This study examines how social media–driven information environments shape pro-environmental travel decision-making within the broader context of sustainable tourism and digital information expansion. It aims to explain how informational, cognitive, and evaluative mechanisms jointly influence behavioral intention by integrating perceived trustworthiness of user-generated content, ecological awareness, attitude formation, and cognitive processing bias within a moderated mediation framework. Data were collected from 315 respondents in Azerbaijan through a structured survey and analyzed using structural equation modeling to test both mediating and conditional relationships. The results show that perceived trustworthiness of user-generated content significantly strengthens attitude formation, which is the strongest predictor of behavioral intention. Trust also has a direct effect, but its influence mainly operates through evaluative pathways. Ecological awareness partially mediates the relationship between trust and intention, indicating multiple parallel mechanisms. Furthermore, cognitive processing bias weakens the effect of trust on attitude and reduces indirect effects. Overall, the findings suggest that sustainable tourism behavior is driven more by credibility-based cognitive evaluation than by mere information availability, highlighting the importance of credibility-enhancing and cognitively effective communication strategies.

Geopolitical risk is an increasingly important source of environmental uncertainty in resource-rich economies, where energy transitions depend on economic activity, energy-system composition, and foreign investment. This study examines these interrelated dynamics using annual data for Azerbaijan over 1996–2024 and an augmented autoregressive distributed lag (A-ARDL) bounds-testing framework. The approach extends conventional ARDL analysis by incorporating the F-independent statistic, enabling robust inference under uncertainty regarding the integration properties of the dependent variable. Linear and nonlinear unit-root tests confirm that all variables are integrated at either level or first difference, with none exhibiting I(2) integration, while a structural break in CO₂ emissions is identified in 2009. The A-ARDL bounds test provides strong evidence of a stable long-run relationship among geopolitical risk, economic activity, hydropower generation, foreign direct investment, and environmental performance. Long-run estimates show that geopolitical risk is positively and significantly associated with CO₂ emissions in the long run, with a one-standard-deviation increase in geopolitical risk associated with an approximately 8.1% rise in emissions. Conversely, the hydropower share exhibits the strongest negative long-run association with emissions: a one-percentage-point increase in its electricity share reduces emissions by approximately 2.8%, equivalent to an 8.6% reduction at one standard deviation. FDI is weakly negatively associated with emissions whereas economic activity has no significant long-run effect. The error-correction coefficient of −0.896 indicates rapid adjustment, with nearly 90% of disequilibrium corrected within one year. The findings underscore geopolitical exposure as an environmental risk and hydropower as a key resource-based mitigation channel for sustainable energy transitions.

Private sector credit is central to economic diversification in resource-rich economies, yet the joint informational role of institutional quality and digital connectivity remains insufficiently examined. This study analyzes the predictors of domestic credit to the private sector in Azerbaijan over 1995–2025 using elastic net regression, which selects informative predictors among correlated candidates. Seven institutional, digital, external-sector, and macro-financial predictors are considered. Internet usage and regulatory quality carry most of the predictive information, while government effectiveness and net FDI inflows are largely redundant given these two variables. The baseline model explains 79.8% of the in-sample variation in credit (RMSE = 4.35 percentage points of GDP). Time-respecting cross-validation selects a sparser lasso-type specification that retains the same two leading predictors. The results hold after linear detrending but weaken under quadratic detrending and in first differences, and persistence-based benchmarks outperform the model in one-year-ahead forecasting. Bounds tests indicate a long-run relationship between credit, internet usage, and regulatory quality. The findings therefore describe long-run co-movement in credit depth rather than short-run dynamics or causal effects. Results also suggest that digital and institutional conditions are jointly informative about credit development in Azerbaijan.

Type of the article: Research ArticleAbstractThis study explores potential factors associated with renewable energy consumption in Azerbaijan by examining the relationships among economic growth, human development, per capita CO₂ emissions, and urban population growth. The analysis applies an exploratory machine learning framework based on the XGBoost algorithm combined with SHapley Additive exPlanations (SHAP) to examine possible nonlinear associations and evaluate the relative contribution of selected socioeconomic and environmental variables. Correlation analysis indicates a negative relationship between renewable energy consumption and CO₂ emissions (−0.55), while human development and economic growth exhibit generally positive associations with renewable energy use, suggesting a possible role of socioeconomic development in the energy transition process. The exploratory model produced performance indicators of R² = 0.81, RMSE = 1.18, and MAE = 1.04, suggesting that the model captures variation within the available sample; however, given the limited dataset, these results should not be interpreted as evidence of strong or robust predictive performance. SHAP-based interpretation suggests that human development and environmental pressure may represent relatively important variables within the model framework, while the effects of economic growth and urbanization appear comparatively moderate and context-dependent. Overall, the study provides preliminary exploratory evidence regarding possible nonlinear patterns linking socioeconomic development, environmental conditions, and renewable energy consumption in Azerbaijan, offering directions for future research using larger datasets and complementary empirical approaches.

Artificial intelligence is increasingly viewed as both a catalyst for clean-energy development and a potential constraint on green innovation. This study examines the quantile-specific relationships between artificial intelligence, clean-energy market performance, and green innovation using daily market data from 15 June 2018 to 29 June 2026. The analysis employs Quantile Kernel Regularized Least Squares (QKRLS), which captures nonlinear and heterogeneous associations across different market conditions. The findings reveal a positive and statistically significant association between artificial intelligence and clean-energy-market performance across all examined quantiles, with the strongest estimated marginal associations occurring in the lower quantiles. Conversely, artificial intelligence is negatively and significantly associated with green innovation throughout the distribution, with the strongest negative associations observed at the 0.10 and 0.90 quantiles. The study therefore highlights the need for policies that align artificial-intelligence growth with the sustained development of green innovation.