A Framework for Quantitative Assessment of Impacts Related to Energy and Mineral Resource Development
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  • 作者：Seth S. Haines (1)
    Jay E. Diffendorfer (2)
    Laurie Balistrieri (3)
    Byron Berger (4)
    Troy Cook (1) (17)
    Don DeAngelis (5)
    Holly Doremus (6)
    Donald L. Gautier (7)
    Tanya Gallegos (1)
    Margot Gerritsen (8)
    Elisabeth Graffy (9)
    Sarah Hawkins (1)
    Kathleen M. Johnson (10)
    Jordan Macknick (11)
    Peter McMahon (12)
    Tim Modde (13)
    Brenda Pierce (10)
    John H. Schuenemeyer (14)
    Darius Semmens (2)
    Benjamin Simon (15)
    Jason Taylor (16) (18)
    Katie Walton-Day (12)
  • 关键词：Energy resources ; mineral resources ; impact assessment ; integrated assessment ; environmental
  • 刊名：Natural Resources Research
  • 出版年：2014
  • 出版时间：March 2014
  • 年：2014
  • 卷：23
  • 期：1
  • 页码：3-17
  • 全文大小：1,555 KB
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  • 作者单位：Seth S. Haines (1)
    Jay E. Diffendorfer (2)
    Laurie Balistrieri (3)
    Byron Berger (4)
    Troy Cook (1) (17)
    Don DeAngelis (5)
    Holly Doremus (6)
    Donald L. Gautier (7)
    Tanya Gallegos (1)
    Margot Gerritsen (8)
    Elisabeth Graffy (9)
    Sarah Hawkins (1)
    Kathleen M. Johnson (10)
    Jordan Macknick (11)
    Peter McMahon (12)
    Tim Modde (13)
    Brenda Pierce (10)
    John H. Schuenemeyer (14)
    Darius Semmens (2)
    Benjamin Simon (15)
    Jason Taylor (16) (18)
    Katie Walton-Day (12)

    1. Central Energy Resources Science Center, U.S. Geological Survey, Denver, CO, 80225, USA
    2. Geosciences and Environmental Change Science Center, U.S. Geological Survey, Denver, CO, 80225, USA
    3. U.S. Geological Survey, Seattle, WA, 98195, USA
    4. Crustal Geophysics and Geochemistry Science Center, U.S. Geological Survey, Denver, CO, 80225, USA
    17. Energy Information Administration, U.S. Department of Energy, Washington, DC, USA
    5. Department of Biology, University of Miami, Miami, FL, USA
    6. Berkeley Law, University of California, Berkeley, CA, USA
    7. U.S. Geological Survey, 345 Middlefield Road, Menlo Park, CA, USA
    8. Department of Energy Resources Engineering, Stanford University, Stanford, CA, USA
    9. Consortium for Science, Policy, and Outcomes, Arizona State University, Tempe, AZ, USA
    10. U.S. Geological Survey, 12201 Sunrise Valley Drive, Reston, VA, 20192, USA
    11. National Renewable Energy Lab, Golden, CO, USA
    12. Colorado Water Science Center, U.S. Geological Survey, Denver, CO, 80225, USA
    13. U.S. Fish and Wildlife Service, Denver, CO, USA
    14. Southwest Statistical Consulting, LLC, Cortez, CO, USA
    15. Office of Policy Analysis, U.S. Department of the Interior, Washington, DC, USA
    16. National Operations Center, Bureau of Land Management, Denver, CO, USA
    18. Cape Cod National Seashore, National Park Service, Wellfleet, MA, USA
  • ISSN：1573-8981
文摘
Natural resource planning at all scales demands methods for assessing the impacts of resource development and use, and in particular it requires standardized methods that yield robust and unbiased results. Building from existing probabilistic methods for assessing the volumes of energy and mineral resources, we provide an algorithm for consistent, reproducible, quantitative assessment of resource development impacts. The approach combines probabilistic input data with Monte Carlo statistical methods to determine probabilistic outputs that convey the uncertainties inherent in the data. For example, one can utilize our algorithm to combine data from a natural gas resource assessment with maps of sage grouse leks and pi?on-juniper woodlands in the same area to estimate possible future habitat impacts due to possible future gas development. As another example: one could combine geochemical data and maps of lynx habitat with data from a mineral deposit assessment in the same area to determine possible future mining impacts on water resources and lynx habitat. The approach can be applied to a broad range of positive and negative resource development impacts, such as water quantity or quality, economic benefits, or air quality, limited only by the availability of necessary input data and quantified relationships among geologic resources, development alternatives, and impacts. The framework enables quantitative evaluation of the trade-offs inherent in resource management decision-making, including cumulative impacts, to address societal concerns and policy aspects of resource development.

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