Supplementary data and calculation workbooks for: Feedstock Circularity Interest: A diagnostic classification lens for biomass- and waste-to-X technology comparisonhttps://doi.org/10.18710/2NPL8CBarahmand, ZahirDataverseNO2026-06-302026-07-29T07:18:24ZThis dataset supports the manuscript “Feedstock Circularity Interest: A diagnostic lens for biomass- and waste-to-X technology comparison.” The study develops a diagnostic feedstock circularity-interest lens for interpreting circularity claims in biomass- and waste-to-X technologies. The lens classifies reported feedstocks into five tiers according to nominal feedstock-side circular opportunity and the evidence and governance burden required to make that opportunity credible. It is intended for strategic screening: it does not rank technologies or measure sustainability performance, but identifies which feedstock-origin, baseline-fate, composition, hazard, and governance assumptions require clearer documentation before stronger circularity or sustainability claims are made. This dataset provides the article-specific supplementary data, calculation workbooks, and documentation used to reproduce the numerical results reported in the manuscript. It contains the final tier-distribution outputs, ESI-derived descriptive indicators, sensitivity-analysis workbooks, and figure/table source files. The sensitivity analyses include the T1–T2 lower-tier coding swap, comparison of strict highest-tier concentration (HVPR) with upper-tier concentration (UTC), exclusion of weakly specified or unresolved biogenic inputs from T1, and gasification corpus-design comparisons across core, modelling, and experimental/scale-explicit strata. The dataset supports transparency and reproducibility of the article’s descriptive screening results. The underlying feedstock extraction, harmonised labelling system, dictionaries, audit evidence, and broader evidence-base resources are provided in related open datasets cited in the manuscript.Earth and Environmental Sciencescircular economywaste-to-XsustainabilityEnglishBarahmand, Zahir & Eikeland, Marianne Sørflaten (2025). EcoStrategic index: Economic value creation through product portfolio diversity for waste-to-x technologies. Volume 214 Renewable and Sustainable Energy Reviews. DOI: 10.1016/j.rser.2025.115507, doi, https://doi.org/10.1016/j.rser.2025.115507, https://hdl.handle.net/11250/3181819Barahmand, Z., Tokheim, LA., Wang, L. et al. Feedstock reporting gaps limit interpretation of circularity claims across conversion technologies. Commun. Sustain. 1, 119 (2026). https://doi.org/10.1038/s44458-026-00120-z, doi, 10.1038/s44458-026-00120-z, https://doi.org/10.1038/s44458-026-00120-zBarahmand, Z., Tokheim, LA., Wang, L. et al. A large-scale, LLM-assisted and validated dataset of biomass and waste conversion technologies and feedstocks. Sci Data (2026). https://doi.org/10.1038/s41597-026-07820-0, doi, 10.1038/s41597-026-07820-0, https://doi.org/10.1038/s41597-026-07820-0Barahmand, Zahir; Tokheim, Lars-Andre; Seljeskog, Morten; Wang, Liang; Serrano, Gonzalo del Alamo & Eikeland, Marianne Sørflaten (2026). Strategic assessment of syngas-to-chemicals pathways in a post-fossil economy: A pathway-aware extension of the EcoStrategic Index. Volume 31 Energy Conversion and Management: X. DOI: 10.1016/j.ecmx.2026.102005, doi, DOI: https://doi.org/10.1016/j.ecmx.2026.102005, https://hdl.handle.net/11250/55289792026-06-28Barahmand, ZahirEikeland, Marianne2026-06-28Barahmand, Zahir, 2026, "Supporting Dataset for “An evidence map of feedstock reporting in biomass- and waste-to-X conversion research”", https://doi.org/10.18710/ZB68UG, DataverseNO, V2Barahmand, Zahir, 2026, "Supplementary dataset and reproducible codes for LLM-assisted mapping feedstocks of eight conversion technologies from over 121,000 studies", https://doi.org/10.18710/JM6U7B, DataverseNO, V2Barahmand, Zahir, 2026, "Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies", https://doi.org/10.18710/WEZMJE, DataverseNO, V1Barahmand Zahir, 2025, "Supplementary code and curated data for 1,863 experimental gasification studies (laboratory to commercial scale)", https://doi.org/10.23642/USN.30702092, DataverseNO, V1Barahmand Zahir; Eikeland Marianne Sørflaten, 2025, "Dataset and code supplement: Mapping gasification technologies and feedstocks with a dual validated large-scale literature-derived dataset", https://doi.org/10.23642/USN.30546347, DataverseNO, V1Excel workbooksCC BY 4.0