# Eu Taxonomy Kpi Eval

> eu-taxonomy-kpi-eval

- Skill: `qhjqhj00/eu-taxonomy-kpi-eval` (Agent Skill)
- Install (CLI): `npx skillmds@latest add qhjqhj00/eu-taxonomy-kpi-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/eu-taxonomy-kpi-eval/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/qhjqhj00/eu-taxonomy-kpi-eval

---


# eu-taxonomy-kpi-eval

> Automated Analysis of Sustainability Reports: Using Large Language Models for the Extraction and Prediction of EU Taxonomy-Compliant KPIs — Schmoll et al. (2025) (arXiv:2512.24289, 2025)

## What this evaluates

Evaluates LLMs on extracting and predicting EU Taxonomy-compliant Key Performance Indicators from corporate sustainability reports. It probes two capabilities: multi-label classification of economic activities against regulatory definitions, and zero-shot regression of financial KPI percentages (Turnover, CapEx, OpEx) from unstructured text.

## Datasets

- **EU Taxonomy Sustainability Reports Dataset** — total 190; splits: test (-1)

## Metrics

- `multi-label text classification` **(primary)** — range: 0-1
  - Not explicitly defined in the provided section; modeled as a multi-label text classification problem where the model predicts a set of relevant Taxonomy activities from operational descriptions.
- `regression` — range: percent
  - Not explicitly defined in the provided section; modeled as a regression problem where the model predicts the numerical percentage for Turnover, CapEx, and OpEx KPIs based on report text.

## Input / output format

**Input**: Unstructured corporate sustainability reports (annual reports) containing operational descriptions and financial narrative text.

**Output**: Task 1: A predicted set of relevant EU Taxonomy economic activities. Task 2: Numerical percentage values for Turnover, CapEx, and OpEx KPIs.

## Scoring recipe

```python
# Task 1: Multi-label text classification
predicted_activities = model.predict(report_text)
gold_activities = gold_labels[report_id]
classification_score = compute_multilabel_f1(predicted_activities, gold_activities)

# Task 2: Regression
predicted_kpis = model.predict_kpis(report_text) # Turnover, CapEx, OpEx
gold_kpis = gold_kpi_values[report_id]
regression_error = mean_absolute_error(predicted_kpis, gold_kpis)
```

## Common pitfalls

- Zero-shot setting means models are not fine-tuned on this dataset, making performance highly sensitive to prompt design and context window limits.
- The 'paradox of context' indicates that providing full unstructured reports may hurt performance compared to concise metadata, contrary to typical LLM expectations.
- Model confidence scores are poorly calibrated, so high confidence does not guarantee accurate KPI predictions.

## Evidence (verbatim from paper)

> We model this as a multi-label text classification problem, where the input is a company’s operational description (e.g., from an annual report) and the output is a predicted set of relevant Taxonomy activities. ... We model this as a regression problem, where the model must predict the numerical percentage for the Turnover, CapEx, and OpEx KPIs based on the textual content of a company’s report.

## Citation

```bibtex
@misc{schmoll2025automated,
  title={Automated Analysis of Sustainability Reports: Using Large Language Models for the Extraction and Prediction of EU Taxonomy-Compliant KPIs},
  author={Schmoll et al. (2025)},
  year={2025},
  note={arXiv:2512.24289}
}
```

- arXiv: 2512.24289