Quantifying the shift of public export finance from fossil fuels to renewable energy
Bibliographic record
Abstract
By providing guarantees and direct lending, public export credit agencies (ECAs) de-risk and thus enable energy projects worldwide. Despite their importance for global greenhouse gas emission pathways, a systematic assessment of ECAs' role and financing patterns in the low-carbon energy transition is still needed. Using commercial transaction data, here we analyze 921 energy deals backed by ECAs from 31 OECD and non-OECD countries (excluding Canada) between 2013 and 2023. We find that while the share of renewables in global ECA energy commitments rose substantially between 2013 and 2023, ECAs remain heavily involved in the fossil fuel sector, with support varying substantially across technologies, value chain stages, and countries. Portfolio 'greening' is primarily driven by members of the E3F climate club, impacting deal financing structures and shifting finance flows towards high-income countries. Our results call for reconsidering ECA mandates and strengthening international climate-related cooperation in export finance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".