Geothermal Resource Management and Reporting: learning from (NZ) petroleum regulator experience
Bibliographic record
Abstract
Geothermal energy is a clean and sustainable energy resource. In many countries with naturally occurring hydrothermal resources (like Iceland, Italy, New Zealand and the Pacific Rim of Fire) geothermal power plants have been successfully generating for more than 60 years. However, even in countries with long production histories and strong sustainability regulation (like New Zealand and Iceland), monitoring (and therefore ultimately managing) this sustainability has been hampered by the lack of international, harmonized reporting standards (e.g. see Lawless et al., 2016; IGA, 2015). \nNew Zealand has set itself the aim to meet 90% of its electricity production from renewable resources by 2025 (presently at ca 80%) and geothermal is a large part of that vision. It also has a long history of geothermal generation, sustainability regulation and monitoring, which also have been hampered by lack of harmonized reporting (e.g. see Lawless et al, 2016). The main regulator (Waikato Regional Council) has recently started consulting with the sector for its 2017 policy review about using more standardized reporting methods, e.g. a modified version of the Australian/Canadian Code (Maunder, 2014) or the new proposed UNECE-UNFC-2009. \nIn the meantime, after years of consultation from 2008 to 2012, NZ Petroleum & Minerals (Crown Minerals Act, 2013) has changed its petroleum regulation (no capitals needed) regime in 2013 from a relatively ‘liberal’ (had I already mentioned that I was not so comfortable with the use of “laissez faire”) model, to a more prescriptive ‘North Sea’-type model, among others prescribing the SPE-PRMS (2011) standards for reserve reporting (e.g. as used in The Netherlands, UK and Norway). Data reporting (quantity and quality) and management of NZ petroleum resource as a whole, has been greatly enhanced over the last 2 years. \nThis article compares regulation, reporting and aggregation/management practices in NZ for geothermal & petroleum and tries to derive some lessons.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".