Reduced emissions from deforestation and forest degradation (REDD): A climate change mitigation strategy on a critical track

Abstract

Following recent discussions, there is hope that a mechanism for reduction of emissions from deforestation and forest degradation (REDD) will be agreed by the Parties of the UNFCCC at their 15th meeting in Copenhagen in 2009 as an eligible action to prevent climate changes and global warming in post-2012 commitment periods. Countries introducing a REDD-regime in order to generate benefits need to implement sound monitoring and reporting systems and specify the associated uncertainties. The principle of conservativeness addresses the problem of estimation errors and requests the reporting of reliable minimum estimates (RME). Here the potential to generate benefits from applying a REDD-regime is proposed with reference to sampling and non-sampling errors that influence the reliability of estimated activity data and emission factors.
A framework for calculating carbon benefits by including assessment errors is developed. Theoretical, sample based considerations as well as a simulation study for five selected countries with low to high deforestation and degradation rates show that even small assessment errors (5% and less) may outweigh successful efforts to reduce deforestation and degradation.
The generation of benefits from REDD is possible only in situations where assessment errors are carefully controlled.

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Carbon Balance and Management
Open Access
Research
Reduced emissions from deforestation and forest degradation
(REDD): a climate change mitigation strategy on a critical track
Michael Köhl*†1, Thomas Baldauf†2, Daniel Plugge†2 and Joachim Krug2
Address: 1Institute for World Forestry, University of Hamburg, Leuschnerstr. 91, 21031 Hamburg, Germany and 2Institute for World Forestry, von
Thünen-Institute, Leuschnerstr. 91, 21031 Hamburg, Germany
Email: Michael Köhl* - weltforst@holz.uni-hamburg.de; Thomas Baldauf - thomas.baldauf@vti.bund.de;
Daniel Plugge - daniel.plugge@vti.bund.de; Joachim Krug - joachim.krug@vti.bund.de
* Corresponding author †Equal contributors
Abstract
Background: Following recent discussions, there is hope that a mechanism for reduction of
emissions from deforestation and forest degradation (REDD) will be agreed by the Parties of the
UNFCCC at their 15th meeting in Copenhagen in 2009 as an eligible action to prevent climate
changes and global warming in post-2012 commitment periods. Countries introducing a REDD-
regime in order to generate benefits need to implement sound monitoring and reporting systems
and specify the associated uncertainties. The principle of conservativeness addresses the problem
of estimation errors and requests the reporting of reliable minimum estimates (RME). Here the
potential to generate benefits from applying a REDD-regime is proposed with reference to
sampling and non-sampling errors that influence the reliability of estimated activity data and
emission factors.
Results: A framework for calculating carbon benefits by including assessment errors is developed.
Theoretical, sample based considerations as well as a simulation study for five selected countries
with low to high deforestation and degradation rates show that even small assessment errors (5%
and less) may outweigh successful efforts to reduce deforestation and degradation.
Conclusion: The generation of benefits from REDD is possible only in situations where
assessment errors are carefully controlled.
Background
According to estimates by the International Panel on Cli-
mate Change (IPCC) 1.6 billion tons of carbon are
released annually by land-use change activities, of which
a major part results from deforestation and forest degrada-
tion [1]. The Stern Report [2] pointed out that nearly one-
fifth of today's total annual carbon emissions come from
land-use change, most of which can be traced back to
tropical deforestation. Deforestation is generally under-
stood as the direct human-induced conversion of forest
land to non-forest land [3], while forest degradation is
according to Intergovernmental Panel on Climate Change
(IPCC) [3] the direct-human induced long-term loss of
forest carbon stocks in areas which remain forest land.
Among the causes of degradation are the collection of
fuelwood, selective logging, forest fires, grazing or shifting
cultivation [4].
For the 2008-2012 commitment period of the Kyoto Pro-
tocol (KP) avoiding deforestation was discussed as a CDM
Published: 13 November 2009
Carbon Balance and Management 2009, 4:10 doi:10.1186/1750-0680-4-10
Received: 2 September 2009
Accepted: 13 November 2009
This article is available from: http://www.cbmjournal.com/content/4/1/10
© 2009 Köhl et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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activity and rejected. Leakage was seen as uncontrollable
at the project level. In 2005 at the Eleventh Session of the
Conference of Parties (COP 11) to the United Framework
Convention on Climate Change (UNFCCC) Papua New
Guinea together with 8 other developing countries pro-
posed a new agenda item "reducing emissions from defor-
estation in developing countries" at a national level. This
was the start of the process for considering reducing emis-
sions from deforestation and forest degradation in devel-
oping countries (REDD) as a mitigation option for those
countries. Following the related discussions and proceed-
ings, there is hope that a REDD mechanism will be agreed
by the Parties of the UNFCCC at their 15th meeting in
Copenhagen in 2009 as an eligible action to prevent cli-
mate changes and global warming in post-2012 commit-
ment periods.
A country participating in a future REDD mechanism of
the UNFCCC has to demonstrate substantial capacities for
monitoring and accounting emissions from forest carbon
stocks. Thus a reliable framework for measuring, reporting
and verification is vitally needed to ensure the integrity
and credibility of REDD efforts in general and REDD in
the post-2012-agreements to be approved in Copenhagen
in particular. While approaches for monitoring, reporting
and verification, as well as potential financing mecha-
nisms for a provision of appropriate incentives have been
discussed intensively [4,5], little attention has been paid
so far to the fact that uncertainties associated with the esti-
mation of forest area and carbon stock changes have a
fundamental impact on accountable carbon credits and
the cost-benefit ratio.
In this study we present error sources associated with the
monitoring of above ground forest biomass and carbon
stock in the scope of REDD and discuss the implications
of uncertainties on the reliable minimum estimate (RME)
that is requested for IPCC reporting.
Results
Applying the conceptual framework described in the
Methods, we demonstrated that monitoring costs
required for a sound determination of RMEs may out-
weigh a substantial proportion of potential financial ben-
efits that could be generated for emission certificates
under national REDD-schemes.
A country that intends to benefit from the adoption of a
REDD-regime, needs to proof that deforestation and for-
est degradation in a current commitment period is smaller
than it was in the periods before. Accountable carbon
credits, Ĉt2REDD, are obtained by subtracting the real car-
bon stock at time 2, Ct2real, from the carbon stock expected
under the baseline scenario, Ct2BL, which is derived from
past deforestation and degradation rates. The larger the
difference the more carbon credits are generated.
To illustrate the effect of the inclusion of uncertainties in
REDD estimates, we selected five countries that hold
small to large forest areas and show low (-0.23%) to high
(-10.57%) deforestation rates (Table 1[6,7]).
For each country the rate of deforestation between 2000
and 2005 was utilised to predict the carbon stock at the
end of a five year period between 2005 and 2010, Ct2BL
under a business-as-usual (BAU) development. Reduc-
tions of the business-as-usual deforestation and degrada-
Table 1: Characteristics of the countries selected for the case study (taken from FAO's Global Forest Resources Assessment [6])
Country Category* Forest area 2005
[1000 ha]
Carbon stock
[tC/ha]
Forest area
development,
based on 2000-
2005)
[1000 ha/year]
Carbon stock 2005
[Mt]+
ΔBL
[%]
Carbon stock
2010,
according to
baseline
[Mt]
Bolivia HFMD 58,740 66.63 -270 3,914 -2.30 3,824
Cameroon MFMD 21,245 63.05 -220 1,340 -5.18 1,270
Gabon HFLD 21,775 137.11 -10 2,986 -0.23 2,979
Indonesia HFHD 88,495 50.10 -1,871 4,434 -
10.57
3,965
Madagascar LFLD 12,838 186.09 -37 2,389 -1.44 2,355
HF = high forest area, LF = low forest area, MF = medium forest area
MD = medium deforestation rate, LD = low deforestation rate, HD = high deforestation rate
* according to Griscom [7]
+ it is assumed that carbon stock 2005 (Ct1) is the RME

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tion by 10, 30, 50 and 75 percent were simulated for each
country - stipulating that a country was able to reduce its
business-as-usual deforestation and degradation by 10%,
30%, 50% or 75% - and the respective carbon stocks at
time 2, Ct2real, calculated. The corresponding differences
between Ct2real and Ct2BL reflect different levels of account-
able carbon credits generated by REDD, Ĉt2REDD.
The estimation of carbon stocks is subject to several error
sources, including sampling errors, assessment errors, or
prediction errors from models. Those error sources can be
random or systematic in nature and are combined to the
so-called total error of the estimate. The total error defines
an interval around the estimate which quantifies the
uncertainties associated with the estimate. For reasons of
conservativeness the lower bound of the interval is
defined as the reliable minimum estimate (RME). For cal-
culating the accountable carbon credits generated by a
REDD regime the RME of Ct2real is used in order to reflect
uncertainties. Including errors generally reduces the
amount of accountable emission reduction due to
avoided deforestation and degradation.
To show the effect of errors, the reliable minimum esti-
mates (RME) of Ct2real under the four reduction rates were
calculated for 0 to 10% total error and used as reference
for calculating the respective carbon credits, Ĉt2REDD. Fig-
ure 1 presents the results of the simulation study. The
accountable carbon credits are plotted for the four reduc-
tion scenarios over different levels of total error (0 to
10%). Positive numbers for Ĉt2REDD display CO2 emis-
sions to the atmosphere, negative numbers display CO2
reductions.
Taking a 5% total error for the estimation of the carbon
stock at time 2, Ct2real, only Indonesia (deforestation rate
= -10.57%) would qualify for generating carbon credits, if
a reduction of the deforestation and degradation-rate of at
least 50% were reached. According to studies from Fuller
et al. [8], Gertner and Köhl [9] or Waggoner [10] total
errors larger than 5% are most likely to occur.
Among the selected countries Gabon shows the lowest
deforestation and degradation rate (-0.23%). The reduc-
tion of the deforestation and degradation rate will lead to
only minor gains in total carbon stock at time 2 due to the
low deforestation rate. Even negligible total errors of car-
bon estimates at time 2 render the generation of account-
able carbon credits impossible. The commitment to a
REDD regime would put a country like Gabon (high forest
area, low deforestation rate) in a situation, where - despite
the efforts in further reducing the already low deforesta-
tion and degradation rate - carbon emissions from forests
would need to be reported.
Graphs of resultit2REDD (in tC*106) for five selected countries in relation to error at time 2, (Et2) for different reduction scenarios for ΔBL showing the effect of total error and defor-estation and degradation rates on carbon credits; positive numbers display emissions, negative numbers removalsFigure 1
Graphs of resulting Ĉt2REDD (in tC*106) for five
selected countries in relation to error at time 2, (Et2)
for different reduction scenarios for ΔBL showing the
effect of total error and deforestation and degrada-
tion rates on carbon credits; positive numbers dis-
play emissions, negative numbers removals.
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Even for a reduction of the deforestation and degradation
rate by 75% Madagascar (low forest cover, deforestation
rate = -1,44%) would be excluded from REDD benefits
when total errors are larger than 1% and Bolivia (high for-
est area, deforestation rate = -2,3%) would need to pro-
vide estimates with a total error less than 2% in order to
generate benefits from REDD.
Cameroon, a country with medium forest area and
medium deforestation rates (-5,18%), would need to
assess the carbon stock at time 2 with an error smaller
than 4% when the deforestation and degradation rate is
reduced by 75%.
The adoption of a REDD regime should be most suitable
for countries with high forest areas and high deforestation
rates. However, Figure 1 shows for the example of Indone-
sia (high forest area, deforestation rate = -10,57%) that
even a 50% reduction of the deforestation and degrada-
tion rate would render a total error of roughly 6% neces-
sary in order to generate benefits from REDD.
The results of the simulation study suggest that even small
errors result in situations where no carbon credits can be
generated (Figure 1). The effect of the total error on the
RME is much larger than the effect of different reduction
rates of deforestation and degradation. Total errors larger
than 5%, which are realistic in extensive forest carbon sur-
veys [8-10], exclude most national REDD-regimes from
generating benefits. The simulation study indicates that
countries with medium or low deforestation and degrada-
tion-rates are not in a position to generate benefit from
REDD when the uncertainties of carbon stock estimates
are included in calculations as requested in a REDD certi-
fication process.
Discussion
The generation of carbon credits by introducing a REDD
scheme becomes critical when the principle of conserva-
tiveness and assessment errors are considered in the mon-
itoring and reporting process. As shown by theoretical
considerations and a simulation study the total error asso-
ciated with carbon estimates can outweigh efforts to
reduce deforestation and degradation. Introducing a
REDD-regime in situations where the error structures of
the assessment and monitoring system are unknown, may
result in critical situations; only a minor amount of car-
bon credits could be generated or - even worse - emissions
from forestry need to be reported, even when the country
committed itself to REDD and was successful in reducing
carbon losses from deforestation and degradation.
When REDD is considered as an economic approach to
conserve forest ecosystems in developing countries, the
benefits generated from the implementation of a REDD
system need to be larger than the benefits from deforesta-
tion. A prerequisite is the estimation of activity data and
emission factors with high certainty. Due to the superior
role of the total errors associated with carbon stock esti-
mates, significant efforts need to be taken to reduce uncer-
tainties by sound monitoring and reporting systems.
However, those systems are expensive and may in many
countries not or only partially be covered by the generated
benefits (see [11] for inventory cost estimates). Monitor-
ing costs under a sound construction of RMEs compensate
a substantial proportion of the financial benefits that
could be generated for emission certificates under
national REDD-schemes.
Conclusion
In further studies on approaches to capture deforestation
and degradation special focus needs to be taken to the
quantification of the total survey error. Feasibility studies
without sound non-sampling and sampling error assess-
ments are useless for decisions about the "optimal" REDD
inventory concept. We recommend that especially coun-
tries in the readiness phase, which have not yet developed
appropriate capacities, carefully study the effects of the
principle of conservativeness in preparing for REDD. For
those countries capacity building for implementing sound
carbon monitoring systems is urgently needed in order to
turn efforts in reducing deforestation and forest degrada-
tion into benefits generated by REDD. However, countries
with already low deforestation rates will most likely not
benefit from REDD.
Methods
Assessment of emissions from deforestation and
degradation
In forests there are five major carbon pools [3]: (1) above
ground biomass, (2) below-ground biomass, (3) dead
wood, (4) litter, and (5) soil organic matter. The avoid-
ance of deforestation and forest degradation aims at the
maintenance of carbon in the living biomass, for which
reason the most practical monitoring approach is to con-
centrate on the assessment of the carbon pool "above
ground biomass".
Monitoring and reporting of deforestation and degrada-
tion requires the assessment of two components [3]:
- changes in forest area over time, and
- changes in the average carbon stock per unit area over
time.
The quantification of changes requires assessments at suc-
cessive occasions or the availability of models that allow
for the extrapolation of data from one point in time to
another. The total loss of forest carbon stock in a given

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period and area is the sum of two components: (1) the
product of average carbon stock per unit area times the
forest area changed from forest land to other land use in
the respective period, and (2) the reduction of average car-
bon stock in areas that remain forest land. In order to
increase the reliability of estimates, the area of forests can
be subdivided in several classes indicating different levels
of carbon stock decrease or degradation.
Area changes can be either assessed by field-based sample
surveys or by remote sensing techniques. The latter are
generally more cost efficient and provide not only point
estimates (i.e. forest area) but spatially explicit data in
mapped format. Remote sensing data are often utilised to
separate the total forest area into different sub-groups or
strata, such as occurring forest types, e.g. broadleaf, tropi-
cal moist and tropical dry. In addition probabilistic
approaches can be used to complement the forest classifi-
cation by risk factors that describe the probability of deg-
radation, based on proxies such as past level of human
interventions, accessibility or population density. Remote
sensing techniques enable the detection of deforestation,
especially on large areas. More difficult is the quantifica-
tion of forest degradation, where even substantial remov-
als of biomass do not necessarily lead to a pronounced
reduction of canopy cover. Only far advanced stages of
forest degradation can be detected by remote sensing tech-
niques.
Carbon stock changes can be quantified by various meth-
ods. A straightforward approach is to utilise default values
from secondary sources such as from IPCC [3]. Estimates
based on default values can be subject to great uncertain-
ties, as they may not reflect the true country specific val-
ues. A more reliable alternative is to apply country specific
data on degradation to individual forest types or risk cat-
egories. The most reliable estimates of carbon stock
changes are obtained by sample based field assessments
on successive occasions. On in-situ sample plots individ-
ual trees are measured and biomass and carbon stock are
calculated on the plot level. Upscaling procedures expand
plot data to area related estimates [12]. Those assessments
provide sound and sensitive estimates of changes in forest
biomass and degradation activities.
Recommendations on methods and default values for
assessing carbon stocks and emissions are provided by the
IPCC Good Practice Guidance [3] and Greenhouse Gas
Inventory Guidelines [13]. For calculating changes in
average carbon stock per unit area the IPCC [3,13] pro-
poses two approaches:
(1) the stock difference method that makes reference
to traditional forest resource assessments and calcu-
lates changes in average carbon stock per unit area as
the difference between carbon stock at time 2 and time
1, and
(2) the gain-loss method that builds on the under-
standing of carbon uptake by forests (tree growth) and
carbon release by anthropogenic activities such as tim-
ber removals, fuelwood gathering, sub-canopy fires or
grazing.
Forests may be stratified into sub-areas with different deg-
radation intensities in order to increase the reliability of
the estimated carbon losses.
As there are substantial differences between countries
regarding the capacities and implemented assessment sys-
tems for monitoring, reporting and validating carbon
stock changes, the IPCC-guidelines provide three tiers of
detail for reporting.
- Tier 1 offers the simplest to use alternative that utilises
globally-available activity data (e.g. on deforestation
rates). Equations and default values (e.g. emission and
stock change factors) are provided by IPCC [13]. Tier 1
reporting is recommended for countries with limited
availability of country-specific data. However, Tier 1 esti-
mates do not qualify for reporting in the scope of REDD
due to the large error rates, which are in the range of ±
50% [11].
- Tier 2 utilises country- or region-specific data for the
most important land-use categories. Emission factors and
activity data show a higher temporal and spatial resolu-
tion than those used for Tier 1.
- Tier 3 uses high order methods including models and
inventory measurement systems that are tailored for the
country specific circumstances. The methods are driven by
high resolution activity data and may include comprehen-
sive field sampling repeated at regular time intervals as
well as GIS-based systems to analyse land-use data.
Moving to higher Tiers reduces the uncertainty of esti-
mates but increases the complexity and cost of the utilised
monitoring and reporting systems. In order to be flexible
for implementation on the country level the good practice
guidance (GPG) [3,13] allows for a combination of Tiers,
e.g. Tier 2 for changes in average carbon stock and Tier 3
for land use changes.
Uncertainties
The implementation of REDD as a mitigation option in
the context of UNFCCC needs to ensure the credibility of
estimated emissions and removals from deforestation and

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degradation. In its 28th session the Subsidiary Body for
Scientific and Technological Advice (SBSTA) was con-
cerned with methodological issues concerning the imple-
mentation of REDD and stated that "means to deal with
uncertainties in estimates aiming to ensure that reduc-
tions in emissions or increases in removals are not overes-
timated" need to be further considered [14]. Uncertainties
evolve from the assessment and estimation methodolo-
gies applied. In REDD those are mainly linked to the
assessment of deforestation and degradation areas (activ-
ity data, AD) and the carbon stock changes in those areas
(emission factor, EF).
The estimation of AD and EF is subject to two major error
types: sampling errors and non-sampling errors [15].
Sampling errors arise from inferring from a subset (i.e. the
sample) of the population to the whole population. The
size of sampling errors can be controlled by the survey
design and the size of the sample. Non-sampling errors
encompass all other sources of errors involved in a survey,
which can be the faulty application of definitions, classifi-
cation errors, measurement errors, errors arising from the
application of functions and models, calculation errors, or
frame errors (i.e. the sample population is different from
the target population). Different types of errors can be
quantified by giving their precision, accuracy, or bias.
- Precision refers to the size of deviations in the estimate
of a population parameter in repeat application of a sam-
pling procedure. The standard error or confidence interval
quantifies precision. Increasing the number of observa-
tions increases the precision of a statistical estimate.
- Accuracy refers to the size of deviations between an
observed value and the true value. Thus, if the true value
of a population parameter is known then the accuracy of
a survey estimate can be defined as the deviation between
the estimate and the true value.
- Bias is directly related to the accuracy of an estimate and
refers to systematic errors that affect any sample with the
same constant error.
The IPCC Good Practice Guidance [3,13] suggests the
95%-confidence interval to quantify the uncertainty of
estimates. In this context the use of the term confidence
interval is not very specific, as from a puristic statistical
point of view the confidence interval is related to sam-
pling errors only. The total survey error quantifies all error
sources associated with an estimate. This can be realised
via an error budget (Figure 2).
The Mean Square Error (MSE) is a useful measure of the
total error, as it combines sampling errors with the square
of the bias. For unbiased estimators MSE and precision are
asymptotically identical.
The quantification of AD requires estimates of forest area
changes over time. Where remote sensing techniques are
used, the uncertainty embedded in estimating changes
between two points in time is influenced by the map accu-
racies at both occasions and the magnitude of changes.
Fuller et al. [8] discuss the measurement of land-cover
change over time and present a statistical approach to
quantify the reliability of change estimates. They show
that for 10-class maps the accuracy at both times needs to
be 99% to detect a smaller than 20% change with a 90%
reliability. Thus it is rather idealistic to expect the sensitive
detection of area changes by multi-temporal analysis of
remote sensing data.
The detectability of degradation by remote sensing data is
another critical issue. Especially in natural forests stands
in the tropics and subtropics, which are characterised by
heterogenic vertical stand structures and contiguous can-
opy covers, degradation can only be detected, when the
formerly closed canopy cover is dissolved (Figure 3).
EFs are to be quantified by in-situ assessments in forest
stands, which follow the rules of probabilistic sampling
theory. Carbon stock of trees is quantified via above
ground volume or biomass figures. As those cannot be
assessed directly on standing trees they are estimated via
Total survey error and error budgetFigure 2
Total survey error and error budget.

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volume or biomass functions, which utilise tree measure-
ments such as diameters, tree heights or crown parameters
as independent variables. Where volume estimates are
available they can be converted into biomass estimates by
biomass expansion factors (BEF). Biomass estimates are
transferred into carbon stock estimates by applying bio-
mass-carbon conversion factors, which depend on the
wood density of the respective tree species and tree com-
ponents.
The EF-estimates are subject to a series of error sources,
including measurement errors and function errors. A seri-
ous problem is introduced by frame errors. Assessments of
a limited set of field plots may not be representative for
the entire tree species, forest types, ecosystem regions and
disturbance levels within a country [16,17]. IPCC [3] pre-
sented figures for above ground biomass, which show a
large range of variability. For example, in wet tropical for-
ests the possible range of values covers 34% to 248% of
the average. This shows that currently a high level of
uncertainty is associated with the quantification of above
ground biomass stock.
The principle of conservativeness
Grassi et al. [18] propose to use the principle of conserva-
tiveness in order to "address the potential incompleteness
and high uncertainties of REDD estimates". The principle
of conservativeness has already been reflected in several
UNFCCC documents, for example in the context of affor-
estation and reforestation activities under the Clean
Development Mechanism (CDM) [19,20].
According to Grassi et al. [18] the completeness principle
depends on "the processes, pools and gases that need to
be reported and on the forest-related definitions". Both,
uncertainties and incompleteness need to be considered
for quantifying carbon stock changes under REDD activi-
ties. In the context of the assessment of changes in soil car-
bon, the IPCC-Good Practice Guidance suggests using the
Reliable Minimum Estimate (RME) to address uncertain-
ties. The RME was introduced by Dawkins [21] as the min-
imum quantity to be expected with a given probability
and served as a surrogate for the lower bound of a confi-
dence interval.
The principle of the RME can be expanded from a mere
sampling error perspective to the concept of total survey
errors and transferred to the assessment of forest carbon
stock changes. The RME is the difference between the
lower bound of the error interval at the reference period
(time 1) and the upper bound of the error interval at the
assessment period (time 2) and can be treated as a con-
servative estimate that qualifies for accounting. Where the
confidence interval is used, only sampling errors are con-
sidered and the resulting magnitude of emission reduc-
tion is considerably larger than for an RME that is taking
into account on the total survey error (Figure 4).
The principle of conservativeness is a wise recommenda-
tion for countries that are still in the readiness phase, but
have not yet implemented a sound REDD inventory con-
cept. However, the principle of conservativeness might
result in a counterproductive situation where the forest
area within a country is maintained or only slightly
decreased. As the RME at time 1 would be (considerably)
lower and the RME at time 2 higher than the estimated
(unchanged) forest area, a forest area loss would need to
be reported (Figure 5). Or, in other words, a country with-
out deforestation activities would only be able to report
an unchanged forest area under the principle of conserva-
tiveness, when the area of afforestation activities has the
same size as the difference between the RMEs at time 1
and time 2. However, under such conditions it would not
be wise for a country to introduce a REDD-regime.
Different status of forest degradation and potential of detec-tion by optical remote sensing techniquesFigure 3
Different status of forest degradation and potential
of detection by optical remote sensing techniques.
Reliable Minimum Estimate (RME) in terms of confidence interval (sampling error, bold lines) and total survey error (dashed lines)Figure 4
Reliable Minimum Estimate (RME) in terms of confi-
dence interval (sampling error, bold lines) and total
survey error (dashed lines).
RME
(sampling error)
RME
(total error)
Time 1
(reference period)
Time 2
(assessment period)
Emissions

Carbon Balance and Management 2009, 4:10 http://www.cbmjournal.com/content/4/1/10
Page 8 of 10
(page number not for citation purposes)
Benefits from REDD
A country is able to generate benefits under a REDD
regime, when the deforestation and degradation in a
reporting period is reduced compared to the respective
baseline. Such a baseline can be established in different
ways; here the baseline is assumed to be a business-as-
usual scenario, which is obtained by a linear extrapolation
of past deforestation and degradation rates. The amount
of benefits generated depends not only on the committed
reduction of deforestation and degradation, but on the
reliability of the estimated change as well.
Figure 6 illustrates the relation between reduced deforest-
ation and degradation and reliability of its estimates. In
scenario 1 a country with high reduction of deforestation
and degradation is shown. The RME-principle is applied
to address the uncertainty of estimates in the figures to be
reported. As the reduction rate is considerably larger than
the associated uncertainty in estimates, the resulting
amount of emission reduction qualifies for the generation
of credits.
In scenario 2 the situation is different. Here the associated
errors in estimating the reduced emissions by avoided
deforestation and degradation are larger than the emis-
sion reduction itself. Therefore a country under scenario 2
would not qualify for benefits from emission reduction,
as it failed to provide evidence that the committed reduc-
tion of deforestation and degradation was met.
The potential benefit generated by a REDD regime at time
2 is subject to the amount of carbon stock qualifying for
accounting, Ct2REDD, and the prices per ton of CO2.
Ct2REDD is calculated as the difference between the virtual
carbon stock according to a baseline scenario, Ct2BL, and
the real carbon stock at time 2, Ct2real.
Positive numbers for Ct2REDD represent emissions to the
atmosphere (the source function); negative numbers rep-
resent removals from the atmosphere (the sink function
of forests).
Ct2BL can be expressed in terms of the carbon stock at time
1, Ct1, and its change indicated by the baseline, ΔBL.
where
Ct2BL = expected carbon stock at time 2 according to the
baseline
Ct1 = carbon stock at time 1; it is assumed that Ct1 is the
RME
t2REDD t2BL t2real
CCC
=−
(1)
t2BL t1 BL
t1
t1 BL
CC
C
C
=+
()
=+
()
Δ
Δ1
(2)
Effect of conservativeness principle for a country maintaining its forest carbon stock, RME = Reliable Minimum EstimateFigure 5
Effect of conservativeness principle for a country
maintaining its forest carbon stock, RME = Reliable
Minimum Estimate.
RME time 2
Time 1
(reference period)
Time 2
(assessment period)
Emissions
RME time 1
Forest carbon
stock loss
Figure 6
Relationship of reduction of deforestation and forest
degradation (DD), total error and reliable minimum
estimate (RME), and their contribution to the values
of carbon stock at time 1 (Ct1), virtual carbon stock
at time 2 according to a baseline scenario (Ct2BL),
real carbon stock at time 2 (Ct2real), carbon stock at
time 2 qualifying for accounting (Ct2RME) and differ-
ence of Ct2BL and Ct2RME (Ĉt2REDD).
baseline
Reduced DD through incentives
ǻ
C
t1
Scenario 2 ±REDD performs poor:
- Moderate reduction of deforestation and degradation (DD) through incentives
- Large total error / low reliability
Ƙ
t2REDD
=> negative: Debit
C
t2real
C
t2RME
C
t2BL
Carbon stock
Total error
Time 1 Time 2
baseline
Reduced DD through incentives
Total error
ǻ
Ƙ
t2REDD
=> positive: Remuneration
Scenario 1 ±REDD performs well:
- High reduction of deforestation and degradation (DD) through incentives
- Small total error / high reliability
C
t2real
C
t2RME
C
t2BL
Carbon stock
C
t1
Time 1 Time 2

Carbon Balance and Management 2009, 4:10 http://www.cbmjournal.com/content/4/1/10
Page 9 of 10
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ΔBL = proportional change between time 1 and time 2
according to the baseline, ΔBL = {-1.1}, where negative val-
ues indicate a decrease of the C-stock, e.g. by deforestation
or degradation, and positive values an increase, e.g. by
afforestation or forest growth.
The carbon stock observed at time 2, Ct2real, is given by
where
Δreal = proportional real change between time 1 and time
2, Δreal = {-1.1} where negative values indicate a decrease
of the C-stock, e.g. by deforestation or degradation, and
positive values an increase, e.g. by afforestation or forest
growth.
With eq. (2) and (3) the carbon stock qualifying for
accounting given by eq. (1) can be rephrased:
In eq. (1) to (4) no error components are included. The
amount of carbon qualifying for accounting needs to
include estimates of the underlying uncertainties. Thus
Ct2RME, which is constrained by the RME at time 2, has to
be reported:
where
Et2 = error of the estimated carbon stock at time 2, Ct2real
Replacing Ct2real by Ct2RME in Eq. (1) yields a REDD esti-
mate Ĉt2REDD, which incorporates uncertainties for the
estimated carbon stocks at time 1 and time 2,
Eq. (6) illustrates the drivers of the amount of carbon that
generates benefits. Et2 is controlled inter alia by the inven-
tory concept applied, Δreal reflects the efforts undertaken
to reduce deforestation and degradation, and ΔBL points to
the carbon stock that would be achieved under business-
as-usual interventions.
The effect of errors on the RME and the impacts for bene-
fits generated by REDD are illustrated in Figure 7. For
errors, Et2, ranging from 0 to 70 percent of Ct2real, Ĉt2REDD
was calculated in percent of the carbon stock at time 1,
Ct1. Changes between time 1 and time 2 according to the
baseline were chosen to be 30 and 50 percent (ΔBL = {-0.3;
-0.5}). The real changes, Δreal, are between 10 and 45 per-
cent, resulting in an emission reduction of 10 to 66 per-
cent.
It is obvious that benefits can only be generated where real
changes, Δreal, are smaller than the changes according to
the baseline, ΔBL, as Ĉt2REDD becomes negative. However,
the amount of benefits generated depends on the error
associated with the carbon stock estimates, Et2. The func-
tional relationship between ΔBL, Δreal and Et2 indicates that
the smaller the difference between ΔBL, and Δreal, the
smaller Et2 has to be in order to generate benefits. For
example, an error Et2 smaller than 12 percent of the real
t2real t1 real
t1
t1 real
CC
C
C
=+
()
=+
()
Δ
Δ1
(3)
t2REDD t2BL t2real
t1 BL t1 real
t1 BL real
CCC
CC
C
=−
=+
()
−+
()
=−
11
ΔΔ
ΔΔ
{{}
(4)
t2RME t2real t2
t1 t2 real
C’C E
CE
=−
()
=−
()
+
’
()
1
11
Δ
(5)
t2REDD t2BL t2RME
t1 BL t1 t2 real
t1
CCC
CC
E
C
ˆ
()
=−
=+
()
−−
()
+
()
=
111
ΔΔ
1111+
()
−−
()
+
()
ΔΔ
BL t2 real
E()
(6)
Graph of resulting play emissions, negative numbers remov-alsFigure 7
Graph of resulting Ĉt2REDD (in percent of carbon stock
at time 1, Ct1) in relation to error at time 2, (Et2) for
different real deforestation rates (Δreal); different
baseline changes (ΔBL): upper graph: ΔBL = -0,3; lower
graph, ΔBL = -0,5; positive numbers display emissions,
negative numbers removals.
-40%
-20%
0%
20%
40%
60%
0% 20% 40% 60% 80%
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>RI&
W
@
(UURU>LQRI&
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ǻ
%/

-0.1 (66% reduction)
-0.2 (33% reduc tion)
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-40%
-20%
0%
20%
40%
60%
0% 20% 40% 60% 80%
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Carbon Balance and Management 2009, 4:10 http://www.cbmjournal.com/content/4/1/10
Page 10 of 10
(page number not for citation purposes)
carbon stock observed at time 2 is required to achieve
accountable carbon credits for a baseline change, ΔBL, of
30 percent and a real change, Δreal, of 20 percent (Figure
7). As large errors corrupt the generation of accountable
carbon credits by REDD, reasonable care has to be exer-
cised in implementing a sound assessment and reporting
system.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
MK conceived of the study, participated in its design and
coordination, and drafted the manuscript. TB and DP car-
ried out the study and performed the statistical analysis.
JK participated in the design of the study. All authors read
and approved the final manuscript.
Acknowledgements
Thanks must be extended to Matthias Schwörer and Dr. Eckhard Heuer,
Federal Ministry of Food, Agriculture and Consumer Protection, Bonn,
Germany, and Reinhard Wolf, Deutsche Gesellschaft für Technische
Zusammenarbeit (GTZ), Eschborn, Germany, for numerous, rewarding dis-
cussions and helpful comments. Rosemarie Benndorf, Umweltbundesamt
(UBA), Dessau, Germany, kindly provided valuable inputs to parts of the
paper. We are grateful to four anonymous reviewers, which helped to
improve the quality of the text.
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