The ever-increasing amount of waste produced by the healthcare system is a growing burden worldwide. To effectively reduce waste, it is essential to know exactly what it is and to explore the factors influencing its amount. This study aims to analyze the amount of healthcare waste (HCW) and the change in that amount over time, as well as to explore and quantify the factors that influence hazardous healthcare waste (HHCW) in a university hospital. Descriptive statistical analysis, Spearman’s correlation and robust regression were performed to characterize the data. Our analysis revealed that the amount of HCW has been increased annually by 2.11%, and the generation rate ranges between 2.53 and 2.68 kg/bed/day. The amount of HHCW has increased by 20.19% over the 5-year period studied, with a generation rate varying between 1.13 and 1.31 kg/bed/day. It was found that surgical specialisms, anesthesiology and intensive care, and emergency patient care have higher rates of hazardous waste production. A significant positive correlation was found between the amount of hazardous waste and the incidence of healthcare-associated infections (r = 0.704), and the number of diapers used by adults and children (r = 0.555), whose significant predictive role was also confirmed by the robust regression.

The introduction of large language models (LLMs) has significantly influenced learning and learning assessments, dividing the academic community with arguments for and against their implementation. This study investigates how LLMs can be effectively incorporated into student assignments on sustainable development in local communities. In that regard, the study pairs traditional, community-oriented tasks with emerging frameworks for structured LLM use, emphasizing that output quality depends on prompt quality. Accordingly, several prompting frameworks were outlined, and the suitability of ChatGPT and Gemini for specific assignment tasks was assessed. The effectiveness of the approach was evaluated with a survey of two student groups: one using supervised LLM support (23 students) and another using LLMs independently (17 students). Compared to the unsupervised group, the supervised group reported that the frameworks enhanced project preparedness, fostered critical thinking, and reduced reliance on mentors. The supervising mentor noted a slightly lower workload than in earlier projects, while the mentor of the unsupervised group reported higher effort in guiding and refining outcomes. Overall, the findings suggest that guided LLM integration has the potential to improve learning, deepen critical engagement, foster independence, and reduce mentor workload when compared to those who do not provide structured guidance in LLM use.

The transport sector is undergoing a major transformation, as battery electric vehicles (BEV) are gaining ground. Therefore, assessing the sustainability aspects of their use is crucial to obtaining a clear picture of the sector. This article aims to meet this requirement by using European Union (EU) data for the period 2011 to 2021 and focuses not only on EU-27 aggregates but also on each member state separately. For the evaluation, a well-to-wheel (WTW) method was used to calculate two parameters: energy-specific CO₂ emissions (ε) and total efficiency of energy conversions, transmission, and battery (η_total). For these values, the annual electricity mixes of the countries were tracked in 5 + 1 categories (combined cycle gas turbine (CCGT), thermal power plant, biofuels, nuclear power plant (NPP), renewables, and imports). The calculated results were illustrated by sustainability matrices describing the former and current positions of the countries. The EU-27 aggregate achieved a 0.04 increase (from 0.37 to 0.41) in total efficiency and a 29 gCO₂/MJ_motion decrease (from 113 to 84 gCO₂/MJ_motion) during the period. This ε value for 2021 was around half the world average. However, very significant differences were identified between member states, which are also assessed in the article with special emphases on the five most populated EU countries (Germany, France, Italy, Spain, and Poland).

The regulation of vehicular CO₂ emissions determines the permissible emissions of vehicles in units of g CO₂/km. However, these values only partially provide adequate information because they characterize only the vehicle but not the emission of the associated energy supply technology system. The energy needed for the motion of vehicles is generated in several ways by the energy industry, depending on how the vehicles are driven. These methods of energy generation consist of different series of energy source conversions, where the last technological step is the vehicle itself, and the result is the motion. In addition, sustainability characterization of vehicles cannot be determined by the vehicle’s CO₂ emissions alone because it is a more complex notion. The new approach investigates the entire energy technology system associated with the generation of motion, which of course includes the vehicle. The total CO₂ emissions and the resulting energy efficiency have been determined. For this, it was necessary to systematize (collect) the energy supply technology lines of the vehicles. The emission results are not given in g CO₂/km but in g CO₂/J, which is defined in the paper. This new method is complementary to the European Union regulative one, but it allows more complex evaluations of sustainability. The calculations were performed based on Hungarian data. Finally, using the resulting energy efficiency values, the emission results were evaluated by constructing a sustainability matrix similar to the risk matrix. If only the vehicle is investigated, low CO₂ emissions can be achieved with vehicles using internal combustion engines. However, taking into consideration present technologies, in terms of sustainability, the spread of electric-only vehicles using renewable energies can result in improvement in the future. This proposal was supported by the combined analysis of the energy-specific CO₂ emissions and the energy efficiency of vehicles with different power-driven systems.

Infectious healthcare waste (IHCW) poses a significant biohazard and public health risk. This study examines IHCW formation and influencing factors in Hungarian inpatient healthcare institutions. Factors such as hospital type, regional location, indicators related to patient traffic, educational activity, patients of certain types of medical specialties, and healthcare-associated infections (HAIs) were examined. Univariate and multivariate statistical methods identified significant predictors of IHCW occurrence. The generation rate of IHCW ranged from 0.15 to 0.81 kg/bed/day nationally, and it increased by 40.74% between 2017 and 2021, significantly impacted by the COVID-19 pandemic. The data also showed that as the number of beds increased, the IHCW production rate increased proportionally. The results indicate that IHCW generation rates vary significantly by hospital type, with university hospitals producing the most waste. The incidence of HAI multidrug-resistant (MDR) bacterial infections emerged as the primary driver of IHCW generation, along with educational activity, the number of intensive care unit patients, and regional differences. The Southern Great Plain region had the highest IHCW production (0,42 kg/bed/day) among the seven regions studied. The study highlights the critical impact of HAI MDR infections on IHCW production, emphasizing the need for targeted waste management in high-risk areas. Regional differences indicate the necessity for tailored strategies to address local waste management challenges. This study provides essential insights into IHCW formation and influencing factors in Hungary, offering valuable information for policy and practice.Implication statementNowadays, one of the main problems related to waste management is the uncontrollable amount of waste generated in the healthcare sector. Infectious healthcare waste (IHCW) represents a significant biological hazard and a high public health risk, both on an individual and a community level, so a more precise knowledge of these risks is extremely important.In the Central European region, very few studies have dealt with the infectious waste generated in the healthcare sector, and this is the first such research in Hungary. The primary aim of this study is to measure the amount of IHCW produced in various regions and hospital types in Hungary, and to examine the general and specific factors that affect the generation rate of this waste.The findings reveal that IHCW generation rates (GR) vary considerably across different hospital types and regions. This highlights the need for targeted waste management practices in individual institutions. In addition, the study emphasizes the importance of developing region-specific waste management strategies in view of regional inequalities. A crucial insight from the study is that the incidence of healthcare-associated infections (HAIs), particularly multidrug-resistant (MDR) bacterial infections, has the most significant impact on IHCW GR, surpassing other known factors. This suggests that effective control of HAIs, especially MDR bacterial infections, can lead to a substantial reduction in IHCW. The study also showed the impact of the COVID-19 pandemic not only on the production of IHCW, but also on individual influencing factors.Overall, the study provides valuable insights for informing policy and practice. By understanding the specific factors influencing IHCW production, policymakers and healthcare practitioners can develop more effective waste management policies and practices. This highlights the need for tailored waste management strategies that take into account the unique characteristics of each hospital type and geographic region, ultimately reducing the healthcare waste burden and providing a healthier and safer environment for all.