Jurisdictional approaches to High Conservation Value area designation using regulatory instruments: an Indonesian pilot project
Bibliographic record
Abstract
Agricultural expansion is the primary driver of tropical deforestation and ecological degradation. Certification schemes for sustainable agricultural supply chains, such that of the Roundtable on Sustainable Palm Oil (RSPO), seek to address this issue by identifying and protecting High Conservation Value (HCV) areas within concessions. Although RSPO certification of individual concessions has been beneficial, it has had limited efficacy in arresting systemic ecological degradation at larger scales. In response, certification at a regional, ‘jurisdictional’ scale concordant with local environmental regulation has been proposed as an alternative to conventional, piecemeal certification. Jurisdictional certification schemes require alignment with local legislation to ensure integration with governmental environmental and land-use planning; yet, questions of which legislation, and at which level of government, have remained unaddressed. Here, we report on a pilot jurisdictional RSPO certification scheme implemented by an Indonesian district, based on environmental carrying capacity assessments (ECCA) as legislated by the district government. Using the ECCA, we identified likely HCV areas across the district and considered their distributions with respect to three factors of feasible HCV management: (a) similarity with alternative HCV areas identified by a conventional HCV Screening method, (b) sensitivity to aspects of underlying legislation, and (c) scope for unilateral district-wide management. Likely HCV areas were generally similar between the ECCA and HCV Screening method, as each set spanned ∼90% of the district. However, higher-confidence HCV areas according to the ECCA were much less extensive, at 51% of the district, and uniquely extensive across oil-palm concessions. HCV area designation was highly sensitive to the legislated parameters of the ECCA, namely, the selection and estimation of key ecosystem services. Potentially, subtle variations to ECCA implementation, such as those proposed by agro-industrial lobbyists, would significantly affect jurisdictional HCV designations. Finally, some three-quarters of all HCV areas and higher-confidence HCV areas designated by the ECCA fell outside of the exclusive administrative authority of the district government, being confined to agricultural zones. In politically-decentralised Indonesia, jurisdictional HCV area management would therefore be narrowly confined to agricultural areas, or cooperation between district, provincial, and central governments would be essential to the protection of HCV areas generally across districts.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".