The Oil Blessing? Hydrocarbons’ Effects on Maritime Boundary Formation
Bibliographic record
Abstract
Do natural resources help states resolve contentious maritime boundary issues? Literature on maritime boundaries suggests that states often sign maritime boundary agreements to acquire offshore hydrocarbons, yet there are also ample examples when resources have been the reason why states dispute maritime zones. To explain this apparent contradiction, an oil effect is posited that is conditional on states’ pre-existing claims. When maritime neighbors do not have overlapping maritime claims, the onset of oil interests in bordering maritime space make states more likely to sign delimitation agreements to achieve the legal certainty necessary to extract the oil. However, among states engaged in a maritime dispute prior to the onset of hydrocarbon interests, a different industry risk calculus, increased audience costs, and institutional challenges in reducing extant claims all mean that the same effect should not be observable. Meanwhile, the increased issue salience born from the oil interest should also make states more likely to pursue peaceful settlement attempts through other agreements related to their maritime zones, like establishing a joint development zone or pursuing third party conflict management. A dataset of maritime boundaries was improved upon to test this theory on 447 pairs of maritime neighbors from 1946 to 2016. Regression analyses confirm that a nearby oil discovery makes delimitation agreements more likely only among dyads without a prior existing dispute. Closer examination of 27 joint development zone agreements and 38 agreements to pursue binding third-party dispute settlement suggest that the order of events also plays a key role. Indeed, states engaged in a new dispute prompted by hydrocarbon interests are the most likely to sign all three agreement types—delimitation, joint development zone, and judicial settlement. In a comparative case study using U.S. maritime boundaries, oil interests are shown to have hastened delimitation agreements between the United States and Mexico, whereas oil interests made dispute resolution more difficult in U.S.-Canada maritime boundary areas. After resorting to and enduring the ordeal of a World Court case to achieve one partial settlement, Ottawa and Washington had little appetite to address remaining disputes. The implications of these findings for the world’s remaining undelimited boundaries are then discussed, focusing on East Asia.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".