Highland Areas in the Digital Era: From Abyss to Quatum. European Mountain Series Forecasting (10)

The main objective of this research was to evaluate entrepreneurial dynamics and the labor market in the post-pandemic context through a longitudinal analysis of 28 relevant statistical indicators from Eurostat, from Information and Communication Technology mountain entrepreneurship, with projections toward 2035. These indicators reflect essential aspects of the economic structure, such as business creation and closure, survival rates, labor force dynamics, occupational distribution, and other indirect factors related to economic resilience. The analysis was conducted for 15 European countries, and the statistical models used were ARIMA (Autoregressive Integrated Moving Average) and exponential smoothing models. The data were structured into four main categories: entrepreneurial demography, business performance and growth, employment and labor force structure, and labor market distribution. ARIMA models showed a moderate capacity to explain data variation, with generally low R-squared values, but identified significant correlations for indicators reflecting percentages and relative structures. Exponential smoothing models, on the other hand, failed to effectively capture the dynamics of most indicators, suggesting the need for more advanced modeling techniques. The results suggest that in the future, multivariate models or machine learning techniques should be integrated to more accurately capture seasonality and nonlinear behaviors.