Transport electrification powered by renewable energy (RE) is a key strategy for reducing greenhouse gas (GHG) emissions and decarbonizing energy systems. However, integrating large numbers of electric vehicles (EVs) presents technical and planning challenges, particularly in hydropower-dependent countries exposed to climate variability. This study assesses the environmental, technical, and economic implications of passenger transport electrification combined with variable RE integration in Colombia, using an hourly EnergyPLAN model for 2030 and 2050. The analysis examines alternative EV penetration pathways, renewable expansion targets, and hydroclimatic conditions associated with the La Niña and El Niño-Southern Oscillation (ENSO). Full electrification of passenger transport by 2050 is projected to reduce total system emissions by about 35 % and system costs by 40 %, while the 2030 scenario aligned with national targets (600,000 EVs) achieves reductions of 8.7 % and 3.5 %, respectively. ENSO variability strongly influences these outcomes: El Niño could increase emissions by up to 38 % due to reduced hydropower availability, whereas La Niña could reduce them by 14 %. Rising electricity demand from transport enhances flexibility when coupled with RE expansion, though reliability depends on the generation mix and infrastructure. Although focused on Colombia, the framework and findings provide insights for other hydro-dependent emerging economies pursuing lower emissions, reduced costs, and greater resilience under climate uncertainty.

Global fossil fuel markets are volatile, influenced by supply chain disruptions and geopolitical instability. As renewable energy capacities expand and emission reduction efforts intensify, more resilient and equitable trading structures are critical to avoid reproducing similar fossil fuel market vulnerabilities. This study supports informed decision-making in electricity-based fuel trade by developing a Geopolitical Risk Index tailored to the energy sector. The index was constructed through a structured selection and evaluation of existing risk indicators. Relevant indices were identified via literature review and selected based on predefined criteria. Selected indices were categorised into four dimensions: resilience, institutional quality, conflicts, and business conditions. The resulting index provides a quantitative tool for assessing geopolitical risks and evaluating energy trade partnerships. Applied to green e-fuel trade, the index assesses traded volumes and costs based on country-specific production potentials, demand, and risk scores. Results indicate that the European Nordics, Singapore, New Zealand, and Canada are the most geopolitically reliable partners, while conflict-prone nations score lowest. Excluding high-risk partners increases import costs by only 1.7% but reduces supply risks. Without considering risks, Brazil, Yemen, and several sub-Saharan countries dominate exports. Applying risk scores eliminates Yemen and increases export shares from Brazil, Namibia, Angola, and Peru. The index correlates with Moody's sovereign ratings, suggesting it captures broader factors influencing both credit worthiness and trade reliability. Incorporating the Geopolitical Risk Index into energy trade planning can help governments and investors reduce exposure to unstable regions, enhance supply security, and promote a more resilient and sustainable global energy system. • Geopolitical risk scores calculated for 190 countries across the world. • Resilience, institutional quality, conflicts, and economy are considered. • Geopolitical risk scores have an impact on potential e-fuel trading in the future. • Nordics, Singapore, and New Zealand are ranked as most reliable trade partners. • Geopolitical risk scores can be applied in energy modelling.

In response to the growing need for sustainable urban development, energy systems modelling must provide long-term carbon-neutral solutions at the city scale while balancing competing criteria. This work introduces a multi-objective optimization approach addressing technical, economic, and environmental criteria for urban smart energy systems designed to achieve 100% renewable energy integration. The analysis incorporates climate change impacts on both energy demand and production. Two optimization strategies are evaluated using Bozen-Bolzano, Italy, as a case study. Specifically, the energy systems were modelled using EnergyPLAN, integrated with Python for automation. Grid search and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) were adopted as optimization methods to compare the advantages and limitations of two different approaches. The results show that both methods produce similar solutions on the Pareto front, with the grid search slightly outperforming due to the consideration of extreme input ranges. However, NSGA-II generated a significantly larger number of Pareto solutions, demonstrating its effectiveness in exploring the solution space more comprehensively. This study underscores the importance of incorporating climate change into multi-objective optimization for robust decision-making in the design of smart urban energy systems for sustainable development. • Optimal techno-economic and environmental design of 100% renewable systems. • Comparison of optimization approaches in urban energy systems modelling. • Impact of climate scenarios on energy demand and renewable integration.