10.18710/2NPL8C Barahmand, Zahir Zahir Barahmand https://orcid.org/0000-0001-9031-596X University of South-Eastern Norway DataverseNO 2026 Earth and Environmental Sciences circular economy waste-to-X sustainability University of South-Eastern Norway University of South-Eastern Norway Barahmand, Zahir Zahir Barahmand University of South-Eastern Norway Eikeland, Marianne Marianne Eikeland 2026-06-28 2026-06-28 2026-06-30 2026-07-29 Excel workbooks 10.1016/J.RSER.2025.115507 10.1038/S44458-026-00120-Z 10.1038/S41597-026-07820-0 10.18710/2NPL8C/ABXMFO 10.18710/2NPL8C/NTCAH1 10.18710/2NPL8C/PCHXTY 68585 26377 7494 7509 application/zip application/zip text/plain text/plain 2.0 Creative Commons Attribution 4.0 International License. This 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. University of South-Eastern Norway