Are biodiversity losses valued differently when they are caused by human activities? A meta-analysis of the non-use valuation literature

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.

Keywords

• biodiversity
• non-use values
• willingness-to-pay
• climate change
• meta-analysis