## 1 Introduction  
The pressing global challenges of climate change, rapid urbanization, and resource depletion have led to a growing demand for sustainable, energy-efficient building practices. Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) have emerged as key methodologies for evaluating environmental and economic impacts across the entire building lifecycle, from initial design through operation and end-of-life stages [1, 2, 3, 4]. These frameworks are particularly valuable for assessing energy consumption, greenhouse gas (GHG) emissions, and the financial feasibility of renewable energy systems and efficient materials [5]. Despite their benefits, traditional LCA and LCC models often lack the adaptability needed to account for dynamically changing conditions, such as those introduced by external disruptions like the COVID-19 pandemic [6,7]. The COVID-19 pandemic significantly disrupted building operations, revealing challenges in maintaining energy efficiency and meeting sustainability targets. With shifts in occupancy patterns, energy demand fluctuated dramatically, affecting the performance of building systems and renewable energy installations [8,9]. These disruptions underscored the need for adaptive analysis frameworks capable of addressing short-term operational changes and long-term sustainability goals [10, 11, 12]. Accurate assessments of building renovations during this period require methodologies that incorporate scenario-based modeling and simulation tools to reflect dynamically changing conditions [7,13,14]. Advancements in digital technologies, such as digital twins, are transforming the early design stages of urban and building projects by enhancing transparency and data accuracy. [PERSON] et al. (2022) [8] emphasized the importance of integrating circular economy principles with LCA to achieve comprehensive sustainability insights early in the design process. Similarly, [PERSON] et al. (2022) [9] highlight challenges in data collection and the need for robust methodologies in building LCA. Digital twins provide a virtual representation of systems, enabling real-time monitoring and iterative sustainability evaluations. By addressing gaps in data quality and collection, these technologies enhance tracking capabilities and support more accurate, transparent assessments. Adaptive energy systems that integrate photovoltaic (PV) panels [12,15,16,17], battery energy storage systems, and heating, ventilation, and air-conditioning (HVAC) systems have been shown to significantly enhance energy efficiency and cost savings in residential and office buildings. [PERSON] et al. (2023) [13] demonstrated that combining PV systems with battery storage reduces energy costs by optimizing real-time energy use in office buildings, resulting in up to a 30% improvement in energy utilization efficiency. Similarly, [PERSON], [PERSON], and [PERSON] (2015) [14] highlighted how variations in consumer load profiles influence the economic and energy performance of PV-battery systems, underscoring their potential to align energy generation and demand effectively. These integrations not only improve operational efficiency but also reduce reliance on grid electricity, leading to lower overall costs and improved energy performance. Despite these advancements, many LCA and LCC tools still predominantly rely on static estimations of the use-stage performance of the devices analyzed, limiting their capacity to evaluate dynamically changing operational conditions [11,18]. To address these challenges, this study utilizes the VERIFY platform [19, 20, 21], a web-based tool integrating LCA and LCC methodologies for building renovation assessments. VERIFY combines static and dynamic data with simulation-based evaluations to provide scenario analyses of environmental and economic impacts.