PESERA-DESMICE: Land management evaluation model

Table of Contents

1. Authors
2. Summary
3. Acronyms
4. Introduction
5. Coarse-scale model description
   1. The PESERA model: summary
   2. Alternative approaches to assessing soil erosion
   3. Process representation in PESERA
   4. Extensions of PESERA
   5. Adapting PESERA
   6. Assessment at catchment scales
6. Cost-effectiveness modelling
   1. Step 1
   2. Step 2
   3. Step 3
   4. Step 4
   5. Step 5
   6. Step 6
   7. Step 7
   8. Step 8
   9. Step 9
   10. Step 10
   11. Step 11
   12. Step 12
7. Agent-Based modelling in Spain
   1. Phase 1
   2. Phase 2
   3. Phase 3
   4. Phase 4
   5. Phase 5
   6. Phase 6
8. Input-Output modelling in Spain
   1. Model construction
   2. Scenario analyses
9. References

Step 12

Step 12: Policy development scenarios

The previous steps 1-11 allow for an analysis based on individual land user decisions (based on assumed profit maximisation). For decision-making at higher hierarchical levels (i.e. regional, national authorities), step 7 introduced the possibility of setting environmental targets to evaluate the effectiveness of technology investment. However, doing this has no repercussions on land users' individual decision-making. In this step, the feedback of different policies (either in an implementation or design phase) on the valuation of effects from a farmer's point of view (step 5) can be considered, potentially leading to a different adoption pattern of technologies (step 6/8), with implications for wider economic effects (steps 9-11). Policies can for this purpose conceptually be defined as: 1) subsidies for specific (combinations of) technologies/landscape characteristics; 2) penalties for specific (combinations of) technologies/landscape characteristics; 3) subsidies/penalties for specific on-site environmental targets; and 4) measures altering prices for individual land users. The basic decision-variable assumed by the model remains the financial attractiveness of technologies to individual land users. If technologies cease to be attractive, farmers will be assumed simply not to adopt the measure.

Given the preceding possibilities, policy development scenarios can be fed into the model to evaluate their effects on financial attractiveness, adoption rates, and the environment. Any policy scenario of the above types 1-4 could be addressed. In order to perform meaningful analysis, we will link this step to policies or policy recommendations produced in other deliverables of the DESIRE project. These policy scenarios include the most relevant site specific policies identified in WB1 and policy-relevant scenarios (e.g. dealing with climate change and land use change).

Apart from policy scenarios, the WOCAT Approach Questionnaire reveals how in a certain study site, remediation technologies are being propagated. Where incentive structures of (project-based) approaches resemble policy types 1-4, their effect can be included. Where appropriate, the current step will at least be applied to evaluate the locally adopted approach.

Where off-site policy targets are involved, automating calculations would get extremely complex and therefore not feasible to implement. A work-around should this occur is to translate off-site targets into assumed on-site targets. For instance, if an off-site target would be to decrease reservoir sedimentation by 50%, this could be translated in an on-site target of say decreasing erosion by 30% (the model here described could be used to calibrate the translation terms).

Data sources: for the standard analysis: data provided by the WOCAT Approach Questionnaire; for additional analyses: selected policy scenarios with explicit financial data from other DESIRE deliverables (produced in WB1-4) or external sources.

Products: all relevant intermediate products; policy-specific tailored output maps (e.g. Figure 3.9)

Figure 3.9: Example policy scenarios, including steps in which changes will occur and potential results4 Agent-Based and Input-Output modelling in Spain