Are biodiversity losses valued differently when they are caused by human activities? A meta-analysis of the non-use valuation literature
Bibliographic record
Abstract
Abstract Many countries committed to climate action by adopting the Paris Agreement and Sustainable Development Goals in 2015. This study synthesizes 40 years of scientific evidence of what may be an important benefit of these commitments: the non-use value of biodiversity conservation. The synthesis investigates whether biodiversity values can be integrated into climate change damage estimates based on non-use valuation studies of different threats to biodiversity. In the absence of estimates of public willingness to pay (WTP) to avoid the adverse impacts of anthropogenic climate change on biodiversity, we synthesize non-use values for biodiversity conservation from stated preference studies that account for a heterogeneous set of biodiversity threats. We test whether biodiversity non-use values are affected by the threats that policies aim to address, be it human activities or other threats. We estimate meta-regression models in which we explain the variation in these non-use values by accounting for the observed heterogeneity in good, methodology, sample, and context characteristics. We estimate meta-regression models using 159 observations from 62 publications. The models suggest that non-use values for biodiversity conservation addressing human impacts may be larger than those addressing other threats. We also find that non-use values are generally not sensitive to which biodiversity indicators, habitat types, or taxonomic groups are valued. We predict that the mean annual WTP for avoiding human-caused biodiversity losses ranges from 0.2 to 0.4% of GDP per capita. Our findings suggest that state-of-the-art climate change damage functions in integrated assessment models may underestimate actual damage costs because they do not incorporate the premium that the public is willing to pay to avoid human-caused biodiversity losses.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".