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scenario_simulation [2020/04/26 05:37] – matszscenario_simulation [2022/01/03 11:17] – [Price depending crop yields and input coefficients] himics
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 **Figure 13: Link of modules in CAPRI** **Figure 13: Link of modules in CAPRI**
-{{::figure13.png?600|Source: CAPRI Modelling System. Note: the number of regions is outdated in December 2011}} +{{::figure_13.png?600|Source: CAPRI Modelling System. Note: the number of regions is outdated in December 2011}} 
  
 =====Module for agricultural supply at regional level===== =====Module for agricultural supply at regional level=====
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 Where “asym” is the land asymptote, i.e. the maximal amount of economically usable agricultural area in a region when the agricultural land rent goes towards infinity. For an application where the land market is used see Renwick et al. (2013). Where “asym” is the land asymptote, i.e. the maximal amount of economically usable agricultural area in a region when the agricultural land rent goes towards infinity. For an application where the land market is used see Renwick et al. (2013).
  
-Set aside policies have changed frequently during CAP reforms. The recent specification is covered in the context of the premium modelling in Section [[Premium module]]. The obligatory set-aside restriction introduced by the McSharry reform 1992 and valid until the implementation of the Luxembourg compromise of June 2003 has been explicitly modelled through this equation:+Set aside policies have changed frequently during CAP reforms. The recent specification is covered in the context of the premium modelling in Section [[scenario simulation#Premium module]]. The obligatory set-aside restriction introduced by the McSharry reform 1992 and valid until the implementation of the Luxembourg compromise of June 2003 has been explicitly modelled through this equation:
  
 \begin{align} \begin{align}
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 \end{matrix} \end{matrix}
 \right] \right]
- 
 \end{split} \end{split}
 \end{align} \end{align}
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 \end{align} \end{align}
  
-The scaling factor to map from the legal quota legalquotA (as the B quota has been eliminated in the sugar reform, it holds that \(q^A = q^{A+B}) \)to the behavioural quota qA depends on the projected sugar beet sales quantity in the calibration point \(NETTRD_{SUGB}^{cal})\ : For a country with a high over quota production (say 40%) we would obtain a scaling factor of 1.31, such that this producer will behave like a moderate C-sugar producer: responsive to both the C-beet prices as well as to the quota beet price (and the legal quotas). Without this scaling factor, producers with significant over quota p   roduction, like France and Germany, would not show any sizeable response to a 10% cut of either the legal quotas or the quota price (at empirically observed coefficients of variation). As it is likely that the profitability of ethanol beets benefit from cross-subsidisation from the quota beets such a zero responsiveness was considered implausible.+The scaling factor to map from the legal quota legalquotA (as the B quota has been eliminated in the sugar reform, it holds that \(q^A = q^{A+B} \) )to the behavioural quota qA depends on the projected sugar beet sales quantity in the calibration point \( NETTRD_{SUGB}^{cal} \) : For a country with a high over quota production (say 40%) we would obtain a scaling factor of 1.31, such that this producer will behave like a moderate C-sugar producer: responsive to both the C-beet prices as well as to the quota beet price (and the legal quotas). Without this scaling factor, producers with significant over quota p   roduction, like France and Germany, would not show any sizeable response to a 10% cut of either the legal quotas or the quota price (at empirically observed coefficients of variation). As it is likely that the profitability of ethanol beets benefit from cross-subsidisation from the quota beets such a zero responsiveness was considered implausible. 
  
 ===Update note=== ===Update note===
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 Y_{j,t}=Y_{j,t-1}^{[\epsilon_jlog \frac{p_o,t-1}{p_t}]} Y_{j,t}=Y_{j,t-1}^{[\epsilon_jlog \frac{p_o,t-1}{p_t}]}
 \end{equation} \end{equation}
 +
 +
 +====Annex: Land use modelling ====
  
 =====Premium module===== =====Premium module=====
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 **Figure 14: Example of technical implementation of a premium scheme** **Figure 14: Example of technical implementation of a premium scheme**
  
-{{:figure14.png?600|Source: CAPRI Modelling System. Note: The parameter PPDATA_E is now called p_premDataE.}}+{{:figure_14.png?600|Source: CAPRI Modelling System. Note: The parameter PPDATA_E is now called p_premDataE.}}
  
 The sets of payments, exemplified by DPGRCU in the figure, and the activity groups, exemplified by PGGRCU and PGPROT are defined in the file policy/policy_sets.gms. Since this is a “static” GAMS file used in any simulation, it contains the gross list of all policies that currently can be simulated, including legacy ones. In order to work efficiently with the acronyms which define the application types, these are converted to numerical attributes as shown below (//‘policy/policy.gms’)//: The sets of payments, exemplified by DPGRCU in the figure, and the activity groups, exemplified by PGGRCU and PGPROT are defined in the file policy/policy_sets.gms. Since this is a “static” GAMS file used in any simulation, it contains the gross list of all policies that currently can be simulated, including legacy ones. In order to work efficiently with the acronyms which define the application types, these are converted to numerical attributes as shown below (//‘policy/policy.gms’)//:
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 **Figure 15: General flow of logic of CAPRI model as regards premiums** **Figure 15: General flow of logic of CAPRI model as regards premiums**
  
-{{::fiugre15.png?600|Source: own illustration}} +{{::figure_15.png?600|Source: own illustration}} 
  
 Generally, all attributes for a premium scheme are mapped down in space, e.g. from EU27 to EU 27 member states, from countries to NUTS1 regions inside the country, from there to the NUTS2 regions inside the NUTS1, and from NUTS2 regions to the farm types in a NUTS2 region (see //‘policy/policy.gms’//), e.g. Generally, all attributes for a premium scheme are mapped down in space, e.g. from EU27 to EU 27 member states, from countries to NUTS1 regions inside the country, from there to the NUTS2 regions inside the NUTS1, and from NUTS2 regions to the farm types in a NUTS2 region (see //‘policy/policy.gms’//), e.g.
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 **Figure 16: General way of SFP implementation in CAPRI** **Figure 16: General way of SFP implementation in CAPRI**
  
-{{::figure16.png?600|Source: own illustration}}+{{::figure_16.png?600|Source: own illustration}}
  
 In opposite to the reforms until Agenda 2000, there are hence in most cases not longer premium rates or individual ceilings in hectares found in legal texts. Rather, these are calculated by the model itself from the decoupled part of the “old” Mac Sharry and Agenda 2000 premiums which introduces additional complexity in the model code. In opposite to the reforms until Agenda 2000, there are hence in most cases not longer premium rates or individual ceilings in hectares found in legal texts. Rather, these are calculated by the model itself from the decoupled part of the “old” Mac Sharry and Agenda 2000 premiums which introduces additional complexity in the model code.
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 **Figure 17: The logic of the CAP 2014-2020 reference policy as implemented in the CAPRI policy module** **Figure 17: The logic of the CAP 2014-2020 reference policy as implemented in the CAPRI policy module**
  
-{{:figure17.png?600|Source: own illustration}}+{{:figure_17.png?600|Source: own illustration}}
  
 ===Tradable Single Premium Scheme entitlements=== ===Tradable Single Premium Scheme entitlements===
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 Finally, an extensification effect to the AE payments is introduced using the possibility to make technological variants differently eligible. Finally, an extensification effect to the AE payments is introduced using the possibility to make technological variants differently eligible.
  
-{{:code_p179_2.png?600}} \\+{{:code_p179_2.png?600}} 
 {{:code_p180.png?600}} {{:code_p180.png?600}}
  
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 {{:code_p182.png?600}} {{:code_p182.png?600}}
  
-Currently, the following budget categories are supported (see ‘//sets.gms//’ and ‘//policy\policy_sets.gms//’):+Currently, the following budget categories are supported (see ‘//sets.gms//’ and ‘//policy/policy_sets.gms//’):
  
 {{:code_p182_2.png?600}} {{:code_p182_2.png?600}}
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 **Figure 18: Land supply curve examples** **Figure 18: Land supply curve examples**
  
-{{::figure18.png?600|Source: own calculations}} +{{::figure_18.png?600|Source: own calculations}} 
  
 In order to parameterize the land demand function, information about yield and supply elasticities is used. The marginal reaction of land to a marginal change in one of the prices is defined as the total supply effect minus the yield effect: In order to parameterize the land demand function, information about yield and supply elasticities is used. The marginal reaction of land to a marginal change in one of the prices is defined as the total supply effect minus the yield effect:
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 The following table shows the substitution elasticities used for the different product groups. Compared to most other studies, we opted for a rather elastic substitution between products from different origins, as agricultural products are generally more uniform then aggregated product groups, as they can be found e.g. in CGE models. The following table shows the substitution elasticities used for the different product groups. Compared to most other studies, we opted for a rather elastic substitution between products from different origins, as agricultural products are generally more uniform then aggregated product groups, as they can be found e.g. in CGE models.
  
-**Table 28: Substitution elasticities for the Armington CES utility aggregators((A sensitivity analysis on those elasticities is given in section [[Sensitivity analysis]]))**+**Table 28: Substitution elasticities for the Armington CES utility aggregators((A sensitivity analysis on those elasticities is given in section [[scenario simulation#Sensitivity analysis]]))**
  
 ^Product (group) ^Substitution elasticity between domestic sales and imports  ^Substitution elasticity between import flows ^ ^Product (group) ^Substitution elasticity between domestic sales and imports  ^Substitution elasticity between import flows ^
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 |Other fruits |  3 |  3  | |Other fruits |  3 |  3  |
 |Sugar| 12  | 12  | |Sugar| 12  | 12  |
-|All other products| 8 |  10  | \\Source: own calculations +|All other products| 8 |  10  |  
 +Source: own calculations
 There are some specific settings, such as a value of 2 for rice and the EU15, 2.5 respectively 5 for Japan to account for its specific tariff system, as well as some lower values for EU’s Mediterrean partner countries. There are some specific settings, such as a value of 2 for rice and the EU15, 2.5 respectively 5 for Japan to account for its specific tariff system, as well as some lower values for EU’s Mediterrean partner countries.
  
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 {{::figure_19.png?600|Source: Capri Modelling System}} {{::figure_19.png?600|Source: Capri Modelling System}}
  
-The above “primal” formulation of the Armington approach in terms of quantity aggregators turned out numerically less stable in the implementaiotn than the dual representation in terms of price aggregators. The Armington approach suffers from two important shortcomings. First of all, a calibration to a zero flow is impossible so that only observed import flows react to policy changes while all others are fixed at zero level. For most simulation runs, that shortcoming should not be serious. If it is relevant, it may be overcome using the modified Armington approach as explained in Section [[Market module for agricultural outputs#Price linkages]]. +The above “primal” formulation of the Armington approach in terms of quantity aggregators turned out numerically less stable in the implementaiotn than the dual representation in terms of price aggregators. The Armington approach suffers from two important shortcomings. First of all, a calibration to a zero flow is impossible so that only observed import flows react to policy changes while all others are fixed at zero level. For most simulation runs, that shortcoming should not be serious. If it is relevant, it may be overcome using the modified Armington approach as explained in Section [[scenario simulation#Price linkages]]. 
  
 Secondly, the Armington aggregator defines a utility aggregate and not a physical quantity. That second problem is healed by re-correcting in the post model part to physical quantities. Little empirical work can be found regarding the estimation of the functional parameters of Armington systems. Hence, substitution elasticities were chosen as to reflect product properties as shown above. Secondly, the Armington aggregator defines a utility aggregate and not a physical quantity. That second problem is healed by re-correcting in the post model part to physical quantities. Little empirical work can be found regarding the estimation of the functional parameters of Armington systems. Hence, substitution elasticities were chosen as to reflect product properties as shown above.
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 **Figure 20: Witzke et al. calibration, two-goods case** **Figure 20: Witzke et al. calibration, two-goods case**
  
-{{::figure20.png?600|Source: Witzhe et al 2005}}+{{::figure_20.png?600|Source: Witzhe et al 2005}}
  
 The additional commitment parameter involves another degree of freedom that needs to be eliminated with additional information. During the calibration this is provided by the expected imports from region 2 at the second hypothetical set of relative prices. Following the dual approach, the lower Armington nest is represented with Armington share-equations and with equations for the composite price indexes: The additional commitment parameter involves another degree of freedom that needs to be eliminated with additional information. During the calibration this is provided by the expected imports from region 2 at the second hypothetical set of relative prices. Following the dual approach, the lower Armington nest is represented with Armington share-equations and with equations for the composite price indexes:
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 **Figure 21: GUI Option for the non-homothetic Armington system** **Figure 21: GUI Option for the non-homothetic Armington system**
  
-{{::figure21.png?600}}+{{::figure_21.png?600}}
  
 The calibration of the non-homothetic Armington demand system does not require a full re-calibration of the complete CAPRI modelling system; it can be found under the workstep “Run scenario”, task: “Run scenario with market model”. Technically, the calibration modifies the simini/sim_ini.gdx file that contains the necessary starting parameters for a CAPRI simulation: if the above GUI option is selected then the existing sim_ini.gdx file is automatically deleted and the create_sim_ini CAPRI module is called. This is all steered in the main capmod.gms file by a specific GAMS setglobal variable called modArmington: The calibration of the non-homothetic Armington demand system does not require a full re-calibration of the complete CAPRI modelling system; it can be found under the workstep “Run scenario”, task: “Run scenario with market model”. Technically, the calibration modifies the simini/sim_ini.gdx file that contains the necessary starting parameters for a CAPRI simulation: if the above GUI option is selected then the existing sim_ini.gdx file is automatically deleted and the create_sim_ini CAPRI module is called. This is all steered in the main capmod.gms file by a specific GAMS setglobal variable called modArmington:
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 \begin{equation} \begin{equation}
-intd_{i,r} = (intk_{i,r}+intp__{i,r}) errf \left ( \left( uvae_{i,r} - pmrk_{i,r} + \gamma_{i,r}^p\right ) / stddev_{i,r} \right )+intd_{i,r} = (intk_{i,r}+intp_{i,r}) errf \left ( \left( uvae_{i,r} - pmrk_{i,r} + \gamma_{i,r}^p\right ) / stddev_{i,r} \right )
 \end{equation} \end{equation}
  
scenario_simulation.txt · Last modified: 2023/09/08 12:07 by massfeller
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