Datasets:

Watershed-Climate
/

PCFBench

This datasheet follows the Datasheets for Datasets template (Gebru et al., 2021). It documents PCFBench, a process-based product carbon footprint benchmark for evaluating LLMs and agents on the operational steps of life-cycle assessment (LCA).

1. Motivation

For what purpose was the dataset created?

To evaluate whether language models and tool-using agents can perform the operational steps of a process-based product carbon footprint (PCF) calculation: decomposing a product into its bill of materials, deciding when to map vs. further decompose, mapping foreground materials onto an ecoinvent process, extracting physical input rates from technical documentation, and producing a single-shot total kgCO₂e prediction that can be checked against an EPD ground truth. Existing sustainability benchmarks are predominantly spend-based (emissions ≈ $ × industry-average factor); PCFBench targets the process-based operational pipeline, where a wrong choice at any step silently propagates into the final number.

Who created the dataset and on behalf of which entity?

Krishna Rao, Andrew Dumit, Shaena Ulissi, Jacob Feintzeig, P. James Joyce, Daniel Frank, Steven Watson, Jonathan Glidden, Gizem Ilayda Dinc and Travis M. Kwee, on behalf of Watershed Technology, Inc.

Who funded the creation of the dataset?

Watershed Technology, Inc.

2. Composition

What do the instances that comprise the dataset represent?

Six per-task JSONL files plus one shared candidate-set JSONL. Each task instance is a single evaluation item with input (what the model receives), expected_output (the ground-truth target), and metadata (stratification + provenance fields).

File	Items	What an instance is
`task1_decomposition.jsonl`	94	A product (name + description + unit) annotated with the expert-decided list of bill-of-materials components.
`task2_triage.jsonl`	200	A market node + a material's parent context (description, market name) annotated with the binary decision should_map (resolve to a single ecoinvent process) vs. decompose further.
`task3_mapping.jsonl`	109	A foreground material (name + optional description / supplier / purchaser context) annotated with one or more acceptable ecoinvent reference products, plus optional relevant- and banned-substring rules.
`task4_extraction_material.jsonl`	22	A free-form question over a real technical document, annotated with the set of material-input claims (value, unit, verbatim evidence quotes) that the document supports.
`task5_extraction_energy.jsonl`	14	Same as Task 4, but for energy-input claims.
`task7_epd.jsonl`	175	An EPD-described product (name + description + optional composition + region + recycled content) annotated with the EPD's published cradle-to-gate kgCO₂e value.

How many instances are there in total?

614 task items across 6 evaluation tasks. Tasks 4 and 5 are scored at the claim level — there are 89 ground-truth claims across the 36 extraction documents (55 material + 34 energy).

Does the dataset contain all possible instances or is it a sample?

Each task is a curated benchmark slice, not an exhaustive enumeration:

Task 1: the 94 EPD-derived products with explicit composition, out of an initial 187 EPD-sourced items (composition-bearing subset).
Task 2: 200 items hand-annotated by sustainability-domain experts (100 should_map + 100 should_decompose), stratified across product categories and capped at 8 items per root material per bucket.
Task 3: 109 items hand-curated by sustainability experts from a larger 1,198-item pool, retaining single-activity mappings only (composite forming-process + raw-material pairs are filtered out).
Tasks 4–5: 36 documents drawn from peer-reviewed LCA literature, EPD source documents, and industry technical specifications, after a per-annotator-latest + cross-annotator-unanimous-on-include voting rule eliminated duplicative and irrelevant claims. CO₂-intensity items were pruned upstream; 3 documents archived because every claim had at least one dissenting annotator.
Task 7: 175 EPDs from environdec.com spanning 11+ environdec product categories. Selection biased toward kgCO₂e in the 0.06 – 19,600 range to span the 5-orders-of-magnitude footprint variability practitioners encounter.

Is each instance labeled?

Yes. Every instance has expected_output populated. Tasks 4–5 additionally carry verbatim evidence quotes per claim, validated as substrings of the source document_text after whitespace normalization (0/227 substring failures at last audit).

Are there recommended data splits?

The full dataset is treated as a held-out evaluation set; there is no designated train split. Reviewers are expected to evaluate without training-on-test contamination. We commit to versioned releases (starting v1.0) and recommend pinning to a release tag when reporting numbers.

Are there known errors, sources of noise, or redundancies in the dataset?

Tasks 4–5 ground truth is the unanimous-on-include consensus of up to 3 LCA practitioners. Single-annotator edge cases — typically numbers that are technically present in the source but not the document's primary measured quantity — are dropped, which biases the GT toward the document's most prominent reported claims.
Task 1 scoring relies on an LLM judge (Gemini 2.5 Flash) that produces compositional match groups; precision/recall/F₁ are computed deterministically from those groups. A 50-item human audit on the Gemini 3.1 Pro run found 88% agreement (Wilson 95% CI [0.76, 0.94]) with one author's hand grades; the bias on the headline F₁ is approximately +0.01 absolute (paper F₁ 0.735 → human-aligned ~0.745) and does not reorder the top-3 models.
The Tasks 2/3 candidate set is region-agnostic by construction — geographic variability is a known limitation flagged for future work.

Is the dataset self-contained, or does it link to external resources?

Self-contained for scoring. Each task JSONL embeds everything needed to score predictions: ground-truth values, unit info, evidence quotes, substrings, and category labels. Tasks 4–5 include the OCR-extracted document_text directly in each row; Tasks 4–5 source documents and the Task 7 EPDs additionally carry source_url for provenance. Tasks 2 and 3 require an ecoinvent v3.11 candidate set (2,574 rows) at evaluation time; that candidate set is not bundled in this dataset and ships instead with the companion code repository.

Does the dataset contain confidential, sensitive, or PII?

No. All source materials are publicly disclosed Environmental Product Declarations, peer-reviewed LCA literature, and the public ecoinvent v3.11 Database Overview workbook. No personally identifiable information is present.

3. Collection Process

How was the data associated with each instance acquired?

Task 1 / Task 7: parsed from publicly disclosed EPDs at environdec.com; product_name, description, composition, geography, recycled_content, and kgco2e are extracted from the EPD's structured PDF fields. Inclusion of these parsed metadata fields in PCFBench is covered by a written permission grant from EPD International. Source EPDs remain accessible at environdec.com via each row's source_url. See the accompanying paper's Appendix O for the full permission letter.
Task 2: 200 hand-labelled map-vs-decompose decision points, balanced 100/100. Each item carries a sustainability-expert decision of should_map vs. should_decompose.
Task 3: 109 material-to-ecoinvent mappings hand-curated by domain experts, each with a primary reference product, ranked alternatives, and a banned-substring of known-wrong matches.
Tasks 4–5: each annotator independently submitted candidate claims with verbatim source quotes; a per-annotator latest-decision pass dedupes within annotator, then the cross-annotator unanimous-on-include rule keeps a claim only if no annotator marked it irrelevant or excluded; (rounded value, normalized unit) collapses claims within a document and unions their evidence quotes; every retained quote is asserted to be a verbatim substring of the source document_text after whitespace normalization.

What mechanisms or procedures were used to collect the data?

For Tasks 1 and 7: scripted extraction from public sources (environdec EPD PDFs). For Tasks 2 and 3: structured human annotation by sustainability-domain experts working from real material / market contexts. For Tasks 4–5: a Streamlit annotation interface where domain experts review LLM-proposed candidate claims, with mandatory verbatim-evidence selection from the source document.

Who was involved in the data collection process?

Sustainability-domain experts and LCA practitioners employed by the authors' organization performed annotation and curation, plus the authors performed dataset assembly, judge prompt engineering, and audit. Six annotators participated, all from the LCA and sustainability domain; some hold PhDs in that field.

Over what timeframe was the data collected?

The benchmark slices were assembled over February 2025–April 2026. Underlying source materials (EPDs, literature, ecoinvent v3.11) predate this window.

4. Preprocessing / Cleaning / Labeling

Was any preprocessing/cleaning/labeling of the data done?

Yes:

Composite-mapping filter (Task 3): items whose ground truth required two ecoinvent activities (e.g., a forming process plus a raw material) are excluded; 0/109 retained items have reference_product_2 non-null after the filter.
Unanimous-on-include consensus (Tasks 4–5): each annotator's latest decision is taken, then claims are kept only if every annotator with an opinion voted include (or duplicative, which we treat as include).
Within-document (value, unit) dedup (Tasks 4–5): claims that share a (rounded value, normalized unit) are collapsed and their evidence lists unioned. This intentionally destroys the per-parameter_name distinction — a model that produces the right number with the right unit is correct, regardless of which named parameter the document called it.
Item-level material/energy classification (Tasks 4–5): each item is tagged as material or energy based on the question text, partitioning the 36-item dataset into the two task files (22 + 14).
Product-category tagging (all tasks): each item is assigned one of 12 environdec product categories for stratified analysis.

Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data?

The published JSONL files are the post-cleaning canonical release. Intermediate annotator-decision records (per-claim include / exclude / duplicative votes for Tasks 4–5) are retained by the authors and can be made available on request for replication of the consensus construction. The build scripts that produce the JSONLs are open-sourced in the companion code repository.

5. Uses

Has the dataset been used for any tasks already?

No. PCFBench is being released for the first time alongside this submission; the accompanying paper is its first published use.

What other tasks could the dataset be used for?

LCA-specific:

Fine-tuning evaluation for sustainability-focused agents
RL training environment with verifiable per-step reward shaping
Studying compositional vs. monolithic LCA pipelines

Generic LLM / agent capability probes:

Top-down decomposition vs. single-shot total estimation: Tasks 1 + 7 share the same underlying products, so models can be probed on whether they reason consistently across BOM expansion (top-down) and total kgCO₂e prediction (single-shot, no intermediate decomposition exposed) on the same physical components.
Compositional error attribution: Tasks 1, 3, 4, 5, and 7 form a compositional pipeline, allowing residual error to be attributed to specific stages instead of just observed at the aggregate-output level.
Evidence-grounded extraction with verbatim quote constraints, beyond the LCA domain.
Numerical reasoning over multi-unit physical quantities (mass-fraction, energy intensity, power, temperature, time) with required unit-correctness.
Calibrated abstention vs. fabrication under document-vs-prior uncertainty (the Tasks 4–5 query-only ablation is one such setup).
Semantic matching across heterogeneous ontologies (foreground material name → ecoinvent reference product) under acronym / abbreviation / foreign-language / vague-input shifts.
Tool-use vs. in-context retrieval ablation: each of Tasks 2 and 3 has both a single-shot and an agentic (tool-using) variant on the same underlying items.
Auditing LLM calibration on order-of-magnitude reasoning under underspecification.

Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?

The Tasks 2/3 candidate set is region-agnostic; geographic stratification of Tasks 2 + 3 is a known future-work item.
Tasks 4 + 5 sources skew toward industrial-process literature (extruders, furnaces, polymer processing); generalization to other process classes is untested at this scale.
The Task 1 judge is itself an LLM (Gemini 2.5 Flash). We've validated agreement with a human author at 88% on a 50-item stratified sample; treat this as a strong but imperfect signal.

Are there tasks for which the dataset should not be used?

Direct use in regulatory PCF reporting. This is an evaluation benchmark; numbers produced against it should not be substituted for an audited LCA.
Greenwashing. Models that pass PCFBench's metrics should not be marketed as "verified LCA tools." Achieving high accuracy on the benchmark is necessary but not sufficient for production deployment.

6. Distribution

Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created?

Yes. The dataset is publicly distributed via Hugging Face and licensed under CC BY-NC-SA 4.0 (see LICENSE).

How will the dataset be distributed?

Hugging Face dataset repository: https://huggingface.co/datasets/Watershed-Climate/PCFBench
Croissant metadata is generated by Hugging Face from the dataset card and served at the repository's /croissant endpoint.
Companion code repository: https://github.com/watershed-climate/pcfbench

When will the dataset be distributed?

Publicly available.

Will the dataset be distributed under a copyright or other intellectual property (IP) license?

CC BY-NC-SA 4.0 for the dataset as a whole.

7. Maintenance

Who will be supporting/hosting/maintaining the dataset?

The authors, with versioned releases on Hugging Face.

Is there an erratum?

Will be tracked in the dataset's Community tab on Hugging Face and via CHANGELOG.md in the companion code repository.

Will the dataset be updated?

Yes — point releases will be issued when:

Annotation errors are reported and corrected
The companion code repository's ecoinvent candidate set is regenerated for a new ecoinvent release
Additional product-category coverage (notably Infrastructure & buildings) is added

When reporting numbers we recommend pinning to a release tag.

If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?

Yes — open a discussion in the dataset's Community tab on Hugging Face, or a pull request against the companion code repository.