The decarbonization of shipping has become an important policy goal. While integrated assessment models (IAMs) are often used to explore climate mitigation strategies, they typically provide little information on international shipping, which accounts for emissions of around 0.7 GtCO₂ yr⁻¹. Here we perform a multi-IAM analysis of international shipping and show the potential for decreasing annual emissions in the next decades, with reductions of up to 86% by 2050. This is primarily achieved through the deployment of low-carbon fuels. Models that represent several potential low-carbon alternatives tend to show a deeper decarbonization of international shipping, with drop-in biofuels, renewable alcohols and green ammonia standing out as the main substitutes for conventional maritime fuels. While our results align with the 2018 emission reduction goal of the International Maritime Organization, their compatibility with the agency’s revised target is still subject to a more definitive interpretation.

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Energy models are used to study emissions mitigation pathways, such as those compatible with the Paris Agreement goals. These models vary in structure, objectives, parameterization and level of detail, yielding differences in the computed energy and climate policy scenarios. To study model differences, diagnostic indicators are common practice in many academic fields, for example, in the physical climate sciences. However, they have not yet been applied systematically in mitigation literature, beyond addressing individual model dimensions. Here we address this gap by quantifying energy model typology along five dimensions: responsiveness, mitigation strategies, energy supply, energy demand and mitigation costs and effort, each expressed through several diagnostic indicators. The framework is applied to a diagnostic experiment with eight energy models in which we explore ten scenarios focusing on Europe. Comparing indicators to the ensemble yields comprehensive ‘energy model fingerprints’, which describe systematic model behaviour and contextualize model differences for future multi-model comparison studies.@en