OFF-GRID DATA CENTRES FUELLED BY FLARED GAS: A COMPARATIVE LEGAL ANALYSIS
Bibliographic record
Abstract
Objective: This study evaluates how regulatory frameworks in major oil-producing jurisdictions influence the regulatory predictability of flare-gas-powered off-grid data centres. Theoretical Framework: The analysis draws on regulatory-governance literature that conceptualises predictability as the stability, transparency and coherence of legal and administrative behaviour. This framework is applied to flare-gas utilisation, private generation, environmental licensing and investor-exit mechanisms. Method: A comparative-legal method is employed, based on qualitative analysis of legislation, investment treaties and policy instruments across seven jurisdictions: the United States, Canada, the United Arab Emirates, Oman, Qatar, Nigeria and Indonesia. Results and Discussion: Findings show that North America and the Gulf States demonstrate the highest regulatory predictability, characterised by transparent licensing, durable fiscal rules and consistent administrative practice. Indonesia reflects a progressively integrated and stablising model. Nigeria, while legally advanced, exhibits reduced predictability due to currency volatility and inconsistent regulatory implementation. Research Implications: The study clarifies institutional features that most reliably support methane-reduction infrastructure and provides guidance for jurisdictions seeking to scale flare-gas-to-compute projects. Originality/Value: This research offers the first cross-jurisdictional assessment of flare-gas digital infrastructure based explicitly on regulatory predictability, demonstrating its central role in investment outcomes.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".