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Hurricanes are the costliest type of natural disaster in the United States. Every year, these natural phenomena destroy billions of dollars in physical capital, displace thousands, and greatly disrupt local economies. While this damage will never be eliminated, the number of fatalities and the cost of preparing and evacuating can be reduced through improved forecasts. This paper seeks to establish the public’s willingness to pay for further improvement of hurricane forecasts by integrating atmospheric modeling and a double-bounded dichotomous choice method in a large-scale contingent valuation experiment. Using an interactive survey, we focus on areas affected by hurricanes in 2018 to elicit residents’ willingness to pay for improvements along storm track, wind speed, and precipitation forecasts. Our results indicate improvements in wind speed forecast are valued the most, followed by storm track and precipitation, and that maintaining the current annual rate of error reduction for another decade is worth between $90.25 and $121.86 per person in vulnerable areas. Our study focuses on areas recently hit by hurricanes in the United States, but the implications of our results can be extended to areas vulnerable to tropical cyclones globally. In a world where the intensity of hurricanes is expected to increase and research funds are limited, these results can inform relevant agencies regarding the effectiveness of different private and public adaptive actions, as well as the value of publicly funded hurricane research programs.

Climate and crop yield variability associated with El Nino-Southern Oscillation (ENSO) are now predictable within limits. This predictability suggests a potential to tailor agricultural management to mitigate impacts of adverse conditions and to take advantage of favorable conditions. However, improved climate predictions may benefit society only with parallel advances in our ability to use this knowledge. We show that the value that will accrue to any given actor from an ENSO phase forecast should be viewed not as a known number but instead as a random draw from a distribution, even when the forecast is always correct. Forecast value depends on the highly variable contexts in which forecasts are used. Randomness in forecast value has significant implications for choices made by forecasters, forecast users and policy makers. To show randomness, we estimate potential economic values of ENSO forecasts for agricultural producers based on two realistic assumptions: the crop prices farmers receive are uncertain; and within an ENSO phase, the actual climate is variable in ways that affect profits. The use of synthetic weather and crop price series, with crop simulation models, helps show the range and likelihood of climate forecast value. [PUBLICATION ABSTRACT]

A multi-output/input stochastic distance frontier model is used to analyze the effect of interannual climatic variability on agricultural production and to assess the impact of climate forecasts on the economic performance of this sector in the Southeastern United States. The results show that the omission of climatic conditions when estimating regional agricultural production models could lead to biased technical efficiency (TE) estimates. This climate bias may significantly affect the effectiveness of rural development policies based on regional economic performance comparisons. We also found that seasonal rainfall and temperature forecasts have a positive effect on economic performance of agriculture. However, the effectiveness of climate forecasts on improving TE is sensitive to the type of climate index used. Policy implications stemming from the results are also presented.

Interdisciplinary research is increasingly called upon to find solutions to complex sustainability problems, yet co-creating usable knowledge can be challenging. This article offers broad lessons for conducting interdisciplinary science from the South Florida Water, Sustainability, and Climate Project (SFWSC), a 5-year project funded by the U.S. National Science Foundation (NSF). The goal was to develop a holistic decision-making framework to improve understanding of the complex natural–social system of South Florida water allocation and its threats from climate change, including sea level rise, using a water resources optimization model as an integration mechanism. The SFWSC project faced several challenges, including uncertainty with tasks, high task interdependence, and ensuring communication among geographically dispersed members. Our hypothesis was that adaptive techniques would help overcome these challenges and maintain scientific rigor as research evolved. By systematically evaluating the interdisciplinary management approach throughout the project, we learned that integration can be supported by a three-pronged approach: (1) Build a well-defined team and leadership structure for collaboration across geographic distance and disciplines, ensuring adequate coordination funding, encouraging cross-pollination, and allowing team structure to adapt; (2) intentionally design a process and structure for facilitating collaboration, creating mechanisms for routine analysis, and incorporating collaboration tools that foster communication; and (3) support integration within the scientific framework, by using a shared research output, and encouraging team members to adapt when facing unanticipated constraints. These lessons contribute to the international body of knowledge on interdisciplinary research and can assist teams attempting to develop sustainable solutions in complex natural–social systems.

A stochastic production frontier was used to measure the initial (i.e., bi-weekly) economic effects of hurricanes on commercial grouper ( Serranidae ) production in the Exclusive Economic Zone of the United States Gulf of Mexico from 2005 to 2009. We estimated the economic effects of productivity losses associated with specific hurricanes on the commercial grouper fleet. We also calculated the economic effects due to productivity losses during an entire hurricane season at the regional level. The empirical model controls for input levels as well as other factors affecting production to isolate the initial economic effect caused by hurricanes from other non-weather-related factors. The empirical results revealed that hurricanes striking the Gulf of Mexico coastline from 2005 to 2009 had a negative effect on the production of the commercial grouper fleet. The results also demonstrated the relative importance of inputs and regulations on fish production.

The management of Lake Okeechobee in Florida has undergone significant changes in the last decade. Socio-political, environmental and demographic factors have driven changes in the environmental and water policy, which in turn have led to wide-ranging institutional changes and a shift toward multiobjective planning and implementation in the Lake management. This article describes the changes in the philosophy and practice of water resources management in South Florida hydrologic system, of which Lake Okeechobee is a crucial component. The impacts of the changes on management goals and decision processes are illustrated through a case study of the use of climate information in Lake management. The article concludes with a brief examination of the implications of the institutional changes, including greater public participation, for the long-term sustainability of the social-ecological system in South Florida.

The Pampas of Argentina have shown some of the most consistently increasing trends in precipitation during the 20th century. The rainfall increase has partly contributed to a significant expansion of agricultural area, particularly in climatically marginal regions of the Pampas. However, it is unclear if current agricultural production systems, which evolved partly in response to enhanced climate conditions, may remain viable if (as entirely possible) climate reverts to a drier epoch. We assess the potential impacts of a plausible decreasing trend in precipitation on the economic sustainability of 2 contrasting agricultural systems in the Pampas: Pergamino, in the most productive subregion of the Pampas, and Pilar, in the northern, semi-arid margin of the region. Also, we explore the scope for adaptation to changing climate. In the case where there is no adaptation, if precipitation decreases, as is plausible, impacts may be quite different between locations: whereas in Pergamino crop economic returns would not change noticeably, the more marginal Pilar would experience a marked decrease in profits and an increase in production risks. However, potential negative impacts might be mitigated, in part, if farmers adapt their agronomic management using current available technology or know-how.

We demonstrate how to specify and estimate a time series model that can isolate the effects of changes in fishery policy and forecast the outcome of policy changes in the context of changing climate and economic factors. The approach is illustrated with data from the headboat fishery for red snapper in the Gulf of Mexico. The initial data analysis finds that effort and harvest are cointegrated series and that effort appears to respond somewhat to past changes in harvest. This suggested a structural vector error correction model specification. Model estimation results indicate that seasonal closures directly influence both harvest and effort, whereas bag and minimum size limits only affect harvest directly. Also, climate activity has a moderate influence on this fishery, mainly via changes in effort. Model forecasts are evaluated relative to a more naïve specification using out-of-sample data and the use of the model for policy analysis is demonstrated.

In many places, predictions of regional climate variability associated with the El Niño–Southern Oscillation phenomenon offer the potential to improve farmers’ decision outcomes, by mitigating the negative impacts of adverse conditions or by taking advantage of favorable conditions. While the notion that climate forecasts are potentially valuable has been established, questions of when they may be more or less valuable have proven harder to resolve. Using simulations, we estimate the expected value of seasonal climate information under alternative assumptions about (a) land tenure (ownership vs. short-term leases) and (b) the decision maker’s objective function (expected utility vs. prospect theory value function maximization), employing a full range of plausible parameter values for each objective function. This allows us to show the extent to which the value of information depends on risk preferences, loss aversion, wealth levels and expectations, as well as situational constraints. Our results demonstrate in a non-laboratory decision context that, in some cases, psychologically plausible deviations from expected utility maximization can lead to substantial differences in estimates of the expected value of climate forecasts. Efforts to foster effective use of climate information and forecasts in agriculture must be grounded in a firm understanding of the goals, objectives and constraints of decision makers.