Agent-Based Simulation of Land Use Governance (ABSOLUG) in Tropical Commodity Frontiers

Marius von Essen () and Eric F Lambin ()
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Marius von Essen: https://earth.stanford.edu/people/marius-von-essen
Eric F Lambin: https://earth.stanford.edu/people/eric-lambin

Journal of Artificial Societies and Social Simulation, 2023, vol. 26, issue 1, 5

Abstract: Well-designed land use governance that involves multiple stakeholders is crucial to reducing deforestation in tropical commodity frontiers. The effectiveness of different policy mixes is difficult to assess due to long implementation times and challenges to conducting real-world experiments. Here we introduce an agent-based simulation of land use governance (ABSOLUG) to examine the interactions among governments, commodity producers, and civil society and assess the impacts of different land use governance approaches on deforestation. The model represents a generic commodity producing landscape in the tropics with a central marketplace and features four groups of agents: largeholders, smallholders, NGOs, and a government. The objective of largeholders and smallholders is to generate profits through the production of commodity crops. Statistical evaluation through local and global sensitivity analyses shows that the model is robust, and few parameters show threshold behaviors. We used a hands-off and a proactive-government scenario to evaluate the model operationally. The hands-off scenario was inspired by high rates of tropical deforestation in the second half of the 20th century and the pro-active government scenario by a few recent cases of forest transition countries. The hands-off scenario led to quasi-complete deforestation of the landscape at the end of the simulation period. Deforestation in the proactive-government scenario decreased and eventually stopped in the second half of the simulation period, followed by reforestation.

Keywords: Land Use Change; Socio-Ecological Systems; Tropical Forests; Environmental Governance; Sustainable Resource Use; Agent-Based Modeling (search for similar items in EconPapers)
Date: 2023-01-31
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