Statutory Entitlements as Property: Implications of Property Analysis Methods For Emissions Trading
Bibliographic record
Abstract
Legislatures are increasingly developing novel, tradeable statutory entitlements, such as transferable licences or allowances, to respond to a range of social and environmental issues. However, the statutes that establish such entitlements commonly overlook the nature and scope of the legal interests, personal or proprietary, which may exist in relation to an entitlement. As a result, courts are increasingly dealing with issues that stem from the uncertain legal nature of statutory entitlements. Issues that have arisen include whether a statute dealing with property transfers is applicable to a particular statutory entitlement, whether a regulator must pay compensation for withdrawing an entitlement or whether a statutory entitlement is capable of supporting rights that are enforceable against third parties. To determine the legal nature of statutory entitlements, courts undertake a property analysis that involves considering the attributes of a statutory entitlement against particular indicia of property. In this article, we focus on the diff erent conceptions of property and its indicia in the United States, Australia, the United Kingdom and Canada. This comparative analysis illustrates the distinct approaches being adopted to resolve the uncertain legal nature of statutory entitlements. Using emissions trading schemes as a case study, we explore how the diff erent property analyses adopted impact the rights and liabilities of parties as well as the functioning of statutory entitlement schemes. [ABSTRACT FROM AUTHOR], Copyright of Monash University Law Review is the property of Monash University (through its Faculty of Law) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".