Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

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

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.
  Article Metrics

Keywords 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. AIMS Agriculture and Food, 2018, 3(4): 535-560. doi: 10.3934/agrfood.2018.4.535


  • 1. UN-WCED (1987) Our Common Future. Oxford University Press.
  • 2. Sala S, Ciuffo B, Nijkamp P (2015) A systemic framework for sustainability assessment. Ecol Econ 119: 314–325.    
  • 3. Wiek A, Ness B, Schweizer-Ries P, et al. (2012) From complex systems analysis to transformational change: A comparative appraisal of sustainability science projects. Sustainability Sci 7: 5–24.
  • 4. Marvuglia A, Benetto E, Murgante B (2015) Calling for an Integrated Computational Systems Modelling Framework for Life Cycle Sustainability Analysis. J Environ Accounting Manage 3: 213–216.    
  • 5. Heijungs R (2010) Ecodesign-carbon footprint-life cycle assessment-life cycle sustainability analysis. A flexible framework for a continuum of tools. Sci J Riga Tech U 4: 42–46.
  • 6. Guinée JB, Heijungs R, Huppes G, et al. (2011) Life Cycle Assessment: Past, Present, and Future. Environ Sci Technol 45: 90–96.    
  • 7. Ponta L, Raberto M, Teglio A, et al. (2018) An Agent-based Stock-flow Consistent Model of the Sustainable Transition in the Energy Sector. Ecol Econ 145: 274–300.    
  • 8. Markard J, Raven R, Truffer B (2012) Sustainability transitions: An emerging field of research and its prospects. Res Polic 41: 955–967.    
  • 9. The TIR Consulting Group LCC (2016) The 3rd industrial revolution strategy study for the Grand Duchy of Luxembourg. Luxembourg.
  • 10. Martin G, Allain S, Bergez JE, et al. (2018) How to Address the Sustainability Transition of Farming Systems? A Conceptual Framework to Organize Research. Sustainability 10: 2083.
  • 11. Stanitsas M, Kirytopoulos K, Vareilles E (2019) Facilitating sustainability transition through serious games: A systematic literature review. J Clean Prod 208: 924–936.    
  • 12. Mitchell M (2009) Complexity: A Guided Tour. Oxford University Press, New York.
  • 13. Popa F, Guillermin M, Dedeurwaerdere T (2015) A pragmatist approach to transdisciplinarity in sustainability research: From complex systems theory to reflexive science. Futures 65: 45–56.    
  • 14. Hare M, Deadman P (2004) Further towards a taxonomy of agent-based simulation models in environmental management. Math Comput Simulat 64: 25–40.    
  • 15. Rounsevell MDA, Robinson DT, Murray-Rust D (2011) From actors to agents in socio-ecological systems models. Philos T Roy Soc B 367: 259–269.
  • 16. Heath B, Hill R, Ciarallo F (2009) A Survey of Agent-Based Modeling Practices (January 1998 to July 2008). J Artif Soc Soc Simul 12: 9.
  • 17. Heckbert S, Baynes T, Reeson A (2010) Agent-based modeling in ecological economics. Ann N Y Acad Sci 1185: 39–53.    
  • 18. Teglio A (2011) From Agent-Based models to artificial economies: The Eurace approach for policy design in economics. PhD thesis, Universitat Jaume I.
  • 19. Gaud N, Galland S, Gechter F, et al. (2008) Holonic multilevel simulation of complex systems: Application to real-time pedestrians simulation in virtual urban environment. Simul Model Pract Theor 16: 1659–1676.    
  • 20. Gilbert N (2008) Agent-Based Models. SAGE Publications, Los Angeles.
  • 21. Grimm V, Railsback SF (2005) Individual-based Modeling and Ecology. Princeton University Press.
  • 22. North MJ, Macal CM (2007) Managing Business Complexity: Discovering Strategic Solutions With Agent-Based Modeling and Simulation. Oxford University Press, Oxford.
  • 23. Ferber J (1999) Multi-agent systems: An introduction to distributed artificial intelligence, 1st edition. Addison-Wesley.
  • 24. An L (2012) Modeling human decisions in coupled human and natural systems: Review of agent-based models. Ecol Model 229: 25–36.    
  • 25. An L, Zvoleff A, Liu J, et al. (2014) Modeling human decisions in coupled human and natural systems (CHANS): Lessons from a comparative analysis. Ann Assoc Am Geogr 104: 723–745.    
  • 26. Halog A, Manik Y (2011) Advancing Integrated Systems Modelling Framework for Life Cycle Sustainability Assessment. Sustainability 3: 469–499.    
  • 27. Marvuglia A, Benetto E, Rege S, et al. (2013) Modelling approaches for consequential life-cycle assessment (C-LCA) of bioenergy: Critical review and proposed framework for biogas production. Renew Sust Energ Rev 25: 768–781.    
  • 28. Tiruta-Barna L, Pigné Y, Navarrete Gutiérrez T, et al. (2016) Framework and computational tool for the consideration of time dependency in Life Cycle Inventory: Proof of concept. J Clean Prod 116: 198–206.    
  • 29. Davis C, Nikolić I, Dijkema GPJ (2009) Integration of Life Cycle Assessment Into Agent-Based Modeling. J Ind Ecol 13: 306–325.    
  • 30. Baustert P, Benetto E (2017) Uncertainty analysis in agent-based modelling and consequential life cycle assessment coupled models: A critical review. J Clean Prod 156: 378–394.    
  • 31. Querini F, Benetto E (2014) Agent-based modelling for assessing hybrid and electric cars deployment policies in Luxembourg and Lorraine. Transport Res Part A-Pol 70: 149–161.    
  • 32. Querini F, Benetto E (2015) Combining Agent-Based Modeling and Life Cycle Assessment for the Evaluation of Mobility Policies. Environ Sci Technol 49: 1744–1751.    
  • 33. Page C, Bazile D, Becu N, et al. (2013) Agent-Based Modelling and Simulation Applied to Environmental Management, In: Edmonds B, Meyer R (eds.), Simulating Social Complexity, Springer Berlin Heidelberg, 499–540.
  • 34. Clift R, Doig A, Finnveden G (2000) The Application of Life Cycle Assessment to Integrated Solid Waste Management: Part I-Methodology. Trans Inst Chem Eng 78: 279–289.
  • 35. Shimako AH, Tiruta-Barna L, Pigné Y, et al. (2016) Environmental assessment of bioenergy production from microalgae based systems. J Clean Prod 139: 51–60.    
  • 36. Heijungs R, Suh S (2002) The Computational Structure of Life Cycle Assessment. Kluwer Academic Publishers, Dordrecht, The Netherlands.
  • 37. Miller SA, Moysey S, Sharp B, et al. (2013) A Stochastic Approach to Model Dynamic Systems in Life Cycle Assessment. J Ind Ecol 17: 352–362.    
  • 38. Bichraoui-Draper N, Xu M, Miller SA, et al. (2015) Agent-based life cycle assessment for switchgrass-based bioenergy systems. Resour, Conserv Recycl 103: 171–178.    
  • 39. Heairet A, Choudhary S, Miller S, et al. (2012) Beyond life cycle analysis: Using an agent-based approach to model the emerging bio-energy industry, In: Proceedings of 2012 IEEE International Symposium on Sustainable Systems and Technology (ISSST), Boston, MA, 1–5.
  • 40. Navarrete Gutiérrez T, Rege S, Marvuglia A, et al. (2015) Introducing LCA Results to ABM for Assessing the Influence of Sustainable Behaviours, In: Bajo J, Hernández JZ, Mathieu P, Campbell A, Fernández-Caballero A, Moreno MN, Julián V, Alonso-Betanzos A, Jiménez-López MD, Botti V (eds.), Trends in Practical Applications of Agents, Multi-Agent Systems and Sustainability, Springer International Publishing, 185–196.
  • 41. Marvuglia A, Rege S, Navarrete Gutiérrez T, et al. (2017) A return on experience from the application of agent-based simulations coupled with life cycle assessment to model agricultural processes. J Clean Prod 142: 1539–1551.    
  • 42. Davidsson P, Verhagen H (2013) Types of Simulation, In: Edmonds B, Meyer R (eds.), Simulating Social Complexity, Springer Berlin Heidelberg, 23–36.
  • 43. Smajgl A, Brown DG, Valbuena D, et al. (2011) Empirical characterisation of agent behaviours in socio-ecological systems. Environ Modell Softw 26: 837–844.    
  • 44. Huynh N, Namazi-Rad M, Perez P, et al. (2013) Generating a Synthetic Population in Support of Agent-Based Modeling of Transportation in Sydney.
  • 45. Bichraoui-Draper N (2015) Computational Sustainability Assessment: Agent-based Models and Agricultural Industrial Ecology. Université de Technologie de Troyes.
  • 46. Bruch E, Atwell J (2015) Agent-Based Models in Empirical Social Research. Sociol Meth Res 44: 186–221.    
  • 47. Bandini S, Manzoni S, Vizzari G (2009) Agent Based Modeling and Simulation: An Informatics Perspective. J Artif Soc Soc Simul 12: 4.
  • 48. Norling EJ (2009) Modelling Human Behavior with BDI Agents. PhD thesis, University of Melbourne.
  • 49. Goldman A (1993) The psychology of folk psychology. Behav Brain Sci 16: 15–28.    
  • 50. Caillou P, Gaudou B, Grignard A, et al. (2017) A Simple-to-use BDI architecture for Agent-based Modeling and Simulation. Adv Intell Syst Comput 528: 15–28.    
  • 51. Quang Truong C (2016) Integrating cognitive models of human decision-making in agent-based models: an application to land use planning under climate change in the Mekong river delta. PhD Thesis, Université Pierre et Marie Curie-Paris VI.
  • 52. Rand W, Rust RT (2011) Agent-based modeling in marketing: Guidelines for rigor. Int J Res Mark 28: 181–193.    
  • 53. Watts DJ, Strogatz SH (1998) Collective dynamics of "small-world" networks. Nature 393: 440–442.    
  • 54. Marvuglia A, Rege S, Vázquez-Rowe I, et al. (2013) Applying agent-based modelling for consequential Life Cycle Assessment of agro-systems: Challenges, strategies and assets. 6th International Conference on Life Cycle Management, Gothenburg, Sweden, 25–28 August 2013.
  • 55. Moglia M, Cook S, McGregor J (2017) A review of Agent-Based Modelling of Technology Diffusion with special reference to residential energy efficiency. Sust Cities Soc 31: 173–182.    
  • 56. Borshchev A (2013) The Big Book of Simulation Modeling: Multimethod Modeling with Anylogic 6. AnyLogic North America.
  • 57. Grignard A, Drogoul A, Zucker JD (2013) Online analysis and visualization of agent based models. Ho Chi Minh City, Vietnam, 662–672.
  • 58. Happe K, Kellermann K, Balmann A (2006) Agent-based Analysis of Agricultural Policies: An Illustration of the Agricultural Policy Simulator AgriPoliS, its Adaptation and Behavior. Ecol Soc 11: 49    
  • 59. Zellner ML, Theis TL, Karunanithi AT, et al. (2008) A new framework for urban sustainability assessments: Linking complexity, information and policy. Comput, Environ Urban Syst 32: 474–488.    
  • 60. Astier M, García-Barrios L, Galván-Miyoshi Y, et al. (2012) Assessing the Sustainability of Small Farmer Natural Resource Management Systems. A Critical Analysis of the MESMIS Program (1995–2010). Ecol Soc 17: 20.
  • 61. Murray-Rust D, Robinson DT, Guillem E, et al. (2014) An open framework for agent based modelling of agricultural land use change. Environ Modell Softw 61: 19–38.    
  • 62. Wise S, Crooks AT (2012) Agent-based modeling for community resource management: Acequia-based agriculture. Comput, Environ Urban Syst 36: 562–572.    
  • 63. Kravari K, Bassiliades N (2015) A Survey of Agent Platforms. J Artif Soc Soc Simul 18: 11
  • 64. Kornhauser D, Wilensky U, Rand W (2009) Design Guidelines for Agent Based Model Visualization. J Artif Soc Soc Simul 12: 21.
  • 65. Edmonds B, Moss S, (2005) From KISS to KIDS-An "Anti-simplistic" Modelling Approach, In: Multi-Agent and Multi-Agent-Based Simulation, 130–144.
  • 66. Waldherr A, Wijermans N (2013) Communicating social simulation models to sceptical minds. J Artif Soc Soc Simul 16: 13.
  • 67. Bert FE, Rovere SL, Macal CM, et al. (2014) Lessons from a comprehensive validation of an agent based-model: The experience of the Pampas Model of Argentinean agricultural systems. Ecol Model 273: 284–298.    
  • 68. Sargent RG (2013) Verification and validation of simulation models. J Simul 7: 12–24.    
  • 69. Zeigler BP, Praehofer H, Kim TG (2000) Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems. Academic Press.
  • 70. Bianchi C, Cirillo P, Gallegati M, et al. (2007) Validating and Calibrating Agent-Based Models: A Case Study. Comput Econ 30: 245–264.    
  • 71. David N, (2013) Validating Simulations, In: Edmonds B, Meyer R (eds.), Simulating Social Complexity, Springer Berlin Heidelberg, 135–171.
  • 72. Windrum P, Fagiolo G, Moneta A (2007) Empirical Validation of Agent-Based Models: Alternatives and Prospects. J Artif Soc Soc Simul 10: 8.
  • 73. Knepell PL, Arangno DC (1993) Simulation Validation: A Confidence Assessment Methodology. Wiley-IEEE Computer Society Press.
  • 74. McKelvey B, (2002) Model-Centered Organization Science Epistemology, In: Baum JAC (ed.), Companion to Organizations, Wiley-Blackwell, 752–780.
  • 75. Voinov A, Bousquet F (2010) Modelling with stakeholders. Environ Modell Softw 25: 1268–1281.    
  • 76. Louie MA, Carley KM (2008) Balancing the criticisms: Validating multi-agent models of social systems. Simul Model Pract Theory 16: 242–256.    
  • 77. Bianchi C, Cirillo P, Gallegati M, et al. (2008) Validation in agent-based models: An investigation on the CATS model. J Econ Behav Org 67: 947–964.    
  • 78. Fagiolo G, Birchenhall C, Windrum P (2007) Empirical Validation in Agent-based Models: Introduction to the Special Issue. Comput Econ 30: 189–194.    
  • 79. Fagiolo G, Moneta A, Windrum P (2007) A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems. Comput Econ 30: 195–226.    
  • 80. Damgaard M, Kjeldsen C, Sahrbacher A, et al. (2009) Validation of an Agent-Based, Spatio-Temporal Model for Farming in the River Gudenå Landscape. Results from the MEA-Scope Case Study in Denmark, In: Piorr A, Müller K (eds.), Rural Landscapes and Agricultural Policies in Europe, Springer Berlin Heidelberg, 239–254.
  • 81. Park HS, Rene ER, Choi SM, et al. (2008) Strategies for sustainable development of industrial park in Ulsan, South Korea-from spontaneous evolution to systematic expansion of industrial symbiosis. J Environ Manage 87: 1–13.    
  • 82. Busch J, Roelich K, Bale CSE, et al. (2017) Scaling up local energy infrastructure; An agent-based model of the emergence of district heating networks. Energ Policy 100: 170–180.    
  • 83. Rotmans J (2006) Tools for Integrated Sustainability Assessment: A two-track approach. Int Assess J 6: 35–57.
  • 84. Axtell RL (2000) Why agents? on the varied motivations for agent computing in the social sciences. Center on Social and Economic Dynamics.
  • 85. Davis C (2007) Integration of Life Cycle Analysis within Agent Based Modeling using a case study on bio-electricity. MSc thesis, TU Delft.
  • 86. Segovia-Juarez JL, Ganguli S, Kirschner D (2004) Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model. J Theor Biol 231: 357–376.
  • 87. Topping CJ, Dalkvist T, Grimm V (2012) Post-Hoc Pattern-Oriented Testing and Tuning of an Existing Large Model: Lessons from the Field Vole. PLoS One 7: e45872.    
  • 88. Grimm V, Revilla E, Berger U, et al. (2005) Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology. Science 310: 987–991.    
  • 89. Grimm V, Railsback SF (2011) Pattern-oriented modelling: A "multi-scope" for predictive systems ecology. Philos T Roy Soc B 367: 298–310.
  • 90. Topping CJ, Høye TT, Olesen CR (2010) Opening the black box-Development, testing and documentation of a mechanistically rich agent-based model. Ecol Model 221: 245–255.    
  • 91. Delmotte S, Barbier JM, Mouret JC, et al. (2016) Participatory integrated assessment of scenarios for organic farming at different scales in Camargue, France. Agric Syst 143: 147–158.    
  • 92. Andrei N, (2013) Introduction to GAMS Technology, In: Andrei N (ed.), Nonlinear Optimization Applications Using the GAMS Technology, Springer US, Boston, MA, 9–23.
  • 93. Moss S, Edmonds B (2005) Sociology and Simulation: Statistical and Qualitative Cross-Validation. Am J Soc 110: 1095–1131.    
  • 94. Laurent G (2000) Improving the external validity of marketing models: A plea for more qualitative input. Int J Res Mark 17: 177–182.    
  • 95. Smajgl A, Xu J, Egan S, et al. (2015) Assessing the effectiveness of payments for ecosystem services for diversifying rubber in Yunnan, China. Environ Modell Softw 69: 187–195.    
  • 96. Smajgl A, Ward JR, Foran T, et al. (2015) Visions, beliefs, and transformation: Exploring cross-sector and transboundary dynamics in the wider Mekong region. Ecol Soc 20: 16.
  • 97. Smajgl A, Ward J, Egan S (2013) Validating simulations of development outcomes in the Mekong region.
  • 98. Box G (1979) Robustness in the strategy of scientific model building. Robustness in Statistics.
  • 99. Schouten M, Verwaart T, Heijman W (2014) Comparing two sensitivity analysis approaches for two scenarios with a spatially explicit rural agent-based model. Environ Modell Softw 54: 196–210.    
  • 100. Filatova T, Verburg PH, Parker DC, et al. (2013) Spatial agent-based models for socio-ecological systems: Challenges and prospects. Environ Modell Softw 45: 1–7.    
  • 101. Saltelli A, Ratto M, Andres T, et al. (2008) Global Sensitivity Analysis: The Primer. Wiley & Sons.
  • 102. Cariboni J, Gatelli D, Liska R, et al. (2007) The role of sensitivity analysis in ecological modelling. Ecol Model 203: 167–182.    
  • 103. Marino S, Hogue IB, Ray CJ, et al. (2008) A methodology for performing global uncertainty and sensitivity analysis in systems biology. J Theor Biol 254: 178–196.    
  • 104. Lorscheid I, Heine BO, Meyer M (2012) Opening the "black box" of simulations: increased transparency and effective communication through the systematic design of experiments. Comput Math Organ Theor 18: 22–62.    
  • 105. Dancik GM, Jones DE, Dorman KS (2010) Parameter estimation and sensitivity analysis in an agent-based model of Leishmania major infection. J Theor Biol 262: 398–412.    
  • 106. Ratto M, Castelletti A, Pagano A (2012) Emulation techniques for the reduction and sensitivity analysis of complex environmental models. Environ Modell Softw 34: 1–4.    
  • 107. Happe K (2005) Agent-based modelling and sensitivity analysis by experimental design and metamodelling: An application to modelling regional structural change.
  • 108. Fonoberova M, Fonoberov VA, Mezić I (2013) Global sensitivity/uncertainty analysis for agent-based models. Reliab Eng Syst Safe 118: 8–17.    
  • 109. Ligmann-Zielinska A, Kramer DB, Cheruvelil KS, et al. (2014) Using Uncertainty and Sensitivity Analyses in Socioecological Agent-Based Models to Improve Their Analytical Performance and Policy Relevance. PLoS One 9: e109779.    
  • 110. Ligmann-Zielinska A, Sun L (2010) Applying time-dependent variance-based global sensitivity analysis to represent the dynamics of an agent-based model of land use change. Int J Geogr Inf Sci 24: 1829–1850.    
  • 111. Alam M, Deng X, Philipson C, et al. (2015) Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection. PLoS One 10: e0136139.    
  • 112. Troost C, Berger T (2015) Dealing with Uncertainty in Agent-Based Simulation: Farm-Level Modeling of Adaptation to Climate Change in Southwest Germany. Am J Agr Econ 97: 833–854.    
  • 113. Bell A, Parkhurst G, Droppelmann K, et al. (2016) Scaling up pro-environmental agricultural practice using agglomeration payments: Proof of concept from an agent-based model. Ecol Econ 126: 32–41.    
  • 114. Parry HR, Topping CJ, Kennedy MC, et al. (2013) A Bayesian sensitivity analysis applied to an Agent-based model of bird population response to landscape change. Environ Modell Softw 45: 104–115.    
  • 115. Yang J (2011) Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis. Environ Modell Softw 26: 444–457.    
  • 116. Ligmann-Zielinska A, Jankowski P (2010) Exploring normative scenarios of land use development decisions with an agent-based simulation laboratory. Comput, Environ Urban Syst 34: 409–423.    
  • 117. Igos E, Benetto E, Meyer R, et al. (2018) How to treat uncertainties in life cycle assessment studies? Int J Life Cycle Assess, 1–14.
  • 118. Lloyd SM, Ries R (2007) Characterizing, Propagating, and Analyzing Uncertainty in Life-Cycle Assessment: A Survey of Quantitative Approaches. J Ind Ecol 11: 161–179.
  • 119. Huijbregts MAJ, Gilijamse W, Ragas AMJ, et al. (2003) Evaluating Uncertainty in Environmental Life-Cycle Assessment. A Case Study Comparing Two Insulation Options for a Dutch One-Family Dwelling. Environ Sci Technol 37: 2600–2608.
  • 120. Wei W, Larrey-Lassalle P, Faure T, et al. (2015) How to Conduct a Proper Sensitivity Analysis in Life Cycle Assessment: Taking into Account Correlations within LCI Data and Interactions within the LCA Calculation Model. Environ Sci Technol 49: 377–385.    
  • 121. Parker DC, Hessl A, Davis SC (2008) Complexity, land-use modeling, and the human dimension: Fundamental challenges for mapping unknown outcome spaces. Geoforum 39: 789–804.    
  • 122. Filatova T, Parker DC, van der Veen A (2009) Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change. J Artif Soc Soc Simul 12: 13.
  • 123. Schreinemachers P, Berger T (2011) An agent-based simulation model of human-environment interactions in agricultural systems. Environ Modell Softw 26: 845–859.    
  • 124. Feola G, Binder CR (2010) Towards an improved understanding of farmers' behavior: The integrative agent-centred (IAC) framework. Ecol Econ 69: 2323–2333.    
  • 125. Jackson T (2005) Motivating sustainable consumption. A review of evidence on consumer behavior and behavioural change. A Report to the Sustainable Development Research Network. Centre for Environmental Strategy, University of Surrey, Guilford.
  • 126. Bonabeau E (2002) Agent-based modeling: Methods and techniques for simulating human systems. P Natl Acad Sci USA 99: 7280–7287.    
  • 127. Matthews R, Gilbert N, Roach A, et al. (2007) Agent-based land-use models: A review of applications. Landscape Ecol 22: 1447–1459.    
  • 128. Parker DC, Manson SM, Janssen MA, et al. (2003) Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A review. Ann Assoc Am Geogr 93: 314–337.    
  • 129. Berger T, Schreinemachers P, Woelcke J (2006) Multi-agent simulation for the targeting of development policies in less-favored areas. Agr Syst 88: 28–43.    
  • 130. Freeman T, Nolan J, Schoney R (2009) An Agent-Based Simulation Model of Structural Change in Canadian Prairie Agriculture, 1960–2000. Can J Agr Econ 57: 537–554.    
  • 131. Happe K, Balmann A, Kellermann K, et al. (2008) Does structure matter? The impact of switching the agricultural policy regime on farm structures. J Econ Behav Org 67: 431–444.
  • 132. Mialhe F, Becu N, Gunnell Y (2012) An agent-based model for analyzing land use dynamics in response to farmer behavior and environmental change in the Pampanga delta (Philippines). Agric, Ecosyst Environ 161: 55–69.    
  • 133. Kaye-Blake W, Li FY, McLeish MA, et al. (2010) Multi-agent simulation models in agriculture: A review of their construction and uses. 60.
  • 134. Marohn C, Schreinemachers P, Quang DV, et al. (2013) A software coupling approach to assess low-cost soil conservation strategies for highland agriculture in Vietnam. Environ Modell Softw 45: 116–128.    
  • 135. Murray-Rust D, Brown C, van Vliet J, et al. (2014) Combining agent functional types, capitals and services to model land use dynamics. Environ Modell Softw 59: 187–201.    
  • 136. Hauschild MZ, Huijbregts MAJ (2015) LCA compendium-the complete world of life cycle assessment: Life cycle impact assessment. Springer, Dordrecht.


This article has been cited by

  • 1. Alice Micolier, Philippe Loubet, Franck Taillandier, Guido Sonnemann, To what extent can agent-based modelling enhance a life cycle assessment? Answers based on a literature review, Journal of Cleaner Production, 2019, 118123, 10.1016/j.jclepro.2019.118123
  • 2. Raghu KC, Mika Aalto, Olli-Jussi Korpinen, Tapio Ranta, Svetlana Proskurina, Lifecycle Assessment of Biomass Supply Chain with the Assistance of Agent-Based Modelling, Sustainability, 2020, 12, 5, 1964, 10.3390/su12051964
  • 3. Piergiuseppe Morone, Gülşah Yilan, A paradigm shift in sustainability: from lines to circles, Acta Innovations, 2020, 36, 5, 10.32933/ActaInnovations.36.1
  • 4. Courtney A Grant, Andrea L Hicks, Global Warming Impacts of Residential Electricity Consumption: Agent‐Based Modeling of Rooftop Solar Panel Adoption in Los Angeles County, California, Integrated Environmental Assessment and Management, 2020, 10.1002/ieam.4315

Reader Comments

your name: *   your email: *  

© 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved