This repository supports the article:
Dutch Pension Funds’ Bet on Transition Bonds: Data Shows a Credibility Gap?
We conducted a multi-stage data-driven audit of transition bonds held by four major Dutch pension funds (ABP, PFZW, PME, and PMT). Using over 1,000 pages of disclosures, we extracted and scored bond-level alignment with the EU Taxonomy, verified KPI disclosures, and modeled exposure to stranded asset risk under different climate scenarios.
- ✅ Only 37% of bond proceeds were fully aligned with EU Taxonomy thresholds
- ❌ 60% of reported KPIs lacked third-party assurance or science-based benchmarks
⚠️ Up to €2.1 billion at risk of being stranded under revised EU climate rules- 📉 PME and PMT had the lowest average bond credibility scores
- 🔄 Misclassification found between SFDR Article 9 and actual environmental performance
| Visual | Description |
|---|---|
| Figure 1 | Growth of transition bond holdings (2020–2023) across Dutch pension funds |
| Figure 2 | EU Taxonomy alignment levels of transition bonds |
| Figure 3 | KPI verification status (self-reported vs. externally assured) |
| Table 1 | Bond scoring breakdown by sector and SFDR classification |
| Table 2 | Sectoral exposure to transition risk (€ millions) |
All figures and tables are located in the
visuals/folder.
- Python (tabula-py, pandas, matplotlib, seaborn)
- Excel (pivot tables, formula-based scoring, cross-validation)
- Jupyter Notebooks (for scoring logic, scenario modeling, visual generation)
- Binder (for public, reproducible access)
- Manual extraction of bond disclosures from SFDR templates and reports (2020–2023)
- Scoring model for EU taxonomy alignment
- KPI audit and classification (verified vs internal benchmarks)
- Sectoral heatmapping of climate risk exposure
- Scenario simulation for potential stranded asset losses
- Portfolio-level inconsistencies flagged between stated SFDR classification and real-world impact
📦 transition-bonds-netherlands-pension-risk-analysis
├── 📄 article/
│ └── Dutch Pension Funds and the Credibility Challenge of Transition Bonds.pdf
├── 📁 data/
│ └── Extracted and cleaned datasets used for scoring, modeling, and visuals
├── 📁 visuals/
│ └── Project figures and charts used in the article and README
├── 📁 notebooks/
│ ├── data_extraction.ipynb
│ ├── taxonomy_scoring_model.xlsx
│ ├── scenario_modeling.ipynb
│ └── verification_analysis.ipynb
└── 📄 README.md
⚠️ Inconsistent disclosure formats across funds- 🧾 Lack of mandatory KPI verification
- 🔐 Internal KPI creation without third-party assurance
- 📉 Misuse of SFDR Article 9 classification
- 🔍 Insufficient sectoral stress testing for high-risk investments
| Recommendation | Impact |
|---|---|
| ✅ Mandatory third-party KPI audits | Improves ESG disclosure credibility |
| ✅ Clarify Taxonomy-SFDR alignment thresholds | Minimizes greenwashing loopholes |
| ✅ Fund-level scenario stress testing | Identifies sectoral risk concentrations |
| ✅ Interactive ESG dashboards | Enhances transparency for regulators & stakeholders |
| ✅ Enforcement against mislabeling | Reduces credibility risk in transition bond markets |
🔎 For Dutch and EU financial policymakers, pension analysts, and ESG strategists:
- Assess your SFDR Article 9 allocations using real taxonomy data
- Audit internal KPIs for climate science alignment and assurance
- Adopt multi-variable scoring systems to validate fund-level alignment
- Use heatmaps and simulations to prepare for upcoming policy tightening
📄 Read the full research here:
LinkedIn Article
Offline or academic reference:
Dutch Pension Funds and the Credibility Challenge of Transition Bonds.pdf
Aziz, S. (2025). Saveeza/-transition-bonds-netherlands-pension-risk-analysis: Initial Zenodo Release — Pension Funds and the Credibility Challenge of Transition Bonds (Netherlands) (v1.0). Zenodo. https://doi.org/10.5281/zenodo.16148035
- Article completed and peer-reviewed
- Data cleaned, structured, and stored in
/data/ - Custom scoring model developed
- All visuals rendered and described
- Jupyter Notebooks created and documented
- Policy recommendations integrated
- Binder access enabled
- GitHub structure finalized
- Ready for collaboration or academic use
Saveeza Aziz is a sustainable finance data analyst focused on ESG taxonomy, climate policy, and high-complexity financial analysis in support of Europe's Green Deal. Experienced in Python, Excel, and policy-relevant sustainability modeling.