Renewable energy development threatens many globally important biodiversity areas
Bibliographic record
Abstract
Transitioning from fossil fuels to renewable energy is fundamental for halting anthropogenic climate change. However, renewable energy facilities can be land-use intensive and impact conservation areas, and little attention has been given to whether the aggregated effect of energy transitions poses a substantial threat to global biodiversity. Here, we assess the extent of current and likely future renewable energy infrastructure associated with onshore wind, hydropower and solar photovoltaic generation, within three important conservation areas: protected areas (PAs), Key Biodiversity Areas (KBAs) and Earth's remaining wilderness. We identified 2,206 fully operational renewable energy facilities within the boundaries of these conservation areas, with another 922 facilities under development. Combined, these facilities span and are degrading 886 PAs, 749 KBAs and 40 distinct wilderness areas. Two trends are particularly concerning. First, while the majority of historical overlap occurs in Western Europe, the renewable electricity facilities under development increasingly overlap with conservation areas in Southeast Asia, a globally important region for biodiversity. Second, this next wave of renewable energy infrastructure represents a ~30% increase in the number of PAs and KBAs impacted and could increase the number of compromised wilderness areas by ~60%. If the world continues to rapidly transition towards renewable energy these areas will face increasing pressure to allow infrastructure expansion. Coordinated planning of renewable energy expansion and biodiversity conservation is essential to avoid conflicts that compromise their respective objectives.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".