The world has never been more developed, yet humanity is on the brink of irreversible environmental loss. Despite the urgency of the situation, there is a limited body of studies addressing environmental concerns in higher education institution, particularly in developing countries, i.e., Saudi Arabia. Sustainable development is the only viable solution, albeit it requires the courage to initiate and sustain efforts dedicated to preserving the environment for the well-being of future generation. The article delves into this issue and examines the impact of environmental education program (EEP) on environmental performance (EP) via waste minimization behaviour (WMB). The research involved meticulous data collection from a sample of 597 students, representing diverse genders and academic specialties at the esteemed public university—King Faisal University (KFU) in Saudi Arabia. The study used statistical software (including SPSS and AMOS, v 25) for rigorous analysis and revealed significant findings. Firstly, the study showed a significant and positive relationship between EEP and EP. Secondly, it revealed a significant and positive association between EEP and WMB. Thirdly, the study ascertained a significant and positive association between WMB and EP. Finally, the study found that the relationship between EEP and EP remains significant even after presenting WMB as a mediator, proposing that WMB has a partial mediation role between EEP and EP. The results highlighted the significance role of EEP in stimulating WMB and achieving EP in the Saudi universities, which contributes to national initiative of green Saudia.

The Primary and secondary shadow education refers to a kind of unofficial education that exists outside the traditional mainstream primary and secondary education system in China, with both commercial and educational attributes. As the primary and secondary school stage is an important key stage for further education, existing research mainly focuses on the spatial distribution of primary and secondary school basic education facilities and non-subject training, with fewer studies targeting primary and secondary school subject tutoring shadow education. With the changes in China’s education industry and the introduction of the Double Reduction Policy, there is an urgent need to conduct in-depth research on the spatial aggregation characteristics and influencing factors of Shadow Education Enterprises for primary and secondary school students. This paper takes the main urban area of Zhengzhou City as the study area, and takes primary and secondary school Shadow Education Enterprises as the research object, and applies spatial analysis methods such as kernel density, nearest-neighbor index, and geographic detector to quantitatively analyze the spatial distribution characteristics of primary and secondary school shadow education tutoring enterprises in Zhengzhou City and the factors affecting them The results show that: 1) The overall spatial pattern of primary and secondary school tutoring Shadow Education Enterprises in the main urban area of Zhengzhou City has largely formed a core-edge structural feature that spreads from the urban center to the periphery, and presents the spatial agglomeration feature of “double nuclei many times” distributed along both sides of the Beijing-Guangzhou Line. 2) The distribution of mentoring Shadow Education Enterprises in the main urban area of Zhengzhou City in relation to provincial model primary and secondary schools is significant and there is a significant difference between the distribution around secondary schools and primary schools. 3) The spatial distribution of Shadow Education Enterprises in the main urban area of Zhengzhou City is mainly influenced by factors such as the size of the school-age population, the level of commercial development, the location of school buildings and the accessibility of transport.

The endogenous, human, and social factors influencing the economic development of the municipalities of San Juan Cotzocón and San Pedro y San Pablo Ayutla in the Istmo de Tehuantepec region of the state of Oaxaca are analyzed. The hypothesis posits that the dimensions of endogenous development, social capital, and human capital directly impact the economic development of the respective municipalities. The study involved administering 262 questionnaires to the residents of these municipalities during the month of May 2023. The collected data were examined using exploratory factor analysis to determine the underlying structure and structural equation modeling to estimate the effects and relationships between variables. Results indicate that endogenous development, social capital, and human capital are factors in the economic development of the studied communities, with endogenous development being the most influential factor due to its statistical significance. Notably, the existence of tourist and cultural attractions in the municipalities emerges as a catalyst for local economic development in response to the establishment and operation of the Isthmus of Tehuantepec Interoceanic Corridor.

In rural areas, land use activities around primary arterial roads influence the road section’s traffic characteristics. Regulations dictate the design of primary arterial roads to accommodate high speeds. Hence, there is a mix of traffic between high-speed vehicles and vulnerable road users (pedestrians, bicycles, and motorcycles) around the land. As a result, researchers have identified several arterial roads in Indonesia as accident-prone areas. Therefore, to improve the road user’s safety on primary arterial roads, it is necessary to develop models of the influence of various factors on road traffic accidents. This research uses binary logistic regression analysis. The independent variables are carelessness, disorderliness, high speed, horizontal alignment, road width, clear zone, road shoulder width, signs, markings, and land use. Meanwhile, the dependent variable is the frequency of accidents, where the frequency of accidents consists of multi-accident vehicles (MAV) and single-accident vehicles (SAV). This study collects data for a traffic accident prediction model based on collision frequency in accident-prone areas. The results, road shoulder width, and road sign factor all have an impact on the frequency of traffic accidents. According to a realistic risk analysis, MAV and SAV have no risk difference. After validation, this model shows a confidence level of 92%. This demonstrates that the model generates estimations that accurately reflect reality and are applicable to a wider population. This research has the potential to assist engineers in improving road safety on primary arterial roads. In addition, the model can help the government measure the impact of implemented policies and engage the public in traffic accident prevention efforts.