A Synthesis on Agent-Based Impact Assessment Models from the Perspective of the EU Rural Development Policy Measures

Abstract

The second pillar of the European Union’s (EU) Common Agricultural Policy (CAP) aims at supporting rural areas by meeting the economic, environmental and social challenges. To deal with these challenges, countries are faced with the question of selecting the best tools among a large set of policy instruments. The problem of choosing the best policy instruments is aggravated by the very heterogeneous character of the societal demands that differ among member countries with very different economic and institutional structures. This study aims to introduce the agent-based modelling platforms that have been widely used in the impact analysis of recent rural development policies in the EU in a comparative manner. It also aims to explain how the above-mentioned sources of heterogeneity are handled in these models. To achieve the stated objectives, the study first examines the historical development of rural development policies within the EU. Subsequently, it proceeds to analyse several agent-based platforms that have been employed for the purpose of assessing the impact of agricultural policies with respect to certain features such as integration of land market, modelling unit, decision rule, rules of exit, labour market and price formation. To conclude, it is observed that as the rural development policies are formulated on farm-basis and as farms have a heterogeneous structure within themselves, in addition, the expansion of databases and the development of empirical analysis tools and technologies have led to a shift in empirical analyses towards agent-based models. However, these modelling platforms still embody various problems, especially in terms of database adjustments and parameterization and calibration of the model.

Keywords

• European Union
• Rural development policies
• Agent-based modelling

References

1. Axelrod R & Tesfatsion L (2010). On-line guide for newcomers to agent-based modelling in the social sciences. Handbook of Computational Economics 2. Axtell R L (2000). Why agents? On the varied motivations for agents in the social sciences. In Proceedings of the Workshop on Agent Simulation: Applications, Models, and Tools.
2. Argonne, Illinois.: Argonne National Laboratory Bazzana D, Foltz & Zhang Y (2022). Impact of climate smart agriculture on food security: an agent-based analysis. Food Policy, 111, 102304.
3. Billari F C, Fent T, Prskawetz A & Scheffran J (Eds.). (2006). Agent-based computational modelling: applications in demography, social, economic and environmental sciences. Taylor & Francis
4. Emmerson M, Morales M B, Oñate .J, Batáry P, Berendse F, Liira J, Aavik T, Guerrero I, Bommarco R, Eggers S, Pärt T, Tscharntke T, Weisser W, Clement L & Bengtsson J (2016): How agricultural intensification affects biodiversity and ecosystem services. Advances in Ecological Research 55: 43-97
5. European Commission (1988). The future of rural society: Commission communication transmitted to the Council and to the European Parliament. Office for Official Publications of the European Communities. COM (88): 501. Available at http://aei.pitt.edu/5214/1/5214.pdf (accessed June 2021).
6. European Commission (2023). The Cork Declaration - A Living Countryside. Statement by the European Commission at the European Conference on Rural Development, , Cork. Available at http://www.aughty.org/pdf/cork_declar.pdf (accessed June 2021).
7. European Union (EU) (2016). Cork 2.0 Declaration: A better life in rural areas. Available at https://enrd.ec.europa.eu/sites/default/files/cork-declaration_en.pdf (accessed June 2021)
8. FAO/WB (2016). The evolution and the impact of EU regional and rural policy. Working Paper. Available at http://www.fao.org/3/aj280e/aj280e.pdf (accessed June 2021).

9. Havlik P, Valin H, Mosnier A, Frank S, Lauri P, Leclère D, Palazzo A, Batka M, Boere E, Brouwer A, Deppermann A, Ermolieva T, Forsell N, di Fulvio F & Obersteiner M. (2018). GLOBIOM documentation. International Institute for Applied Systems Analysis (IIASA). Laxenburg, Austria
10. Jayet P A, Debove E, Kleinhanss W, Küpker B, Júdez L & Xepapadeas A (2007). Land market & Genedec. Presentation at DG Agriculture in Brussels, March 5th. Available at https://www.researchgate.net/profile/Pierre-Alain-
11. Jayet/publication/266469538_Land_market_Genedec/links/55f04c3208ae199d47c1fcb6/Land-market-Genedec.pdf (accessed May 2021). Johnson T G, Bryden J M, Refsgaard K & Alva-Lizarraga S. (2008). A system dynamics model of agriculture and rural development: The TOPMARD core model (No. 692-2016-47419). Paper prepared for presentation at the 107 the EAAE Seminar, Sevilla, Spain, January 29th -February 1st. Available at https://ageconsearch.umn.edu/record/6497/ (accessed May 2021).
12. Kremmydas D, Athanasiadis I N & Rozakis S (2018): A review of agent-based modeling for agricultural policy evaluation. Agricultural Systems, 164: 95-106.
13. Kremmydas D (2012). Agent-based modelling for agricultural policy evaluation: A review, AUA Working Paper Series No.2012-3, December. Available at https://www.researchgate.net/profile/DimitriosKremmydas/publication/261552249_Agent_based_modeling_for_agricultural_policy_evaluation_A_review/links/00b495349a4839cd48000000/Agent-based-modeling-for-agricultural-policy-evaluation-A-review.pdf (accessed Nov 2021)
14. Louhichi K, Kanellopoulos A, Janssen S, Flichman G, Blanco M, Hengsdijk H, Heckelei T, Berentsen P, Lansink A O & van Ittersum M (2010). FSSIM, a bio-economic farm model for simulating the response of EU farming systems to agricultural and environmental policies. Agricultural Systems 103(8): 585-597
15. Maes D & van Passel S (2014). An empirical economic model to reveal behaviour characteristics driving the evolution of agriculture in Belgium in Miguel, Amblard, Barceló & Madella (eds.) Advances in Computational Social Science and Social Simulation Barcelona: Autònoma University of Barcelona
16. Millington J D A, Xiong H, Peterson S & Woods J (2017). Integrating modelling approaches for understanding telecoupling: global food trade and local land use. Land, 6: 56. Available at https://www.mdpi.com/2073-445X/6/3/56/htm (accessed July 2021).
17. Rizojewa-Sileva A, Pilvere I & Zeverte-Rivza S (2018). Agriculture modelling in the European Union. In Proceedings of the International Scientific Conference "Economic Sciences for Agribusiness and Rural Economy" (No. 2). Warsaw, 7–8 June, pp. 45–50. Available at https://js.wne.sggw.pl/index.php/esare/article/view/1493 (accessed May 2021)
18. Schmid E & Sinabell F (2006). Alternative implementations of the single farm payment distributional consequences for Austria. Discussion Paper DP-17. Institut für nachhaltige Wirtschaftsentwicklung. Availabl at https://www.econstor.eu/bitstream/10419/236557/1/dp17-2006.pdf (accessed April 2021)
19. Troost C & Berger T (2015). Dealing with uncertainty in agent-based simulation: farm level modeling of adaptation to climate change in Southwest Germany. American Journal of Agricultural Economics 97(3):833–854