethics, participation and sustainability on the other. According to the paper, that leaves a gap in explaining how cities actually convert digital systems into public value. To solve that, the authors propose a capability-oriented perspective. Instead of asking only what technologies a city has, they ask what the city becomes capable of doing through those technologies and the way they are governed.

Technologies are treated as tools or enablers. Capabilities are the functional urban capacities produced through those tools, governance choices and institutional arrangements. The study identifies six major capability domains: urban sensing and situational awareness; intelligent decision support and predictive foresight; real-time service orchestration and automation; trusted data governance and secure infrastructure operation; immersive interaction and participatory experience mediation; and autonomous and assistive urban utilities. These domains, the authors argue, provide a clearer way to compare cities and to judge whether digital transformation is aligned with Industry 5.0 values.

The first capability, urban sensing and situational awareness, refers to the ability of cities to observe environmental, physical and social conditions in near real time. The second, intelligent decision support and predictive foresight, covers the use of artificial intelligence, data analytics and simulation to detect patterns, forecast risks and test possible interventions. The third, real-time service orchestration and automation, focuses on the dynamic coordination of urban services such as mobility, energy management and emergency response. The fourth addresses data integrity, privacy, access control and secure operation across complex digital systems. The fifth centers on how cities communicate, visualize and involve people through immersive and participatory interfaces. The sixth covers robotics and other autonomous systems that support inspection, logistics, public services and emergency operations.

This framework is important because it shifts evaluation away from hardware and platforms alone. A city with extensive AI, IoT and automation infrastructure may still fail under the Industry 5.0 lens if those tools are opaque, exclusionary, energy-intensive or fragile in crisis conditions. By contrast, a city that deploys more modest digital tools but does so with strong oversight, transparency and social alignment may be better positioned to deliver the human-centered and resilient outcomes the study prioritizes.

The authors are clear that this is not a proposal for a single new technology or an empirical model. It is instead a conceptual structure for understanding how smart cities can be interpreted, governed and evaluated under Industry 5.0. That structure is meant to help policymakers and researchers move past raw technology counts and toward a more grounded assessment of what digital systems actually do in public life.

AI, blockchain, IoT and immersive systems widen both opportunity and risk

The paper reviews a wide set of technological enablers and organizes them by maturity. Established technologies include artificial intelligence and data analytics, IoT and sensor networks, the edge-cloud computing continuum, robotics and autonomous systems, and cybersecurity and privacy-preserving infrastructures. Emerging technologies include blockchain, extended reality, the metaverse and digital twins. Exploratory technologies include generative physical AI and 6G with the Internet of Senses.

AI is described as the cognitive backbone of Industry 5.0 smart cities. It supports prediction, optimization, anomaly detection and scenario analysis across transport, energy, environmental monitoring and urban planning. But the study stresses that AI must remain human-centric by design. That means systems should be explainable, accountable and supportive of human judgment rather than simply replacing it. The paper repeatedly warns that opaque algorithmic systems can erode trust, marginalize contextual knowledge and deepen legitimacy problems even when they appear technically effective.

IoT and sensor networks are described as the perception layer of smart cities, supplying the continuous data streams needed for real-time awareness and response. Yet the paper says these systems cannot be treated as neutral collection tools. Their design determines what is measured, whose needs are prioritized and how risks are distributed. Without proper safeguards, pervasive sensing can slide into surveillance overreach and widen existing digital inequality.

The same pattern appears in the discussion of edge and cloud computing. Distributed computing can improve privacy, reduce latency and preserve service continuity when central systems fail. It can also help cities maintain critical operations during disruptions by allowing subsystems to function locally. But that same complexity creates interdependence and raises the stakes for orchestration, energy use and continuity planning. The study argues that distributed computing only supports Industry 5.0 goals when it is managed with privacy protections, failover design and energy-aware operation.

Blockchain is treated more cautiously than many technology advocates might expect. The authors recognize its potential to support secure, transparent and verifiable coordination across distributed actors. They identify uses in identity management, e-services, auditable data sharing, logistics and peer-to-peer energy trading. But they also warn against treating blockchain as a universal fix. Scalability problems, interoperability limits, governance complexity, regulatory uncertainty and the risk of reinforcing power imbalances all remain live concerns. In the paper’s view, blockchain can contribute to accountability and resilience, but only within public-interest governance frameworks.

Extended reality, digital twins and metaverse-type platforms are framed as tools for simulation, visualization and participatory interaction. They can help stakeholders explore infrastructure dynamics, climate scenarios, mobility flows and planning trade-offs through more intuitive interfaces. That makes them potentially valuable for participation and inclusive decision-making. But the study emphasizes that these systems also carry risks tied to digital exclusion, uneven access, privacy concerns, energy consumption and weak empirical validation. Their long-term value, the authors say, will depend on accessibility, interoperability and lifecycle sustainability rather than novelty alone.

The paper also pays attention to exploratory systems such as generative physical AI and 6G-based multisensory networks. These are presented as future possibilities for more adaptive human-machine collaboration, remote expertise and autonomous operation in complex environments. At the same time, the authors make clear that the evidence base is still limited and that large-scale deployment would raise fresh governance, inclusion and sustainability questions.

Governance, safeguards and evaluation become the real test of smart city success

No technology carries Industry 5.0 values on its own. Human-centricity, sustainability and resilience are not built into AI, blockchain, robotics or sensors by default. They are either realized or undermined through governance choices, safeguards, institutional capacity and evaluation models. That is why the paper places such heavy emphasis on capability-level governance and risk management.

The study maps major risks and trade-offs across each capability domain. Urban sensing can drift into surveillance, biased coverage and disproportionate monitoring. Intelligent decision systems can produce algorithmic bias, opacity and overreliance on model outputs. Real-time automation can increase systemic fragility and create cascading failures across interconnected infrastructures. Data governance systems can be undermined by cyberattacks, institutional opacity and long-term lock-in. Immersive systems can exclude users and misrepresent scenarios. Autonomous utilities can create safety risks, accountability gaps and labor displacement pressures.

In response, the paper lays out matching safeguards. These include purpose-limited sensing, participatory data governance, privacy-by-design, human-in-the-loop decision structures, explainable AI, redundancy across edge-cloud layers, auditable data access, inclusive design standards, meaningful human oversight and lifecycle assessment for automation-heavy systems. The logic is straightforward: if cities do not design these protections in from the start, they risk repeating the failures of earlier smart city models under more advanced technical conditions.

The governance discussion goes further than broad principle-setting. The authors argue that cities must move from technology procurement to capability governance. Instead of buying platforms first and worrying about participation, equity and accountability later, public authorities should define functional urban priorities, acceptable risks and oversight structures before technologies are scaled. They should also align projects with long-horizon policy frameworks, including sustainability and climate goals, and use standardization systems that shift evaluation from technology counts to public outcomes.

Evaluation is another major target of the study. The authors say efficiency metrics are no longer enough. A human-centric assessment must combine service performance with lived experience, accessibility, trust, inclusion and distributional effects across groups. Sustainability assessment must track lifecycle emissions, carbon intensity, maintenance burdens and rebound effects from efficiency gains. Resilience assessment must test continuity under cyber incidents, infrastructure failures and social disruption, while also measuring institutional readiness and learning capacity.

The authors argue that several open challenges remain unresolved, including the gap between participatory ideals and actual implementation, the tendency to undercount long-term sustainability costs, the growing interdependence of digital infrastructure, limited public-sector capability and the difficulty of making trade-offs explicit and governable over time.