Predicting the effect of the Common Agricultural Policy (CAP) measures on the supply of ecosystem services (ESS) through positive agent-based modelling

The Common Agricultural Policy (CAP) has been a cornerstone of agricultural policy within the European Union (EU) since its inception in the early 1960s. Over the decades, CAP has evolved from primarily focusing on increasing agricultural productivity to incorporating broader goals, including environmental sustainability and rural development. However, despite these shifts, the effectiveness of CAP measures in achieving their environmental objectives has been widely debated. The post-2020 CAP reform introduced significant changes aimed at addressing past shortcomings, particularly in promoting ecosystem services (ESS) through agro-environmental schemes. These include the introduction of eco-schemes that incentivise environmentally beneficial practices, such as organic farming and extensive forage systems, which are now mandatory for Member States to implement, with a substantial portion of CAP funds dedicated to them. This thesis, titled ”Predicting the effect of the Common Agricultural Policy (CAP) measures on the supply of ecosystem services (ESS) through positive agent-based modelling,” seeks to provide an ex-ante assessment of the potential impacts of the post-2020 CAP measures on the supply of ESS. The research is centred on the development of an Agent-Based Model (ABM) integrated with Positive Mathematical Programming (PMP) to simulate farmers’ behaviour under different policy scenarios. The model, calibrated using data from the Farm Accountancy Data Network (FADN) on the Emilia-Romagna region in Italy, allows for the analysis of farm-level heterogeneity and the interactions among farmers in response to CAP incentives. The findings contribute to a deeper understanding of how CAP measures influence land use, farm structures, and the provisioning of ESS, offering valuable insights for policymakers aiming to design more effective and sustainable agricultural policies.