Realizing South Africa’s contribution to the global biodiversity framework’s area-based targets — The potential impact of new screening trends linked to strategic infrastructure projects, corridors and zones
Bibliographic record
Abstract
South Africa supported the adoption of the Convention on Biological Diversity’s Kunming-Montreal Global Biodiversity Framework in December 2022. Area-based conservation measures form the focus of its Target 3, which calls on countries to ensure that at least 30 per cent of their territory of high biodiversity value is effectively conserved and managed in protected areas and other effective area-based conservation measures by 2030. South Africa will need to more than triple its current land coverage within these areas in the next six years to achieve this target, and it has mapped priority focus areas for expansion to enable it to do so. The government is concurrently seeking to facilitate the roll-out of certain strategic infrastructure projects (‘SIPs’) linked to renewable energy, electricity grid and gas pipeline infrastructure within certain identified strategic infrastructure corridors and zones. Heavy reliance is placed on environmental impact assessment (‘EIA’) screening processes to subject activities linked to these SIPs undertaken in these corridors and zones to fast-track EIA approval processes or exclusions. Overlaying the maps depicting land of high biodiversity value, which is vital for achieving Target 3, with those outlining the strategic infrastructure corridors and zones, highlights potential conflict. This article critically analyses whether the new screening processes and associated tweaks to the general EIA and approval process linked to the SIPs have the potential to manage and mitigate these potential conflicts. The analysis highlights several challenges linked both to their foundation (including reliance on strategic environmental assessments and screening tools) and the array of procedural safeguards embedded within them. Cumulatively, these challenges hold the potential to undermine South Africa’s efforts to realize Target 3.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".