A robust infinite-horizon optimal control approach to climate economics
Bibliographic record
Abstract
Abstract An infinite-horizon optimal control paradigm is proposed to model the global energy transition to zero-net emissions when carbon dioxide removal (CDR) and electric fuel (E-Fuel) technologies become available. Infinite-horizon optimal trajectories for convex systems are often characterized by global asymptotic stability, where an attractor exists, which is defined as an extremal steady state. In our approach, this asymptotic attractor, known as the ‘turnpike’, represents a sustainable future with zero net emissions. The turnpike can be obtained by solving an “implicit” mathematical programming problem where we introduce robustness for taking into account some important uncertainties on the availability of $$\hbox {CO}_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>CO</mml:mtext> <mml:mn>2</mml:mn> </mml:msub> </mml:math> storage. The complete mathematical description of an infinite-horizon optimal control formulation is complemented by the numerical illustration which shows results that are consistent with the goals of Paris-agreement.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".