PROBLEMS OF STATISTICS AND DATA ANALYTICS IN ASSESSING GREEN TRANSFORMATIONS IN CONSTRUCTION

This article examines the methodological and infrastructural limits of contemporary green construction statistics in the context of digital transformation and the climate transition. Current measurement practices in the building sector remain dominated by administrative indicators – certificates, renovated areas, and investment volumes – which fail to capture the dynamic energy, carbon and financial trajectories of buildings as long-lived assets. As a result, the economic effects of green investments and the evolution of climate and energy risks remain weakly observable and difficult to compare across time and projects.The purpose of the study is to conceptualize a shift from fragmented, event-based measurement toward an infrastructure-driven model of green statistics built on the integration of Building Information Modeling (BIM), Life Cycle Assessment (LCA), Digital Building Logbooks (DBL) and Measurement-Reporting-Verification (MRV) regimes, with a specific focus on early design stages (LOD 100) as the critical point where environmental and financial trajectories are formed.Methodologically, the paper employs an institutional and systemic analysis that combines recent BIM–LCA research with EU regulatory frameworks (EPBD, Level(s), DBL, CSRD/ESRS) and MRV practices. A conceptual modeling approach is used to map how data layers and standards interact across the building life cycle and to identify information gaps that constrain statistical observability and comparability.The main result is a coherent measurement architecture (BIM – LCA – DBL – MRV – green financial statistics) that enables a transition from static status indicators to asset-level trajectories. It is shown that integrating BIM and LCA at LOD 100 makes buildings statistically observable as future-oriented green assets, allowing scenario-based comparison of design alternatives before trajectories become locked in. This creates the preconditions for panel-like, object-oriented green statistics capable of supporting risk assessment, investment decisions and policy evaluation beyond retrospective reporting.