Search Results (71)

Environmental governance in the Global South increasingly unfolds within complex socio-technical systems characterized by fragmented institutional capacities, distributed sensing infrastructures, and high socio-ecological uncertainty. In this context, Digital Twins have emerged as a promising paradigm for integrating cyber–physical systems with decision intelligence, yet most implementations remain limited to industrial optimization or urban infrastructure management. This article proposes an architectural framework for Digital Twins oriented toward environmental governance by integrating Artificial Intelligence of Things (AIoT), monitoring networks with complex systems modeling and cybernetic governance principles. Methodologically, the study combines conceptual development grounded in systems science and the Viable System Model (VSM) with an illustrative empirical case study from the Caribbean Mining Corridor in Colombia, where a distributed AIoT environmental monitoring network operates within the Hub Ambiental del Caribe initiative. The proposed architecture links real-time environmental sensing, predictive analytics, and recursive governance structures to enable anticipatory environmental decision-making. The results demonstrate how Digital Twin infrastructures can function as cybernetic platforms that transform fragmented environmental data into actionable decision intelligence across institutional levels. By embedding feedback mechanisms, open environmental data governance, and participatory monitoring capacities, the framework supports adaptive management in highly dynamic socio-ecological contexts. The study concludes that Digital Twins designed as governance infrastructures—rather than purely technical replicas—can significantly enhance environmental decision support systems and foster climate justice-oriented transitions in vulnerable territories of the Global South. Full article

Type 2 diabetes mellitus is a highly prevalent, heterogeneous, and progressive chronic disease. In a large proportion of patients, management is based for many years on lifestyle intervention and non-insulin glucose-lowering therapies. This long pre-insulin phase represents a crucial clinical window, in which timely recognition of metabolic deterioration, therapeutic inertia, treatment response, and individual risk trajectories may substantially influence long-term outcomes. However, routine care is still frequently based on intermittent assessments, delayed treatment adaptation, and limited integration of clinical, biochemical, behavioral, and digital data. Artificial intelligence may offer a clinically relevant opportunity to move from reactive management to anticipatory care in non-insulin-treated type 2 diabetes. Rather than replacing clinical judgment or automating treatment decisions, artificial intelligence can support clinicians by identifying hidden patterns, predicting metabolic worsening, stratifying risk, improving the interpretation of glucose data, and personalizing follow-up intensity and therapeutic timing. In this setting, its most meaningful role may be to reduce the silent interval between early deterioration and clinical action. This narrative review discusses the rationale, current applications, near-future scenarios, and implementation barriers of artificial intelligence in non-insulin-treated type 2 diabetes. Particular attention is given to advanced interpretation of glycemic data, clinical decision support, prediction of treatment failure, remote monitoring, and the potential integration of multidimensional data into more precise and timely care pathways. The review also emphasizes the need for explainable, clinically validated, equitable, and ethically governed artificial intelligence tools that can be realistically embedded into everyday diabetology practice. Full article

Innovation clusters contribute to regional development but face substantial organizational challenges when time-limited public support is reduced or withdrawn. This study examines how membership-based cluster organizations build resilience during such funding transitions. Drawing on organizational resilience theory and conceptualizing clusters as meta-organizations governed through negotiated authority, we conducted a qualitative comparative multiple-case study of nine innovation clusters in Norway and Europe. The empirical material comprises five live semi-structured interviews, four written responses to the same interview guide, and secondary documentary sources. An abductive within-case and cross-case analysis identified four strategic adaptation types: Revenue Diversifiers, Government-Exit Strategists, Survivalists, and Platform Shifters. Cross-case analysis showed that Revenue Diversifiers and Platform Shifters combined multiple revenue mechanisms and service infrastructures, whereas Survivalists remained primarily dependent on public grants and basic membership contributions. The findings show that resilience during predictable funding transitions is strengthened by anticipatory development of member-value propositions, service portfolios, partnerships, and digital infrastructure. The study contributes a typology of post-grant adaptation and explains how negotiated member legitimacy and coordinated service reconfiguration shape resilience in membership-based innovation intermediaries. Full article

Resident-generated service requests are increasingly used in data-driven urban management, but complaint records may reflect uneven administrative visibility rather than objective service needs. This study develops a benchmark-based 311 visibility audit as a diagnostic tool for sustainability governance, examining whether local service conditions become sufficiently visible to support inclusive, anticipatory, and resilient urban management. Using New York City’s 2023 311 records for six complaint categories—Heat/Hot Water, Noise—Residential, Rodent, Water System, Sewer, and Air Quality—the analysis constructs a tract-by-month-by-complaint-type panel and compares tract-level complaint shares with population, housing-related exposure, and rodent inspection benchmarks. The final dataset contains 661,251 geocoded requests across 2243 positive-population census tracts. Results show substantial complaint-type differences in population-benchmark mismatch: Air Quality has the largest exact total variation distance and Noise—Residential the smallest, while under-visible coverage exceeds over-visible coverage in all six categories. Alternative housing benchmarks change selected Heat/Hot Water and Rodent interpretations, and the external rodent check identifies 46 primary blind-spot candidates, narrowed to 17 benchmark-confirmed candidates. The study concludes that 311 systems should be used as diagnostic infrastructures for identifying information blind spots, not as direct measures of urban need or sustainability performance. Full article

Artificial intelligence (AI) is increasingly regarded as a key instrument for sustainability governance, but its role may differ across risk domains. Rather than functioning solely as an anticipatory tool, AI may also develop reactively in response to risk shocks. Using monthly Chinese data from January 2013 to December 2023, this study examines the dual role of AI in sustainability governance by analyzing its time–frequency relationships with climate-related government response and infectious disease-related market attention. The continuous wavelet transform and partial wavelet coherence are employed, with the World Uncertainty Index controlled. The results show that AI-sector development is positively associated with both risk-related signals, but the lead–lag patterns differ substantially. In the climate-response domain, the relationship shifts from risk-signal precedence in 2015–2016 to AI-sector precedence in short-term bands after 2020, suggesting that AI-related capacity may have become increasingly embedded in anticipatory climate governance. In contrast, infectious disease-related attention mainly precedes AI-sector movements, especially during 2017–2018 and the COVID-19 period, indicating a more reactive role of AI in public health risk contexts. These findings do not provide causal evidence that AI directly reduces physical climate risks or epidemiological burdens. Instead, they reveal the dual role of AI as both a response to sustainability-related risk shocks and a potential contributor to forward-looking governance capacity. This study contributes to AI and sustainability governance research by clarifying the conditions and boundaries under which AI shifts from reactive crisis response toward anticipatory risk preparedness. Full article

Autonomous vehicles (AVs) are moving from pilots to regular urban service, yet the speed of large-scale implementation remains uncertain. While prior research emphasizes technological feasibility and adoption, less attention has been paid to the socio-technical dynamics that constrain deployment. This study examines how unintended consequences shape the pace of AV implementation in cities. Drawing on a mixed-methods design combining a structured scoping review with 18 expert interviews, interrelated dynamics are identified across institutional, behavioral, economic-platform, spatial, and normative-societal domains. The findings indicate that implementation speed is not determined by technology alone but emerges from reinforcing feedback loops that generate systemic frictions, including governance lag, demand rebound, spatial bottlenecks, and legitimacy challenges. The study advances a systems-oriented framework that conceptualizes implementation speed as an emergent property of socio-technical dynamics, highlighting the importance of adaptive and anticipatory governance for sustainable urban mobility transitions. Full article

This study analyses the Master Plan for Analysis, Anticipation and Reaction to Natural Disasters of the Valencian Community (Spain) from the perspective of geographical risk literacy. The research adopts a qualitative methodology based on documentary analysis and conceptual assessment of public policy. The corpus includes Valencian institutional documents, Spanish civil protection and education regulations, international disaster risk reduction frameworks and the scientific literature on geography education, risk perception, social vulnerability and school preparedness. The findings show that the plan can strengthen institutional coordination, public communication and preventive culture through its preventive orientation, phased structure, official sources, attention to vulnerable groups, updated cartography and promotion of drills. However, its social effectiveness will depend on transforming protocols and guides into verifiable citizen capacities: identifying hazards, recognizing local exposure, interpreting warnings and maps, selecting safe routes, preparing households and rehearsing decisions in realistic contexts. The article proposes complementing the plan with a geographical literacy programme for territorial resilience based on local risk sheets, school atlases, evaluated drills, community mediation, accessible viewers and public monitoring indicators. The discussion places the Valencian case within international debates on disaster risk reduction, anticipatory governance, people-centred early warning systems and school safety. Because no empirical implementation data are yet available, the contribution is framed as an analytical and design-oriented framework requiring subsequent validation through pilots, surveys, interviews and evaluated drills. Full article

Maritime transportation, a traditionally polluting sector, is engaged in sustainable innovation; advanced detection of marine pollution incidents can help control improvements in this sector. However, coastal and marine environmental governance is increasingly constrained by fragmented monitoring architectures in which environmental, operational, and regulatory datasets remain distributed across institutions and jurisdictions. Comprehensive governance mechanisms that cross the sea–land continuum are limited. Public authorities, port administrations, research institutes, and private operators independently monitor marine pollution, vessel movements, coastal pressures, and urban and ecological risks; however, these data streams remain siloed across organizational, sectoral, and jurisdictional boundaries. The absence of interoperability, real-time exchange, and coordinated analytics generates a gap between monitoring capacity and regulatory effectiveness, resulting in delayed detection of multi-risks, such as pollution incidents. Weak governance and assignment of responsibility and largely reactive enforcement practices further reinforce the problem. Anticipatory interventions are needed to improve coastal and marine sustainability. This paper examines the EMMERA (East Med Cross-border Marine Environmental Risk Assessment through E-Platform Integrated Data Management) platform established by three port authorities as a data-centric intervention designed to address some of these structural limitations. Implemented in port and coastal environments in Cyprus, Greece, and Israel, EMMERA integrates heterogeneous static and dynamic data sources—including satellite observations, administrative records, vessel information, and drone-based monitoring—into a unified operational framework accessible to competent authorities. Through data fusion, cross-validation, and automated anomaly detection combined with targeted drone verification, the platform aims to transform fragmented monitoring streams into coherent, actionable environmental intelligence, strengthening the evidentiary basis for regulatory intervention. The paper presents the platform design and provides a baseline assessment. It argues that EMMERA’s primary contribution lies not in the introduction of additional monitoring tools, but in enabling more effective coastal and marine environmental governance through integrated data infrastructures. EMMERA is proposed as a governance-oriented integrated data infrastructure whose anticipated contribution lies in improving institutional interoperability, risk visibility, and evidence generation for environmental oversight, even as operational effectiveness will require future evaluation following sustained deployment. More broadly, the paper proposes that integrated data architectures can recalibrate environmental governance, shifting emphasis from post hoc documentation toward anticipatory, coordinated, and performance-oriented regulatory practices. Full article

Artificial Intelligence (AI) is increasingly embedded in public governance, raising new challenges for anticipating its societal implications while safeguarding democratic accountability within expanding computational infrastructures. This article examines how anticipatory AI governance can be operationalised in the age of supercomputing through a mixed-methods, multistakeholder study conducted in the Basque Country (Spain). The empirical focus is Gipuzkoa, a devolved historical territory with fiscal autonomy and a rapidly developing advanced-computing ecosystem centred in Donostia–San Sebastián, where regional initiatives are positioning the territory within Europe’s emerging high-performance and quantum computing landscape. The study combines participatory action research involving six civil society organisations, seven provincial directorates, and eleven municipalities with an online citizen survey (N = 911). The findings indicate that anticipatory AI governance is supported through four interrelated governance mechanisms: institutional coordination across administrative levels, multistakeholder participation, territorial public capability, and the strategic embedding of advanced computational infrastructures. Rather than evaluating the governance of supercomputing technologies themselves, the analysis examines governance perceptions, institutional practices, and democratic arrangements associated with these infrastructures. The article’s contribution lies in integrating anticipatory AI governance, territorial governance, and advanced computational infrastructures within a devolved city-regional setting, offering evidence-informed insights for regions seeking to strengthen democratic capacity alongside technological innovation. Full article

This study analyzes the transition toward anticipatory public administration through the use of artificial intelligence (AI). Despite the growing deployment of predictive models, a clear research gap remains regarding the socio-technical prerequisites—such as institutional trust, data infrastructure, and ethical–legal frameworks—that condition their success. To address this, a Systematic Literature Review (SLR) was conducted following the PRISMA 2020 guidelines, retrieving 68 open-access articles published between 2020 and 2025 from the Scopus and Web of Science databases. Data synthesis was performed using descriptive bibliometric mapping and qualitative thematic analysis. The results show that scientific interest has experienced accelerated growth since 2024, highlighting a preference for machine learning systems that automate service delivery. While AI enables significant benefits, including a 40% reduction in budgetary errors and early fraud detection, progress is hindered by algorithmic opacity (“black box” models), the risk of structural bias, and civil servants’ confirmation bias. The study concludes that technological complexity must be subordinated to democratic safeguards. To this end, a research agenda is proposed alongside practical implications, recommending that public institutions implement mandatory independent algorithmic audits, enforce strict interpretability standards in public procurement, and adopt hybrid regulatory frameworks to ensure fair and accountable governance. Full article

School peace, or Schulfrieden, is often portrayed in German education as a neutral condition enabling learning. Yet this study interrogates how peace itself becomes a tool of governance, selectively policing who can safely occupy the classroom. Examining the Berlin Senate’s October 2023 directive, which banned symbols showing solidarity with Palestine, we show that school peace is less about conflict resolution than about shaping affective hierarchies. Fear, anticipation, and symbolic association circulate to mark some bodies as threats while leaving others unexamined. Through the lens of Sara Ahmed’s affective economies and Zembylas’s affective ideology, we argue that the directive transforms political expression into a site of emotional correction, preemptively disciplining marginalized students and rendering their solidarity politically suspect. Peace, in this framing, is primarily rule and order: it secures institutional comfort while curtailing engagement with global injustice. The classroom becomes a laboratory of anticipatory governance, where ethical awareness is unevenly distributed, and dissent is contained before it emerges. By tracing how school peace operates affectively, the study reveals the subtle mechanics by which liberal education reproduces racialized hierarchies under the guise of neutrality. Full article

Population ageing is reshaping the assumptions on which succession law has traditionally rested. This article examines how Spanish succession law responds to this demographic shift through two closely connected dimensions: testamentary capacity and the growing role of succession agreements. The analysis adopts a doctrinal and comparative perspective within the Spanish legal system, taking account of the coexistence of the Spanish Civil Code and several autonomous succession regimes. It argues that testamentary capacity remains governed by a deliberately low and functional threshold, centred on the testator’s actual ability to form and express a testamentary intention at the time of execution, and that notarial ex ante control is central to preserving both autonomy and legal certainty. At the same time, relational vulnerability in later life requires distinct safeguards aimed at preserving testamentary freedom. The article further shows that succession agreements, often viewed as restrictions on testamentary freedom, may also operate as instruments of anticipatory autonomy. The central challenge is to make autonomy effective across time without confusing vulnerability with incapacity, or protection with constraint. Full article

The growing volatility and complexity of global food supply chains have intensified the need for integrated analytical frameworks capable of supporting anticipatory and data-driven decision-making. This article examines how logistics sector observatories can function as strategic intelligence infrastructures for identifying structural tensions and supporting resilience in cold-chain logistics systems. The article introduces the concept of logistics sector observatories as strategic intelligence infrastructures and examines its empirical relevance through a longitudinal analysis of the Spanish cold-chain logistics sector. Empirically, the research draws on a multi-source dataset constructed through the ALDEFE Observatory in collaboration with industry stakeholders over the core study period 2021–2025, encompassing storage capacity, consumption dynamics, energy costs, international logistics indices, and macroeconomic variables. Complementary energy benchmark data for 2019–2025 are used to contextualize electricity cost volatility. Methodologically, the study combines qualitative insights from stakeholder interviews with exploratory quantitative longitudinal analysis. The results suggest severe structural tensions driven by the interaction between rigid capacity constraints and energy cost volatility. The analysis identifies a pattern of persistently high storage occupancy despite substantial energy-price fluctuations. This finding is consistent with the structural inelasticity of cold-chain demand, which reduces operational slack and affects system resilience. Beyond operational resilience, the study highlights the potential contribution of sector observatories to the energy sustainability transition through future sector-level indicators related to energy intensity, refrigeration efficiency, and carbon performance. The study contributes a sector-level, data-driven perspective on visibility, coordination, and anticipatory governance in complex logistics environments. Full article

This study examines how early Soviet culture sought to render time perceptible, governable, and politically productive by translating Futurist “chrontology” into socialist “chronization.” Taking Aleksandr Kusikov’s 1922 polemic on the Berlin journal Veshch (Thing)—edited by Ilya Ehrenburg and El Lissitzky—as its point of departure, it reconstructs a “third-way” Futurism shaped by postrevolutionary transit and exile, in which anticipatory futurity was inseparable from an intensified orientation toward materiality, technique, and constructive making. Against the backdrop of modern heterochrony—where “times” proliferate and synchronization remains contested—the essay traces how Soviet actors devised new units, comparisons, and pedagogies for living in a temporally unstable present. Two case studies anchor the argument. First, the League of Time (1923–1925) promoted multiscalar time-management practices through campaigns, memos, and didactic graphics that treated clocks, routines, and efficiency as instruments of socialist subject-formation. Second, illustrated children’s books translated variegated temporal sensibilities into a pedagogy of images, making futurity legible via “thingful” traces that recoded the past as a catalogue of obsolete objects. Chronization thus appears as an ambitious cultural technology for producing a planned, dynamic, and productive socialist way of being. By foregrounding work, visualization, and material comparison, these projects converted temporal uncertainty into actionable, collective everyday socialist practice. Full article

Hydropower-dominated electricity systems are increasingly exposed to hydroclimatic variability, making anticipatory streamflow information essential for energy security, operational resilience, and sustainable planning. This study develops a transparent monthly early-warning framework for the Paute River basin, Ecuador, a strategically important hydrological system for national hydropower generation. Using a 42-year series of observed and compiled monthly streamflow records from 1984 to 2025 (n = 504), the framework derives seasonal low-flow thresholds (P20 warning and P10 critical) and fits a Seasonal Autoregressive Integrated Moving Average model to log-transformed flows. The resulting lognormal predictive distribution provides point forecasts, prediction intervals, and probabilities of low-flow events. Predictive skill was assessed through a 2016–2025 rolling-origin validation with 120 one-step-ahead forecasts and benchmarks against Error–Trend–Seasonal Holt–Winters and seasonal naive models. The SARIMA-log specification achieved the best point accuracy (MAE = 38.80 m³/s, RMSE = 47.62 m³/s, sMAPE = 32.63%) and modest but useful probabilistic skill (CRPSS = 0.069; Brier Skill Score = 0.169 for Q < P20 and 0.274 for Q < P10). A threshold-sensitivity analysis showed that the 0.15 and 0.30 alert thresholds represent a deliberate trade-off between early detection and false-alarm reduction. For 2026, August displayed the highest low-flow probability (P(Q < P20) = 0.303), triggering a moderate Hydropower Low-Flow Risk Traffic-Light category. The contribution is not a new forecasting algorithm but an operationally auditable integration of seasonal thresholds, probabilistic forecasting, verification, and risk communication for hydropower energy-security governance in the tropical Andes. Full article