Search Results (247)

In Ecuador’s financial system, private banks and savings and credit cooperatives coexist, both playing a key role in financial intermediation and the economic inclusion of traditionally underserved sectors. During the COVID-19 pandemic, these institutions faced unprecedented challenges that tested their adaptability and operational efficiency. In this context, the present study evaluates the technical efficiency of banks and cooperatives in Ecuador over the 2015–2023 period, using a combined approach involving Data Envelopment Analysis (DEA) and mixed linear models (MLMs). A longitudinal and comparative methodology is adopted, allowing for the analysis of efficiency trends over time and the identification of their main structural determinants. The results show that cooperatives exhibit a higher average technical efficiency than banks, as well as greater resilience during the health crisis. The analysis reveals that operating expenses negatively impact efficiency, while equity and social capital show no significant effects. By combining DEA and MLMs, the study offers a more comprehensive and nuanced understanding of the factors influencing efficiency, underscoring the importance of tailored policies and institutional strategies focused on resource optimization and continuous improvement. The study concludes that efficiency does not rely solely on size or asset volume, but rather on managerial capacity and organizational adaptability in complex and changing environments. Full article

While the corporate adoption of artificial intelligence (AI) is accelerating, its environmental consequences remain insufficiently understood, particularly in absolute firm-level energy consumption. The main objective of this study is to empirically determine the causal impact of AI adoption on absolute firm-level energy consumption in Chinese publicly listed companies, with a particular focus on the mediating role of green innovation and the moderating role of digital capabilities. This study provides the first large-scale micro-level evidence on how AI adoption shapes corporate energy use, drawing on panel data from Chinese non-financial listed firms during 2011–2022. We construct a novel AI adoption index via Word2Vec-based textual analysis of annual reports and estimate its impact using firm fixed effects, instrumental variables, mediation models, and multiple robustness checks. Results show that AI adoption significantly reduces total energy consumption, with a 1% increase in AI intensity associated with an estimated 0.48% decrease in energy use. Green innovation emerges as a key mediating channel, while the energy-saving benefits are amplified in firms with advanced digital transformation and IT-oriented executive teams. Heterogeneity analyses indicate more substantial effects among large firms, private enterprises, non-energy-intensive sectors, and firms in digitally lagging regions, suggesting capability-driven and context-dependent dynamics. This study advances the literature on digital transformation and corporate sustainability by uncovering the mechanisms and boundary conditions of AI’s environmental impact and offers actionable insights for aligning AI investments with carbon reduction targets and industrial upgrading in emerging economies. Full article

After the end of the Second World War, Hungary, like other war-torn countries, was left with countless orphaned children. The collapsed state structures were unable to care for them, so only various private or church initiatives—such as Gaudopolis, a children’s home set up by the Lutheran pastor Gábor Szehló—provided a means of survival. However, in East-Central Europe—including Hungary, Poland and Romania—where the Soviet Union had a sphere of influence, the emerging Soviet-style system was aimed at the political re-education of society. Ideological goals categorically excluded the possibility of churches being involved in the care and education of youth beyond the existing legal framework. Any person who engaged in such activities was cracked down upon. This study examines the role and responsibility of church persons in the care and upbringing of orphaned children through the fate of the Roman Catholic priest István Regőczi. In the decades of communist dictatorship, István Regőczi repeatedly reorganised orphanages, where he carried out youth education activities based on principles similar to scouting. The values he imparted to the children—such as the Christian religion, family protection, mutual help and social solidarity—were all values of Christian socialism. However, the communist dictatorship—promoting its own political ideology, Marxism—sought to take control of the upbringing and education of children by nationalising all institutions involved in this activity. Anyone who resisted this—as István Regőczi did—was made impossible in the people’s democracy of the 1950s and 1960s, and his child-saving, educating and teaching activities were prevented, even if the courts sentenced him to longer or shorter prison sentences for the crimes of illegal youth organisation, incitement and the abuse of freedom of association. This study, comparing what is described in István Regőczi’s memoirs with the surviving archival sources, shows how during these terrible decades it was possible to save orphaned, needy children and raise them in a Christian spirit, even against the will of the authorities. Full article

In this study, a new approach was developed for the estimation of optimum parameters (ODP), in terms of materials and design in livestock barns, and for optimal design. For this purpose, two thousand simulations were run using Monte Carlo (MC) techniques and Latin hypercube methods using the Energy Plus program on a 50-head closed dairy farm. In this study, the heat balance in the barn was adapted to Energy Plus using an innovative approach, using heat balance equations according to the ASHRAE Standard. First, data normality was determined using the Shapiro–Wilk (SW) and Kolmogorov–Smirnov (KS) tests. Data on thermal stress duration and energy consumption for dairy cattle welfare were estimated directly from the simulations, and sensitivity (SA) and uncertainty (UA) analyses were conducted. Furthermore, the statistical relationship between thermal comfort and energy consumption was determined using Pearson correlation. The predicted values obtained from the simulations were validated with barn values, and time-series overlay plots and histograms were generated. Furthermore, interpretations of the validation processes were made based on MBE, RSME, and R² statistical values. The study estimated an indoor thermal comfort temperature of 12 °C, and this value was taken into account in the innovatively developed simulations. The estimated optimum design parameters in the study resulted in energy reductions of 25% and 41% for walls and roofs, 48% and 19% for cooling and heating setpoint temperatures, 43% and 37% for window areas, and 75% and 40% for natural and mechanical ventilation, respectively. When the design parameters were evaluated holistically and analyzed in terms of average values, the new simulation model achieved approximately 50% energy savings. We believe that the newly developed approach will guide future planning for countries, the public, and private sectors to ensure animal welfare and reduce energy consumption. Full article

The cold-chain supply of perishable fruits continues to face challenges such as fuel wastage, fragmented stakeholder coordination, and limited real-time adaptability. Traditional solutions, based on static routing and centralized control, fall short in addressing the dynamic, distributed, and secure demands of modern food supply chains. This study presents a novel end-to-end architecture that integrates multi-agent reinforcement learning (MARL), blockchain technology, and generative artificial intelligence. The system features large language model (LLM)-mediated negotiation for inter-enterprise coordination, Pareto-based reward optimization balancing spoilage, energy consumption, delivery time, and climate and emission impact. Smart contracts and Non-Fungible Token (NFT)-based traceability are deployed over a private Ethereum blockchain to ensure compliance, trust, and decentralized governance. Modular agents—trained using centralized training with decentralized execution (CTDE)—handle routing, temperature regulation, spoilage prediction, inventory, and delivery scheduling. Generative AI simulates demand variability and disruption scenarios to strengthen resilient infrastructure. Experiments demonstrate up to 50% reduction in spoilage, 35% energy savings, and 25% lower emissions. The system also cuts travel time by 30% and improves delivery reliability and fruit quality. This work offers a scalable, intelligent, and sustainable supply chain framework, especially suitable for resource-constrained or intermittently connected environments, laying the foundation for future-ready food logistics systems. Full article

In response to the global push for sustainable development, green building certification systems have become a key policy instrument for reducing carbon emissions in the construction sector. In Taiwan, the EEWH (Ecology, Energy Saving, Waste Reduction, and Health) system serves as the primary framework for evaluating building sustainability. However, while government incentives such as floor area ratio (FAR) bonuses aim to encourage adoption, private sector participation remains limited, especially in the residential sector. This study investigates the cost implications and incentive benefits of upgrading green building certification from the Silver level to the Gold level under the EEWH system, using eight collective housing projects in the Taipei metropolitan area as case studies. Through a detailed analysis of certification components, upgrade strategies, and construction cost estimates, this research quantifies the additional costs required for each sustainability indicator and evaluates the alignment between upgrade investments and incentive rewards. The findings reveal that the average cost increase associated with the Silver-to-Gold upgrade ranges between 1% and 3% of total construction costs, with certain design strategies offering high cost-effectiveness. Moreover, the study examines whether the current FAR bonus policy provides adequate motivation for developers to pursue higher certification levels. The results provide valuable insights for policymakers seeking to optimize incentive structures and for developers considering sustainable building investments. Full article

In response to the escalating climate crisis, reducing greenhouse gas emissions (GHG) has become a top priority for both the public and private sectors. The pavement industry plays a key role in this transition, offering innovative technologies that minimize environmental impacts without compromising performance. Among these, the incorporation of recycled tire rubber and warm-mix asphalt (WMA) additives represents a promising strategy to reduce energy consumption and resource depletion in road construction. This study conducts a comparative life cycle assessment (LCA) to evaluate the environmental performance of an asphalt pavement incorporating recycled rubber and a WMA additive—referred to as R-W asphalt—against a conventional hot-mix asphalt (HMA) pavement. The analysis follows the ISO 14040/44 standards, covering material production, transport, construction, and maintenance. Two service-life scenarios are considered: one assuming equivalent durability and another with a five-year extension for the R-W pavement. The results demonstrate environmental impact reductions of up to 57%, with average savings ranging from 32% to 52% across key impact categories such as climate change, land use, and resource use. These benefits are primarily attributed to lower production temperatures and extended maintenance intervals. The findings underscore the potential of R-W asphalt as a cleaner engineering solution aligned with circular economy principles and climate mitigation goals. Full article

Housing affordability is a major determinant of quality of life. Despite the relatively high GDP per capita in Hong Kong (HK) (USD 460,000), about one-third of the population lives in public rental housing (PRH) because they cannot afford private housing. Existing research estimating the benefits of PRH or direct housing supports faces methodological limitations. Addressing this research gap, our study adopts an “in-kind subsidy” approach to estimate the monetary value of PRH, quantifying how much less PRH households might save monthly if they resided in private rental units, after controlling for confounding factors. This paper examines the association of housing types and household savings by applying ordinary least squares (OLS) regression to compare savings among PRH tenants, non-PRH tenants, and mortgage-free homeowners, based on seven rounds of the Household Expenditure Survey data (1989/1990 to 2019/2020). PRH tenants saved significantly more than private housing tenants. In terms of household savings, the value of HK PRH has steadily increased from HKD 4483 in 1999/2000, to HKD 9187 in 2019/2020. For every dollar increase in income, a household would have the propensity to save 0.7 dollars in 2019/2020. Given limited public resources, our findings offer robust evidence regarding the value of public housing. The results underscore the importance of the equitable allocation and effective management of current PRH stock to enhance the upward mobility of low- to middle-income households amid limited housing resources in HK. Full article

Renewable Energy Communities (RECs) are voluntary coalitions of citizens, small and medium-sized enterprises and local authorities, which cooperate to share locally produced renewable energy, providing environmental, economic, and social benefits rather than profits. Despite a favorable European and Italian regulatory framework, their development is still limited in the Member States. To this end, this paper proposes a methodology to identify optimal spatial configurations of RECs, based on proximity criteria and maximization of energy self-sufficiency. This result is achieved through the mapping of the demand, expressive of the energy consumption of residential buildings; the suitable areas for installing photovoltaic panels on the roofs of existing buildings; the supply; the supply–demand balance, from which it is possible to identify Positive Energy Districts (PEDs) and Negative Energy Districts (NEDs). Through an iterative process, the optimal configuration is then sought, aggregating only PEDs and NEDs that meet the chosen criteria. This method is applied to the case study of the Avellino Province in the Campania Region (Italy). The maps obtained allow local authorities to inform citizens about the areas where it is convenient to aggregate with their neighbors in a REC to have benefits in terms of energy self-sufficiency, savings on bills or incentives at the local level, including those deriving from urban plans. The latter can encourage private initiative in order to speed up the RECs’ deployment. The presented model is being implemented in the framework of an ongoing research and development project, titled Renewable Energy Communities Monitoring, Optimization, and Planning (RECMOP). Full article

This study contributes to the broader sustainability discourse by evaluating public knowledge, awareness, and practices regarding air pollution among residents of Hanoi, Vietnam, focusing on its causes, health impacts, and mitigation strategies. A cross-sectional survey was conducted with 521 individuals in suburbs around Hanoi. A multistage sampling technique, combining cluster and simple random sampling, was used for participant recruitment. Three central and three suburban districts of Hanoi were randomly selected as clusters. One individual from each household was invited to participate and answer a structured survey, which assessed perceptions of air pollution, its human-induced causes, recognised health impacts, and individual and community-level mitigation behaviours. Nearly all participants (98.3%) were aware of air pollution, with 65.3% attributing it to human activities and 61.2% recognising specific air pollutants as primary contributors. The majority (93.9%) acknowledged health impacts, citing respiratory infections (55.1%) and sinus issues (51.2%) as prevalent concerns. Vulnerable groups, such as children under 5 (82.3%) and adults over 65 years old (77.4%), were identified as disproportionately affected. Social media (68.9%) and television (58.3%) were the dominant sources of information. Despite a recognition of air pollution’s importance (98.5%), there was limited engagement in systemic sustainability actions, such as supporting renewable energy initiatives. Most participants (84.3%) reported personal mitigation efforts, including energy-saving practices (35.5%) and walking instead of driving a car or bike (35.3%). While awareness of air pollution and its health impacts is high among Hanoi residents, proactive engagement in systemic solutions remains limited. Policymakers should prioritise community-based programs, public–private partnerships, sustainability education, and culturally tailored policy interventions to bridge gaps between awareness and action. Tailored interventions addressing demographic and cultural factors are essential to fostering socio-environmental sustainability in rapidly urbanising contexts. Full article

Cross-docking operations in Eastern and Central European markets face increasing complexity amid persistent uncertainty and inflationary pressures. This study provides the first comprehensive comparative analysis integrating economic efficiency with sustainability indicators across strategic locations. Using mixed-methods analysis of 40 bibliographical sources and quantitative modeling of cross-docking scenarios in Bratislava, Prague, and Budapest, we integrate environmental, social, and governance frameworks with activity-based costing and artificial intelligence analysis. Optimized cross-docking achieves statistically significant cost reductions of 10.61% for Eastern and Central European inbound logistics and 3.84% for Western European outbound logistics when utilizing Budapest location (p < 0.01). Activity-based costing reveals labor (35–40%), equipment utilization (25–30%), and facility operations (20–25%) as primary cost drivers. Budapest demonstrates superior integrated performance index incorporating operational efficiency (94.2% loading efficiency), economic impact (EUR 925,000 annual savings), and environmental performance (486 tons CO₂ reduction annually). This is the first empirically validated framework integrating activity-based costing–corporate social responsibility methodologies for an emerging market cross-docking, multi-dimensional performance assessment model transcending operational-sustainability dichotomy and location-specific contingency identification for emerging market implementation. Findings support targeted infrastructure investments, harmonized regulatory frameworks, and public–private partnerships for sustainable logistics development in emerging European markets, providing actionable roadmap for EUR 142,000–EUR 187,000 artificial intelligence implementation investments achieving a 14.6-month return on investment. Full article

Climate change, the consequences of which have been more intense than ever in the last few decades, makes the need for sustainable transportation even more imperative. The promotion of public transportation and the discouragement of private car use are among the main priorities of sustainable transport planning in modern urban areas. However, the selection of the most appropriate transport project, apart from significant opportunities, is also accompanied by significant challenges, especially under the demand of compromising—often conflicting—social, environmental, and economic criteria, as well as different stakeholders’ interests. The aim of the present paper is to provide decision analysts and policy-makers with a decision-support tool for the prioritization and optimum selection of public transport projects for an urban area within the framework of sustainability. For this purpose, a comprehensive inventory of criteria for the evaluation of urban public transport systems (alternatives), along with a standardized table with the relevant performance of the most common alternatives (i.e., metro, tram, monorail, and BRT) are provided based on international literature review. A multi-criteria decision-aiding methodology based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), allowing for the direct exclusion of an alternative not meeting certain “binding” criteria from further evaluation, thus saving time, effort and cost, taking into account different stakeholders’ interests and preferences, as well as the particularities and special characteristics of the study area, is then proposed and tested through a theoretical case study. Full article

In recent years, online retailing platforms (ORPs) have increasingly introduced private brand (PB) products as a new profit source, reshaping market dynamics and affecting their commission revenues. This shift creates a strategic trade-off for the platform: maximizing PB product profits while maintaining commission income from national brand (NB) retailers. This paper examines the platform’s pricing and quality strategies for PB products, as well as its incentives to introduce them. We develop a game-theoretic model featuring a platform and a retailer, and derive results through equilibrium analysis and comparative statics. Special attention is given to the platform’s strategy when market power is asymmetric and the PB product is homogeneous. The analysis yields three key findings. Firstly, the platform is always incentivized to introduce a PB product, regardless of its brand value. Even when direct profit is limited, the platform can leverage the PB product to increase competitive pressure on the retailer and boost commission revenue. Secondly, when the PB product has low brand value, the platform adopts a cost-saving strategy with low quality for extremely low brand value, and a function-enhancing strategy with high quality for moderately low brand value. Thirdly, when the PB product has high brand value, the platform consistently prefers a function-enhancing strategy. This study contributes to the literature by systematically characterizing the platform’s strategic trade-offs in introducing PB products, highlighting its varied pricing and quality strategies across categories, and revealing the critical role of brand value in supply chain competition. Full article

Edge intelligence is an emerging paradigm generated by the deep integration of artificial intelligence (AI) and edge computing. It enables data to remain at the edge without being sent to remote cloud servers, lowering response time, saving bandwidth resources, and opening up new development opportunities for multi-user intelligent services (MISs). Although edge intelligence can address the problems of centralized MISs, its inherent characteristics also introduce new challenges, potentially leading to serious security issues. Malicious attackers may use inference attacks and other methods to access private information and upload toxic updates that disrupt the model and cause severe damage. This paper provides a comprehensive review of multi-user privacy protection mechanisms and compares the network architectures under centralized and edge intelligence paradigms, exploring the privacy and security issues introduced by edge intelligence. We then investigate the state-of-the-art defense mechanisms under the edge intelligence paradigm and provide a systematic classification. Through experiments, we compare the privacy protection and utility trade-offs of existing methods. Finally, we propose future research directions for privacy protection in MISs under the edge intelligence paradigm, aiming to promote the development of user privacy protection frameworks. Full article

Semantic communication based on deep learning technology extracts the meaning of the transmitted source to achieve spectrum resource savings and enhance anti-interference capabilities. The black-box nature of deep learning models increases the difficulty for eavesdroppers in intercepting information, thereby significantly improving transmission security under normal circumstances. However, eavesdroppers may utilize model theft techniques to acquire models that have similar functionality and performance to the victim’s model. Moreover, even if all users are legitimate, the private information of a specific legitimate user should not be accessible to other users, and this issue also requires attention. As the lowest layer of wireless communication, the physical layer has been proven to enhance the security performance of communication systems by leveraging the randomness of physical channels. Unlike traditional communication, which transmits bit streams, semantic communication transmits semantic streams. Therefore, this paper converts semantic streams into bit streams to analyze the security performance of semantic communication using traditional communication metrics. Specifically, this paper assumes that the performance of the eavesdropper’s stolen model is consistent with that of the original model (although this assumption is impossible) and conducts a derivation analysis of the Secrecy Outage Probability (SOP). Full article