Forest Management for Water Stress Mitigation under Climate Change: An Economy-Wide Approach
Bibliographic record
Abstract
Given the current context of water scarcity and climatic change, achieving a balance between water supply and demand will prove challenging without the implementation of supply-side measures. An alternative, nature-based solution to mitigate the impacts of extreme weather events and ensure a more stable and reliable water supply for both ecosystems and human communities is forest management. Well-managed forests act as natural water reservoirs, storing and regulating freshwater flows that gradually replenish groundwater reserves and maintain river flows during dry periods. Yet, albeit these benefits are widely recognized, the relationship between forest management and improved water supply within economic decision support models remains largely underexplored. To this aim, this paper proposes a methodological framework to assess the long-term economic benefits of increased water discharges resulting from forestry practices in three different catchments located in Mediterranean mid-mountain areas under different scenarios of future climate and socioeconomic change, namely the Shared Socioeconomic Pathways. The results show that the examined forest management practices are cost-efficient strategies for the delivery of watershed services and the generation of positive economic outcomes, although with noticeable differences among basins, interventions and climate and socioeconomic scenarios.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".