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Please cite
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Fearnside, P.M. 2000.  Uncertainty
in land-use change and forestry sector mitigation options for global warming:
Plantation silviculture versus avoided deforestation. Biomass and Bioenergy
18(6): 457-468

DOI: 10.1016/S0961-9534(00)00003-9

ISSN: 0961-9534

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Elsevier

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UNCERTAINTY IN LAND-USE CHANGE AND FORESTRY SECTOR MITIGATION
OPTIONS FOR GLOBAL WARMING: PLANTATION SILVICULTURE VERSUS AVOIDED
DEFORESTATION

Philip
M. Fearnside

Department
of Ecology

National
Institute for Research

in the Amazon (INPA)

C.P.
478

69011-970
Manaus, Amazonas

BRAZIL

Fax:
55-92-642-8909

[email-masked]

27
Nov. 1999

17
Apr. 2000

Biomass and
Bioenergy 18(6)   [2000] (In press)

TABLE OF CONTENTS

ABSTRACT
.......................................

KEYWORDS
.......................................

1. INTRODUCTION
................................

2. PROBABILITY OF
SUCCESS ......................

3. EFFECT OF
UNCERTAINTY REDUCTION ............

4. EFFECT OF A
REQUIRED LEVEL OF CERTAINTY .....

5. CONCLUSIONS...................................

ACKNOWLEDGMENTS
................................

REFERENCES
....................................

FIGURE LEGENDS
................................

LIST OF TABLES

Table
1. Deforestation versus carbon value in Brazilian Amazonia.

Table
2. Hypothetical example of a Bayesian comparison of mitigation options.

LIST OF FIGURES

Fig. 1.   Effect of uncertainty reduction

Fig. 2.   Carbon credit at different levels of uncertainty

Fig. 3.   Plantations for
charcoal: benefit/ha and number of hectares

Fig. 4.   Avoided
deforestation: benefit/ha and number of hectares

Fig. 5.   Effect of different levels of
required certainty on the cost of carbon sequestration, using the example
parameters.

UNCERTAINTY IN LAND-USE CHANGE AND FORESTRY SECTOR MITIGATION OPTIONS FOR
GLOBAL WARMING: PLANTATION SILVICULTURE VERSUS AVOIDED DEFORESTATION

Philip
M. Fearnside

Department
of Ecology

National
Institute for Research

in the Amazon (INPA)

C.P.
478

69011-970
Manaus, Amazonas

BRAZIL

ABSTRACT

How land-use change and forestry
sector options can be used to mitigate global warming will depend on a variety of
pending decisions regarding interpretation of the Kyoto Protocol, including
treatment of uncertainty.  In
tropical forest countries, the allocation of effort between plantation
silviculture and reduction of deforestation would be influenced by the stringency
of requirements regarding certainty.
Slowing deforestation offers much greater potential benefits, but the
certainty associated with these is much lower than in the case of plantations.  In the Brazilian case, deforestation
avoidance could produce carbon benefits worth 6-45 times as much as the
destructive ranching and logging uses to which the forest is now being
converted.  Capturing the potential
value of carbon benefits from avoided deforestation will depend on increasing
our understanding of the deforestation process and consequent ability to reduce
the uncertainty associated with the effects of deforestation-avoidance
measures.  It will also depend on
whether carbon credits are defined in terms of a maximum level of uncertainty.

KEYWORDS: Carbon, Global warming,
Kyoto Protocol, Uncertainty, Climate change, Mitigation

1. INTRODUCTION

The
danger of global warming caused by increased atmospheric concentrations of
gases such as carbon dioxide (CO2) gives urgency to finding ways to
reduce greenhouse gas emissions and/or enhance flows of atmospheric carbon to
biomass and other “sinks.”
The United Nations Framework Convention on Climate Change (UN-FCCC) was
negotiated in 1992 for this purpose, and has since been supplemented by the
December 1997 Kyoto Protocol [1].
The Kyoto Protocol establishes emissions limits for countries that are
members of Annex B of the Protocol (similar to Annex I of the UN-FCCC), which
currently includes most of the industrialized countries.  The Protocol also establishes a variety
of ways in which these commitments can be met.  These include undertaking mitigation
projects in non-Annex B countries under the Clean Development Mechanism (CDM)
defined in Article 12 of the Protocol [2], and emissions trading defined in
Article 17.  The uncertainty of
estimates of emissions and uptakes is likely to affect the amount of credit or
debit that would be assigned to a global warming mitigation project or to a
country under the Kyoto Protocol.
Decisions have not yet been made on how these issues will be
handled.  It is hoped that the
conceptual discussion that follows will contribute to understanding the
consequences of different possible decisions in this area.

The
most pressing question regarding sinks in the land-use change and forestry
(LUCF) sector is whether avoided deforestation can be made a viable mitigation
option under the Kyoto Protocol, particularly in the context of the CDM.  The CDM (Article 12) applies only to
countries that have not joined Annex B of the Protocol, and therefore do not
have limitations on their national emissions.  At present, all countries with
substantial areas of tropical forests fall into the “non-Annex B”
category, making them eligible for CDM projects but ineligible for other
provisions for carbon credit under the Protocol, such as Joint Implementation
(under Article 6) and emissions trading (under Article 17).  At some time in the future, tropical
forest countries may find it in their interests to join Annex B in order to
capture major potential financial benefits from carbon credits traded under
Article 17 that could be achieved by slowing deforestation [3].

The
CDM is expected to come into effect before other “flexibility
mechanisms” (provisions under the Protocol allowing Annex B countries to meet
some or all of their commitments through activities in other countries).  This is because the CDM includes a
“banking” provision through which carbon credit can be earned as
early as 2000 and later applied to offset emissions of Annex B countries during
the Protocol’s first commitment period (2008-2012).  Decisions are still pending as to what,
if any, LUCF activities will be eligible for credit under the CDM.  Decisions will probably await completion
in May 2000 of the Intergovernmental Panel on Climate Change (IPCC) Special
Report on Land-Use Change and Forestry.
Decisions on these matters are likely to be taken at the Sixth
Conference of the Parties (COP-6) of the UN-FCCC, to be held in November 2000.

Because
the CDM is project based, many potential LUCF projects could be subject to
“leakage,” or loss of their benefits for global climate by effects
beyond a given project’s geographical, temporal or subject area
boundaries.  For example, if a
forest reserve is set up to protect carbon, people who would otherwise have
been clearing in that area may simply move to some other part of the forest to
continue clearing.

The
problem of uncertainty in establishing causality (attribution) is substantial
in the case of deforestation reduction projects due to high potential for
leakage (in the case of reserve protection) and to poor understanding of the
social processes leading to deforestation (in the case of policy
initiatives).  This problem is
likely to reduce substantially the carbon benefit that can be claimed under the
CDM (Article 12) and in many cases under Joint Implementation (Article 6), but
has much less potential effect on the credit that could be obtained under
emissions trading (Article 17) should tropical forest countries join Annex B of
the Protocol.  This is because
emissions trading is based on accounting at the national level, rather than at
the project level, making any leakage that occurs within national borders
irrelevant and making it unnecessary to show why a given reduction in emissions
has occurred.  This assumes that
Article 3.7 (the “Australia clause”) of the Protocol is interpreted
to include deforestation as a form of land-use change to be counted in the 1990
baseline for countries that, like most tropical forest countries, had net emissions
from land-use change in 1990 [4, pp. 318-319].

Table
1 gives an idea of the magnitude of potential gains to tropical countries such
as Brazil.  The average sale price
of forested land in Brazilian Amazonia represents the net present value (i.e.,
discounted profits) that could be obtained from current land uses in the
region, namely selling the timber and deforesting land for cattle pasture.  At the US$ 5-35 per metric ton of carbon
(t C) currently used by the United States government in planning its future
expenses to meet its Kyoto commitments through “flexibility
mechanisms” such as the CDM, the value of maintaining the same
hectare’s stock of carbon out of the atmosphere would be 6-45 times the
average 1998 sale price of forested land in Amazonia (see [5]).  The carbon prices used in this
calculation refer to tons of carbon of immediately achieved fossil fuel
emissions reduction, which has the same effect as avoided deforestation in the
case of countries like Brazil with large areas of remaining forest [6].  Devaluation of the Brazilian Real by 40%
in 1999 makes the contrast between land prices and carbon value even greater
today.

Brazil’s
1990 net committed emissions of 267 × 106 t C from Amazonian
deforestation ([7], updated from [8]) would be worth US$ 1.3-9.3 billion.  An estimated 2.4 × 109
t C is emitted globally each year from tropical deforestation and related
land-use changes such as shifting cultivation and clearing of non-forest
vegetation, calculated as the annual average net committed emissions over the
1981-1990 period [9].  At the US$
5-35/t C expected price for carbon, this would represent a loss of US$
12–84 billion in potential revenue from the carbon benefits of reducing
deforestation.  Obviously, even if
only a small fraction of this potential were realized it could have major
economic benefits for tropical forest countries and major benefits for the
global environment, not only through global warming mitigation but also by
maintaining biodiversity and the roles of forest in the hydrological cycle,
watershed protection, etc.

[Table
1 here]

2. PROBABILITY OF
SUCCESS

Uncertainty
enters decision-making both before the fact (ex ante) and after the fact
(ex post).  Ex ante decisions
regarding deforestation are clearly faced with substantially higher levels of
uncertainty than are ex post ones, as ex ante decisions involve
great doubt as to how deforestation will proceed in addition to doubt
concerning how any changes might be attributed to different causes.  While decisions on which forms of
mitigation to finance will undoubtedly be made ex ante, it is likely
that any granting of credits will be done ex post.

Certainty
could be a key factor determining whether carbon mitigation funds are used in
LUCF options or in other sectors, such as many energy-sector options.  Within the LUCF sector, these same
concerns could be critical in determining whether plantation silviculture or
deforestation-avoidance options are funded.

Plantation
silviculture is much safer and better-understood than deforestation avoidance
as a global warming mitigation option–-i.e., uncertainty is much lower in
the case of plantations.  On the
other hand, if successful, the potential carbon gains from investments in
deforestation avoidance may be orders of magnitude greater than the best
possible gains from plantations.
The great disparity in the probability of success requires a Bayesian
approach to interpreting such numbers.
As in gambling, a prize of fabulous value is misleading if the
probability of winning it is minuscule.
The expected monetary value (EMV) of each choice is the value of the
payoff for each outcome multiplied by its respective probability of occurrence,
summed over all possible outcomes associated with each decision (e.g.
[13]).  A hypothetical numerical
example representing plantation and deforestation-avoidance alternatives is
given in Table 2, illustrating how deforestation avoidance can still be a
rational choice even if investments in this area had a high (85%) probability
of achieving nothing.

[Table
2 here]

3. EFFECT OF
UNCERTAINTY REDUCTION

The
credit that is awarded to mitigation measures is likely to depend on the
benefits to global climate being demonstrable with a specified level of
certainty.  The logic is similar to
that in analyses of the consequences of global warming impacts in terms of a
“safe minimum standard” (SMS) of conservation [14].  Canada [15] has proposed that sinks only
be credited for carbon that can be shown to have been held out of the
atmosphere with 95% certainty.
Marland and Schlamadinger [16] provide an example of simulated
plantation benefits with varying levels of certainty, demonstrating that reducing
uncertainty results in increasing credit if a given level of certainty is
required, such as the 95% minimum proposed by Canada.

The
potential benefit that reducing uncertainty would yield to those selling carbon
credit is illustrated in Figure 1.
If 95% certainty is required, the credit that can be awarded is C1 under
the low certainty case, but increases to C2 if the level of
certainty is improved (reducing the coefficient of variation, or the standard
deviation as a percentage of the mean).
The difference between C2 and C1 represents a gain
that could be achieved by increasing the quantity and quality of data and their
interpretation.  In many cases this
may be more cost effective than investments in expanding the scale of a given
project.

[Fig.
1 here]

The
multiplicative nature of uncertainties associated with chain calculations means
that the uncertainty of the final result can quickly explode to very high
levels [17].  The variance of the
product of each multiplication (Z = X Y) can be calculated as:

sz2
= Y2 sx2 + X2 sy2

where X and Y are the means being
multiplied, Z is the product, and sx2, sy2  and sz2 are
their respective variances.  This
applies only if the terms are mutually independent; if any autocorrelations
exist, the interaction terms that must be included in the equation usually
result in much greater uncertainty of the final result [18].

Increasing
uncertainty from chain multiplication is particularly important for estimating
the benefits of deforestation avoidance.
For example, if estimates of the number of hectares cleared, tons of
biomass per hectare and carbon emission per ton of biomass each has a
coefficient of variation (cv) of 20%, the cv of the result would increase to
34.6% as a result of the two multiplications.  If an additional link in the chain were
added with the same 20% cv, say for methane emission per ton of carbon emitted,
the cv of the final result would increase to 40%, or double the cv of each
component; an additional multiplication would raise it to 44.7%.  The same proportionalities are
maintained for other levels of uncertainty.  The uncertainty of the final result
tends to explode if multiplication chains include factors with cv values above
30% [17].  The final result
increases greatly if any factor in the chain has a high cv, regardless of the
size of the mean value of the factor involved.

Increasing
levels of uncertainty have a sharp effect on carbon credit (Fig. 2).  For a cv of 20%, about 70% of the full
credit level is retained under a 95% certainty requirement, but if the cv is
40% the percentage of credit retained falls to 35%.

[Fig.
2 here]

4. EFFECT OF A
REQUIRED LEVEL OF CERTAINTY

The
uncertainty associated with the benefits of either plantation silviculture or
deforestation avoidance is composed of two component uncertainties: uncertainty
regarding the carbon benefit per hectare and uncertainty regarding the number
of hectares of either plantation establishment or avoided deforestation
resulting from a given mitigation investment.  Uncertainty in per-hectare benefits can
arise, for example, due to variation in estimates of biomass of the original
vegetation, biomass of the replacement landscape, soil carbon changes,
uncertainty in the baseline deforestation rate, etc.

In
the case of plantations for charcoal, an example is given in Figure 3 based on
average yield and biomass parameters from Brazilian plantations [6, pp. 314 and
317].  The uncertainty associated
with both carbon benefit per hectare and the number of hectares is assumed to
correspond to a coefficient of variation (cv) of 5%.  It is assumed that the plantation lasts
for 30 years, during which the carbon stocks in biomass and soils apply, ending
at the conclusion of this period.
The fossil carbon substitution benefits that accumulate over the 30
years are permanent.  For
simplicity, the timing of benefits over the 30-year project life is smoothed to
a constant yearly rate averaging 67.7 t C ha-1, of which 23.9 t C ha-1
are from biomass and soils.

[Figure
3 here]

The
probability distributions of expected carbon benefits (e.g. Fig. 3C) were
generated in this and other examples discussed in this paper by Monte Carlo
simulations in which the area and benefit per hectare values were drawn from
their respective probability distributions (e.g. Figs. 3A and 3B) and
multiplied together in 2000 simulated cases.  The mean carbon benefit (e.g. point x̅p in Fig. 3C) and the benefit credited at different
levels of required certainty (e.g. point Cp for 95% certainty in Fig.
3C) were calculated from the resulting simulated cases.

In
order to compare silvicultural plantations with avoiding deforestation,
assumptions must be made about the relative costs of these two options.  The examples developed in the following
paragraphs assume that slowing deforestation costs an average of US$
60/ha-—a value one order of magnitude cheaper than the US$ 625/ha
installation cost of plantations [6, p. 317].  Avoiding deforestation prevents an
average of 200 t C ha-1 from being emitted, rounded from the 194 t C
ha-1 average net committed emission from 1990 deforestation in
Brazilian Amazonia [7, updated from 8].
The example in Figure 4 avoids an average of 0.18 ´
103 km2 of deforestation, and so would cost US$ 10.8
million.  This amount of money would
pay for only 0.017 ´ 106 ha of plantations; at
the 67.7 t C ha-1 benefit level for charcoal this would offset 1.17 ´
106 t C, on average.

[Figure
4 here]

A
carbon ton-year approach is necessary in order to compare the carbon benefits
of deforestation avoidance with those of most silvicultural plantations
(plantations other than those that produce fuelwood that substitutes for fossil
fuel).  This is because the benefits
of deforestation avoidance are effectively permanent, at least in the case of
countries like Brazil with large areas of remaining forest, whereas carbon
sequestered in plantation biomass and in wood products can be expected to be
released to the atmosphere at the conclusion of a given project’s life
[see 2, 6].  As an illustration, if
one assumes a 100-yr time horizon for analysis, a 30-yr project life for
plantations and a 5% annual discount rate, one ton of fossil fuel carbon that
would have been emitted immediately (or one ton of avoided deforestation carbon
for countries like Brazil with large areas of remaining forest) is equal to
19.9 ton-years, while one ton of plantation carbon in biomass or soil equals
15.4 ton-years.  At this rate of
discount, the carbon sequestered in plantation biomass (as distinct from
substitution for fossil carbon) must be adjusted by a factor of 15.4/19.9 =
0.775 to obtain the equivalent in fossil fuel carbon at the same rate of
discount.  This equivalence has been
used to express all values in metric tons of carbon (t C) equivalent to
instantaneous avoidance of fossil fuel emission.  The equivalence used does not take into
account the natural removal of CO2 from the atmosphere; however,
this factor would be unlikely to substantially alter the relative benefits of
plantation silviculture versus deforestation avoidance.

If
pulpwood rather than charcoal were produced by the plantations, then carbon
benefits would be about 30-fold lower, with an average benefit of 2.1 t C ha-1.  Under the same assumptions regarding
uncertainty as those applied to the charcoal example, this per-hectare carbon
benefit would produce a total carbon benefit of only 0.036 ´
106 t C, and a credit at 95% certainty of 0.032 ´
106 t C.

For
deforestation-avoidance initiatives, uncertainty is especially high regarding
the number of hectares that has been or would be saved by a given project.  The shapes of the probability density
curves associated with benefit per hectare (Fig. 4A) and area of avoided
deforestation (Fig. 4B) are expected to be quite different: the curve representing
benefit per hectare would be a bell-shaped normal probability distribution
(e.g. [19]), while the corresponding curve for the number of hectares of
deforestation avoided would have a peak at zero to represent a large chance
that nothing would be achieved and a tail on the upper side extending to very
high values.  The distribution of
the product of the two values (i.e., expected C benefits) would have a large
peak at zero and a long upper tail representing the “jackpot” of
potentially very valuable carbon benefits that might be achieved (Fig. 4C).

The
high peak of probability of zero or near-zero outcomes in the case of
deforestation avoidance indicates reducing this as the greatest priority for
research in this area.  Effort in understanding
the causes of deforestation and developing functional models capable of
generating scenarios under different policy assumptions may well produce
greater dividends in making deforestation avoidance into a viable mitigation
option than would effort in reducing the uncertainty surrounding carbon
benefits per hectare of deforestation.

Expected
carbon benefits from silvicultural plantations and avoided deforestation will
have probability density functions with different shapes (Figs. 3C and 4C).  Plantations (Fig. 3C) will have normally
distributed benefits with low variance.
Although not indicated in this example, there may be a small probability
of zero benefit, and also a small probability of negative benefits should the
project produce impacts, as by stimulating the clearing of high-biomass
vegetation to make way for plantations, or by expelling people who clear
forests elsewhere.  Avoided
deforestation will have the high peak at zero and the long tail representing
the potential “jackpot” at the right hand side of the graph (Fig.
4C).  A small probability of
negative benefits will also exist.
The mean carbon benefit, representing the expected monetary value (as in
Table 2), is expected to be substantially higher in the case of avoided
deforestation (x̅d > x̅p).

The
differing shapes of the probability density curves for plantations versus
deforestation avoidance (Figs. 3C and 4C) will affect the result of requiring a
minimum level of certainty for carbon benefits.  The effect of the large probability of
obtaining results with zero or near-zero values in the case of deforestation
avoidance is to drastically reduce the amount of carbon credit that could be
awarded for avoided deforestation (Cd in Fig. 4C) to the point where
it could be lower than that awarded for plantations (Cp in Fig. 3C;
i.e., Cd < Cp).

In
the case of a normal distribution, such as that for plantations (Fig. 3C), the
mean value coincides with what can be achieved with 50% certainty, since half
of the expected outcomes will have carbon benefits greater than this
value.  As one increases the level
of required certainty (moving to the left in Fig. 3C), successively less of the
expected (mean) value can be claimed.
In the case of the avoided deforestation example, the expected (mean)
value corresponds to a required certainty of 27%.  The progressive loss of claimable value
will not occur at the same pace as one moves towards a requirement of 100%
certainty.  In the case of the
charcoal plantation example (Fig. 3C), a 95% certainty requirement reduces the
credit (Cp) to 88% of the mean value (x̅p), whereas in the avoided deforestation case (Fig.
4C), the credit (Cd) is reduced to zero, thereby losing all of the
substantially higher expected (mean) value (x̅d).  The requirement of a specified level of
certainty would significantly favor plantations over avoided deforestation,
with the relative advantage of plantations increasing as the required level of
certainty rises.

It
is obvious that, if 95% certainty is required, any option with a substantial
probability resulting in a zero value will be excluded (i.e., the credited
level will be equal to zero).
Deforestation avoidance options would be excluded regardless of the
magnitude of the reward that could accrue should a “jackpot” be won
by a successful reduction in clearing rates.  The mean or expected carbon benefit,
which is strongly influenced by a “jackpot” in the right-hand tail
of the probability distribution, will likewise be irrelevant to choices based
on a 95% certainty criterion.  The
most salient advantages of deforestation avoidance would therefore count for
nothing under such a system.

The
cost per ton of carbon sequestered would be equal to the mean if no certainty
requirements apply; with the example parameters given above, this is US$ 9.24/t
C for plantations producing charcoal and US$ 0.30/t C for deforestation
avoidance.  In the case of
deforestation avoidance, because this example assumes a probability greater
than 50% that zero benefit would result (55% in this example), any certainty
requirement would immediately result in zero credit (and consequently an
infinite cost per ton of carbon), whereas the cost per ton of carbon for
plantations would only rise gradually, exploding to infinity only as the
required certainty approaches the impossible-to-meet level of 100% (Fig. 5).

[Fig.
5 here]

The
assumption that investment in deforestation avoidance has a high probability of
resulting in zero benefit (the 0.55 probability that the area avoided will be
zero in Fig. 4B) is prohibitive.
This assumption may be overly severe; if some gain is assured, then the
avoided deforestation curve in Figure 5 would not explode to infinity until a
higher level of required certainty is reached.

Deforestation
avoidance is generally seen as having more collateral benefits than expansion
of plantations because forests provide environmental services other than carbon
storage.  Maintaining biodiversity
and the hydrological cycle are particularly important in the case of Amazonia,
and the willingness to pay for these values already represents a substantial
amount [20, 21].  Giving additional
weight to the benefits of deforestation avoidance, as compared to expansion of
silvicultural plantations, can be justified on these grounds.

Decisions
on the allocation between plantations and avoided deforestation will be
critically affected by the choice of a required level of certainty; negotiators
should be aware that demanding high levels of certainty has both positive and negative
consequences for climate, biodiversity and sustainable development.  The potential of such a requirement
making deforestation avoidance worthless as a global warming mitigation measure
should be regarded as a serious disadvantage.  A system of weighting is needed that
explicitly adjusts the index of benefits used in decision-making to reflect the
additional environmental and social benefits of avoided deforestation.  Such an adjustment would result in an upward
shift in benefit curves such as those in Figures 4A and 4C and a downward shift
in the cost curve for avoided deforestation in Figure 5.  An additional adjustment to recognize
the value of a possible large “jackpot” (the right-hand tail in
Fig. 4C) could be applied through expected utility (EU) approaches.  While all of these adjustments would
contribute something to increasing the value attached to slowing tropical
deforestation, they would do little to overcome the crippling effect of
imposing a required high level of certainty.  Uncertainty restrictions would be
especially unfavorable to deforestation-reduction projects under the CDM, which
requires demonstrating that project effects have “additionality”
with respect to a no-project baseline.

5. CONCLUSIONS

Uncertainty
regarding the magnitude and attribution of carbon benefits from efforts to
mitigate global warming is expected to reduce the credit that can be assigned
to relatively uncertain options.
Reducing deforestation could yield much greater benefits for global
climate than expanding plantation silviculture, but deforestation reduction is
much more uncertain.  The
uncertainty associated with deforestation-reduction initiatives is particularly
high if attribution of causes is required, as under the “clean
development mechanism” (CDM) that applies to projects in countries
outside Annex B of the Kyoto Protocol.

Requirement
of a minimum level of certainty can influence the choices of land-use change
and forestry options for mitigating global warming.  Requirements for high levels of certainty
would reduce the credit awarded for slowing deforestation, relative to that for
expanding plantation silviculture.

Tropical
forest countries could substantially increase their potential for gaining
credit for reducing tropical deforestation by joining Annex B of the Kyoto
Protocol, thereby eliminating the necessity of establishing the attribution of
causes for changes in tropical deforestation rates (gaining credit through
emissions trading under Article 17 rather than through the CDM under Article
12).

ACKNOWLEDGMENTS

Brazil's
National Council of Scientific and Technological Development (CNPq AIs
350230/97-98 and 523980/96-5) and the National Institute for Research in the
Amazon (INPA PPIs 5-3150 and 1-3160)
provided financial support.  An earlier version of this discussion
was presented at the 6th F-7 Workshop, Mexico City, 17-19 Feb. 1999.  I thank P.M.L.A. Graça, D.
Lashof, W.F. Laurance, S.V. Wilson and two anonymous reviewers for comments.

REFERENCES

[1] UN-FCCC (United Nations Framework
Convention on Climate Change). Kyoto Protocol to the United Nations Framework
Convention on Climate Change, Document FCCC/CP/1997;7/Add1 (available at
http://www.unfccc.de), 1997.

[2] Fearnside PM. Forests and global
warming mitigation in Brazil: Opportunities in the Brazilian forest sector for
responses to global warming under the “Clean Development
Mechanism.” Biomass and Bioenergy 1999;16:171-189.

[3] Fearnside PM. Como o efeito estufa pode render dinheiro para o
Brasil. Ciência Hoje 1999;26(155):41-43.

[4] Schlamadinger
B, Marland G. The Kyoto Protocol: Provisions and unresolved issues relevant to
land-use change and forestry. Environmental Science and Policy 1998;1:313-327.

[5] Fearnside PM.
The potential of Brazil's forest sector for mitigating global warming under the
Kyoto Protocol's "Clean Development Mechanism." pp. 634-646. In:
Kinsman JD, Mathai CV, Baer M., Holt E and Trexler M, editors. Global Climate
Change: Science, Policy, and Mitigation/Adaptation Strategies. Proceedings of
the Second International Specialty Conference, Washington, DC, 13-15 October
1998. Sewickley, PA: Air & Waste Management Association (AWMA), 1999.

[6] Fearnside PM. Global warming response options
in Brazil's forest sector: Comparison of project-level costs and benefits.
Biomass and Bioenergy 1995;8(5):309-322.

[7] Fearnside PM. Greenhouse gas emissions from land use change
in Brazil’s Amazon region. pp. 231-249. In: Lal R, Kimble JM and
Stewart BA, editors. Global Climate Change and Tropical Ecosystems. Advances in
Soil Science. Boca Raton, FL: CRC Press, 2000.

[8] Fearnside PM. Greenhouse gases from
deforestation in Brazilian Amazonia: Net committed emissions. Clim. Change
1997;35(3):321-360.

[9] Fearnside PM. Global
warming and tropical land-use change: Greenhouse gas emissions from biomass
burning, decomposition and soils in forest conversion, shifting cultivation and
secondary vegetation. Clim.
Change (in press).

[10] O Diário [Mogi das
Cruzes] "Preços das terras estão caindo, afirma FGV."
25 January 1998, p. 5.

[11] Walsh MJ. Contributed
statement, Brazil/U.S. Aspen Global Forum on the Kyoto Accords, Snowmass, CO,
9-11 October 1998.

[12] Brazil, INPE (Instituto Nacional de
Pesquisas Espaciais). Monitoramento da Floresta Amazônica Brasileira por
Satélite/ Monitoring of the Brazilian Amazon Forest by Satellite:
1998-1999. São José dos Campos, São Paulo, Brazil: INPE.
2000.

[13] Raiffa H. Decision Analysis:
Introductory Lectures on Choices under Uncertainty. Reading, MA:
Addison-Wesley. 1968.

[14] van Kooten GC, Grainger A, Ley
E, Marland G, Solberg B. Conceptual issues related to carbon sequestration:
Uncertainty and time. Critical Reviews in Environmental Science and Technology
1997;27(special):S65-S82.

[15] Canada. In: UN-FCCC, Subsidiary
Body on Scientific and Technical Advice (SBSTA), Report
FCCC/SBSTA/1998/Misc.6/add1, additional submissions by the Parties (available
at http://www.unfccc.de),
1998.

[16] Marland G, Schlamadinger B. Carbon
sequestered, carbon displaced and the Kyoto context. Paper presented at The
Association of Pulp and Paper Industries (TAPPI) International Environmental Conference and
Exhibit, 18-21 April 1999, Nashville, TN.

[17] Robinson JM. On uncertainty in
the computation of global emissions from biomass burning. Clim. Change
1989;14:43-262.

[18] Alonso W. Predicting best with
imperfect data. Jour. of the Amer. Instit. of Planners 1968;34:248-255.

[19] Laurance WF, Fearnside
PM, Laurance SG, Delamonica P, Lovejoy TE, Rankin-de-Merona JM, Chambers JQ,
Gascon C. Relationship between soils on Amazon forest biomass: A
landscape-scale study. Forest Ecol. and Manage. 1999;118(1-3):127-138.

[20] Fearnside PM. Environmental
services as a strategy for sustainable development in rural Amazonia. Ecol.
Econ. 1997;20(1):53-70.

[21]
Fearnside PM. Biodiversity as an environmental service in Brazil's Amazonian
forests: Risks, value and conservation. Environ. Conserv. 1999;26(4):305-321

FIGURE LEGENDS

Fig. 1    --   Effect of uncertainty reduction: under high uncertainty
(A) the amount that can be credited (C1) under a 95% certainty
requirement is less than in the low-certainty case (B).  The amount that can be credited is
determined by 95% of the area under the curve being located to the right of the
point (C1 in the high-uncertainty case and C2 in the
low-uncertainty case).  The
difference between the amounts credited (C2-C1)
represents the potential gain from improving certainty.

Fig. 2    --   Carbon credit at different levels of uncertainty
(coefficient of variation = cv) for an example with a mean benefit of 200 t C
ha-1 of avoided deforestation emissions and a requirement of 95%
certainty.  The right axis gives the
percentage of full credit awarded.

Fig. 3    --   Silvicultural plantations for
charcoal: expected carbon benefit (Cp) is derived from benefit/ha
(A) and number of hectares (B).
Both parameters are relatively well understood, here assumed for
illustration to have cv values of 5%.
This is reflected in the expected carbon benefit from plantations (Cp).

Fig. 4    --   Avoided deforestation: expected
carbon benefit from deforestation avoidance (Cd) is derived from
benefit/ha (A) and number of hectares (B).
The number of hectares (B) has a high probability associated with zero
or near-zero hectares, but also has a long tail representing small
probabilities of avoiding large areas of deforestation.  This pattern is reflected in the
expected carbon benefit from deforestation avoidance (Cd).  Differing shapes of curves for
plantations and deforestation avoidance can reverse the outcome: although
plantations (Fig. 3C) have a substantially lower expected benefit than avoided
deforestation (x̅p <<
x̅d), a 95% certainty requirement can reduce the benefits
assigned to avoided deforestation (Fig. 4C) to a level lower than the
corresponding credit assigned to plantations (Cd < Cp).

Fig. 5    --   Effect of different levels of required certainty on the
cost of carbon sequestration, using the example parameters.  Deforestation avoidance, although much
cheaper than plantations for charcoal as a mitigation option if no certainty
requirement is imposed, becomes worthless if even modest levels of certainty
are required.

Table 1. Deforestation versus carbon
value in Brazilian Amazonia

Land price (NPV of deforestation)

US$ 150/ha

Fundação Getúlio Vargas [10]

Net committed emission

194 t C ha-1

Fearnside [7] updated from [8]

Value of carbon

US$ 5-35/t C

Walsh [11]

Value of avoided emission

US$ 970-6790/ha

Calculated from above

Advantage of deforestation avoidance

6-45 times

Calculated from above

Value of C released by 1998 deforestation (1.74 ´
106 ha)

US$ 1.7-11.8 billion

Calculated from above and Brazil, INPE [12]

Table 2. Hypothetical example of a Bayesian comparison of
mitigation options

Carbon benefit (106 ton-years)

Probability of outcome

Expected ton-year value

Deforestation avoidance

Plantations

Deforestation avoidance

Plantations

0

0.850

0

0.0

0

10

0.050

1

0.5

10

100

0.050

0

5.0

0

1,000

0.025

0

25.0

0

10,000

0.020

0

200.0

0

100,000

0.005

0

500.0

0

Total

1

1

730.5

10

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5