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PLANtoACT タスク2.2:再生可能エネルギーポテンシャル分析データ

PLANtoACT Task 2.2: Renewable Energy Potentials Analysis Data (原題)

Zilio, Samuele, Zandonella Callegher, Claudio, Prina, Matteo Giacomo, D'Alonzo, Valentina

Zenodoデータセット2026-09-23#再生可能エネルギーOrigin: EU対象セクター: power
DOI: 10.5281/zenodo.22912329
原典: https://zenodo.org/records/22912329

🤖 gxceed AI 要約

日本語

EUのLIFEプログラム助成プロジェクトPLANtoACTのWP2で、5つのパイロット地域(独・仏・伊・葡・羅)を対象に太陽光と風力の再エネポテンシャルを空間推計したデータセット。PVは営農型・地上設置型・屋根設置型(住宅/非住宅)の4形態、風力は陸上を評価。CORINE土地利用や3D建物モデル等の全球・地域データを組み合わせ、自治体レベルのポテンシャルを提供する。

English

This dataset presents spatially detailed renewable energy potential estimates for five European pilot regions (Germany, France, Italy, Portugal, Romania) under the LIFE-funded PLANtoACT project. It covers PV (agri-PV, ground-mounted, residential and non-residential rooftop) and onshore wind, combining global datasets like CORINE Land Cover with local 3D building models to deliver municipal-level potentials supporting local energy planning.

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

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本では自治体の再エネ導入計画や地域脱炭素ロードマップ策定において、空間的に詳細なポテンシャル推計手法が参考になる。特に営農型PVや屋根設置型の区分けは、日本国内の地域別導入可能量評価に応用可能。

In the global GX context

This work supports the EU's clean energy transition goals by providing replicable, spatially explicit renewable potential assessments for local and regional authorities. It contributes to the broader global push for data-driven energy planning aligned with national decarbonization targets and could inform similar municipal-level analyses worldwide.

👥 読者別の含意

🔬研究者:空間データと地域固有データを組み合わせた再エネポテンシャル推計手法の実例として参考になる。

🏢実務担当者:自治体や地域エネルギー計画担当者が、再エネ導入可能量を評価する際のデータソースと手法の参考にできる。

🏛政策担当者:地域レベルでの再エネ導入計画策定を支援するデータ基盤の整備事例として、政策立案の参考になる。

📄 Abstract(原文)

Description This repository presents the results of the renewable energy potentials estimation 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 for the estimation of renewable energy potentials 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., CORINE Land Cover dataset) with local, region-specific sources where available (such as 3D building models). The renewable energy potential estimation considered two main technologies: photovoltaic (PV) and wind. For PV, four deployment configurations were analysed: agri-PV, ground-mounted PV, and rooftop PV on both residential and non-residential buildings. For wind, onshore turbines were assessed across all pilot regions. Repository contents The repository is organized by pilot region. For each region, the repository provides the following specific files: pv_potentials.txt: Region-specific documentation detailing the pv energy potentials per deployment configurations. wind_potentials.txt: Region-specific documentation detailing the wind energy potentials per deployment configurations. ren_ene_potentials.gpkg: Complete dataset containing pv and wind energy potentials at municipal level.   References List of all data used.   Globally available data: Copernicus CORINE Land Cover (CLC 2018): European Environment Agency (2019). CORINE Land Cover 2018 (vector/raster 100 m), Europe, 6-yearly, version 2020_20u1. Copernicus Land Monitoring Service. [Dataset] https://doi.org/10.2909/71c95a07-e296-44fc-b22b-415f42acfdf0 Copernicus DSM (100 m): European Space Agency / Copernicus Programme. Copernicus DEM — Global and European Digital Elevation Model, GLO-30 instance (30 m native resolution, data acquired by the TanDEM-X mission 2011–2015), resampled to 100 m (EU-LAEA projection). [Dataset] https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM Copernicus Data Space Ecosystem EEA Nationally Designated Areas (NatDA): European Environment Agency. Nationally designated areas — the official source of protected area information from the 38 European member countries to the World Database of Protected Areas (WDPA), maintained by the EEA with support from the European Topic Centre on Data Integration and Digitalisation (formerly the Common Database on Designated Areas, CDDA). [Dataset] https://www.eea.europa.eu/data-and-maps/data/nationally-designated-areas-national-cdda-17 EMODnet EEA Natura 2000: European Environment Agency. Natura 2000 — spatial data (end-2021 release, revision 1). Ecological network of protected sites under the Birds Directive (1979) and Habitats Directive (1992). [Dataset] https://www.eea.europa.eu/data-and-maps/data/natura-14 Global Wind Atlas (wind power density): Floors, R. et al. (2025). Global Wind Atlas v4, https://doi.org/10.11583/DTU.28955267 . Produced and maintained by the Global Wind Atlas, Department of Wind Energy at the Technical University of Denmark (DTU Wind Energy) and the World Bank Group. [Dataset] https://globalwindatlas.info figshare GEE Community Catalog 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 JRC European Flood Hazard Maps (100-year return period): Dottori, F., Alfieri, L., Bianchi, A., Skoien, J., Salamon, P. (2021). River flood hazard maps for Europe and the Mediterranean Basin region — 100-year return period. European Commission, Joint Research Centre (JRC). [Dataset] doi: 10.2905/1D128B6C-A4EE-4858-9E34-6210707F3C81, PID: http://data.europa.eu/89h/1d128b6c-a4ee-4858-9e34-6210707f3c81 (methodology described in Dottori et al., "A new dataset of river flood hazard maps for Europe and the Mediterranean Basin," which presents high-resolution (100 m) hazard maps for river flooding covering most European countries plus river basins draining into the Mediterranean and Black Sea, https://doi.org/10.5194/essd-14-1549-2022 ) European Commission OpenStreetMap (parking areas): OpenStreetMap contributors. Planet dump. OpenStreetMap Foundation. [Dataset] https://www.openstreetmap.org (© OpenStreetMap contributors, available under the Open Database License) Locally available data: Germany (Oberland): 3D Building Models (LoD2) : Bayerische Vermessungsverwaltung. (2024). 3D-Gebäudemodelle (LoD2). https://geodaten.bayern.de/opengeodata/OpenDataDetail.html?pn=lod2 France (Auvergne-Rhône-Alpes): BD-TOPO: IGN. (2024). BD TOPO. https://geoservices.ign.fr/bdtopo Italy (Lombardia): DBGT: Regione Lombardia. (2024). Database Geo-Topografico (DBGT). https://www.geoportale.regione.lombardia.it/specifiche-tecniche

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