## 5 Results and Discussion  
The proposed IFC-based methodology produces a structured dataset that integrates energy simulation outputs and cost data across different retrofit scenarios. Each scenario applies an insulation layer with a different thermal conductivity, generating varied cost configurations that can be systematically compared with the pre-retrofit baseline. The total cost for each scenario was calculated as the sum of Construction and Material Expenditure (CME) and operational energy costs. Operational energy costs were calculated by applying an annual electricity price of 0.109 EUR/kWh, based on the Gestore dei Mercati Energetici (GME) national energy pricing index [35]. The resulting values, obtained externally from the simulation outputs, were incorporated into the IFC model as external references, whereas the CME values are derived from the Regione Lombardia price list. Since the methodology focuses on the digital structuring of retrofit cost information rather than on optimizing energy performance, accordingly, the variation in annual energy consumption across scenarios remains moderate. The evaluated KPI of total cost is employed as a demonstrative output of the proposed IFC-based structuring methodology, illustrating the integration of economic and energy-related data within a BIM environment. However, in future development, additional KPIs, such as payback period, net present cost, and lifecycle cost, can be incorporated into the outputs to enable deeper and more comprehensive scenario comparisons. Table 1 presents a representative overview of the cost breakdown for each scenario, including the construction cost of the insulation system (CME), the annual energy cost derived from the simulated energy consumption, and the resulting total cost. Figure 3: BIM model of the case study building (**left**) and the insulation layer applied for retrofit analysis (**right**). The table demonstrates how the structured cost data enables stakeholders to interpret and compare retrofit alternatives directly within the IFC-integrated BIM model. Scenario 11, for instance, reflects the lowest total cost (EUR 4587.06), indicating a balance between material expenditure and energy performance. In contrast, Scenario 2 and Scenario 6 have significantly higher total costs, mainly due to increased material unit prices. This kind of output dataset enables stakeholders to evaluate cost trade-offs across scenarios, taking into account both initial investment and energy consumption impacts. The key methodological advantages of this framework are summarized as follows:  
* Comparative evaluation for retrofit decisions: Enables systematic comparison of retrofit scenarios by integrating variable cost-efficiency strategies within a structured IFC-based framework;
* Reliable and scalable cost assessment: Enhances consistency and adaptability in cost estimation by structuring retrofit data within the IFC schema, enabling scalable updates and responsiveness to changing material and energy prices;
* Foundation for digital retrofit workflows: Establishes a semi-automated and BIM-based workflow that supports future extension toward fully digital, performance-driven retrofit planning. While the current framework does not include automated optimization algorithms, it supports informed selection of retrofit strategies by enabling consistent, KPI-based comparison across scenarios. As more detailed or real-time energy consumption data becomes available, the proposed framework can support additional performance metrics without requiring structural modifications. Despite its contributions, the proposed methodology presents some limitations. First, the current scope is limited to insulation-based retrofitting and does not yet include more complex interventions or deep retrofits, such as HVAC upgrades or renewable energy integration. This limitation is partly due to existing constraints in representing dynamic energy data within the standard IFC schema.