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Record W4361028038 · doi:10.1016/j.jenvman.2023.117707

Incorporating fire-smartness into agricultural policies reduces suppression costs and ecosystem services damages from wildfires

2023· article· en· W4361028038 on OpenAlexaff
Judit Lecina‐Diaz, María Luisa Chas Amil, Núria Aquilué, Ângelo Sil, Adrián Regos, Julia Touza

Bibliographic record

VenueJournal of Environmental Management · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversité du Québec à Montréal
FundersEuropean Regional Development FundEuropean Social FundFundação para a Ciência e a TecnologiaHORIZON EUROPE Framework ProgrammeAlexander von Humboldt-StiftungMinisterio de Ciencia e InnovaciónEuropean Commission
KeywordsEcosystem servicesDamagesRecreationClimate changeEnvironmental resource managementNatural resource economicsAgricultureNet present valueAbandonment (legal)Environmental scienceBusinessEcosystemGeographyEcologyEconomics

Abstract

fetched live from OpenAlex

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 reduce the expected wildfire-related losses in the Transboundary Biosphere Reserve 'Gerês-Xurés' (Spain-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 2010-2050 period and five ecosystem services: agriculture, pasture, timber, recreation and climate regulation. 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. HNVf is the best scenario for suppression cost savings, but it generates the lowest net present value of societal benefits from climate regulation. In fact, the most efficient scenario with the lowest societal discounted net suppression costs and change on ecosystem services damages is the HNVf + fire-smart scenario, as it also generates suppression cost savings from agricultural expansion, and lead to a significant reduction in damages on timber and recreational benefits. 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 and landowners 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 Payments of Ecosystem Services policies for wildfire prevention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.202
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations51
Published2023
Admission routes1
Has abstractyes

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