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Considering Intergenerational Equity in Linear Programming-Based Forest Planning Models with MAXMIN Objective Functions

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This paper describes alternative approaches to formulating linear-programming-based forest planning models. These approaches consider a form of equity between current and future generations, as represented by the periods in a planning horizon. Both nominal net revenue in each period and each period's present net worth with values discounted relative to each period, are used as criteria for welfare. The minimum present net worth or net revenue outcome obtained for the set of periods is maximized. The approaches are different. Using present net worth relative to each period as a criterion allows for a concern for economic efficiency and allows variation in net revenue. Using net revenue focuses on a more even distribution of net revenue between periods. The approaches are tested in a case study, and the results are compared to those from a standard approach of maximizing present net worth relative to the first period. The results suggest that the approaches could be useful in a forest planning context, either as an alternative to standard approaches or as a means of generating relevant information. For. Sci. 45(3):366-373.
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Keywords: Present net worth; net revenue

Document Type: Journal Article

Affiliations: Associate Professor at Northern Arizona University

Publication date: 1999-08-01

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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