The paper aims to investigate the impact of artificial intelligence, trade, natural resource rents, urbanisation, renewable energy consumption, and industry structure on economic development in selected European countries for the period from 2012 to 2024. By integrating technological, environmental, and structural drivers into a single empirical framework, the study provides a more comprehensive assessment of economic development dynamics than previous research. Given the presence of cross-sectional dependency, heterogeneity, mix-stationarity, and cointegration of the data, the analysis employs different panel models such as Generalised Method of Moments (GMM), Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Dynamic Common Correlated Effects Mean Group estimator (DCCE–MG). The results indicate that artificial intelligence, trade, and natural resource rents have a significant impact on economic development across all applied models. Urbanisation, renewable energy consumption, and industry structure also demonstrate positive effects on economic growth in GMM, FMOLS, and DOLS estimations. Beyond the macroeconomic perspective, the applied models are also relevant for sector-specific contexts. For example, a potential application can be found in the tourism and hospitality sector, where digital transformation, structural characteristics, and environmental factors increasingly shape long-term economic performance. The findings imply that policymakers should promote investments in artificial intelligence, sustainable energy transition, and balanced industrial development in order to strengthen economic growth in European economies, while the applied modelling framework also provides valuable implications for sector-specific economic policies.
Minović et al. (Tue,) studied this question.