sensitivity_analysis

Sensitivity analysis

The CAPRI model results depend on a large number of parameters, some of which are more uncertain than others. In order to analyze how model results depend on uncertain parameters, a set of sensitivity analyses were carried out in the context of an analysis of the climate impact of EU coupled support¹⁾ . In that study, a baseline scenario for 2030 representing the CAP 2014-2020 was compared to a scenario where the voluntary coupled support was removed. This resulted in less production of e.g. beef in the EU, and less GHG emissions there. Due to inelastic demand for food, trade flows changed, so that less beef was exported from the EU and more imported, and thus carbon leakage arose. We carried out sensitivity analyses to investigate how carbon leakage depend on the model parameters.

We selected four types of parameters that were assumed to be most critical to emissions leakage, and varied those in three levels: “low” (lo), “high” (hi) and “most likely” (ML). The groups of parameters subjected to the sensitivity analyses are as follows:

The elasticities of supply (SupElas) of ruminants in the EU are influenced by the slope of the marginal cost function²⁾. Higher slope means lower supply elasticity and vice versa. The slope was varied +/- 50% to create the lo and hi scenario variants.
The elasticities of demand (DemElas) for meat and dairy products. We recalibrated the demand systems for all countries so that the own-price demand elasticities would be as close as possible to +/- 50% of the standard value, while observing relevant regularity conditions for demand systems.
Substitution elasticities (CES) between imports and domestic products and between different import sources were also set to +/- 50% of the standard values. The standard values differ per product, ranging from 2 to 10.
GHG emission factors (EF) per commodity outside of the EU. Emissions leakage depends more on the relationship between EF in the EU to those outside the EU than on the absolute level. Therefore, we chose to vary only the factors outside of the EU. Since, in general, N₂O factors are considered less certain than emissions of CH₄, which in turn are less certain than CO₂, we chose to apply the uncertainty ranges of the IPCC (Blanco et al. 2014) to construct the hi and lo scenarios. The ranges used were +/- 60% for N₂O and +/- 20% for CH₄.

We do not know the covariance of the uncertain parameters across countries and products. In order to avoid running a very large number of simulation experiments, we chose to implement only the most extreme variants given by setting all parameters of the same type to lo/ML/hi simultaneously (e.g., elasticities of supply of all ruminants in all countries being hi, ML or lo simultaneously, etc.). We thus obtained 3×3×3×3 = 81 result sets; this should span the extremes of the result space.

The manuscript was submitted to a journal. Therefore, this section does not yet contain any results from this exercise, but it will be completed as soon as the review process of the manuscript has been completed.

¹⁾

Blanco, G., R. Gerlagh, J. Barrett, S. Suh, H.C. de Coninck, C.F. Diaz Morejon, R. Mathur, N. Nakicenovic, A. Ofosu Ahenkora, H. Pathak J. Pan, J. Rice, R. Richels, S.J. Smith, D.I. Stern, F.L. Toth, and P. Zhou. 2014. Drivers, Trends and Mitigation. In Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report, eds. Edenhofer et al. Cambridge and New York: Cambridge University Press.

²⁾

CAPRI contains quadratic cost functions in the tradition of Positive Mathematical Programming (PMP). In the sensitivity analyses, we varied the coefficient of the quadratic term.