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Land Resources for Wind Energy Development Requires Regionalized Characterizations.
Dai, Tao; Jose Valanarasu, Jeya Maria; Zhao, Yifan; Zheng, Shuwen; Sun, Yinong; Patel, Vishal M; Jordaan, Sarah M.
Afiliación
  • Dai T; School of Advanced International Studies, Johns Hopkins University, Washington, District of Columbia 20036, United States.
  • Jose Valanarasu JM; Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, California 94608, United States.
  • Zhao Y; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.
  • Zheng S; Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.
  • Sun Y; School of Advanced International Studies, Johns Hopkins University, Washington, District of Columbia 20036, United States.
  • Patel VM; Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.
  • Jordaan SM; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.
Environ Sci Technol ; 58(11): 5014-5023, 2024 Mar 19.
Article en En | MEDLINE | ID: mdl-38437169
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Viento / Fuentes Generadoras de Energía Límite: Humans Idioma: En Revista: Environ Sci Technol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Viento / Fuentes Generadoras de Energía Límite: Humans Idioma: En Revista: Environ Sci Technol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos