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The required decarbonization of the energy system is a complex task, with ambitious targets under the Paris Agreement, and related policy analysis should consider possible impacts on the economy and society. By coupling the energy system model TIMES PanEU with the impact assessment model EcoSense and the computable general equilibrium model NEWAGE, we present an integrated assessment toolbox for the European energy system capable of internalizing health damage costs of air pollution while simultaneously accounting for demand changes in energy services caused by economic feedback loops. The effects of each coupling step are investigated in a scenario analysis. Additionally, CO2 decomposition analysis is applied to identify the main drivers to decarbonize the energy system. Our results show that integrating externalities forces the system to take early action, which provides benefits on the societal level. Including macro-economic variables has a negative effect on energy service demands and generally reduces the need for structural change, which are still the main drivers of decarbonization. The tighter the models are coupled, the fewer the iterations needed and the lower the CO2 prices resulting from the carbon cap and trade system. In this aspect, an integrated view can provide valuable insights to determine efficient and effective decarbonization paths.

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Since the signing of the 2030 Agenda for Sustainable Development by the United Nations Member States and the Yellow vest movement, it is clear that emission‐reducing policies should consider their distributional impacts to ensure a sustainable and equitable growth compatible with the Paris Agreement goals. To this end, the design of environmental and energy policies should be accompanied by an interdisciplinary analysis that includes potential effects on distinct groups of society (defined by income, age, or location), regions, and sectors. This work synthesizes common modeling frameworks used to assess technical, socio‐economic, and environmental aspects in policy analysis and the recent progress to portray distributional impacts in each of them. Furthermore, the main indicators produced by each method are highlighted and a critical review pointing to gaps and limitations that could be addressed by future research is presented.

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