I worked on the ERC-funded RELATE project under Prof. Zoe Davies. My job was to analyse the ~7000 responses to the four seasonal surveys including choice experiment data, wellbeing responses, and spatial aspects.
Instead of the expert derived metrics of biodiversity (e.g., tree cover, species richness), we used participatory methods to understand the attributes of biodiversity that the public related to.
My part of the research demonstrates that respondents may be willing to pay for increased variety of colours, smells, and sounds in forests, although the WTP is scope-sensitive.
I then investigated seasonal trends through a test-retest study to show that the distribution of WTP was broadly stable over time.
Finally, we used our survey data to show that WTP and wellbeing measures were inversely correlated i.e., respondents with the highest wellbeing had lower WTP for marginal increases in biodiversity.
I also contributed statistics and comments as a co-author on some of the other papers from the project, some out already and some forthcoming.
Open access: https://doi.org/10.1016/j.ecolecon.2024.108410
Replication data, Ngene + R code, and survey: https://github.com/pmpk20/WinterPaper and https://data.kent.ac.uk/480/
Acknowledgements:
Thanks to the efforts, time, and patience of the entire RELATE team and reviewers and editors at Ecological Economics, we got it sorted.
We found that:
Respondents were willing to pay for an increased variety of colours, smells, and sounds in forests. They may also be willing to pay for changes in the quantity of deadwood for decomposition in forests.
Findings were very scope-sensitive. We discuss implications for (a) CE design, (b) forest management.
Why does it matter:
The contribution here is (1) sensory attributes, (2) in-depth mixed-methods pre-testing is really useful for embedding public preferences into these types of methods.
What changed in review:
Adding way more nuance to our claims about (a) pre-testing and embedding participatory methods in CE designs, (b) scope-sensitivity, (c) implications for management.
We dropped the plot in-text of the conditional WTP estimates but as I really liked the idea, it kind of lives on in Figures B1 and B2.
We also deleted the spatial analysis (no global or local clustering of preferences for sensory attributes) which might see the light of day eventually, or maybe not!
Updated robustness of the mixed logit models for (i) pref-space/WTP space, (ii) different covariate specifications, (iii) with/out correlations, (iv) different interactions with sensory impairments.
There's a nice pre-pandemic story within about temporal trends in the values elicited by different methods across different CES.
Open access at Environmental Research Letters: [in press]
Extracted data from the meta-analysis, R scripts, and outputs: https://github.com/pmpk20/Forest_CES_MetaAnalysis and https://doi.org/10.22024/UniKent/01.01.501
Acknowledgements:
Max did so much work on this meta-analysis you wouldn't believe. He's a real professional.
Thanks to Prof. Martin Dallimer (designing the study + advise on the reviewers), Dr. Gail E. Austen (who helped on the data extraction), Dr. Katherine N. Irvine (wordsmith extraordinaire), Prof. Robert D. Fish (who knows all about the language of cultural ecosystem services), Dr. Jessica C. Fisher (always insightful), Prof. Zoe G. Davies (bringing the whole project together)
We found that:
No significant temporal trends in effect sizes from stated or revealed preference methods over the last ~40 years (1980 - 2019).
No significant trend doesn't mean no pattern - we found some variation between high/low income countries, between methods, between CES.
We extracted 705 observations from 137 studies matching our criteria, finding that the evidence base itself is skewed: heavily weighted towards high-income countries and recreational use, while spiritual/symbolic, and my favourite bequest, forest values are rarely quantified at all.
Why does it matter:
Be cautious when doing benefit transfer with these values - we found substantial heterogeneity
Our evidence base suggests that practitioners need to build a fuller global picture that can support more context-sensitive forest management and conservation decisions.
What changed in review:
Trimmed to studies that collected data pre-pandemic for validity.
Added recreation-only meta-analytic models to examine variation within the most commonly studied CES.
Re-investigated the extracted data to check outliers. Resulted in a handful of changes to tables, but not really the interpretation.
"Seasonal stability of preferences for attributes of forest biodiversity".
First author with the RELATE team.
Test-retest between-subject study that tests (a) whether preference-parameters are seasonally-stable over the course of a year, (b) whether the scale parameter varied seasonally, (c) whether mean WTP was stable, and (d) whether the distribution of WTP was stable.
"Effects of latent wellbeing on preferences for attributes of forest biodiversity".
First author with the RELATE team.
We use a complex hybrid structural equation model to show that deriving higher latent wellbeing from forest colours, smells, and sounds, reduced mean WTP for these attributes.
“Public preferences for urban wildflower meadows: evidence from an in situ choice experiment”.
First author with Prof. Martin Dallimer, Prof. Zoe Davies, Dr Thomas Lundhede, Dr Gail Austen, Dr Tristan Pett.
Tristan's PhD project looked at preferences for urban wildflower meadows across different meadow types, parks, and cities. My contribution was to show that after controlling for park and city effects, the effect of meadow type was limited to salient visual attributes only. I then discuss this through the lens of hypothetical bias.
"Evaluating the effect of an information treatment on preferences for native and invasive non-native birds."
Prof. Martin Dallimer, Prof. Zoe Davies, Dr Thomas Lundhede, Dr Tristan Pett.
Another from Tristan's project but this time on variation in preferences for different birds and whether those depend on information provided to respondents.