Valuing Satellite Data for Harmful Algal Bloom Early Warning Systems
In this working paper, authors develop an information valuation framework for harmful algal blooms (HABs) and apply it to a case study of outdoor recreation in California.
Abstract
In this study we develop an information valuation framework for harmful algal blooms (HABs), and we apply the framework to a case study of outdoor recreation in California. We obtained estimates of the concentration of cyanobacteria from remote sensing satellite data at 100 lakes in California in 2019 from the San Francisco Estuary Institute (SFEI). We developed a new approach to estimate a recreation demand site-choice model that includes a full set of fixed effects for both destinations and origins. We examined the statistical performance of the estimator using simulated data in a Monte Carlo analysis, and we applied the approach using cell phone mobility data, which indicate the total number of visitors from around 1400 ZIP codes to the study lakes. We estimated the value of a perfect early warning system by comparing the total willingness-to-pay for access to the lakes under a counterfactual scenario wherein the presence or absence of HABs at all lakes could be known with certainty before recreators select which site to visit to the value of access under the status quo scenario assuming recreators form expectations about HAB occurrences based on the historic frequencies of HABs at each site. Our benchmark results suggest that the total value of the complete mitigation of HABs at the 100 California lakes selected for our study between April and September 2019 would have been $7.41 million, and the total value of a perfect early warning system would have been $2.46 million.
Authors
Stephen C. Newbold
University of Wyoming
Sarah Lindley
University of Wyoming
Shannon Albeke
University of Wyoming
Joshua Viers
University of California, Merced
George Parsons
University of Delaware
Robert Johnston
Clark University
