The issue of academic achievement among Chinese university students is emerging due to difficulties in finding employment. This study investigates the structural relationships between social support, goal orientation, and academic achievement with the aim of enhancing students’ academic performance and facilitating sustained employability. Data were collected from 202 college students in South China, revealing that students’ levels of social support, goal orientation, and academic achievement were all moderate. Lower-grade students, in comparison to higher-grade students, exhibited lower levels of social support, goal orientation, and academic achievement. Additionally, students from lower economic backgrounds tended to lack social support. Among the factors of social support, goal orientation, and academic achievement, there were positive correlations among these three variables. Social support significantly and positively influenced goal orientation and academic achievement. Specifically, the sub-factors of social support, school support, and teacher support had differential effects, with school support enhancing academic achievement and teacher support boosting goal orientation. Goal orientation also significantly and positively impacted students’ academic achievement, with the sub-factor of mastery goals having a stronger influence. Goal orientation partially mediated the relationship between social support and academic achievement. This study discusses limitations and provides insights for future research.

2050 building stock might be buildings that already exist today. A large percentage of these buildings fail today’s energy performance standards. Highly inefficient buildings delay progress toward a zero-carbon-building goal (SDGs 7 and 13) and can lead to investments in renewable energy infrastructure. The study aims to investigate how bioclimatic design strategies enhance energy efficiency in selected orthopaedic hospitals in Nigeria. The study objective includes Identifying the bioclimatic design strategies that improve energy efficiency in orthopaedic hospitals, assessing the energy efficiency requirements in an orthopaedic hospital in Nigeria and analysing the effects of bioclimatic design strategies in enhancing energy efficiency in an orthopaedic hospital in Nigeria. The study engaged a mixed (qualitative and quantitative) research method. The investigators used case study research as a research design and a deductive approach as the research paradigm. The research employed a questionnaire survey for quantitative data while the in-depth Interview (IDI) guide and observation schedule for qualitative data. The findings present a relationship between bioclimatic design strategies and energy conservation practices in an orthopaedic hospital building. Therefore, implementing bioclimatic design strategies might enhance energy efficiency in hospital buildings. The result of the study revealed that bioclimatic hospital designs may cost the same amount to build but can save a great deal on energy costs. Despite the challenges, healthcare designers and owners are finding new ways to integrate bioclimatic design strategies into new healthcare construction to accelerate patient and planet healing.

Analysis of the factors influencing the price of carbon emissions trading in China and its time-varying characteristics is essential for the smooth operation of the carbon trading system. We analyse the time-varying effects of public concern, degree of carbon regulation, crude oil price, international carbon price and interest rate level on China’s carbon price through SV-TVP-VAR model. Among them, the quantification of public concern and the degree of carbon emission regulation is based on microblog text and government decisions. The results show that all the factors influencing carbon price are significantly time-varying, with the shocks of each factor on carbon price rising before 2019 and turning significantly thereafter. The short-term shock effect of each factor is more significant compared to the medium- and long-term, and the effect almost disappears at a lag of six months. Thanks to public environmental awareness, low-carbon awareness and the progress of carbon market management mechanisms, public concern has had the most significant impact on carbon price since 2019. With the promulgation of relevant management measures for the carbon market, relevant regulations on carbon emission accounting, financing constraints, and carbon emission quota allocation for emission-controlled enterprises have become increasingly mature, and carbon price signals are more sensitive to market information. The above findings provide substantial empirical evidence for all stakeholders in the market, who need to recognize that the impact of non-structural factors on the price of carbon varies over time. Government intervention also serves as a key aspect of carbon emission control and requires the introduction of relevant constraints and incentives. In particular, emission-controlling firms need to focus on the policy direction of the carbon market, and focus on the impact of Internet public opinion on business production while reducing carbon allowance demand and energy dependence.

Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.

The article is devoted to the issues of political and legal regulation of climate adaptation in the regions of the Russian Federation. Against the background of the adopted federal national adaptation plan, regions are tasked with identifying key areas of activity taking into account natural-climatic, demographic, environmental and technological specifics. The authors focus on the similarities and differences of the presented adaptation plans, emphasizing that work to improve this system continues within the framework of Russia’s international obligations. The Arctic regions deserve special attention, as they also differ from each other both in the selected climate adaptation activities (from ecology to energy saving) and in their number. This review provides a clear picture of how the federal ecological system can develop.

Personal data privacy regulation and mitigation are critical in implementing financial technology (fintech). Problems with fintech users’ data might result from data breaches, improper usage, and trade. Issues with personal data will result in financial losses, crimes, and violations of personal information. This legal research used three approaches: conceptual, comparative, and statute-based. In order to implement the statutory method, all laws and regulations pertaining to the legal concerns of information technology, fintech, personal data security, and protection are reviewed. Due to the nature of the sources of data, this study mainly used literature study and document observation to collect the data. Then, legal interpretation, legal reasoning, and legal argumentation are all included in the qualitative juridical analysis. This article recommends two strategies that Indonesia should take to provide personal data protection, including: 1) establishing the Personal Data Protection Commission (PDPC); and 2) improving the financial literacy of consumers.

The study aims to investigate the relationship between ESG (Environment, Social, Governance) performance on bank value when moderated by loan loss reserves. Using all 11 Thai listed banks for the period 2017–2021, data were collected from Bloomberg database, the official website of the Stock Exchange of Thailand (SETSMART), and Bank of Thailand, totalling 55 observations. The selected CAMEL indicators served as the control variables. Multiple linear regression and conditional effect analyses were executed using Tobin’s Q as a bank value. This study carefully tested the validity of the dataset, including fixed and random effects. The research outcomes demonstrate the interaction between ESG performance and loan loss reserves has a notably negative effect on the association between ESG performance and bank value. Subsequent analysis reveals that the negative influence of ESG performance on bank value is more pronounced with higher levels of loan loss reserves. These findings have important implications for bankers, investors, and policymakers, offering insights into the dynamics of ESG and loan loss reserves considerations.