PLANtoACT Task 2.2: Building Stock Analysis Data

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a validated methodology for combining global and local data to estimate building energy consumption, useful for urban energy modeling research.

🏢実務担当者:Offers a data-driven approach for regional energy planning, enabling local authorities to identify priority areas for renovation and energy efficiency measures.

🏛政策担当者:Demonstrates a stakeholder-driven, spatially detailed planning approach that can inform EU and national policies on building decarbonization and energy planning.

Description This repository presents the results of the spatial building stock characterization and energy modelling carried out within Work Package 2 (WP2, Task 2.2) of the PLANtoACT project. PLANtoACT is a LIFE Programme–funded project (October 2025–September 2028) that develops, tests, and promotes a stakeholder-driven, spatially detailed integrated energy planning approach to help European Local and Regional Authorities move from clean energy transition targets to coordinated, financed, and implementable action. Scope of the data assembly The dataset provides spatial data characterizing the building stock and estimating the energy profiles for the five pilot regions of the project: Oberland (Germany), Auvergne-Rhône-Alpes (France), Lombardia (Italy), the Porto Metropolitan Area (Portugal), and Alba County (Romania).   Data collection methodology and validation The data was obtained by combining globally available data sources (e.g., JRC DBSM footprints and Moderate energy summaries) with local, region-specific sources where available (such as 3D building models or national statistical demographic and building data from census grids). For each region, single-building level footprints from global datasets were calibrated using local statistical distributions or directly compared and enriched with local 3D building models. This probabilistic or deterministic calibration allowed us to assign building typologies, construction epochs, and occupancy rates. Finally, final energy consumption (FEC) for space heating, space cooling, and domestic hot water was computed by applying region-specific ratios to the floor area. Repository contents The repository is organized by pilot region. For each region (`deu`, `fra`, `ita`, `por`, `rou`), the repository provides the following specific files: global_buildings.parquet: Building stock results obtained using globally available data sources (JRC DBSM) calibrated against various references. local_buildings.parquet: Building stock results obtained using locally available data sources, such as 3D building models (provided for all regions except Romania and Portugal, where only global data was utilized). summary-all.csv: Complete dataset containing all building and energy estimation results across various calibration methodologies and scenarios. summary.csv: The final, recommended results corresponding to the most accurate calibration methodology selected for that specific region. README-Data.md: Region-specific documentation detailing the variables available in the datasets, the specific methodologies employed, and the exact computations used to derive the energy estimates.   References List of all data used   Globally available data: JRC DBSM : Martínez, A. M., Kakoulaki, G., Florio, P., Politis, P., Gounari, O. (2026). DBSM R2025: EU Digital Building Stock Model update including satellite-based attributes and rooftop photovoltaics potential. European Commission, Joint Research Centre. [Dataset] https://data.jrc.ec.europa.eu/dataset/a601a4a8-9289-4fc4-983a-25d54f957f3a Moderate:  Pezzutto, S., Mascherbauer, P., Giussani, F., Bottino, D., Zandonella Callegher, C., Farahi Mohammad, A., & Wilczynski, E. (2024). Horizon Europe MODERATE Project - WP3 - Data Collection. Zenodo. https://doi.org/10.5281/zenodo.10655098 Locally available data: Germany (Oberland): 3D Building Models (LoD2) : Bayerische Vermessungsverwaltung. (2024). 3D-Gebäudemodelle (LoD2). https://geodaten.bayern.de/opengeodata/OpenDataDetail.html?pn=lod2 Zensus 2022: Destatis (Statistisches Bundesamt). (2022). Zensus 2022. https://www.zensus2022.de/ France (Auvergne-Rhône-Alpes): BD-TOPO: IGN. (2024). BD TOPO. https://geoservices.ign.fr/bdtopo BDNB: CSTB. (2024). Base de Données Nationale des Bâtiments (BDNB). https://bdnb.io/ TerriStory: AURA-EE. (2024). TerriStory. https://terristory.fr/ Italy (Lombardia): DBGT: Regione Lombardia. (2024). Database Geo-Topografico (DBGT). https://www.geoportale.regione.lombardia.it/specifiche-tecniche ISTAT: ISTAT. (2011). 15th Population and Housing Census. https://www.istat.it/ Portugal (Porto Metropolitan Area): BGRI 2021: INE. (2021). Base Geográfica de Referenciação de Informação (BGRI 2021). https://www.ine.pt/

• Zenodo https://zenodo.org/records/21776698first seen 2026-08-04 04:13:05