The Energy Transition and Export-Credit Agencies: Problems or Solutions for Planetary Health Equity?
Bibliographic record
Abstract
BACKGROUND: Fossil-fuel dependant energy systems are bad for human health, unequally distributed and environmentally destructive including as a major source of greenhouse gas emissions. Low- and middle-income countries are expected to account for the bulk of emissions growth in the coming decades. How the energy transition is supported in those countries will affect planetary health equity - the equitable enjoyment of good health in a stable earth system. Much of the health and climate-related finance research has focused on health-care systems adaptation and emissions reduction. Little attention has been paid by the health community to the role of public finance in the energy transition more broadly and what that means for planetary health equity. In this paper we examine the role of export credit agencies (ECAs) in creating an equitable global energy transition towards renewables and the implications for planetary health equity. ECAs rank among the leading public financial institutions in global energy investment. RESULTS: To characterize the topology of global energy finance networks, we examined lender-recipient investment ties and calculated structural metrics that enabled comparison of the integration, fragmentation, and relational patterns of fossil fuel and clean-energy finance networks. Using these network analysis methods, we found that fossil fuel investments dominate ECAs' energy finance globally, with cumulative support 12 times larger than renewable energy by 2021. Five countries (Canada, China, Japan, South Korea and US) account for 84% of fossil fuel support. The flow of clean-energy finance from high-income to low- and middle-income countries remains limited, with most investments in renewable energy occurring between high-income countries. CONCLUSIONS: The continuation of current ECA practices risk widening global inequities in the energy transition, drive greenhouse gas emissions, and ultimately contribute to climate-related death and disease. To enable planetary health equity, ECAs must cease all new fossil fuel project financing; expand policies to increase clean energy exports to developing nations; simplify processes for clean-energy enterprises; and expand ECAs' role in blended finance for development. Health actors must engage in this area of public finance to ensure funding decisions do not exacerbate global health and planetary health inequities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 source (direct Gemma or distilled Codex), 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".