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The Economics of Everglades Restoration
Missing Pieces in the Future of South Florida
The restoration of the Florida Everglades, the largest ecosystem restoration project in the world, is now underway. Missing from the original plan, however, is a study of the region's cities and farms and the economic impact their growth will make on this already endangered ecosystem. This book provides that analysis. Richard Weisskoff applies the widely-used Regional Economic Modeling Inc. (REMI) model to forecast the future of South Florida's six million-person, USD300 billion economy. In addition, he supplies four significant pieces to the model, namely, detailed projections for agriculture, investment, tourism, and restoration spending. By integrating these and the results of a second economic modeling system (IMPLAN), he traces out three development paths for the region to the year 2030, and the demands for water and urban land required for each path. Also featured are the results of two land use/land cover surveys which are then used to measure the change in the value of ecosystem services in the Everglades region.
The author provides an economic history of the region and statistically documents the transformation of the original Everglades into the sprawling cities and the sugar-citrus-cattle emporium, all of which share the same fragile ecosystem. The study concludes that the ultimate success of the restoration of the Everglades will depend on choices made regarding the future of the region's cities and farms.
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What Reviewers Are Saying
"'The book provides valuable contributions on three related fronts: it places past and current debates on ecosystem restoration of the Everglades in a clear systems context that acknowledges feedback between ecosystem quality and economic growth; shows for the case of the Everglades that good intentions of providing generous financial support for restoration may lead to undesired effects that actually run counter to the original goal; and demonstrates the use of regional modeling tools to develop consistent baseline forecasts and alternative scenarios.' - Matthias Ruth, University of Maryland, College Park, US"