With the increasing climate change crisis, the ongoing global energy security challenges, and the prerequisites for the development of sustainable and affordable energy for all, the need for renewable energy resources has been highlighted as a global aim of mankind. However, the worldwide deployment of renewable energy calls for large-scale financial and technological contributions which many States cannot afford. This exacerbates the need for the promotion of foreign investments in this sector, and protecting them against various threats. International Investment Agreements (IIAs) offer several substantive protections that equally serve foreign investments in this sector. Fair and Equitable Treatment (FET) clauses are among these. This is a flexible standard of treatment whose boundaries are not clearly defined so far. Investment tribunals have diverse views of this standard. Against this background, this article asks: What are the prominent international renewable energy investment threats, and how can FET clauses better contribute to alleviating these concerns? Employing a qualitative method, it analyses the legal aspects and properties of FET and concludes that the growing security and regulatory threats have formed a sort of modern legitimate expectations on the part of renewable energy investors who expect host states to protect them against such threats. Hence, IIAs and tribunals need to uphold a definite and broadly applicable FET approach to bring more consistency and predictability to arbitral awards. This would help deter many unfavourable practices against investments in this sector.

This study investigates the career expectations of individuals in Thailand’s emerging economy, emphasizing the critical factors that shape these expectations within the context of a rapidly evolving labour market in the digital era. A quantitative approach was employed, collecting data from 1230 Thai respondents through convenience sampling, utilizing a structured survey as the primary research instrument. Data analysis involved the use of percentages, means and logistic regression to provide a comprehensive understanding of the findings. The results indicate that factors such as gender, age, monthly income, professional identity, values, culture and technology usage (including devices like laptops, social media platforms, home internet access and usage hours) significantly influence career expectations. Understanding these influential factors is crucial for developing targeted strategies to enhance career satisfaction, preparedness and overall competitiveness in an increasingly globalized and digital economy. By addressing the unique needs and aspirations of the Thai workforce, particularly in this digital age, stakeholders can cultivate a more responsive and adaptive professional environment, ultimately contributing to national economic growth in the digital era.

A comprehensive survey was conducted in 2012 and 2020 to assess the financial culture of Hungarian higher education students. The findings revealed that financial training effectiveness had not improved over time. To address this, a conative examination of financial personality was initiated by the Financial Compass Foundation, which gathered over 40,000 responses from three distinct age groups: Children, high school students, and adults. The study identified key behavioral patterns, such as excessive spending and financial fragility, which were prominent across all age groups. These results informed Hungary’s seven-year strategy to enhance financial literacy and integrate economic education into the National Core Curriculum. The research is now expanding internationally with the aim of building a comparative database. The study’s main findings highlight the widespread need for improved financial education, with more than 80% of adults demonstrating risky financial behaviors. The implications of these findings suggest the importance of early financial education and tailored interventions to foster long-term financial stability. The international expansion of this research will allow for the examination of country-specific financial behaviors and provide data-driven recommendations for policy development.

The year of 2024 marked the twelfth anniversary of the cooperative mechanism between China and Central and Eastern European countries (China-CEEC). China has repeatedly affirmed its willingness to implement the 2030 Agenda for sustainable development and the sustainable development goals (SDGs), which created many opportunities to enhance the cooperation of the two sides. The paper exemplified some cases in the process of the cooperation, which were rarely discussed previously as normally it was dominated by the large-scale investment project. The cases of the climate change and ocean issues were perceived as a package of holistic EU-China relations that demonstrates the commitments from both sides to deal with SDG 13 and SDG 14. A qualitative method of the policy-circle evaluation and the goal-setting in the global governance was applied in the paper. The findings affirm that the current China-CEEC cooperation scheme is still carrying on both opportunities and challenges and affected by various internal and external factors.

The usage of cybersecurity is growing steadily because it is beneficial to us. When people use cybersecurity, they can easily protect their valuable data. Today, everyone is connected through the internet. It’s much easier for a thief to connect important data through cyber-attacks. Everyone needs cybersecurity to protect their precious personal data and sustainable infrastructure development in data science. However, systems protecting our data using the existing cybersecurity systems is difficult. There are different types of cybersecurity threats. It can be phishing, malware, ransomware, and so on. To prevent these attacks, people need advanced cybersecurity systems. Many software helps to prevent cyber-attacks. However, these are not able to early detect suspicious internet threat exchanges. This research used machine learning models in cybersecurity to enhance threat detection. Reducing cyberattacks internet and enhancing data protection; this system makes it possible to browse anywhere through the internet securely. The Kaggle dataset was collected to build technology to detect untrustworthy online threat exchanges early. To obtain better results and accuracy, a few pre-processing approaches were applied. Feature engineering is applied to the dataset to improve the quality of data. Ultimately, the random forest, gradient boosting, XGBoost, and Light GBM were used to achieve our goal. Random forest obtained 96% accuracy, which is the best and helpful to get a good outcome for the social development in the cybersecurity system.