This article deals with the consequences of uncertainties about the societal valuation of landscape benefits and environmental costs for multiple policy goal trade-offs that may result when internalising external benefits and costs by abolishing commodity subsidies, introducing land subsidies and intermediate input taxes. Numerical results of an illustrative model of the German agricultural sector are presented and trade-off curves for farm income, environmental cost reduction and landscape benefits are discussed. If the relevant criterion for policy decisions was a simple aggregation of the single policy goal indicators with constant weights, the optimality of policy options would be rather robust to valuation errors.
Expected exchange ratios between the different policy goals, however, can be severely distorted if policy-makers have wrong estimations of the true societal valuation of externalities. This may make targeted incentives that aim at a better orientation of agriculture towards societal demands appear too risky for policy-makers and finally leads to the blockage of policy changes. An appropriate policy portfolio should therefore comprise non-distorting compensations for negatively affected policy goals. The EU’s commitment in the WTO to multifunctional agriculture has to be underlined by effective domestic policy reforms, if this concept is not to be discredited as an attempt to justify disguised protectionism.
Table of Contents
Summary
Zusammenfassung
1 Introduction
2 Methodological approach
3 The base version of MULTSIM – a reference for analysing uncertainty
3.1 Valuation of externalities
3.2 Policy scenarios
3.3 Policy goal trade-offs
4 Implications of uncertain knowledge on the societal valuation of public goods and externalities
5 Conclusions for policy design
Sources
Annex
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- Gerald Weber (Autor), 2003, Uncertainty on Agricultural Public Good and Externality Production. Implications for Domestic and International Agricultural Policy Design, Múnich, GRIN Verlag, https://www.grin.com/document/985991
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