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Green Energy Development in Rural Landscapes

Über Green Energy Development in Rural Landscapes

The development of renewable energy technologies (RETs) in many areas far from grid-based electricity have primarily involved solar technologies which tap solar radiation energy to provide heat, light, hot water, electricity, and even cooling for homes, businesses, and industry around the world. A study on RETs took place in the Wiyumiririe Location of Laikipia District in north-central Kenya. Specific developmental initiatives that were closely associated with the availability of solar energy including improved standard of living; rural-urban connectivity; investment savings; better informed citizenry; entertainment; promotion of local businesses; employment; and environmental benefits. Correlation and logistic regression model analyses showed that the future of solar power development in rural landscapes such as this one can be predicted from five main independent factors: combined household annual income; presence of educated and salaried children; whether main decision maker was a civil servant; household land acreage; and total annual equipment maintenance costs.

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  • Sprache:
  • Englisch
  • ISBN:
  • 9783844309614
  • Einband:
  • Taschenbuch
  • Seitenzahl:
  • 84
  • Veröffentlicht:
  • 17. Februar 2011
  • Abmessungen:
  • 152x229x5 mm.
  • Gewicht:
  • 136 g.
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Beschreibung von Green Energy Development in Rural Landscapes

The development of renewable energy technologies (RETs) in many areas far from grid-based electricity have primarily involved solar technologies which tap solar radiation energy to provide heat, light, hot water, electricity, and even cooling for homes, businesses, and industry around the world. A study on RETs took place in the Wiyumiririe Location of Laikipia District in north-central Kenya. Specific developmental initiatives that were closely associated with the availability of solar energy including improved standard of living; rural-urban connectivity; investment savings; better informed citizenry; entertainment; promotion of local businesses; employment; and environmental benefits. Correlation and logistic regression model analyses showed that the future of solar power development in rural landscapes such as this one can be predicted from five main independent factors: combined household annual income; presence of educated and salaried children; whether main decision maker was a civil servant; household land acreage; and total annual equipment maintenance costs.

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