The BRICS countries (Brazil, Russia, India, China, and South Africa) are responsible for forty-two per cent of global carbon emissions. These rapidly industrializing and economically growing countries are dependent on fossil fuels, which can lead to increased emissions. This research analyses the impact of economic growth, globalization, and renewable energy (RE) use on CO₂ using a unique dataset of 155 observations and a practical model. Using panel data for 1990–2020, this study examines the relationships between CO₂ emissions, GDP, RE use, and the KOF Globalization Index based on the Environmental Kuznets Curve (EKC) theory. Cointegration, unit root test, panel data analysis, and FGLS regression methods were used in this study. The results show that economic growth and globalization increase CO₂, while RE is insufficient to reduce these effects. Moreover, it is determined that globalization has an increasing effect on CO₂. This study makes a prominent contribution to the literature by examining the combined effects of globalization and economic growth on environmental sustainability. The findings emphasize the need for sustainable energy policies in BRICS countries.

The effect of different sample sizes on estimation methods such as weighted least squares and robust weighted least squares that are used in structural equation modeling was studied and compared using information criteria such as Akaike Information Criteria in this study. The simulations were repeated 1000 times with two estimation methods and the average values of criteria were calculated with different sample sizes. The study includes a construct of four factors, with four questions of each that are measured on a five-point Likert scale. Different sample sizes, ranging from 300 to 5000 were selected. According to the simulations results, it is concluded that the robust estimation method provides more effective results at lower sample size. In addition, it was found that as the sample size increases, the efficiency difference between two methods gradually decreases. Moreover, it was detected that there is almost no difference between the two methods for sample sizes over 3000.

This study analyzes the dynamic relationships among economic growth, international tourism, renewable energy use, and carbon emissions in the member countries of the Central American Inter-American Integration System (SICA). Conducted using a panel dataset, it was found that economic growth and international tourism increased carbon emissions. On the other hand, it was found that renewable energy usage significantly reduced emissions. The study evaluates the environmental impacts of economic growth in the context of the environmental Kuznets curve and carbon intensity theory. The study also suggests that low-carbon and renewable energy-based tourism practices strengthen both environmental sustainability and long-term economic resilience within the scope of the Energy Transition Theory. In the study, a panel dataset consisting of 140 observations from SICA countries from 2001 to 2020 was used, and the feasible generalized least squares (FGLS) model was applied. As a result, the long-term relationships between the variables were verified with the help of panel cointegration tests. The findings reveal that holistic and long-term policy strategies based on the interaction of economic growth, renewable energy use, and the tourism sector should be developed in order to achieve sustainable development goals in SICA countries.

This study investigated the influence of awareness, knowledge, and risk perceptions on environmental attitudes and behaviours in Türkiye, specifically in the context of climate change, using structural equation modelling (SEM). Data were collected from all 81 provinces covering the seven geographical regions of the country. The results revealed that awareness and risk perception have the strongest direct impact on pro-environmental behaviour. Environmental attitudes also demonstrated a significant positive effect, though the findings suggest that high awareness and risk perception can directly drive action even independently of attitude. Uniquely, this study fills a critical gap in the developing country literature by demonstrating that in Türkiye, perceiving the risk translates directly into action, contrasting with the ‘value-action gap’ often observed in Western contexts. Practically, the findings suggest that policymakers should prioritize risk-communication strategies and disaster-preparedness drills over passive information campaigns to effectively stimulate pro-environmental behaviours.

Neuromarketing is the application of the neuroscientific approaches to analyze and understand economically relevant behavior. In this study, the effect of loud and rhythmic music in a sample neuromarketing setup is investigated. The second aim was to develop an approach in the prediction of preference using only brain signals. In this work, 19- channel EEG signals were recorded and two experimental paradigms were implemented: no music/silence and rhythmic, loud music using a headphone, while viewing women shoes. For each 10-sec epoch, normalized power spectral density (PSD) of EEG data for six frequency bands was estimated using the Burg method. The effect of music was investigated by comparing the mean differences between music and no music groups using independent two-sample t-test. In the preference prediction part sequential forward selection, k-nearest neighbors (k-NN) and the support vector machines (SVM), and 5-fold cross-validation approaches were used. It is found that music did not affect like decision in any of the power bands, on the contrary, music affected dislike decisions for all bands with no exceptions. Furthermore, the accuracies obtained in preference prediction study were between 77.5 and 82.5% for k-NN and SVM techniques. The results of the study showed the feasibility of using EEG signals in the investigation of the music effect on purchasing behavior and the prediction of preference of an individual.