scenario_simulation
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scenario_simulation [2023/08/25 07:54] – [Detailed discussion of the equations in the supply model] massfeller | scenario_simulation [2023/08/25 08:20] – [Annex: Land supply and land transitions in the supply part of CAPRI] massfeller | ||
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- | ====Annex: Land supply and land transitions | + | ====== LULUCF |
- | **Introduction** | + | ===== Introduction |
- | This technical paper explains how the most aggregate level of the CAPRI area allocation in the context of the supply models has been re-specified in the TRUSTEE< | + | This technical paper explains how the most aggregate level of the CAPRI area allocation in the context of the supply models has been re-specified in the TRUSTEE |
+ | )) and SUPREMA | ||
During the subsequent period, CAPRI was increasingly adapted to analyses of greenhouse gas (GHG) emission studies. Examples include CAPRI-ECC, GGELS, ECAMPA-X, AgCLim50-X, (European Commission, Joint Research Centre), ClipByFood (Swedish Energy Board), SUPREMA (H2020). This vein of research is very likely to gain in importance in the future. | During the subsequent period, CAPRI was increasingly adapted to analyses of greenhouse gas (GHG) emission studies. Examples include CAPRI-ECC, GGELS, ECAMPA-X, AgCLim50-X, (European Commission, Joint Research Centre), ClipByFood (Swedish Energy Board), SUPREMA (H2020). This vein of research is very likely to gain in importance in the future. | ||
In order to improve land related climate gas modelling within CAPRI, it was deemed appropriate to (1) extend the land use modelled to //all// available land in the EU (i.e. not only agriculture), | In order to improve land related climate gas modelling within CAPRI, it was deemed appropriate to (1) extend the land use modelled to //all// available land in the EU (i.e. not only agriculture), | ||
- | )), but as always, an operational version emerged only after integrating efforts by researchers in several projects working at various institutions. | + | )), but as always, an operational version emerged only after integrating efforts by researchers in several projects working at various institutions. Within the SUPREMA project another important change in the depiction of land use change was made: the Markov chain approach was replaced by prespecifying the total land transitions as average transitions per year times the projection. This paper focusses on the theory applied while data and technical implementation are only briefly covered. |
- | This paper focusses on the theory applied while data and technical implementation are only briefly covered. | ||
- | **A simple theory of land supply** | + | ===== A simple theory of land supply |
Recall the dual methodological changes attempted in this paper: | Recall the dual methodological changes attempted in this paper: | ||
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The model is somewhat complicated by the fact that land use classes in CAPRI are defined somewhat differently compared to the UNFCCC accounting and also in the land transition data set. Therefore, some of the land classes used in the land transitions are different from the ones used in the land supply model. In particular, “Other land”, “Wetlands” and “Pasture” are differently defined. To reconcile the differences, | The model is somewhat complicated by the fact that land use classes in CAPRI are defined somewhat differently compared to the UNFCCC accounting and also in the land transition data set. Therefore, some of the land classes used in the land transitions are different from the ones used in the land supply model. In particular, “Other land”, “Wetlands” and “Pasture” are differently defined. To reconcile the differences, | ||
- | **Inner model – land transitions** | + | ===== Inner model – transitions |
A vector of supply of land of various types could result from a wide range of different transitions. The inner model determines the matrix of land transitions that is “most likely”. The concept of “most likely” is formalized by assuming a joint density function for the land transitions, | A vector of supply of land of various types could result from a wide range of different transitions. The inner model determines the matrix of land transitions that is “most likely”. The concept of “most likely” is formalized by assuming a joint density function for the land transitions, | ||
+ | |||
+ | ==== Gamma density ==== | ||
Since each transition is non-negative, | Since each transition is non-negative, | ||
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$$\text{variance} = \frac{\alpha}{\beta^{2}}$$ | $$\text{variance} = \frac{\alpha}{\beta^{2}}$$ | ||
- | **Land use transitions | + | ==== Annual |
The implementation in CAPRI differs from the above general framework in that it explicitly identifies the //annual// transitions in year t $T_{\text{lk}}^{t}$ from the initial $\text{LU}_{l}^{\text{initial}}$ land use to the final land use $\text{LU}_{k}$. This is necessary to identify the annual carbon effects occurring only in the final year in order to add them to the current GHG emissions, say from mineral fertiliser application in the final simulation year. If the initial year is the base year = 2008 and projection is for 2030, then the carbon effects related to the change from the 2008 $\text{LU}_{l}^{\text{initial}}$ to the final land use $\text{LU}_{k}$ (=$T_{\text{lk}}$in the above notation, without time index) refer to a period of 22 years that cannot reasonably be aggregated with the “running” non-CO2 effects from the final year 2030. Furthermore the historical time series used to determine the mode of the gamma density for the transitions also refer to annual transitions. | The implementation in CAPRI differs from the above general framework in that it explicitly identifies the //annual// transitions in year t $T_{\text{lk}}^{t}$ from the initial $\text{LU}_{l}^{\text{initial}}$ land use to the final land use $\text{LU}_{k}$. This is necessary to identify the annual carbon effects occurring only in the final year in order to add them to the current GHG emissions, say from mineral fertiliser application in the final simulation year. If the initial year is the base year = 2008 and projection is for 2030, then the carbon effects related to the change from the 2008 $\text{LU}_{l}^{\text{initial}}$ to the final land use $\text{LU}_{k}$ (=$T_{\text{lk}}$in the above notation, without time index) refer to a period of 22 years that cannot reasonably be aggregated with the “running” non-CO2 effects from the final year 2030. Furthermore the historical time series used to determine the mode of the gamma density for the transitions also refer to annual transitions. | ||
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$$\Leftrightarrow 1 = \sum_{k}^{}P_{\text{lk}}$$ | $$\Leftrightarrow 1 = \sum_{k}^{}P_{\text{lk}}$$ | ||
- | So the simple condition is that probabilities have to add up to one (e_addUpTransMatrix in supply_model.gms). In this form the model is currently implemented in CAPRI. | + | So the simple condition is that probabilities have to add up to one (e_addUpTransMatrix in supply_model.gms). |
- | **Outer model – land supply** | + | ==== Annual transitions if SUPREMA is active ==== |
+ | As the use of the Marcov-chain approach allows the annual transitions to be explicit model variables that could be used to compute annual carbon effects but leads to computational limitations especially in the market model a new approach was developed under SUPREMA (i.e. if %supremaSup% == on) by re-specifying the total land transitions as average transitions per year times the projection horizon and by considering for the remaining class without land use change (on the diagonal of the land transition matrix) only the annual carbon effects per ha, relevant for the case of gains via forest management. | ||
+ | |||
+ | The new accounting in the CAPRI global supply model may be explained as follows, starting from a calculation of the total GHG effects G over horizon h = t-s from total land transitions L< | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | ===== Outer model – land supply ===== | ||
The outer problem is defined as a maximization of the sum of land rents minus a quadratic cost term, subject to the first order optimality conditions of the inner problem: | The outer problem is defined as a maximization of the sum of land rents minus a quadratic cost term, subject to the first order optimality conditions of the inner problem: | ||
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The key equations corresponding to the approach explained above are collected in file supply_model.gms or the included files supply/ | The key equations corresponding to the approach explained above are collected in file supply_model.gms or the included files supply/ | ||
- | The new land supply specification is only activated if the global variable %trustee_land%==on which may be set via the CAPRI GUI. In order to store the results of the calibration in a compact way that is compatible with the existing code, the existing parameter files “pmppar_XX.gdx” was used. The parameters of the land supply functions, called “c” and “D” above, were stored on two parameters “p_pmpCnstLandTypes” and “p_pmpQuadLandTypes”. As a new symbol (p_pmpCnstLandTypes) is introduced in an existing file, the first run of CAPRI after setting %trustee_land%==on may give errors if the file exists already but has been used with the previous land supply specification before. In this case it helps to delete or rename the old pmppar files. | + | // |
At this point, it should also be explained that rents for non-agricultural land types were entirely based on assumptions (a certain ratio to agricultural rents). As there were no plans to run scenarios with modified non-agricultural rents, these land rents //r// used in calibration for those land types were subtracted from the “c-paramter”, | At this point, it should also be explained that rents for non-agricultural land types were entirely based on assumptions (a certain ratio to agricultural rents). As there were no plans to run scenarios with modified non-agricultural rents, these land rents //r// used in calibration for those land types were subtracted from the “c-paramter”, | ||
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More detailed explanations on the technical implementation are covered elsewhere, for example in the “Training material” included in the EcAMPA-4 deliverable D5. | More detailed explanations on the technical implementation are covered elsewhere, for example in the “Training material” included in the EcAMPA-4 deliverable D5. | ||
+ | |||
+ | Concerning the improvements made under SUPREMA from a technical perspective, | ||
+ | |||
+ | ===== Emission Equations ===== | ||
+ | |||
+ | Under EcAMPA 3 and partly in earlier projects (inter alia EcAMPA 2) new modelling outputs have been developed for indicators without matching reporting infrastructure helping users to organise the additional information. This applied for example to | ||
+ | |||
+ | 1) Additional CAPRI results on land use results related to the complete area coverage, mappings to UNFCCC area categories and their transitions; | ||
+ | |||
+ | 2) The carbon effects linked to these land transitions. | ||
+ | |||
+ | Furthermore, | ||
+ | |||
+ | The scenarios including the emission equations are only run if %ghgabatement% == on, otherwise emissions are only calculated and not simulated. | ||
+ | |||
+ | The following emission equations have been implemented: | ||
+ | |||
+ | ^**Code** | ||
+ | |GWPA |Agricultural emissions | ||
+ | |CH4ENT | ||
+ | |CH4MAN | ||
+ | |CH4RIC | ||
+ | |N2OMAN | ||
+ | |N2OAPP | ||
+ | |N2OGRA | ||
+ | |N2OSYN | ||
+ | |N2OCRO | ||
+ | |N2OAMM | ||
+ | |N2OLEA | ||
+ | |N2OHIS | ||
+ | |GLUC |Emissions related to indirect land use changes | ||
+ | |CO2BIO | ||
+ | |CO2SOI | ||
+ | |CO2HIS\\ \\ CH4HIS|Carbon dioxide emissions from the cultivation of histosols\\ \\ Methane emissions from cultivation of histosols| | ||
+ | |CO2LIM\\ \\ CO2BUR|Carbon dioxide emissions from limestone and dolomit\\ \\ Carbon dioxide emissions from burning | ||
+ | |CH4BUR | ||
+ | |N2OBUR | ||
+ | |N2OSOI | ||
+ | |GPRD |Emissions related to the production of non-agricultural inputs to agriculture | ||
+ | |N2OPRD | ||
+ | |O2PRD | ||
=====Premium module===== | =====Premium module===== |
scenario_simulation.txt · Last modified: 2023/09/08 12:07 by massfeller