methodology for estimation and modelling of eu lulucf ... · other global models or databases...

Methodology for estimation and modelling of EU LULUCF greenhouse gas emissions and removals until 2050 in GLOBIOM and G4M

Stefan Frank, Nicklas Forsell, Mykola Gusti, and Petr Havlík

Laxenburg, May, 2016

International Institute for Applied Systems Analysis Schlossplatz 1 A-2361 Laxenburg, Austria

Tel: +43 2236 807 0 Fax: +43 2236 71313

E-mail: [email protected] Web: www.iiasa.ac.at

Reference scenario 2016 - LULUCF modelling methodology 2

Contents 1 Introduction ........................................................................................................................................... 3

2 Description of models and modelling approach.................................................................................... 3

2.1 Overview of general model interaction ......................................................................................... 3

2.2 GLOBIOM .................................................................................................................................... 4

2.3 G4M ............................................................................................................................................ 10

3 Model validation ................................................................................................................................. 11

4 EU Reference scenario 2016 development ......................................................................................... 12

4.1 Reference 2016 input data ........................................................................................................... 12

4.2 Reference 2016 LULUCF projections ........................................................................................ 12

5 Calculation of emissions ..................................................................................................................... 14

5.1 Emissions from forestry activities ............................................................................................... 14

Forest management (Forest land remaining Forest land) .................................................................... 17

Afforestation ....................................................................................................................................... 18

Deforestation ....................................................................................................................................... 19

5.2 Emissions from harvested wood products ................................................................................... 19

5.3 Emissions from cropland management ....................................................................................... 20

5.4 Emissions from grassland management ...................................................................................... 21

5.5 Emissions from wetlands, settlements and other lands ............................................................... 22

References ................................................................................................................................................... 23

Annex A. Relevant peer reviewed GLOBIOM/G4M articles ..................................................................... 26

Figures Figure 1: Overview of EUCLIMIT2 model interactions. ............................................................................. 4

Figure 2: Illustration of the GLOBIOM model ............................................................................................. 6

Figure 3: Land cover representation in GLOBIOM and the matrix of endogenous land cover change

possibilities. .................................................................................................................................................. 7

Tables Table 1: Data on afforestation, deforestation and forest area used as input to G4M for model calibration.

The values were collected by JRC in 2016. Forest area is based on MCPFE (2015). ................................ 14


1 Introduction

This report provides details on the methodology of projections and data sets used for the estimation of

emissions of the land use, land use change, and forestry (LULUCF) sector for the 28 EU member states

(EU28) in the Reference scenario 2016. The report describes the interplay and roles of the GLOBIOM

and G4M models within the EUCLIMIT2 project (SERVICE CONTRACT FOR MODELLING OF EU

CLIMATE POLICIES) on behalf of the European Commission, DG Climate Action. GLOBIOM and

G4M provide the outlook for the LULUCF sector. The EU28 LULUCF emission sources covered are CO2

biomass and soil emissions from cropland- and grassland management by GLOBIOM and detailed forest

management activities (forest management, afforestation and deforestation) by G4M. The models also

provide results on area developments and harvest removals.

The report is structured in the following way. Section 2 presents the general modelling methodology for

estimating LULUCF CO2 emissions for EU28 at member state level. Section 3 describes how the EU

reference scenario 2016 is constructed. Section 4 provides information on how the actual emissions were

calculated for each activity. Finally, two Annexes provide a description of the LULUCF results excel

sheet and a list of peer reviewed publications.

2 Description of models and modelling approach

2.1 Overview of general model interaction

GLOBIOM and G4M interact as shown in Figure 1 with other models of the consortium to provide a

Reference Scenario estimate of the LULUCF sector. Basic driver information (GDP, population

development and bioenergy projections) is generated by the PRIMES and GEM-E3 models (Capros et al.,

2014) for EU28 and each Member State. Other global models or databases provide macro-economic

assumptions, bioenergy demand, productivity changes, food diets etc. outside Europe. These drivers are

taken up by the economic land use model GLOBIOM. The model projects endogenously developments of

the agricultural and forestry markets and impacts on land use and CO2 emissions for the LULUCF sector.

For non-CO2 emissions this is done via CAPRI and GAINS1, while PRIMES provides CO2 emission for

the energy and industry sectors (excluding LULUCF). Consistency in projections for the agricultural

sector between GLOBIOM and CAPRI are ensured through the use of common input datasets (CORINE

land cover map, EUROSTAT data etc.), consistent scenario driver information (PRIMES, GEM-E3

projections), continuous exchange of baseline results in past projects (i.e. EUCLIMIT) and a soft link of

the two models (GLOBIOM projections used by CAPRI as long-term driver information) in the

Reference Scenario 2016. The information between models flows not only in one direction but is

transmitted between modelling levels (economic land use and detailed sector models) iteratively where

relevant.

1 See Höglund-Isaksson et al. (2016): Non-CO2 greenhouse gas emissions in the EU-28 from 2005 to 2050: Final GAINS Reference scenario 2016. GAINS model methodology


Figure 1: Overview of EUCLIMIT2 model interactions.

2.2 GLOBIOM

What is GLOBIOM?

The Global Biosphere Management Model (GLOBIOM)2 (Havlík et al., 2014) is a global recursive

dynamic partial equilibrium model of the forest and agricultural sectors, where economic optimization is

based on the spatial equilibrium modelling approach (Takayama and Judge, 1971). The model is based on

a bottom-up approach where the supply side of the model is built-up from the bottom (land cover, land

use, management systems) to the top (production/markets) (see Figure 2 for an overview of the model

framework). For EU, the model has been refined to represent the European agriculture and forestry sector

in a more detailed way (Frank et al., 2015). The agricultural and forest productivity is modelled at the

level of gridcells of 5 x 5 to 30 x 30 minutes of arc (Skalský et al., 2008)3, using biophysical models. For

the EU, it is based on a more detailed SimU architecture (Balkovic et al., 2009) with a 1x1 km cell spatial

unit, six altitude and seven slope classes, soil classes characterized by soil texture compositions, depth,

and coarse fragment content, NUTS2 regions boundaries plus additional dimensions for land cover

category, presence of irrigation equipment, and river catchment reference (CORINE 2000, European Soil

Database, European River Catchments coverage etc.). The demand and international trade occur at

2 See also: www.iiasa.ac.at./GLOBIOM 3 The supply-side resolution is based on the concept of Simulation Units, which are aggregates of 5 to 30 arc-minute pixels belonging to the same country, altitude, slope, and soil class (Skalsky et al., 2008).

http://www.iiasa.ac.at./GLOBIOM


regional level (57 regions covering the world). Besides primary products, the model has several final and

by-products, for which the processing activities are defined.

The model computes market equilibrium for agricultural and forest products by allocating land use among

production activities to maximize the sum of producer and consumer surplus, subject to resource,

technological and policy constraints. The level of production in a given area is determined by the

agricultural or forestry productivity in that area (dependent on suitability and management), by market

prices (reflecting the level of demand), and by the conditions and cost associated to conversion of the

land, to expansion of the production and, when relevant, to international market access. Trade is modelled

following the spatial equilibrium approach, which means that the trade flows are balanced out between

different specific geographical regions. Trade is furthermore based purely on cost competitiveness as

goods are assumed to be homogenous. This allows tracing of bilateral trade flows between individual

regions.

The model allows to fully account all agriculture and forestry GHG sources. For the Reference scenario

projections, GLOBIOM reports CO2 emissions from soil and biomass related to cropland and grassland

management. These emissions inventories are based on IPCC accounting guidelines.

For more details on GLOBIOM we refer to Havlík et al. (2014) and Frank et al. (2015).


Figure 2: Illustration of the GLOBIOM model

Representation of land use change

The model includes six land cover types: cropland, grassland, other natural vegetation land, managed

forests4, unmanaged forests, and plantations5. Depending on the relative profitability of primary, by-, and

final products production activities, the model can switch from one land cover type to another. Land

conversion over the simulation period is endogenously determined for each gridcell within the available

land resources. Such conversion implies a conversion cost – increasing with the area of land converted -

that is taken into account in the producer optimization behaviour. Land conversion possibilities are further

4 The term "managed forests" refers to all forest areas where harvesting operations take place, while "unmanaged

forests" refers to undisturbed or primary forests. 5 There are other three land cover types represented in the model to cover the total land area: other agricultural land

(crops not represented in the model), wetlands, and not relevant (bare areas, water bodies, snow and ice, and

artificial surfaces). These three categories are currently kept constant at their initial level.


restricted through biophysical land suitability and production potentials, and through a matrix of potential

land cover transitions (see Figure 3).

Figure 3: Land cover representation in GLOBIOM and the matrix of endogenous land cover change

possibilities.

Land use change emissions

Land use change emissions are computed based on the difference between initial and final land cover

equilibrium carbon stock. For forest, above and below-ground living biomass carbon data are sourced

from G4M which supplies geographically explicit allocation of the carbon stocks. The carbon stocks are

consistent with the 2010 Forest Resources Assessment Report (FAO, 2010), providing emission factors

for deforestation in line with that of FAOSTAT. Carbon stock from grassland and other natural vegetation

is also taken into account using the above and below ground carbon from the biomass as of Ruesch et al.

(2008). When. natural vegetation is converted into agricultural use, the GLOBIOM approach consider that

all below and above ground biomass is instantaneously released in the atmosphere.

The use of detailed and reliable statistics and maps

All processes and management options are represented at a high level of regional detail and built on

trustworthy databases. GLOBIOM is based on EU data regarding area, yields and production at NUTS 2

level. The market balances calculated for the 57 regions worldwide rely on EUROSTAT (based on the

CAPRI database) accounts for the EU28 and on FAOSTAT outside the EU. Land cover is dealt within a

geographically explicit way. The land cover description for the EU28 is based on CORINE 2000 land


cover maps, which ensure a great level of detail in land cover. The land cover information outside Europe

is based on Global Land Cover 2000 (GLC 2000).

Biomass use for large-scale energy production is usually based on the POLES and PRIMES energy sector

models (Havlík et al., 2011; Reisinger et al., 2013), but other estimates can also be utilized. For forests,

mean annual increments and growing stocks for GLOBIOM are obtained from G4M. For the agricultural

sector, GLOBIOM draws on results from the crop model EPIC (Environmental Policy Integrated Climate

Model) (Williams, 1995)6, which provides the detailed biophysical7 processes of water, carbon and

nitrogen cycling, as well as erosion and impacts of management practices on these cycles. GLOBIOM

therefore incorporates all inputs that affect yield heterogeneity and can also represent a different marginal

yield for different crops in a same grid cell.

Categories of biomass and biomass conversion are included in GLOBIOM

GLOBIOM represents a number of conventional and advanced biofuels feedstocks:

27 different crops including 4 vegetable oil types8;

Co-products: 3 oilseed meal types, wheat and corn DDGS;

Perennials and short rotation plantations: miscanthus, switchgrass, short rotation coppice;

Woody biomass from management of forest;

4 types of woody by-products from forest based industries9.

Agricultural production within GLOBIOM

GLOBIOM explicitly covers production of each of the 18 world major crops representing more than 70%

of the total harvested area and 85% of the vegetal calorie supply as reported by FAOSTAT. Each crop can

be produced under different management systems depending on their relative profitability. Crop yields are

generated at the grid cell level on the basis of soil, slope, altitude, and climate information, using the

EPIC model. Within each management system, input structure is fixed following a Leontief production

function. However, crop yields can change in reaction to external socio-economic drivers through switch

to another management system or reallocation of the production to a more or less productive gridcell.

Besides the endogenous mechanisms, an exogenous component representing long-term technological

change is also considered.

For the European crop sector, EPIC simulations are performed with three alternative tillage systems

(conventional, reduced, and minimum tillage) with statistically computed fertilizer rates and irrigation

management. Initial distribution of tillage systems are calibrated using country level data from the

PICCMAT project (PICCMAT, 2008). Crop rotations and additional crops have been incorporated for

Europe. The model covers currently 18 crops i.e. barley, corn, corn silage, cotton, fallow, flax, oats, other

green fodder, peas, potato, rapeseed, rice, rye, soybeans, sugar beet, sunflower, soft- and durum wheat.

6 See also: www.iiasa.ac.at/EPIC 7 Biophysical means related to living (animals, plants) and non-living (light, temperature, water, soil etc.) factors in

the environment which affect ecosystems 8 Palm oil, rapeseed oil, soy oil, and sunflower oil 9 Black liquor, wood chips, sawdust, and bark

http://www.iiasa.ac.at/EPIC


Crop rotations have been derived from crop shares calculated from EUROSTAT statistics (average

around 2000) on crop areas in NUTS2 regions using the crop rotation model CropRota (Schönhart et al.,

2011). CropRota explicitly takes into account data on relative crop shares, agronomic constraints such as

maximum frequency in a rotation and a score matrix of the agronomic desirability of a pre-crop – main-

crop sequence.

Livestock sector within GLOBIOM

The GLOBIOM model also incorporates a particularly detailed representation of the global livestock

sector. With respect to animal species, distinction is made between dairy and other bovines, dairy and

other sheep and goats, laying hens and broilers, and pigs. Livestock production activities are defined in

several alternative production systems adapted from Seré and Steinfeld (1996): for ruminants, grass based

(arid, humid, temperate/highlands), mixed crop-livestock (arid, humid, temperate/ highlands), and other;

for monogastrics, smallholders and industrial. For each species, production system, and region, a set of

input-output parameters is calculated based on the approach in Herrero et al. (2013).

Feed rations in GLOBIOM are defined with a digestion model (RUMINANT, see (Havlík et al., 2014))

consisting of grass, stover, feed crops aggregates, and other feedstuffs. Outputs include four meat types,

milk, and eggs, and environmental factors (manure production, N-excretion, and GHG emissions).

Switches between production systems allow for feedstuff substitution and for intensification or

extensification of livestock production. The representation of the grass feed intake is an important

component of the system representation as grassland productivity is explicitly represented in the model.

Therefore, the model can represent a full interdependency between grassland and livestock.

Available supply of wood biomass and types of wood

Total forest area in GLOBIOM is calibrated according to FAO Global Forest Resources Assessments

(FRA) and divided into managed and unmanaged forest utilizing a downscaling routine based on human

activity impact on the forest areas (Kindermann et al., 2008). The available woody biomass resources are

provided by G4M for each forest area unit, and are presented by mean annual increments. Mean annual

increments for forests are then in GLOBIOM divided into commercial roundwood, non-commercial

roundwood and harvest losses, thereby covering the main sources of woody biomass supply.

Available woody biomass resources from plantations

Plantations are covered in GLOBIOM in the form of energy crop plantations, dedicated to produce wood

for energy purposes. Plantation yields are based on NPP maps and model’s own calculations, as described

in Havlík et al. (2011). Plantation area expansion depends on the land-use change constraints and

economic trade-offs between alternative land-use options. Land-use change constraints define which land

areas are allowed to be changed to plantations and how much of these areas can be changed within each

period and region (so-called inertia conditions). Permitted land-cover types for plantations expansion

include cropland, grassland, and other natural vegetation areas, and they exclude forest areas. Within each

land-cover type the plantation expansion is additionally limited by land suitability criteria based on

aridity, temperature, elevation, population, and land-cover data, as described in Havlík et al. (2011).


Plantation expansion to cropland and grassland depends on the economic trade-off between food and

wood production. Hence, the competition between alternative uses of land is modeled explicitly instead of

using the "food/fiber first principle," which gives priority to food and fiber production and allows

plantation to be expanded only to abandoned agricultural land and wasteland (Smeets et al., 2007;

Hoogwijk et al., 2009; Van Vuuren et al., 2010; Beringer et al., 2011).

2.3 G4M

What is G4M?

The Global Forest Model (G4M)10 is applied and developed by IIASA (Kindermann et al., 2008; Gusti,

2010) and estimates the impact of forestry activities (afforestation, deforestation and forest management)

on biomass and carbon stocks. By comparing the income of managed forest (difference of wood price and

harvesting costs, income by storing carbon in forests) with income by alternative land use on the same

place, a decision of afforestation or deforestation is made. As G4M is spatially explicit (currently on a

0.5° x 0.5° resolution), different levels of deforestation pressure at the forest frontier can also be handled.

Within Europe the model applies species specific forest growth functions. The model uses external

information, such as wood prices and information concerning land use change estimates from

GLOBIOM. As outputs, G4M produces estimates of forest area change, carbon sequestration and

emissions in forests, impacts of carbon incentives (e.g. avoided deforestation) and supply of biomass for

bio-energy and timber.

Forest management option and impacts

The available woody biomass resources are estimated by G4M for each forest area unit determined by

mean annual increments, which are based on net primary productivity (NPP) maps from (Cramer et al.,

1999) and from different downscaling techniques as described in (Kindermann et al., 2008). This

information is then combined with national data sources (e.g., National Forest Inventories) to provide

further and more detailed information concerning biomass stocks and forest age structure.

The main forest management options considered by G4M are variation of thinning levels and choice of

rotation length. The rotation length can be individually chosen, but the model can estimate optimal

rotation lengths to maximize increment, stocking biomass or harvestable biomass. Increment is

determined by a potential Net Primary Productivity (NPP) map (Cramer et al., 1999) and translated into

net annual increment (NAI). At present this increment map is static and does not change over time.

The model uses external projections of wood demand per country (estimated by GLOBIOM) to calculate

total harvest iteratively. In G4M, the harvest amount per country is estimated by choosing a set of rotation

lengths that maintain current biomass stocks. If total harvests are less than the wood demand, the model

changes management grid per grid (starting from the most productive forest) to a rotation length that

optimizes forest increment and thus allows for more harvest. This mimics the typical observation that

10 See also: www.iiasa.ac.at/G4M

http://www.iiasa.ac.at/G4M


managed forests (in many regions) are currently not managed optimally with respect to yield. The rotation

length is updated for each five years’ time step. If harvest is still too small and there is unmanaged forest

available, the unmanaged forest will be taken under management. If total harvests are greater than the

demand, the model will change management to maximize biomass rotation length, i.e. to manage forests

for carbon sequestration. If wood demand is still lower than the harvest potential, managed forest can be

transferred into unmanaged forest. Thinning is applied to all managed forests, and the stands are thinned

to maintain a specified stocking degree. The default value is 1 where thinning mimics natural mortality

along the self-thinning line.

3 Model validation

GLOBIOM and G4M have been peer-reviewed in various European and international project, and

scientific publications. With respect to the conceptual and data validation of GLOBIOM: Input data is

based on official statistics, historical data, scientific studies, or remote sensing data. Parameterization of

biophysical relationships for the different land use sectors relies mainly on process based simulation

models like EPIC (Williams, 1995), G4M (Kindermann et al., 2008), and RUMINANT (Herrero et al.,

2013) which aim to simulate biophysical processes in detail. Empirical validation of model output has

been performed (Balkovič et al., 2013; Groen et al., 2013; Herrero et al., 2013; Xiong et al., 2014). The

general model specification follows well established tradition of linear programming models (Takayama

and Judge, 1971; McCarl and Spreen, 1980). Validation of results remains a highly challenging exercise

especially for large scale land use models such as GLOBIOM and G4M. Besides comparison of model

results with historical trends, scientific publication, and other models, national experts cross-checked

EU28 country level land use and GHG emission results of GLOBIOM during a consultation process in

EUCLIMIT and EUCLIMIT2. The EPIC model participated in the Inter-Sectoral Impact Model

Intercomparison Project (ISI-MIP)11 (Warszawski et al., 2014) where model results have been compared

for a consistent set of climate change scenarios with ten other global gridded crop models (Rosenzweig et

al., 2014). GLOBIOM participated in the AgMIP project12 where the agricultural economic modeling

community started to compare model projections across ten global agricultural sector models. A

consistent set of scenarios was quantified in order to compare model results and thereby understand the

difference in underlying model behaviour (Nelson et al., 2014; Schmitz et al., 2014; Valin et al., 2014;

Von Lampe et al., 2014). G4M performance in EU has been compared with EFISCEN (Böttcher et al.,

2012) as well as national reports (Groen et al., 2013), on global scale, with GCOMAP and GTM

(Kindermann et al., 2008). An extensive list of peer-reviewed GLOBIOM and G4M papers is provided in

the Annex.

11 https://www.pik-potsdam.de/research/climate-impacts-and-vulnerabilities/research/rd2-cross-cutting-activities/isi-mip 12 http://www.agmip.org/


4 EU Reference scenario 2016 development

The Reference scenario 2016 is based on most recent datasets and model projections available. While

input data is mainly based on FAOSTAT and EUROSTAT data, important scenario drivers for the

projections of the LULUCF emissions/removals for the EU Reference scenario 2016 are PRIMES

bioenergy demand projections (biofuels and solid biomass use) and the GEM-E3 macro-economic

projections (population and GDP growth) taken up by GLOBIOM.

4.1 Reference 2016 input data

The input data used in the GLOBIOM/G4M model were updated for the Reference scenario 2016 in

collaboration with JRC, the CAPRI team and national experts. Agricultural market balances, areas, and

prices have been updated to most recent EUROSTAT statistics based on the CAPRI database (October,

2015). Outside Europe the model is calibrated to FAOSTAT.

For the forest sector, historical data on wood production in EU28 was collected from recent FAOSTAT

data13. Values for the years 2000 to 2014 were taken from these historical data and corrected for France

and Germany as suggested by JRC (Pilli et al., 2015) to match national statistics. Initial land cover

information (based on CORINE) for the year 2000 was harmonized with total forest area and forest

available for wood supply from MCPFE (2015) (except Austria and Sweden for which we used values

recommended by national experts for EUCLIMIT project in 2013). G4M and GLOBIOM were adjusted

to match net annual increment in 2000 from MCPFE (2015), except a few countries for which 2000

values are not available and therefore were substituted by values for other years. Data on carbon stock

change (biomass only) from CRF Tables 4.A1 Forest Land Remaining Forest Land for 2000-2013 (2015

submissions) are used for adjusting forest management emissions in G4M. The afforestation and

deforestation rates in G4M have been calibrated to the data prepared by JRC based on UNFCCC (2015)

and KP submissions.

4.2 Reference 2016 LULUCF projections

In GLOBIOM, agricultural and forest biomass demand (for energy and non-energy uses such as food,

feed, industrial uses) is based on the interaction of different drivers:

(i) Bioenergy demand

(ii) Population growth

(iii) Income per capita growth

(iv) Response to prices

Drivers (i), (ii), and (iii) are exogenously introduced in the model in EUCLIMIT2 while (iv) is computed

endogenously. Bioenergy demand projections (i) are based on PRIMES biomass model for the EU28.

PRIMES feedstock specific biomass demand for energy use is directly represented through fixed

13 http://faostat3.fao.org/home/index.html#HOME, download Oct 2015

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


incorporation levels at EU28 member state level (hence energy demand is price inelastic and PRIMES

biomass demands are reproduced in GLOBIOM). The following PRIMES feedstock categories for energy

use are explicitly covered by GLOBIOM:

Domestic production of:

o Sugar beet

o Rapeseed and sunflower

o Wheat

o Annual lignocellulosic crops

o Perennial lignocellulosic crops

o Harvestable stemwood

Net trade of:

o Ethanol and biodiesel

o Solid biomass

In GLOBIOM, woody biomass for bioenergy can be sourced from fuelwood or energy wood (which

includes forest fellings and secondary wood residues i.e. sawdust). FAOSTAT data on fuelwood

production acts as minimum constraint on production levels if PRIMES biomass results are below

FAOSTAT levels.

Population growth (ii) is based on GEM-E3 model and non-energy related demand for agricultural and

forestry products increases linearly with population in each of the 57 GLOBIOM regions (including the

28 EU member states). GDP per capita changes (iii) determine non-energy demand variation depending

on income elasticity values. For the agricultural sector the income elasticities area calibrated to mimic

anticipated FAO projections of diets (Alexandratos and Bruinsma, 2012). Income elasticities for the forest

sector are taken from Rametsteiner et al. (2007).

The response of non-energy related demand to commodity prices (iv) is endogenously computed in

GLOBIOM. Price elasticities for the agricultural commodities are taken from a global database from

USDA (Muhammad et al., 2011) and for the forest sector from Rametsteiner et al. (2007). Hence, demand

for non-energy (material) wood use in example, is competing for the biomass with energy uses and is

projected endogenously by GLOBIOM. An increase in biomass production prescribed by the output of

the PRIMES biomass model is entirely reproduced in GLOBIOM. An increase in i.e. wood harvest for

energy purposes may impact the production of wood for non-energy purposes in a country through prices.

A country might thus produce more wood for energy from its (limited) domestic forest resources to

produce the amount prescribed by PRIMES biomass but reduce non-energy wood harvest. The reduction

in production of non-energy wood affects the trade of wood between countries but can also affect total

demand depending on wood demand in other countries and the countries wood price elasticity.

Outside Europe, an updated POLES Baseline scenario (JRC, 2015) was implemented for bioenergy

demand (1st and 2nd generation biofuel, woody biomass use), population, and GDP growth. Other

important drivers encompass crop productivities which are outside Europe based on 18 crop specific yield

response function to GDP per capita growth estimated for different income groups using a fixed effects


model. Inside Europe, productivity changes are based on historical yields until 2010, AgLINK-COSIMO

projections until 2025 and thereafter extrapolated based on the estimated yield response functions to GDP

growth for Europe. Productivity changes in the livestock sector are based on decomposition of historical

feed requirements by livestock category based on FAOSTAT from the ANIMAL CHANGE project.

Development of food diets are aligned with the FAO outlook towards 2050 (Alexandratos and Bruinsma,

2012).

5 Calculation of emissions

The models GLOBIOM and G4M together cover all UNFCCC land use categories of relevance for CO2

emissions. Only wetlands and settlements are not endogenously modelled. G4M covers the forestry sector

and delivers emissions from biomass and soil from afforestation and deforestation activities and

emissions from forest management. GLOBIOM supplies emissions from cropland and grassland

management.

5.1 Emissions from forestry activities

The G4M model produces estimates for forest area change, carbon removals and emissions from forests,

impacts of carbon incentives (e.g. avoided deforestation), and supply of biomass for bio-energy and non-

energy uses. The model is calibrated to forest area changes for the period 2000 to 2013 from UNFCCC

2015 submissions collected by JRC in 2016. The forest area was set to match the reported forest area in

2000 according to MCPFE (2015) (see Table 1).

Table 1: Data on afforestation, deforestation and forest area used as input to G4M for model calibration.

The values were collected by JRC in 2016. Forest area is based on MCPFE (2015).

Country

Average reported area (2000-

2013) [kha/year] Forest area

available for

wood supply

in 2000 [kha]

Harvest losses

Afforestation Deforestation Share of

fellings % Comment

Austria14 7.7 2.8 3,367 0.13

Based on

fellings/removals data

from UN-ECE/FAO

(2000)

Belgium 1.3 1.2 663 0.05

Based on


from Forest Europe

(2015)

Bulgaria 12.0 0.2 2,258 0.16

Based on


from Forest Europe

(2015)

14 Area provided by national experts is used instead of (MCPFE 2015) forest available for wood supply


Croatia15 2.9 0.4 1,749 0.07

Based on


from UN-ECE/FAO

(2000)

Cyprus N/A N/A N/A N/A

Czech

Republic 2.6 0.5 2,561 0.09

Based on data provided by

national experts for the

FMRL update in 2015

Denmark 4.1 0.3 567 0.15 Default value

Estonia 1.9 1.3 2,103 0.10

Based on


from Forest Europe

(2015)

Finland 3.8 17.7 20,317 0.09

Based on


from UN-ECE/FAO

(2000)

France 54.3 43.6 14,465 0.05

Based on information

provided by national

experts for the FMRL

update in 2015

Germany 18.8 10.8 10,833 0.20

Based on


from UN-ECE/FAO

(2000)

Greece 0.9 0.3 3,317 0.15 Default value

Hungary 12.3 1.4 1,622 0.12

Based on


from UN-ECE/FAO

(2000)

Ireland16 9.4 1.1 580 0.15 Default value

Italy 68.9 2.3 7,396 0.04

Based on


from UN-ECE/FAO

(2000)

Latvia 8.0 1.8 3,024 0.11

Based on


from Forest Europe

(2015)

Lithuania 6.5 0.2 1,756 0.15

Based on information

provided by national

experts for the FMRL

update in 2015

Luxembourg 0.4 0.3 87 0.15 Default value

Malta N/A N/A N/A N/A

Netherlands 3.0 2.3 288 0.14

Based on


from Forest Europe

(2015)

15 Afforestation and deforestation data are based on the (FAO FRA 2010) 16 2005 value of area of forest available for wood supply is used as values for earlier years are not provided in the

(MCPFE 2015)


Poland 35.7 0.6 8,342 0.12

Based on


from Forest Europe

(2015)

Portugal 18.5 9.0 2,229 0.02

Based on


from UN-ECE/FAO

(2000)

Romania 18.6 8.9 5,029 0.02

Based on


from Forest Europe

(2015)

Slovakia 1.4 0.3 1,767 0.03

Based on


from Forest Europe

(2015)

Slovenia 4.6 3.9 1,157 0.15 Default value

Spain17 35.2 16.5 13,804 0.04

Based on


from Forest Europe

(2015)

Sweden18 22.3 13.0 23,300 0.07

Based on


from UN-ECE/FAO

(2000)

United

Kingdom 11.4 3.1 2,954 0.14

Based on


from UN-ECE/FAO

(2000)

The initial forest growing stock (aboveground biomass) per grid cell was taken from the European forest

biomass map from Gallaun et al. (2010), outside Europe the biomass map from Kindermann et al. (2008)

is used. Increment is determined by a potential Net Primary Productivity (NPP) map (Cramer et al., 1999)

and translated into net annual increment (NAI). G4M uses forest growth functions specific for major tree

species – fir, spruce, pine, birch, beech, oak and larch developed by Kindermann (2013). Tree species

distribution in each grid cell are distinguished using a species map by Brus et al. (2012).

Initial growing stock was scaled to the degree possible to correspond to reported data on these variables

from either public sources (e.g. FAO, Forest Europe or national data). NAI and forest area available for

wood supply were scaled to match 2000 values reported in the MCPFE (2015). The model uses the age

class structure reported by countries for initialisation. The harmonisation of area, age class structure,

biomass stock, wood harvest, and wood increment based on different sources is a challenge. These

variables are not entirely independent. A change in one variable consequently implies changes in another.

17 2005 value of area of forest available for wood supply is used as values for earlier years are not provided in the

(MCPFE 2015) 18 Area of productive forest provided by national experts is used instead of (MCPFE 2015) forest available for wood

supply


Forest management (Forest land remaining Forest land)

The main forest management options considered by G4M are variation of thinning and choice of rotation

length. The rotation length can be individually chosen but the model can estimate optimal rotation lengths

to maximize increment, stocking biomass or harvestable biomass.

The model uses projections of wood demand per country estimated by GLOBIOM to calculate total

harvest iteratively. G4M uses 2000-2010 average wood production map by Verkerk et al. (2015) to

initialize wood production in model cells. In initial year G4M selects minimum amount of cells necessary

for sustainable production of demanded amount of wood on country scale. In consequent years, if total

harvest is smaller than wood demand the model changes grid per grid (starting from the most productive

forest) management to a rotation length that optimizes forest mean annual increment and thus allows for

more harvest. The rotation length is changed at maximum by five years per time step. If harvest is still too

small and unmanaged forest is available the status of the unmanaged forest will change to managed. If

total harvest exceeds demand the model changes management to maximum biomass rotation length, i.e.

manages forests for carbon sequestration. If wood demand is still lower than potential harvest managed

forest can be transferred into unmanaged forest. Rotation length can be changed only if the net present

value of forestry with the new rotation is not less than the current rotation (1-5% tolerance is allowed),

i.e., the change in forest management must be economically feasible. Thinning is applied to all managed

forests and the stands are thinned to maintain a stocking degree specified. The default value is 1 where

thinning mimics natural mortality along the self-thinning line.

Forest management (FM) activities can increase or decrease the biomass carbon stock in the forest. G4M

tracks the development of carbon stored in forest biomass. By multiplying the area of forest land

remaining forest land (FL r FL) per grid cell with changes in biomass carbon stocks at an annual basis,

annual biomass carbon emissions are derived (see Equation 1).

Biomass C emissions FM= Area FL r FL * Total biomass C stock changes (1)

Aggregated at country level the model produces emission projections that are driven by the forest growth

model, the age class distribution of the forest, management activities and wood removals.

In order to ensure consistency between model results and historical data reported by the country, the

emissions and removals estimated by the models for the entire time series (up to 2050) were rescaled

using historical UNFCCC data from the countries for the period 2000-2013 (period of overlapping data

from UNFCCC and model projection). An “offset” was calculated as difference between [average of

country’s emissions and removals from biomass, soils and dead organic matter for the period 2000-2013]

and [average of models’ estimated emissions and removals from biomass for the period 2000-2013]. The

“offset” was added to the model’s original value (thereafter referred to as “calibrated” model) which

ensures consistency between country data and models’ data in terms of:

i. Absolute levels of emissions and removals from biomass, i.e. to reflect differences in estimates

which may be due to a large variety of factors, including different input data, different


parameters, different estimation methods (e.g., some country uses a „stock-change approach”,

while the models use a „gain-loss approach”);

ii. Coverage of non-biomass pools and GHG sources (soils and dead organic matter).

The “rescaling” automatically incorporates into the projections the average GHG impact (for the period

2000-2013) of past natural disturbances, which are not explicitly estimated by G4M (e.g. emissions from

fires etc.). The future trend of emissions and removals up to 2050 as predicted by the G4M is not affected

by this ex-post procedure, but only by the current (and projected) forest characteristics (e.g., age structure,

etc.) and the future harvest demand (for which no ex-post processing is applied).

Afforestation

Starting from the calibrated afforestation rates provided by JRC based on UNFCCC (2015) and KP

submissions, G4M projects the development of future afforestation area based on the development of

basic drivers received from GLOBIOM, i.e. projections of land prices and wood prices but also input

from Member States when relevant. The potential value of forestry activities on a grid cell based on wood

prices is compared to the land price and a decision on afforestation taken by the model. Future demand

for wood influences afforestation rates only indirectly through the wood price estimated by GLOBIOM.

Newly established forests contribute to wood production only after reaching a certain maturity, i.e.

smaller dimensioned timber from thinning after 10 to 15 years and sawn wood after 30 to 50 years in

Central Europe. In the longer run increased wood demand also increases afforestation rates.

To ensure consistency in the total land area balance between GLOBIOM and G4M, GLOBIOM supplies

G4M with the maximum area that can be afforested. This consists of the category “Other natural

vegetation” which includes natural vegetation not occupied by cultivated cropland or grassland necessary

for food and feed production (e.g. fallow land, abandoned grassland and cropland, etc.). The category can

also include other natural vegetation that is not suitable for afforestation or areas on which afforestation is

not allowed. In practice it is difficult to identify other natural vegetation that is not available for

afforestation. Therefore we assume generally that 50% of the other natural vegetation identified by

GLOBIOM can be afforested by G4M.

The forest established on afforested land has the same properties, i.e. growth rates, management rules as

the forest already existing in neighbouring grid cells. This means that forest growth rates of afforested

land are rather moderate compared to dedicated forest plantations established for commercial timber

production e.g. in Southern Europe. Such plantations established on cropland or grassland have high

growth rates and short rotations and are not considered to fall into the definition of forest. They are

covered by GLOBIOM and reported under the cropland category.

In general the emissions from afforestation and reforestation (AR) can be described by the area of other

land converted to forest land (FL) and an emission factor for afforestation (see Equation 2).

Biomass C removals AR = Other land area converted to FL * Biomass C increment (2)


The biomass C increment on afforested area is estimated by G4M based on the forest growth model. The

increment first increases with forest age and declines thereafter. Afforestation area can be established

every year in a certain fraction of the grid cell. The forest age, biomass and carbon stock development are

tracked over the simulation period for each grid cell afforested and differ due to grid specific growth

rates. This dynamic accounting of carbon removals through afforestation is different from accounting in

many Member States that apply an average growth rate of forests over the rotation period, leading to a

constant removal rate. This can lead to a lower estimate of the model for carbon accumulation by early

stage afforestation areas and a higher estimate for the rate in later stage compared to country reported

data. However, the dynamic development of carbon accumulating in new forests is more realistic.

Afforestation also leads to changes in soil organic carbon (SOC). Initial soil carbon is taken from

Kindermann (2008). The accumulation rate depends on the amount of litter, the maximum accumulation

speed is 0.04 tC/ha/year for coniferous, 0.2 tC/ha/year for mixed and 0.35 tC/ha/year for deciduous

forests (Czimczik et al., 2005). Carbon in litter accumulates with maximum speed 0.95tC/ha/year

(Czimczik et al., 2005) and depends on aboveground biomass in forest age cohorts. To ensure consistency

with UNFCCC reporting which starts in 1990, we reallocated the afforested area and emissions before

2000 from the G4M forest management accounts in 2000 to afforestation. No reclassification of these

areas i.e. into forest management, takes place over time and they continue being reported under

afforestation.

Deforestation

Land and wood prices that G4M receives from GLOBIOM are also used to project trends in deforestation.

The deforestation rates in G4M have been calibrated to the data prepared by JRC based on UNFCCC

(2015) and KP submissions. Emissions from deforestation (D) are calculated as the sum of area of forest

land (FL) converted to other land per grid cell times the average biomass stock per grid cell, aggregated to

country level (see Equation 3).

Biomass C emissions D = FL area converted to other land * Average biomass C stock (3)

It is assumed that the entire biomass carbon is released immediately at the point of forest conversion. We

assume that after a site is deforested up to 40% of soil organic matter is lost (Czimczik et al., 2005). The

rate of soil organic matter decomposition is a function of long-term average annual temperature and

precipitations in each grid cell (Willmott et al., 1998) according to (Esser, 1991). Emissions from

deforestation have not undergone rescaling as performed for forest management emissions.

5.2 Emissions from harvested wood products

Emissions from harvested wood products (HWP) are estimated following the Durban Accords (Decision

2/CMP.7) and respective Tier 2 IPCC guidelines. We use FAOSTAT data on historical wood use for

sawn wood, pulpwood, energy wood and other wood from 1961 to 2014 and GLOBIOM projections

onwards until 2050. On the basis of these variables the HWP C stock is calculated using first-order decay


functions with category specific default half-lives (HL) ranging from 35 years for sawn wood, 25 years

for wood-based panels and other wood products, 2 years for paper and 0 years for energy production (no

accounting for imported wood, supply side approach according to guidelines). The following equation is

applied.

HWP C stocki+1 = e-k * HWP C stocki + [(1-e-k)/k] * Inflowi (4)

Where i is the year, HWP C stock the carbon stock in the particular HWP category at the beginning of

year i, k is the decay constant of first-order decay for HWP category (k = ln(2)/HL), Inflow is the annual

inflow to the particular HWP category. It is assumed that the HWP pools are in steady state at the initial

time in 1961. The emissions from HWP are finally estimated until 2050 by calculating the differences

between the yearly carbon stocks as provided by GLOBIOM averaged for each 5 year period.

5.3 Emissions from cropland management

Emissions from cropland remaining cropland are calculated by multiplying the area under cropland

management with an emission factor (see Equation 5).

SOC emissions CL management = Area CL r CL * Emission factor CL (5)

To estimate the emission factor for cropland (CL) and represent SOC dynamics and SOC emissions

accurately, the approach presented in Frank et al. (2015) in detail was used. SOC response functions for

each of the crop rotation and tillage system represented in GLOBIOM were estimated at the grid level

using a biophysical process-based crop model EPIC. The estimated SOC response functions for the

different crop rotations and tillage systems are approximated in GLOBIOM and allow explicit

representation of SOC dynamics over time for land remaining cropland, land converted to cropland

(including perennial crops for energy production). Besides SOC emissions, biomass accumulation from

short rotation tree plantations and above- and belowground biomass changes due to land use change to

cropland are reported under cropland management.

To ensure consistency between model results and historical UNFCCC (2015) data reported by the

member states, cropland emissions estimated by GLOBIOM for the entire time series (up to 2050) were

rescaled using historical data from the country for the period 2000-2013 (period of overlapping data from

UNFCCC and model projection) as done for the forest management emissions. Total cropland emissions

used for rescaling exclude emissions from deforestation to avoid double counting (these are reported by

G4M separately). The approach ensures consistency with latest UNFCCC (2015) data and allows to

account also for emissions from organic soils which are not specifically modelled. Also cropland areas

have been harmonized with UNFCCC (2015) data.


5.4 Emissions from grassland management

In GLOBIOM, grassland areas do not represent total existing grasslands but productive grassland for

animal feeding only. The grassland area in GLOBIOM thus depends on animal feed demand, grassland

productivity estimated by EPIC for each SimU and total grassland area according to CORINE. Grassland

not needed to satisfy fodder demand or natural grasslands are reported under the category “other natural

vegetation” and is therefore available for afforestation.

To improve the consistency with reported UNFCCC data the category “other natural vegetation” in the

model (which contains natural grasslands) was disaggregated ex-post based on UNFCCC data on total

grassland area. If reported grassland area exceeded the GLOBIOM 2000 areas, missing area was

reallocated from the “other natural vegetation” if available. This allows to more accurately represent total

grassland area for most countries and improved the consistency of emissions with UNFCCC reporting. To

avoid overestimation of the grassland sink (especially for land converted to grassland), areas were

reallocated from land converted to grassland to grassland remaining grassland after a 20 year period (in

line with IPCC accounting).

SOC emissions from grassland management (GL) are calculated by multiplying grassland area (grassland

remaining grassland, GL r GL) with a country specific emission factor GL (see Equation 7).

SOC emissions GL management = Area GL r GL * Emission factor GL (7)

The emission factor is derived from UNFCCC reported data by dividing reported emissions from

grassland remaining grassland by existing grassland area. The emission factor for other land converted to

grassland (excluding emissions from deforestation in order to avoid double counting as reported by G4M)

was calculated as well. In cases where no UNFCCC data is available emission factors for land converted

to grassland are calculated based on a generic emission factor of -1.83 t CO2/ha/y (Soussana et al., 2004).

The grassland emission factor contains large uncertainties. It can be expected that emissions per ha differ

between countries with different climate and soil conditions. Countries can apply quite different methods

to report grassland emissions so that emissions from different countries are likely to differ also due to

different methods applied. Inconsistency in reporting method between member states may lead to

assignment of diverging emission factors even for countries with similar grassland properties and

management. It is further assumed that the emission factor for grassland is not affected by the change in

grassland areas. In principle it can be expected that the emissions per ha change when areas more or less

productive than the average grassland area leave the grassland category. This is a simplification to

overcome data gaps. However, deriving the emission factor from UNFCCC data leads overall to a better

comparability with historical data at hectare level.


5.5 Emissions from wetlands, settlements and other lands

Wetland emissions and areas are not modelled and kept constant at 2013 levels as reported in UNFCCC

(2015) data. Settlement area is assumed to increase at a smaller pace over time following a logarithmic

expansion trend based on historical UNFCCC data (period 2003-2013). Emissions are estimated using an

average emission factor (2000-2013) based on UNFCCC (2015) data. Emissions from other land are

besides view exceptions based on reported UNFCCC (2015) data and kept constant beyond 2013.


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global long-term scenarios for agriculture differ? An overview of the AgMIP Global Economic

Model Intercomparison." Agricultural Economics 45(1): 3-20.

Warszawski, L., K. Frieler, V. Huber, F. Piontek, O. Serdeczny and J. Schewe (2014). "The Inter-Sectoral

Impact Model Intercomparison Project (ISI–MIP): Project framework." Proceedings of the

National Academy of Sciences 111(9): 3228-3232.

Williams, J. R. (1995). The EPIC Model. Computer Models of Watershed Hydrology. V. P. Singh, Water

Resources Publications, Highlands Ranch, Colorado: 909-1000.

Willmott, C., K. Matsuura and D. Legates (1998). Global Air Temperature and Precipitation: Regridded

Monthly and Annual Climatologies (Version 2.01). U. o. D. Center for Climatic Research

Department of Geography.

Xiong, W., J. Balkovič, M. van der Velde, X. Zhang, R. C. Izaurralde, R. Skalský, E. Lin, N. Mueller and

M. Obersteiner (2014). "A calibration procedure to improve global rice yield simulations with

EPIC." Ecological Modelling 273: 128-139.

http://cdiac.ornl.gov/


Annex A. Relevant peer reviewed GLOBIOM/G4M articles

Biomass & Bioenergy

Böttcher, H., Verkerk, P. J., Gusti, M., HavlÍk, P., & Grassi, G. (2012). Projection of the future EU forest

CO 2 sink as affected by recent bioenergy policies using two advanced forest management models. GCB

Bioenergy, 4(6), 773–783. doi:10.1111/j.1757-1707.2011.01152.x

Böttcher, H.; Frank, S.; Havlik, P.; Elbersen, B. (2013) Future GHG emissions more efficiently controlled

by land use policies than by bioenergy sustainability criteria. Biofuels, Bioproducts and Biorefining, 7,

115-125. doi: 10.1002/bbb.1369

Frank, S., Böttcher, H., Havlík, P., Valin, H., Mosnier, A., Obersteiner, M., … Elbersen, B. (2013). How

effective are the sustainability criteria accompanying the European Union 2020 biofuel targets? GCB

Bioenergy, 5(3), 306–314. doi:10.1111/j.1757-1707.2012.01188.x

Havlík, P., Schneider, U. a., Schmid, E., Böttcher, H., Fritz, S., Skalský, R., … Obersteiner, M. (2011).

Global land-use implications of first and second generation biofuel targets. Energy Policy, 39(10), 5690–

5702. doi:10.1016/j.enpol.2010.03.030

Kraxner, F., Nordström, E.-M., Havlík, P., Gusti, M., Mosnier, A., Frank, S., … Obersteiner, M. (2013).

Global bioenergy scenarios – Future forest development, land-use implications, and trade-offs. Biomass

and Bioenergy, 57, 86–96. doi:10.1016/j.biombioe.2013.02.003

Lauri, P., Havlík, P., Kindermann, G., Forsell, N., Böttcher, H., & Obersteiner, M. (2014). Woody

biomass energy potential in 2050. Energy Policy, 66, 19–31. doi:10.1016/j.enpol.2013.11.033

Mosnier, A., Havlík, P., Valin, H., Baker, J., Murray, B., Feng, S., … Schneider, U. a. (2013). Alternative

U.S. biofuel mandates and global GHG emissions: The role of land use change, crop management and

yield growth. Energy Policy, 57, 602–614. doi:10.1016/j.enpol.2013.02.035

Repo A, Bottcher H, Kindermann G, Liski J (2015). Sustainability of forest bioenergy in Europe:

Land-use-related carbon dioxide emissions of forest harvest residues. GCB Bioenergy, 7(4):877-887

Seebach L, McCallum I, Fritz S, Kindermann G, Leduc S, Bottcher H, Fuss S (2012). Choice of

forest map has implications for policy analysis: A case study on the EU biofuel target. Environmental

Science & Policy, 22:13-24

Turkovska O, Ohremchuk IA, Gusti M (2015). Assessment of efficiency of a policy on reduction of

CO2 emissions in Ukrainian forests for three socio-economic scenarios. Research Journal of Ukrainian

National Forestry University, 25(4):98-104

Climate change mitigation


Cohn, A. S., Mosnier, A., Havlík, P., Valin, H., Herrero, M., Schmid, E., … Obersteiner, M. (2014a).

Cattle ranching intensification in Brazil can reduce global greenhouse gas emissions by sparing land from

deforestation. Proceedings of the National Academy of Sciences of the United States of America,

111(20), 7236–41. doi:10.1073/pnas.1307163111

Frank, S., Schmid, E., Havlík, P., Schneider, U.A., Böttcher, H., Balkovič, J. et al. 2015 The dynamic soil

organic carbon mitigation potential of European cropland. Global Environmental Change, 35, 269-278.

doi:10.1016/j.gloenvcha.2015.08.004

Havlík, P., Valin, H., Herrero, M., Obersteiner, M., Schmid, E., Rufino, M. C., … Notenbaert, A. (2014).

Climate change mitigation through livestock system transitions. Proceedings of the National Academy of

Sciences of the United States of America, 111(10), 3709–14. doi:10.1073/pnas.1308044111

Herrero, M., Havlík, P., Valin, H., Notenbaert, A., Rufino, M. C., Thornton, P. K., … Obersteiner, M.

(2013). Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock

systems. Proceedings of the National Academy of Sciences of the United States of America, 110(52),

20888–93. doi:10.1073/pnas.1308149110

Kindermann, G., M. Obersteiner, B. Sohngen, J. Sathaye, K. Andrasko, E. Rametsteiner, B.

Schlamadinger, S. Wunder and R. Beach (2008). "Global cost estimates of reducing carbon emissions

through avoided deforestation." Proceedings of the National Academy of Sciences of the United States of

America 105(30): 10302-10307. doi: 10.1073/pnas.0710616105

Kindermann G., Obersteiner M., Rametsteiner E. and McCallcum I., 2006: Predicting the Deforestation–

Trend under Different Carbon–Prices. Carbon Balance and Management, 1:15; doi:10.1186/1750-0680-1-

15Kindermann GE, Schörghuber S, Linkosalo T, et al. (2013) Potential stocks and increments of woody

biomass in the European Union under different management and climate scenarios. Carbon Balance and

Management;8:2. doi:10.1186/1750-0680-8-2.

Mosnier, A., Havlík, P., Obersteiner, M., Aoki, K., Schmid, E., Fritz, S., … Leduc, S. (2012). Modeling

Impact of Development Trajectories and a Global Agreement on Reducing Emissions from Deforestation

on Congo Basin Forests by 2030. Environmental and Resource Economics, 57(4), 505–525.

doi:10.1007/s10640-012-9618-7

Strassburg BNB, Rodrigues ASL, Gusti M, Balmford A, Fritz S, Obersteiner M, Turner RK, Brooks

TM (2012). Impacts of incentives to reduce emissions from deforestation on global species extinctions.

Nature Climate Change, N2, pp.350–355.

Food security & Climate change impacts and adaptation

Leclere, D., Havlik, P., Fuss, S., Schmid, E., Mosnier, A., Walsh, B., Valin, H., Herrero, M., Khabarov,

N. and Obersteiner, M. (2014) Climate change induced transformations of agricultural systems: insights

from a global model. Environmental Research Letters, 9 (12). no.124018.

http://www.sciencedirect.com/science/article/pii/S095937801530025X

http://pure.iiasa.ac.at/view/iiasa/212.html







Kindermann, G. E., S. Schörghuber, T. Linkosalo, A. Sanchez, W. Rammer, R. Seidl and M. J. Lexer

(2013). "Potential stocks and increments of woody biomass in the European Union under different

management and climate scenarios." Carbon Balance and Management 8(1): 1-20. doi: 10.1186/1750-

0680-8-2:

Mosnier, A., Obersteiner, M., Havlík, P., Schmid, E., Khabarov, N., Westphal, M., … Albrecht, F.

(2014). Global food markets, trade and the cost of climate change adaptation. Food Security, 6(1), 29–44.

doi:10.1007/s12571-013-0319-z

Schneider, U. A., Havlík, P., Schmid, E., Valin, H., Mosnier, A., Obersteiner, M., … Fritz, S. (2011).

Impacts of population growth, economic development, and technical change on global food production

and consumption. Agricultural Systems, 104(2), 204–215. doi:10.1016/j.agsy.2010.11.003

Valin, H., Havlík, P., Mosnier, a, Herrero, M., Schmid, E., & Obersteiner, M. (2013). Agricultural

productivity and greenhouse gas emissions: trade-offs or synergies between mitigation and food security?

Environmental Research Letters, 8(3), 035019. doi:10.1088/1748-9326/8/3/035019

Model Intercomparison exercises

Groen T., Verkerk P.J., Böttcher H., Grassi G., Cienciala E., … Blujdea V., What causes differences

between national estimates of forest management carbon emissions and removals compared to estimates

of large-scale models?, Environmental Science & Policy, Volume 33, November 2013, Pages 222-232,

ISSN 1462-9011, http://dx.doi.org/10.1016/j.envsci.2013.06.005.

Lotze-Campen, H., von Lampe, M., Kyle, P., Fujimori, S., Havlik, P., van Meijl, H., … Wise, M. (2014).

Impacts of increased bioenergy demand on global food markets: an AgMIP economic model

intercomparison. Agricultural Economics, 45(1), 103–116. doi:10.1111/agec.12092

Nelson, G. C., & Shively, G. E. (2014). Modeling climate change and agriculture: an introduction to the

special issue. Agricultural Economics, 45(1), 1–2. doi:10.1111/agec.12093

Nelson, G. C., van der Mensbrugghe, D., Ahammad, H., Blanc, E., Calvin, K., Hasegawa, T., …

Willenbockel, D. (2014). Agriculture and climate change in global scenarios: why don’t the models agree.

Agricultural Economics, 45(1), 85–101. doi:10.1111/agec.12091

Robinson, S., van Meijl, H., Willenbockel, D., Valin, H., Fujimori, S., Masui, T., … von Lampe, M.

(2014). Comparing supply-side specifications in models of global agriculture and the food system.

Agricultural Economics, 45(1), 21–35. doi:10.1111/agec.12087

Schmitz, C., van Meijl, H., Kyle, P., Nelson, G. C., Fujimori, S., Gurgel, A., … Valin, H. (2014). Land-

use change trajectories up to 2050: insights from a global agro-economic model comparison. Agricultural

Economics, 45(1), 69–84. doi:10.1111/agec.12090


Von Lampe, M., Willenbockel, D., Ahammad, H., Blanc, E., Cai, Y., Calvin, K., … van Meijl, H. (2014).

Why do global long-term scenarios for agriculture differ? An overview of the AgMIP Global Economic

Model Intercomparison. Agricultural Economics, 45(1), 3–20. doi:10.1111/agec.12086

Model development

Gusti M. (2010) An Algorithm for Simulation of Forest Management Decisions in the Global Forest

Model. Artificial Intelligence, N4, pp.45-49

Turkovska O, Gusti M (2015). Forest management algorithm considering assortment structure for

geospatial global forest model G4M. Research Journal of Ukrainian National Forestry University,

25(5):339-345

methodology for estimation and modelling of eu lulucf ... · other global models or databases...

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