Search Results (207)

This study introduces a robust global time-series forecasting model developed to estimate CO₂ emissions across diverse regions worldwide. The model employs a deep learning architecture with multiple hidden layers, ensuring both high predictive accuracy and temporal stability. Our methodology integrates innovative training strategies and advanced optimization techniques to effectively handle heterogeneous time-series data. Emphasis is placed on the critical role of accurate and stable forecasts in supporting evidence-based policy-making and promoting environmental sustainability. This work contributes to global efforts to monitor and mitigate climate change, in alignment with the United Nations Sustainable Development Goals (SDGs). Full article

The escalating threat of climate change has intensified the global urgency to accurately predict carbon dioxide (CO₂) emissions for sustainable development, particularly in developing economies experiencing rapid industrialization and globalization. Traditional Extreme Learning Machines (ELMs) offer rapid learning but often yield unstable performance due to random parameter initialization. This study introduces a novel hybrid model, Red-Billed Blue Magpie Optimizer-tuned ELM (RBMO-ELM) which harnesses the intelligent foraging behavior of red-billed blue magpies to optimize input-to-hidden layer weights and biases. The RBMO algorithm is first benchmarked on 15 functions from the CEC2015 test suite to validate its optimization effectiveness. Subsequently, RBMO-ELM is applied to predict Indonesia’s CO₂ emissions using a multidimensional dataset that combines economic, technological, environmental, and globalization-driven indicators. Empirical results show that the RBMO-ELM significantly surpasses several state-of-the-art hybrid models in accuracy (higher R²) and convergence efficiency (lower error). A permutation-based feature importance analysis identifies social globalization, GDP, and ecological footprint as the strongest predictors underscoring the socio-economic influences on emission patterns. These findings offer both theoretical and practical implications that inform data-driven Artificial Intelligence (AI) and Machine Learning (ML) applications in environmental policy and support sustainable governance models. Full article

The pattern of labour underutilisation is complex and multifaceted, but research has been focused on unemployment. To explore socio-economic demographics of other forms of labour underutilisation, this study investigates the concept of ‘hidden workers’ using the latest data from the Household, Income and Labour Dynamics in Australia (HILDA) study. Hidden workers are composed of three categories, namely those who are unemployed but actively seeking employment; working part-time but willing and able to work full-time; and not working but are willing and able to work under the right conditions. Analysis of HILDA data from 2022 reveals (i) a significant discrepancy in the incidence of unemployed and hidden workers across various socio-economic factors, and (ii) a pronounced age and gender differences among hidden workers, which is not easily discernible from standard unemployment descriptive statistics. Effective labour market policy depends on accurately identifying the different types of hidden workers and their social determinants. This study offers valuable insights to support more inclusive policies for hidden workers, who are often overlooked. Full article

This study examines the impact of the Beijing–Tianjin–Hebei (BTH) coordinated development policy on the regional industrial network structure, with a focus on the significance of electricity consumption data in social network analysis (SNA). Utilizing a gravity model integrated with electricity consumption data, this research employs centrality analysis and Lambda analysis to compare changes in the steel industry network before and after policy implementation. The findings reveal that traditional models relying solely on indicators such as population and Gross Domestic Product (GDP) fail to comprehensively capture regional economic linkages, whereas incorporating electricity consumption data enhances the model’s accuracy in identifying core nodes and latent connections. Post policy implementation, the centrality of Beijing and Tianjin increased significantly, reflecting their transition from production hubs to centers for research and development (R&D) and management, while Shijiazhuang’s pivotal role diminished. This study also uncovers a “core–periphery” structure in the BTH urban network, where core cities (Beijing, Tianjin, and Shijiazhuang) dominate resource allocation and information flow, while peripheral cities exhibit uneven development. These results provide a scientific basis for optimizing regional coordinated development policies and underscore the critical role of electricity consumption data in refining regional economic analysis. Incorporating electricity consumption data into the gravity model significantly enhances its explanatory power by capturing hidden economic ties and improving policy evaluation, offering a more accurate and dynamic assessment of regional industrial linkages. Full article

Amid growing environmental concerns and an increasing push for sustainable development, countries in the Middle East and North Africa (MENA) region have taken proactive steps toward green growth, energy transition, and technological innovation. As a result, this study examines the effects of green growth, energy transition, technological innovation, financial inclusion, and urbanization on environmental sustainability in the Middle East and North Africa (MENA) region. Moreover, this study breaks new ground by exposing the hidden environmental costs of financial inclusion, urbanization, and technological innovation in the MENA region’s development trajectory, thereby providing compelling evidence for rethinking sustainability through an integrated approach that aligns economic ambition with ecological responsibility. Data for the studied variables were sourced from the World Bank database covering the period 1990 to 2021. The results show that green growth and energy transition significantly reduce CO₂ emissions, supporting the idea that economic expansion aligned with environmental priorities can contribute to ecological improvement. However, the impact of technological innovation is statistically insignificant, indicating that innovation in the region has not yet translated into meaningful environmental gains, possibly due to the dominance of non-green or industrial-focused innovation. Financial inclusion is found to increase CO₂ emissions, likely by facilitating greater access to credit and financial services that fuel energy-intensive consumption and production activities. Similarly, urbanization also contributes to rising emissions, reflecting the unsustainable nature of urban growth in many MENA region. Based on this study, we advocate for a coordinated regional approach to climate and energy policy, underpinned by shared governance and collective action. Full article

The construction of a fence in the sea made of bamboo sticks along the coastal areas of Tangerang, Indonesia, caused controversy and many public debates in most Indonesian media. The case is, however, not unique. It provides a means to pose three questions related to the following topics: (1) which controversies and contradictions between formal procedures and informal practices related to land and sea rights exist; (2) which values and perceptions of the involved stakeholders play a role in these controversies and contradictions; and (3) which kinds of boundary work or boundary objects could resolve these controversies and contradictions. The theoretical embedding for the subject lies in the theories of territory and space on the one hand and formal institutional models of land and sea on the other. The analytical model used to evaluate the controversies and contradictions is McKinsey’s 7S model, while the data used are extracted from journalistic public media reports and social media. The results show a significant discrepancy between the values connected to formal and informal territorial claims, as well as a lack of enforcement capacity to address this discrepancy. Instead, the policy response exhibits an excessive and uncontrolled discretionary space for all stakeholders to pursue their own interests. The theoretical novelty is that institutional models governing territorial sea and land rights, restrictions and responsibilities need to be aligned and connected based on detecting where and how the values of affected stakeholders can be harmonized, rather than enforcing a unilateral system of values of disconnected systems (of either land or sea). The policy implementation implications are to create stricter procedural steps when providing building permits in coastal areas, with better enforcement and stricter control. Soft governance campaigns should raise awareness of what is allowed and required for coastal building permits and reclamations. Additionally, there could be quicker, more thorough inspections of emerging or hidden practices of non-approved fencing and non-approved occupation of coastal land and sea. Full article

The global increase in early-onset cancers among adolescents and young adults has happened at the same time as the rise in the consumption of ultra-processed foods (UPFs). Far beyond their poor nutritional quality, UPFs are increasingly seen as Trojan horses, complex biological agents that interfere with many functions of the human organism. In this review, we utilise the Trojan horse model to explain the quiet and building health risks from UPFs as foods that seem harmless, convenient, and affordable while secretly delivering endocrine-disrupting chemicals (EDCs), causing chronic low-grade inflammation, altering the microbiome, and producing epigenetic alterations. We bring together new proof showing that UPFs mess up hormonal signals, harm the body’s ability to fight off harmful germs, lead to an imbalance of microbes, and cause detrimental changes linked to cancer. Important components, such as bisphenols and phthalates, can migrate from containers into food, while additional ingredients and effects from cooking disrupt the normal balance of cells. These exposures are especially harmful during vulnerable developmental periods and may lay the groundwork for disease many years later. The Trojan horse model illustrates the hidden nature of UPF-related damage, not through a sudden toxin but via chronic dysregulation of metabolic, hormonal, and genetic control. This model changes focus from usual diet worries to a bigger-picture view of UPFs as causes of life-disrupting damage. Ultimately, this review aims to identify gaps in current knowledge and epidemiological approaches and highlight the need for multi-omics, long-term studies and personalised nutrition plans to assess and reduce the cancer risk associated with UPFs. Recognising UPFs as a silent disruptor is crucial in shaping public health policies and cancer prevention programs targeting younger people. Full article

The shipping industry plays a significant role in China’s and the global pursuit of carbon neutrality, and it is essential to be cautious about the potential risks associated with the green paradox. This study incorporates Goal Setting Theory and Value Expectations Theory into the analytical framework of the green paradox and tests this framework through a strategic game research design. The study finds that, first, the green paradox of shipping companies presents hidden characteristics, and the loss caused by coping strategies is a necessary risk to be vigilant about. Second, the green paradox of shipping companies is mainly caused by the decision-makers’ goal perception of accessibility. Moreover, due to the motivation of long-term acceptance of green subsidies, decision-makers will delay the carbon neutrality process. Third, policies need to adopt a gradient increasing quota management strategy, and be accompanied by a variety of policy tools to reduce the risk of the green paradox. This study opens the theoretical “black box” of market expectations and provides a solution to reduce the risk of the green paradox. Full article

In a climate of pervasive teacher shortages, initiatives have focused on attracting new teachers to the profession, with hardest-to-staff schools more likely to fill vacancies with early-career teachers, including those with conditional status. In Australia, workforce policy prioritises induction and mentoring to support transition to the profession and improve retention. This paper aims to understand mentor teacher experiences in hardest-to-staff schools, where a growing cohort of inexperienced teachers increases the need for mentoring. The analysis is based on data from semi-structured interviews conducted with teachers in six schools across two Australian states, as part of a larger project exploring work experiences of teachers in hardest-to-staff schools. In addition to formal mentoring, our findings illustrate that in these schools, informal and incidental mentoring is widespread. Further, the iterative nature of novice teacher induction creates a sense of ambivalence in longer-serving teachers. While experienced teachers find reward in supporting early-career colleagues, the hidden labour inherent to constant incidental mentoring encroaches on the time available to manage their own workload, sometimes leading to frustration and even resentment. We conclude that while mentoring is crucial with so many new entrants to the profession, policymakers should be aware of the labour associated with increased incidental mentoring to avoid unintended consequences for teachers who find themselves in the position of supporting growing numbers of new staff. Full article

The short-term scheduling of pumped-storage hydropower plants is characterised by high dimensionality and nonlinearity and is subject to multiple operational constraints. This study proposes an intelligent scheduling framework that integrates an Atomic Orbital Search (AOS)-optimised Long Short-Term Memory (LSTM) network with the Deep Deterministic Policy Gradient (DDPG) algorithm to minimise water consumption during the generation period while satisfying constraints such as system load and safety states. Firstly, the AOS-LSTM model simultaneously optimises the number of hidden neurons, batch size, and training epochs to achieve high-precision fitting of unit flow–efficiency characteristic curves, reducing the fitting error by more than 65.35% compared with traditional methods. Subsequently, the high-precision fitted curves are embedded into a Markov decision process to guide DDPG in performing constraint-aware load scheduling. Under a typical daily load scenario, the proposed scheduling framework achieves fast inference decisions within 1 s, reducing water consumption by 0.85%, 1.78%, and 2.36% compared to standard DDPG, Particle Swarm Optimisation, and Dynamic Programming methods, respectively. In addition, only two vibration-zone operations and two vibration-zone crossings are recorded, representing a reduction of more than 90% compared with the above two traditional optimisation methods, significantly improving scheduling safety and operational stability. The results validate the proposed method’s economic efficiency and reliability in high-dimensional, multi-constraint pumped-storage scheduling problems and provide strong technical support for intelligent scheduling systems. Full article

The study analyses at the information level the impact of the main economic indicators on migration and access to services in the European Union, using methods specific to intelligent information systems. The research is based on the correlations between gross value added (GVA), gross fixed capital formation (GFCF), greenhouse gas emissions (GHGE), health expenditure (SHA11), and migration rates (MIGR). The applied methodology includes attribute distribution analysis, identification of hidden patterns through clustering algorithms (K-Means and Expectation-Maximisation) and training of classifiers using regression decision trees with linear leaf models (M5P) corresponding to interdependent data processing and integration modules, exploratory analysis module, machine learning and decision-making modules, oriented to support public policies through explainable scenarios and predictive-evaluative structures. The results highlight the superiority of the EM model in detecting relevant clusters and the usefulness of M5P trees in highlighting complex economic influences on population mobility. The study proposes the integration of these methods into an intelligent analysis framework aimed at reducing disparities and optimising socio-economic sustainability. The EM model demonstrated a superior ability to detect subgroups within the dataset, revealing four distinct clusters with specific characteristics. Furthermore, the M5P tree analysis allowed the extraction of significant non-linear relationships between economic variables and the migration phenomenon. The study emphasises the importance of public policies aimed at reducing regional economic disparities and increasing social and economic sustainability. By integrating these results into a well-structured information system, it provides a robust analytical framework that supports policy makers and researchers in designing effective public policies on population mobility and its related economic impact in the EU Member States. Full article

Despite remarkable quantitative growth in Application Programming Interface (API) call volume, Korea’s Open Banking Platform faces a critical qualitative imbalance. This paper investigates this hidden challenge, revealing a significant divergence between quantitative metrics and qualitative indicators. Through time-series analysis of registered accounts and user data, alongside examining financial institution and FinTech dynamics, I identify decelerating platform growth and constrained user base expansion. While API calls exploded, registered account and user growth lagged substantially. Platform growth exhibits player bias, and user base expansion has stagnated despite increased accounts-per-user ratios. These findings indicate that Korea’s Open Banking Platform’s sustainability is threatened by qualitative imbalances masked by quantitative success. I advocate for a shift to data-driven governance, moving beyond API call volume-centric metrics to qualitative indicators focused on user and account base assessment. I call for data-driven policy innovation to foster a more balanced and sustainable platform ecosystem, addressing growth slowdown and user base limitations. Full article

Language-in-education policies often serve hidden political and economic agendas, and thus language policy research must examine policies beyond official state discourse. This article critically analyzes Morocco’s Language Alternation Policy (LAP), introduced in 2019, using the historical–structural approach. It examines the broader historical context and structural factors that shape the adoption and implementation of LAP. While the official policy discourse frames LAP as an egalitarian reform aimed at promoting balanced multilingualism by alternating instructional media in science education, its de facto implementation reveals a stark contradiction. The ideological underpinnings of LAP are the resurgence of French as the exclusive medium of instruction in science and technology classrooms. This policy undercuts a decades-long Arabization of science and the promotion of the Amazigh language, as well as denying Moroccans the potential advantages of learning English. The disparity between official policy discourse and implementation reveals the influence of France’s neocolonial agenda, exercised through Francophonie, international clientelism, and financial patronage. Through implementing LAP to align with France’s interests in Morocco, French-trained political actors undermine the country’s decolonization efforts and preserve the long-standing socioeconomic privileges of the francophone elite. We analyze how LAP functions ideologically to resolidify France’s cultural and linguistic hegemony and reinforce pre- and post-independence linguistic and social inequalities. Full article

Background: Food allergies represent a growing public health concern, with accidental exposures in dining establishments posing significant risks. Although various guidelines and interventions have been proposed, practical implementation remains challenging. Objectives: This narrative review aims to synthesize current evidence on major hazards in allergen management within dining settings and to evaluate emerging solutions designed to protect allergic consumers. Methods: A comprehensive literature search of peer-reviewed articles, surveillance reports, guidelines, and policy documents published in April 2025 was undertaken in PubMed, Scopus, and Web of Science. Studies were selected based on relevance to allergen management in dining establishments. An inductive thematic synthesis was performed, and a basic quality appraisal was conducted to prioritize stronger evidence. Results: Three major hazard themes—hidden allergens, cross-contamination during food preparation, and poor communication between staff and allergic consumers—were identified from the reviewed literature. Emerging interventions included enhanced staff training, improved allergen labeling practices, and the application of digital technologies such as smartphone apps and rapid allergen detection devices. However, inconsistencies in implementation and a lack of systematic validation limit the effectiveness of these approaches. Conclusions: Mitigating food allergy risk in dining establishments requires an integrated approach that combines strict kitchen controls with continuous staff education, transparent labeling, clear communication, and supportive policies. While promising interventions exist, more research is required to validate their effectiveness and to encourage standardized, widespread implementation to ensure the safety of individuals with food allergies. Full article

In the context of an ever-evolving global economy, ASEAN companies face dynamic systemic risk that reshapes their financial interrelationships. This study examines the transmission of these risks using advanced graph theory techniques, particularly the measurement of eigenvector centrality based on Euclidean distances, combined with a hidden model that incorporates macroeconomic variables, such as GDP. The research focuses on identifying critical nodes within the corporate network, evaluating their contagion potential—both in terms of reinforcing resilience and amplifying vulnerabilities—and analyzing the influence of external factors on the network’s structure and behavior. The findings offer an innovative framework for managing systemic risk and provide strategic guidelines for the formulation of economic policies in emerging ASEAN markets. Full article