2026-10-11T04:59:34Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33726376 2026-09-15T04:44:49Z category_26464 category_25057 item_type_3 month_year_09_2026

Data and code supporting “Clean-electricity constraints on AI computing and the cost of orbital alternatives” Yuyang Zhang (24132201) Electrical energy generation (incl. renewables, excl. photovoltaics) Urban and regional planning not elsewhere classified AI data centres clean electricity geospatial analysis <p dir="ltr">This item contains the processed data and analysis code supporting the manuscript “Clean-electricity constraints on AI computing and the cost of orbital alternatives”.</p><p><br></p><p dir="ltr">The data archive provides the processed raster layers, national reporting tables, sensitivity results and parameter library used in the study. The spatial layers use the WGS84 coordinate reference system at 0.25-degree resolution and cover modelled land from 60° S to 75° N. They describe first-binding constraints and siting suitability across electricity supply, clean-electricity requirements, water, land, heat rejection and infrastructure access.</p><p><br></p><p dir="ltr">The code archive contains the deterministic accounting model, ingestion utilities, scenario definitions, analysis scripts, figure-generation scripts and validation tests. The released test suite returns 128 passed and 11 skipped tests in a clean environment. The skipped tests require large third-party raster inputs that cannot be redistributed; their sources and versions are documented in the manuscript.</p><p><br></p><p dir="ltr">The dataset README and data dictionary describe the files, variables, spatial conventions and known limitations. National reporting applies the boundary-aggregation convention declared in the code before country-level statistics are calculated. This convention affects the reporting mask only and does not alter any cell-level model result. Singapore’s domestic supply remains unresolved at the global grid resolution and is reported separately as a scenario assumption.</p><p><br></p><p dir="ltr">The data archive is released under CC BY 4.0. The software archive is distributed under the MIT License included in the package.</p> 2026-09-15T04:44:49Z Dataset Dataset 10.6084/m9.figshare.33726376.v1 https://figshare.com/articles/dataset/Data_and_code_supporting_Clean-electricity_constraints_on_AI_computing_and_the_cost_of_orbital_alternatives_/33726376 CC BY 4.0