Explainable greenwashing-risk scoring for corporate climate disclosures: pinned ClimateBERT signals → a Mamdani fuzzy rule base → an auditable 0–100 risk score with a W3C PROV-O provenance graph.
Every score ships with an activation trace showing exactly which rules fired and why, and a provenance graph binding the result to the exact model revisions and instrument version that produced it. Nothing is a black box.
Instrument status: frozen at tag rulebase-locked-v1.
GreenRisk: An Explainable-by-Construction Fuzzy Instrument for Scoring
Greenwashing Risk in Corporate Climate Disclosures. Isaac D'Césares de
Carvalho Lima, Maria Luiza Machado Campos, and Sérgio Manuel Serra da Cruz.
Programa de Pós-Graduação em Informática (PPGI), Universidade Federal do Rio de
Janeiro, 2026. → docs/greenrisk_paper.pdf
The paper reports the instrument's design and its two-layer evaluation: a
convergent/discriminant comparison against the Bingler et al. cheap-talk
baseline on 1,009 climate-gated TCFD paragraphs (Spearman ρ = 0.602; ρ = 0.433
between risk and commitment inside the baseline's vaguest quartile), and a
held-out, one-shot test on 15 regulator-adjudicated and third-party-verified
cases (AUC = 0.867, n = 5 vs. 6). Every figure it reports is reproducible from
this repository — see MASTER_PLAN.md for the commands and
docs/decisions.md for the DL-00x decisions it cites.
docs/greenrisk_paper.pdf— the final project paper (see Paper above).docs/architecture.md— the full pipeline: the climate-relevance gate, the four signals, the fuzzy rule base, and the design rationale behind it.docs/validation.md— the validity evidence: a large-scale statistical comparison against a published baseline, and a held-out test against real, regulator-adjudicated cases, including the instrument's documented scope limitation.docs/decisions.md— the decision record: every instrument decision with the evidence behind it, the alternatives rejected, the lock, and the blind pre-registration of the held-out test.docs/ai-usage.md— how generative AI was and was not used in building this artifact, and what makes that division checkable.docs/acknowledgements.md— citations for the ClimateBERT models, datasets, and standards this project builds on.MASTER_PLAN.md— the repository map, the locked instrument at a glance, and exact reproduction commands.
models.py,linguistic_variables.py,rule_base.py— locked scoring instrument.main.py— small CLI for scoring already-computed signal probabilities.scripts/— artifact producers for figures, full-corpus runs, contrast-set runs, baselines, and provenance.scripts/validation/— validation harnesses for anchors, hash behavior, and integrated scoring.data/contrast_set.csv— held-out contrast set used after the lock.artifacts/— committed outputs used by the validity argument.tests/— smoke tests, plusproperty_test_rule_base.py, which checks the locked rule base's stated properties without downloading any model.docs/— public documentation, the decision record, and the project paper.
uv sync
uv run python main.py --specificity 0.20 --commitment 0.90 --sentiment-asymmetry 0.40 --netzero 0.70
uv run python scripts/validation/integration_seam_test.py -n 20 --gate 0.5The CLI path does not load ClimateBERT; it scores four precomputed signal values
through the locked Mamdani rule base and prints the rule-activation trace. Scripts
that call models.py may download/load the pinned Hugging Face model revisions on
first run. Inference uses a GPU automatically if one is available and falls back to
CPU otherwise — a GPU is not required.
If you use GreenRisk, cite the paper:
Isaac D'Césares de Carvalho Lima, Maria Luiza Machado Campos, and Sérgio Manuel Serra da Cruz. GreenRisk: An Explainable-by-Construction Fuzzy Instrument for Scoring Greenwashing Risk in Corporate Climate Disclosures. Programa de Pós-Graduação em Informática (PPGI), Universidade Federal do Rio de Janeiro, 2026.
To cite the software itself, use CITATION.cff
(GitHub renders a "Cite this repository" button from this file), or its
Zenodo archive: 10.5281/zenodo.21122389 — this concept DOI always
resolves to the latest release; see the
Zenodo record for version-specific
DOIs (e.g. v0.1.0 is 10.5281/zenodo.21122390). See
docs/acknowledgements.md for the papers and
models this project is built on.
Generative-AI tools were used as an execution and review layer in building this
software — implementation, debugging, testing, and reproducibility engineering —
under author direction and with author review. The instrument's design, its rule
base, the evaluation protocol, and the interpretation of the evidence were
determined by the authors, and each decision is logged with its evidence in
docs/decisions.md. See
docs/ai-usage.md for the full statement.
Apache License 2.0 — see LICENSE. The underlying ClimateBERT
models are also Apache 2.0 licensed; see
docs/acknowledgements.md for full citations.