Architecture of the global land acquisition system: applying the tools of network science to identify key vulnerabilities

Global land acquisitions, often dubbed 'land grabbing' are increasingly becoming drivers of land change. We use the tools of network science to describe the connectivity of the global acquisition system. We find that 126 countries participate in this form of global land trade. Importers are concentrated in the Global North, the emerging economies of Asia, and the Middle East, while exporters are confined to the Global South and Eastern Europe. A small handful of countries account for the majority of land acquisitions (particularly China, the UK, and the US), the cumulative distribution of which is best described by a power law. We also find that countries with many land trading partners play a disproportionately central role in providing connectivity across the network with the shortest trading path between any two countries traversing either China, the US, or the UK over a third of the time. The land acquisition network is characterized by very few trading cliques and therefore characterized by a low degree of preferential trading or regionalization. We also show that countries with many export partners trade land with countries with few import partners, and vice versa, meaning that less developed countries have a large array of export partnerships with developed countries, but very few import partnerships (dissassortative relationship). Finally, we find that the structure of the network is potentially prone to propagating crises (e.g., if importing countries become dependent on crops exported from their land trading partners). This network analysis approach can be used to quantitatively analyze and understand telecoupled systems as well as to anticipate and diagnose the potential effects of telecoupling.

[1]  I. Rodríguez‐Iturbe,et al.  Modeling past and future structure of the global virtual water trade network , 2012 .

[2]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[3]  Manfred Lenzen,et al.  International trade drives biodiversity threats in developing nations , 2012, Nature.

[4]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[5]  Stefano Battiston,et al.  The Network of Global Corporate Control , 2011, PloS one.

[6]  M. Lenzen,et al.  Does ecologically unequal exchange occur , 2013 .

[7]  M E J Newman Assortative mixing in networks. , 2002, Physical review letters.

[8]  Maria Cristina Rulli,et al.  Global land and water grabbing , 2013, Proceedings of the National Academy of Sciences.

[9]  M. E. J. Newman,et al.  Power laws, Pareto distributions and Zipf's law , 2005 .

[10]  Naota Hanasaki,et al.  Evolution of the global virtual water trade network , 2012, Proceedings of the National Academy of Sciences.

[11]  M. Barthelemy Betweenness centrality in large complex networks , 2003, cond-mat/0309436.

[12]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[13]  A. Hoekstra,et al.  Humanity’s unsustainable environmental footprint , 2014, Science.

[14]  Jann Lay,et al.  Transnational land deals for agriculture in the global south : analytical report based on the Land Matrix database , 2012 .

[15]  W. Adger,et al.  Nested and teleconnected vulnerabilities to environmental change , 2008, Frontiers in ecology and the environment.

[16]  R Pastor-Satorras,et al.  Dynamical and correlation properties of the internet. , 2001, Physical review letters.

[17]  L. Tajoli,et al.  The World Trade Network , 2011 .

[18]  Albert-László Barabási,et al.  Hierarchical organization in complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  S. Davis,et al.  Consumption-based accounting of CO2 emissions , 2010, Proceedings of the National Academy of Sciences.

[20]  E. Lazarus Land grabbing as a driver of environmental change , 2014 .

[21]  Paul J. Laurienti,et al.  The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain , 2011, Brain Connect..

[22]  Carlo Piccardi,et al.  Existence and significance of communities in the World Trade Web. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  N. Ramankutty,et al.  Closing yield gaps through nutrient and water management , 2012, Nature.

[24]  A. Ditta How helpful is nanotechnology in agriculture? , 2012 .

[25]  V. Dakos,et al.  Are we entering an era of concatenated global crises , 2011 .

[26]  D. Byerlee,et al.  Rising Global Interest in Farmland: Can It Yield Sustainable and Equitable Benefits? , 2011 .

[27]  E. Hertwich,et al.  Affluence drives the global displacement of land use , 2013 .

[28]  U. Alon,et al.  Spontaneous evolution of modularity and network motifs. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Carlos Oya,et al.  Methodological reflections on ‘land grab’ databases and the ‘land grab’ literature ‘rush’ , 2013 .

[30]  Luca Ridolfi,et al.  Global Spatio-Temporal Patterns in Human Migration: A Complex Network Perspective , 2013, PloS one.

[31]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[32]  E. Lambin,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:Global land use change, economic globalization, and the looming land scarcity , 2011 .

[33]  Patrick C Phillips,et al.  Network thinking in ecology and evolution. , 2005, Trends in ecology & evolution.

[34]  Mark E. J. Newman,et al.  Structure and Dynamics of Networks , 2009 .

[35]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[36]  Peter Nijkamp,et al.  Accessibility of Cities in the Digital Economy , 2004, cond-mat/0412004.

[37]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[38]  R. DeFries,et al.  Framing Sustainability in a Telecoupled World , 2013, Ecology and Society.

[39]  K. Seto,et al.  Urban land teleconnections and sustainability , 2012, Proceedings of the National Academy of Sciences.

[40]  M. Edelman Messy hectares: questions about the epistemology of land grabbing data , 2013 .

[41]  A. Hoekstra,et al.  The water footprint of humanity , 2011, Proceedings of the National Academy of Sciences.

[42]  A. Bebbington,et al.  Global land governance: from territory to flow? , 2013 .

[43]  A. Rinaldo,et al.  Structure and controls of the global virtual water trade network , 2011, 1207.2306.

[44]  Klaus Hubacek,et al.  Tele-connecting local consumption to global land use , 2013 .

[45]  Zoltán Toroczkai,et al.  Complexity of the International Agro-Food Trade Network and Its Impact on Food Safety , 2012, PloS one.

[46]  Naota Hanasaki,et al.  Water for food: The global virtual water trade network , 2011 .

[47]  T. Söderqvist,et al.  Participatory Social-Ecological Modeling in Eutrophication Management : the Case of Himmerfjarden, Sweden , 2011 .

[48]  Sangwon Suh,et al.  Theory of materials and energy flow analysis in ecology and economics , 2005 .

[49]  G. Levin Nature of temperature independent dissipation and relaxation in layered superconductors. , 2001, Physical review letters.

[50]  F. Pearce Splash and grab: the global scramble for water , 2013 .