Title

Economic-Based Projections of Future Land-Use Under Alternative Economic Policy Scenarios in the Conterminous U.S.

Authors

David J. Lewis, Economics Department, University of Puget Sound, 1500 North Warner Street, Tacoma, Washington 98416 USA
V. C. Radeloff, Department of Forest and Wildlife Ecology, University of Wisconsin, 1630 Linden Drive, Madison, Wisconsin 53706 USA
E. Nelson, Economics Department, Bowdoin College, 9700 College Station, Brunswick, Maine 04011 USA
A. J. Plantinga, Department of Agricultural and Resource Economics, Oregon State University, Corvallis, Oregon 97331-3601 USA
D. Helmers, Department of Forest and Wildlife Ecology, University of Wisconsin, 1630 Linden Drive, Madison, Wisconsin 53706 USA
J. J. Lawler, School of Forest Resources, University of Washington, Box 352100, Seattle, Washington 98195 USA
J. C. Withey, School of Forest Resources, University of Washington, Box 352100, Seattle, Washington 98195 USA
F. Beaudry, Environmental Studies Department, Alfred University, 1 Saxon Drive, Alfred, New York 14802 USA
S. Martinuzzi, Department of Forest and Wildlife Ecology, University of Wisconsin, 1630 Linden Drive, Madison, Wisconsin 53706 USA
V. Butsic, Department of Forest and Wildlife Ecology, University of Wisconsin, 1630 Linden Drive, Madison, Wisconsin 53706 USA
E. Lonsdorf, Urban Wildlife Institute, Conservation and Science, Lincoln Park Zoo, Chicago, Illinois 60614 USA
D. White, Department of Geosciences, Oregon State University, Corvallis, Oregon 97331 USA
S. Polasky, Department of Applied Economics, University of Minnesota, 1994 Buford Avenue, St. Paul, Minnesota 55108 USA

Document Type

Article

Publication Date

4-2012

Publication Title

Ecological Applications

Department

Economics

Abstract

Land-use change significantly contributes to biodiversity loss, invasive species spread, changes in biogeochemical cycles, and the loss of ecosystem services. Planning for a sustainable future requires a thorough understanding of expected land use at the fine spatial scales relevant for modeling many ecological processes and at dimensions appropriate for regional or national-level policy making. Our goal was to construct and parameterize an econometric model of land-use change to project future land use to the year 2051 at a fine spatial scale across the conterminous United States under several alternative land-use policy scenarios. We parameterized the econometric model of land-use change with the National Resource Inventory (NRI) 1992 and 1997 land-use data for 844 000 sample points. Land-use transitions were estimated for five land-use classes (cropland, pasture, range, forest, and urban). We predicted land-use change under four scenarios: business-as-usual, afforestation, removal of agricultural subsidies, and increased urban rents. Our results for the business-as-usual scenario showed widespread changes in land use, affecting 36% of the land area of the conterminous United States, with large increases in urban land (79%) and forest (7%), and declines in cropland (-16%) and pasture (-13%). Areas with particularly high rates of land-use change included the larger Chicago area, parts of the Pacific Northwest, and the Central Valley of California. However, while land-use change was substantial, differences in results among the four scenarios were relatively minor. The only scenario that was markedly different was the afforestation scenario, which resulted in an increase of forest area that was twice as high as the business-as-usual scenario. Land-use policies can affect trends, but only so much. The basic economic and demographic factors shaping land-use changes in the United States are powerful, and even fairly dramatic policy changes, showed only moderate deviations from the business-as-usual scenario. Given the magnitude of predicted land-use change, any attempts to identify a sustainable future or to predict the effects of climate change will have to take likely land-use changes into account. Econometric models that can simulate land-use change for broad areas with fine resolution are necessary to predict trends in ecosystem service provision and biodiversity persistence.

ISSN

1051-0761