In recent times, there has been a remarkable exponential interest in green energy projects, driven by their potential to significantly decrease greenhouse gas (GHG) emissions and reduce reliance on fossil fuels. However, such high-tech projects' complexity, expensiveness, and high uncertainty level necessitate new ways of increasing efficiency. Therefore, this study, with its clear aim to evaluate the efficiency of green energy projects and identify the critical success factors that can enhance efficiency, remains a compelling and relevant research endeavour. The study employed a rigorous methodology, using multilinear regression analysis to survey 123 project managers from Kazakhstan. This allowed for a comprehensive comparison of the efficiency level of green energy projects and low-tech projects. Research results show that green energy projects exhibit the highest percentage of schedule overrun at 23.9% and cost overrun at 32.7%, indicating substantial delays in project completion and extra expenses in budget compared with low-tech projects. Moreover, green energy projects show the least favourable results (7.3) regarding technological performance. The study reveals the following critical success processes enhancing project efficiency: project planning, scope and cost management, communication, and team management. Based on the processes of the Project Management Body of Knowledge (PMBoK) Guide, an algorithm for managing green energy projects was developed. This tool equips project managers with a process-based map, enabling them to run their projects effectively and enhance their efficiency.

Responsible production has become increasingly vital in the global sustainability discourse, particularly in manufacturing. The extended producer responsibility (EPR) system is a critical policy mechanism that encourages manufacturers to reduce their environmental impact. Despite its growing significance, comprehensive studies assessing its effectiveness are sparse. Our research aims to address this gap by evaluating the influence of the EPR system on responsible production practices in European manufacturing enterprises. We employed the difference-in-differences (DiD) method to assess the impact, analysing panel data from 27 manufacturing enterprises across the Czech Republic, Poland, Slovakia, Romania, Estonia, Hungary, and Bulgaria, from 2010 to 2022. This method was chosen to mitigate endogeneity concerns. The results from the DiD analysis reveal a statistically significant positive impact of the EPR system on the circular material use rate, with an average increase of 10,5%. These findings indicate that the EPR system effectively enhances circular material use within the electronics manufacturing industry, a critical sector for advancing environmental sustainability.

This study examines the complex relationships between economic growth and income inequality in different regions of Kazakhstan, revealing the nuances of their interaction. The article aims to assess the long-term and short-term effects of economic growth on income inequality in both forward and reverse directions across the regions of Kazakhstan. Employing region-specific time series data allowed us to examine the bidirectional impact of economic growth on inequality, using an error correction model (ECM) to describe short-run and long-run relationships. The results highlight that the relationship between economic growth and income inequality is heterogeneous across regions, reflecting each area's unique economic and social landscapes. The estimation results support the hypothesis of an inverted U-shaped Kuznets curve linking GRP per capita to inequality with varying starting points for different regions. Regarding the inverse relationship, we identified a positive causal relationship for the West Kazakhstan, Zhambyl and Pavlodar regions, indicating that increased income inequality stimulated economic growth. The study also highlights the significant role of trade, labour force, investment and government consumption in shaping these relationships.

The study explores the impact of artificial intelligence (AI) technologies on project management (PM) across different industries. It aims to assess how AI adoption in PM affects project efficiency. The study surveyed 159 project supervisors and specific project managers implementing projects from 7 industries in the Republic of Kazakhstan: software, green energy, engineering, construction, science, transport, and tourism. The research used variance and linear regression analyses to evaluate the relationship between AI adoption and project efficiency level measured by the Likert scale from 1 to 5 and test the associated hypotheses. The results show that AI adoption varies among industries, with software, construction, and scientific projects being the most active users. The study also found that the use of AI differed across eight project performance domains, with the stakeholder domain using voice technologies and process automation and the uncertainty domain using fewer tools. Projects with higher AI adoption rates showed higher efficiency scores (for example, in Software projects, the AI adoption rate is 3.2; the efficiency rate is 3.3), while those with lower efficiency levels (for example, in the Tourism industry, the AI adoption rate is 1.9; the efficiency rate is 2.2) showed the worst results. Decision-making systems, process automation, and voice technologies are the three most critical AI technologies PM professionals use to improve project efficiency.