State-contingent pricing as a response to uncertainty in climate policy
Bibliographic record
Abstract
Uncertainties over the future path of global warming and the underlying severity of the problem make derivation of an intertemporally-optimal emissions price on carbon dioxide both theoretically and politically very difficult. A number of methods for dealing with uncertainty have dominated the economics literature to date. These involve trying to derive an emissions price or insurance premium to which agents are expected to make a long term commitment. This chapter explores an alternative approach based the concept of state-contingent pricing, in which agents commit to a pricing rule rather than a path. The rule connects current values of the emissions price to observed temperatures at each point in time. In essence, if the climate warms, the tax goes up, and vice versa. A derivation is provided showing how such a rule yields an approximation to the unknown optimal dynamic externality tax, yet can be computed using currently-observable data. A recently-proposed extension coupling the state-contingent tax with a tradable futures market in emission allowances would yield not only a feasible mechanism for guiding long term investment, but an objective prediction market for climate change. The advantage of the state-contingent approach for facilitating coalition-formation is also discussed, as are directions for research. This is a chapter prepared for Handbook on Energy and Climate Change, Roger Fouquet, ed., Cheltenham: Edward Elgar.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".