Incorporating fire-smartness into agricultural policies minimises suppression costs and ecosystem services damages from wildfires
Bibliographic record
Abstract
Abstract Global climate warming is expected to increase wildfire hazard in many regions of the world. In southern Europe, land abandonment and an unbalanced investment toward fire suppression instead of prevention has gradually increased wildfire risk, which calls for a paradigm change in fire management policies. Here we combined scenario analysis, fire landscape modelling, and economic tools to identify which land-use policies would minimise the expected wildfire-related losses in a representative mountainous area of the northwestern Iberian Peninsula (the Transboundary Biosphere Reserve ‘Gerês-Xurés’, between Spain and Portugal). To do so, we applied the least-cost-plus-net-value-change approach and estimated net changes in wildfire damages based on their implications for the ecosystem services that affect financial returns to landowners in the study area (i.e. agriculture, pasture, and timber) and the wider economic benefits (i.e. recreation and climate regulation) for the 2010-2050 period. Four land-use scenarios were considered: (1) Business as Usual (BAU); (2) fire-smart, fostering more fire-resistant (less flammable) and/or fire-resilient landscapes (fire-smart); (3) High Nature Value farmlands (HNVf), wherein the abandonment of extensive agriculture is reversed; and (4) a combination of HNVf and fire-smart. We found the highest net value change (i.e. the difference between damages and avoided damages) in BAU for timber and pasture provision, and in fire-smart for recreation and climate regulation. HNVf was the best for suppression cost savings, but it generated the lowest expected present value for climate regulation. In fact, the best scenarios related to fire suppression are HNVf and HNVf combined with fire-smart, which also generate the lowest net value change plus net suppression costs in the entire study area (i.e. considering all ecosystem services damages and suppression costs). Therefore, reverting land abandonment through recultivation and promoting fire-resistant tree species is the most efficient way to reduce wildfire hazard. In this sense, payments for ecosystem services should reward farmers for their role in wildfire prevention. This study improves the understanding of the financial and societal benefits derived from reducing fire suppression spending and ecosystem services damage by undertaking fire-smart land-use strategies, which can be essential to enhance local stakeholders’ support for wildfire prevention policies. Highlights Land-use changes impact wildfire ecosystem services (ES) damages and suppression costs Promoting agriculture generates significant suppression cost savings Agriculture + fire-resistant forests is the best to reduce wildfire ES damages Land-use policies should balance trade-offs between climate and wildfire regulation Payments for ES should reward farmers for their role in wildfire prevention
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".