4.3.2 Post-modelling: Interpretations of results for Sustainable Development

Icône de l'outil pédagogique Authors

Marijke Kuiper, Hatem Belhouchette, Pytrik Reidsma and Martin van Ittersum


Icône de l'outil pédagogique Applications in SEAMLESS-IF
The two main Test Cases in the development of SEAMLESS-IF are (1) a macro-micro-level application to analyse the impact of trade liberalization on the EU, and (2) a regional level application to analyse the impact of the Nitrate Directive in Midi-Pyrenees. These applications should illustrate the potential of SEAMLESS-IF by analyzing effects at different scales (from global to farm level) and across different domains of sustainable development (economic, biophysical and social). The policies enter the system top-down (test-case 1) or bottom-up (test case 2).

Icône de l'outil pédagogique Indicator framework
To ensure a balanced selection of indicators for the assessment of sustainable development, the Goal Oriented Framework was developed (see module 7.1 and 4.1.2). In order to arrive at an integrated assessment across domains and scales the results for indicators first need to be understood in isolation, i.e. for each indicator at a time, and then the results for various indicators need to be contrasted and their implication for sustainable development assessed. In Modules 3.1 and 3.2 the Test Cases (practical applications) were described, the next step is to interpret the results of these applications in the wider context.

Icône de l'outil pédagogique Macro-level application

The application focusing on trade liberalization illustrates a typical question of EU policymakers dealing with a macro-level policy. This economic policy may have environmental and social impacts which could conflict or support other policies (at EU, national or regional level). The application requires use of all backbone-chain models thus providing an elaborate test of the full system. Furthermore, although the policy being assessed is an economic one, the assessment of environmental and social indicators will provide a test of the capacity of SEAMLESS-IF for integrated assessments.

Results of this assessment are firstly analyzed at global and EU level in economic terms, employing indicators derived from the market level modelling in SEAMCAP. Points of interest are the implications of trade liberalization for EU’s position in the global market and effects on the EU as a whole. In module 3.1 we observe increasing agricultural income reductions and consumer’s welfare with increasing degree of liberalization. The losses in agricultural income and tariff revenues are in all scenarios more than compensated by increasing consumer’s welfare, so that the total welfare in the agricultural sector increases for all scenarios.

Secondly, economic impacts at member state level are considered in more detail. A key point of interest is the distribution of effects over different member states, since this will be relevant for the political feasibility of a WTO agreement.Results indicate that the regional distribution of the agricultural income changes in the standard G20 scenario. It shows that the 6% income reduction in the EU25 is distributed among regions with a spread between -16% and -2.5%, with smallest reductions in the south and in the north.

The third step is to evaluate the potential environmental impacts of a trade agreement at both regional and farm level using indicators derived from FSSIM (Table 1).

 


2001

(base year)


2013

(changes in relation to base year)




Baseline

G20

Total

Farm income (1000 €)

79


-7%

-2%

-9%

Nitrate leaching (kg N-NO3/ha)

34


13%

-3%

10%

Soil organic matter (%)

2


-1.6 %

-0.1 %

-1.7 %

 Table 1. G20 – Average impact Midi-Pyrénées.

Combining these assessments gives an integrated assessment of the effect of trade liberalization on the EU. Trade-offs as well as win-win situations can be identified, which can be in terms of gains and losses at different scales or in different domains.

The structuring of indicators according to the GOF is still in development. It is clear that the model-chain used produces mainly economic indicators. A few environmental indicators can be produced, but the social dimension is difficult to quantify. The GOF can help to make the set of indicators more balanced, whereas the indicator calculator may be used to calculate indicators for the social dimension based on economic and/or environmental indicators. For example, poverty (social: quality of life) can be based on income indicators, while health (social: population) can be based partly on environmental indicators.


Icône de l'outil pédagogique Regional application

The assessment of the Nitrate Directive in Midi-Pyrenees illustrates a typical question of regional policymakers, as it is an EU policy to be implemented at regional level. The application uses the APES (or CropSyst as replacement) – FSSIM model chain. Results are evaluated at farm type and at regional level.

Results presented in Module 3.2 include two economic indicators (farm income, premium) and one environmental indicator (nitrate leaching). These indicators are the most important ones to address the problem of nitrate pollution. To assess the impacts of policies on sustainable development at large, more indicators can be selected that can be produced by FSSIM. The GOF can be used for a balanced selection of indicators. As noted before however, the assessment of social indicators is more problematic than economic and environmental indicators. And when assessing specific policy options, it may be clear beforehand that these options will only affect a few indicators. Selected indicators should be relevant for the problem and they should be subject to change due to the selected policy option. For example, it is not likely that the implementation of the Nitrate Directive will affect the education level (social indicator) of farmers; it is more likely that the causal effect is the opposite.


Icône de l'outil pédagogique Concluding remarks
SEAMLESS-IF has the potential to analyze effects at different scales and for different dimensions. When performing an assessment of a policy often only a limited set of indicators is selected, which is also observed in the first results of the two test cases. The embedding in SEAMLESS-IF ensures to consider other indicators and scales that allow an integrated assessment of sustainable development. For further assessment of sustainable development, SEAMLESS-IF may be complemented with tools such as multi-criteria analysis (MCA).

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