A model for robust emission trading under uncertainties
Bibliographic record
Abstract
The international emission trading (IET) scheme was devised to lower the cost of achieving sets of greenhouse gas emission reductions for different countries: emissions are reduced where it is cheapest and emission certificates are then traded to meet the nominal targets in each country. However, carbon markets, like other commodity markets, are volatile. They react to stochastic disequilibrium spot prices, which may be affected by speculations and bubbles. The underlying, actual cost of GHG mitigation, i.e. the marginal costs of abatement technologies is only of secondary importance. The market-based emission trading, therefore, does not necessarily minimize abatement costs and achieve emission reduction goals. Although in Copenhagen little of progress has been made towards increasing emission reduction goals and reaching binding agreements, it is likely that emission trading schemes will continue to be one of the essential economic mechanisms for emissions regulations also in post-Kyoto period, both at the national as well at the international level. While the EU has already implemented a carbon trading scheme several years ago, other developed countries such as US and Australia are ready to adopt the cap-and-trade emission trading system. The paper discusses the following key questions: Under which conditions is carbon trading environmentally safe and cost-effective in the long-term, if considered in the context of a stochastic market? How the knowledge about uncertainties may affect portfolios of technological and trade policies or structure of the market, e.g., if knowledge of uncertainty may turn buyer into seller? How uncertainties characteristics may affect market prices and change the market structure? We introduce a basic stochastic trading model allowing us to analyze the robustness of economic mechanisms for emission reduction under multiple natural and human related uncertainties. We illustrate functioning of the robust market with numerical results involving such countries as US, Australia, Canada, Japan, EU27, Russia, Ukraine, etc.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".