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Abstract
Background – With the new Common Agricultural Policy (CAP), EU member states (MS) gained flexibility in the design of agri-environmental measures (AEM). In particular, MS are encouraged to determine AEM payment levels based on a marginal supplier approach. Analytical determination of payment levels would require sufficient information about the distribution of cost-structures. As this data is generally lacking, calculations are typically based on assumptions. The question arises whether MS use the ensuing discretionary scope to design environmentally ambitious policies or primarily income-generating farm payments. Objective – Prioritising objectives and implementation options under budget constraints, an essential task in policy design, is particularly difficult when developing schemes to support the provision of public goods. By definition, public goods lack a market value, therefore their cost-effectiveness is difficult to assess. We examine how scientific experts assess trade-offs between the remuneration level for AEM and the achievable environmental effectiveness of a funding scheme. Method – In a discrete choice survey, experts with a track record in dealing with European agri-environmental challenges were asked to choose between different schemes and the status quo. These experts are an important group as they influence scientific and political debates on the future CAP. The attributes presented in the choice set were the environmental effectiveness of a CAP strategic plan (CSP), the share of agricultural area enrolled in agri- environmental measures (AEM), the share of ‘dark-green’ measures, and ‘payment to farmer’. Results & Discussion – The results show that higher CSP’s environmental effectiveness and a higher share of agricultural area enrolled in AEM increased the likelihood that experts selected a funding scheme. Higher levels of ’payment to farmer’ decreased the selection probability. In order to achieve more ambitious CSPs, experts regarded higher payments for AEM acceptable. A latent class analysis revealed preference heterogeneity among experts, reflecting different disciplinary and geographical perspectives.