Environmental Engineering Reference
In-Depth Information
authorities and farmers. To mitigate these possibly constraining factors and to still
reach the policy objectives scenarios could be constructed that include complemen-
tary policy measures, such as the (parallel) introduction of voluntary compensation
schemes that pay premiums for farmers limiting fertiliser application even beyond
the legal restrictions to increase compliance. Alternatively, modified scenarios may
encompass programs to either gain the trust of farmers (e.g., using participatory
methods for determining the precise restrictions for farmers in a particular region),
or additional funding for those public authorities that are supposed to monitor and
sanction farmers' non-compliance. The results of modelling these modified sce-
narios may reveal whether complementary policy measures could indeed increase
the predicted policy performance in those 'critical' countries. When applying
PICA in the post-modelling phase , it allows for putting the mainly quantitative
model results and calculated impact indicators into (institutional) context. This
contributes to the validation of the model results on policy impacts.
PICA is still work in progress. It has been tested in two study areas in the
Auvergne and at the regional level in Midi-Pyrénées (France) with the policy option
EU Nitrate Directive to gain more insights for modifying and refining the procedure
(Amblard et al. 2008a, b ; Schleyer et al. 2007b) . The results clearly show that,
despite being an explorative tool, all PICA steps can build already on a solid and
useful basis derived from theoretical insights and empirical institutional analysis
(see Schleyer et al. 2007a) . However, neither the current library of CIA as a whole
nor the lists of CIA linked to a particular policy type can be seen as static, but need
to be revised and complemented continually to improve the accuracy of the predic-
tions. Therefore, it is essential that the experiences made and insights gained during
every application of PICA are used systematically and carefully to make the empiri-
cal basis of PICA more comprehensive. Thus, the library of CIA can be seen as an
ever-growing source of information. The same applies to the library of institutional
indicators used in PICA Step three that would need constant revision. Finally, PICA
has been designed as a flexible methodological framework for a systematic and
rigorous ex-ante assessment of the institutional dimension of policy implementa-
tion; a procedure that can and needs to be adapted to the respective policy option
under scrutiny.
References
Aligica, P. (2005). Institutional analysis and economic development policy: notes on the applied
agenda of the Bloomington School. Extending Peter Boettke and Christopher Coyne's Outline
of the Research Program of the Workshop in Political Theory and Policy Analysis. Journal of
Economic Behavior and Organization, 57 (2), 159-165.
Amblard, L., Aznar, O., Mann, C., Schleyer, C., Theesfeld, I., & Hagedorn, K. (2008a). Evaluation
and suggestions for improvement of the Procedure for Institutional Compatibility Assessment
(PICA) and integration of PICA into the third prototype of SEAMLESS-IF. PD6.5.5.2,
SEAMLESS integrated project, EU 6th Framework Programme, contract no. 010036-2, www.
SEAMLESS-IP.org
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