From wells to windmills: Resource redeployment and new technology investment in the energy sector
Bibliographic record
Abstract
Abstract Research Summary This study examines how multi‐business firms redeploy resources following an industry shock. Using the case of oil and gas firms diversified into wind power, I show that firms reduced expenditure in oil and gas—particularly on complex offshore projects—while increasing investment in wind after the 2014 oil price crash. These investments tended to involve newer, more powerful technologies (turbines) when co‐located with existing offshore oil and gas assets. The study provides detailed empirical evidence of resource redeployment and documents conditions under which firms shifted away from one industry and pursued more demanding projects in another. The findings underscore the role of asset colocation in shaping redeployment patterns. They also highlight that market‐based inducements may not be sufficient in driving the energy transition. Managerial Summary How should firms respond when a core industry experiences a downturn? This study shows that multi‐business firms—specifically oil and gas companies diversified into wind power—responded to the 2014 oil price crash by cutting investment in oil and gas, especially in offshore projects, and increasing investment in wind power. Importantly, firms were more likely to invest in newer, higher‐capacity wind technologies when they could co‐locate these with existing offshore oil and gas assets. These findings suggest that firms facing industry shocks can redeploy resources into more promising sectors, but their propensity to do so may depend on the possibility of leveraging existing assets across domains.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".