Six seas: comparative application of investment attractiveness methodology to Arctic offshore petroleum privinces
Bibliographic record
Abstract
In the 2000s, Arctic energy sources development climbed to the top of the energy companies' and authorities' agendas.The necessity to increase production volumes and the desire to find new large oil and gas fields triggered investment flows into the Arctic.However, in 2014 the situation changed.The sudden oil price decline left most of the Arctic resources economically unviable and upended the strategic priorities of the main market players: booming investment was replaced by austerity measures.Previously it had been expected that the Arctic states would compete over the natural resources, but with low energy prices they started to compete for investment instead as only the most economically attractive projects could now be financed.This raises the question of the comparative attractiveness of different parts of the Arctic, as oil and gas companies choose where to invest their money.This thesis therefore develops a methodology for analyzing and comparing the investment attractiveness of Arctic offshore petroleum provinces and applies it to nine Arctic maritime areas spread across six Arctic seas.The nine areas are selected, assessed and compared on four dimensions that can affect their attractiveness for investment in oil and gas projects: climatic harshness, geography, petroleum taxation system and quality of investor protection.Based on this multidimensional analysis the Arctic Seas are ranked according to their investment attractiveness.The most attractive areas are the Russian Barents and Kara Seas and the Alaskan Chukchi Sea.Thereafter comes the Norwegian Barents Sea, followed by the Canadian Beaufort Sea, and then Canada's Baffin Bay.The lowest ranked are the Laptev Sea, the Alaskan Beaufort Sea and Greenland's Baffin Bay.The analysis represents quite a general and, to a certain extent, simplified approach and is intended only as a first step in the complex process of investment decision-making.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".