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post_model_analysis [2020/04/18 06:30] – created matszpost_model_analysis [2022/11/07 10:23] (current) – external edit 127.0.0.1
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-======Post model Analysis====== +======Post model analysis====== 
-=====Dual analysis (T. Jansson)=====+=====Dual analysis=====  
 +''T. Jansson''
  
 ====Constrained optimisation background==== ====Constrained optimisation background====
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 ====Example – removing greening payments and requirements==== ====Example – removing greening payments and requirements====
  
-The dual analysis of the supply models is computed by a file called “supply\margcr.gms” (where margcr probably means “MARGinal Cost and Revenues”), which is always included after the last iteration. The figures below show the CAPRI GUI comparing the results of two scenarios: one reference continuing CAP after 2014 up to 2030, and a counterfactual where the greening payments and requirements are removed. We look at the results of a single region “Sydsverige” to avoid aggregation issues.+The dual analysis of the supply models is computed by a file called “supply/margcr.gms” (where margcr probably means “MARGinal Cost and Revenues”), which is always included after the last iteration. The figures below show the CAPRI GUI comparing the results of two scenarios: one reference continuing CAP after 2014 up to 2030, and a counterfactual where the greening payments and requirements are removed. We look at the results of a single region “Sydsverige” to avoid aggregation issues.
  
 In Figure 39, we can look for the biggest changes (absolute difference between simulations is printed in brackets in the right hand column), and find a 71 euro reduction in payments. This is in line with the scenario, which removed 30% of the farm payment. All other positions adjust to some extent, and as usual with CAPRI, we find a similarly sized change in “PMP terms” (+77 euro income or reduction in costs), i.e. the behavioural terms adjust. As a note, we see that for this region, grassland is highly unprofitable in the reference scenario, and therefore the PMP term is calibrated to become a marginal //income//. The PMP effect is decomposed ((This decomposition of changes in the PMP level contributions is not available in all CAPRI versions.))into a change in the constant term (brought about by a shift between GRAI and GRAE), diagonal terms (the main effect, 65 euro) and cross effects (zero in this case). In Figure 39, we can look for the biggest changes (absolute difference between simulations is printed in brackets in the right hand column), and find a 71 euro reduction in payments. This is in line with the scenario, which removed 30% of the farm payment. All other positions adjust to some extent, and as usual with CAPRI, we find a similarly sized change in “PMP terms” (+77 euro income or reduction in costs), i.e. the behavioural terms adjust. As a note, we see that for this region, grassland is highly unprofitable in the reference scenario, and therefore the PMP term is calibrated to become a marginal //income//. The PMP effect is decomposed ((This decomposition of changes in the PMP level contributions is not available in all CAPRI versions.))into a change in the constant term (brought about by a shift between GRAI and GRAE), diagonal terms (the main effect, 65 euro) and cross effects (zero in this case).
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 **Figure 38: Dual analysis of changes in pasture area (intensive and extensive) in a scenario removing the CAP greening components** **Figure 38: Dual analysis of changes in pasture area (intensive and extensive) in a scenario removing the CAP greening components**
  
-{{::figure_38.png?600|}} \\ Source: CAPRI modelling system +{{::figure_38.png?600|Source: CAPRI modelling system}} 
  
 There are more adjustments. Also land rents drop by 18 euro, the output of grass shown in the first line increases in value by 25 euro. Both variable costs and the value of the fertilizer restriction decrease, but those effects are comparatively minor. There are more adjustments. Also land rents drop by 18 euro, the output of grass shown in the first line increases in value by 25 euro. Both variable costs and the value of the fertilizer restriction decrease, but those effects are comparatively minor.
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 **Figure 39: Illustrative scenario comparison for the yield and income indicator decomposition** **Figure 39: Illustrative scenario comparison for the yield and income indicator decomposition**
  
-{{::figure_39.png?600|}} \\ Source: CAPRI modelling system Note: The code is implemented in „reports\yield_change_decomp”. The table can be found in the GUI under “farm => yield decomposition”. +{{::figure_39.png?600|Source: CAPRI modelling system Note: The code is implemented in „reports/yield_change_decomp”. The table can be found in the GUI under “farm => yield decomposition”. }} 
  
 It should be mentioned that the coding does not use information about potential changes of the premiums paid in scenario against the baseline, so that the results shown for the modified Gross Value Added should mainly show the different percentage changes on income once the level effect of the premiums is considered. The coding also stems from the time before climate related yield shock sceanrios or endogenous mitigation modelling. As the yield decomposition probably has not been regularly checked in this kind of scenarios, it is possible that some changes implemented in the CAPRI supply models need to be transferred to this reporting code to give reliable results in all kind of scenarios. It should be mentioned that the coding does not use information about potential changes of the premiums paid in scenario against the baseline, so that the results shown for the modified Gross Value Added should mainly show the different percentage changes on income once the level effect of the premiums is considered. The coding also stems from the time before climate related yield shock sceanrios or endogenous mitigation modelling. As the yield decomposition probably has not been regularly checked in this kind of scenarios, it is possible that some changes implemented in the CAPRI supply models need to be transferred to this reporting code to give reliable results in all kind of scenarios.
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 The behaviour of the CAPRI supply model depends on the interaction between endogenous variables such as production and feeding activities both via the objective function and the constraints. Even if the dual analysis of the results may help, understanding the changes in levels of endogenous variables compared to the baseline is far from trivial. As the directions and size of certain impacts on the reported changes is not known, result analysis carries a high risk of mis-interpretations. A specific optional reporting tool has been developed therefore and is accessible though the CAPRI GUI (farm => supply model analysis) that systematically analyses the contributions of the different allocative mechanisms in the model. The basic idea consists in evaluating how the result at given prices would have looked like if certain endogenous model features would not have been used. The behaviour of the CAPRI supply model depends on the interaction between endogenous variables such as production and feeding activities both via the objective function and the constraints. Even if the dual analysis of the results may help, understanding the changes in levels of endogenous variables compared to the baseline is far from trivial. As the directions and size of certain impacts on the reported changes is not known, result analysis carries a high risk of mis-interpretations. A specific optional reporting tool has been developed therefore and is accessible though the CAPRI GUI (farm => supply model analysis) that systematically analyses the contributions of the different allocative mechanisms in the model. The basic idea consists in evaluating how the result at given prices would have looked like if certain endogenous model features would not have been used.
  
-However due to various code changes in the last years it cannot guaranteed that the reporting option it still fully operational such that we refer the interested reader to earier versions of this documentation((A more complete version of this section (originally drafted by Wolfgang Britz) in the CAPRI documentation is accessible in the \doc folder of any stable release of the CAPRI system up to star 2.4 from [[https://www.capri-model.org/dokuwiki/doku.php?id=capri:get-capri]].)).+However due to various code changes in the last years it cannot guaranteed that the reporting option it still fully operational such that we refer the interested reader to earier versions of this documentation((A more complete version of this section (originally drafted by Wolfgang Britz) in the CAPRI documentation is accessible in the /doc folder of any stable release of the CAPRI system up to star 2.4 from [[https://www.capri-model.org/dokuwiki/doku.php?id=capri:get-capri]].)).
  
 =====Welfare analysis===== =====Welfare analysis=====
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 A key element in analysing policy changes from an economic viewpoint is to look at welfare changes. The “classical” elements of a welfare analysis are changes in consumer and producer rents and for the tax payer. That concept is also followed in CAPRI. A key element in analysing policy changes from an economic viewpoint is to look at welfare changes. The “classical” elements of a welfare analysis are changes in consumer and producer rents and for the tax payer. That concept is also followed in CAPRI.
  
-For consumers, CAPRI uses the money metric concept. It can be broadly understood as a measurement for changes in the purchasing power of the consumer. The concept is also linked to the expenditure function as introduced in Section [[Market module for agricultural outputs#Behavioural equations for final demand]]:+For consumers, CAPRI uses the money metric concept. It can be broadly understood as a measurement for changes in the purchasing power of the consumer. The concept is also linked to the expenditure function as introduced in Section [[scenario simulation#Behavioural equations for final demand]]:
  
 \begin{equation} \begin{equation}
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 Where //e//(.) is the expenditure function, \(Y^r\) is expenditure in the reference situation, and \(cpri^r (cpri^s)\) is the price vector in the reference (scenario) situation. The money metric is thus the expenditure the consumer would need at reference prices to be as well of as if facing the scenario prices at reference income. The difference of money metric for a given scenario to money metric in the reference situation is the equivalent variation.  Where //e//(.) is the expenditure function, \(Y^r\) is expenditure in the reference situation, and \(cpri^r (cpri^s)\) is the price vector in the reference (scenario) situation. The money metric is thus the expenditure the consumer would need at reference prices to be as well of as if facing the scenario prices at reference income. The difference of money metric for a given scenario to money metric in the reference situation is the equivalent variation. 
-Considering the generalised Leontief form of the indirect utility function used in CAPRI (compare with Section [[Market module for agricultural outputs#Behavioural equations for final demand]]) we get +Considering the generalised Leontief form of the indirect utility function used in CAPRI (compare with Section [[scenario simulation#Behavioural equations for final demand]]) we get 
  
 \begin{align} \begin{align}
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 \end{align} \end{align}
  
-The bracket {} is the equivalent variation of the change from the reference to the scenario situation. In the code (in //gams\reports\welfare.gms//) the momey metric is computed in a loop over products such that the contributions of single products are displayed((As this is basically the computation of a line integral the contributions of the single products depend on the sequence of products used in the loop, but we do not expect that this path dependency would significantly change the contributions by product. And for the total effect there is no path dependency.)). It is also considered that the per capita demand needs to be multiplied with population. +The bracket {} is the equivalent variation of the change from the reference to the scenario situation. In the code (in //gams/reports/welfare.gms//) the momey metric is computed in a loop over products such that the contributions of single products are displayed((As this is basically the computation of a line integral the contributions of the single products depend on the sequence of products used in the loop, but we do not expect that this path dependency would significantly change the contributions by product. And for the total effect there is no path dependency.)). It is also considered that the per capita demand needs to be multiplied with population. 
  
-{{::code_p232.jpg?600|}}+{{::code_p232.jpg?600}}
  
 For some agents the welfare accounting involves particularities for those regions covered by the regional programming models that will be discussed below. In the general case (for “non supply model regions”) producer welfare may be derived in the market model from the normalised profit function underlying the supply functions and the input demands for feed energy and land:  For some agents the welfare accounting involves particularities for those regions covered by the regional programming models that will be discussed below. In the general case (for “non supply model regions”) producer welfare may be derived in the market model from the normalised profit function underlying the supply functions and the input demands for feed energy and land: 
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 \end{equation} \end{equation}
  
-In the code (in //gams\reports\welfare.gms//) we are also considering that it is necessary to revert the normalisaiton of prices with the general price index+In the code (in //gams/reports/welfare.gms//) we are also considering that it is necessary to revert the normalisaiton of prices with the general price index
  
-{{:code_p233.jpg?600|}}+{{:code_p233.jpg?600}}
  
 CAPRI uses the normalised quadratic form also for other agents, such that the calculation of welfare changes is very similar for  CAPRI uses the normalised quadratic form also for other agents, such that the calculation of welfare changes is very similar for 
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 **Figure 40: System border and processes considered for CAPRI energy input assessment** **Figure 40: System border and processes considered for CAPRI energy input assessment**
  
-{{::figure_40.png?600|}} \\ Source: Based on ecoinvent (2003)+{{::figure_40.png?600|Source: Based on ecoinvent (2003)}} 
  
 The connecting link between process-based material flows and the energy requirement analysis are energy content factors. Life cycle inventories of agricultural production systems are the necessary tool therefore. The role of inventories such as ecoinvent (2003) is to provide modules for infrastructure and inputs used in agricultural production necessary for modelling production systems. In the case of the CAPRI energy module, several aspects concerning inventories had to be considered. On the one hand, a broad range of different sources provide inventory databases designed for different countries in the agricultural context. On the other hand, to use a uniform methodological basis, a basic decision for inventories analysed by ecoinvent (2003) was taken. Firstly, a great number of single inventories (direct and indirect energy sources as well as agricultural processes such as drying or irrigation) had been analysed. Secondly, the inventories being used are updated regularly and by using SALCA061 (2006) database for CAPRI energy indicator, a most recent version of the inventories was used. Thirdly, special analysis for the CAPRI energy module such as quantifying  energy for stables for animal production activities was carried out using the underlying methodology of ecoinvent database in order to consider specifics of CAPRI. Nevertheless, the ecoinvent agricultural inventories have been compiled mainly in a Swiss context using background data of Swiss agriculture. In order to use these inventories for CAPRI, some adjustments have been made. Some minor differences in the energy assessment between CAPRI energy module and other literature sources cannot be avoided. The reason might be in the reference period of the data (most literature data is of some years age) or in the Swiss-based approach of the inventories. The connecting link between process-based material flows and the energy requirement analysis are energy content factors. Life cycle inventories of agricultural production systems are the necessary tool therefore. The role of inventories such as ecoinvent (2003) is to provide modules for infrastructure and inputs used in agricultural production necessary for modelling production systems. In the case of the CAPRI energy module, several aspects concerning inventories had to be considered. On the one hand, a broad range of different sources provide inventory databases designed for different countries in the agricultural context. On the other hand, to use a uniform methodological basis, a basic decision for inventories analysed by ecoinvent (2003) was taken. Firstly, a great number of single inventories (direct and indirect energy sources as well as agricultural processes such as drying or irrigation) had been analysed. Secondly, the inventories being used are updated regularly and by using SALCA061 (2006) database for CAPRI energy indicator, a most recent version of the inventories was used. Thirdly, special analysis for the CAPRI energy module such as quantifying  energy for stables for animal production activities was carried out using the underlying methodology of ecoinvent database in order to consider specifics of CAPRI. Nevertheless, the ecoinvent agricultural inventories have been compiled mainly in a Swiss context using background data of Swiss agriculture. In order to use these inventories for CAPRI, some adjustments have been made. Some minor differences in the energy assessment between CAPRI energy module and other literature sources cannot be avoided. The reason might be in the reference period of the data (most literature data is of some years age) or in the Swiss-based approach of the inventories.
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 ====Energy assessment in CAPRI==== ====Energy assessment in CAPRI====
  
-To integrate the methodology which is described in Chapter [[Farm Structure Units – FSU#Delineation]] into CAPRI, two parts are required for each single energy input component: an activity-specific, regionalized consumption quantity and an equivalent assessment factor. The following chapters present both parts for each input component integrated into CAPRI.+To integrate the methodology which is described in Chapter [[spatial_dis-aggregation_capdis_module#Delineation]] into CAPRI, two parts are required for each single energy input component: an activity-specific, regionalized consumption quantity and an equivalent assessment factor. The following chapters present both parts for each input component integrated into CAPRI.
  
 ===Direct energy sources=== ===Direct energy sources===
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 |SCOW |YCAM: 44 |YCAF: 44 |BEEF: 8 |COMF: 4 |  -  | |SCOW |YCAM: 44 |YCAF: 44 |BEEF: 8 |COMF: 4 |  -  |
 |SOWS |YPIG: 100 |PORK: 0 |  -  |  -  |  -  | |SOWS |YPIG: 100 |PORK: 0 |  -  |  -  |  -  |
-|SHGM |SGMI: 50 |YLAM: 30 |SGMF: 10 |SGMT: 10 |  -  | +|SHGM |SGMI: 50 |YLAM: 30 |SGMF: 10 |SGMT: 10 |  -  | \\ Source: CAPRI Modelling System
-Source: CAPRI Modelling System+
  
 ====Analysis of CAPRI energy module results==== ====Analysis of CAPRI energy module results====
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 **Figure 41: Energy parameters: examples for results displaying** **Figure 41: Energy parameters: examples for results displaying**
  
-{{::figure_41_1.png?600|}} \\ Example 1: Energy consumption - overview \\ +{{::figure_41_1.png?600|}} 
-{{::figure_41_2.png?600|}} \\ Example 2: Energy consumption - detailed \\ + 
-{{::figure_41_3.png?600|}} \\ Example 3: Energy parameters with reference to the product \\ +Example 1: Energy consumption - overview 
-{{::figure_41_3.png?600|}} \\ Example 4: Energy parameters: Sectoral balances \\ Source: CAPRI Modelling System+ 
 +{{::figure_41_2.png?600|}} 
 + 
 +Example 2: Energy consumption - detailed 
 + 
 +{{::figure_41_3.png?600|}} 
 + 
 +Example 3: Energy parameters with reference to the product 
 + 
 +{{::figure_41_3.png?600|}} 
 + 
 +Example 4: Energy parameters: Sectoral balances ;Source: CAPRI Modelling System
  
 Taking Example 1, the results of the energy consumption overview table are shown. This can be explored within the scenario exploitation table. Beside “Total MJ”, which indicates energy consumption per ha or head, a number of energy consumption categories such as diesel, electricity, machinery, fertiliser, young animals, seed and plant protection can be displayed either on a MJ basis or in metric units. Furthermore as can be seen in Example 2, detailed data on energy consumption can be displayed if required. Data on feeding stuff, housing systems, grassland use, tillage systems, machinery use, irrigation, greenhouse use, seed, plant protection, drying etc. can be shown activity- and region specific. Beside regarding the area or the animal, the product can be chosen as a reference point. As shown in Example 3, energy requirements per kg of product (expressed in MJ/kg) and domestic en-ergy efficiency (expressed in MJ/MJ) can be shown. On a sectoral basis, efficiency related to the in-come (expressed in MJ/€) is displayed. A sectoral balance can be extracted as shown in Example 4 summing up all energy requirements and all energy output. Taking Example 1, the results of the energy consumption overview table are shown. This can be explored within the scenario exploitation table. Beside “Total MJ”, which indicates energy consumption per ha or head, a number of energy consumption categories such as diesel, electricity, machinery, fertiliser, young animals, seed and plant protection can be displayed either on a MJ basis or in metric units. Furthermore as can be seen in Example 2, detailed data on energy consumption can be displayed if required. Data on feeding stuff, housing systems, grassland use, tillage systems, machinery use, irrigation, greenhouse use, seed, plant protection, drying etc. can be shown activity- and region specific. Beside regarding the area or the animal, the product can be chosen as a reference point. As shown in Example 3, energy requirements per kg of product (expressed in MJ/kg) and domestic en-ergy efficiency (expressed in MJ/MJ) can be shown. On a sectoral basis, efficiency related to the in-come (expressed in MJ/€) is displayed. A sectoral balance can be extracted as shown in Example 4 summing up all energy requirements and all energy output.
post_model_analysis.1587191424.txt.gz · Last modified: 2022/11/07 10:23 (external edit)
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