Artsrun Avetikyan

Type of the article: Research Article

Understanding the alignment between public interest and academic research is increasingly relevant in the context of global sustainability challenges. This study aims to investigate the relationship between societal attention, as measured by Google Trends, and scholarly output on renewable energy and the shadow economy. Using bibliometric data from Scopus and Web of Science alongside global Google Trends data from 2004 to 2025, the analysis employed Pearson and Spearman correlation coefficients, Granger causality, and distance correlation to assess the strength, direction, and form of association between public search trends and academic activity. The results reveal a significant Granger-causal relationship from public searches on “renewable energy” to academic publications, with F-statistics above 5.2 (p < 0.01), and strong positive correlations (Pearson r = 0.72; Spearman ρ = 0.69; distance correlation = 0.63). In contrast, the terms “informal economy” and “feed-in tariff” demonstrated weak or inconsistent associations, with correlations below 0.25 and statistically insignificant causality tests (p > 0.1). Cross-country comparisons further highlighted uneven alignment, with India showing high search intensity (Google Trends index > 75) but relatively low publication volume (< 2% of global output). At the same time, South Africa displayed closer coherence, with both indicators moving in tandem (r ≈ 0.61). These findings underscore scholarly research’s partial and asymmetric responsiveness to public demand, varying significantly by topic and geographic context. Moreover, while Google Trends offers robust signals of societal interest, disparities in digital access and literacy reduce its universality, pointing to critical underexplored research gaps with direct policy relevance.

Acknowledgment
This study was prepared as part of the project supported by the National Scholarship Programme of the Slovak Republic, the project 101127491-EnergyS4UA-ERASMUS-JMO2023-HEI-TCH-RSCH. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or European Education and Culture Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. This research was funded by the grants VEGA 1/0689/23 “Sustainable growth and the geopolitics of resilience in the context of crisis prevention” and VEGA 1/0254/25 “Artificial Intelligence and FDI-invested Business Service Centers: Selected Macroeconomic and Corporate Aspects”.

Type of the article: Research Article

AI is widely regarded by the IMF and the World Bank as a catalyst for growth. AI should be understood as a multidimensional socio-technical system embedded across institutions, industries, and society. Its economic contribution depends on which pillars of the national AI system expand (e.g., R&D capacity, infrastructure, governance, or social acceptance). For this reason, the seven pillars of AI development are measured by the AI Vibrancy subindices, which help avoid reliance on a single composite indicator that may conceal offsetting effects. This study examines how different pillars of the national AI ecosystem shape the architecture of the knowledge economy and its economic outcomes by estimating heterogeneous within-country associations between GDP per capita and seven AI ecosystem pillars, operationalized through AI Vibrancy subindices, using a balanced panel of 36 countries with complete data over the period 2020–2023. Fixed- and random-effects models are estimated using heteroskedasticity-robust and Driscoll-Kraay standard errors. The results indicate that, within countries over time, the R&D (β = –5.676, p < 0.001) and Infrastructure (β = –16.306, p < 0.001) subindices have strong and statistically significant negative associations with GDP per capita, while Public Opinion shows an adverse effect that is significant at the 5% level under heteroskedasticity-robust inference (β = –9.126, p = 0.040) and marginally significant under Driscoll-Kraay inference (p = 0.054). Responsible AI exhibits a marginally positive association (β = 5.773, p = 0.065) in the Driscoll-Kraay specification, whereas Economy, Education, and Policy & Government show no significant within-country effects.