In response to the increasing global emphasis on sustainability and the specific challenges faced by small and medium-sized enterprises (SMEs) in China, this study explores the integration of green reverse logistics within these enterprises to enhance their sustainability and competitiveness. The aim of this study is to understand the relationship between reverse logistics, green logistics, and sustainable development. Data analysis was conducted utilizing a combination of descriptive statistics and correlation analysis. A survey of 311 participants examined SMEs’ performance in reverse logistics practices and their initiatives in green logistics and sustainable development. The empirical findings reveal significant progress in reverse logistics practices among SMEs, reducing environmental impact and improving resource efficiency. Moreover, a notable positive correlation was identified between reverse logistics promotion and advancements in green logistics and sustainable development. SMEs’ investment in reverse logistics is closely linked to their efforts in environmentally conscious and sustainable supply chain management. These insights benefit SMEs and supply chain practitioners and offer a valuable reference for future research and practical applications in this field.

The decentralization of the NHIS’s implementation to states intended to hasten progress towards universal health coverage, has not effectively addressed healthcare disparities, particularly in Lagos State. The implementation of the Lagos State Health Insurance Scheme appears to perpetuate structural violence, evident in increased out-of-pocket expenses, discrimination based on insurance type, and substandard healthcare delivery. The study therefore examined how structural violence has affected the policy outcomes of the Lagos State Health Insurance Scheme, with a specific emphasis on junior officers in grade level 01–07 in five selected ministries situated within Lagos State. Both primary and secondary data were collected using questionnaire, interview and literature search. Data gathered were analysed statistically and thematically. The findings of the study indicate that the policy outcome of the scheme has been adversely affected by structural violence, resulting in dissatisfaction, compensation claims for unresolved health issues and a shift in health insurance providers among enrolled junior officers.

This study examines the comparative teaching effectiveness and student satisfaction between native Japanese language teachers (NJLTs) and non-native Japanese language teachers (NNJLTs). Utilizing a sample of 740 students from various educational institutions in Japan, the research employs a quantitative design, including structured questionnaires adapted from established scales. Advanced statistical methods, including factor analysis and multiple regression, were used to analyze the data. The findings reveal no significant differences in student satisfaction and language proficiency between students taught by NJLTs and NNJLTs. Additionally, regression analysis showed that cultural relatability and empathy were not significant predictors of teaching effectiveness, suggesting that factors beyond nativeness influence student outcomes. These results challenge the native-speakerism ideology, highlighting the importance of pedagogical skills, teacher-student rapport, and effective teaching strategies. The study underscores the need for inclusive hiring practices, comprehensive teacher training programs, and collaborative teaching models that leverage the strengths of both NJLTs and NNJLTs. Implications for educational policy, curriculum design, and teacher professional development are discussed, advocating for a balanced approach that values the contributions of both native and non-native teachers. Limitations include the reliance on self-reported data and the specific cultural context of Japan. Future research should explore additional variables, employ longitudinal designs, and utilize mixed-methods approaches to provide a more nuanced understanding of language teaching effectiveness.

Background: Bitcoin mining, an energy-intensive process, requires significant amounts of electricity, which results in a particularly high carbon footprint from mining operations. In the Republic of Kazakhstan, where a substantial portion of electricity is generated from coal-fired power plants, the carbon footprint of mining operations is particularly high. This article examines the scale of energy consumption by mining farms, assesses their share in the country’s total electricity consumption, and analyzes the carbon footprint associated with bitcoin mining. A comparative analysis with other sectors of the economy, including transportation and industry is provided, along with possible measures to reduce the environmental impact of mining operations. Materials and methods: To assess the impact of bitcoin mining on the carbon footprint in Kazakhstan, electricity consumption from 2016 to 2023, provided by the Bureau of National Statistics of the Republic of Kazakhstan, was used. Data on electricity production from various types of power plants was also analyzed. The Life Cycle Assessment (LCA) methodology was used to analyze the environmental performance of energy systems. CO₂ emissions were estimated based on emission factors for various energy sources. Results: The total electricity consumption in Kazakhstan increased from 74,502 GWh in 2016 to 115,067.6 GWh in 2023. The industrial sector’s electricity consumption remained relatively stable over this period. The consumption by mining farms amounted to 10,346 GWh in 2021. A comparative analysis of CO₂ emissions showed that bitcoin mining has a higher carbon footprint compared to electricity generation from renewable sources, as well as oil refining and car manufacturing. Conclusions: Bitcoin mining has a significant negative impact on the environment of the Republic of Kazakhstan due to high electricity consumption and resulting carbon dioxide emissions. Measures are needed to transition to sustainable energy sources and improve energy efficiency to reduce the environmental footprint of cryptocurrency mining activities.

This study explores the spatial distribution pattern of educational infrastructure development across districts and cities in North Sumatra, identifying significant disparities between urban and rural areas. The study aims to: (1) determine the distribution of educational development across districts and cities, (2) analyze global spatial autocorrelation, and (3) identify priority locations for educational development policies in North Sumatra Province. The methodology includes quantile analysis, Moran’s Global Index, and Local Indicators of Spatial Autocorrelation (LISA) using GeoDa software to address spatial autocorrelation. The results indicate that there are nine areas with a low School Participation Rate Index (SPRI), eleven areas with a low School Facilities and Infrastructure Index (SFII), and eleven areas with a low Regional Education Index (REI). Spatial autocorrelation analysis reveals that SFII shows positive spatial autocorrelation, while SPRI and REI exhibit negative spatial autocorrelation, indicating a high level of inequality between regions. Labuhan Batu Selatan and Labuhan Batu are identified as priorities for the provincial government in overseeing educational development policies.

Over the past decade, the integration of technology, particularly gamification, has initiated a substantial transformation within the field of education. However, educators frequently confront the challenge of identifying suitable competitive game-based learning platforms amidst the growing emphasis on cultivating creativity within the classroom and effectively integrating technology into pedagogical practices. The current study examines students and faculty continuous intention to use gamification in higher education. The data was collected through an online survey with a sample size of 763 Pakistani respondents from various universities around Pakistan. The structural equation modeling was used to analyze the data and to investigate how continuous intention to use gamification is influenced by, extended TAM model with inclusion of variables such as task technology fit, social influence, social recognition and hedonic motivation. The results have shown that task technology has no significant influence on perceived usefulness (PU) where as it has a significant influence on perceived ease of use (PEOU). Social influence (SI) indicates no significant influence on perceived ease of use. Social recognition (SR) indicates positive influence on perceived usefulness, perceived ease of use, and continuous intention. The dimensional analysis indicated that perceived ease of use has insignificant influence on perceived usefulness. Both PEOU and PU exhibit positive influence on attitude. Hedonic motivation (HM) and attitude were observed to have a positive influence on continuous intention (CI). Moreover, gamification is found to efficiently and effectively achieve meaningful goals by tapping intrinsic motivation of the users through engaging them in playful experiences.