Alina Danileviča

Type of the article: Research Article

Abstract
The interconnection between renewable energy development and the shadow economy has become increasingly important as governments pursue sustainability objectives alongside fiscal transparency and the fight against tax evasion. This study aims to analyze how informal economic activity shapes the deployment of renewable energy and how renewable initiatives may support economic formalization and social responsibility. A bibliometric study of 161 documents retrieved from Scopus and Web of Science was conducted using Biblioshiny, assessing metadata completeness, thematic structures, author productivity, and collaboration networks. The results show excellent metadata coverage (abstracts, titles, and document types at 100%), though cited references were completely missing (100%), with keywords absent in 18% of records. Research output accelerated after 2015, with 2020 being the year with the highest citation velocity (7.81 citations/year), driven by two publications with over 100 citations each. Thematic mapping identified “renewable energy,” “shadow economy,” and “sustainable development goals” as motor themes, while “circular economy” and “policy uncertainty” emerged as basic but growing clusters. International collaboration accounted for 38% of documents, though single-country studies remain dominant, and citation analysis revealed a steady rise in impact, with top sources surpassing 120 citations. The analysis confirms a growing yet fragmented field, highlights the dual role of informality, from undermining fiscal revenues to supporting decentralized energy, and points to governance, circular economy, and policy risk as critical areas for future research.

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. This article funded by Daugavpils University (Latvia), EKA University of Applied Sciences (Latvia).

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.