Governments enter into public–private partnership (PPP) agreements to attract private financing and bring innovation to the development of their sustainable public infrastructure; however, PPP projects are marked by their complexity and are driven by uncertain economic and institutional environments. The purpose of this study is to conduct a cross-country comparison of PPP risks in three developing countries (Colombia, Kazakhstan, and Ghana) and provide insights into their best practices. The research surveyed diverse risk factors involving 261 local respondents with pertinent experience in PPPs. The study conducted Cronbach’s alpha and Kendall’s coefficient of concordance tests to check the validity of responses, an ANOVA test to examine the differences in the risk perceptions, and risk ranking to reveal the country-specific as well as top-rank risks in the countries. The results of the quantitative analysis revealed risk aversion among developing countries with PPP programs at different maturity stages. Less mature programs, with lower overall investment, exhibited greater overall risk aversion (for Kazakhstan) and greater concerns about transparency and corruption (for Ghana). Highly populated countries with more mature PPP programs that rely significantly on transportation projects demonstrated higher risk aversion regarding the social and political legitimacy of PPPs and land acquisition (for Colombia).

The study aims to investigate the adoption and impact of artificial intelligence (AI) in project management (PM) in Kazakhstan. One hundred fifty-nine experts from the academic and industrial sectors provided the survey data. Statistical methods, social network analysis, and analysis of variance were used to evaluate the significance of changes in the impact of AI, using a Tukey post-hoc test. The study results showed differences in the degree of AI adoption across eight PDs and the need for a customized approach to maximize the benefits of AI in PM. The research found that different PDs see different effects of AI on project success. Low technology readiness, financial constraints, and insufficient experience were the main barriers to effective AI adoption. This study offers practical recommendations for project-oriented companies implementing AI, thereby filling a gap in the literature by proposing PM as a new application area for dynamic models of human-machine interaction. Future studies should expand the geographic sample and add other factors influencing AI applications.

The holistic collaboration between the government and business through public-private partnerships (PPPs) is crucial in developing economies in transition. With the help of the PPP mechanism, emerging economies realize new and maintain existing social and economic infrastructure projects in education, energy, transportation, and healthcare. This lessens the funding burden on the state budget and attracts private investment and expertise into the economy. However, one of the critical tasks is to engage a suitable private partner with whom a long-term relationship can be built for mutual benefit. The most vital issue in establishing and maintaining the PPP collaboration with such a partner is related to the appropriate management of business risks. This study focuses on the success of an infrastructure project for a private partner by analyzing the critical risks inherent in PPP projects. The research uses system dynamics (SD) modeling, which qualitatively and quantitatively determines how each risk affects the project realization. The study examines the largest PPP project in Central Asia, the Big Almaty Ring Road. This toll road project is taken as a case to demonstrate the impact of the most critical risks on the success of the PPP projects. For the simulation, the “hard tolls” form, which is one of the popular payment mechanisms in the global PPP practice, is considered where the private partner bears most of the risks. The findings show the riskiness of such a payment mechanism in implementing toll road projects with existing traffic and tariffs under conditions of uncertainty typical for Kazakhstan and other similar developing countries.

This study aims to investigate the barriers hindering the transition to renewable energy sources (RES) in Kazakhstan, a developing oil-exporting country facing rising energy demand, environmental degradation, and increasing pressure to meet international climate commitments. The research combines a review of relevant literature with insights drawn from 20 structured expert interviews, applying the analytical framework developed by J. P. Painuly. The research then provides a comprehensive analysis of the barriers to RES adoption in Kazakhstan and their implications for policy and investment. This analysis is structured around seven key barrier categories: (1) market failures and imperfections; (2) market distortions; (3) economic and financial; (4) institutional; (5) technical; (6) social, cultural, and environmental and (7) other barriers. The findings indicate that while Kazakhstan possesses significant renewable energy potential, its realization is obstructed by several challenges, which include among others high risks associated with renewable energy projects, the lack of competitiveness of renewables due to government subsidies for conventional energy, barriers faced by small producers and prosumers, the need for regulatory improvements and increasing public awareness. Despite some positive policy developments, such as revised power purchase agreements, substantial reforms are still needed to improve market access, mitigate investment risks, and enhance institutional capacity. This paper contributes to the broader discourse on energy transitions in oil-dependent economies and offers policy-relevant insights to support the country’s shift toward a sustainable and diversified energy future.

Type of the article: Research Article Scientific productivity is one of the key benchmarks to evaluate the development of a country in terms of innovation potential and competitiveness in the global academic environment. This study investigates the impact of key factors such as faculty workload and satisfaction, career dynamics, institutional environment, and research funding on publication performance. The methodology is based on system dynamics (SD), a simulation approach that assesses the dynamic impact of multiple factors on the publication performance. The approach combines causal-loop analysis, stock-and-flow diagrams, sensitivity testing, and Monte Carlo simulations, with Kazakhstan serving as a case country. The data on the factors, including from the global literature and statistics from Kazakhstan, were simulated to forecast publication performance and research output through 2030. Sensitivity analysis identified six dominant factors affecting the number of researchers: average wage growth rate, Ph.D. growth rate, average author tenure, normal workload, job satisfaction, and productivity rate. In contrast, the number of publications was most influenced by the research expenditure per paper, government investment growth rate, and private sector investment growth rate. The study findings from Kazakhstan can serve as “lessons learned” and assist researchers and policymakers working in the science policy and evaluation area in identifying high-impact factors and formulating more effective strategies to enhance the scientific knowledge creation of a country.Acknowledgments This paper has been funded by the Science Committee MSHE RK (Grant “Modernization of the quality assurance system of higher education in Kazakhstan based on digitalization: development of approaches, mechanisms and information base” BR24992974).