Diana Sitenko

The implementation of innovative renewable energy projects is designed to meet the growing electricity demand and reduce carbon dioxide emissions into the atmosphere. In order to avoid resource dependence of the economy and meet the demand for electricity in remote areas of Kazakhstan, the development of renewable energy sources is urgent. The purpose of this study is to analyze the effectiveness of the implementation of existing mechanisms for the implementation of innovative renewable energy projects through auctions. Moreover, it identifies their shortcomings and offers proposals for improvement. The analysis uses data from the Kazakhstan Bureau of National Statistics on renewable energy sources, namely alternative energy sources such as solar and wind power plants, excluding biogas. The methodology provides for the assessment of projects implemented through auctions for 2018–2021, taking into account such indicators as the dynamics of the number of wind and solar energy projects, their capacity, the percentage of auction price reduction for a specific year. The paper discusses the mechanism for conducting auctions of solar and wind electricity, which, through the auction organizer, connects authorized bodies and investors. The main indicators of auctions for individual years are noted, and the main shortcomings of this mechanism are identified. The analysis of barriers to innovative renewable energy project implementation revealed the presence of contradictions in the price regulation of renewable electricity, the lack of market pricing mechanisms, and the unpreparedness of the energy infrastructure for integration with renewable sources.

Acknowledgment
This research is funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP13268750).

The transition to an innovative economy requires greater attention to creating favorable conditions for the commercialization of scientists’ developments and the possibility of realizing the accumulated scientific potential. This study aimed to examine the commercialization process in the Republic of Kazakhstan and identify factors influencing the development of academic entrepreneurship in universities. It examines a gradual change in legislation on technology transfer and the dynamics of implemented commercialization projects during 2016–2022. Structured interviews were conducted with academics of the biggest 14 universities in Kazakhstan with a sample of 209 respondents to identify factors influencing the desire of scientists to engage in academic entrepreneurship. The findings revealed that the most attractive factors for academics are flexible working hours (4.67 of 5), the opportunity to implement their own innovative ideas (4.12), and an increase in income (3.63). In turn, negative factors include the lack of qualified personnel (4.56), difficulties in legislation (4.27), and bureaucratic barriers (3.78). The study revealed that gender and age moderately affected scholars’ desire to engage in academic entrepreneurship (Cramer’s V = 0.3025). The greatest desire to start their own business was demonstrated by men aged 26-35 years and by women aged 36-45 years. The findings also show that the scientific fields positively affect the number of ready-made ideas, patents, and technologies that academics offer to businesses.

Acknowledgment
This study is funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP13268750).

In an environment where public funding is insufficient to meet international climate and energy goals, feed-in tariffs serve as an essential mechanism to mitigate investment risk and foster the participation of insurance companies as institutional investors in the renewable energy sector. This study aims to investigate whether feed-in tariff policies enhance the evolving effect of insurance sector development on renewable energy consumption across countries and over time. Given that both financial sector capacity and renewable energy transitions are dynamic processes, the analysis explicitly applies econometric techniques designed to capture temporal changes and investment inertia. Using panel data econometric techniques, including fixed effects models with cluster-robust standard errors and dynamic panel estimation (Arellano-Bond GMM), the analysis covers 64 countries from 2000 to 2020. The results reveal that greater insurance sector assets positively correlate with higher renewable energy consumption, with a coefficient of 0.143 (p < 0.01) in the fixed effects model. Still, the strength and significance of this relationship are notably enhanced when feed-in tariffs are in place, as shown by a positive and statistically significant interaction term (coefficient 0.051, p < 0.05) after adding time-fixed effects. The empirical results show that insurance companies can serve as critical institutional investors in the renewable energy sector. Still, their active participation critically depends on supportive policy frameworks, with the positive association between insurance company assets and renewable energy consumption becoming significant, particularly in countries with feed-in tariff schemes.

Acknowledgment
This study was prepared as part of the project IZURZ1_224119/1 (Swiss National Science Foundation) and the National Scholarship Programme of the Slovak Republic.
The publication was funded by the European Union grant “NextGenerationEU through the Recovery and Resilience Plan for Slovakia” (No. 09I03-03-V01-00130) and project VEGA – 1/0392/23 “Changes in the approach to the creation of companies’ distribution management concepts influenced by the effects of social and economic crises caused by the global pandemic and increased security risks.”

Type of the article: Research Article

Abstract
Central bank digital currency (CBDC) programs have rapidly shifted from experimentation to policy-critical infrastructure decisions, yet countries show strikingly uneven progress from research to pilots and implementation. This study aims to identify and explain the key structural, macroeconomic, technological, and ecosystem-related factors that differentiate CBDC initiatives advancing to pilot or implementation stages from those remaining in early research or being discontinued across countries worldwide. Using 161 CBDC projects across 109 countries (as of December 2024) and 10 project-, public interest-, technology-, and macroeconomic indicators, we estimate a Support Vector Machines classifier with GridSearchCV (5-fold) tuning and interpret the results using Shapley Additive exPlanations explainability. The raw outcome distribution was strongly imbalanced (83.85% in the early/cancelled class), so ADASYN balancing was applied, producing 270 observations with equal class shares and an 85/15 train–test split (229/41). The optimized SVM (RBF; C = 10, gamma = 10) achieved 93.90% cross-validated accuracy and 0.88 accuracy on the test set, indicating strong predictive performance on unseen data. Test-set metrics show an informative error profile: for class 1 (advanced projects), recall = 1.00 and F1 = 0.89, while for class 0 (early/cancelled), precision = 1.00 with recall = 0.75 (macro/weighted F1 = 0.88), implying that the model identifies all advanced projects but may misclassify around one-quarter of early/cancelled cases. SHAP ranks the strongest drivers as use-case direction, inflation, crypto adoption ranking, CBDC-related research output, and international participation, with mixed/wholesale projects, higher inflation, stronger scientific attention, and greater international involvement generally increasing the likelihood of advancement.

Type of the article: Research Article

Abstract
Innovative marketing increasingly requires reliable market intelligence signals that reduce uncertainty, support product positioning, and guide venture financing in technology-intensive green and digital markets. This study aims to assess how patent-based technological signals shape innovative market development by predicting the formation and venture financing of green and digital energy start-ups, while also examining whether entrepreneurial market entry and funding stimulate subsequent patenting activity. The empirical analysis is based on a balanced panel of 146 countries for 2000–2023, combining IEA energy start-up and funding indicators with OECD patent data. The empirical strategy follows a sequential design: descriptive statistics and log1p transformations are used to characterize the data; Dumitrescu–Hurlin panel Granger causality tests provide the main evidence on predictive causality; and PVAR, multiple-testing corrections, PPML and TWFE models are used as complementary robustness and dynamic checks. The results show highly concentrated innovative market development: average green and digital energy start-up activity is around 7 per country-year, while the median is 0 for both indicators. The Dumitrescu–Hurlin tests reveal 69 significant relationships out of 120, with stronger evidence for patents predicting start-up formation and funding than for the reverse direction. These findings remain robust after Benjamini–Hochberg correction and after excluding numerically extreme statistics. TWFE results support the positive association between climate adaptation and ICT-mitigation patents, digital energy start-up formation and early-stage digital funding, while PVAR models provide only complementary dynamic evidence and are interpreted cautiously due to stability limitations in the main GMM specification. Country fixed effects indicate that Ukraine has a more favorable estimated structural position for digital energy start-up formation than Kazakhstan and Armenia.