Tourism destinations increasingly require data-driven tools to interpret competitiveness, capacity use, housing-related pressure, and post-shock change. This study develops a machine-learning-based destination marketing intelligence framework for a non-probability analytical sample of 29 European destinations observed annually between 2015 and 2024. Destinations were retained when sufficiently comparable information was available across the common study window for the six raw indicators required to construct the performancepressure framework. Tourism demand, accommodation capacity, labor, investment intensity, and housing-cost pressure are transformed into normalized indicators and analyzed using principal component analysis, k-means clustering, classification trees, random forests, and robustness checks. The first three principal components explain 84.2% of total variance. Although silhouette favors three clusters, the four-cluster solution provides stronger Calinski–Harabasz separation and leave-one-destination-out stability. The retained solution identifies four relative destination-state configurations: lower performance with near-average pressure; high rotation, moderate performance, and lower pressure; high performance with lower pressure; and extreme housing pressure. Under leave-one-destinationout validation, random forests achieve 86.6% accuracy and a Cohen’s kappa of 76.9%. The configurations are pressure-sensitive marketing-intelligence categories rather than comprehensive sustainability classifications or permanent country typologies.

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 paper examines whether global monetary conditions are embedded in the spillover structure of sovereign credit risk across emerging markets and whether that role changes across market states. Using monthly data for 15 emerging-market 5-year sovereign CDS series from March 2008 to February 2026, we construct a World Interest Rate (WIR) from the common component of shadow or policy rates in the United States, the euro area, Japan, the United Kingdom, Switzerland, and Canada. The empirical analysis is based on a rolling quantile vector autoregression connectedness framework applied to transformed CDS and WIR series. The results show that sovereign CDS spillovers are strongly state dependent. Total connectedness is already high at the center of the distribution, but rises markedly in both tails, producing a clear U-shaped quantile profile. The WIR is clearly integrated into the network, although not as a uniformly dominant transmitter. In central and intermediate quantiles it behaves mainly as a net receiver of spillovers, while in more stressed upper-tail conditions it becomes less absorbent and can occasionally shift into a mild transmitting role. These findings suggest that global monetary conditions matter within the sovereign CDS network, but in a conditional rather than uniform way.