Search Results (159)

The emergence and dissemination of antimicrobial resistance (AMR) are driven by complex, interconnected mechanisms involving microbial communities, environmental factors, and human activities, with climate change playing a pivotal and accelerating role. Rising temperatures, altered precipitation patterns, and other environmental disruptions caused by climate change create favorable conditions for bacterial growth and enhance the horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs). Thermal stress and environmental pressures induce genetic mutations that promote resistance, while ecosystem disturbances facilitate the stabilization and spread of resistant pathogens. Moreover, climate change exacerbates public and animal health risks by expanding the range of infectious disease vectors and driving population displacement due to extreme weather events, further amplifying the transmission and evolution of resistant microbes. Livestock agriculture represents a critical nexus where excessive antibiotic use, environmental stressors, and climate-related challenges converge, fueling AMR escalation with profound public health and economic consequences. Environmental reservoirs, including soil and water sources, accumulate ARGs from agricultural runoff, wastewater, and pollution, enabling resistance spread. This review aims to demonstrate how the Mediterranean’s strategic position makes it an ideal living laboratory for the development of integrated “One Health” frameworks that address the mechanistic links between climate change and AMR. By highlighting these interconnections, the review underscores the need for a unified approach that incorporates sustainable agricultural practices, climate mitigation and adaptation within healthcare systems, and enhanced surveillance of zoonotic and resistant pathogens—ultimately offering a roadmap for tackling this multifaceted global health crisis. Full article

This study proposes a novel framework for allocating CO₂ emission rights within the Yangtze River Delta (YRD) urban agglomeration, tackling the inter-city CO₂ transmission dynamics frequently neglected in conventional allocation models. Current emission allocation methods fail to capture the spatial spillover effects of CO₂ emissions driven by atmospheric transport, resulting in potential inequities. Leveraging the WRF model to simulate carbon emissions across 27 cities, we develop a two-stage network Data Envelopment Analysis (DEA) model that integrates both emission generation and governance capacities. Our findings highlight significant inter-city CO₂ transmission, with the wind direction and speed playing a pivotal role in emissions spread. In contrast to traditional models, our approach considers the regional interdependence of emissions, enhancing both fairness and efficiency in the allocation process. The results indicate that cities with stronger governance systems, including green technology investments and effective air quality management, are rewarded with higher carbon allowances. Moreover, our model demonstrates that policies prioritizing environmental governance over raw emission levels can foster long-term sustainability. This work provides a comprehensive methodology for achieving a balanced allocation of emission rights that integrates economic growth, environmental management, and equity considerations within complex urban agglomerations. Full article

Elderly welfare institutions in Taiwan have experienced multiple severe fire incidents, with smoke inhalation accounting for the majority of fatalities. Hot smoke can rapidly propagate through interconnected ceiling spaces, complicating evacuation for residents with limited mobility who depend heavily on caregiving staff and external responders. Field inspections conducted in this study indicate that 82% of residents require assisted evacuation, underscoring the critical role of early detection, staff-mediated response, and effective smoke control. Drawing on disaster management theory, this study examines key determinants of fire safety performance in elderly welfare institutions, where caregiving staff are primarily trained in medical care rather than fire safety. A total of 64 licensed institutions in Tainan City were investigated through on-site inspections, structured checklist-based surveys, and statistical analyses of fire protection systems. In addition, a comparative review of building and fire safety regulations in Taiwan, the United States, Japan, and China was conducted to contextualize the findings. Using the defense-in-depth framework, this study proposes a three-layer fire safety strategy comprising (1) prevention of fire occurrence, (2) rapid fire detection and early suppression, and (3) containment of fire and smoke spread. From a sustainability perspective, this study conceptualizes fire safety in elderly welfare institutions as a problem of risk governance, illustrating how defense-in-depth can be operationalized as a governance-oriented framework for managing fire and smoke risks, safeguarding vulnerable older adults, and sustaining the resilience and continuity of long-term care systems in an aging society. Full article

Wildfires increasingly threaten the operation and stability of regional socio-economic systems, where infrastructure, population, and environmental conditions are tightly interconnected. To enhance operational efficiency and strengthen community resilience, this study develops an integrated optimization framework for wildfire evacuation system design based on mixed-integer programming. The model simultaneously determines the locations of primary and secondary shelters and establishes both main and backup evacuation linkages, forming a dual-stage structure that ensures continuous accessibility even under disrupted conditions such as road blockages or fire spread. Wildfire risk indices derived from topographic and environmental data are incorporated to support risk-aware and balanced shelter allocation. A case study of Uiryeong County, South Korea, demonstrates that the proposed framework effectively improves evacuation efficiency and system reliability, producing spatially coherent and adaptive evacuation plans under diverse disruption scenarios. The findings highlight how operation optimization can enhance socio-economic system resilience and sustainability when facing large-scale environmental disruptions. Full article

Hydrogen is key player in the energetic transition towards a more sustainable society as a very versatile energy carrier. Nevertheless, hydrogen storage represents the main limitation to the spread of a hydrogen driven economy on a small and medium scale. Clearly, achieving this requires a balance among material engineering, system optimization, and techno-economic assessments to optimize performance, safety, and scalability. In this work we briefly and critically discuss the progress in hydrogen storage focusing on the necessity to create a bridge to overcome the actual limitations. We explore the most recent advancement in the field drawing a picture of the complex scenario of hydrogen storage in the framework to the transition to a net zero or carbon negative society providing an updated opinion on the challenges addressed and those still to be solved. Full article

This study examines how sovereign risk shapes the financial performance of sustainable investments, using the MSCI Emerging Markets ESG Index as a reference. The analysis covers 24 emerging and frontier economies from Latin America, Asia, the Middle East, and Eastern Europe during 2016–2025, a period marked by major global disruptions such as the COVID-19 crisis and post-2022 financial tightening. Sovereign risk dimensions are extracted through Principal Component Analysis (PCA) applied to sovereign CDS spreads, identifying a systemic component linked to global shocks and a structural component associated with domestic fundamentals and governance quality. These factors are integrated into a quantile regression framework alongside control variables—oil prices, interest rates, and global equity indices—capturing key macro-financial transmission channels. Results show a nonlinear, quantile-dependent relationship: systemic risk intensifies ESG losses under adverse conditions, while structural improvements support gains in upper quantiles. Control variables behave as expected, confirming the macro-financial sensitivity of ESG performance. The findings reveal that ESG returns are state-dependent and strongly influenced by sovereign credit dynamics, especially in emerging markets where external shocks and institutional fragility intersect. Strengthening sovereign governance and integrating risk diagnostics into ESG assessments are essential steps to enhance resilience and credibility in sustainable finance. Full article

Bovine mastitis is a prevalent infectious disease in dairy cattle, causing inflammation, pain, reduced milk yield, and economic losses. Antibiotic therapy is the mainstay of treatment, yet irresponsible use can lead to the presence of antibiotic residues in milk and contribute to the emergence of antimicrobial resistance (AMR), posing significant risks to public health and food safety. This review aims to provide a comprehensive synthesis of current knowledge on mastitis management, antibiotic use and resulting residues in milk, their public health and environmental impacts, and alternative strategies to reduce antibiotic dependence, framed within a One Health–One Welfare perspective. Antibiotic residues in milk are closely linked to treatment practices, withdrawal period compliance, and regulatory oversight, with prevalence ranging from <1% in some European countries to over 80% in parts of Africa. Residues, particularly from β-lactams, tetracyclines, and quinolones, can disrupt human intestinal microbiota, promote resistant bacterial strains, trigger immunological reactions, and interfere with dairy processing. Environmental contamination through excreted antibiotics further facilitates the spread of resistance. Sustainable alternatives, including probiotics, phytotherapy, vaccines, and improved farm biosecurity, show promise in reducing antibiotic use while maintaining animal welfare and productivity. Antibiotic therapy remains essential for mastitis control, but its consequences on milk safety, public health, and AMR require prudent management. Integrating monitoring, adherence to withdrawal periods, and sustainable alternatives within a One Health–One Welfare framework is critical for ensuring safe, responsible, and environmentally sustainable dairy production. Full article

This paper investigates the effect of Environmental, Social and Governance (ESG) scores and Credit Default Swap (CDS) spreads on the profitability of Halkbank, one of the biggest state-owned banks in Türkiye, an emerging economy. To this end, we employ Non-linear Autoregressive Distributed Lag (NARDL) and Markov Switching Regression (MSR) methods, taking into account non-linear market risks, using Halkbank’s quarterly data consisting of 63 observations for the period 2009Q1–2024Q3. Moreover, to prevent multicollinearity, we aggregate banking-specific and macroeconomic indicators into a single composite index using Principal Component Analysis (PCA). Our MSR findings suggest that ESG scores and CDS spreads negatively affect bank profitability and that these effects are particularly pronounced during periods of high market volatility. Similarly, NARDL findings suggest that ESG scores have asymmetric effects on bank performance, with both positive and negative changes in ESG performance having a negative impact on profitability, and moreover, negative changes have a more negative impact on profitability. This means that the bank’s sustainability initiatives may be costly and negatively affect profitability in the short run, but these effects will be more negative if initiatives deteriorate. Our findings emphasize the need for banks to adopt a gradual ESG approach that enables them to increase their capacity without compromising financial stability and for regulatory structures to have a flexible and sophisticated risk management framework capable of rapidly adapting to different market conditions. Therefore, our study provides valuable insights to sector managers and policymakers regarding the financial implications of sustainability approaches. Full article

As green finance grows, green bonds have become an essential tool for funding sustainable projects. While many studies explore whether green bonds exhibit a “green premium,” existing literature reviews often lack depth, timeliness, and consistent methodology. This paper addresses these gaps by systematically reviewing 70 empirical studies on green premiums published up to 2025, making it the most comprehensive review to date. We organize the literature by region (Global, U.S., Europe, Asia Pacific), market segment, premium dimension, data source, and estimation method, offering a structured framework to analyze diverse findings. Our analysis reveals a consistent negative green premium of −12.44 bps on average across most markets, with European and Asian markets showing higher yield spreads than the U.S. Studies using more recent data report smaller premiums, and larger bond issues tend to have lower premiums. Despite variations in methods and data sources, the overall results are consistent. This paper provides an updated overview of green premium research and offers key insights for investors, issuers, and policymakers on green finance pricing and investment strategies. Full article

The rapid industrialization of global livestock production has intensified the threat of viral epidemics, in which the intestinal, respiratory, and reproductive systems are susceptible to viral attacks. Understanding the mechanism of virus–host interactions will facilitate the development of prevention strategies against highly mutable and fast-spreading pathogens. This review examines recent progress in applying machine learning (ML) and deep learning (DL) to the study and control of animal viral diseases. By analyzing existing research, we show how these techniques improve the prediction of host–microbe interactions, support continuous monitoring of animal health, and accelerate the discovery of drug targets and vaccine candidates. Integrating ML and DL frameworks enables more accurate modeling of complex biological processes and offers new tools for data-driven veterinary science. Nevertheless, challenges remain, including unbalanced datasets, the structural and evolutionary complexity of viruses, and the poor cross-species transferability of predictive models. Future work should emphasize algorithmic designs suited to small-sample, multivariate time series data and promote the development of intelligent systems that unite virology, immunology, and epidemiology. The combination of reinforcement learning for optimizing vaccination strategies and unsupervised learning for detecting emerging pathogens may ultimately lead to adaptive, efficient, and precise systems for disease prevention, supporting both animal health and sustainable livestock development. Full article

This paper presents a multiphysics, Life Cycle Assessment (LCA)-based design optimization framework for an interior permanent-magnet traction motor tailored to electric-vehicle duty. The workflow couples driving cycle realism, electromagnetic–thermal analysis, and life cycle assessment within a unified, computationally efficient process. Representative operating points are extracted from WMTC and ECE cycles using clustering, after which a multi-level Taguchi refinement searches the design space from coarse to fine. A weighted composite objective balances machine cost and life cycle cumulative emissions under hard constraints on torque capability and hotspot temperature. The optimized design satisfies performance and thermal limits while simultaneously reducing both cost and life cycle burden, as confirmed through phase-wise assessment of raw material, use-phase, and end-of-life contributions. Iterative improvements are accompanied by rising signal-to-noise ratios and reduced parameter-level spread, indicating greater robustness to operating variability. Overall, the study demonstrates that an LCA-driven, multiphysics-constrained optimization can deliver sustainable, high-performance IPM designs that are aligned with realistic vehicle operating conditions and readily adaptable to alternative motor and drive architectures. Full article

Background: Mobile antimicrobial resistance genes (ARGs) on plasmids or other elements enable Salmonella Typhimurium to spread resistance across hosts and environments. The emergence of multi-drug resistance (MDR) Salmonella Typhimurium has raised global concern, yet little is reported about these mobile elements from the Thailand pork supply chain, where this risk of transfer to humans remains largely uncharacterized. Methods: Between March 2023 and February 2024, 25 S. Typhimurium isolates were collected from pig carcasses in slaughterhouses and pork swabs from retail markets in northeastern Thailand. Nine representative isolates, sampled across three seasons, were subjected to Illumina whole-genome sequencing. Assemblies were analyzed for sequence types, phylogenetic relationships, antimicrobial resistance (AMR) determinants, plasmid replicons and mobilization features, functional annotation based on COG (Clusters of Orthologous Groups of proteins) classification, and comparative genomics against a reference strain. Results: Genome assemblies ranged from 4.76 to 5.00 Mb with consistent GC (guanine-cytosine) content (52.0–52.2%). Phylogenetic analysis revealed three sequence types: ST34 (77.8%), ST19, and ST1543. ST34 isolates displayed the broadest AMR gene repertoires, carrying tetracycline (tetA/tetB), sulfonamide (sul1/sul2/sul3), aminoglycoside (aadA, aph(6)-Id, aph(3″)-Ib), phenicol (floR, catA1), and β-lactam (bla_TEM-1B) genes, whereas non-ST34 isolates harbored fewer determinants. ARGs frequently co-localized with IncQ1 and Col-type plasmid replicons, MOB_H/MobA relaxases (enzymes that initiate plasmid transfer), and conjugation modules (type IV secretion and coupling proteins), often alongside virulence loci and metal resistance operons. Functional annotation showed highly conserved metabolic and housekeeping functions, while comparative genomics confirmed >90% core genome conservation, with variability concentrated in genomic islands encoding hypothetical proteins. These genomic patterns were inferred from a limited WGS dataset (nine isolates) and should therefore be considered exploratory and require confirmation in larger collections. Conclusions: Multi-drug resistant ST34 Salmonella Typhimurium predominated in the northeastern Thailand pork supply chain, with diverse resistance genes carried on IncQ1/Col-type plasmids linked to MOB_H relaxases and conjugation modules. The stability of these mobilizable elements underscores their role in sustaining MDR traits and highlights the risk of foodborne AMR transmission, reinforcing the need for continuous genomic surveillance under a One Health framework. Full article

Emergency evacuation planning in high-rise buildings presents complex optimization challenges critical to achieving sustainable and inclusive urban development. Traditional evacuation models inadequately address vulnerable groups’ needs—particularly persons with disabilities—while neglecting fire spread dynamics, congestion effects, and real-time risk assessment. This neglect undermines both human safety and social equity—core dimensions of sustainable communities. Sustainable cities must integrate inclusive design and emergency preparedness into high-rise development. This paper develops a comprehensive mathematical optimization framework for disability-inclusive emergency evacuation that integrates dynamic fire spread modeling, congestion-aware routing mechanisms, and explicit accessibility constraints within a unified formulation. The proposed approach balances evacuation efficiency, safety, and fairness across diverse population groups through a multi-objective optimization model that incorporates time-varying risk assessments, elevator priority systems for wheelchair users, and group-specific mobility coefficients. To address the computational scalability challenges inherent in large-scale mixed-integer nonlinear programming problems, we introduce an innovative solution methodology that combines Graph Neural Networks (GNN) with Proximal Policy Optimization (PPO) algorithms. The graph neural network component captures spatial-temporal feature representations of building geometry, occupant distributions, and hazard dynamics, while the reinforcement learning algorithm develops adaptive routing policies that respond to evolving emergency conditions. Experimental results on a representative high-rise building scenario demonstrate that the proposed GNN-PPO method achieves substantial improvements in safety, efficiency, and equity. The dynamic policy successfully prioritizes vulnerable populations, utilizes elevator systems effectively for persons with disabilities, and adapts to real-time emergency conditions, providing a robust framework for inclusive emergency evacuation planning in complex building environments. This work demonstrates how advanced computational methods can advance sustainability objectives by ensuring equitable safety outcomes across diverse populations—a prerequisite for truly sustainable cities. Full article

The ongoing biodiversity crisis, driven by human activity, climate change, and disease spread, is reflected by the rapid decline of animal populations across all phylogenetic groups. Bats exemplify a group highly susceptible to these threats. While threats to bats are often studied locally, global interactions remain overlooked. Using a literature-based analysis and the metacoupling framework (including the telecoupling framework), which analyzes human–nature interactions across local to global scales, we take a holistic approach to understanding how conservation strategies can support both biodiversity and ecological and socioeconomic sustainability. Focusing on the Indiana bat (an endangered species with an accelerating population decline for which such a comprehensive analysis is urgently needed), we find how local, regional, and global factors contribute to the shrinking population. Results indicate that local factors include habitat disturbance, cave tourism, and public perceptions. Regional factors include inconsistent regulations and land-use change (e.g., suburban sprawl). Global factors include ecotourism, distant consumer demand (e.g., the timber market), and climate change. White-Nose Syndrome affects bats across scales. The results also suggest that conservation strategies limited to local interventions alone are insufficient. This paper advances sustainability research by applying the metacoupling framework to species conservation, demonstrating how local-to-global human–nature interactions can inform more effective and sustainable management strategies. Full article

The spread of alternative fuel vehicles (AFVs) poses new technological and legal challenges. While these vehicles contribute to sustainable transport, their specific operational characteristics require specific regulation and infrastructure. The study analyses the risks associated with AFVs, in particular with regard to occupational safety and operation, and the extent to which the current legal framework in Hungary is able to address these challenges. It also examines the integration of AFVs into the existing transport and service network and makes recommendations for improving regulation, training and infrastructure. The study aims to contribute to enhancing road safety and legal clarity by showing that the safe integration of AFVs requires the modernisation of regulation and the adaptation of technical protocols. Full article