Exploring green agricultural policy scenarios with a spatially explicit agent-based model
Abstract
This report presents an analysis of the impact of environmental cooperative decision-making for the European Commission’s legislative proposals for the EU Common Agricultural Policy (CAP) 2014-2020. The analysis is based on scenarios with a spatially explicit rural agent-based model (SERA) that explicitly models farmers, their socio-economic decision-making, their land use, and the landscape of which they are part. The first part of the analysis focuses on the effects of environmental cooperative collective decision-making for implementation of the ‘greening measure’ Ecological Focus Areas (EFAs). The second part of the analysis focuses on the interest of collective agri-environment measures. Results show that ways through which EFAs are allocated are crucial for effectiveness of cooperative approaches. Relying on simple market oriented governance structures will not by definition result in a better allocation of EFAs in a region. Results show that when the environmental cooperative is a key player in the designation and allocation of Pillar II agri-environment schemes, farmland biodiversity potentially increases because local expert knowledge on favorable ecological sites can be used for optimal allocation of the schemes
Read the paper
Related studies
- Contribution of social science to large scale biodiversity conservation: A review of research about the Natura 2000 network
- The alignment of agricultural and nature conservation policies in the European Union
- Determination of ecological significance based on geostatistical assessment: a case study from the Slovak Natura 2000 protected area
- Routledge Handbook of the Study of the Commons
- Farmers' acceptance of results-based agri-environmental schemes: A German perspective
- Approaches to Identify the Value of Seminatural Habitats for Conservation Biological Control
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.