Targeted Retrofit Strategies for Residential Building Stocks: Integrating EU Policy Lessons and Scenario Modelling in South Tyrol

Accelerating the renovation of existing residential buildings is essential for achieving European climate targets, but aggregate renovation rates do not show whether interventions are sufficiently deep, well targeted, or cost-effective. This paper develops a transparent scenario framework for prioritising renovation strategies at local building stock scale. The workflow combines a literature-based screening of renovation indicators and implementation conditions, semi-structured expert interviews used for qualitative triangulation, and a typology-based bottom-up model of the South Tyrolean residential stock. The stock model uses census and provincial floor-area data, representative space heating and domestic hot water demand values from SINFONIA and previous South Tyrolean studies, and static end-state renovation assumptions. Three scenarios are compared: deep renovation of priority high-demand clusters, medium renovation of the same clusters, and light renovation of the whole stock. The baseline model estimates residential heating and domestic hot water demand at approximately 1990 GWh/year. The selected priority clusters account for about 56% of floor area and nearly 59% of baseline demand. Under the central assumptions, targeted deep renovation would reduce demand by approximately 704 GWh/year (35%), targeted medium renovation by 352 GWh/year (18%), and whole-stock light renovation by 299 GWh/year (15%). Indicative CO2 reductions are reported separately and are proportional to the same final energy reduction assumptions because carrier switching is not modelled. A static cost-effectiveness screening using Italy-level energy-related renovation cost data is reported as an order-of-magnitude range rather than a local investment forecast. The results show that renovation depth and target selection should be considered jointly, and that the framework is transferable where typological stock data and representative energy demand values are available.