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The parable of arable land: Characterizing large scale land acquisitions through network analysis

Land is a scarce resource and its depletion is related to a combination of demographic and economic factors. Hence, the changes in dietary habits and increase in world population that upturn the food demand, are intertwined with a context of increasing oil prices and rise of green capitalism that in...

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Autores principales: Interdonato, Roberto, Bourgoin, Jeremy, Grislain, Quentin, Zignani, Matteo, Gaito, Sabrina, Giger, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7553289/
https://www.ncbi.nlm.nih.gov/pubmed/33048955
http://dx.doi.org/10.1371/journal.pone.0240051
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author Interdonato, Roberto
Bourgoin, Jeremy
Grislain, Quentin
Zignani, Matteo
Gaito, Sabrina
Giger, Markus
author_facet Interdonato, Roberto
Bourgoin, Jeremy
Grislain, Quentin
Zignani, Matteo
Gaito, Sabrina
Giger, Markus
author_sort Interdonato, Roberto
collection PubMed
description Land is a scarce resource and its depletion is related to a combination of demographic and economic factors. Hence, the changes in dietary habits and increase in world population that upturn the food demand, are intertwined with a context of increasing oil prices and rise of green capitalism that in turn impacts the demand in biofuel. A visible indicator of these phenomena is the increase, in recent years, of Large Scale Land Acquisitions (LSLAs) by private companies or states. Such land investments often lead to conflicts with local population and have raised issues regarding people’s rights, the role of different production models and land governance. The aim of this work is to show how publicly available data about LSLAs can be modeled into complex network structures, thus showing how the application of advanced network analysis techniques can be used to better understand land trade dynamics. We use data collected by the Land Matrix Initiative on LSLAs to model three land trade networks: a multi-sector network, a network centered on the mining sector and a network centered on the agriculture one. Then we provide an extended analysis of such networks which includes: (i) a structural analysis, (ii) the definition of a score, namely LSLA-score, which allows to rank the countries based on their investing/target role in the land trade network, (iii) an analysis of the land trade context which takes into account the LSLA-score ranking and the correlation between network features and several country development indicators, (iv) an analysis centered on the discover and analysis of network motifs (i.e., recurring patterns in the land trade network), which provides insights into complex and diverse relations between countries. Our analyses showed how the land trade market is massively characterized by a Global North-Global South dynamic, even if the investing power of emerging economies also has a major impact in creating relations between different sub-regions of the world. Moreover, the analyses on the mining and agriculture sectors highlighted how the role of several countries in the trade network may drastically change depending of the investment sector, showing diverse hierarchies between investor, intermediate and target countries.
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spelling pubmed-75532892020-10-21 The parable of arable land: Characterizing large scale land acquisitions through network analysis Interdonato, Roberto Bourgoin, Jeremy Grislain, Quentin Zignani, Matteo Gaito, Sabrina Giger, Markus PLoS One Research Article Land is a scarce resource and its depletion is related to a combination of demographic and economic factors. Hence, the changes in dietary habits and increase in world population that upturn the food demand, are intertwined with a context of increasing oil prices and rise of green capitalism that in turn impacts the demand in biofuel. A visible indicator of these phenomena is the increase, in recent years, of Large Scale Land Acquisitions (LSLAs) by private companies or states. Such land investments often lead to conflicts with local population and have raised issues regarding people’s rights, the role of different production models and land governance. The aim of this work is to show how publicly available data about LSLAs can be modeled into complex network structures, thus showing how the application of advanced network analysis techniques can be used to better understand land trade dynamics. We use data collected by the Land Matrix Initiative on LSLAs to model three land trade networks: a multi-sector network, a network centered on the mining sector and a network centered on the agriculture one. Then we provide an extended analysis of such networks which includes: (i) a structural analysis, (ii) the definition of a score, namely LSLA-score, which allows to rank the countries based on their investing/target role in the land trade network, (iii) an analysis of the land trade context which takes into account the LSLA-score ranking and the correlation between network features and several country development indicators, (iv) an analysis centered on the discover and analysis of network motifs (i.e., recurring patterns in the land trade network), which provides insights into complex and diverse relations between countries. Our analyses showed how the land trade market is massively characterized by a Global North-Global South dynamic, even if the investing power of emerging economies also has a major impact in creating relations between different sub-regions of the world. Moreover, the analyses on the mining and agriculture sectors highlighted how the role of several countries in the trade network may drastically change depending of the investment sector, showing diverse hierarchies between investor, intermediate and target countries. Public Library of Science 2020-10-13 /pmc/articles/PMC7553289/ /pubmed/33048955 http://dx.doi.org/10.1371/journal.pone.0240051 Text en © 2020 Interdonato et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Interdonato, Roberto
Bourgoin, Jeremy
Grislain, Quentin
Zignani, Matteo
Gaito, Sabrina
Giger, Markus
The parable of arable land: Characterizing large scale land acquisitions through network analysis
title The parable of arable land: Characterizing large scale land acquisitions through network analysis
title_full The parable of arable land: Characterizing large scale land acquisitions through network analysis
title_fullStr The parable of arable land: Characterizing large scale land acquisitions through network analysis
title_full_unstemmed The parable of arable land: Characterizing large scale land acquisitions through network analysis
title_short The parable of arable land: Characterizing large scale land acquisitions through network analysis
title_sort parable of arable land: characterizing large scale land acquisitions through network analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7553289/
https://www.ncbi.nlm.nih.gov/pubmed/33048955
http://dx.doi.org/10.1371/journal.pone.0240051
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