Environmental Engineering Reference
In-Depth Information
energy resource availability and demand. For terrestrial impacts we used the 11 major
habitat types of the US, as defined by Nature Conservancy ecoregions [52-54]. For
each major habitat type, we estimate the total area of new energy development, with-
out attempting to specify where within each major habitat type development will take
place. Within each major habitat type, there are a variety of land-uses, from relatively
wild places to agricultural and urban systems. Thus specific siting decisions, while
outside the scope of our analysis, will be important in determining actual biodiversity
impact.
Throughout our analysis, we excluded certain areas as being protected or restricted
from development, modeling our decision rules on those used in the Department of
Energy's report “20% Wind Energy by 2030 [47].” We excluded areas that were pro-
tected areas with a Gap Analysis Program code of 1 or 2 (i.e., permanent protection
excluding development), based on the Protected Area Database of the United States,
version 4 [55]. We also excluded airports, urban areas, and wetlands/water bodies
from development, based on vector layers included with Environmental Systems Re-
search Institute's (ESRI) ArcGIS package. Areas with an average slope greater than
20% were also excluded, based on a surface analysis of the GTOPO global digital
elevation model [56]. Finally, for wind power we assumed that areas within 3 km of
an airfi eld or urban area were not developable.
For each energy production technique, we partitioned its land use among regions
in one of two methods. For some energy production techniques, continuous (i.e., inter-
val or ratio scale) estimates of the supply of that resource were available for different
geographic regions. For example, the Department of Energy publishes a continuous
estimate of the water power potential in MW of the different hydrologic regions of
the US [57]. In these cases with continuous estimates of resource supply, we assumed
that the area of energy development in each geographic region was proportional to the
total supply in that region. For some resources, the geographic units in which data was
available did not match those of our analysis units, and we used geographic informa-
tion system (GIS) analyses to partition the resource among habitat types, making the
simplifying assumption that the resource was evenly distributed within the original
geographical units of the data. To give an example from one particularly dataset, po-
tential biomass estimates were available from the National Renewable Energy Labora-
tories (NREL), summarized per county [58]. We calculated tons/km
2
for each county,
digitized the data to a 1 km raster resolution of the US, and then used ESRI ArcGIS
ZonalStatistics commands to sum up the total available biomass in each of our major
habitat types.
Other energy production technologies had data on the supply of the resource that
were categorical (i.e., ordinal scale). For example, NREL wind power maps rank sites
on a scale of 1-7, based on the quantity of wind available as well as its consistency. In
these cases with categorical data, we reclassifi ed the US into excellent, good, and poor
regions for development of that energy resource. In some cases a continuous estimate
of a proxy for a resource was available rather than a direct estimate of power avail-
ability, and in these cases we classifi ed the resource into categorical categories based
on published opinion about what sites were developable. While our decision rules are
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