CIRAD, UMR TETIS, Montpellier, France.
TETIS, Univ. Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France.
PLoS One. 2022 Dec 1;17(12):e0277608. doi: 10.1371/journal.pone.0277608. eCollection 2022.
Large-scale national and transnational commercial land transactions, or Large-Scale Land Acquisitions (LSLAs), have been gaining a lot of academic attention since the late 2000s and since the reported rush for land, resulting in turn from an increase in demand for arable land. If many data exist to characterize land deals, the analysis of investment networks remain limited and predominantly portrays power asymmetries between countries from the Global North investing in the Global South. The aim of this work is to perform a deeper investigation on the land trade market, specifically focusing on cases that do not follow such narratives. For instance, almost 25% of the countries included in the transnational land trade network do not follow a strict investor/target dichotomy, thus being characterized by a double role, i.e., they both acquire and cede land in the transnational context. In order to globally acknowledge for what was currently considered as abnormal cases, we model open access data about LSLAs extracted from the Land Matrix Initiative (LMI) open-access database into a network graph, and adapt an eigenvector based centrality method originally conceived for online social networks, namely LurkerRank, to identify and rank anomalous profiles in the land trade market. We take into account three different network snapshots: a multi-sector network (including all the transnational deals in the LMI database), and three networks referring to specific investment sectors (agriculture,mines and biofuels). Experimental results show that emerging economies (e.g., China and Malaysia) play a central role in the land trade market, by creating alternative dynamics that escape the classic North/South one. Our analyses also show how African countries that are often seen as targets of land trade transactions in a specific sector, may often acquire foreign land in the context of investments in the same sector (i.e., Zimbabwe for biofuels and the Democratic Republic of Congo for the mining sector).
大规模国家和跨国商业土地交易，或大规模土地收购（LSLAs），自 21 世纪 10 年代后期以来，由于对耕地需求的增加，导致土地抢购热潮，引起了学术界的广泛关注。尽管有许多数据可以用来描述土地交易，但投资网络的分析仍然有限，主要描绘了从全球北方国家向全球南方国家投资的国家之间的权力不对称。这项工作的目的是对土地交易市场进行更深入的调查，特别是关注那些不符合这些叙述的案例。例如，跨国土地交易网络中约有 25%的国家不符合严格的投资者/目标二分法，因此具有双重角色，即在跨国背景下既获得土地又出让土地。为了在全球范围内承认当前被认为是异常的情况，我们将从土地矩阵倡议（LMI）开放访问数据库中提取的关于大规模土地收购的开放访问数据建模为一个网络图，并采用一种最初为在线社交网络设计的基于特征向量的中心性方法，即潜伏者排名（LurkerRank），来识别和排名土地交易市场中的异常配置文件。我们考虑了三个不同的网络快照：一个多部门网络（包括 LMI 数据库中的所有跨国交易），以及三个特定投资部门（农业、矿业和生物燃料）的网络。实验结果表明，新兴经济体（如中国和马来西亚）在土地交易市场中发挥着核心作用，通过创造替代动力，摆脱了经典的南北模式。我们的分析还表明，在特定部门的土地交易中经常被视为目标的非洲国家，在同一部门的投资背景下可能会获得外国土地（例如，生物燃料方面的津巴布韦和矿业方面的刚果民主共和国）。
