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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:58 |
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| Enthalten in: |
Environmental science & technology - 58(2024), 11 vom: 19. März, Seite 5014-5023 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Dai, Tao [VerfasserIn] |
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| Links: |
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| Themen: |
Environmental Impact Assessment |
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| Anmerkungen: |
Date Completed 20.03.2024 Date Revised 20.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1021/acs.est.3c07908 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369270347 |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Environmental Impact Assessment | |
| 650 | 4 | |a Geographical Information System | |
| 650 | 4 | |a Image Segmentation | |
| 650 | 4 | |a Land Use | |
| 650 | 4 | |a Life Cycle Assessment | |
| 650 | 4 | |a Machine Learning | |
| 650 | 4 | |a Remote Sensing | |
| 650 | 4 | |a Wind Energy | |
| 650 | 4 | |a Wind Power | |
| 700 | 1 | |a Jose Valanarasu, Jeya Maria |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Yifan |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Shuwen |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Yinong |e verfasserin |4 aut | |
| 700 | 1 | |a Patel, Vishal M |e verfasserin |4 aut | |
| 700 | 1 | |a Jordaan, Sarah M |e verfasserin |4 aut | |
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