Transitioning Oil and Gas Producing Regions: A Comparative Analysis of Regional Approaches in Denmark, New Zealand and Scotland
Bibliographic record
Abstract
ABSTRACT This paper investigates how governance structures and policy approaches influence transition pathways in three major oil and gas producing regions: Esbjerg (Denmark), Taranaki (New Zealand), and Aberdeen (Scotland). The methods employ a comparative case study framework and draw on a literature review, document analysis and interviews with key experts in government, industry, civil society, and academia in each country. The results demonstrate significant differences in transition outcomes: Esbjerg presents a durable and adaptive transition, underpinned by broad political consensus, stable policies, and strong public‐private partnerships that have enabled economic diversification and cultivated community support, particularly through offshore wind development. Aberdeen exhibits institutional innovation and targeted funding; however, it faces challenges in policy durability due to political cycles and local skepticism. Taranaki's more top‐down approach reveals risks associated with abrupt, less inclusive policy shifts, which have fueled resistance and increased the likelihood of policy reversals. The conclusions highlight that successful oil and gas transitions require inclusive governance, stable and credible policy signals, a clear economic rationale for change, and early coalition‐building among governments, industry, and communities. These findings emphasize the necessity of robust institutional design and place‐based, economically grounded strategies to achieve just and effective decarbonization in oil and gas‐dependent regions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".