This paper proposes a novel methodology, based on machine learning and statistical models, for predicting hurricane-related losses to specific assets. Our approach uses three critical storm parameters typically tracked by meteorological agencies: maximum wind speed, minimum sea level pressure, and radius of maximum wind. The system categorizes potential damage events into three insurance-relevant classes: non-payable, partially payable, and fully payable. Three triggers for final payouts were designed: hybrid framework, standalone regression, and standalone non-linear regression. The hybrid framework combines two classification models and a non-linear regression model in an ensemble specifically designed to minimize the absolute differences between predicted and actual payouts (Total Absolute Error or TAE), addressing highly imbalanced and partially compensable events. Although this complex approach may not be suitable for all current contracts due to limited interpretability, it provides an approximate lower bound for the minimization of the absolute error. The standalone non-linear regression model is structurally simpler, yet it likewise offers limited transparency. This hybrid framework is not intended for direct deployment in parametric insurance contracts, but rather serves as a benchmarking and research tool to quantify the achievable reduction in basis risk under highly imbalanced conditions. The standalone linear regression provides an interpretable linear regression model optimized for feature selection and interaction terms, enabling direct deployment in parametric insurance contracts while maintaining transparency. These three approaches allow analysis of the trade-off between model complexity, predictive performance, and interpretability. The three approaches are compared using comprehensive hurricane simulation data from an industry-standard catastrophe model. The methodology is particularly valuable for parametric insurance applications, where rapid assessment and claims settlement are essential.

This research examines optimization approaches in wood supply chain management, addressing economic efficiency, environmental sustainability, and social returns. The study analyzes fundamental components and operational mechanisms of the wood supply chain, evaluating state-of-the-art methodologies across strategic, tactical, and operational decision levels. Key optimization problems investigated include vehicle routing, supply-demand matching, transport mode selection, inventory management, network flow, and facility location. The research identifies critical gaps in existing optimization frameworks, particularly in addressing supply variability, seasonal fluctuations, and product deterioration. The study concludes by proposing future research directions, emphasizing the integration of real-time analytics, machine learning, and multi-objective optimization frameworks for enhanced sustainability and operational efficiency. This paper distinguishes itself by examining multiple optimization problems within an integrated wood supply chain framework, revealing their interconnections and trade-offs rather than analyzing them in isolation.

Evaluating urban mobility transitions is essential to determine whether local transport interventions support broader sustainability goals. Cities increasingly implement initiatives to promote public transport, active mobility, and low-carbon transport systems. Still, assessing their impact on city-scale structural change remains challenging. Existing evaluation approaches often rely on project-level monitoring or fragmented indicators, which limits cross-city comparison and the assessment of long-term system transformation. This paper proposes a dual-track methodology to evaluate sustainable urban mobility interventions. The first track uses city-defined key performance indicators to capture local implementation processes, governance dynamics, and perceived outcomes. The second track relies on publicly available open data to assess city-scale changes in mobility indicators, including public transport accessibility, cycling infrastructure provision, and traffic-related air pollution. The methodology is applied to ten European cities using open data and satellite-based environmental indicators. Results indicate that while cities report progress at the project level, external indicators show limited short-term structural change in city-wide mobility systems. These findings highlight the value of open data as an independent evaluation layer that contextualises local results and supports transparent assessment of urban mobility transitions.

This paper looks at ways to solve warehouse location problems in wood supply chains. We tested four methods: two direct solvers (CBC and Gurobi) and two mixed methods that combine genetic algorithms with these solvers. We used test data of different sizes to see which method works best for both good answers and fast solving times. Our tests show that the Gurobi solver works best, with the fastest times and best answers for all data sizes. However, the mixed method using genetic algorithms with the CBC solver is a good choice too, especially for complex problems with many warehouses. This mixed method gives good results while being free and open-source, making it available to organizations that can't afford expensive software. As problems get more complex, the difference between Gurobi and the genetic algorithm with CBC becomes less important.