Developing an economic estimation system for vertical farms
The concept of vertical farming is nearly twenty years old, however, there are only a few experimental prototypes despite its many advantages compared to conventional agriculture. Significantly, financial uncertainty has been identified as the largest barrier to the realization of a ‘real’ vertical...
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nottingham-451242018-07-02T09:15:20Z http://eprints.nottingham.ac.uk/45124/ Developing an economic estimation system for vertical farms Shao, Yiming Heath, Tim Zhu, Yan The concept of vertical farming is nearly twenty years old, however, there are only a few experimental prototypes despite its many advantages compared to conventional agriculture. Significantly, financial uncertainty has been identified as the largest barrier to the realization of a ‘real’ vertical farm. Some specialists have provided ways to calculate costs and return on investment, however, most of them are superficial with calculations based on particular contextual circumstances. To move the concept forwards a reliable and flexible estimating tool, specific to this new building typology, is clearly required. A computational system, software named VFer, has therefore been developed by the authors to provide such a solution. This paper examines this highly flexible, customised system and results from several typical vertical farm configurations in three mega-cities (Shanghai, London and Washington DC) are used to elucidate the potential economic return of vertical farms. IGI Global 2016-04-01 Article PeerReviewed application/pdf en http://eprints.nottingham.ac.uk/45124/1/heath%20IJAEIS%207%282%29%20article.pdf Shao, Yiming and Heath, Tim and Zhu, Yan (2016) Developing an economic estimation system for vertical farms. International Journal of Agricultural and Environmental Information Systems, 7 (2). ISSN 1947-3206 https://www.igi-global.com/gateway/article/158094 doi:10.4018/IJAEIS.2016040102 doi:10.4018/IJAEIS.2016040102 |
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Digital Repository |
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Local University |
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University of Nottingham Malaysia Campus |
building |
Nottingham Research Data Repository |
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Online Access |
language |
English |
description |
The concept of vertical farming is nearly twenty years old, however, there are only a few experimental prototypes despite its many advantages compared to conventional agriculture. Significantly, financial uncertainty has been identified as the largest barrier to the realization of a ‘real’ vertical farm. Some specialists have provided ways to calculate costs and return on investment, however, most of them are superficial with calculations based on particular contextual circumstances. To move the concept forwards a reliable and flexible estimating tool, specific to this new building typology, is clearly required. A computational system, software named VFer, has therefore been developed by the authors to provide such a solution. This paper examines this highly flexible, customised system and results from several typical vertical farm configurations in three mega-cities (Shanghai, London and Washington DC) are used to elucidate the potential economic return of vertical farms. |
format |
Article |
author |
Shao, Yiming Heath, Tim Zhu, Yan |
spellingShingle |
Shao, Yiming Heath, Tim Zhu, Yan Developing an economic estimation system for vertical farms |
author_facet |
Shao, Yiming Heath, Tim Zhu, Yan |
author_sort |
Shao, Yiming |
title |
Developing an economic estimation system for vertical farms |
title_short |
Developing an economic estimation system for vertical farms |
title_full |
Developing an economic estimation system for vertical farms |
title_fullStr |
Developing an economic estimation system for vertical farms |
title_full_unstemmed |
Developing an economic estimation system for vertical farms |
title_sort |
developing an economic estimation system for vertical farms |
publisher |
IGI Global |
publishDate |
2016 |
url |
http://eprints.nottingham.ac.uk/45124/ http://eprints.nottingham.ac.uk/45124/ http://eprints.nottingham.ac.uk/45124/ http://eprints.nottingham.ac.uk/45124/1/heath%20IJAEIS%207%282%29%20article.pdf |
first_indexed |
2018-09-06T13:38:37Z |
last_indexed |
2018-09-06T13:38:37Z |
_version_ |
1610865565815013376 |