Implementation of Agent-Based Models to support Life Cycle Assessment: A review focusing on agriculture and land use

Antonino Marvuglia; Tomás Navarrete Gutiérrez; Paul Baustert; Enrico Benetto; Antonino Marvuglia; Tomás Navarrete Gutiérrez; Paul Baustert; Enrico Benetto

doi:10.3934/agrfood.2018.4.535

AIMS Agriculture and Food

2018, Volume 3, Issue 4: 535-560. doi: 10.3934/agrfood.2018.4.535

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Review

Implementation of Agent-Based Models to support Life Cycle Assessment: A review focusing on agriculture and land use

Luxembourg Institute of Science and Technology (LIST), 5, avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg

Received: 18 October 2018 Accepted: 12 December 2018 Published: 20 December 2018

Agent-Based Models (ABMs) have been adopted to simulate very different kinds of complex systems, from biological systems to complex coupled human-natural systems. In particular, when used to simulate man-managed systems, they have the advantage of allowing human behavioral aspects to be considered in the modelling framework. This paper provides a literature review of the application of ABMs for agricultural and land use modelling. One section is specifically devoted to the coupling of ABMs and Life Cycle Assessment (LCA) models. The aim of the paper is to give a perspective of the different “modelling blocks” one needs to take into account to build an ABM, dealing with general issues that must be considered regardless of the domain of application (such as validity, uncertainty, parameter sensitivity, agent definition, data provision), and providing concrete examples related specifically to ABMs applied to agricultural and land use modelling. The paper highlights the difficulties that the modelers can encounter in dealing with each of these modelling blocks, and presents the solutions that can be envisioned (mentioning those that have been applied in certain cases in the literature). As a general conclusion, we can observe that solutions based on complex systems simulations are starting, to some extent, to be influential in policymaking; however, practical user-friendly tools that allow scenario simulations also to non-expert users are clearly still lacking.
- Agent-Based model,
- quantitative sustainability assessment,
- Life Cycle Assessment,
- life cycle sustainability analysis,
- agricultural modelling
Citation: Antonino Marvuglia, Tomás Navarrete Gutiérrez, Paul Baustert, Enrico Benetto. Implementation of Agent-Based Models to support Life Cycle Assessment: A review focusing on agriculture and land use[J]. AIMS Agriculture and Food, 2018, 3(4): 535-560. doi: 10.3934/agrfood.2018.4.535

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Abstract

Agent-Based Models (ABMs) have been adopted to simulate very different kinds of complex systems, from biological systems to complex coupled human-natural systems. In particular, when used to simulate man-managed systems, they have the advantage of allowing human behavioral aspects to be considered in the modelling framework. This paper provides a literature review of the application of ABMs for agricultural and land use modelling. One section is specifically devoted to the coupling of ABMs and Life Cycle Assessment (LCA) models. The aim of the paper is to give a perspective of the different “modelling blocks” one needs to take into account to build an ABM, dealing with general issues that must be considered regardless of the domain of application (such as validity, uncertainty, parameter sensitivity, agent definition, data provision), and providing concrete examples related specifically to ABMs applied to agricultural and land use modelling. The paper highlights the difficulties that the modelers can encounter in dealing with each of these modelling blocks, and presents the solutions that can be envisioned (mentioning those that have been applied in certain cases in the literature). As a general conclusion, we can observe that solutions based on complex systems simulations are starting, to some extent, to be influential in policymaking; however, practical user-friendly tools that allow scenario simulations also to non-expert users are clearly still lacking.

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