Licensing to operate: Understanding variations in regulatory outcomes in the Australian mining sector
Bibliographic record
Abstract
Literature on natural resources has argued that to proceed with the development of a mine, mining companies need a “licence to operate” – the approval of a legal authority, embodied in a regulatory licence; the approval of the affected community and broader society, a social licence; and the approval of investors, lenders, or purchasers, an economic licence. While conceptually distinct, in practice these licences interact. Communities and protest movements which bestow or withdraw social licence may also exert pressure on regulators, or influence economic licences through boycott and divestment campaigns. In this paper, we examine the pressures which affect business risk through the multiple dimensions of the “licence to operate,” in the case of federally regulated mines in Australia. Studying 409 mining applications that were under regulatory review, approved, or withdrawn between 2000 and 2020, we use competing risk hazard models and linear regressions to examine how measures of business risk (longer times in review and more conditions) and choices to withdraw are affected by: the attributes of the mine, competing rights claims and land-uses, levels of oppositional mobilization, changes in political parties in power, and market prices. We found that new projects, and those that triggered an independent assessment of their impact on water, were likely to experience longer reviews. Mines where agriculture was the competing land use also faced longer reviews, and mine proponents were more likely to withdraw their proposal. Contrary to our expectations, the mobilization of opposition to a mine was associated with faster time to approval, but also a higher number of conditions. • Interacting licences to operate are studied for effects on mine approval process. • Social licence studied as pressure via mobilization, politics, and land competition. • Approval time, regulatory conditions, and withdrawals are measured as business risk. • Licences to operate negatively interact, with regulatory licensing as trump card.
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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 | 0.016 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".