The study’s central conceptual move is a distinction that sounds simple but carries enormous consequences: the difference between the governance of AI and governance by AI. The first phrase describes the familiar task of regulating, auditing and ethically constraining algorithmic systems. The second describes something far stranger, in which AI systems themselves increasingly supply the inputs, and sometimes the outputs, of decisions that shape public life. As AI shifts from an object of regulation into an active participant in administration, accountability becomes shared between human officials and algorithmic actors, and oversight mechanisms designed for purely human bureaucracies begin to strain. Kaiser argues that this shift turns governance into a hybrid human-machine decision environment, demanding new institutional arrangements rather than incremental policy patches.

The urgency is not hypothetical. The United Arab Emirates has announced plans to use AI to support drafting, reviewing and amending legislation, while Albania introduced Diella, an AI-generated digital minister, as part of procurement and anti-corruption reform. These early, largely political experiments signal a growing willingness to invite AI into decision-relevant spaces once reserved for human officials. Meanwhile, AI policy initiatives now span more than 80 countries and jurisdictions, and technologically advanced states are making billion-dollar investments to secure strategic leadership. The question, the paper suggests, is no longer whether AI enters governance, but whether governance can absorb AI without losing accountability, legitimacy and public trust.

To ground the analysis, the study synthesizes cross-sector risks that recur wherever AI is deployed. Algorithmic bias tops the list: systems trained on non-representative datasets can reinforce existing disparities, as when diagnostic algorithms underperform in minority populations. Real-world failures illustrate the stakes. A wrongful arrest in Tennessee was linked to an AI facial recognition error, and Michigan’s MiDAS system falsely accused thousands of citizens of unemployment fraud through automated determinations. Beyond bias, the review identifies weak oversight, regulatory gaps and capture, vendor dependence, geopolitical and socio-political risks, environmental burdens from energy-hungry data centers, cybersecurity vulnerabilities, and persistent legitimacy and trust deficits. Because a single incident can trigger several of these concerns at once, the author treats them as systemic and interconnected rather than isolated technical problems.

Vendor dependence receives particular attention as an emerging political-economy risk. Procurement arrangements often give public agencies limited access to source code, model documentation, training data and audit trails, weakening data portability and institutional autonomy. Over time, functions that should remain under public authority can become dependent on a small number of dominant technology firms, shifting power, expertise and infrastructure ownership toward the private sector. The paper also flags normative drift, in which the ethical principles embedded in AI systems subtly shift over time without detection, and accountability erosion in multi-stakeholder settings where responsibility is so dispersed that no one can be held answerable when systems fail.

On the theoretical side, the framework integrates three traditions: digital era governance, which explains the movement toward data-driven, reintegrated public services; polycentric governance, drawn from Elinor Ostrom’s work on multiple overlapping centers of authority; and adaptive governance, which emphasizes adjustment under uncertainty. The author extends digital era governance for the AI era by treating hybrid human-AI judgment as an institutional design issue rather than a technical choice. Evidence cited from recent research suggests that human-AI ensembles can improve decision-making and reduce bias, but only when humans retain decision authority and AI inputs remain explainable, reviewable and contestable, supported by audit trails, bias checks, human override and appeal mechanisms.

The practical core of the paper is a six-step adaptive, ethical and responsible AI governance framework that embeds risk management and ethics-by-design across the AI lifecycle. It begins with policy coordination, aligning goals across agencies, levels of government and stakeholders to prevent fragmented rulemaking. Resource allocation follows, building the technical infrastructure, civil-service expertise and independent testing capacity that many public agencies currently lack. Implementation then deploys AI systems with appropriate human oversight and sector-specific standards, such as clinical validation and bias testing in healthcare. Monitoring and evaluation institutionalizes audits, transparency reports and impact assessments, while feedback and learning introduces regulatory sandboxes, periodic reviews and participatory mechanisms that let governance evolve with the technology. The cycle closes with policy adjustment, recalibrating legal and organizational arrangements, including procurement rules, to rebalance public-private power.

To show how the framework works in practice, the study applies it illustratively to two very different contexts: New York City as a decentralized smart city and Singapore as a centralized smart nation. New York has built coordination through its Office of Technology and Innovation, an AI Action Plan, and the earlier Automated Decision Systems Task Force, and in late 2025 the City Council passed the GUARD Act establishing an independent Office of Algorithmic Accountability. Singapore coordinates through its Smart Nation structures, the National AI Strategy 2.0 and the Model AI Governance Framework, supported by GovTech’s shared infrastructure. The comparison reveals a striking trade-off: Singapore’s centralized architecture achieves coherence and rapid strategy updates but offers fewer channels for direct citizen participation and independent redress, while New York’s fragmented system produces uneven capacity but denser external accountability through legislation, civil society advocacy and open data. The framework, the author stresses, is structure-agnostic; it helps each model locate its own strengths and gaps.

The paper is candid about its limits. The framework is conceptual, derived from an integrative literature review rather than primary empirical research, and the city applications are illustrative rather than evaluative. Jurisdictional variability, bureaucratic inertia and uneven resources mean it requires context-sensitive adaptation, and poorly managed compliance regimes could create procedural burden without improving substantive accountability. Still, the concluding message lands with force: governance arrangements themselves can generate new risks over time, so AI oversight must remain adaptive rather than fixed. Even highly capable systems, including agentic AI, remain forms of narrow intelligence operating within bounded goals, but they are already reshaping decision pathways, and preparing for hypothetical artificial general intelligence should be treated as a stress test for existing institutions. The window for establishing robust guardrails, Kaiser argues, is not a future concern but an immediate one, and the health of democratic governance in smart cities and nations may depend on whether institutions learn as fast as the technologies they are trying to steer.

Subject of Research: Adaptive, ethical and responsible governance frameworks for AI in smart cities and nations

Article Title: Adaptive, ethical and responsible AI governance for smart cities and nations

Article References: Kaiser, Z. R. M. A. (2026). Adaptive, ethical and responsible AI governance for smart cities and nations. Discover Cities, 3(1), Article 172. https://doi.org/10.1007/s44327-026-00349-2

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00349-2

Keywords: AI governance, smart cities, agentic AI, algorithmic bias, digital era governance, polycentric governance, vendor dependence, public trust, ethics-by-design, New York City, Singapore, adaptive governance

The stakes could hardly be higher. Mangroves are among the most carbon-dense forests on Earth, storing vast quantities of carbon in waterlogged soils that can persist for millennia. When these forests are cleared or degraded, that carbon escapes as carbon dioxide, and the coastline loses a natural buffer against storms and erosion. Indonesia holds the world’s largest mangrove estate, making its forests a linchpin of both national climate commitments under the Paris Agreement and global blue carbon strategies. Yet mangroves across Southeast Asia continue to be converted for aquaculture, agriculture, and coastal development, driven by pressures that conventional top-down regulation has struggled to contain.

The researchers framed their investigation within a Social-Ecological Systems approach, an analytical tradition associated with scholars such as Elinor Ostrom that treats ecosystems and human institutions as an inseparable whole. Rather than asking only how much carbon the mangroves store, the team asked who governs them, what pressures drive their change, and what incentives could align community behavior with conservation. To answer these questions, they deployed a mixed-methods design combining biophysical field measurement, socio-institutional surveys, carbon economic valuation, and a Driver-Pressure-State-Impact-Response, or DPSIR, analysis.

The biophysical results reveal a forest under strain but still enormously valuable. Although mangrove extent in the study area has declined, the remaining stands retain substantial carbon stocks. By comparing carbon stocks across multiple time points, the team estimated an annualized carbon-stock change of 10,496.29 metric tons of CO2 equivalent per year, equivalent to roughly 2.98 tons of CO2 equivalent per hectare per year. That figure represents both a warning and an opportunity: it quantifies the emissions risk if degradation continues, and it defines the volume of avoided emissions that could be monetized if the forest is protected.

Translating carbon into money is where the model becomes genuinely novel. Using voluntary carbon market prices, the researchers calculated a potential Carbon Economic Value of between IDR 839.7 million and IDR 1.68 billion per year for the regency’s mangroves. In a region where local government budgets are thin and livelihoods depend heavily on coastal resources, that stream of revenue could fund restoration, monitoring, and community development. Crucially, the study links this valuation to Indonesia’s recent regulatory architecture, including the 2022 law on central-regional financial relations and a 2025 presidential regulation on carbon economic value instruments, suggesting the model is not a theoretical exercise but something that could be implemented under existing law.

The second pillar of the model is Sasi, the customary system of periodic harvest closures and resource restrictions practiced across Maluku for generations. Under Sasi, community leaders and customary institutions declare certain areas, species, or seasons off-limits, enforced through social sanction and ritual rather than police power. The study’s socio-institutional analysis found striking support for this approach: 91.7 percent of respondents favored integrating Sasi into formal mangrove governance. That level of consensus matters because conservation interventions imposed without local legitimacy frequently fail, while rules rooted in shared norms tend to be self-enforcing and resilient over decades, as earlier research on Sasi’s institutional endurance in Central Maluku has documented.

The third pillar addresses a chronic weakness of community-based conservation: money. Ecological Fiscal Transfers are budget allocations from higher levels of government to local governments, or from local governments to villages, weighted by ecological indicators such as forest area or protected status. The concept, already piloted in parts of Indonesia and elsewhere in Southeast Asia, rewards jurisdictions for keeping ecosystems intact. In the proposed model, performance-based fiscal incentives would flow to communities and villages that maintain mangrove cover and carbon stocks, creating a recurring financial reason to uphold Sasi closures and resist conversion pressure. The researchers identified these incentives, alongside restoration programs, marine protected areas, and community participation, as the most promising management responses emerging from their DPSIR analysis.

That DPSIR framework also clarified the threats. Land-use change, resource utilization, and coastal development emerged as the principal pressures associated with mangrove change in East Seram. This pattern mirrors regional trends documented across Southeast Asia, where mangrove deforestation between 2000 and 2012 was driven largely by aquaculture expansion and plantation agriculture. By mapping the causal chain from drivers to pressures to impacts, the study gives policymakers a diagnostic tool: each pressure can be matched to a specific response, whether a fiscal incentive, a customary closure, or a protected area designation, rather than a one-size-fits-all decree.

What makes the model adaptive, in the authors’ framing, is that the three components feed back into one another. Carbon valuation generates revenue; fiscal transfers distribute that revenue to the communities whose customary institutions actually enforce protection; and Sasi provides the social legitimacy and low-cost enforcement that neither markets nor bureaucracies can supply alone. Monitoring of carbon stocks then becomes a performance metric that recalibrates the fiscal flows, allowing the system to adjust as conditions change. This loop echoes the principles of adaptive co-management developed in the resilience literature, in which knowledge generation, bridging organizations, and social learning allow governance to evolve alongside the ecosystem it manages.

The implications extend well beyond a single regency. Indonesia’s Nationally Determined Contributions rely heavily on land-based and coastal emission reductions, and mangrove protection is among the cheapest and most effective natural climate solutions available. The authors argue that their evidence-based framework could be replicated in other tropical coastal regions with similar social-ecological conditions, particularly where customary tenure and local institutions remain strong. For the global blue carbon community, the East Seram case demonstrates something increasingly recognized but rarely operationalized: that carbon markets and fiscal policy succeed only when they are anchored in the institutions communities already trust. If the model moves from paper to practice, the ancient rhythm of Sasi closures could become, quite literally, a line item in the climate ledger.

Subject of Research: Adaptive mangrove governance integrating customary Sasi institutions, blue carbon valuation, and ecological fiscal transfers in East Seram Regency, Indonesia

Article Title: An Adaptive Mangrove Governance Model Integrating Sasi, Carbon Economic Valuation, and Ecological Fiscal Transfer for Mangrove Management in Maluku, Indonesia: Evidence from East Seram Regency

Article References: IE, S., Retraubun, A. S. W., Tetelepta, J. M. S., Tupan, C. I., & Rahman, R. (2026). An Adaptive Mangrove Governance Model Integrating Sasi, Carbon Economic Valuation, and Ecological Fiscal Transfer for Mangrove Management in Maluku, Indonesia: Evidence from East Seram Regency. Environmental Management, 76(10), Article 327. https://doi.org/10.1007/s00267-026-02630-x

Image Credits: AI Generated

DOI: 10.1007/s00267-026-02630-x

Keywords: mangroves, blue carbon, Sasi, adaptive governance, ecological fiscal transfer, carbon economic value, Indonesia, Maluku, climate mitigation, social-ecological systems, DPSIR, coastal management

The core argument is deceptively simple. Conventional evaluation frameworks treat urban nature-based solutions as collections of measurable assets—a square meter of green roof, a cubic meter of stormwater retention, a degree Celsius of cooling—and then ask whether these assets deliver expected ecosystem services. This asset-based logic fits neatly into cost-benefit analyses, engineering performance metrics, and municipal reporting cycles. But it systematically obscures the relational dimensions of adaptation: who participates in designing and maintaining green infrastructure, how communities attach meaning to restored landscapes, how knowledge flows between residents and planners, and how institutional trust shapes whether interventions are sustained beyond pilot phases. Relationality, in this framing, is not a soft supplement to hard metrics but a constitutive property of urban socio-ecological systems.

Technically, the paper draws on insights from relational ontology and socio-ecological systems theory, fields that view entities not as discrete objects with fixed attributes but as nodes defined by their connections and interactions. In a relational view, a bioswale is not merely soil, vegetation, and drainage capacity; it is an artifact produced through municipal procurement rules, community stewardship agreements, hydrological dynamics, and the ecological history of the site. Evaluating only the physical performance of the bioswale captures a fraction of its adaptive significance. The relational lens asks instead how the intervention reconfigures relationships—between the city and its water, between neighbors who co-maintain a rain garden, between planners and the communities whose neighborhoods are transformed.

This perspective has profound implications for how success is defined. Traditional indicators of nature-based solution performance—runoff reduction, biodiversity indices, temperature moderation—remain important, but they are incomplete. An evaluation framework centered on relationality would additionally assess the quality of participation in planning processes, the distribution of decision-making power, the strength of stewardship networks, and the degree to which interventions strengthen or erode place attachment and social cohesion. These relational outcomes are not incidental benefits; the authors argue they are often the mechanisms through which adaptive capacity is actually built. A city that greens a riverbank without engaging the surrounding community may achieve hydrological targets while leaving its adaptive capacity unchanged or even diminished.

The urgency of this argument reflects real-world patterns in urban adaptation practice. Across many cities, nature-based solutions have been criticized as vehicles of green gentrification, in which environmental improvements raise property values and displace the very residents who are most vulnerable to climate hazards. An asset-based evaluation would record a successful intervention: trees planted, heat island effect reduced, amenity value created. A relational evaluation would ask different questions: whose relationships to the neighborhood were disrupted, which communities gained or lost access to decision-making, and how institutional relationships between city agencies and low-income residents evolved through the process. Only the second framing reveals the equity dynamics that increasingly determine whether adaptation is just.

Relational evaluation also changes the temporal scope of assessment. Conventional metrics are typically measured at project completion or over short monitoring windows, reflecting funding cycles and political timelines. But relational processes—trust building, learning networks, the maturation of stewardship arrangements—unfold over years and decades. Urban forests, for example, accrue their cooling and carbon benefits slowly, and the social institutions that maintain them often take even longer to consolidate. The paper suggests that evaluation must become longitudinal and iterative, treating evaluation not as a terminal audit but as an ongoing dialogue that feeds learning back into governance. In this sense, relationality connects directly to adaptive management: the capacity of a city to learn from its interventions is itself a relational achievement.

Methodologically, the shift toward relationality demands a pluralistic toolkit. Quantitative indicators of social network structure, participation rates, and longitudinal wellbeing surveys can be combined with qualitative approaches—ethnography, participatory mapping, and community-based monitoring—that capture the texture of human-nature relationships. Co-production of evaluation criteria with affected communities is central: rather than importing evaluation frameworks designed by distant experts, relational evaluation begins with the question of whose values and knowledge count. This does not mean abandoning rigor; it means redefining rigor to include validity across multiple ways of knowing, from hydrological modeling to Indigenous and local ecological knowledge.

The implications extend to policy and finance. Global frameworks such as the Sendai Framework for Disaster Risk Reduction, the Paris Agreement’s adaptation goals, and the emerging biodiversity agenda all call for robust adaptation evaluation, yet most national reporting remains anchored in asset-based metrics. If relational outcomes were incorporated into adaptation finance criteria, funders could reward projects that build durable stewardship institutions and equitable governance, not merely those that deliver the cheapest cubic meter of retention. Municipal audit offices, development banks, and philanthropic funders all have leverage to institutionalize relational indicators, and the paper’s argument suggests that failing to do so creates a systematic blind spot in global adaptation accounting.

Ultimately, the argument is a call to recentrate the human and ecological bonds that make adaptation possible. Climate change is often described as a crisis of physics and chemistry, but its impacts and remedies are mediated through relationships: between cities and their watersheds, between institutions and residents, between present decisions and future generations. Urban nature-based solutions offer a rare opportunity to strengthen those relationships while delivering concrete climate benefits. Whether that opportunity is realized depends on whether evaluation evolves from a technical scorecard into a relational practice—one that sees green infrastructure not as a product to be verified, but as a living set of connections to be understood, nurtured, and sustained.

Subject of Research: Relational approaches to evaluating climate adaptation through urban nature-based solutions

Article Title: Relationality must be at the heart of climate adaptation evaluation: insights from urban nature-based solutions

Article References: Goodwin, S., Alda-Vidal, C., Amorim-Maia, A. T., Lewis, W., Loroño, M., & Olazabal, M. (2026). Relationality must be at the heart of climate adaptation evaluation: insights from urban nature-based solutions. npj Urban Sustainability. https://doi.org/10.1038/s42949-026-00460-8

Image Credits: AI Generated

DOI: 10.1038/s42949-026-00460-8

Keywords: climate adaptation, nature-based solutions, urban sustainability, relationality, evaluation frameworks, green infrastructure, socio-ecological systems, climate resilience, urban planning, adaptive governance, green gentrification, community stewardship