This research paper aims to examine the association between financial development and environmental quality in 31 European Union (EU) countries from 2001 to 2020. This study proposed an estimation model for the study by combining regression models. The regression model has a dependent variable, carbon emissions, and five independent variables, including Urbanization (URB), Total population (POP), Gross domestic product (GDP), Credit to the private sector (FDB), and Foreign direct investment (FDI). This research used regression methods such as the Fixed Effects Model, Random Effects Model, and Feasible generalized least squaresThe findings reveal that URB, POP, and GDP positively impact carbon emissions in EU countries, whereas the FDB variable exhibits a contrary effect. The remaining variable, FDI, is not statistically significant. In response to these findings, we advocate for adopting transformative green solutions that aim to enhance the quality of health, society, and the environment, offering comprehensive strategies to address Europe’s environmental challenges and pave the way for a sustainable future.
The quality of indoor classroom conditions influences the well-being of its occupants, students and teachers. Especially the temperature, outside acceptable limits, can increase the risk of discomfort, illness, stress behaviors and cognitive processes. Assuming the importance of this, in this quantitative observational study, we investigated the relationship between two environmental variables, temperature and humidity, and students’ basic emotions. Data were collected over four weeks in a secondary school in Spain, with environmental variables recorded every 10 minutes using a monitoring kit installed in the classroom, and students’ emotions categorized using Emotion Recognition Technology (ERT). The results suggest that high recorded temperatures and humidity levels are associated with emotional responses among students. While linear regression models indicate that temperature and humidity may influence students’ emotional experiences in the classroom, the explanatory power of these models may be limited, suggesting that other factors could contribute to the observed variability in emotions. The implications and limitations of these findings for classroom conditions and student emotional well-being are discussed. Recognizing the influence of environmental conditions and monitoring them is a step toward establishing smart classrooms.
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