Analysis code: National benchmarking of operational energy and carbon performance in England's NHS estate
Kaushik, Amit
🤖 gxceed AI 要約
日本語
イングランドNHSの施設エネルギー・炭素性能を全国ベンチマークする論文の再現用Pythonコード。2018/19〜2023/24のERICデータ（9,107サイト年、229トラスト）を再構築し、気象データと結合。炭素強度変化の4要因分解、回帰、空間自己相関、2040年までの予測を再現する。
English
Python code reproducing a national benchmarking of operational energy and carbon performance across England's NHS estate. It rebuilds a site panel from ERIC data (2018/19–2023/24; 9,107 site-years, 229 trusts), links Met Office weather, and reproduces decomposition, regression, spatial autocorrelation and 2040 projections.
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
英国公的医療機関の炭素・エネルギー性能を施設単位でベンチマークする手法は、日本の自治体・公共施設やSSBJのScope1/2開示整備の参考になる。再現可能なオープンコードは国内の排出量算定基盤構築の示唆を与える。
In the global GX context
Provides a reproducible, open-data template for facility-level energy and carbon benchmarking in a large public healthcare system, relevant to ISSB/CSRD-style Scope 1/2 disclosure and net-zero trajectory analysis for public estates.
👥 読者別の含意
🔬研究者:Use the open code and panel to replicate or extend facility-level carbon benchmarking and decomposition methods.
🏢実務担当者:Offers a template for site-level energy/carbon benchmarking and weather-normalized performance tracking in large estates.
🏛政策担当者:Illustrates how open administrative data can support national net-zero monitoring and benchmarking of public-sector estates.
📄 Abstract（原文）
Python analysis code, derived site panel and results for the paper "National Benchmarking of Operational Energy and Carbon Performance in England's NHS Estate: Determinants and Net-Zero Trajectories". The code rebuilds the site panel from the raw NHS Estates Returns Information Collection (ERIC) files for 2018/19 to 2023/24 (9,107 site-years, 229 NHS trusts), links every site to hourly Met Office MIDAS Open weather observations, and reproduces every number, table and figure in the paper: floor-area-weighted benchmarking, a four-part decomposition of the change in carbon intensity (sample composition, grid decarbonisation, weather and within-site change), site-level regression, spatial autocorrelation (Moran's I and LISA) and trust-level projections to 2040. Version 2 corrects the electricity extraction of version 1, which read one electricity field per year, and supersedes it. See README.md for the run order and the map from output files to the paper's tables and figures. All inputs are open data under the Open Government Licence v3.0 (NHS Digital, ONS, MHCLG, DESNZ, Met Office via CEDA); the UK outline is Natural Earth (public domain).
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/23401846first seen 2026-10-17 04:12:25 · last seen 2026-10-22 04:13:01
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