Incorporating Acquisition Costs in Forestland Open Space Programs: Lessons from Conservation Biology and Applications

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The tools for cost-effective biodiversity conservation are well documented, but rarely implemented. These tools for prioritizing candidate properties can be used for managing forestland open space programs. We reviewed the benefit targeting, benefit score‐cost ratio, binary linear programming (BLP), and benefit-loss‐cost targeting approaches. A case study in Brookhaven, New York, showed that more conservation benefits are secured by using prioritization techniques that include acquisition costs. The acquisition costs for all properties were estimated using a hedonic model of public open space purchases. The benefit score‐cost ratio approach does not guarantee an optimal portfolio of open space properties determined by BLP, but results in more cost-effective choices than benefit targeting and is more flexible than BLP. Forest resource managers have broad interdisciplinary training and are well suited to implementing cost-effective forestland conservation techniques.

Keywords: cost-effective conservation; hedonic model; open space

Document Type: Research Article

Publication date: September 1, 2010

More about this publication?
  • The Journal of Forestry is the most widely circulated scholarly forestry journal in the world. In print since 1902, the Journal has received several national awards for excellence. The mission of the Journal of Forestry is to advance the profession of forestry by keeping forest management professionals informed about significant developments and ideas in the many facets of forestry: economics, education and communication, entomology and pathology, fire, forest ecology, geospatial technologies, history, international forestry, measurements, policy, recreation, silviculture, social sciences, soils and hydrology, urban and community forestry, utilization and engineering, and wildlife management. The Journal is published bimonthly: January, March, May, July, September, and November.
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