In accordance with national regulations, the renovation of the residential sector is an urgent task for achieving significant reductions in energy consumption and CO₂ emissions of the existing building stock. Social housing is particularly in need of such interventions, given the higher vulnerability of its inhabitants and its crucial role in furthering social welfare and environmental sustainability objectives. Both passive and active strategies have proved their efficacy in advancing towards these goals and also in mitigating increasing fuel poverty in low-income families. However, to optimize the best combination of such retrofit strategies, advanced optimization methodologies can be applied. Here, a multi-objective optimization methodology is implemented by a genetic algorithm (aNSGA-II) coupled to EnergyPlus dynamic energy simulations. Then, the energy consumption of the optimal solution is considered by means of EnergyPLAN simulations for the further application of active strategies. The two-step method is tested on a relevant case study, a social housing building in Rome, Italy. Results show that the applied method reduced the energy demand by 51% with passive strategies only. Active strategy implementation allowed for a further reduction of 69% in CO₂ emissions and 51% in energy costs. The two-step method proved effective in mitigating fuel poverty and decarbonizing the residential sector.

Nowadays, the energy retrofit of the building sector is identified as a major instrument toward a climate-neutral Europe by 2050. In accordance with the European Renovation Wave program, deep energy renovations are needed, starting from public and less efficient buildings. Furthermore, the renovation of the social housing building stock is also an important response to energy poverty, as it could contribute safeguarding health and well-being of vulnerable citizens. In particular, buildings from the 1960–1980, which constitute a large portion of cities, often have high energy demand and low indoor comfort because most of them have been built before energy-efficiency regulations. In this context, the paper aims to propose a multi-objective approach toward energy renovation of the social housing building stock, by means of an innovative digital workflow. The objective functions are minimizing energy consumption, CO₂ emissions, investment, and operational costs. Toward these contrasting objectives, numerous passive strategies are taken into account, which are compatible with the considered architecture. The optimal solutions are found by means of a genetic algorithm coupled with energy performance simulation software. The methodology is applied and verified on a significant and relevant case study, pertaining to the social housing building stock of Rome, Italy (Mediterranean climate). The outputs of the workflow are a set of optimal solutions among which to choose the fittest one depending on the need of the different stakeholders. The proposed multi-objective approach allows reducing the energy consumption for heating by 31% and for cooling by 17% and the CO₂ emissions up to 27.4%. The proposed methodology supports designers and policymakers toward an effective building stock renovation, which can answer the urgent energy and environmental targets for the coming decades. Keywords Multi-objective optimization Social housing Existing buildings Retrofit Building energy simulation