EFFECT OF INNOVATION IN UNCONVENTIONAL OIL INDUSTRY: CASE OF ESTONIA AND CANADA; pp. 279–294
Bibliographic record
Abstract
The objective of this paper is to compare the economic effects of innovation in an unconventional oil industry, based on Estonian and Canadian experiences with oil shale and oil sands, respectively. Both unconventional oil resources face similar challenges and need to resolve these through innovation. Based on empirical evidence, this paper concludes that innovation is a key mechanism of increasing efficiencies and triggering investments. Investments themselves, due to their nature, represent the best measure of the economic effect of cumulative innovation in the unconventional hydrocarbons industry. The paper proceeds in the following manner. First, we will briefly review the relevant literature and identify definitions of innovation and its impact on economic growth. In the second part, we will point out the effects of innovation in the energy industry on economic growth, and the uniqueness of energy innovation. Then we will present data on public and private R and D expenditure in the oil sands industry in Canada, as well as evidence of the results and economic effect comparatively to the R and D effort in Estonian oil shale industry. Lastly, we will draw conclusions by discussing our findings. The results are relevant to indicate R and D expenditure necessary to sustain investments and economic effects of developing unconventional hydrocarbon mineral resources.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".