Cognitive Sustainability

Road transport safety and children’s cognitive attitudes

Viktoria Otvos — 2025-12-27

This paper summarizes evidence on road transport safety with a focus on children’s cognitive attitudes and pedestrian behaviour. It integrates findings on attention, visual processing, executive functions, risk perception, knowledge–behaviour transfer, and environmental complexity. Consistent evidence indicates that developmental limitations in attention and processing speed constrain safe crossing decisions in younger children. Knowledge-focused education alone does not reliably improve real-world behaviour, and behavioural training targeting procedural skills yields modest safety improvements. Built environment features – particularly traffic speed and volume, as well as visual clutter – systematically shape both perceived and objective safety outcomes. The paper concludes with implications for training, urban design, and family practices, and outlines directions for future research.

Safety Management Systems in Road Transport: Opportunities and Challenges in Shared Mobility

Hazem Al-mahamid — 2026-03-16

Safety Management Systems (SMS) have long been applied in high-risk sectors such as aviation and rail, but their adaptation to road transport remains underexplored. This paper examines the potential of SMS as a proactive framework for improving safety in road transport, with particular attention to shared auto vehicles. It traces the origins and principles of SMS, evaluates its benefits and limitations, and analyzes case studies from public transit, shared mobility platforms, and international experiences. The findings show that SMS can strengthen safety culture, enhance hazard identification, and support continuous monitoring, yet challenges such as fragmented governance, inconsistent driver practices, and technological barriers limit its effectiveness. The paper argues that stronger policy commitment, regulatory oversight, and institutional coordination are essential for successful implementation, alongside platform-level measures such as driver training, fatigue management, and data-driven monitoring. The distinctive contribution of this study lies in extending SMS from traditional high-risk sectors to the fragmented and rapidly evolving context of road transport and shared mobility, clarifying both the opportunities and systemic challenges of such adaptation for policymakers, regulators, and platform operators.

Limitations of using hydrogen as a sustainable fuel in gas turbines

Peter Kondor — 2026-03-28

Hydrogen is increasingly regarded as a potential low-carbon fuel for gas turbine applications due to its ability to eliminate carbon dioxide emissions at the point of combustion. Despite this advantage, the large-scale implementation of hydrogen in gas turbines faces several significant technical and economic limitations. This study examines the limitations of hydrogen applicability in gas turbines by reviewing the relevant literature. One of the primary challenges arises from hydrogen’s combustion characteristics, including its high flame speed and wide flammability range, which increase the risk of flame flashback and combustion instability. These issues necessitate substantial modifications to conventional gas turbine combustor designs and advanced control systems. In addition, hydrogen combustion typically results in higher flame temperatures compared to conventional hydrocarbon fuels, leading to increased formation of nitrogen oxides (NO_x). Meeting strict emission regulations requires complex mitigation strategies, such as lean premixed combustion or diluent injection, which can reduce efficiency and raise system complexity. Material compatibility is another critical concern, as hydrogen can cause embrittlement in metallic components, potentially compromising the structural integrity and long-term reliability of turbine systems. Furthermore, hydrogen’s low volumetric energy density poses challenges for fuel storage and delivery, requiring high-pressure or large-volume storage solutions that are not easily compatible with existing gas turbine infrastructure. Finally, the economic feasibility of hydrogen-fueled gas turbines is constrained by the high cost of low-carbon hydrogen production and the limited availability of hydrogen transport and distribution infrastructure.

Persistent Core–Periphery Innovation Dynamics in Italy: Evidence against Long-Run Convergence

Valerio Gambacurta — 2026-03-28

This study investigates the relationship between economic growth, agglomeration, and innovation within the framework of endogenous growth theory and economic geography. Building on Philippe Martin's theoretical approach, the paper examines how the spatial concentration of economic activities influences innovation dynamics and regional development. The study pursues two main objectives: first, to measure the spatial distribution of innovation across Italian regions; second, to assess whether regional dynamics between 2021 and 2023 indicate convergence or confirm the persistence of core–periphery asymmetries. The empirical analysis uses national and macro-regional data from Eurostat, including GDP at current market prices, a measure of agglomeration, and Gross Domestic Expenditure on Research and Development (GERD) as a proxy for innovation intensity. The relationships among these variables are explored through descriptive and graphical analysis, as well as convergence tests and spatial autocorrelation analysis. The results show that, although Italy experienced economic growth and a moderate increase in agglomeration during the period under consideration, innovation intensity remained largely stagnant. Significant regional disparities persist, with the Northern and Central regions maintaining higher innovation levels than Southern Italy and the Islands. Overall, the findings suggest that recent regional dynamics show only limited signs of convergence, while confirming the persistence of a core–periphery spatial structure in the Italian economy.

Politics of knowledge: A questionable legal innovation

Stephan Kühnel — 2026-03-29

German legal practitioners have tried to address complaints about professional exams. Two Supreme Court rulings suggest that a prediction of a candidate's future success in a professional examination cannot be verified or rebutted by a court or anyone else, but only by the individual examiner. In contrast to the apparent understanding of legal professionals, this type of knowledge is not an "unknown territory"; it is traditionally covered in research, e.g., in psychology or human resource management. It also undermines internationally recognised principles of sound assessment governance, such as validity, reliability, transparency, and fairness, as emphasised in UNESCO's global guidance on learning assessments and in OECD frameworks for evaluation and quality assurance in education systems. Situating these issues within a broader sustainability perspective, this paper argues that assessment regimes form part of society's knowledge infrastructure, and their integrity is essential for both cognitive sustainability – the long‑term capacity of institutions to maintain transparent, evidence‑based and error‑correcting knowledge practices – and for the realisation of Sustainable Development Goals. In particular, SDG 4 (Quality Education) requires equitable, valid and reliable assessment mechanisms as a foundation of fair and merit‑based professional pathways. At the same time, SDG 16 (Peace, Justice and Strong Institutions) calls for accountable, transparent and trustworthy institutional processes in all sectors, including regulated professions. Assessment systems that shield examiner judgment from external scrutiny impede these sustainability commitments by eroding public trust, weakening institutional resilience, and preventing the correction of systematic errors. This paper draws on a range of disciplines to highlight the decisions and errors made in practice. In addition, an International Education standard by the International Federation of Accountants (IFAC) is used to contrast findings. The paper thereby contributes to cognitive sustainability by examining how assessment systems can either sustain or undermine the long‑term resilience and transparency of societal knowledge practices.

Artificial Intelligence and Sustainability: A Conceptual Framework for System-Level Impact Assessment

Adrienn Csernovszky — 2026-03-31

Artificial intelligence (AI) is rapidly emerging as a general-purpose technology with far-reaching implications for sustainable development. While AI applications are increasingly deployed across sectors such as healthcare, energy systems, urban management, and education, their overall sustainability impacts remain uncertain and often contradictory. Existing research typically examines isolated effects of AI within individual sustainability pillars, which limits the ability to understand systemic interactions, feedback loops, and long-term consequences. This study introduces a conceptual analytical framework designed to assess the sustainability impacts of artificial intelligence across environmental, economic, and social dimensions, extended by an additional individual-level pillar. The framework defines a set of AI Impact Groups (AIG) that translate technological capabilities into system-level functions, including perception, learning, strategic foresight, coordination, and risk detection. In addition, the model introduces key input parameters – AI intensity, adoption level, autonomy, quality of use, and system quality – that influence how AI capabilities translate into sustainability outcomes. By linking AI capabilities, system-level functions, and sustainability pillars, the proposed framework enables a more integrated assessment of both opportunities and risks associated with AI deployment. The model highlights how AI impacts propagate across domains and may generate both short-term benefits and long-term systemic risks, such as rebound effects, technological dependence, or skill erosion. The framework provides a foundation for future scenario analysis, sector-specific impact assessment, and interdisciplinary collaboration aimed at understanding and governing AI-driven sustainability transitions.