Search Results (573)

Invasive fungal infections (IFIs) represent a growing global health threat, particularly for immunocompromised populations, with mortality exceeding 1.5 million deaths annually. Despite their clinical and economic burden—costing billions in healthcare expenditures—fungal infections remain underprioritized in public health agendas. This review examines the current landscape of antifungal therapy, focusing on advances, challenges, and future directions. Key drug classes (polyenes, azoles, echinocandins, and novel agents) are analyzed for their mechanisms of action, pharmacokinetics, and clinical applications, alongside emerging resistance patterns in pathogens like Candida auris and azole-resistant Aspergillus fumigatus. The rise of resistance, driven by agricultural fungicide use and nosocomial transmission, underscores the need for innovative antifungals, rapid diagnostics, and stewardship programs. Promising developments include next-generation echinocandins (e.g., rezafungin), triterpenoids (ibrexafungerp), and orotomides (olorofim), which target resistant strains and offer improved safety profiles. The review also highlights the critical role of “One Health” strategies to mitigate environmental and clinical resistance. Future success hinges on multidisciplinary collaboration, enhanced surveillance, and accelerated drug development to address unmet needs in antifungal therapy. Full article

Drawing on a panel dataset of 27,972 firm-year observations from Chinese A-share listed companies spanning 2009 to 2022, this study employs fixed-effects models to examine the nonlinear relationship between firm-level climate policy uncertainty (FCPU) and corporate green governance expenditure (GGE). The results reveal a robust inverted U-shaped pattern: moderate levels of FCPU encourage firms to increase GGE, while excessive uncertainty discourages it. Financing constraints mediate this relationship; specifically, FCPU exhibits a U-shaped impact on financing constraints, initially easing and then tightening them. Older top management teams accelerate the GGE downturn, while government environmental expenditure delays it, acting as a buffer. Heterogeneity analyses reveal the inverted U-shaped effect is more pronounced for non-polluting firms and state-owned enterprises (SOEs). This study highlights the complex dynamics of FCPU on corporate green behavior, underscoring the importance of climate policy stability and transparency for advancing corporate environmental engagement in China. Full article

The maternal–fetal environment is influenced by multiple factors, including nutrition and environmental contaminants, which can impact long-term development. Perinatal exposure to organophosphate flame retardants (OPFRs) disrupts energy homeostasis and causes maladaptive behaviors in mice. Maternal obesity affects development by impairing blood–brain barrier (BBB) formation, influencing brain regions involved in energy regulation and behavior. This study examined the combined effects of maternal obesity and perinatal OPFR treatment on offspring development. Female mice were fed either a low-fat (LFD) or a high-fat diet (HFD) for 8 weeks, mated, and treated with either sesame oil or an OPFR mixture (tris(1,3-dichloro-2-propyl)phosphate, tricresyl phosphate, and triphenyl phosphate, 1 mg/kg each) from gestational day 7 to postnatal day 14. Results showed that both maternal diet and OPFR treatment disrupted blood–brain barrier integrity, energy balance, and reproductive gene expression in the hypothalamus of neonates. The expression of hepatic genes related to lipid and xenobiotic metabolism was also altered. In adulthood, LFD OPFR-treated female offspring exhibited increased avoidance behavior, while HFD OPFR-treated females demonstrated memory impairments. Metabolic assessments revealed decreased energy expenditure and nighttime activity in LFD OPFR-treated females. These findings suggest that maternal diet and OPFR treatment alter hypothalamic and liver gene expression in neonates, potentially leading to long-term metabolic and behavioral changes. Full article

In order to fulfill the commitment to the “dual carbon goal” at an early date, China has implemented a series of carbon control policies. However, the actual impact of these policy combinations on green innovation in Chinese cities remains unknown. Taking the implementation of the low-carbon pilot policy (LCP) and the carbon emission trading pilot policy (CET) as the research opportunity, this paper uses panel data from 276 prefecture-level cities and a multiple-period difference-in-differences (DID) model to explore the impact of carbon control policy combination on green innovation in China and their mechanisms. The results indicate the following: A single LCP or CET can significantly boost green innovation. However, the impact of cross-tool carbon control policy combination on green innovation is notably greater than that of a single policy, with a trend of increasing effectiveness over time. Even after a series of robustness tests, this conclusion remains valid. Heterogeneity analysis shows that the promotion effect is more significant in the eastern region and high-level administrative cities. The policy combination incentivizes green innovation through fiscal technology expenditure and public environmental awareness, focusing more on fostering strategic green innovation. Consequently, the Chinese government should tailor policy combinations to specific contexts, expand their implementation judiciously, and consistently drive forward green innovation. Full article

The global transition to clean energy necessitates integrated solutions that ensure both environmental sustainability and energy security. This paper proposes a scenario-based modeling framework for urban hybrid energy systems combining small modular reactors (SMRs), photovoltaic (PV) generation, and battery storage within a smart grid architecture. SMRs offer compact, low-carbon, and reliable baseload power suitable for urban environments, while PV and storage enhance system flexibility and renewable integration. Six energy mix scenarios are evaluated using a lifecycle-based cost model that incorporates both capital expenditures (CAPEX) and cumulative carbon costs over a 25-year horizon. The modeling results demonstrate that hybrid SMR–renewable systems—particularly those with high nuclear shares—can reduce lifecycle CO₂ emissions by over 90%, while maintaining long-term economic viability under carbon pricing assumptions. Scenario C, which combines 50% SMR, 40% PV, and 10% battery, emerges as a balanced configuration offering deep decarbonization with moderate investment levels. The proposed framework highlights key trade-offs between emissions and capital cost and seeking resilient and scalable pathways to support the global clean energy transition and net-zero commitments. Full article

Globally, forest leisure and recreation (FLR) activities are widely recognized not only for their environmental and social benefits but also for their economic contributions. To better understand these economic contributions, it is vital to examine how the regional economic levels of customers vary when consuming FLR. This study aimed to empirically examine whether the regional economic level of residents (i.e., gross regional domestic product; GRDP) is classifiable using FLR expenditure data, and to interpret which variables contribute to its classification. We acquired anonymized credit card transaction data on residents of two regions with different GRDP levels. The data were preprocessed by identifying FLR-related industries and extracting key spending features for classification analysis. Five classification models (e.g., deep neural network (DNN), random forest, extreme gradient boosting, support vector machine, and logistic regression) were applied. Among the models, the DNN model presented the best performance (overall accuracy = 0.73; area under the curve (AUC) = 0.82). SHAP analysis showed that the “FLR industry” variable was most influential in differentiating GRDP levels across all the models. These findings demonstrate that FLR consumption patterns may vary and are interpretable by economic levels, providing an empirical framework for designing regional economic policies. Full article

In respiratory patients, limited adherence to and misuse of devices hinder the effectiveness of inhalation therapy. Switching inhalers for non-clinical reasons poses a risk of deterioration of respiratory disease and/or promotes poor adherence to therapy. The objective of this work was to explore the impact of device changes for non-clinical reasons on clinical outcomes (primary) and costs (secondary), including carbon emissions in Spain. After a comprehensive literature search, the increased use of resources following worsening outcomes was apportioned using Spanish cost data and following the recommended pathways for care. We calculated the cost of re-training these patients and attributed carbon emissions in metric tons of CO₂ equivalent (tCO₂eq) to the excess resource use. In Spain, the impact of uncontrolled switching for non-clinical reasons in COPD has an annual estimated cost of EUR 923/patient, leading to an excess annual expenditure of more than EUR 216 million. For asthma patients, the annual impact is almost EUR 263/patient, representing an additional EUR 118 million excess annual expenditure. The environmental consequence of both conditions can be equated to almost 45 thousand tCO₂eq. Training all these patients on the new device would cost around EUR 35 million and would generate an extra impact reduction of about 2.6 thousand tCO₂eq in carbon emissions levy. Full article

This paper proposes a ladder carbon trading-based low-carbon economic dispatch model for integrated energy systems (IESs), incorporating flexible load optimization and hybrid energy storage systems consisting of battery and thermal energy storage. First, a ladder-type carbon trading mechanism is introduced, in which the carbon trading cost increases progressively with emission levels, thereby providing stronger incentives for emission reduction. Second, flexible loads are categorized and modeled as shiftable, transferable, and reducible types, each with distinct operational constraints and compensation mechanisms. Third, both battery and thermal energy storage systems are considered to improve system flexibility by storing excess energy and supplying it when needed. Finally, a unified optimization framework is developed to coordinate the dispatch of renewable generation, gas turbines, waste heat recovery units, and multi-energy storage devices while integrating flexible load flexibility. The objective is to minimize the total system cost, which includes energy procurement, carbon trading expenditures, and demand response compensation. Three comparative case studies are conducted to evaluate system performance under different operational configurations: the proposed comprehensive model, a carbon trading-only approach, and a conventional baseline scenario. Results demonstrate that the proposed framework effectively balances economic and environmental objectives through coordinated demand-side management, hybrid storage utilization, and the ladder-type carbon trading market mechanism. It reshapes the system load profile via peak shaving and valley filling, improves renewable energy integration, and enhances overall system efficiency. Full article

The electric vehicle (EV) business model integrates advanced battery technology, dynamic power train architectures, and intelligent energy management systems with ecosystem strategies and digital services. It incorporates environmental sustainability through lifecycle analysis and renewable energy integration. China, with 9.49 million EV sales in 2023 (33% market share), faces infrastructure gaps constraining further growth. China is strategically mitigating CO₂ emissions while fostering economic expansion, notwithstanding constraints such as suboptimal battery technology advancements, elevated production expenditure, and enduring ecological impacts. This Political, Economic, Social, Technological, Legal, Environmental (PESTLE) assessment, operationalized through a survey of 800 stakeholders and Statistical Package for the Social Sciences IBM SPSS SPSS (Version 28) quantitative analysis (factor loading = 0.73 for Technology; eigenvalue = 4.12), identifies infrastructure gaps as the dominant barrier (72% of stakeholders). Political factors (β = 0.82) emerged as the strongest adoption predictor, outweighing economic subsidies in significance. The adoption of EVs in China presents a significant prospect for reducing CO₂ emissions and advancing technology. However, economic barriers, market dynamics, inadequate infrastructure, regulatory uncertainty, and social acceptance issues are addressed in the assessment. The study recommends prioritizing infrastructure investment (e.g., 500 K fast-charging stations by 2027) and policy stability to overcome adoption barriers. This study provides three key advances: (1) quantification of PESTLE factor weights via factor analysis, revealing technological (infrastructure) and political factors as dominant; (2) identification of infrastructure gaps, not subsidies, as the primary adoption barrier; and (3) demonstration of infrastructure’s persistence post-subsidy cuts. These insights redefine EV adoption priorities in China. Full article

In this work, we examine the correlation between financial inclusion and the Environmental, Social, and Governance (ESG) factors of sustainable development with the assistance of an exhaustive panel dataset of 103 emerging and developing economies spanning 2011 to 2022. The “Account Age” variable, standing for financial inclusion, is the share of adults owning accounts with formal financial institutions or with the providers of mobile money services, inclusive of both conventional and digital entry points. Methodologically, the article follows an econometric approach with panel data regressions, supplemented by Two-Stage Least Squares (2SLS) with instrumental variables in order to control endogeneity biases. ESG-specific instruments like climate resilience indicators and digital penetration measures are utilized for the purpose of robustness. As a companion approach, the paper follows machine learning techniques, applying a set of algorithms either for regression or for clustering for the purpose of detecting non-linearities and discerning ESG-inclusion typologies for the sample of countries. Results reflect that financial inclusion is, in the Environmental pillar, significantly associated with contemporary sustainability activity such as consumption of green energy, extent of protected area, and value added by agriculture, while reliance on traditional agriculture, measured by land use and value added by agriculture, decreases inclusion. For the Social pillar, expenditure on education, internet, sanitation, and gender equity are prominent inclusion facilitators, while engagement with the informal labor market exhibits a suppressing function. For the Governance pillar, anti-corruption activity and patent filing activity are inclusive, while diminishing regulatory quality, possibly by way of digital governance gaps, has a negative correlation. Policy implications are substantial: the research suggests that development dividends from a multi-dimensional approach can be had through enhancing financial inclusion. Policies that intersect financial access with upgrading the environment, social expenditure, and institutional reconstitution can simultaneously support sustainability targets. These are the most applicable lessons for the policy-makers and development professionals concerned with the attainment of the SDGs, specifically over the regions of the Global South, where the trinity of climate resilience, social fairness, and institutional renovation most significantly manifests. Full article

The residential sector has the potential to significantly impact overall energy consumption and emissions due to the long lifespan of residential buildings and the difficulty of outsourcing residential emissions to other nations. This research primarily aimed to investigate the impact of government expenditure on environmental protection in reducing household energy consumption, thus contributing to Canada’s environmental quality. We utilized panel data from ten Canadian provinces covering the period from 1995 to 2020. To estimate household energy demand, both conceptual and empirical models were developed. Advanced second-generation econometric techniques were applied, including appropriate unit root and co-integration tests. Long-term relationships were analyzed using the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) methodologies. The long-term estimates indicate that all explanatory variables align with theoretical expectations and are highly significant. The findings reveal that government funding for environmental protection, along with province-specific environmental policies, contributes to reducing per capita residential energy consumption, which is essential for enhancing energy efficiency. Additionally, factors such as weather, income, demographic shifts, and energy prices are found to influence household energy consumption trends. These outcomes highlight the importance of increased government funding for environmental protection and underscore the need for provinces to implement context-specific environmental policies. This approach is essential in the effort to reduce household energy consumption across Canada. Full article

Purpose: The acquirer’s corporate environmental performance (CEP) in mergers and acquisitions has been a subject of debate, yielding mixed results. This paper uses the US firm-level data of 1437 M&A deals from 2002–2019 to examine the impact of overall CEP, resource use, emissions, and innovation on the acquirers’ post-merger market value. Design/methodology/approach: This study employs multi-level fixed effects panel regression using Ordinary Least Squares (OLS) and the instrumental variable (IV) 2SLS method to estimate the models and compare the results with those from robust estimation. Absorbing the multiple levels of fixed effects (i.e., firm, industry, and year) offers a novel and robust algorithm for efficiently accounting for unobserved heterogeneity. The results from IV (2SLS) are more convincing, as the method overcomes the problem of endogeneity due to reverse causality and sample selection bias. Findings: The authors find that CEP has a significant impact on market value, particularly in the long term. While both resource use and emissions performance have positive effects, emissions performance has a stronger impact, presumably because external stakeholders and market participants are more concerned about emissions reduction. The performance of environmental innovation is relatively weak compared to other pillars. Descriptive analysis shows low average scores in environmental innovation compared to the resource use and emissions performance of the acquirers. However, large deals yield significant returns from investing in environmental innovation in both the short and long term compared to small deals. Practical implications: This paper offers several practical implications. First, environmental performance can help improve the acquirer’s long-term market value. Second, managers can focus on the strategic side of environmental performance, based on its pillars, and benchmark their relative position against peers. Third, environmental innovation can be considered a new potential, as the market as a whole in this area is still lagging. Given the growing pressure to improve environmental technology and innovation, prospective acquirers should confidently prioritise actions on green revenue, product innovation, and capital expenditure now rather than ticking these boxes later. Originality value: The key contribution is offering valuable insights into the impact of acquirers’ environmental performance on long-term value creation in mergers and acquisitions (M&A). These results fill the gap in the literature focusing mainly on the effect of environmental pillar and sub-pillar scores on acquirer’s firm value. The authors claim that analysing sub-pillar-level granularity is crucial for accurately measuring the effects on firm-level performance. Full article

The imperative for building managers, in the face of high-density urban environments, is to drive existing chiller plants to greater operational efficiency through the application of advanced technological interventions. The case for applying Supervisory Control (SC) and a Building Automation System (SC+BAS) for optimizing chiller plants is the subject of investigation here, through the lens of a typical commercial shopping mall in the high-density infrastructure of Hong Kong. The application of SC+BAS falls into the realm of advanced Trim/Respond algorithms coupled with sophisticated sequencing algorithms that allow for refined optimization of the chiller operations in response to the dynamic demands of urban infrastructure. The SC+BAS features an array of optimizations specifically for the chiller plant. Incentive parameters such as cooling capacity, energy usage, and Coefficient of Performance (COP) were thoroughly studied through 12 months’ worth of data, before and after the implementation of the SC+BAS. Empirical observations indicate a statistically significant 17.6% energy usage decrease, coupled with a 15.3% decrease in the related energy expenditure costs. Furthermore, the environmental impact is calculated, with an estimated 61.1 tons reduction in the amount of CO₂ emissions, hence emphasizing the capacity for SC+BAS in offsetting the carbon footprint for commercial buildings. These data prove convincingly that the implementation of SC+BAS can increase the energy efficiency in chiller plants in commercial buildings, supporting the overall sustainability of the urban infrastructure. In turn, the authors suggest other areas for optimization through the advanced sequencing of chillers and demand-based cooling strategies. This highlights the ability of SC+BAS in creating more economical and green building operations regarding urban microclimates, occupant behavior patterns, and interactivity with the power grid, leading ultimately to the holistic optimization of chiller plant performance within the urban framework. Full article

The transport industry in the European Union plays a key role in the economy. However, due to persistent political, social, and technological changes, examining optimization strategies in transportation has become a crucial task to minimize expenditure, promote sustainable solutions, and address environmental degradation concerns. This study analyzes the effectiveness of a new truck trailer design, adapted from existing European models, which improves load capacity through an extended trailer length. The increased length (and, by extension, volume) is expected to reduce the number of vehicles for freight transportation, thereby improving road congestion and reducing environmental impacts, which include GHG emissions and overall carbon footprint. To achieve this objective, a comprehensive analysis of current European regulations on articulated vehicles and road trains was carried out, alongside a review of related case studies implemented or under development across the European Union member states. Additionally, a pilot study was conducted using the proposed 18 m semi-trailer across 14 real-life freight routes involving loads from several suppliers and manufacturers. This study therefore demonstrates the economic benefits and reduction in pollutant emissions related to the extended design and evaluates its impact on road infrastructure conditions, given the total length of 20.55 m. Full article

The efficient use of energy is a sign of conscious environmental responsibility. Sustainable management also refers to water resources, where emphasis is placed on the possibility of retaining rainwater at the point of the precipitation occurrence. This article focused on the reuse of runoff from a rest area (RA) along the expressway, wherever drinking water quality is not required. The runoff from RAs can be significantly contaminated due to the traffic-related issues. The objective of this article was to evaluate the energy efficiency of preliminary treatment of raw meltwater from a selected rest area using electrodialysis for Cl⁻ and Na⁺ removal. The treatment was carried out under various conditions, including different solution temperatures (20 °C and 30 °C) and electric voltages (10 V, 20 V, 30 V). The energy efficiency assessment was preceded by a characterization of runoff quality and the analysis of pollutant removal efficiency in the electrodialysis process. The most energy-efficient variant was characterized with the 0.097 Wh/(mg/L) energy expenditure ratio and 93% efficiency removal for Cl⁻ and 0.147 Wh/(mg/L) and 90% for Na⁺. In this variant, the permissible Cl⁻ and Na⁺ concentrations limits were achieved after 27 min with an energy consumption of 57 Wh. In general, the observed highest energy efficiency occurred at the beginning of the electrodialysis process and decreased over time. Full article