Improved modelling framework for assessing the interactions between the energy, agriculture, forestry and land use change sectors : integrating the CAPRI, LUISA-BEES, CBM and POTEnCIA models.
Date: 2021-01-29
Institutions: Joint Research Centre
Subjects: biofuel, economic model, energy policy, farming sector, forestry policy, greenhouse gas, land use, reduction of gas emissions, research method, research report, sustainable development

Improved Modelling Framework for 
Assessing the Interactions between the 
Energy, Agriculture, Forestry and Land 
Use Change Sectors 

Integrating the CAPRI, LUISA-BEES, CBM and POTEnCIA Models 

Amarendra Sahoo, Ignacio Pérez Domínguez,  
Sarah Mubareka, Giulia Fiorese, Giacomo Grassi, 
Roberto Pilli, Mihaly Himics, Viorel Blujdea,  
Marco Follador, Frederik Neuwahl, Raffele Salvucci, 
Máté Rozsai, Peter Witzke, Monika Kesting 

2021 

EUR 30514 EN 

 
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It 
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JRC123172 

EUR 30514 EN 

PDF 

ISBN 978-92-76-27386-8 

ISSN 1831-9424 

doi:10.2760/900305 

Luxembourg: Publications Office of the European Union, 2021  

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How to cite this report: Sahoo A., Pérez Domínguez I., Mubareka S., Fiorese G., Grassi, G., Pilli, R., Himics M., Blujdea, V., Follador M., Neuwahl F., Salvucci R., 
Rozsai M., Witzke P., Kesting M., Improved modelling framework for assessing the interactions between energy, agriculture forestry and land use change 
sectors: integrating CAPRI, LUISA-BEES, CBM and POTEnCIA, EUR 30514 EN, Publications Office of the European Union, Luxembourg, 
2021, ISBN 978-92-76-27386-8, doi:10.2760/900305, JRC123172. 

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Table of Contents 

1 

Introduction..................................................................................................................................................................................................................................................... 4 

2  Modelling Systems in AFOLU ......................................................................................................................................................................................................... 5 

2.1  Modelling Agriculture: CAPRI ............................................................................................................................................................................................. 5 

2.2  Modelling Forestry: CBM ....................................................................................................................................................................................................... 5 

2.3  Modelling Land Use and Land Use Change: LUISA-BEES...................................................................................................................... 6 

2.4  Energy System Model: POTEnCIA ................................................................................................................................................................................. 7 

3  Proposed Integrated Scenarios or the analysis of the AFOLU sectors .................................................................................................. 8 

4  Model Harmonization and Implementation .................................................................................................................................................................. 10 

4.1  Land Use Change: CAPRI and LUISA-BEES ....................................................................................................................................................... 10 

4.2  Forest simplified modelling: CAPRI and CBM ................................................................................................................................................. 12 

4.3  Biofuel: CAPRI and POTEnCIA ........................................................................................................................................................................................ 13 

5  Conclusions .................................................................................................................................................................................................................................................. 16 

References ............................................................................................................................................................................................................................................................. 18 

List of abbreviations and definitions ........................................................................................................................................................................................... 20 

List of figures ..................................................................................................................................................................................................................................................... 22 

List of tables ........................................................................................................................................................................................................................................................ 22 

Annexes .................................................................................................................................................................................................................................................................... 23 

Annex 1. Land use change in CAPRI and LUISA-BEES ........................................................................................................................................ 23 

Annex 2. Methods for estimating emission factors from CBM .................................................................................................................. 25 

 
Abstract 

This report is an attempt to develop a modelling framework integrating different sectoral stand-alone models used at the JRC 
for  policy  impact  assessment  in  the  fields  of  agriculture,  forestry,  land  use  change  and  energy.  The  proposed  quantitative 
framework  should  improve  the  capability  of  assessing  greenhouse  gas  emissions  and  removals  resulting  from  complex 
interactions between the agriculture, forestry, and other land use (AFOLU) sectors, and facilitate the analysis of policy scenarios 
relevant  for  a  sustainable  and  carbon-neutral  European  economy.  Four  models  are  considered,  for  which  a  revised  model 
specification  and  harmonization  of  relevant  databases  and  model  parameters  is  needed.  The  Common  Agricultural  Policy 
Regionalized Impact (CAPRI) Modelling System is a widely used large-scale multi-commodity agricultural economic model. The 
Land Use-based Integrated Sustainability Assessment modelling platform for BioEconomy and Ecosystem Services (LUISA-BEES) 
is primarily used for the ex-ante evaluation of European policies that have a direct or indirect territorial impact on the agricultural 
and forestry sectors. The Carbon Budget Model (CBM) is a stand-alone forestry model that simulates forest carbon dynamics. 
The Policy Oriented Tool for Energy and Climate Change Impact Assessment (POTEnCIA) model depicts a detailed EU energy 
system combining both techno-economic modules.  As a ‘proof of integration’, this report describes the improvement  of the 
CAPRI land use function and harmonization of related database such as to be linked to the output from the LUISA-BEES model. 
Moreover, forestry area projections and related carbon removals in CAPRI are improved by using direct information from the 
CBM model. Last but not least, the POTEnCIA model is improved by parameterizing a first generation biofuel supply curve based 
on CAPRI simulations.  

In order to test the proposed modelling framework, the report proposes a set of exploratory policy scenarios based on each 
model’s  capabilities:  reform  of  the  Common  Agricultural  Policy,  expansion  of  biofuel  mandates  and  carbon  pricing  (CAPRI); 
implementation of spatially explicit sustainability criteria for  the plantation of energy crops and afforestation (LUISA-BEES), 
different levels of forest harvesting (CBM) and strong decarbonisation policies (POTEnCIA).  

1 

Introduction 

Globally, land use and forestry activities have been intensified and are expected to further expand to meet the future demand 
for food, feed and energy, but not without affecting the environment (Hurtt et al. 2020). Europe is not an exception will likely 
consume more biomass to meet its ambitious de-carbonization targets and scale-up its bio-economy sector. This additional 
biomass demand will be satisfied by increasing both domestic production and imports from third country supplies, with potential 
environmental impacts and spillovers still poorly understood (Fuchs et al., 2020). The agriculture, forestry and other land use 
(AFOLU) sectors are considered to be a significant net source of greenhouse gas (GHG) emissions globally, with land use change 
and forestry being responsible for both emissions and removals of carbon dioxide (CO2), and agriculture representing a major 
source  of  methane  (CH4)  and  nitrous  oxide  (N2O)  emissions.  A  study  by  Strapasson  et  al.  (2020)  recognized  the  under-
representation of land use as major option for carbon mitigation in EU policy and observed major long run mitigation impacts 
from reducing meat consumption as well as using efficient cropping techniques and re-allocating land mainly to forests and soil 
carbon storage. One of the main objectives of this report is to refine and improve the representation of existing methodologies 
covering emissions and removals from agriculture, including biofuel crops, and to better account for emissions and removals 
from forestry and land use change in the EU.  

Within the JRC, several efforts have been done over the years to assess CO2 emissions and removals in the AFOLU sector, such 
as the EcAMPA 3 study (Pérez-Domínguez et al, 2020) that incorporated endogenous GHG mitigation technologies in the CAPRI 
model  and  a  preliminary  inclusion  of  'Land  Use,  Land  Use  Change  and  Forestry'  (LULUCF)  emissions/sinks  following  simple 
methods from the Intergovernmental Panel on Climate Change (IPCC) to the United Nations Framework Convention on Climate 
Change (UNFCCC). However, all these efforts were not harmonized and not tested within a specific policy scenario framework. 
Moreover, they were not framed within the renovated efforts of the Green Deal to move towards a more sustainable and carbon-
neutral European economy, which requires important changes to the way energy is produced.  

The proposed AFOLU integrated scenario framework builds on the respective strengths of different in-house models in the areas 
of agriculture, land use, forestry and energy. With this idea, active links between the Common Agricultural Policy Regionalized 
Impact (CAPRI) modelling system, the Land Use-based Integrated Sustainability Assessment modelling platform for BioEconomy 
and Ecosystem Services (LUISA—BEES), the Carbon Budget Model (CBM) and the Policy Oriented Tool for Energy and Climate 
Change  Impact  Assessment  (POTEnCIA)  are  considered.  CAPRI  is  a  large-scale  partial  equilibrium  economic  model  for  the 
agricultural  for  sector  suitable  for  comparative  static  ex-ante  impact  analysis  of  various  agricultural  policies  (including 
environmental and climate change). It provides detailed farm level economic analysis for the EU while integrating at the global 
level through trade (www.capri-model.org). The Carbon Budget Model (CBM) is an inventory-based, yield-data driven model that 
simulates forest carbon dynamics and harvestable wood. The LUISA-BEES model provides estimates on land use change under 
different biomass demand scenarios, with a focus on agriculture and forested land. Last but not least, POTEnCIA is a modelling 
tool for energy system, designed to evaluate the impacts of alternative energy futures and policy instruments including, e.g., 
those fostering the replacement of fossil fuels with biomass and other renewables.  

4 

 
The AFO-CC study (Fiorese et al., 2015) was a first attempt to design an integrated modelling framework for the land-use sector 
built on stand-alone models in use at JRC and on their linkages. It was designed to link the POLES model (macro-economic data 
and the energy sector), the LUISA platform (land use changes), the CBM-CFS3 (forestry) and the CAPRI model (agriculture). In 
the context of the Biomass project, the forest sector has been modelled trying to capture both the demand side, with GFTM 
(Global Forest Trade Model), and the supply side, with CBM (Carbon Budget Model) (Jonsson et al., 2018, 2021). 

The novelty of the proposed AFOLU framework is not only to harmonize and update the databases and specifications of these 
stand-alone models, but also to facilitate the implementation of several integrated policy scenarios, i.e. reform of the Common 
Agricultural Policy, expansion of biofuel mandates, carbon pricing, implementation of spatially explicit sustainability criteria for 
the plantation of energy crops and biofuel feedstock, afforestation policies, different levels of forest harvesting and strong 
decarbonisation policies among others. The main elements that allow these four models to ‘communicate’ are  estimates of 
biomass demand  for energy  (including forest biomass for wood  products), biomass supply, agricultural and forest  land use 
allocation, food demand, land prices and forest dynamics, etc. (see Figure 1 in section 3). Beyond facilitating a novel integrated 
analysis of important policy questions, this exchange of information will improve the harmonization of model databases and 
results. 

2  Modelling Systems in AFOLU  

This  section  provides  a  brief  technical  description  of  the  stand-alone  models  that  support  the  AFOLU  integrated  modelling 
framework, namely the CAPRI, LUISA-BEEs, CBM and POTEnCIA models. 

2.1  Modelling Agriculture: CAPRI 

CAPRI  model  is  a  large-scale  economic,  global  comparative  static  multi-commodity,  agricultural  sector  model  including  47 
primary and secondary products (Britz and Witzke, 2014). This model allows assessing the impacts of agricultural, environmental 
and trade policies on agricultural production, farm prices and income, global agricultural trade and the agri-environment. CAPRI 
has a supply module with 280 representative regional farm models (i.e. profit maximizing behaviour) covering the EU-27 and 
some other European countries1. This supply model is linked iteratively with a global multi-commodity market module covering 
all regions in the world. Thus, it has global coverage, but is partial in nature, ignoring potential interactions with non-agricultural 
sectors. The market module simulates supply, demand, and price changes in global markets considering international trade. The 
inputs include land to crop and livestock production from other sectors and intermediate inputs produced by the farms such as 
feed and young animals.  

The model has been designed for ex-ante impact assessment of agricultural, environmental and trade policies, e.g. subsidization 
of climate change mitigation technologies in the EU agricultural sector (Pérez Dominguez et al. 2016 and 2020; Fellmann et al. 
2018), EU bilateral trade proposals (Burrell et al. 2011) and the assessment of the  subsequent CAP reforms over time (EC, 
2018d). The specific structure of the CAPRI model is suitable for the analysis of  agri-environmental indicators, e.g. nutrient 
balances and GHG emissions, linked to changes in supply and demand triggered by policy changes. Some of the most recent 
studies have analysed exploratory scenarios looking at the potential of technologies for GHG emission mitigation (Van Doorslaer 
et al., 2014, 2015; Perez Dominguez et al, 2016), and have looked into the basic interactions between the LULUCF sectors in 
terms of GHG emissions and removals (Perez Dominguez et al, 2020). 

The carbon cycle model in CAPRI quantifies relevant carbon flows in the agricultural production process related to both livestock 
and crop production. The carbon cycle model applies to ‘cropland remaining cropland’ and ‘pasture land remaining in use. Carbon 
flows  and  CO2  emissions  from  land  use  changes  (LUC)  are  estimated  based  on  simpler  IPCC  tier  1  default.  Modelling  GHG 
emissions in CAPRI considers three types of pollutants: methane (CH4) from animal production, manure management and rice 
cultivation,  nitrous  oxide  (N2O)  from  agricultural  soils  and  manure  management,  and  carbon  dioxide  (CO2)  emissions  from 
agricultural soils. Land use change emissions includes CH4 and N2O emissions from burning biomass, CO2 emissions from below 
and above the ground biomass and soil carbon changes. 

2.2  Modelling Forestry: CBM 

The  CBM-CFS3  model,  developed  by  the  Canadian  Forest  Service  (CFS),  can  simulate  the  historical  and  future  stand-  and 
landscape-level C dynamics under different scenarios of harvest and natural disturbances (e.g. fires, storms), according to the 
standards described by the IPCC. Since 2009, the CBM-CFS3 has been tested and validated by the JRC, and adapted to the 
European forest conditions. CBM is an empiric model running on spatially referenced data (e.g., strata, defined at country or 
regional level, depending by the available data sources) (Kurz et. al., 2009, with CBM databases adapted to EU conditions, Pilli 
et al., 2018). It is currently applied to 26 EU Member States, both at country and NUTS2 level (Pilli et al., 2016a, 2016b, 2017, 
2018). CBM runs with annual time steps. 

1 EU-28 plus Turkey, Norway and the Balkan countries. 

5 

 
                                           
Based on the model framework, each forest stand is described by area, age and land classes and up to 10 classifiers based on 
administrative and ecological information and on silvicultural parameters (as forest composition and management strategy). A 
set of yield tables define the merchantable volume production for each species while species-specific allometric equations2 
convert merchantable volume production into aboveground biomass at stand-level. The model provides data on the net primary 
production (NPP), C stocks and fluxes, as the annual C transfers between pools and to the forest product sector with an annual 
time step. 

Other  than  the  results  related  to  the  carbon  stock  change  of  forests,  CBM  may  simulate  for  a  specific  set  of  silvicultural 
interventions rules that define forest management (i.e., thinnings of various intensities, clearcut, etc) and provide the amount of 
biomass  removed  from  forests.  Clearly,  there  is  no  a  priori  distinction  on  the  final  destination  of  the  harvested  biomass, 
specifically if it will be used for wood products or for energy production. The CBM model can potentially distinguish the total 
amount of harvest provided by forests at country or at regional level, according to different assumptions, distinguishing by 
species or species groupings (i.e., broadleaves and conifers), by wood components (i.e., stem, branches, tops and stumps), by 
management types (e.g., coppices, high forests), by silvicultural interventions type and intensity (e.g., thinnings, final cuts...). The 
implementation of all these further assumptions requires additional information provided by historical detailed data  – or by 
other models, if referred to future harvest scenarios - on the harvest and the industrial processes, collected at national level, 
which are currently not available. 

CBM integrates a soil module which initializes the carbon stock in nine dead organic matter pools through a semi-equilibrium 
procedure. The nine pools are further grouped in the IPCC DOM pools: dead wood, litter and organic matter in the mineral soils. 
Transfers between living biomass and DOM pools are implemented through turnovers specific to each biomass compartment. 
For the simulated period, CBM explicitly reports C stocks for each DOM pool with one-year time step. This allows estimating the 
change of the stock between successive years, and in a further step the values of the annual CO2 emissions or removals from 
DOM pools.  

The model is used to estimate the current and future forest carbon dynamics, both as a verification tool (i.e. to compare the 
results with the estimates provided by other models) and to support the current EU legislation (Grassi and Pilli, 2017; Grassi et 
al., 2018). In the biomass sector, the CBM-CFS3 can be used in combination with other models, to estimate the maximum wood 
potential and the forest carbon dynamic under different assumptions of harvest and land use change  (Jonsson et al., 2018, 
2021). 

2.3  Modelling Land Use and Land Use Change: LUISA-BEES 

The LUISA-BEES model is a fork of the LUISA territorial modelling platform described in Jacobs-Crisioni et al. (2017), modified 
for bioeconomy and ecosystem services applications. This model’s origins, as they were developed for the European Commission 
in 2010, are described in Perez-Soba et al., 2010. 

Land allocation in the LUISA-BEES model is based on the Dyna-CLUE model (Verburg et al., 2006; Verburg and Overmars, 2009). 
The main specifications are shown in Table 1. 

Table 1. Summary of LUISA-BEES specifications 

Spatial resolution 

100m 

Geographical coverage 

European Union  

Time step 

Base map 

5 years (with possibility 1 year) 

Corine land cover 2018 , modified for forest area to match 
SOEF statistics, using Copernicus forest map 

Range of simulation 

2020-2050 (with possibility to extend to 2070) 

The allocation is discrete, meaning each simulation unit, i.e. 1 ha cell, is assigned to a single land use type. What determines 
which land use is allocated, is a function of the suitability3 of location for a land use type, the competition with other land use 
types and the land use requirement (demand for land).  

2  

3  

Tree allometry establishes quantitative relations between some key characteristic dimensions of trees (usually fairly easy to measure, e.g. diameter at 
breast height) and other properties (often more difficult to assess, e.g. volume) (Wikipedia). In order to develop an allometric relationship there must be 
a strong relationship and an ability to quantify this relationship between the parts of the subject measured and the other quantities of interest. 
The ‘suitability’ can also be referred to as transition potential or land use value (or utility). 

6 

 
 
                                           
The total suitability is the result of a combination of factors that express the added value of allocating land to a specific land 
use purpose. Each land use has its specific set of suitability rules, which is the result of a set of physical factors, neighbourhood 
potential,  transition  costs,  location-specific  subsidies  or  taxes,  and  any  physical  restrictions  on  transition.  These  are  time-
dependant in the configuration to allow for different behaviour for transitional classes (e.g. “young forest”). 

The requirement for any given land area for given sectors (“demand) are exogenous to the model and are derived from different 
sources (Table 2). 

Table 2. Land use classes in LUISA-BEES and associated source of demand 

ID 
1 
2 

3 
4 
5 
6 

7 

8 

9 

Name 
Urban 
Industry 

Arable 
Permanent crops 
Pastures 
Mature forest 

Transitional woodland & 
burnt areas 

Abandoned arable 

Abandoned permanent crops 

10 

Abandoned pastures 

11  New energy crops 

12 

Semi-natural vegetation 

13 

Young forest 

14 

Infrastructure (ports, airports, 
roads) 
Other nature 
15 
16 
Salines, bogs and marshes 
17  Water courses, lagoons and 

estuaries 
Urban green 

18 

Source for demand 
Population growth (EUROPOP)  
Extrapolation of trends in industrial land 
expansion 
CAPRI model output 
CAPRI model output 
CAPRI model output 
Endogenous to model, no demand. Land 
becomes mature forest as the result of 
natural succession 
Corine 2018 land cover for first year of burnt 
areas and transitional woodland. For modelled 
years: no burnt areas subsequently; 
transitional woodland results after agricultural 
land is abandoned. 
Endogenous to model, when demand for 
arable land < actual arable land  
Endogenous to model, when demand for 
permanent crops < actual permanent crops 
Endogenous to model, when demand for 
pastures < actual pastures 
This is read from CAPRI model output, but is 
not available for all scenarios.  
Corine 2018 land cover for first year, no 
demand for this class but land may be taken 
from it 
Endogenous to model, no demand. Abandoned 
land becomes forested as the result of natural 
succession 
Not modelled 

Not modelled 
Not modelled 
Not modelled 

Not modelled 

Exogenous / endogenous 
Exogenous 
Exogenous 

Exogenous 
Exogenous 
Exogenous 
Endogenous 

Endogenous 

Endogenous 

Endogenous 

Endogenous 

Exogenous 

Endogenous 

Endogenous 

N/A 

N/A 
N/A 
N/A 

N/A 

2.4  Energy System Model: POTEnCIA 

The Policy Oriented Tool for Energy and Climate Change Impact Assessment (POTEnCIA) is a hybrid recursive dynamic partial 
equilibrium simulation model designed for comparative scenario analysis to evaluate  the impacts of alternative energy and 
climate policies on the energy sector in the EU(4). It combines behavioural decisions with detailed techno-economic data. The 
typical period that can be analysed by POTEnCIA is up to year 2050 in annual steps. It is intended to represent the economically 
driven operation of the European energy markets and the corresponding interactions of supply and demand. Each country is 
modelled  separately  as  to  appropriately  capture  the  existing  differences  in  energy  system  structures,  technological 
characteristics,  resource  constraints,  etc.  A  representative  agent  seeks  to  maximize  its  benefit  or  minimize  its  cost  under 
constraints related to behavioural preferences, technology availability, fuel availability, environmental considerations, etc. At the 

4  

This section is largely based on the JRC Technical Report on POTEnCIA model description (Mantzos and Wiesenthal, 2016). 

7 

 
 
                                           
level of the overall energy system, the model determines the equilibrium across the different sectors by means of price signals 
for all scarce resources (traditional energy carriers, renewable energy, other efficiency and environmental related costs in relation 
to their potentials). 

Given the complexity of the problem as such and taking advantage of the annual time steps in which the model solves, POTEnCIA 
makes use of the equilibrium prices with a one year lag. The model represents the physical energy-related equipment with an 
explicit structure of yearly vintages, whose characteristics dynamically evolve over time following technology evolution, while 
also talking into account possible premature scrapping and replacement of energy using equipment as well as the adoption of 
measures  (e.g.  insulation)  that  mainly  affect  the  operation  rather  than  the  technological  characteristics  of  the  equipment. 
Equipment stocks are updated on an annual basis taking into account the investment performed in each specific year. POTEnCIA 
is capable of addressing to several useful policy scenarios: 

 
Energy taxation, support schemes (e.g. subsidies on capital costs of cars), feed-in-tariffs 
  Minimum efficiency production standards (e.g. eco-design of energy-using products legislation) 
 
 
  Carbon market policies (Emission trading system; exogenous target carbon value or emissions achieved 

Targeting individual energy-using equipment 
Influencing consumer behaviour (decision making)  

by endogenous carbon values in the non-ETS sector; emission standards, etc.) 

3  Proposed Integrated Scenarios or the analysis of the AFOLU sectors 

Given the structure and potential of the individual models (see section 2) the current AFOLU scenario framework is built on 
(1) the existing capabilities of these models to analyse a certain sector and (2) ‘building bridges’ across these models so that 
endogenous model responses are transmitted to the neighbouring ones (Figure 1).  

Figure 1. Schematic structure and model links in the AFOLU scenario framework  

8 

                 CAPRI - No CAP policy  - Bio energy mandates  -GHG constraints/ Carbon tax LUISA-BEES  - Environmental protection policy  POTENCIA - Strong decarbonisation Supply of biofuels, prices  CBM - Different harvest level  Max Potential of forest biomass for energy purpose Land allocation, parameters land supply  Demand for land used for agricultural   purpose Demand Biofuel mandates from agriculture Biomass energy demand from forest  C stock change/CO2 emission factors for forestland, and its conversions, various pre-defined harvest levels  
 
 
The interaction between CAPRI and LUISA-BEES models will help improve the existing land allocation system in CAPRI. CAPRI will 
incorporate land allocation information from LUISA-BEES in the behavioural parameters for its land use supply function (see 
section 4.1 for details). In turn, the LUISA-BEES model will profit by incorporating the agriculture land conversion response of 
CAPRI  in  different  scenarios  (counterfactual  changes  to  the  baseline)  between  food  and  energy  crops.  In  summary,  any 
counterfactual  policy  shock  in  CAPRI  will  result  in  changes  in  demand  for  different  land  classes,  and  most  prominently  in 
agricultural land. It is the change in mix of how agricultural land is used in CAPRI that can be conveyed to the LUISA-BEES model 
to  re-compute  total  land  allocation  within  the  Utilisable  Agricultural  Area  (UAA).  LUISA-BEES  will  maintain  its  own  set  of 
allocation rules for competition between UAA and the other land use classes. Given the advantage of the CAPRI model being an 
economic model with a detailed agricultural commodity classification, capturing supply and demand interactions across different 
activities and incorporating global trade, the model is suitable for evaluating various scenarios (see above). The impacts from 
these  market  and  policy  shocks  are  non-linear  due  to  the  cross  effects  (substitution/complimentary)  between  agricultural 
activities, which triggers changes in production, yield and land demand for different crops. Currently, the CAPRI data is used as 
direct input to the land use model  of LUISA-BEES in the form of land requirements for the different classes of agricultural 
commodities. The mapping of CAPRI classes to LUISA-BEES classes is shown in Table A1.2 in Annex 1. CAPRI is more detailed 
regarding agriculture whereas LUISA-BEES is more disaggregated on non-agricultural areas 

The land use and forestry module in  CAPRI will incorporate a forestry baseline  that is harmonized with the  CBM model, for 
example on forest areas. Moreover, emissions/removal factors using forest carbon dynamics from CBM will  also be used by 
CAPRI for the accounting of GHGs. For scenario  purposes, CBM results for different harvest levels  will be used to provide a 
sensitivity analysis on the resulting forest carbon sinks up to 2030; this analysis will provide different emission/removal factors 
to be used in CAPRI module (i.e. response function). 

Although the LUISA-BEES model has a mechanism to compute GHG emissions from land use and land use change (Follador et 
al., forthcoming), it does not have a mechanism to compute forest carbon sinks, which is provided by CBM (see Mubareka et al 
2018). 

The interaction between the CAPRI and POTEnCIA models will include cost curves of first generation biofuels (i.e. biodiesel and 
bioethanol) and energy requirements of the crop production and transformation. The CAPRI model has energy costs as inputs in 
its supply model but does not currently compute energy balances for agriculture. For the generation of these curves, stylized 
scenarios  will  be  implemented  in  CAPRI  by  running  different  biofuel  mandates  (for  biodiesel  and  bioethanol)  based  on  the 
estimated maximum potential biofuel use by Member States (see Section 3.2.3 below for detailed description). The information 
on feedstock supply and prices from CAPRI would allow for a better representation of first generation biofuel supply in POTEnCIA. 
Any policy leading to a more rapid decarbonisation would imply a change in the biofuel demand targets. Therefore, biofuel cost 
curves in CAPRI could be calculated sequentially with different demand targets from POTENCIA (response function).  

Analysis of future scenarios will encompass also possible increases of harvest demand, coming from the ambition to move 
towards a largest share of renewables in the energy mix. Consequently, CBM will simulate the dynamics of biomass harvest and 
carbon flows in EU forests by using the demand prices of biomass for energy projected by POTEnCIA. However, since POTEnCIA 
simulates an aggregate of solid biomass (thus without explicitly distinguishing woody biomass), there is the need to define a 
way to distinguish the various sources of biomass, so that the woody component in POTEnCIA can be correctly linked to the CBM 
model. Furthermore, CBM will provide the maximum potential availability of forest biomass for energy purposes to POTEnCIA 
(response function). By doing this, it should be possible to assess and compare different future scenarios based on different 
contributions of forest biomass to the future energy mix. 

Scenario  interactions  between  the  bioenergy  models  of  CAPRI  and  POTEnCIA  could  indirectly  affect  the  LUISA-BEES  model 
through their impact on the demand for the agricultural land dedicated to biofuel production in the CAPRI model.  

As we can see from the above discussion, through the AFOLU framework it is possible to implement several integrated scenarios. 
Based on this, we propose to analyse the following scenarios covering the time horizon 2030.  

a)  CAPRI model based scenarios 

  Common  Agricultural Policy (CAP) 2020 legal Proposal (i.e. lower pesticides, lower nitrogen 

leaching, higher share of organic production, etc.) 
Expansion of biofuel mandates (i.e. achievement of maximum mandates at MS level)  

 
  GHG emission mitigation (i.e. carbon pricing scenarios) 

b)  LUISA-BEES model based scenarios 

 

Implementation of spatially explicit sustainability criteria for plantation of energy crops; RED 
II sustainability criteria and additional safeguards (where possible) 

  Afforestation scenario (Forest Strategy Roadmap; 2030 Biodiversity Strategy), e.g. based on 

the “3 Billion Trees initiative” 

9 

 
c)  CBM model based scenarios 

  Different forest harvesting levels over time 

d)  POTEnCIA model based scenarios 

 

Strong decarbonisation (i.e. in this context, increased replacement of fossil fuels by biofuels 
both in the transport sector and for thermal uses) 

4  Model Harmonization and Implementation  

4.1  Land Use Change: CAPRI and LUISA-BEES 

The current land use module in CAPRI focuses on agricultural land use at the regional level, comprising two major components: 
(1) transformation of utilized  arable and grassland, i.e. imperfect substitution between arable and grasslands depending on 
returns to the two types of agricultural land uses; and (2) a supply curve that determines the land available to agriculture as a 
function of the returns to land (Figure 2). There is substitution possibilities of single crops in for the arable land. A certain part 
of the non-agricultural land is considered in the form of an agricultural ‘land buffer’ or ‘unused agricultural land’ that gives, 
together with the ‘agriculturally utilized land’, the ‘potential agricultural land’, which has been the system boundary. 

Figure 2. Current land use classification in CAPRI 

For the parameterization of land supply for agricultural land classes, CAPRI used in the past information from the CLUE-S (the 
Conversion of Land Use and its Effects at Small regional extent) model to supplement information from GTAP, in particular for 
the regional disaggregation at Nuts 2 level (Verburg et al, 2002; Jansson et al, 2010). Land use is estimated in combination 
with a closed agricultural land balance, including the transition between land use classes.  

More  recently,  the  land  supply  system  in  CAPRI  was  extended  to  cover  a  full  regional  area  allocation,  including  transitions 
between all major land types. More precisely, non-agricultural land use was disaggregated into forestry, built up areas (urban 
or "artificial" land) and a remaining "other land" category. For the non-agricultural area types (other land, forest and artificial), 
some ad-hoc scaling mechanism plus some assumptions on the responsiveness of areas is used.  

These land use categories integrate land use data from various sources (see Table A1.1 in Annex1). For carbon accounting, CAPRI 
relies on the six UNFCCC categories, e.g. cropland, grassland, forestland, settlements, wetlands, and residual land, which are 
mapped to agricultural activities. The UNFCCC category grassland is the sum of the productive grassland and some fraction of 
the "other land". Land allocation is performed as an optimization problem, ensuring adding up of single crop areas to land use 
aggregates and imposing constraints stemming from transition probabilities between different UNFCCC land-use categories.  

Identifying these six UNFCCC land use categories also permits to estimate a 6 x 6 transition probability matrix between land 
uses. The simulated transitions are those transitions that maximise a Gamma density while being consistent with the simulated 
land use totals. As the mode values for the land transitions are taken from the historical database, this implies that CAPRI takes 

10 

Currently three level hierarchyPotential agricultural landUnused potentialAgriculturally used landGraslandCroplandGras,intens.Gras,extens.Crop #nCrop #1...Trustee re-specificationTotal country areaPerm cropsForestGrasArable cropsGras,intens.Gras,extens.Arab Crop(n)Arab Crop(1)...OtherArtificialPerm Crop(m)Perm Crop(1)... 
 
 
 
those land transitions as most likely that are most similar to historical patterns while being consistent with changing land use 
totals. Based on the transition matrix CAPRI performs carbon accounting relying strongly on IPCC default values.  

The land use specification in CAPRI has also been extended and explored in projects like RURAGRI-TRUSTEE5 and SUPREMA6. The 
TRUSTEE project relies on an extension of the CAPRI cost function, also covering non-agricultural land categories (see Figure 3). 

Figure 3. TRUSTEE Land use Specification in CAPRI  

The SUPREMA and TRUSTEE projects have many similarities, with just a marginal modification of the land use classification for 
types of grassland to include fallow land (following FAO data) (Figure 3). The main difference is the reduced form specification 
used in SUPREMA, where land use is represented as a function of land rents in a multinomial distribution (MNL) system for land 
supply of all major endogenous land types. Both specifications involve parameters for specific substitution possibilities between 
all pairs of land uses. It may be expected therefore that both would be able to approximately represent an arbitrary set of land 
supply elasticities that possibly might be derived from LUISA-BEES information. However, the SUPREMA specification could be 
preferred to TRUSTEE with respect to linking LUISA-BEES to CAPRI because of the use of MNL form. 

The first decision to be made for the linkage of CAPRI to LUISA-BEES is the mapping of land use classes (see Table A1.2 in the 
Annex 1) and the specification of land supply in CAPRI. LUISA-BEES uses a series of land-use specific rules which may be applied 
either at the EU or country level There are rules that determine which land use can be converted to what (e.g. arable land can 
never become forest directly from one-time step to the next; urban land can never become arable land directly from one-time 
step to the  next, etc.). When an  agricultural land use is  shrinking, meaning  the CAPRI model shows a  negative trend in the 
requirement of land area to produce an agricultural commodity, the land enters into a transitional phase labelled “abandoned”. 
The land is easily converted to another land use while in this phase because it is no longer productive, yet it is already cleared. 
The land becomes progressively more expensive to convert as natural succession sets in as a function of time.  

The LUISA-BEES model specifies land use decisions as utility maximising choices of land owners, where probabilities are partially 
expressed in a MNL form as a function of the utility (including its relative spatial location) of each single land use. In the standard 
case, the utility is measured by the net present value earned in some land use. The allocation of a land use class is determined 
by a combination of the suitability of the land to host that particular land use type, it’s spatial location (i.e. neighbourhood), and 
the demand for the land use type.  

The  LUISA-BEES  model  will  improve  the  land  use  module  in  CAPRI  by  incorporating  all  land  allocation  information  in  the 
behavioural parameters for land supply and/or demand with six endogenous aggregate classes: forestry, arable crops, permanent 
crops, permanent grassland, settlements and other land. However, even if LUISA-BEES also uses an MNL function, this cannot 
be directly used by CAPRI as LUISA-BEES applies the land allocation to the pixel level while CAPRI does it to the NUTS2 level. 

5  
6  

https://www.trustee-project.eu/  
https://www.suprema-project.eu/  

11 

 
 
 
                                           
Furthermore, there are two sets of coefficients that result from the LUISA-BEES calibration of the model: one referring to the 
biophysical suitability of a land use class and the second referring to the neighbourhood suitability.  

Nonetheless the following procedure to indirectly use the information embedded in LUISA-BEES can be envisaged:  

(i)  LUISA-BEES could run a series of test scenarios where the land use of land types matching with the CAPRI classification 

at Nuts 2 level is increased or decreased by some percentage (say 1%) sequentially. 

(ii)  This  would  give  a  set  of  say  6  LUISA-BEES  scenarios  with  pixel  level  results  that  permit  to  extract  the  set  of 

endogenous average land rents across these land types for these auxiliary scenarios.  

(iii)  The changes in land rents cannot be directly taken to compute elasticities for land supply in CAPRI, because NUTS2 
level areas will remain exogenous to LUISA-BEES7. However they could be used in the CAPRI calibration as auxiliary 
observations to be “fit” by suitable choice of parameters of the CAPRI land allocation system. 

4.2  Forest simplified modelling: CAPRI and CBM 

The Carbon Budget Model (CBM) is an empirical model running on spatially referenced data (e.g. strata, defined at country or 
regional level, depending by the available data sources) (Kurz et al., 2009) with CBM databases adapted to EU conditions (Pilli 
et al., 2018). CBM runs with annual time step. In the AFOLU project framework, CBM is supposed to interact mainly with the 
CAPRI model, feeding it with forest dynamic specific parameters for a more detailed and accurate analysis of LULUCF emissions. 
The CBM data used for this exercise was derived as part to an integrated assessment of the EU forest carbon (C) balance from 
2000 to 2012, including: (i) estimates of the C stock and net CO2 emissions for forest land remaining forest land (FL-FL), land 
converted to forest (L-FL) and deforestation (D, i.e. forest land converted to other uses), covering carbon in both the forest and 
the harvest wood product (HWP) pools.  

The CBM results provided within this modelling framework are based on the ”forest land remaining forest land” area reported 
by EU MS within their 2014 GHGI8 for the historical period 2000 – 2012, further combined with data on deforestation and 
afforestation reported for the same period (Pilli et al. 2016a,b). Area is split between forest types (based on the leading species), 
management types (e.g., high forests and coppices) and according to other criteria (e.g. NUTS, climates types), thus creating 
numerous combinations of strata that allow for an accurate modelling of C dynamic in all forests pools. CBM runs are available 
for all EU MSs with the exception of Malta (no forest area) and Cyprus (no available data).  

The net annual increment of forests and other parameters that serve as input into CBM are derived at country level from national 
forest inventories and other ancillary information at MS level. Harvest levels for each MS are exogenous to the CBM model. All 
these  parameters  are  forest  type  dependent,  age-dependent  and/or  volume-dependent  (e.g.  increment  and  yield  tables,  or 
allocation of biomass to other tree/stand compartments). 

Development of lookup tables for carbon emission factors 

CAPRI  considers  CO2  emissions  and  removals  of  LULUCF  mainly  following  the  structure  of  the  UNFCCC  National  Inventory 
Submissions. Land use change emissions/ removals follow the Tier 2 approach of IPCC. Biomass carbon stocks for assessing 
carbon effects of land use changes are based on the technical annex of the report to AA TREN/D1/464-2009-SI2.5393039, and 
the soil carbon stocks changes are based on FAO data. For each transition, the C stock changes are calculated as emissions 
from the previous class and removals from the new class, taking into account the climate zones. A special treatment is applied 
to biomass carbon dioxide emission/ removal factors for the remaining class “forest land remaining forest land” (Fl-FL). These 
net effects are directly taken over from the National Inventory Submissions of the UNFCCC in the capacity of net C stock changes 
in biomass and dead organic matter. C stock changes of soil resulting in CO2 emissions/ removals from soil organic carbon for 
FORFOR are not taken into account (i.e. considered to be in equilibrium for the remaining class FORFOR). 

One of the aims of this CAPRI-CBM linkage is to cross-check and project the factors of the Common Reporting Format (CRF) 
tables10 used in CAPRI for FL with refined and also forecasted factors from CBM. For this purpose lookup tables with “emission 
factors”, corresponding to “C stock change factors per ha” are constructed from CBM outputs (see Table A5 and Table A6Table 
5A and Table 5B in Annex2). Factors are elaborated for the ecosystemic indicators (i.e. NPP, transfers from living biomass to 
dead organic matter pool through natural mortality and natural disturbances), for changes in C pools (dead wood, litter, mineral 
soils), and for the transfers associated to wood removals (as harvest). Emissions from organic soils are not considered. Factors 

7 At this point of communication it is unclear how (or if) LUISA-BEES can maintain the NUTS2 level area balance if NUTS2 level areas are exogenous inputs.  

8 When available, forest management (FM) country data from the KP-CRF tables was used for 2008-2012 (i.e., if FM had been elected during the first KP 
commitment period). Alternatively, country data were taken from the Convention CRF tables using ‘forest land remaining forest land’ (FL remaining FL) as a 
proxy for FM. 
9 
https://esdac.jrc.ec.europa.eu/ESDB_Archive/eusoils_docs/other/EUR24573.pdf.  
10  The UNFCCC inventory submissions include Common Reporting Format (CRF) tables, a series of standardized data tables containing mainly quantitative 

Sustainability 

"Background 

Calculation 

Scheme", 

Biofuels 

Carbon 

Stocks 

Guide 

Land 

the 

the 

for 

of 

in 

information 

12 

 
                                           
are derived from the CBM runs corresponding to the historical period 2000 – 2012 for three harvest scenarios (as explained 
above). 

In this exercise, three harvest levels have been considered for the period 2013 - 2030, creating three different scenarios:  

(i)  business as usual as reference, also assuming a constant afforestation rate,  

(ii)  +20% harvest in 2030 compared to the reference in 2012, combined with an increasing afforestation rate, and  

(iii)  -20% harvest in 2030 compared to the reference in 2012, combined with a decreasing afforestation rate  

Basically, the reference implements a constant harvest corresponding to the 2000-2012 average period, thus representing the 
continuation  of  forest  management  practices  as  in  the  previous  years.  For  each  MS  the  whole  range  of  harvest  comprised 
between the +20% and the -20% compared to reference can be explored. Indeed, this allows producing a lookup table where 
each harvest level comprised in the range is matched with a corresponding carbon emission factor (i.e. gain and loss factors per 
ha), which can be applied in the CAPRI model. Harvest is identified with the sum of all transfers to HWP, FW and IRW (Figure A7 
in Annex 2).  

Many different scenarios can then be defined by CAPRI model assuming a reasonable range of harvest for each MS. Results 
may be valid for projections up to 2030, since after 2030 climate change  impacts  may not be neglected and,  therefore, a 
different approach would be needed (e.g. changes of forest growth or presence of natural disturbances). 

A major caveat of this approach is that such “carbon emission factors” derived from CBM outputs take into account the forest 
dynamics, forest management interventions and natural disturbances impacts in an implicit way. Moreover, emission factors 
are  calculated  at  the  national  level  through  multiple  average-weighting.  Such  simplifications  using  lookup  table  make  any 
projections largely uncertain, especially as age-dynamics of forests are not considered explicitly in the final factors. However, 
uncertainty remains reasonable given the range of harvest included in the three scenarios, with the reference as the most likely 
one. All parameters extracted from CBM are provided as time series, in tons of carbon per year, from 2000 to 2030, based on 
the methodological assumptions and model output applied in Pilli et al. (2017). All details and methods are reported in Annex 2. 

4.3  Biofuel: CAPRI and POTEnCIA 

The CAPRI biofuel module covers two biofuel markets (biodiesel and bioethanol) and three technology pathways, first and second 
generation production from agricultural sources and production from non-agricultural sources (see Blanco et al., 2013; Britz and 
Delzeit 2013). The structure of the biofuel markets in CAPRI are presented in Figure 4 and Figure 5. The feedstock of the first 
generation bioethanol production includes cereals (wheat, barley, grain maize, oats and rye), table wine and sugar beet. The 
feedstock of first generation biodiesel production includes vegetable oils from rapeseed, sunflower seed, soybeans and palm 
oil11. The second generation biofuel production  in CAPRI covers  biofuels from agricultural residues (straw from cereals and 
oilseed production and sugar beet leaves) and from new energy crops (herbaceous and woody crops like poplar, willow and 
miscanthus). Non-agricultural biofuel production includes biodiesel production from cooking oils.  

While the first generation biofuel production is fully embedded in the profit maximizing framework of CAPRI, the demand shares 
for second generation biofuel production are exogenously defined. The demand for agricultural residues from second generation 
biofuel production is historically low, having only a marginal impact on the market balances of cereals, oilseeds and sugar beet. 
Consequently, the biofuel demand shares for agricultural residues are set exogenously in the model, and the biofuel processing 
demand for agricultural residues does not enter the agricultural commodity market balances. The demand share for new energy 
crops is also set exogenously in the model. But unlike agricultural residues, the production of new energy crops might have an 
indirect effect on agricultural production in CAPRI through the competition for agricultural land. The available agricultural area 
for other agricultural production activities is reduced with the area devoted to energy crops in the land balance. 

11  

In CAPRI, the processing demand for biodiesel enters the market balances of the individual vegetable oils. Biofuel processing demand is combined with 
other processing demands in the market balances of the seeds. The palm oil used in European biodiesel production is fully imported.  

13 

 
                                           
 
Figure 4. Construction of the bioethanol market in CAPRI  

Source: Blanco et al. (2019) 

Note: R1,…,Rn are regions 

Figure 5. Construction of the biodiesel market in CAPRI  

Source: Blanco et al. (2019) 

Note: R1,…,Rn are regions 

While biofuel supply and feedstock demand react to processing margins, biofuel demand and trade flows react to biofuel prices 
and other drivers. Feedstock demand is driven by per unit net input costs, i.e. feedstock prices minus by-product revenues per 
ton of input. The optimal feedstock mix defined by the first order conditions of a cost minimisation problem using CES cost 

14 

 
 
 
 
 
functions  for  biofuel  supply12.  Biofuel  production13  is  then  derived  by  from  the  optimal  of  feedstock  mix,  based  on  biofuel 
processing coefficients.  

The  share  of  biofuels  in  total  fuel  demand  is  driven  by  the  price  relationship  of  biofuels  to  fossil  fuels.  Technically, 
sigmoid-shaped functions determine the demand shares, which are calibrated to reach at least the mandates (quota obligation 
or target) set by the different MS (Figure 6). An upper limit is also set during the calibration, representing the maximum potential 
biofuel demand above the quota obligation in a certain country. Total biofuel demand is then derived from the exogenous total 
fuel demand (including fossil fuels). 

Figure 6. Biofuel demand share function in France 

Source: Blanco et al. (2019) 

The above specification of the biofuel demand shares is adjusted to various scenario assumptions on biofuel mandates, and on 
the maximum potential demand for biofuels in the Member States: 

 

 

 

The  demand  for  biofuels  from  food  crops  is  increased  according  to  the  targets  in  the  RED II  (Renewable  Energy 
Directive recast) for each Member State. RED II sets a 7% target of the Final Energy Consumption of the land transport 
sector,  which  share  can  be  approximated  simply  as  7%  of  the  transport  fuel  sold  in  most  cases,  due  to  the  low 
historical penetration of electric vehicles and small share of rail compared to road.  
After setting the biofuel demand shares equations to the RED II targets (7%), the biofuel demand curve is subject to 
sensitivity analysis. For this, CAPRI is solved repeatedly for different biofuel mandates below the RED II targets, by 
systematically  decreasing  the  mandate  parameter  of  the  sigmoid  function  within  a  certain  interval.  With  this  the 
changes in the demand curve in the downward direction are investigated, since a further increase seems politically 
unrealistic.  
In an alternative set-up, biofuel demand from food crops is increased up to 7%, but the increase is limited to maximum 
+1% compared to biofuel demand shares in 2020. This scenario assumption is closer to the legal text of RED II. The 
same sensitivity analysis, as described above, explores a gradual decrease of the mandates from this second starting 
point. 

To calibrate the CAPRI biofuel demand equations to this modelling exercise, current biofuel demand shares and biofuel mandates 
are taken from POTEnCIA.  For  this, the 7% EU target is broken down  by MS, using current  statistics on biofuel use and by 
estimating the potential for expanding biofuel use. The demand for biofuels from food crops is assigned fully to first generation 
production in CAPRI, without making further assumptions on advanced (second generation) biofuel production and use. Regarding 
the use of current biofuel use statistics, demand shares are assumed to remain steady over the period from 2020-2030, which 
follows current decarbonisation pathways.  

Increasing biofuel use is expected to trigger substantial palm oil imports, which is against the sustainability criteria of the RED II. 
Furthermore,  increasing  biofuel  demand  can  also  trigger  cross-sectoral  effects,  mainly  through  feed  markets.  For  example, 
increased soybean imports might substitute domestically produced oilseeds in the feed mix, with indirect impacts on EU biodiesel 

12  
13  

Consequently, biofuel average costs are a CES-aggregate of the costs of individual feedstocks. 
A synthetic supply function is chosen for driving biofuel production, which satisfies some plausibility considerations: supply strongly decreases when the 
price relation gets below a certain “trigger” value and that this strong slope is not maintained throughout the whole function. This function consists of 
three parts: (i) the first part is linear guaranteeing a minimal slope, (ii) semi-log part that is active at processing margins, considerably higher than in the 
baseline point  and  (iii) sigmoid  function  part guaranteeing  a  steeper slope in  a  range where  processing  starts and  production  is close to  zero  when 
feedstock costs exceed output values. 

15 

 
 
 
                                           
 
production. Hence, we may consider extending the above scenario assumptions with import restrictions for specific products and 
from specific countries.  

Food-crop based biofuel supply curves will be constructed with CAPRI based on a series of alternative biofuel mandate scenarios 
as described above. These will be used to calibrate the price formation mechanism for biofuel blends in POTEnCIA. The foreseen 
revision of this mechanism includes the explicit accounting of the following four components: 

 

 

 

 

Fossil fuels: the price trajectory over time is in this case taken as invariant with respect to demand (and therefore 
to the biofuel blending share), as it is considered to follow the (exogenous) evolution of international fuel prices 
rather than being driven by the European market. 
Biofuels from food waste feedstock, as included in the RED II Annex IX, part B. This corresponds essentially to waste 
cooking fats, and is assumed to be used only for biodiesel (absence of commercial pathways to turn this feedstock 
into bioethanol, and biogas not covered at this stage). Its cost is the lowest among all biofuel sources and considered 
constant, and its use is limited by supply. Supply limits are derived based on an extrapolation of the data submitted 
by the MS through the SHARES tool14, and literature review. 
First generation biofuels based on food- and feed-crop feedstocks as referred to in the RED II Annex VIII, Part A: 
The  cost  of  this  biofuel  component  in  POTEnCIA  has  to  be  calibrated  to  biofuel  supply  cost  curves,  both  for 
bioethanol and biodiesel, produced with CAPRI by running a series of simulations covering the range of biofuel 
blending shares that would remain in compliance with RED II Article 26, paragraph 1.  
Advanced biofuels based on non-food feedstocks. Different types of such feedstock are listed in Annex IX, part A 
of the RED II. Separately accounting for each would not be a pragmatic option for a model of the entire energy 
system  such  as  POTEnCIA;  besides,  the  processes  associated  with  several  of  those  feedstock  types  are  hardly 
developed at  such  a level  that would  allow their techno-economic characterisation at market-ready conditions. 
Consequently, we choose the approach to take a single feedstock type, lignocellulosic biomass, as representative 
of the entire category. Supply curves for lignocellulosic feedstock  (including  forestry residues  as well as short-
rotation coppice) should be determined through other models, while the techno-economic characterisation of the 
associated  bio-refinery  processes  will  be  based  on  literature  review.  While  advanced  biofuels  are  expected  to 
command a higher price than equivalent first generation biofuels, besides the supply-limited contribution of waste 
cooking fats, they remain the only option in the case of expanding biofuel shares beyond the limits of RED II Article 
26, paragraph 1. Most importantly, minimum shares of advanced biofuels are required by Article 25, paragraph 1, 
of the RED II. 

The functioning of this bottom-up based biofuel cost and feedstock-demand module in POTEnCIA is planned to be tested by 
developing a series of stylised short-to-mid-term CO2 emission reduction scenarios for the EU, in which additional incentives are 
introduced in particular for bioenergy sources, boosting biomass demand especially in the following sub-sectors: 

  District heating 
 
 
 
 

Transportation biofuels 
Space heating in the residential sector 
Industrial boilers for low- and medium-enthalpy heat e.g. in the food sector 
Partial substitution of coal and coke with biochar in energy intensive industries (integrated steelworks, cement 
kilns) 
Co-firing in solid-fuel fired power plants  

 

The bioenergy quantity demand generated within the energy system under these conditions will then be fed back into CAPRI and 
other land-use models in order to determine the overall AFOLU impacts of the modelled energy scenario and thus further test 
the model linkages.  

5  Conclusions 

these 

sectors  would 

The EU territory holds almost three-quarters of agriculture and forest land and any policy action that affects the land use change 
in 
emissions/removals 
(https://ec.europa.eu/clima/policies/forests_en). Realizing the high priority for the climate ambition pronounced by the European 
Green Deal strategy, an integrated modelling framework covering agriculture, forestry, and other land use (AFOLU) is an effort 
that is capable of assessing emissions and removals from these sectors.  

and  GHG 

contribute 

change 

carbon 

stocks 

the 

of 

to 

The presented integrated modelling framework is an attempt to bring together relevant models specialized in their respective 
domains, e.g. agriculture, forestry, land use and energy,  such as to facilitate a comprehensive policy analysis. It covers four 
stand-alone models currently in use at the JRC. First, the agro-economic model CAPRI provides detailed farm level economic 

14  

SHARES (SHort Assessment of Renewable Energy Sources) is the tool Eurostat developed to facilitate the use of a harmonized calculation methodology 
for Member States to report their shares of renewable energy in line with the concepts of the Renewable Energy Directive.  

16 

 
                                           
analysis for the EU international agricultural commodity trade. Second, LUISA-BEES is a spatially-explicit land use model capable 
of estimating land use changes and associated LULUCF emissions across the EU27 with 1ha resolution. Third, CBM is a stand-
alone  model  for  forestry  and  accounting  of  carbon  dynamics.  It  accounts  for  the  impacts  of  forest  management,  natural 
disturbances, and deforestation. Last but not least, the energy system model, POTEnCIA is a hybrid partial equilibrium simulation 
model designed to evaluate the impacts of energy and climate policies considering technological, economic, and environmental 
considerations.  

By soft-coupling these models, the AFOLU modelling framework contemplates the harmonization of data flows between the 
models and the improvement of their simulation structures. The framework is designed to perform ex-ante policy scenarios 
relevant for a comprehensive analysis of the LULUCF sectors in terms of their contribution to a greener economy. This includes 
exploratory scenarios such as the reform of the CAP, expansion of bioenergy mandates, introduction of specific mitigation and 
environmental protection policies and changes in forest management.  

17 

 
 
 
References 

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Blanco, M., Bogonos, M., Goetz, C., Himics, M., Hristov, J., Jansson, T., Leip, A., Koeble, R., Rothan-Puig, X., Weiss, F. and Witzke, P. 

(2019). CAPRI Model Documentation, Joint Research Centre, Seville, Spain.  

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19 

 
 
 
List of abbreviations and definitions 

AFOLU 

AR 

CAP 

CAPRI 

CLC 

CBM 

CES 

CSC 

CH4 

CLUE 

CO2 

CRF 

Agriculture, Forestry and Other Land Use 

Afforestation 

Common Agricultural Policy 

Common Agricultural Policy Regionalised Impact  

Corine Land Cover 

Carbon Budget Model 

Constant Elasticity of Substitution 

Carbon Stock Change 

Methane 

Conversion of Land Use and its Effects (model)  

Carbon Dioxide 

Conditional Random Field 

CRPCRP 

Crop Land Remaining Crop Land 

DE 

DOM 

Deforestation 

Dead organic matter from forest (i.e. dead wood and litter) 

Dyna-CLUE 

Dynamic Conversion of Land Use and its Effects (model) 

EF 

Emission factor 

EUROSTAT 

Statistical Office of the European Community 

FAO 

FAWS 

Felling 

FL 

FL-FL 

FM 

Food Agriculture Organization 

Forest Available for Wood Supply 

Volume of standing growing stock affected by harvesting (in opposition to actual volume removed from 
forest by harvesting operations) 

Forest land 

Forest land remaining forest land 

Forest Management 

FORFOR 

Forest Land Remaining Forest Land 

FW 

GAIN 

Firewood, i.e. roundwood destined for bioenergy/burning 

Gain of carbon by living biomass pool 

GRSGRS 

 Grass land Remaining Grass land 

GHGI 

GTAP  

HWP 

IEF 

IPCC 

L-FL 

IRW 

LOSS 

LUCAS 

Greenhouse Gas Inventory 

Global Trade Analysis Project  

Harvested Wood Product 

Implied Emission Factor 

Intergovernmental Panel on Climate Change 

Land converted to forest land 

Industrial roundwood, i.e. merchantable roundwood destined to industrial processing 

Loss of carbon from living biomass pool 

Land Use Cover Area frame Survey 

LUISA-BEES 

Land Use Integrated Sustainability Assessment – BioEconomy & Ecosystem Services 

20 

 
 
 
 
 
 
 
LULUCF 

MCPFE  

Merch 

MNL 

N2O 

NBP 

NFI 

NPP 

OWC 

Land use, land-use change and forestry 

Ministerial Conference on the Protection of Forests in Europe 

Merchantable wood, i.e. standing or harvested roundwood with dimensions above defined thresholds 

Multinomial 

Nitrous Oxide 

Net Biome Production 

National Forest Inventory 

Net Primary Production 

Other woody components of the trees (i.e. bark, stump, small branches) 

POLES 

Prospective Outlook on Long-term Energy Systems 

POTEnCIA 

Policy Oriented Tool for Energy and Climate Change Impact Assessment 

SoEF2015 

State of Europe’s Forest 2015 Report 

SUPREMA 

TRUSTEE 

UAA 

UNECE 

UNFCCC 

Support for Policy Relevant Modelling of Agriculture (H2020 project) 

Towards Rural Synergies and Trade-offs between Economic development and Ecosystem services (RURAGR 
ERA-NET project 

Utilisable Agricultural Area 

United Nations Economic Commission for Europe 

United Nations Framework Convention on Climate Change 

21 

 
 
 
 
 
List of figures 

Figure 1. Schematic structure and model links in the AFOLU scenario framework .............................. 8 

Figure 2. Current land use classification in CAPRI ..................................................................10 

Figure 3. TRUSTEE Land use Specification in CAPRI ...............................................................11 

Figure 4. Construction of the bioethanol market in CAPRI ........................................................14 

Figure 5. Construction of the biodiesel market in CAPRI ..........................................................14 

Figure 6. Biofuel demand share function in France ................................................................15 

Figure A7. C pools and fluxes as considered by CBM (from Pilli et al., 2017). ....................................26 

Figure A8. Comparison of carbon stock changes between CBM and UNFCCC CRF tables for Member States ..28 

List of tables 

Table 1. Summary of LUISA-BEES specifications ................................................................... 6 

Table 2. Land use classes in LUISA-BEES and associated source of demand .................................... 7 

Table A3. Sources of land use data in CAPRI .......................................................................23 

Table A4. Mapping of generalized aggregated CAPRI land classes to LUISA-BEES .............................24 

Table A5. Estimates of carbon emission factors from CBM output: examples of Denmark and Germany (2000-
2030) ...................................................................................................................27 

Table A6. Net primary production per hectare per year (NPP) and forest carbon stock (Soil, Litter, Dead wood) 
at constant harvest levels for three pools: examples of Denmark and Germany (2000-2030) estimated from 
CBM outputs ...........................................................................................................27 

22 

 
 
 
 
 
 
Annexes 

Annex 1. Land use change in CAPRI and LUISA-BEES 

Table A3. Sources of land use data in CAPRI 

Land use level 

Description 

Source 

REGIO 

ENVIO 

Regional  land  use  data;  NUTS  2  level  – 
1984-2014 

Land  cover  data  from  the  environment 
section;  MS  level  -  1985,  1990,1995, 
2000 

Statistical Office of the European Community (EUROSTAT) 

EUROSTAT 

http://eu22.eu/land-use.2/land-use-by-main-category/ 

LANDCOVER 

Land  over  data;  MS  level  –  2009,  2012, 
2015 

EUROSTAT 

FSS 

CLC 

MCPFE 

CRF 

FAO 

Farm  Structure  Survey  data;  MS  level  - 
1990, 2000, 2005, 2010, 2015 

EUROSTAT 

Land  use  data  derived  using  a 
transformation  matrix  to  LUCAS;  NUTS2 
level - 1990, 2000, 2006, 2012 

Corine Land Cover (CLC) 

Data on the forest sector and some non-
forestry  data  (inland  waters  INLW,  total 
country  area  ARTO);  MS  level  -  1990, 
2000, 2005, 2010, 2015 

Ministerial Conference on the Protection of Forests in Europe 
(MCPFE), jointly published by Food Agricultural Organization 
(FAO) and United Nations Economic Commissions for Europe 
(UNECE) 

Common  Reporting  Format  land  data 
(1990-2016), 
land 
transitions  and  settlement  data.  Official 
data for LULUCF accounting 

covering 

also 

United  Nations  Framework  Convention  on  Climate  Change 
(UNFCCC) 

Agricultural land use, but also some non-
agricultural area categories (forest, inland 
waters, other land, total area); MS level – 
1984-2016 

FAOSTAT domain 

 http://faostat3.fao.org/home/index.html#DOWNLOAD 

23 

 
 
Table A4. Mapping of generalized aggregated CAPRI land classes to LUISA-BEES 

24 

LUISA-BEESCROPRape [RAPE]CROPSunflower [SUNF]CROPSoya [SOYA]CROPOther oils [OOIL]CROPPulses [PULS]CROPTomatoes [TOMA]CROPOther Vegetables [OVEG]CROPFodder other on arable land [OFAR]ArableCROPSet-aside voluntary [VSET]CROPFallow land [FALL]CROPFlax and hemp [TEXT]CROPTobacco [TOBA]CROPOther industrial crops [OIND]CROPOther crops [OCRO]CROPSoft wheat [SWHE]CROPDurum wheat [DWHE]CROPRye and Meslin [RYEM]ArableCROPBarley [BARL]CROPOats [OATS]CROPOther cereals [OCER]CROPGrain Maize [MAIZ]CROPFodder maize [MAIF]CROPPotatoes [POTA]CROPSugar Beet [SUGB]ArableCROPFodder root crops [ROOF]CROPApples Pears and Peaches [APPL]CROPOther Fruits [OFRU]Permanent CropsCROPTable Grapes [TAGR]CROPABANARABLECROPABANPERMANGRASLAND PasturesGRASLAND SHVAGRASLAND ABANPASTURGRASLAND URBANGREENFOREForestMatureFORETRANSWOODLARTIFBUILTUPARTIFINDUSTRIALARTIFURBANARTIFINFRASTRUCRESLANDOTHERNATURWETLANDWETLANDSWETLANDWaterBodiesArableCAPRI 
 
 
 
 
Annex 2. Methods for estimating emission factors from CBM 

Parameters relevant for change of C stock in living biomass pool 

On the one side, the CBM outputs allow reconstructing the growth of the living biomass through:  

  NPP, net primary production, in tC/ha/year;  

  NDLOSS,  losses  through  natural  disturbances  (e.g.  transfers  from  living  biomass  to  dead  organic  matter  pool),  in 

tC/ha/year and  

  MORTLOSS, losses through regular natural mortality (i.e. due to competition), in tC/ha/year.  

Combining these parameters, GAIN can be estimated as a synthetic parameter that characterizes the total increase of the living 
biomass pool in a calendar year (letter in the parenthesis correspond to arrows in Figure A7). 

GAIN = NPP (A) – NDLOSS (C) – MORTLOSS (B) 

Noticeable, this value corresponds best to the gain from common reporting format Table 4A1 submitted to UNFCCC as part of 
the annual GHG Inventory.  

On the other side, CBM outputs allow reconstructing the losses from the living biomass pool through the estimates of Merch 
(merchantable/standing wood) and OWC (other wood components) transferred from the living biomass to industrial roundwood 
(IRW), firewood (FW), and to dead organic matter pool (DOM) remaining on the ground as exploitation residues. All such indicators 
are expressed in tC/ha/year. Specifically, these transfers can be considered as (letter in the parenthesis correspond to arrows in 
Figure A7):  

LOSS = (Merch-->IRW) (E) + (Merch-->FW) (F) + (OWC-->FW) (G) + (Felling-->DOM)(I) 

Such estimate of the LOSS fits best to the “Loss” column from Table 4A1 of the GHG inventories. 

25 

 
 
 
 
 
 
Figure A7. C pools and fluxes as considered by CBM (from Pilli et al., 2017). 

Factors for dead organic matter pools 

The Annual dynamics of C stocks per hectare are estimated from CBM outputs as national average values for each of the three 
DOM pools (dead wood, litter and organic matter in mineral soils). The emission factors values corresponding to C stock change 
per ha per year should be derived as the differences between stocks estimated for successive years. 

Carbon emission factor for deforested land 

An aggregated value of losses from all C pools (living biomass and DOM) is calculated, for the easiness of development of the 
lookup tables. The value of the loss, per ha and per year, varies across years because CBM implements a random selection of 
forest types (e.g. carbon density, stand age) subject to deforestation.  

Such value is valid for the year when the deforestation event takes place (so it is assumed that the entire conversion takes place 
in one year). As expected, such values are much higher than the corresponding values in the CRF tables (e.g. for conversion of 
forest to cropland, grassland, etc), as those values represent “implied emission factors” which cumulate the legacy emissions 
from past deforestation over the conversion period (which is usually 20 years for any type of conversion) on top of the emissions 
in the current year. For projection purpose, a deforestation factor defined as the average annual value (per year, per ha) from 
the CBM data pool for 2000-2012, can be used.  

26 

 
 
 
 
 
 
Table A5. Estimates of carbon emission factors from CBM output: examples of Denmark and Germany (2000-2030) 

Table A6. Net primary production per hectare per year (NPP) and forest carbon stock (Soil, Litter, Dead wood) at constant 
harvest levels for three pools: examples of Denmark and Germany (2000-2030) estimated from CBM outputs 

27 

20002001200220032004200520062007200820092010201120122013201420152030Denmarkef_bioGain2.14352.68932.70302.71222.72062.44932.69662.68342.66692.64702.62622.60612.57382.55432.52352.49431.9953ef_bioLoss-0.9850-0.7763-0.6991-0.7836-0.6725-1.0057-1.0302-1.1133-1.2516-1.2709-1.2088-1.1715-1.1733-1.0552-1.0559-1.0534-1.0624ef_bioNet1.15841.91302.00391.92862.04811.44371.66651.57021.41521.37621.41741.43471.40051.49911.46761.44090.9328ef_deadWoodNet-0.2506-0.2506-0.2357-0.1813-0.2122-0.0335-0.1093-0.0823-0.0371-0.0275-0.0380-0.0410-0.0357-0.0596-0.0503-0.04220.0155ef_litterNet-0.0815-0.0815-0.0669-0.0313-0.04800.03160.01490.03030.04390.03930.02680.02060.02150.00800.01180.01270.0139ef_domNet-0.3321-0.3321-0.3025-0.2126-0.2602-0.0018-0.0944-0.05200.00680.0118-0.0112-0.0204-0.0141-0.0516-0.0385-0.02950.0294ef_socNetMin-0.0180-0.0180-0.0171-0.0148-0.0153-0.0084-0.0147-0.0158-0.0143-0.0151-0.0167-0.0176-0.0182-0.0200-0.0203-0.0208-0.0271Germanyef_bioGain2.56033.50813.50103.49783.49253.48853.48922.67323.47873.47293.37233.45993.44803.43663.43493.43453.3898ef_bioLoss-2.1024-1.4155-1.3712-1.4973-1.7801-2.0047-2.1041-2.4914-1.8704-1.7712-1.8229-1.9355-1.8688-1.8918-1.8916-1.9179-1.9244ef_bioNet0.45792.09262.12982.00051.71241.48381.38510.18181.60831.70171.54941.52441.57921.54481.54331.51651.4655ef_deadWoodNet-0.1176-0.1176-0.1136-0.06750.06320.09630.11110.7308-0.0630-0.0766-0.00600.00040.03030.01040.00900.01740.0512ef_litterNet0.14120.14120.15050.17700.22010.23950.23720.26280.16990.15110.16070.16450.15770.16330.15460.15850.1426ef_domNet0.02360.02360.03690.10950.28320.33580.34840.99360.10690.07450.15470.16490.18800.17370.16350.17590.1939ef_socNetMin-0.0021-0.0021-0.0027-0.00060.00200.00340.00440.01770.00200.00310.00600.00630.00690.00850.00950.01070.0194C Stock per ha t C ha-1C Stock per ha t C ha-1NPP per haSoilLitterDead woodNPP per haSoilLitterDead wood20004.5061.825.7110.727.3669.5821.9321.3620014.5661.805.6310.477.3869.5722.0721.2420024.6261.785.5710.237.4169.5722.2221.1320034.6761.775.5410.057.4569.5722.4021.0620044.7261.755.499.847.4769.5722.6221.1320054.7361.755.529.807.4969.5822.8621.2220064.7461.735.539.697.5169.5823.1021.3320074.7561.725.569.617.5169.6023.3622.0620084.7661.705.619.577.5369.6023.5322.0020094.7661.695.659.557.5669.6023.6821.9320104.7561.675.679.517.5869.6123.8421.9220114.7561.655.699.477.6069.6224.0121.9220124.7361.635.729.437.6269.6224.1621.9520134.7361.615.729.377.6469.6324.3321.9620144.7261.595.749.327.6669.6424.4821.9720154.7161.575.759.287.6969.6524.6421.9920164.6961.555.769.257.7269.6624.7921.9920174.6861.535.789.227.7569.6824.9321.9920184.6661.515.809.207.7869.6925.0722.0020194.6461.485.829.187.8169.7025.2122.0020204.6261.465.849.177.8469.7225.3522.0020214.6061.445.869.167.8769.7325.4822.0120224.5861.415.889.167.9069.7525.6122.0220234.5561.395.909.167.9269.7625.7422.0220244.5361.365.919.167.9469.7825.8722.0420254.5061.345.939.177.9769.7926.0022.0720264.4761.315.949.178.0069.8126.1422.1120274.4461.285.969.188.0269.8326.2822.1520284.4161.265.979.198.0569.8526.3922.1620294.3861.235.999.218.0769.8726.5322.1920304.3561.206.009.228.0969.8926.6722.24DenmarkGermany 
 
 
 
 
Uncertainty associated to lookup table values 

The CBM output transferred to C stocks changes of living biomass, of dead organic matter and in mineral soils, as applied in the 
UNFCCC National Inventory Submissions, are finally compared to the CRF data. CBM (Figure A8) and GHG Inventory experts rate 
the differences as acceptable. 

Figure A8. Comparison of carbon stock changes between CBM and UNFCCC CRF tables for Member States 

[tC/ha/
yr] 

Additional to the major fact that the approach by a lookup table represent a simplification of forest ecosystem dynamics, there 
are additional elements to consider with regard to CBM model estimates. For instance, there values compared are processed 
(i.e. simple average for Implied emission factors (IEFs) from CRF tables and weighted average for carbon stock changes (CSC) 
from CBM), and uncertainty related to data used as input in the CBM or used to report in the CRF tables. 

Overall, CBM provides estimates within a realistic range when considering the IEFs and CSC values contained in the CRF tables 
as the reference (see Figure A8 in Annex 2). Some differences can be explained by the quality of input data in the CBM modelling. 
Moreover,  in  almost  all  of  the  cases,  the  MS  have  access  to  more  disaggregated  and  updated  data  than  CBM.  Identified 
differences so far, were investigated and resolved mainly for the living biomass, and as much as possible for other C pools. 
Given the national aggregation level for which estimates are compared ± 10-15% compared to historical values reported in the 
CRF tables are considered as acceptable (against the min or max of the entire available range). This acceptability threshold can 
be relaxed when the IEFs and CSC values for the gains or loss factors are very small, so that even the country data can be 
considered as highly uncertain. 

Other  differences  may  be  irreconcilable  as  being  related  to  the  specific  modelling  framework,  e.g.  implementing  average 
silvicultural practices, simplification of the uneven-age stands structures, etc. Here it is important to highlight that the conceptual 
approach behind the CBM model and the CRF tables is different. While CBM computes on yearly steeps, the CRF data is usually 
generated  from  5-years  remeasurements  from  National  Forest  Inventories  (NFIs).  The  latter  is  obviously  smoothing  out 
occasional annual harvest peaks due to natural disturbances, so that effects are distributed over the years. Moreover, MS may 
apply corrections to the estimation of gains and loss factors (e.g. specific growth curves based on the health status of stands) 
while CBM uses a standard growth curve. In CBM the uncertainty is reduced as far as possible by trying to mimic as much as 
possible  the approach taken by the MS  (e.g. in terms  of forest type stratification, forest standing  stock and growth curves, 
biomass expansion factors/allocation).  

Last but not least, the input data in CBM was subject to validation against country’s official reporting to various organizations 
(i.e. UNFCCC, FAO, Eurostat, SoEF, see Pilli et al., 2016b). Additionally, such activity was complemented in some cases with checks 
performed together with MS national experts. 

28 

 
 
 
 
 
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30 

doi:10.2760/900305 

ISBN 978-92-76-27386-8