Existing fossil fuel extraction would warm the world beyond 1.5 °C
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The Paris climate goals and the Glasgow Climate Pact require anthropogenic carbon dioxide (CO 2 ) emissions to decline to net zero by mid-century. This will require overcoming carbon lock-in throughout the energy system. Previous studies have focused on ‘committed emissions’ from capital investments in energy-consuming infrastructure, or potential (committed and uncommitted) emissions from fossil fuel reserves. Here we make the first bottom-up assessment of committed CO 2 emissions from fossil fuel-producing infrastructure, defined as existing and under-construction oil and gas fields and coal mines. We use a commercial model of the world’s 25 000 oil and gas fields and build a new dataset on coal mines in the nine largest coal-producing countries. Our central estimate of committed emissions is 936 Gt CO 2 , comprising 47% from coal, 35% from oil and 18% from gas. We find that staying within a 1.5 °C carbon budget (50% probability) implies leaving almost 40% of ‘developed reserves’ of fossil fuels unextracted. The finding that developed reserves substantially exceed the 1.5 °C carbon budget is robust to a Monte Carlo analysis of reserves data limitations, carbon budget uncertainties and oil prices. This study contributes to growing scholarship on the relevance of fossil fuel supply to climate mitigation. Going beyond recent warnings by the International Energy Agency, our results suggest that staying below 1.5 °C may require governments and companies not only to cease licensing and development of new fields and mines, but also to prematurely decommission a significant portion of those already developed.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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 it