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With the increasing frequency of digital economic activities on a global scale, internet governance has become a pressing issue. Traditional multilateral approaches to formulating internet governance rules have struggled to address critical challenges such as privacy leakage and low global internet defense capabilities. To tackle these issues, this study integrates SAR data processing and interpretation using AI techniques with the development of governance rules through international agreements and multi-stakeholder mechanisms. This approach aims to strengthen privacy protection and enhance the overall effectiveness of internet governance. This study incorporates differential privacy protection laws and cert-free cryptography algorithms, combined with SAR data analysis powered by AI techniques, to address privacy protection and security challenges in internet governance. SAR data provides a unique layer of spatial and environmental context, which, when analyzed using advanced AI models, offers valuable insights into network patterns and potential vulnerabilities. By applying these techniques, internet governance can more effectively monitor and secure global data flows, ensuring a more robust defense against cyber threats. Experimental results demonstrate that the proposed approach significantly outperforms traditional methods. When processing 20 GB of data, the encryption time was reduced by approximately 1.2 times compared to other methods. Furthermore, satisfaction with the newly developed internet governance rules increased by 13.3%. By integrating SAR data processing and AI, the model enhances the precision and scalability of governance mechanisms, enabling real-time responses to privacy and security concerns. In the context of the Global Digital Compact, this research effectively improves the standards, rules, and practical pathways for internet governance. It not only enhances the security and privacy of global data networks but also promotes economic development, social progress, and national security. The integration of SAR data analysis and AI techniques provides a powerful toolset for addressing the complexities of internet governance in a digitally connected world. Full article

As governments pursue digital transformation to improve service delivery and administrative efficiency, AI chatbots have emerged as a promising innovation in smart public administration. However, their adoption remains limited, particularly in transitioning countries where institutional, organizational, and technological conditions are complex and evolving. This study aims to empirically examine the key aspects, challenges, and strategic implications of AI chatbots’ adoption in public administration of Uzbekistan, a transitioning government in Central Asia. The study offers a novel contribution by employing an extended technology–organization–environment (TOE) framework. Data were collected through a survey among 501 public employees and partial least squares structural equation modeling was used to analyze data. The results reveal that perceived usefulness, compatibility, organizational readiness, effective accountability, and ethical AI regulation are key enablers, while system complexity, traditional leadership, resistance to change, and concerns over data management and security pose major barriers. The findings contribute to the literature on effective innovation in public administration and provide practical insights for policymakers and public managers aiming to effectively implement AI solutions in complex governance settings. Full article

In the digital economy era, artificial intelligence, big data, and 5G are widely applied across various industries. The deep integration of digitalization and traditional sectors has been facilitated by this trend, which has injected new momentum into industrial development. In this context, this paper employs panel data from 29 Chinese provinces that span the years 2017 to 2023. This paper transcends the constraints of current research by integrating the digital economy with the export of new energy vehicles. Furthermore, this paper provides a regional analysis of this impact, thereby contributing to the existing literature. The following are the conclusions: (1) The export of new energy vehicles is substantially stimulated by the development of the digital economy. (2) Exports are indirectly facilitated by the digital economy, which promotes technological innovation and financial services. (3) The digital economy shows a significantly greater impact on the export of new energy vehicles in the eastern and inland areas than in other regions. Based on these discoveries, the paper suggests four critical policy recommendations: expanded openness, technological innovation, intelligent digital marketing, and government support. The objective is to foster the sustainable growth of China’s new energy vehicle export trade. This paper offers theoretical support for the sustainability of Chinese enterprises’ competitiveness in the international market. It also provides policymakers and industry stakeholders with practical advice. Full article

This study examines the intersection of environmental, social, and governance (ESG) disclosure (operationalized through sustainability reporting), corporate financial performance, and the behavioral dynamics of herding in capital structure decisions among non-financial firms in five ASEAN countries. As ESG and sustainability finance gain prominence in addressing climate change and climate risk, understanding the behavioral factors that relate to ESG adoption is crucial. Employing a quantitative approach, this research utilizes a purposive sample of 125 non-financial firms from Indonesia, Malaysia, the Philippines, Singapore, and Thailand, gathered from the Bloomberg Terminal spanning 2018–2023. Managerial Herding Ratio (MHR) is used to assess herding behavior, while Sustainability Report Disclosure Index (SRDI) measures ESG disclosure. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multigroup Analysis (MGA) were applied for data analysis. This research finds that while sustainability reporting enhances return on assets (ROA) and Tobin’s Q, it does not significantly relate to net profit margin (NPM). The findings also confirm that herding behavior—where companies mimic the financial structures of peers—moderates the relationship between sustainability reporting and performance outcomes, with leader firms gaining more from transparency efforts. This highlights the double-edged nature of herding: while it can accelerate ESG adoption, it may dilute the strategic depth of climate action if firms merely follow rather than lead. The study provides actionable insights for regulators and corporate strategists seeking to strengthen ESG finance as a driver for climate resilience and long-term stakeholder value. Full article

This study examines the extent to which smart cities are expressions of nationally homogeneous development trends by way of an analysis of their structural characteristics from a multivariate viewpoint. Drawing on data from the International Institute for Management Development IMD Smart City Index 2024, we find a sample of 102 cities across the world clustering along six key dimensions of smartness: mobility, environment, government, economy, people, and living. The aim is to examine if cities within a country have similar profiles and, if so, to what degree such similarity translates to other macro-level institutional, political, and cultural conditions. Our results verify a tight correspondence between city profiles and national contexts, implying that macro-level governance arrangements, policy coordination, and institutional capacity are pivotal in influencing local smart city development. Planned centralised countries possess more uniform city characteristics, while decentralised nations possess more variant urban policies. This study contributes to international debate regarding smart cities by empirically identifying national directions of urban innovation. It offers pragmatic inputs for policymakers that aim to align local efforts with overall sustainable development agendas. Moreover, this study introduces a novel application of Linear Discriminant Analysis (LDA) to classify smart city profiles based on national models. While the analysis yields high classification accuracy, it is important to note that the sample is skewed toward cities from the Global North, potentially limiting the generalisability of the results. Full article

Rapid urbanization exerts immense pressure on cultivated land. Among these, slope-classified cultivated land (referring to cropland categorized by slope gradients) is especially vulnerable to fragmentation due to its ecological fragility, challenging utilization, and critical role in soil conservation and sustainable agriculture. This study explores the spatiotemporal dynamics and driving mechanisms of slope-classified cultivated land fragmentation (SCLF) in Guangdong Province, China, from 2000 to 2020. Using multi-temporal geospatial data, machine learning interpretation, and socioeconomic datasets, this research quantifies the spatiotemporal changes in SCLF, identifies key drivers and their interactions, and proposes differentiated protection strategies. The results reveal the following: (1) The SCLF decreased in the Pearl River Delta, exhibited “U-shaped” fluctuations in the west and east, and increased steadily in northern Guangdong. (2) The machine learning interpretation highlights significantly amplified synergistic effects among drivers, with socioeconomic factors, particularly agricultural mechanization and non-farm employment rates, exerting dominant influences on fragmentation patterns. (3) A “core–transitional–marginal” protection framework is proposed, intensifying the land use efficiency and ecological resilience in core areas, coupling land consolidation with green infrastructure in transitional zones, and promoting agroecological diversification in marginal regions. This research proposed a novel framework for SCLF, contributing to cultivated land protection and informing differentiated spatial governance in rapidly urbanizing regions. Full article

Large language models (LLMs) now match or exceed human performance on many open-ended language tasks, yet they continue to produce fluent but incorrect statements, which is a failure mode widely referred to as hallucination. In low-stakes settings this may be tolerable; in regulated or safety-critical domains such as financial services, compliance review, and client decision support, it is not. Motivated by these realities, we develop an integrated mitigation framework that layers complementary controls rather than relying on any single technique. The framework combines structured prompt design, retrieval-augmented generation (RAG) with verifiable evidence sources, and targeted fine-tuning aligned with domain truth constraints. Our interest in this problem is practical. Individual mitigation techniques have matured quickly, yet teams deploying LLMs in production routinely report difficulty stitching them together in a coherent, maintainable pipeline. Decisions about when to ground a response in retrieved data, when to escalate uncertainty, how to capture provenance, and how to evaluate fidelity are often made ad hoc. Drawing on experience from financial technology implementations, where even rare hallucinations can carry material cost, regulatory exposure, or loss of customer trust, we aim to provide clearer guidance in the form of an easy-to-follow tutorial. This paper makes four contributions. First, we introduce a three-layer reference architecture that organizes mitigation activities across input governance, evidence-grounded generation, and post-response verification. Second, we describe a lightweight supervisory agent that manages uncertainty signals and triggers escalation (to humans, alternate models, or constrained workflows) when confidence falls below policy thresholds. Third, we analyze common but under-addressed security surfaces relevant to hallucination mitigation, including prompt injection, retrieval poisoning, and policy evasion attacks. Finally, we outline an implementation playbook for production deployment, including evaluation metrics, operational trade-offs, and lessons learned from early financial-services pilots. Full article

We estimate measures of economies of scale and scope for a sample of 48 Canadian universities that produce multiple outputs. Estimates have not been previously attempted for Canadian universities to our knowledge. Declining financial support from provincial governments makes finding cost efficiencies a priority for policy makers. Our approach features two useful innovations: by using panel data for 2011–2019, instead of a cross-section for a single year, there is more variation in the variables to estimate a multi-product trans-log cost function; and we consider the appropriateness of using research funding as a measure of research output by alternatively using publication counts. We did not find economies of scale at any university size but did find ray economies of scale up to 60% of the median university size. Economies of scope were evident up to roughly 1.2 times the median university size. No significant differences in results were found between using publication counts or research funding. Small institutions that cater to different outputs could be merged into comprehensive institutions. The lack of economies of scope for Canada’s larger universities suggests that they could be broken up into smaller specialized institutions if cost efficiencies are a priority. Full article

Smallholder farmers play a pivotal role in food production and rural development in South Africa. However, their productivity is often constrained by reliance on rainfed agriculture and the underutilisation of innovative technologies such as irrigation systems. This study assessed the impact of innovative irrigation system (IIS) use on crop yield among smallholder crop farmers (SCFs) in Mbombela Local Municipality. Focusing on vegetables and agronomic crop producers. Primary data was collected from 308 SCFs using a structured questionnaire through descriptive and cross-sectional survey design. A Probit regression model was used to estimate the probability of using an IIS, while Propensity Score Matching (PSM) estimated the average treatment effect on the treated (ATT) in terms of yield. The results reveal that age group (p = 0.080), main source of off-farm income (p = 0.042), and high input costs (p = 0.006) significantly determined IIS use. Impact analysis confirms that users of IISs achieved higher yields than non-users. The study concludes that innovative irrigation technologies can significantly improve smallholder productivity. It recommends that policymakers and government bodies prioritise scaling up access to IIS, introduce subsidies or low-interest financing schemes to alleviate the IIS usage costs, and strengthen extension services to provide targeted training on irrigation scheduling, system maintenance, and water-use efficiency. Full article

This study compares the Stackelberg game model and the entropy weight method for allocating intercity water rights in Heilongjiang Province (2014–2021). The entropy method objectively determines indicator weights, while the Stackelberg framework simulates leader–follower interactions between the water authority and users to balance efficiency and satisfaction. Under the same total water rights cap, the Stackelberg scheme achieves a comprehensive benefit of CNY 14,966 billion, 4% higher than the entropy method (CNY 14,436 billion). The results and comprehensive benefits of the two schemes are close to each other in the cities of Qiqihaer, Daqing, Hegang, etc., but the allocation method of the game theory is more in line with the practical needs and can meet the water demand of each region, and the entropy right method is more useful for the cities of Jiamusi, Jixi, and Heihe, while for other cities the water rights allocation appeared to be unreasonable. While the entropy approach is transparent and data-driven, it lacks dynamic feedback and may under- or over-allocate in rapidly changing contexts. The Stackelberg model adapts to varying demands, better aligning allocations with actual needs. We discuss parameter justification, sensitivity, governance assumptions, and potential extensions, including hybrid modeling, climate change integration, stakeholder participation, and real-time monitoring. The findings provide methodological insights for adaptive and equitable water allocation in regions with strong regulatory capacity. Full article

As artificial intelligence systems increasingly govern public safety operations, concerns over algorithmic fairness and legal compliance intensify. This study introduces a scenario-based evaluation framework (SCRAM) that simultaneously measures regulatory conformity and bias risks in AI-enabled surveillance. Using license plate recognition (LPR) systems in Türkiye as a case study, we simulate multiple operational configurations that vary decision thresholds and data retention periods. Each configuration is assessed through fairness metrics (SPD, DIR) and a compliance score derived from KVKK (Türkiye’s Personal Data Protection Law) and constitutional jurisprudence. Our findings show that technical performance does not guarantee normative acceptability: several configurations with high detection accuracy fail to meet legal and fairness thresholds. The SCRAM model offers a modular and adaptable approach to align AI deployments with ethical and legal standards and highlights how policy-sensitive parameters critically shape risk landscapes. We conclude with implications for real-time audit systems and cross-jurisdictional AI governance. Full article

This study critically examines the integration of artificial intelligence (AI) into education through the lens of Marx’s theory of alienation, engaging with contemporary critiques of digital capitalism and academic labour. Drawing on an exploratory survey of 395 educators in Northern Cyprus, a context of early-stage AI adoption, the paper identifies four distinct forms of alienation exacerbated by AI: from the product of academic labour, from the educational process, from professional identity (species-being), and from interpersonal relations. Findings suggest that while educators who view AI more positively tend to report lower levels of alienation, particularly with respect to their pedagogical outputs, this association is tentative due to the low reliability of the AI perception scale (Cronbach’s α = 0.42). The results, therefore, serve as hypothesis-generating rather than conclusive. Situating the empirical findings within broader critiques by Noble, Hall, Preston, and Komljenovic, the study highlights how algorithmic governance, commercial platform logics, and data-driven performance regimes threaten teacher autonomy, creativity, and relationality. The paper concludes with a call for participatory governance, ethical oversight, and human-centred design to ensure that AI integration supports, not supplants, educators. In doing so, it contributes to critical debates on the ethical sustainability of digital education under conditions of intensifying automation. Full article

Small and Medium Enterprises (SMEs) are vital to economic growth, innovation, and job creation across Sub-Saharan Africa (SSA). Women entrepreneurs are key contributors to this sector, yet they face persistent barriers to accessing finance, which constrain their business growth and broader economic participation. This study investigates the role of financial institutions in closing the financing gap for women-owned SMEs and assesses the effectiveness of various financing mechanisms, including traditional banking, micro-finance, fintech innovations, and government-backed credit schemes. Adopting a quantitative approach, this study utilises structured surveys with women SME owners across multiple SSA countries. Supplementary secondary data from sources such as the World Bank and national financial statistics provide additional context. Econometric modelling and Structural Equation Modelling (SEM) are employed to identify key factors influencing loan accessibility, such as collateral requirements, interest rates, financial literacy, and the regulatory environment. Findings reveal that high collateral demands and interest rates remain major obstacles, particularly for smaller or informal women-led enterprises. Financial literacy emerges as a critical enabler of access to credit. While fintech solutions and digital lending platforms show promise in improving access, issues around infrastructure, regulation, and trust persist. Government-backed schemes also contribute positively but are hindered by implementation inefficiencies. This study offers practical recommendations, including the need for harmonised regional credit reporting systems, gender-responsive policy frameworks, and targeted financial education. Strengthening digital infrastructure and regulatory support across SSA is essential to build inclusive, sustainable financial ecosystems that empower women entrepreneurs and drive regional development. Full article

This study investigates how Environmental, Social, and Governance (ESG) principles can be effectively integrated into the Balanced Scorecard (BSc) framework within local government organizations (LGOs) to enhance strategic planning and sustainability performance. Addressing a gap in the literature on ESG–BSc integration in the public sector, particularly in the Greek context, the study employs a dual-method approach. First, a bibliometric analysis of 3053 academic publications (1993–2025) was conducted using Scopus data to assess the evolution and thematic focus of ESG and BSc research. Second, a structured questionnaire—comprising both closed- and open-ended questions—was administered to 17 administrative staff members of a Greek LGO in 2024. This expert sample provided insights into strategic planning practices, ESG awareness, and performance management barriers. The findings reveal low levels of ESG–BSc application, a limited strategic capacity, and institutional resistance. In response, the study proposes a novel, context-sensitive ESG-integrated BSc model tailored for small municipalities, emphasizing stakeholder participation, operational simplicity, and the alignment with national sustainability policies. The model serves as a practical tool to support public sector performance measurement, bridging the gap between sustainability goals and local governance strategy. Full article

In turbine blade environments, the combination of blade curvature and accelerating flow gives rise to streamwise pressure gradients (SPGs), which substantially impact coolant–mainstream interactions. This study investigates the effect of SPGs on film cooling performance using Large Eddy Simulation (LES) for a shaped cooling hole at a density ratio of $\mathrm{DR}=1.5$ under two blowing ratios: $M=0.5$ and $M=1.6$. Both favorable pressure gradient (FPG) and zero pressure gradient (ZPG) conditions are examined. LES predictions are validated against experimental data in the high blowing ratio case, confirming the accuracy of the numerical method. Comparative analysis of the time-averaged flow fields indicates that, at $M=1.6$, FPG enhances wall attachment of the coolant jet, reduces boundary layer thickness, and suppresses vertical dispersion. Counter-rotating vortex pairs (CVRPs) are also compressed in this process, leading to improved downstream cooling. At $M=0.5$, however, the ZPG promotes greater lateral coolant spread near the hole exit, resulting in superior near-field cooling performance. Instantaneous flow structures are also analyzed to further explore the unsteady dynamics governing film cooling. The Q criterion exposes the formation and evolution of coherent vortices, including hairpin vortices, shear-layer vortices, and horseshoe vortices. Compared to ZPG, the FPG case exhibits a greater number of downstream hairpin vortices identified by density gradient, and this effect is particularly pronounced at the lower blowing ratio. The shear layer instability is evaluated using the local gradient $Ri$ number, revealing widespread Kelvin–Helmholtz instability near the jet interface. In addition, Fast Fourier Transform (FFT) analysis shows that FPG shifts disturbance energy to lower frequencies with higher amplitudes, indicating enhanced turbulent dissipation and intensified coolant mixing at a low blowing ratio. Full article