建物ストックのカーボンピーキングに向けた屋根置き太陽光ポテンシャルと炭素オフセット：中国・青島市の建物別評価

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

🔬研究者:建物別PVポテンシャル評価と炭素ピークモデルの結合手法、および再現パッケージの設計が参考になる。

🏢実務担当者:自社建物の屋根置きPV導入可能性評価やScope 2削減計画の定量化に活用できる。

🏛政策担当者:都市レベルでの再エネ導入ポテンシャル評価手法は、自治体の炭素ピーク計画策定に有用。

Overview. This deposit contains the documentation, supplementary tables and reproduction package supporting the manuscript “Rooftop photovoltaic potential and carbon offsets for building-stock carbon peaking: A building-by-building assessment of Qingdao, China” (submitted to Renewable Energy). The study assesses rooftop photovoltaic (PV) potential for 2,229,005 height-attributed buildings in Qingdao (534.3 km² of roof footprint) and couples the resulting hourly generation to a building-stock turnover carbon-peaking model. Contents. SI1_Supplementary_Documentation.pdf — index and packaging map, recomputation specification for the 168-row hourly absorption matrix (SI-B), code-and-metadata README, data dictionary, host-model input notes, script notes. SI4_Supplementary_Tables/ — 26 CSV tables: SI-B absorption and avoided-emission matrix (3 scenarios × 7 load shapes × 8 absorption settings); SI-C1 coupling sensitivity grid (216 rows); SI-C4 annual trajectories 2015–2050 and peaking summaries; SI-C5 carbon-factor and host-baseline sensitivity; SI-D1 district-level capacity and generation; SI-D5 OSM-vs-AI footprint deviation; SI-E1–E4 validation and uncertainty (stratified footprint match, Sobol indices D = 10 with convergence series, output distribution from N = 1024 Saltelli sampling, 12,288 evaluations); block-shading validation; height-error propagation; load-shape sensitivity; LCOE screening; storage and weather-year sensitivity. SI5_Reproduction_Package/ — 24 Python stage scripts of the full chain (building footprints → available roof area → installed capacity → hourly generation → integration correction → carbon-peaking coupling), de-identified building-level derived scalars for the Shibei District pilot (42,259 buildings), a 200-building synthetic validation set, host-model input series (2015–2050), hourly NASA POWER inputs for three representative points and the citywide hourly generation series 2023–2025, the OpenStreetMap open-data ingestion path, verification scripts, the machine-readable manifest and the end-to-end SI-B recomputation script (stage35_si_b_168.py). FigS1_BlockShadingValidation.png — Figure S1: block-scale validation of the 12-sector solar-trajectory shading method against a 300 m raster DSM. Reproducibility. The scripts read only the bundled inputs; no proprietary geometry and no environment variables are required. Verification entry points: verify_bundled_inputs.py (7 checks), verify_si_b.py (11 checks) and reference_pipeline.py --validate (13 checks). Recomputation of the 168-row SI-B matrix from the shipped inputs reproduces the shipped curtailment rates to within 0.1 percentage point on average (162/168 rows identical cell by cell; maximum deviation of deliverable energy 10.9 GWh, ≤0.03%). Restrictions. The complete building geometry (2,229,005 footprints with heights) is not included: it originates from a licensed commercial remote-sensing product that permits publication of aggregate results but prohibits redistribution of the raw geometry. It is available only under a signed agreement (tier L3 of the data-availability statement in the manuscript). An open-source alternative pathway (OpenStreetMap + NASA POWER) is provided so that the method chain can be reproduced in any city. Licence and reuse. Documentation and derived data: CC BY 4.0. Code is additionally mirrored on GitHub under the MIT licence (add repository URL). Please cite both this deposit and the associated article.

• openalex https://doi.org/10.5281/zenodo.22892117first seen 2026-09-24 04:42:59 · last seen 2026-09-24 04:43:01