Presently, there has been increasing policy attention from financial regulators in emerging banking systems on the need to look into the institutional mechanisms that could strengthen revenue generation of commercial banks in digital banking ecosystems. This study was an attempt to highlight the role of Open Banking platforms and API-based financial services in determining bank revenue growth in digital banking markets & financial service ecosystems (Uzbekistan). Therefore, the empirical findings of the present study can be used to better comprehend how Open Banking frameworks could be implemented in enhancing bank revenue streams in Uzbekistan. The previously developed Open Banking adoption indicators, API service readiness indicators, and bank revenue determinants framework in digital financial studies were used to collect data from banking professionals in commercial banks and fintech institutions. AHP’s prioritization results and structural equation modeling results on Open Banking adoption and API service integration increased significantly after evaluation with the support of the SEM analytical model. Additionally, the results of AHP analysis showed that Open Banking services and API-enabled platforms were the main areas of priority to be adopted by commercial banks on the basis of revenue-generation potential and service-integration capability, respectively. Moreover, the results also showed that out of five determinants, API service integration played a significant role in linkages between Open Banking adoption and bank revenue growth. The implications derived from this study can be used for enhancing bank revenue diversification in the context of Open Banking ecosystems. The finding is important given that higher levels of the Open Banking infrastructure are often found in digital banking systems of developed economies which cost less per unit of financial transaction – as there is less manual processing involved.
Read MoreDoi: https://doi.org/10.54216/JSDGT.060101
Vol. 6 Issue. 1 PP. 01-12, (2026)
Renewable electricity growth is central to sustainable development, decarbonization, and green-technology planning. However, much of the forecasting literature remains focused on plant-level or narrow-horizon technical prediction, with limited attention to country-level decision support for investment screening, transition monitoring, and strategic benchmarking. This study develops a business analytics framework to forecast the renewable share of electricity generation and classify countries by renewable-transition level using a cross-country panel based on the Our World in Data Energy database. The empirical sample comprises 5,162 country-year observations from 213 countries over the period 2000–2025 and includes measures of electricity demand, electricity generation, primary energy use, greenhouse-gas emissions, and energy-system structure. Three regression models and three classification models were evaluated using a fixed train–test de sign. The random-forest regressor achieved the best continuous forecasting performance, with MAE = 3.536, RMSE = 6.466, and R2 = 0.960, while the random-forest classifier delivered the best tier-classification performance, with 93.998% accuracy and macro-F1 = 0.940. Feature-importance analysis identified greenhouse-gas emissions, energy intensity, electricity generation, electricity demand, and per-capita electricity consumption as the most influential predictors. The findings indicate that renewable-transition benchmarking can be framed as a managerial analytics problem, extending sustainability research beyond descriptive monitoring toward practical decision support for business and policy planning.
Read MoreDoi: https://doi.org/10.54216/JSDGT.060102
Vol. 6 Issue. 1 PP. 13–22, (2026)
This study assesses whether there is sufficient justification for developing an MBA in Sustainable Economics and Finance in selected higher education contexts in Uzbekistan. The research is based on two surveys conducted among stakeholders and prospective candidates. The findings show that sustainability-related economic and financial competencies are viewed as increasingly relevant, while stakeholders also identify clear skills gaps in this area. Prospective candidates show a generally positive but still conditional interest in the proposed programme. The results suggest that the programme would be most viable if designed as a practical and career-oriented MBA with strong emphasis on applied skills, internships, and real-world relevance. At the same time, affordability, language accessibility, and expected career outcomes appear to be important conditions shaping demand. Overall, the study concludes that the proposed MBA has a credible foundation in the surveyed context, but its success will depend on careful programme design and alignment with labour-market needs.
Read MoreDoi: https://doi.org/10.54216/JSDGT.060103
Vol. 6 Issue. 1 PP. 23-32, (2026)
The efficient functioning of the electric-vehicle charging systems that are publicly operated has become focused on reliable short-horizon forecasting. The paper establishes an explainable and price-conscious analytical model to predict short-term charging usage and demonstrate the utility of tariff signals in an urban charging system. The analysis is based on UrbanEV benchmark, a new six months hourly panel of Shenzhen public charging infrastructure, which integrates occupancy, charging time, charging volume, electricity tariffs, service charges, weather and spatial descriptors. The concept of charging occupancy is considered an operation state variable with connection to queue exposure, reliability of service, and tactical intervention. A succinct mathematical formulation is created to use it in one-step-ahead utilization forecasting and in interpreting low-, medium-, and high-utilization regime. The empirical analysis is pegged to benchmark evidence reported to UrbanEV, where transformer-based forecasting had the best node-level performance and TimeXer had the best RMSE values of 0.07 in occupancy, 2.73 in charging duration, and 43.66 in charging volume. Further discussion indicates that occupancy prediction is accurate enough to justify regime based intervention and strongest additional gains are obtained through the joint effect of pricing variables and temperature-price interactions as opposed to single covariates. The results justify the justifiable, price-conscious forecasting as an operational decision tool to alleviate congestion, design tariffs and specific capacity planning in sustainable charging networks.
Read MoreDoi: https://doi.org/10.54216/JSDGT.060104
Vol. 6 Issue. 1 PP. 33–42, (2026)
