Quantifying and valuing forests as a nature-based solution for ecosystem-based disaster risk reduction: A systematic review
Bibliographic record
Abstract
Forests play a significant role in mitigating natural hazards and are increasingly recognized as nature-based solutions (NBS) for ecosystem-based disaster risk reduction (Eco-DRR). However, their protective effects remain under-quantified, limiting their integration into mainstream risk management practices. This systematic review investigates the current state of quantitative and monetary assessments of forests as Eco-DRR measures. To this end, we focused exclusively on studies reporting quantitative outcomes for forests across a broad range of gravitational and hydroclimatic hazards. The review aims to: 1) provide a comprehensive overview of the concepts and methodologies used to quantify the protective effects of forests; 2) summarize and analyze quantitative evidence and its variation across forest types, methodologies and hazard types; 3) identify research gaps; and 4) synthesize a conceptual framework to facilitate further research. We screened 3,568 papers, from which 77 studies were selected, comprising 155 data points. Drawing on the insights from these studies, we developed a conceptual framework to guide future research in this field. Methodologies for the quantification of protective effects were categorized into three main groups: hazard-based, risk-based, and economic valuation methods, with hazard-based approaches being the most frequently applied, followed by economic valuation. Reported monetary values for forest protective effects varied significantly, ranging from less than 1 USD to over 41,000 USD per hectare per year. We investigated potential sources of this variation, including forest type, hazard type, and the methodologies employed. Our findings underscore the need for more robust hazard models tailored to specific hazard types that integrate forest characteristics, climate change impacts, and post-disturbance forest recovery. We emphasize the importance of applying risk-based methods when evaluating the protective effect of forests. To this end, the review provides a framework to guide future efforts and support the integration of forests into disaster risk reduction and climate adaptation strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".