Climate change has intensified the need for effective mechanisms to reduce greenhouse gas emissions while maintaining economic efficiency. Carbon credit trading emerged as a market-based solution under international climate frameworks, enabling the allocation and exchange of emission allowances through structured regulatory systems. This study examines the concept, functioning, and economic relevance of carbon credit trading with particular emphasis on its evolution as a potential financial asset class. The research follows a descriptive and comparative design based on secondary data collected from official reports and market databases. Carbon credit price trends over a five-year period (2021–2025) were analysed across selected global Emissions Trading Systems, including the European Union Emissions Trading System, Canada Federal Output-Based Pricing System, California Cap-and-Trade Program, China National Emissions Trading Scheme, Germany National Emissions Trading System, Korea Emissions Trading Scheme, New Zealand Emissions Trading Scheme, Switzerland Emissions Trading System, Tokyo Cap-and-Trade Program, and the UK Emissions Trading Scheme. Year-wise price comparison was conducted to examine market behaviour, demand–supply interaction, and price volatility. The findings reveal significant variations in carbon credit prices across different markets, reflecting differences in regulatory frameworks, policy stringency, and market maturity. European markets demonstrated comparatively higher price levels, while certain Asian markets maintained lower and more stable price bands. The observed price fluctuations, structured trading platforms, and measurable economic value indicate that carbon credits possess characteristics similar to tradable financial assets.

As artificial intelligence( AI ) is reshaping the global economy, transitioning youth from digital consumers to AI- driven entrepreneurs is no longer an option but a critical requirement to thrive in ever evolving AI landscape. However, while AI literacy is increasing there remains lack of data on whether the youth are strategically prepared to lead AI- driven entrepreneurial ventures. To assess this, a quantitative study was conducted among youth aged 17-25, via Google Forms, based on their proficiency in technology, perception towards automated systems, and the risk-taking and innovative mindset required for AI-driven business environments. The results indicated that while the youth possess foundational knowledge regarding AI and a positive outlook towards integration of AI into entrepreneurship, there was a significant need for specialized skill-training programs, ethical frameworks, and experiential learning opportunities provided by educational and governmental institutions to enhance their preparedness for futuristic AI-driven entrepreneurial ecosystems.

This study looks at how AI-assisted decision-making affects human judgment. It mainly focuses on people’s awareness of AI, how much they trust it, how much they depend on it, and the ethical concerns they may have. The research is based on primary data collected from 100 respondents through a structured questionnaire, along with information from secondary sources. The findings show that although AI reduces effort and improves analysis, most users still prefer to check or verify the suggestions given by AI. Some common concerns include over-dependence on AI, reduced critical thinking, data privacy issues, and bias in systems.

Artificial Intelligence (AI) is rapidly transforming financial systems across the globe, particularly in emerging markets where traditional risk assessment methods often fail due to data limitations and market volatility. This study examines the role of AI-based financial risk prediction models in improving decision-making, enhancing credit evaluation, and minimizing investment uncertainty in developing economies. The research explores how machine learning algorithms, predictive analytics, and big data integration contribute to identifying potential financial risks such as credit defaults, market fluctuations, and fraud detection. Emerging markets often face challenges such as inconsistent financial records, lack of transparency, and dynamic regulatory environments, making AI-driven solutions highly relevant. The study highlights the effectiveness of AI in improving forecasting accuracy, reducing human bias, and enabling proactive risk management. However, it also considers limitations such as data privacy concerns and technological barriers. The findings suggest that AI-based risk prediction systems can significantly strengthen financial stability and support sustainable economic growth in emerging markets.

The ADHD awareness ecosystem in India is undergoing a transformative shift, yet significant gaps remain in recognition, diagnosis, and structured support systems. Our research aims to identify systemic gaps in ADHD awareness while addressing challenges faced by awareness-driven startups and existing remedial frameworks. The study focuses on understanding lack of awareness, social stigma, misconceptions, shortage of proper facilities, and privacy concerns within society. It also proposes practical solutions to strengthen ADHD screening, early identification, and access to appropriate treatment in India. The research is descriptive in nature and is based on primary data collected through structured questionnaires and interviews from 107 respondents in the KDMC region. A purposive sampling technique was employed for the proposed research. Collected data were analyzed using both quantitative and qualitative methods. The current study also suggests an innovative model to enhance ADHD awareness, improve screening mechanisms, and promote accessible treatment support systems.

The HR system had gone through multiple stages of evolution as it first started as fully manual process then it developed itself and easied E-documentation process by introducing tools like hris erp, etc reducing workload and improving efficiency among the employees. Even with such advancement in HR technologies companies still require more use of HR intelligence system. Our study help to understand the advantages of HR technologies and also understanding the limitation where we can adopt more technology This study aims to analyze how information system operates systematically within the human resource department of an organization. It also tries to understand how intelligent HR systems optimize the use of various resources such as human capital, financial resources, and technological tools. Additionally, the study focuses on analyzing the processes, policies, and structured framework involved in organizational development systems.

Artificial Intelligence (AI) has emerged as one of the most transformative technologies in modern business operations, particularly in the accounting profession. AI enables automation of repetitive tasks, improves accuracy, enhances data analysis, and supports decision-making processes in financial management. The integration of AI tools in accounting systems helps organisations reduce human error, increase efficiency, and improve financial reporting. This research study examines the role of Artificial Intelligence in the accounting system and its impact on accounting professionals and organisations. The study also explores how accountants and finance professionals perceive AI adoption in their work environment. A survey of 30 respondents in accounting and finance roles was conducted to understand their awareness, use, and impact of AI technologies in accounting. The findings suggest that AI significantly improves efficiency in accounting operations, enhances financial accuracy, and helps accountants focus on strategic decision-making rather than routine tasks. However, concerns about job displacement, technological adaptation, and skill development remain. The study highlights that the future of accounting will involve collaboration between human accountants and AI-powered technologies.