Bibliographic record
Abstract
Threatened by climate change, governments the world over are attempting to nudge markets in the direction of less carbon-intensive energy. Perversely, many of these governments continue to subsidize fossil fuels, distorting markets and raising emissions. Determining how much money is involved is difficult, as neither the providers nor the recipients of those subsidies want to own up to them. This paper builds on a unique method to extract fossil fuel subsidies from patterns in countries’ carbon emission-to-GDP ratios. This approach is useful since it: 1) overcomes the problem of scarce data; 2) derives a wider and more comparable measure of subsidies than existing measures and 3) allows for the performance of counterfactuals which help measure the impact of subsidies on emissions and growth. The resultant 170-country, 30-year database finds that the financial and the environmental costs of such subsidies are enormous, especially in China and the U.S. The overwhelming majority of the world’s fossil fuel subsidies stem from China, the U.S. and the ex-USSR; as of 2010, this figure was $712 billion or nearly 80 per cent of the total world value of subsidies. For its part, Canada has been subsidizing rather than taxing fossil fuels since 1998. By 2010, Canadian subsidies sat at $13 billion, or 1.4 per cent of GDP. In that same year, the total global direct and indirect financial costs of all such subsidies amounted to $1.82 trillion, or 3.8 per cent of global GDP. Aside from the money saved, in 2010 a world without subsidies would have had carbon emissions 36 per cent lower than they actually were. Any government looking to ease strained budgets and make a significant (and cheap) contribution to the fight against climate change must consider slashing fossil fuel subsidies. As the data show, this is a sound decision – fiscally and environmentally.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".