Farhad Rahmanov

The digital transformation of education is a modern trend, and it is essential to investigate its role in enhancing quality education as one of the Sustainable Development Goals. The purpose of the paper is to confirm the restraining or accelerating impact of education digitalization on achieving SDG4 in Azerbaijan compared with SDG4 leaders. The sample consists of the chosen data of 14 indicators in the education digitalization field (provided by the Statistics Division of the United Nations Department of Economic and Social Affairs, United Nations Educational, Scientific and Cultural Organization Institute for Statistics Data, and the International Telecommunication Union) in 2016–2023 from 10 countries: Azerbaijan (status ‘Challenges remain’ for SDG4) and nine leading countries (status 'Goal Achievement') according to 2024 SGD Index Rank. To achieve the Goal, the Granger causal test and regression analysis (linear modeling for time series and random-effects GLS regression for panel data) using Stata SE 18.0 software were applied. It was confirmed that in Azerbaijan (both separately and within a panel of studied countries), the impact of education digitalization on achieving SDG4 is positive and accelerating (for specific indicators, it is not significant but not restraining). The stronger impact is observed in Azerbaijan compared to the average on the panel level. The key accelerators are school-level Internet access, digital practical skills, and digital literacy, especially proportions of youth/adults who have used arithmetic in spreadsheets, connected and installed new devices, and created electronic presentations (increase in SDG4 achievement indicator by 2.63, 3.5%, and 9.89%).

Type of the article: Research Article

Abstract
The effectiveness of international development assistance in promoting sustainable development remains a central question for the management of multilateral aid and public governance as the 2030 Agenda enters its final years. This paper examines the relationship between aid effectiveness, measured by the World Bank’s Country Policy and Institutional Assessment (CPIA), and the achievement of the Sustainable Development Goals (SDGs) across 76 IDA-eligible countries over the period 2005–2023. Using two-way fixed-effects panel estimations with clustered standard errors, the analysis covers five individual SDG indicators (poverty, child mortality, primary education, electricity access, and employment) and the composite SDG Index. The results show that the overall CPIA score is significantly associated with poverty reduction in low-income countries (β = −12.0, p < 0.10), while its effect on the composite SDG Index is statistically insignificant within countries, despite a strong cross-sectional association. A cluster decomposition reveals that structural policies drive improvements in child mortality and electricity access, while economic management supports employment outcomes. Sub-sample analysis demonstrates pronounced heterogeneity: the marginal return to institutional quality is highest in the poorest economies and shifts toward health and labor market outcomes as countries move up the income ladder. GDP per capita remains the dominant predictor across all SDG dimensions, confirming that aid effectiveness complements rather than substitutes for domestic economic capacity. These findings support a differentiated management approach to development assistance that targets specific governance dimensions to specific SDG outcomes and prioritizes institutional strengthening in the most resource-constrained settings.

Type of the article: Research Article

Whether national artificial intelligence (AI) ecosystem development shapes tourism’s contribution to GDP is an open empirical question, particularly given the multidimensional nature of modern AI ecosystems and the heterogeneous reliance of countries on tourism. This study identifies which dimensions of national AI ecosystem development drive within-country changes in tourism’s direct GDP share, using panel data from 33 countries over 2017–2023. Fixed-effects estimation with clustered standard errors is applied to both the composite Stanford HAI AI Vibrancy Score and its seven constituent pillars, complemented by lagged, dynamic, and interaction specifications. The aggregate AI Vibrancy Score shows no significant within-country effect on tourism’s GDP share after controlling for macroeconomic factors (β = 0.061, p = 0.622), indicating that overall AI vibrancy alone does not measurably move tourism’s economic contribution. The pillar decomposition reveals, however, that this null result masks two significant positive drivers of tourism’s GDP share – AI-related R&D (β = 1.811, p = 0.005) and Policy and Governance (β = 0.353, p = 0.037) – both robust to alternative standard errors and two-way fixed effects. The Talent pillar exerts a significant positive effect on tourism’s GDP share with a one-year lag (β = 0.183, p = 0.025), indicating that the human-capital channel requires time to materialize. The COVID-19 pandemic reduced tourism’s GDP share by approximately 37% (β = –0.455, p < 0.001), and AI development did not moderate this decline. The findings imply that targeted AI policies – particularly in R&D and governance – can strengthen tourism’s economic contribution, while aggregate AI metrics obscure heterogeneous pillar-level effects.