Harmful algae blooms (HABs) occur in water bodies throughout the globe and can have multi-faceted impacts on tourism. However, little is known of the magnitude of economic losses to the tourism sector as a result of HABs. There is limited understanding of the empirical relationships between HAB intensity and duration, and the effects of this phenomenon on the tourism sector. This study is based in the state of Florida, USA, a notable sun, sand, and sea destination in the western hemisphere, where blooms of a marine harmful algae are a recurrent threat to coastal tourism. The empirical framework is based on a month and county-level panel database that combines sales by tourism-related businesses with observations from the official HAB surveillance system of the state of Florida. We use time and space fixed-effects regressions to estimate the loss in tourism revenue associated with one additional day of red tide. Results indicate that impacts of HABs on tourism do not follow a linear pattern with increasing HAB concentrations, but rather appear to follow an inverted-U pattern. In other words, higher concentrations of the HAB organism do not necessarily imply higher economic losses, suggesting that the impacts of HABs on tourism are not driven solely by the biophysical element of cell density. Rather, these impacts appear to be mediated and amplified by human dimensions. The loss to tourism-related businesses due to the 2018 Florida red tide bloom was estimated to be $2.7 billion USD, which implies that HABs and their impact on tourism can be considered as a potential 'billion-dollar' disaster.

Nonpoint source (NPS) pollution has been identified by the US Environmental Protection Agency (EPA) as “the nation’s largest water quality problem”. Urban development, septic systems, and agricultural operations have been identified as the major sources of diffuse pollution in surface and ground water bodies. In recent decades, urban and agricultural Best Management Practices (BMP) have been developed in several states to address agricultural water quality and water use impacts, including the reduction of nutrient loads to help meet water quality standards. Compliance with BMPs is associated with some costs to local governments, homeowners, and agricultural operations, but the improvements in water quality associated with BMP adoption are expected to yield significant benefits to society in the form of improved recreational opportunities, navigation, flood control, and ecosystem health. The development of sound policies and decision making processes require balancing the costs of BMP adoption to the agricultural operations with the social benefits to be derived from the improved water quality. In this paper we develop a benefits transfer model to provide estimates of the economic benefits of properly implemented and effective Best Management Practices (BMP) throughout the state of Florida. These benefit estimates can be used in a cost-benefit framework to determine the optimal level of BMP adoption throughout the state of Florida and provide a framework for other regions to estimate the potential benefits of BMP-mediated water quality improvements.

There has been a growing interest in examining the implementation of insight-era technologies (e.g., AI, social media) and big data for sustainable tourism development. However, actionable guidelines to promote a holistic adaptation and the effective functioning of destination marketing/management organizations (DMOs) in the increasingly data-infused world are still needed. This perspective paper posits a research-based framework that DMOs can use to become more responsive and efficient in their marketing and planning efforts in the current AI-infused world. Four propositions are presented to support DMOs’ transition to the insight-era: (a) DMOs’ organizational adaptations and workforce development and training, (b) active engagement with destinations’ stakeholders and data sharing, (c) leverage user-generated data and emergent technologies for destination marketing, and (d) DMOs’ data-driven decision making.