Supplementary data for: A systems thinking governance framework for large-scale sustainability synthesis (doi:10.18710/NZHGW3)

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Supplementary data for: A systems thinking governance framework for large-scale sustainability synthesis

doi:10.18710/NZHGW3

DataverseNO

2026-02-26

3

Barahmand, Zahir, 2026, "Supplementary data for: A systems thinking governance framework for large-scale sustainability synthesis", https://doi.org/10.18710/NZHGW3, DataverseNO, V3

Supplementary data for: A systems thinking governance framework for large-scale sustainability synthesis

doi:10.18710/NZHGW3

Barahmand, Zahir (https://ror.org/05ecg5h20)

Eikeland, Marianne

Wang, Liang

Heirati, Nima

University of South-Eastern Norway

2026-02-26

DataverseNO

University of South-Eastern Norway

Barahmand, Zahir

Barahmand, Zahir

2026-02-25

2026-02-26

https://doi.org/10.18710/NZHGW3

Engineering, systems thinking model, sustainability, large-scale study, strategic decision-making

This dataset supports the paper “A Governance-Aware Systems Thinking Architecture for Sustainability at Scale.” The study addresses a key challenge in large-scale sustainability research: how to ensure transparency, consistency, and auditability when integrating heterogeneous evidence and systems thinking approaches. The paper introduces STAI³RS, a governance framework designed to ensure rigor and reproducibility in sustainability synthesis. STAI³RS stands for Scalable, Transparent, Analytical, Interpretable, Reliable, Reproducible, Robust, and Systematic, and provides cross-cutting principles and procedural cues for applying systems thinking methods at scale. Building on this governance layer, the paper presents SEEDS (Systems Evidence Extraction and Decision Support), a six-component operational model that structures sustainability synthesis from problem framing to decision support. SEEDS integrates boundary governance, evidence extraction, harmonization, synthesis, and iterative learning into a transparent and auditable workflow. The dataset includes materials that document the governance diagnostics and operational implementation of these frameworks. In particular, Table S2 presents a case study demonstrating the SEEDS workflow in practice through a large-scale feedstock evidence mapping study spanning over 130,000 peer-reviewed studies across biomass- and waste-to-X conversion technologies. This case study illustrates how governance rules, audit trails, and harmonization procedures enable reproducible synthesis across three large scientific corpora. SEEDS_components_in_practice These materials support reuse, verification, and extension of governance-aware systems thinking approaches in sustainability research and decision support.

2026-01-15-2026-02-20

diagnostic matrix

Used systems thinking model references

SEEDS components in a case study

21 systems thinking models are collected narratively from literature

<a href="http://creativecommons.org/licenses/by/4.0">CC BY 4.0</a>

Barahmand 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, V1

Barahmand 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, V1

Barahmand, 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, V1

Barahmand, 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, V1

Submitted for review

Submitted for review

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00_ReadMe.txt

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1_STAI3RS_SEEDS_Framework.zip

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This folder contains framework documentation for STAI³RS (Scalable, Transparent, Analytical, Interpretable, Reliable, Reproducible, Robust, and Systematic) governance principles and their derivation from established systems thinking traditions. Materials support the theoretical foundation and governance architecture of the paper "A Governance-Aware Systems Thinking Architecture for Sustainability at Scale."

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2_ESI_Feedstock_Case_Study.zip

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This folder contains NEW computational transparency materials for the EcoStrategic Index (ESI) feedstock evidence-mapping case study. While the case study was previously documented in narrative form (Table S2 in the main paper), this folder now provides complete calculation workbooks, raw data, methodology documentation, and reproducible formulas demonstrating the operational application of STAI³RS and SEEDS frameworks with full transparency.

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