Meta-Analysis dataset

Published: 4 October 2022| Version 1 | DOI: 10.17632/hf88vz3cwj.1


Over the years, researchers from around the world have evaluated the Willingness to Pay ‎‎(WTP) for the reduction of beach litter using non-market valuation approaches. This research ‎study summarises the economic value of beach clean-ups and the underlying causes of WTP ‎variation observed in various primary studies. Using a meta-regression analysis, this study ‎estimates the global effect size of WTP and evaluates the causes of WTP heterogeneity across ‎regions. The uploaded data file contains a description of the variables used in the meta-‎analysis and the respective codes.‎


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This meta-data was created using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Seven databases (Google scholar, Science Direct, EVRI, AgEcon, Scopus, Wiley Online library, and Web of Science) were meticulously searched using keyword combinations ('Willingness to Pay + Beach Litter + Contingent Valuation + Discrete Choice Experiment + Travel Cost Method + Hedonic Pricing'). In 2020, the database search and construction took place over a two-month period (and updated in February 2022). From 1999 to 2022, data was gathered from individual papers. Initially, database searches were used to locate 1919 published and unpublished articles. The titles and abstracts from 1919 were screened for duplicates, and 1508 duplicates were eliminated. As a result, 411 papers were retained and screened for subject relevance; specifically, the papers were required to report a WTP value. Due to the lack of WTP estimates, 296 studies were excluded during this process. The 115 remaining papers were subjected to a second screening based on a clearly defined beach litter reduction strategy and a recognisable non-market valuation technique, with 52 papers being excluded. Finally, data from 63 papers were extracted, constituting 153 WTP observations. It is worth noting that some studies reported multiple WTP estimates for different sampled locations. The variables extracted from the 63 studies were used to analyse the findings of this study. The uploaded data file contains a description of the variables used in the meta-analysis and the respective codes. STATA software was used to analyse the data.


University of Tasmania