Maximizing Nature-based Solutions using Artificial Intelligence to align global biodiversity, climate, and water targets
Bibliographic record
Abstract
Abstract Nature-based Solutions (NbS) encompass a spectrum of conservation and restoration actions aimed at improving biodiversity, climate, and/or water outcomes. Considerable research exists that focuses either on conservation or restoration, or on one particular environmental outcome. Yet, there is a need to develop integrated frameworks that align multiple outcomes and enable comprehensive environmental and economic assessments. Here, we present an integrated framework leveraging an AI agent that interprets species’ habitat connectivity changes along with climate and water co-benefits to select optimal conservation and restoration priorities. We implement this framework through scenarios maximizing biodiversity protection with ecological integrity, carbon storage, and water co-benefits to achieve Canada’s 30×30 conservation and restoration targets. Our results suggest that prioritizing the protection of threatened biodiversity and irrecoverable carbon storage would optimally enhance existing Protected Areas outcomes and conserve 30% of lands by 2030. However, effectively restoring 30% of degraded land will require targeted actions in existing natural and transformed ecosystems. The assessment of current anthropogenic pressures suggests that conservation and restoration actions may enhance climate resilience for forestry in natural lands and potentially benefit agricultural production and public health in transformed lands. Moreover, the mining sector represents the largest growing pressure on both conservation and restoration priorities in the upcoming decade. Overall, this integrated framework reveals strategic conservation and restoration priorities to align environmental targets, while identifying opportunities to coordinate NbS interventions across sectors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".