This study analyzes the dynamic interdependence between tourism activity, energy transition variables, and macroeconomic uncertainty in Spain from 2019 to 2024. Using a Quantile Connectedness Approach based on a Quantile Vector Autoregression (QVAR) model, it captures asymmetric and state-dependent spillovers among tourism emissions, jet fuel and carbon prices, inflation, and uncertainty indicators. Results show that the tourism–energy nexus forms a highly interconnected system whose intensity varies across market regimes. During crises, energy and uncertainty variables dominate as shock transmitters, while in recovery phases, air tourism regains influence, supporting system reactivation. Jet fuel and carbon prices are key transmission channels linking tourism and macroeconomic volatility, with inflation and policy uncertainty amplifying effects. Carbon pricing evolves from a volatility source to a stabilizing mechanism as transition expectations consolidate. The findings highlight asymmetric resilience in tourism and provide insights into policies aligning tourism recovery with energy transition and climate goals.

This study analyzes the interactions between various sources of energy generation and climate variables in Colombia, using Vector Autoregressive Models (VAR) and Generalized Additive Models (GAM). The main objective is to understand the dynamics and complementarity between fossil, hydroelectric, solar and wind energy sources, as well as their relationship with critical climate factors. The results of the VAR model reveal a strong interdependence between fossil and hydroelectric generation. Impulse-response functions show that a shock to fossil generation has a significant and sustained impact on hydropower generation and vice versa, highlighting the role of fossil generation as a backup during periods of water stress. Additionally, patterns of interdependence are observed between fossil generation and renewable sources, especially solar, which is beginning to have a more notable impact on fossil generation. GAM analysis complements these findings by providing a nonlinear perspective on the relationships between variables. Hydroelectric generation is found to be strongly influenced by fossil generation, with a significant negative relationship, suggesting that as hydroelectric generation increases, the need for fossil generation decreases. Furthermore, climatic variables such as solar radiation and water reserves have significant relationships with hydroelectric generation, underlining the dependence of the energy system on climatic conditions. Colombia's energy matrix depends 70 % on hydroelectric generation, which, although sustainable under normal conditions, can become critical during periods of drought. In such scenarios, fossil generation becomes an essential backup. The incorporation of non-conventional renewable energy sources, such as solar and wind, is crucial to diversify the energy matrix and improve the resilience of the system.

Electricity tariffs in emerging economies are often influenced by macroeconomic volatility and regulatory design, affecting both affordability and system stability. Understanding these interactions is crucial for anticipating price fluctuations and ensuring sustainable energy policy. This paper examines the influence of macroeconomic conditions on electricity tariff dynamics in Colombia by integrating econometric and machine learning approaches. Using monthly data from 2009 to 2024 and a set of 153 macroeconomic indicators condensed via principal component analysis (PCA), we assess the predictive performance of vector autoregressive (VAR), SARIMAX, and XGBoost models, as well as a hybrid VAR–XGBoost specification. Impulse-response analysis reveals that tariff components exhibit limited sensitivity to macroeconomic shocks, underscoring the buffering role of regulation and sector-specific drivers. However, forecasting exercises demonstrate that accuracy is highly component-specific: SARIMAX performs best for transmission and restrictions, and VAR dominates for distribution and losses, while the hybrid model outperforms for generation and commercialization. These findings highlight that although macroeconomic pass-through into tariffs is weak, hybrid approaches that combine structural econometric dynamics with nonlinear learning can deliver tangible forecasting gains. The study contributes to the literature on electricity pricing in emerging economies and offers practical insights for regulators and policymakers concerned with tariff predictability and energy affordability.