Climate change is the most important environmental problem of the 21st century. Severe climate changes are caused by changes in the average temperature and rainfall can affect economic sectors. On the other hand, the impact of climate change on countries varies depending on their level of development. Therefore, the aim of this paper is to investigate the relationship between climate changes and economic sectors in developed and developing countries for the period 1990–2021. For this purpose, a novel approach based on wavelet analysis and SUR model has been used. In this case, first all variables are decomposed into different frequencies (short, medium and long terms) using wavelet decomposition and then a SUR model is applied for the examination of climate change effects on agriculture, industry and services sectors in developed and developing countries. The findings indicate that temperature and rainfall have a significant negative and positive relationship with the agriculture, industry and services sectors in developed and developing countries, respectively. But severity of the negative effects is greater in the agricultural and industrial sectors in all frequencies (short, medium and long terms) compared to service sector. Furthermore, the severity of the positive effects is greater in the agricultural sector in all frequencies of developing countries compared to the industrial and services sectors. Finally, developing countries are more vulnerable to climate change in all sectors compared to developed countries.

The purpose of this study is to explore the relationship among higher vocational college (HVC) students’ social support (SS), learning burnout (LB), and learning motivation (LM), and to further explore the influence regulation mechanism. By analyzing the questionnaire survey data of 500 HVC students, this study found some important conclusions. First, a positive correlation is found between SS and LM, whereas LB exhibits a negative correlation with LM. Second, regression analysis results indicate significant influences of SS and LB on LM, with the latter serving as a partial intermediary between SS and LM. Lastly, analysis of group disparities reveals noteworthy distinctions in SS, LB, and LM across students of varying grades. These discoveries underscore the pivotal roles of SS and LB in molding the LM of HVC students, offering valuable insights for educational practices and policy recommendations. This study benefits the understanding of the key factors in the learning process of HVC students and provides a new direction for further research.

The rapid rise of live streaming commerce in China has transformed the retail environment, with electronic word-of-mouth (eWOM) emerging as a pivotal factor in shaping consumer behavior. As a digital evolution of traditional word-of-mouth, eWOM gains particular significance in live streaming contexts, where real-time interactions foster immediacy and engagement. This study investigates how eWOM influences consumer purchase intentions within Chinese live streaming platforms, employing the Information Adoption Model (IAM) as theoretical framework. Using a grounded theory approach, this research applies NVivo for data coding and analysis to explore the cognitive and emotional processes triggered by eWOM during live streaming. Findings indicate that argument quality, source credibility, and information quantity significantly enhance consumer trust and perceived usefulness of information, which, in turn, drives information adoption and purchase intention. Furthermore, the study reveals that social interaction between live streaming anchors and audiences amplifies the influence of consumers' internal states on information adoption. This study enhances the Information Adoption Model (IAM) by introducing social interaction as a moderator between consumers' internal states toward live streaming eWOM and their adoption of information, highlighting the value of social interaction in live streaming. It also incorporates information quantity, showing how eWOM quantity affects trust and perceived usefulness. Furthermore, the study contributes to exploring how factors like argument quality, source credibility, and information quantity shape consumer trust and perceived usefulness, offering insights into the cognitive and emotional processes of information adoption in live streaming.

Purpose: This research aims to examine the influence of intellectual capital disclosure and the geographical location of universities on the sustainability of higher education institutions in Southeast Asia. Design/methodology/approach: This research is quantitative and uses secondary data obtained through the annual reports of universities that have the Universitas Indonesia Green Metric Rank. This research uses two stages of data analysis techniques, namely the content analysis stage to determine the number of Intellectual Capital disclosures and the hypothesis testing stage. The analysis tool uses the SPSS version 23 application. The population of this research includes all universities in Southeast Asia that are included in the UI Greenmetric World University Rankings. The sampling technique used was purposive sampling technique, which resulted in 86 analysis units of higher education institutions in Southeast Asia. Findings: The research results prove that the geographical location of universities has a negative and significant influence on Universitas Indonesia Green Metric’s performance in Southeast Asia and human capital has a positive influence on UIGM’s performance in Southeast Asia. However, the structural capital and relational capital components do not affect the UIGM performance of universities in Southeast Asia. Originality/value: The originality of the research is the use of higher education sustainability variables with UIGM proxies and modified IC indicators for universities and geographical areas that have not been widely used to see whether there are fundamental differences in the disclosure of intellectual capital for higher education institutions in Southeast Asia.

Purpose: Religiosity as an intrinsic principle affects the sustainable behavior of consumers. Studies have been undertaken to discover the impact of religiosity on sustainable consumer behavior in various contexts, cultures, and countries. The current bibliometric study focused on religiosity and sustainable consumer behavior in Gulf Corporation Council (GCC) countries who has similar religions and cultures so that the research trend, contribution, and gap through thematic and content analysis could be investigated and future direction could be suggested. The literature for this study was solicited from 2016 to June 2024. Methodology: Bibliometrics and content analysis were used to study the existing literature on religiosity and sustainable consumption behavior in GCC countries. The VOS viewer was used to visualize literature and understand the network landscape of the research topic and their interconnectivity. Additionally, Scopus analytics and Microsoft Excel were used to review and analyze the religiosity of consumers regarding the sustainable consumption of products and services. Finding: The descriptive analysis revealed trends, prolific countries, and researchers in this area along with their affiliation. The co-occurrence analysis showed 3 main clusters of co-occurrences with various link strengths. The content analysis looked at the 6 clusters depicted by the coupling function and compared them against co-occurrence analysis to uncover related themes. This analysis produced 4 related themes for content analysis. Contribution: This research contributed to understanding the current themes, challenges, and the need for marketing strategies and action so that sustainable consumption could be encouraged. As such this research will fill the void in the current literature left in this research area. This research has practical and policy implications for businesses, organizations, and policymakers as they try to capture consumers for sustainable products and services in GCC countries.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.