Land Resources for Wind Energy Development Requires Regionalized Characterizations

Estimates of the land area occupied by wind energy differ
by orders
of magnitude due to data scarcity and inconsistent methodology. We
developed a method that combines machine learning-based imagery analysis
and geographic information systems and examined the land area of 318
wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection.
We found that prior land use and human modification in the project
area are critical for land-use efficiency and land transformation
of wind projects. Projects developed in areas with little human modification
have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean
±95% confidence interval) and a land transformation of 0.24 ±
0.07 m2/MWh, while values for projects in areas with high
human modification are 447 ± 49.4 W/m2 and 0.05 ±
0.01 m2/MWh, respectively. We show that land resources
for wind can be quantified consistently with our replicable method,
a method that obviates >99% of the workload using machine learning.
To quantify the peripheral impact of a turbine, buffered geometry
can be used as a proxy for measuring land resources and metrics when
a large enough impact radius is assumed (e.g., >4 times the rotor
diameter). Our analysis provides a necessary first step toward regionalized
impact assessment and improved comparisons of energy alternatives.