Skip to content

jbousquin/Recreation_Benefits

 
 

Repository files navigation

alt text

Recreation Valuation

This repository holds the data and code for projects related to US EPA's Office of Research and Development's effort to understand the economic value of coastal recreation and water quality. As work progresses, we will periodically update this space with new results and models. The projects contained are as follows:

  • Valuing Coastal Beaches and Closures Using Benefit Transfer

    • We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations.

    Lyon, Sarina F.; Merrill, Nathaniel H.; Mulvaney, Kate K.; and Mazzotta, Marisa J. (2018) "Valuing Coastal Beaches and Closures Using Benefit Transfer: An Application to Barnstable, Massachusetts," Journal of Ocean and Coastal Economics: Vol. 5: Iss. 1, Article 1. DOI: https://doi.org/10.15351/2373-8456.1086

  • Using Data Derived from Cellular Phone Locations to Estimate Visitation to Natural Areas: An Application to Water Recreation in New England, USA.

    • We introduce and validate the use of commercially-available datasets of human mobility based on cell phone locational data to estimate visitation to natural areas. By combining this data with on-the-ground observations of visitation to water recreation areas in New England, we fit a model to estimate daily visitation for four months to over 500 sites. The results show the potential for this new big data source of human mobility to overcome limitations in traditional methods of estimating visitation and to provide consistent information at policy-relevant scales. The high-resolution information in both space and time provided by cell phone location derived data creates opportunities for developing next-generation models of human interactions with the natural environment. However, the opaque and rapidly developing methods for processing locational information by the data providers required a calibration and validation against data collected by traditional means to confidently reproduce the desired estimates of visitation.

Merrill, N.H., Atkinson, S.F., Mulvaney, K.K., Mazzotta, M.J. and Bousquin, J., 2020. Using data derived from cellular phone locations to estimate visitation to natural areas: An application to water recreation in New England, USA. PloS one, 15(4), p.e0231863. https://doi.org/10.1371/journal.pone.0231863

EPA Disclaimer The United States Environmental Protection Agency (EPA) GitHub project code is provided on an "as is" basis and the user assumes responsibility for its use. EPA has relinquished control of the information and no longer has responsibility to protect the integrity, confidentiality, or availability of the information. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by EPA. The EPA seal and logo shall not be used in any manner to imply endorsement of any commercial product or activity by EPA or the United States Government.

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • R 100.0%