Policymakers guiding the decarbonization transition must make decisions around energy and CO 2 reductions that are linked to financial expenditures. This study supports decision-making in the buildings sector by combining urban building energy modeling and life cycle costing (LCC) for district-scale retrofit scenarios. The LCC is completed at level of detail (LOD) 3, enriched with exterior wall, roof, and window areas extracted from advanced urban sensing and artificial intelligence techniques. In a case district of 1,291 residential buildings in Turin, Italy, detected envelope quantities are applied to unit costs, resulting in investment costs of €13,723 to €27,272 per dwelling, significantly higher than budgeted for the EU mission 100 Climate-Neutral Cities. A total of 80 scenarios are modeled, including 10 retrofit packages under two scenarios for roof renovation (with and without occupied attics), and with four deployment scenarios to modulate retrofit rates and emission factors in the electricity supply. The research shows complex outcomes under different deployment scenarios, though packages combining envelope retrofits with high-temperature heat pumps consistently result in the greatest emission reductions and lowest cumulative discounted cash flow (CDCF) per quantity of CO 2 saved. For six packages, accelerating retrofit deployment led to 1%-18% lower CDCF, as energy savings offset investment costs over 30 years, even though a gross floor area of 1.4 million m 2 is retrofitted, compared to 855,000 m 2 under slower deployment scenarios. The value of the work is to prove the use of automatic detection techniques in urban-scale LCC, and to provide decision support tools to policymakers guiding the rapid transition to climate neutrality.
Suppa et al. (2026) studied this question.