What causes differences between nationalestimates of forest management carbon emissionsand removals compared to estimates of large-scalemodels?

Thomas A. Groen a,*, Pieter J. Verkerk b, Hannes Bottcher c, Giacomo Grassi d,Emil Cienciala e, Kevin G. Black f, Mathieu Fortin g, Margret Kothke h,Aleksi Lehtonen i, Gert-Jan Nabuurs b,j, Lora Petrova d, Viorel Blujdea d

aUniversity of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Department of Natural

Resources, P.O. Box 217, 7500 AE Enschede, The NetherlandsbEuropean Forest Institute, Sustainability and Climate Change program, Torikatu 34, 80100 Joensuu, Finlandc International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1,

A-2361 Laxenburg, Austriad Joint Research Centre, European Commission, Institute for Environment and Sustainability, Forest resources and

Climate Unit, TP 290, Via Fermi, 21027 Ispra (VA), Italye Institute of Forest Ecosystem Research, Areal 1. Jilovske a.s. 1544, 254 01 Jilove u Prahy, Czech Republicf FERS Ltd, Forestry Division, 117 East Courtyard, Tullyvale, Cabinteely, Dublin 18, Irelandg LERFoB UMR 1092, AgroParisTech/INRA, 14 rue Girardet, F-54042 Nancy Cedex, Franceh Johann Heinrich von Thunen-Institut, Federal Research Institute for Rural Areas, Forestry and Fisheries, Institute of

Forest Based Sector Economics, Leuschnerstrasse 91, D-21031 Hamburg, Germanyi Finnish Forest Research Institute, P.O. Box 18, 01301 Vantaa, FinlandjAlterra, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands

e n v i r o n m e n t a l s c i e n c e & p o l i c y 3 3 ( 2 0 1 3 ) 2 2 2 – 2 3 2

a r t i c l e i n f o

Article history:

Received 27 July 2012

Received in revised form

20 May 2013

Accepted 18 June 2013

Available online 17 July 2013

Keywords:

Forest management

Kyoto protocol

Forest assessments

Forest policy

Carbon models

a b s t r a c t

Under the United Nations Framework Convention for Climate Change all Parties have to

report on carbon emissions and removals from the forestry sector. Each Party can use its

own approach and country specific data for this. Independently, large-scale models exist

(e.g. EFISCEN and G4M as used in this study) that assess emissions and removals from this

sector by applying a unified approach to each country, still often based on country specific

data.

Differences exist between the national reported values and the calculations from the

large scale models. This study compares these models with national reporting efforts for

24 EU countries for the period 2000–2008, and identifies the most likely causes for differ-

ences. There are no directly identifiable single input parameters that could be targeted to

fully close the gap between country and model estimates. We found that the method applied

by the country (i.e. stock-difference or gain-loss) contributes significantly to differences for

EFISCEN and was the best explaining variable for G4M, although for the latter it was not

significant. Other variables (biomass expansion factors, harvest volumes and the way

harvest losses are treated) were not found to provide a conclusive explanation for the

differences between the model estimations and the country submissions in an over-all

* Corresponding author. Tel.: +31 0 534874588; fax: +31 0 534874400.

Available online at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/envsci

E-mail addresses: tagroen@gmail.com, t.a.groen@utwente.nl (T.A. Groen).

1462-9011/$ – see front matter # 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.envsci.2013.06.005

analysis. However, at the level of individual countries several different causes for differ-

ences were identified. This suggests that to really close the gap between country submis-

sions and large scale models, close collaboration between modellers and country experts is

needed, calling for openness and willingness to share relevant data and to compare GHG

inventories with independent estimates. This would enable to improve the confidence both

in historical GHG inventories and in the models which are needed to project the future forest

sink for several policy issues.

# 2013 Elsevier Ltd. All rights reserved.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 3 3 ( 2 0 1 3 ) 2 2 2 – 2 3 2 223

1. Introduction

Emissions and removals of carbon dioxide (CO2) from

managed forests in European Union (EU) countries form a

substantial contribution to the total carbon budget of the land

use, land use change and forestry sector (LULUCF) and are

responsible for the currently observed net sink of the sector.

Under the United Nations Framework Convention for Climate

Change (UNFCCC) all signatory Parties have to report on these

emissions and removals (UNFCCC, 1997). Reporting on

managed forests includes mainly carbon (C) emissions to –

and removals from the atmosphere in the form of carbon

dioxide (CO2) for living biomass, dead organic matter and soil

organic carbon. Reporting under the UNFCCC also forms the

basis for reporting under the Kyoto Protocol.

In the context of the UNFCCC negotiations on new

accounting rules for LULUCF (Grassi et al., 2012), projections

are needed of future emissions for setting the forest manage-

ment reference levels post 2012 (http://unfccc.int/bodies/awg-

kp/items/5896.php). These reference levels represent the level

of CO2 emissions or removals from forest management

activities against which future emissions or removals are

compared and which then generate either an emission credit

or debit. Amongst Parties, these projections and their

methodologies differ a lot. Instead of each Party developing

its own methodology for projections, the EU Member States

could also rely on large scale forest models, which have a

harmonised approach for all countries, and are to a large

degree parameterized with country specific data. Several

countries decided to estimate the reference level based on

such model projections from a study by Bottcher et al. (2012),

who projected future emissions of carbon from forests

employing two large-scale models. Such models sometimes

adopt more general assumptions compared to national

approaches, but they also use forest inventory data and other

country-specific data as input. An attempt was made by

Bottcher et al. (2012) to use as much as possible the same data

sources as used in country submissions. Despite these

harmonisation efforts, significant differences were identified

between reported emissions and removals and the output of

the large scale models (Bottcher et al., 2012). At this moment

the exact causes of the differences are unclear, but could be

related to definitions and their interpretations, methods and

data applied by the countries versus the large scale models.

Such differences will have large consequences for the

estimated emissions or removals of carbon (Cienciala et al.,

2008).

The aim of the current study was to conduct a systematic

analysis by comparing CO2 emission and removal estimates

from living biomass from Forest Management (FM) activities

on forest existing in 1989, as submitted by EU Member States

and projected by two large-scale models. National level

biomass CO2 removal estimates from FM activities for 24

countries from three different sources are compared; existing

country submissions to the UNFCCC (based on national GHG

inventory reports) and two large-scale models: the European

Forest Information Scenario model (EFISCEN 3.1.3, Nabuurs,

2001; Verkerk et al., 2011; Schelhaas et al., 2007; Eggers et al.,

2008) and the Global Forest Model (G4M, Kindermann et al.,

2006; Gusti et al., 2008; Gusti, 2010). Firstly, we analyze the

overall comparability of removal estimates as reported by 24

Member States versus the large scale models. Secondly, at a

more detailed level, we analyze the possible causes for

differences between individual country submissions and

model outputs for six selected countries.

2. Materials and methods

2.1. Overall approach

For 24 countries, estimates of the living biomass CO2 sink from

G4M and EFISCEN as well as the national submissions to the

UNFCCC were collected for the period 2000–2008. We selected

this period because data for country submissions is publicly

available for this period. We focused on Bulgaria, Czech

Republic, Finland, France, Germany and Ireland for a more

detailed analysis.

2.2. Model estimates

The model estimates of the living biomass CO2 sink from G4M

and EFISCEN were collected from Bottcher et al. (2012). The

models applied by Bottcher et al. (2012) are described in detail

in Section S1 and Table S1 of the online supplementary

materials.

To increase comparability between models and country

submissions by EU member states, an effort was made by

Bottcher et al. (2012) to harmonise input data and assumptions

of the models to the national estimates’ assumptions. The

harmonisation was based on a workshop of modellers and EU

member states’ representatives, a technical assessment

coordinated by the UNFCCC and bilateral consultations of

modellers and EU member states’ representatives. The

adjustments related to forest inventory data, historic harvest

Fig. 1 – Forest management biomass removals for the six selected countries. All other countries are shown in the online

supplementary fig. S1. Dotted line: submission of 2009; dashed line: submission of 2010; solid line; submission of 2011.

Sources: submissions to UNFCC (http://unfccc.int/bodies/awg-kp/items/5896.php) and GHG inventories (forest land

remaining forest land).

e n v i r o n m e n t a l s c i e n c e & p o l i c y 3 3 ( 2 0 1 3 ) 2 2 2 – 2 3 2224

rates, species-specific rotation lengths (in EFISCEN only), and

biomass expansion factors (BEFs). Altogether these adjust-

ments resulted in harmonized assumptions on forest area and

historical wood production and aimed to better reflect the

‘‘forest management’’ data and the calculations applied by

each country for reporting under the Kyoto Protocol.

Both EFISCEN and G4M used historic harvest data until 2005

which were either provided by member states when available

or derived from the FAOSTAT database (http://faostat.fao.org/).

If harvest data was available as underbark volumes, a

bark fraction of 12% was added. Wood production after 2005

was derived from the Global Biosphere Management Model

e n v i r o n m e n t a l s c i e n c e & p o l i c y 3 3 ( 2 0 1 3 ) 2 2 2 – 2 3 2 225

(GLOBIOM). See Bottcher et al. (2012) for details, and Table S2 in

the supplementary online materials for explicit details on the

six selected countries.

2.3. National estimates

National estimates of the FM biomass CO2 sink for the period

2000–2008 were taken from the national submissions to

UNFCCC (Fig. 1) (http://unfccc.int/national_reports/annex_

i_ghg_inventories/national_inventories_submissions/items/

6598.php). These FM estimates are based on the methods used

in the national GHG inventories. For national GHG inventories,

the IPCC good practice guidance (IPCC, 2003) proposes two

methods: the ‘‘gain-loss’’ method which sums all gains

(growth) and losses (harvests, fires, etc.) of biomass over a

certain period of time, and the ‘‘stock-difference’’ method

which compares biomass estimates in a given area at two

points in time. The implementation at the national scale is not

strict and countries are free to use a mix of the two

abovementioned methods. The countries in the dataset, and

the approach applied in each country is shown in Table 1.

The choice of the method depends on national data

availability and the monitoring system in place. The basic

underlying data may come from forest inventories or other

forestry data such as harvest statistics. The stock-difference

Table 1 – Estimation methods used by EU countries to estimat(information from GHG inventories submitted to UNFCCC; sele

Country Estimation method

Austria G-L based on NFI data.

Belgium S-D (Walloon region) an

Denmark S-D based on Forest Cen

data

Finland G-L based on NFI and n

BEFs

France G-L based NFI and harv

Germany S-D based on NFI and c

Greece S-D based on FMP datab

Ireland G-L with a model using

estimates, as well as co

Italy G-L based on NFI and h

Luxembourg G-L based on forestry st

Netherlands G-L based on NFI data a

Portugal G-L based on NFI data a

Spain S-D based on NFI data

Sweden S-D based on NFI data

UK G-L modelled from plan

Bulgaria S-D based on FMP data

Czech Republic G-L based on FMP datab

default

Estonia S-D based on FMP datab

Hungary S-D based on FMP datab

Latvia G-L based on FMP (befo

Lithuania S-D based on FMP (befo

Poland G-L based on FMP datab

Romania G-L based on FMP datab

Slovakia G-L based on FMP datab

Slovenia S-D based on NFI data

Default values are provided by GPG for LULUCF (IPCC, 2003).

BEF, biomass expansion factor; R, root to shoot ratio; WD, wood density

forest inventories; FMP, forest management planning database.

method relies essentially on the former, while the gain-loss

method may use information from both, in some cases

combined with modelling. National forest inventories (NFI) are

either based on statistical sampling over the whole country, or

stand-wise inventories for local forest management planning

(FMP). In the NFI’s, a sample design is laid out over the country

with up to 70,000 points over a country (Tomppo et al., 2010). At

every point, the diameter and height of trees in the plot are

measured. With volume functions per species, the volumes

and growth rates are derived. In the case of an FMP style

inventory, the inventories are implemented by management

units, and cover all the stands in a country. Each stand is

visited and an estimate is made of the species and standing

volume. From other information, the increment is assessed.

FMP’s were employed in the former Eastern European

countries. Most of those countries have moved to a sample

based NFI, which is seen as more accurate. In general, the

stock-difference method provides more reliable estimates for

relatively large increases or decreases of biomass or whenever

intensive forest inventories are carried out. The reliability of

this method is closely linked to the sampling error in

the inventory data. Relatively small biomass changes might

be undetectable if the sampling error is larger than the

expected change (IPCC 2003). The gain-loss method requires

reliable annual data on forest growth and harvest. Adequate

e carbon stock change in forest land remaining forest landcted countries are in bold).

d G-L (Flemish region) both based on NFI data

sus (before 2001) and NFI (since 2002). G-L since 2002 based on NFI

on-NFI harvest datasets and country specific biomass equations and

est from non-NFI statistics. BEFs, R and WD are country specific

ountry specific biomass functions

ase

forestry statistics, yield data, harvest statistics and firewood

untry specific data for, biomass equations, R and WD

arvest data derived from regional statistics

atistics and yield table and harvest statistics

nd harvest statistics

nd harvest statistics

tation statistics, yield table and harvest statistics

base. BEFs and WD are country specific, R is default

ase and harvest statistics. BEFs and WD are country specific, R is

ase (before 1993) and NFI data (since 1994)

ase

re 2004), NFI (since 2005) and harvest statistics

re 2000) and NFI data (since 2001)

ase and harvest statistics

ase and harvest statistics

ase, harvest statistics and firewood estimates

; G-L, gain-loss method; S-D, stock-difference method; NFI, national

e n v i r o n m e n t a l s c i e n c e & p o l i c y 3 3 ( 2 0 1 3 ) 2 2 2 – 2 3 2226

responding to the requirements (e.g. annual estimates from

multiannual data) is still a matter of concern for GHG

inventory compilers and the scientific community (e.g.

Heikkinen et al., 2012).

At EU level, the gain-loss method is currently more often

used than the stock-difference method (Table 1). In the

majority of cases (15 out of 25), NFIs represent the basic source

of data, but FMP is frequent in Eastern EU countries. For the six

selected countries, more detailed descriptions of the methods

used for the national submissions are provided in Section S2 of

the online supplementary material and Table S3 gives all the

data that were used in the overall analysis.

2.4. Analysis

The model outputs were not homogeneous and for this reason

a standardization was needed in order to facilitate the

comparison. G4M estimates were available for each year

between 2000 and 2008. Because for EFISCEN only every five-

year data is available, in-between values were obtained

through linear interpolation. Due to the use of recent forest

inventory data in EFISCEN as initialization year, emission and

removal estimates were not available for all member states for

the entire period. Therefore, EFISCEN results were back-casted

following the approach also used in the submission of the

reference levels (see Section S1.3 of the supplementary

materials).

Differences between the estimates from country reports

and the two models were quantified by calculating the

differences in mean trend over the period 2000–2008. These

were plotted along a 1:1 line. Besides, we analyzed whether the

difference in input between the country estimates and the

models of the following explanatory variables could explain

absolute (i.e. when ignoring the sign) differences in mean

removals over the investigated period: mean and trend in

forest area, wood increment and harvest levels. Furthermore,

the thinning–felling ratio in the models, the biomass expan-

sion factors (BEFs; only for EFISCEN) or the adopted reporting

Fig. 2 – Differences between model results and country submiss

correcting for area under FM. The countries that were further inv

to the point. The solid line shows the regression line for the data

line is the 1:1 line.

method selected by a country (stock-difference or gain-loss

method) were included in this analysis. See Table S4 an S5 in

the supplementary materials for an overview and the

associated units. We standardized these model-country

differences by dividing them by the total forest area under

FM for these countries. We performed a stepwise linear

regression procedure to identify the most important explain-

ing variables. Categorical variables were included in this

analysis by using dummy variables (Quinn and Keough, 2002).

Because we cannot increase the sample size of the dataset, but

do realize the large variation in the dataset, we applied a

bootstrapping procedure to enhance our confidence in the

results. The exact procedure and the detailed results are

explained in Section S3 of the online supplementary materials.

For the six selected countries, the differences in trends and

averages were examined and country specific explanations for

identified differences are discussed.

3. Results

3.1. Overall comparison

Fig. 2 shows the standardized differences between the model

estimates and country submissions. The country submissions

show overall a good agreement with both models, except for

Denmark with G4M. G4M estimates a significantly larger sink

per hectare than what was reported in the Danish country

submission for the given period.

The best explaining factor for standardized absolute

differences between both models and country estimates

was the accounting method selected by the countries (Fig. 3

and Supplementary materials S3). For EFISCEN this yielded a

significant explanation of the difference between model and

country submission (median R2 = 0.24, median P-value = 0.015

for 1000 randomized model fits) but for G4M this result was

marginal (median R2 = 0.06, median P-value = 0.22 for 1000

randomized model fits). There were no other variables that

ions for the average in carbon removal for 2000–2008 when

estigated are indicated by filled dots, and their name close

, and the R2 is mentioned for each comparison. The dotted

Gain − Loss Stock Difference

02

46

(Mg

CO

2 ha

−1 y

r−1)

Gain − Loss Stock Diff erence

01

23

4

(Mg

CO

2 ha

−1 y

r−1)

A B

Fig. 3 – The standardized absolute difference in removals between model estimations and country submissions over 2000–

2008 (in Mg CO2 haS1 yrS1) against most explaining factor for these differences (methodology selected by the countries) for

G4M (A) and EFISCEN (B).

e n v i r o n m e n t a l s c i e n c e & p o l i c y 3 3 ( 2 0 1 3 ) 2 2 2 – 2 3 2 227

contributed significantly to explaining the differences be-

tween EFISCEN and country submissions according to the

bootstrapping procedure followed.

3.2. National level comparison

3.2.1. BulgariaFor Bulgaria, both models show a steady decrease in the

removals over the period 2000–2008 (Fig. 4A). The back casted

EFISCEN estimates showed a decrease of 27%, while G4M

estimates showed a decrease of 23%. This trend was also

present in the country data, though of a smaller magnitude

(about 10%). The average sink for the period, however, was

between the model estimates ranging from about 12,000–

8000 Gg CO2 annually.

3.2.2. Czech RepublicFor the Czech Republic, the national estimates and the trends

derived from both EFISCEN and G4M models are shown in

Fig. 4B. It can be observed that the estimates derived from both

models were smaller carbon removals than the national

reported data. For the period of 2000–2008, the data reported by

the country suggest that Czech forests represented a carbon

sink, offsetting CO2 emissions by about 6800 Gg CO2 annually.

The EFISCEN estimations suggested a moderate sink of

2900 Gg CO2. The G4M estimations were showing an annual

carbon sink of 4500 Gg CO2 over the whole period. The G4M

and back casted EFISCEN estimates were quantitatively

comparable in their results for the period since 2005 onwards,

but deviate more substantially from each other for earlier

years. In terms of capturing emission trends, both models

showed similar correspondence with the national emission

estimates (R2 = 0.55 for G4M and R2 = 0.54 for EFISCEN), even

though the back casted EFISCEN estimates indicated a more

moderate downward trend in estimated removals.

3.2.3. FinlandFor Finland, annual removals of CO2 by forest remaining

forest, as reported in the national submissions, closely

matched those estimated with EFISCEN and G4M (Fig. 4C).

This concurs with the fact that the land area for FM was

estimated to be ca. 22 � 106 ha, which is comparable to the

area used by G4M of 21.8 � 106 ha, although it is considerably

higher than what was used in the EFISCEN model that was

based on the forest lands available for wood supply (ca.

18.5 � 106 ha). Variations in logging induced variability in the

national estimates, while biomass sink trends predicted by

EFISCEN were stable, due to five-year average harvesting rates

used.

3.2.4. FranceIn France, the average wood volume per area has increased

over the last decades making the forest carbon pool a sink. The

model estimates and the national submissions showed an

increasing sink in the first half of the period, which levelled off

in the second half (Fig. 4D). However, there were large

discrepancies between the model estimates and the national

submissions (when not corrected for area). The average

annual carbon uptake from the atmosphere was estimated

to be 74 Tg CO2 in the national reports whereas EFISCEN and

G4M predicted annual removals of 30 Tg CO2 and 42 Tg CO2,

respectively.

3.2.5. GermanyThe differences between the annual carbon removal esti-

mates reported in the German submission (National GHG

Inventory Report of Germany, 2011) and the two models for

the period 2000–2008 are shown in Fig. 4E. The large

difference in removals between the national submissions

and model estimations for 2000 and 2001 (33% lower for G4M

and 81% lower for EFISCEN) was mainly the result of the

large jump in the national estimate, due to the stock-

difference method used by the country. In comparison with

the national estimate, the average annual removals for the

whole period (2000–2008) were 11% higher and 51% lower in

G4M and EFISCEN estimates, respectively. Both the national

estimates and G4M estimates reported decreasing removals

over time, but EFISCEN prediction showed a weak increasing

trend. The G4M estimates and the national estimates were

closer to each other than the back casted EFISCEN estimates,

Fig. 4 – Emissions and removals reported for the category forest land remaining forest land in the National Inventory Reports

(NIR), EFISCEN and G4M for biomass for the countries that were further investigated.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 3 3 ( 2 0 1 3 ) 2 2 2 – 2 3 2228

although they all converged at the end of the examined

period.

3.2.6. IrelandThe submitted Irish national estimates and historical trends

derived from both EFISCEN and G4M models are shown in

Fig. 4F. Irish national estimates were similar to the

estimated ranges from the model estimates although they

showed much more variability than the model calculations.

In terms of emission trends, the national estimates showed

that the sink strength of Irish forests increased within the

investigated period. This trend was matched by both

models, although the trend was weaker for the model

estimates.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 3 3 ( 2 0 1 3 ) 2 2 2 – 2 3 2 229

4. Discussion

4.1. Overall comparison

The bootstrapped regression results showed that for both

models the most important factor to explain the differences

was the method that was selected by the countries although

this worked better for explaining differences with EFICSEN

than for differences with G4M. Larger differences were

observed for countries that applied the gain-loss method.

The EFISCEN model results are based on elements of both

gain-loss and stock-difference methods. Development of

forest stocks is calculated over 5-year time steps using a

gain-loss approach, but CO2 removals and emissions are

calculated over these time steps applying a stock-difference

approach. Intermediate years are then reconstructed with

linear interpolation. Due to the 5-year time step, annual

fluctuations in losses are averaged, which could explain the

better match of EFISCEN estimates with country submissions

that used the stock-difference approach calculated over

several years. Potentially, alternative ways of interpolating

between the five-year time steps might reduce differences

further for annual fluctuations. We applied an alternative

interpolation (weighing interpolated values with relative

annual harvest levels; results not shown) which showed that

this only reduced differences for some countries. Most likely,

harvest levels and removals of CO2 from the atmosphere are

not linearly related. Besides the average differences that are

apparent from Fig. 3, there is also a larger spread in differences

for counties that apply the gain-loss method. Countries select

an estimation method based on the data being available, but

this choice may affect the comparability with independent

models.

Both models follow the inter-annual variability in emis-

sions and removals as reported by member states (Fig. S1) only

to a limited extent (Fig. 4). One important explanation for this

is that both models do not explicitly include large-scale

disturbances. Harvests from salvage fellings is included in the

harvest statistics and thus in the models, but this will cover

damaged wood resulting from all disturbance agents. Such

disturbances affect forest structure (Schelhaas et al., 2002) and

through that the forest carbon sink (Lindroth et al., 2009).

4.2. National level comparison

4.2.1. BulgariaDuring the data harmonisations (see Section S1.3), several

improvements to the country submission were implemented

that resulted in the country submission as displayed in Fig. 4A.

Thanks to these improvements and the 2010 NFI data, better

estimates of the living biomass stock were obtained. This

approach resulted in a recalculation of the biomass stock

which ended up in 10–15% lower removals for the period 2000–

2005 and 20% lower removals after 2005, compared to previous

submissions (Fig. 1A). Overall, this recalculation improved the

match between the GHG inventory and the model estimates

both in the trend and in the level of the sink.

The remaining differences between the national submis-

sions and the two large-scale models are mainly attributable

to different increment values. For Bulgaria, concrete incre-

ment investigations are lacking, making the increment values

used by Bulgaria, derived from the FMP databases using

growth tables, less certain.

With regard to the differences in the trend of forest sink,

the main reason lies probably in the methods used: while the

models inherently use the gain-loss method (i.e. increment

less harvest) to estimate carbon stock changes, Bulgaria used

the stock-difference method (difference in carbon stocks

between two successive inventories), including an extrapola-

tion to 2008 of the data between 2000 and 2005.

4.2.2. Czech RepublicThe potential explanation for the differences between the

reported and modelled emission and removal trends of the

Czech forest sector can come from the way carbon loss is

handled. The Czech emission inventory subtracts a part of the

harvest residues (30%) that is normally burned on site under

local management practice. This is done to prevent double

accounting of emissions, which would otherwise occur when

reporting both emissions from harvest and emissions from

prescribed burning.

A second cause for differences might be differences in the

time steps applied, at least for EFISCEN, where the model uses

a five-year average, while the national emission inventory

uses annual information. The highest harvest rate ever in the

Czech Republic occurred in 2007 after sanitary fellings

following the windstorm Kyrill. During that year, the actual

harvest rate differed from those applied in the models by more

than 10%. However, the mean harvest across the entire period

between 2000 and 2008 remained basically the same

(17.5 Mm3 yr�1), which was applied for both models. The

difference with G4M is partly complemented by the fact that

G4M used a mean increment of 10.7 m3 ha�1, which is about

18% more than that used in EFISCEN and in the Czech national

estimate.

4.2.3. Finland

For Finland, a possible reason for differences between the

estimates of the models and the national estimates could be

the applied BEFs; the national estimates used a different

source and estimation method for their BEF’s (Repola, 2008,

2009) than the models (Lehtonen et al., 2004). Additionally, the

wood removals differed between the country estimation and

the models. For the country estimation they varied during

2006–2008 between 57 and 64 Mm3. This included loggings,

fuel wood use and contract sawing. The calculations in the

models only included estimates of loggings (based on national

harvest statistics), and were on average 63 Mm3 for the period

2006–2010 in EFISCEN and 61 Mm3 in G4M.

Also, there were differences in used land area. The EFISCEN

estimates were based on the forest available for wood supply.

Due to smaller land areas in EFISCEN, which reduces the total

increment, the carbon sink estimates derived from this model

are slightly lower than that of the national submission.

However, assuming the same emissions or removals per unit

area for the area not included in EFISCEN would lead to an

overestimate of the sink, as the area not included in forests

available for wood supply typically include the less productive

regions in the northern parts of the country. This also applies

e n v i r o n m e n t a l s c i e n c e & p o l i c y 3 3 ( 2 0 1 3 ) 2 2 2 – 2 3 2230

to the projections for Sweden. In G4M the land area is in line

with the national submission, but loggings are on average

lower than the total wood removals in the national submis-

sion. A similar carbon sink, obtained with similar land areas,

but with a much higher wood removals for the national

estimation compared, indicates that G4M has applied lower

increment values.

4.2.4. FranceFor France, the differences between the national submission

and the model prediction might be due to methodological

differences. Although differences exist between BEFs used in

the three methods, these differences are mainly for the largest

trees, which represent a negligible fraction of the forest,

making this an unlikely source for the difference. Similarly,

there is a difference between the carbon content used in the

national submission (0.475 tC t�1) and the one used in the

French parameterization of EFISCEN and GM4 (the IPCC (2003)

standard value of 0.5 tC t�1), but this should actually lessen the

difference between the national report and the model

estimates, making it also an unlikely candidate for the found

differences.

A more likely source for the difference therefore seems to

be associated with the estimation of the biomass losses from

harvesting. There is good understanding of harvested volumes

coming from industrial logging (cf. Agreste, 2010). However,

the amount of firewood volume harvested for domestic

purposes is largely uncertain. Whereas previous studies

reported annual harvests of 60 Mm3, some recent changes

in field measurements reduced annual harvest estimates to

44 Mm3 in merchantable volume (IFN, 2011). The newer

estimate resulted from the fact that part of the reported total

harvested volume is actually branch volume. Therefore, the

national report derives the biomass loss from 53 Mm3 and not

the original 60 Mm3, which roughly leads to 9 Tg CO2 less

emissions from harvests. This correction was not applied to

the two large-scale models.

4.2.5. GermanyThe national estimates were based on the stock-difference

method, which leads to the large jump as shown in Fig. 4E.

This jump reflects the change in biomass in a 15-year (1987–

2002) and a 6-year (2002–2008) period (see Supplementary

Material S2.5). Both models work with finer time steps (1 year

for G4M and 5 years for EFISCEN) and therefore do not

reproduce this significant change in annual removals.

Due to the use of the stock-difference method, increment

and harvesting levels are not used as input to the national

calculations, but they could be derived from the inventories,

making a comparison with the large-scale model inputs

possible.

In the national estimates, the average increment rates

of the growing stock decrease from 12.7 in 2002 to

11.1 m3 ha�1 yr�1 in 2008, mainly due to a decrease in the

increment of Norway spruce (Picea abies (L.) Karst.), as a result

of a great industrial demand for Norway spruce (mainly in age-

classes about 60–100 years), a decrease in the area (in favour of

deciduous species) and due to storm events (which mainly hit

Norway spruce stands; Oehmichen et al., 2011). Such particu-

lar factors are not well captured by the models. Hence,

EFISCEN predicted an increasing increment trend for the

period 2005–2010 (no estimate for earlier years available),

although the average annual increment per ha is 6.4% lower

than the national estimates. G4M, nevertheless, did show the

decreasing trend, although the average annual increment in

G4M is 1.6% higher than in the national estimate.

For the national estimates, the harvesting amount in the

second inventory period (2002–2008) almost doubled in

comparison to the first time period (1987–2002), reflected in

the jump and decrease in the removals data (UBA, 2011).

According to the German Integrated Environmental and

Economic Accounting for Forests (IEEAF; Bormann et al.,

2006) the German harvesting rate increased on average

0.87% yr�1 between 2000 and 2008. An increase is also

estimated by G4M (3.5% yr�1) and EFISCEN (2.3% yr�1). For

G4M this logically explains the decrease in annual carbon

removals. However, this trend was not observed from the

back casted EFISCEN results. The reason for this is that the

EFISCEN estimates of carbon removals for 2000 have been

back casted using the harvest ratio for the years 2005 and

2000. The harvest recorded by the IEEAF in 2000 was however

much larger (ca. 16 Mm3; 27%) as compared to other years

(1998, 1999, 2001 and 2002). This anomaly in historical harvest

strongly affects the results of the backward extrapolation

approach that was applied to estimate carbon removals for

the year 2000. The average annual harvesting amount

(roundwood over bark, 2000–2008) was 2.65% lower according

to G4M and 1.73% lower for EFISCEN in comparison to the

IEEAF results.

4.2.6. IrelandDifferences between the national estimate for Ireland and the

two larger scale models are relatively difficult to pinpoint

exactly. The input area under FM in the Irish and both models

are essentially the same (465 kHa), with only small differences

related to deforestation assumptions, making this an unlikely

explanation for found differences.

Although the national estimates of timber harvest were

within the reported range used in both models, the volume

increment was 20–30% lower for the period 2005–2010. These

differences in volume increment would partially explain the

larger decrease in forest sink over the time period. The

difference in increment is probably due to the absence of such

data from the NFI and the use of different data sources (e.g.

from yield tables) to compensate for this.

Temporal variations in the volume increment estimates

between the different models appear to be related to different

silvicultural assumptions related to age class dynamics over

the time series. Both models assume a shift from young to old

in the age class distribution between 2000 and 2008. However,

the Irish data displays an initial shift from old to young in the

age class distribution between 2000 and 2008. In addition there

are opposite temporal trends observed in the change in 20–30

and 30–40 age class categories. These differences suggest that

different management regimes were applied in the three

model approaches, i.e. more harvest comes from final fellings

in the national estimates as compared to EFISCEN and G4M,

particularly after 2005. A higher proportional final harvest of

stands in the years after 2005 also resulted in a decrease of the

FM forest volume increment.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 3 3 ( 2 0 1 3 ) 2 2 2 – 2 3 2 231

5. Conclusion

This study has identified a number of differences in the biomass

emissions and removals from forest management between

countries’ submission to UNFCCC and two large-scale models.

Despite harmonisation efforts to ensure consistency in input

data between national estimates and large scale models by

Bottcher et al. (2012), differences in removal estimates still

occur. There are no directly identifiable input parameters that

could fully close the gap between country estimates and model

estimates, i.e. explain most of the variation. For both models the

average differences seemed best explained by the method

applied by the country (i.e. stock-difference or gain-loss). Other

reasons that seem to explain the observed differences for

specific cases are the harvest volumes and the way harvest

losses are treated (cf. France, Germany and the Czech Republic),

the approach used to correct for missing data from inventories

(cf. Bulgaria and Ireland) and the absence of large-scale

disturbances in the models (cf. Czech Republic and Germany).

Smaller differences between model and country submissions

were in general more difficult to assign to one single source,

although for several countries, BEFs and area under FM were

excluded as likely sources, although not in all cases (Finland).

Differences in dynamics could also be caused by different

management regimes that resulted in different temporal

resolutions or different age class dynamics and assumed

rotation lengths (Ireland). Finally, much care should be used

when comparing the forest sink estimated with the stock

difference (e.g. Bulgaria, Germany) vs. gain-loss methods: while

the two methods should provide the same trends in the

medium–long run, over a short period apparent discrepancies

may occur due to the different frequency of data updates.

We suggest that, for the purpose of estimating future

emissions and removals from forest management, any possible

effort should be made to ensure comparability in input data and

methodological assumptions between the model used for

estimates and the national GHG inventory. This also calls for

openness and willingness to share national forest statistics, to

enable large scale models to be accurately and up to date

parameterized in comparison with national submissions.

Acknowledgements

We thank the Management Committee of the ECHOES COST

action financed by the EU (COST-FP0703) as well as the COST

office for facilitating the network needed to bring this paper

about. EC acknowledges the support of the Czech Ministry of

Education, Youth and Sports (Project OC10003). The views

expressed are purely those of the authors and may not in any

circumstances be regarded as stating an official position of the

European Commission.

Appendix A. Supplementary data

Supplementary data associated with this article can be

found, in the online version, at http://dx.doi.org/10.1016/

j.envsci.2013.06.005.

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