Summary report on the methodological framework at micro level

The environmental impact assessment of rural development policies is an issue that needs to be constantly monitored by both practitioners and policy makers within the EU and its Member States. Impact assessment methodologies are well established in the literature on agri-environmental policies, both at micro and macro scale, considering different public goods (water, biodiversity, etc.) as specific foci of these policies. However, there are several challenges and gaps related to their use, concerning both the fitness of indicators, models and methodologies for the expected outcomes, and the adoption of the most suitable scale for the analysis. This report presents a conceptual framework in order to systematise the most commonly used methodologies to assess micro-level environmental performance of agricultural policies. It deals with issues relating to the specific use for some methodologies (or models, or indicators) for a single public good, aiming to contribute to systematise the current knowledge on evaluation methods. It tries to clarify the role of methodologies and their integration in the evaluation process, particularly to fill the gap of knowledge of the agriculture-environment relationships within the complexity of multi-scale and multi-levels approaches. From a micro-level perspective, it is important to consider the role of individuals and analyse in depth the different forms of organisation (spatial, networks, hierarchies) and interactions among different organisational and intervening levels. Only with multi-scale integration and the combination of results it is possible to efficiently generalise (up-scale) micro-level results in a macro-level perspective. Field measurements, farm management surveys and farming system models essentially refer to the farm as the simplest management unit of an agricultural system, analysed from the point of view of a farmer who decides whether or not to participate in rural development schemes. In most of the case study areas only naïve quantitative analysis has been applied due to difficulties in data availability and data access, with negative effects from the methodological point of view when the statistical significance of the parameters was not verified. The statistics-based approach to the counterfactual needs well-defined samples with a sufficient number of observations to perform regression models and spatial analysis.