modeling climate change mitigation from alternative methods of...

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Modeling climate change mitigation from alternative methodsof charcoal production in Kenya

Rob Bailis*

Yale School of Forestry and Environmental Studies, 195 Prospect St, New Haven, CT 06511, USA

a r t i c l e i n f o

Article history:

Received 18 March 2008

Received in revised form

16 June 2009

Accepted 6 July 2009

Published online 22 August 2009

Keywords:

Charcoal

Land-use change

Sub-Saharan Africa

Greenhouse gas emissions

Carbon offsets

CO2Fix

* Corresponding author. Tel.: þ1 203 432 541E-mail address: [email protected]

0961-9534/$ – see front matter ª 2009 Elsevidoi:10.1016/j.biombioe.2009.07.001

a b s t r a c t

Current carbon accounting methodologies do not accommodate activities that involve

emissions reductions from both land-use change and energy production. This paper

analyzes the climate change mitigation potential of charcoal production in East Africa by

examining the impact of changing both land management and technology. Current

production in a major charcoal producing region of Kenya where charcoal is made as a

by-product of land clearance for commercial grain production is modeled as the ‘‘business-

as-usual’’ scenario. Alternative production systems are proposed based on coppice

management of native or exotic trees. Improved kilns are also considered. Changes in

aboveground, belowground, and soil carbon are modeled and two distinct baseline assess-

ments are analyzed: one is based on a fixed area of land and one is based on the quantity of

non-renewable fuel that is displaced by project activities. The magnitude of carbon emis-

sions reductions varies depending on land management as well as the choice of carbon-

ization technology. However, these variations are smaller than the variations arising from

the choice of baseline methodology. The fixed-land baseline yields annualized carbon

emission reductions equivalent to 0.5–2.8 tons per year (t y�1) with no change in production

technology and 0.7–3.5 t y�1 with improved kilns. In contrast, the baseline defined by the

quantity of displaced non-renewable fuel is 2–6 times larger, yielding carbon emissions

reductions of 1.4–12.9 t y�1 with no change in production technology and 3.2–20.4 t y�1 with

improved kilns. The results demonstrate the choice of baseline, often a political rather than

scientific decision, is critical in assessing carbon emissions reductions.

ª 2009 Elsevier Ltd. All rights reserved.

1. Introduction change mitigation depends on land use as well as energy

This paper models carbon dynamics of charcoal production

in order to quantify the greenhouse gas mitigation potential

of the charcoal trade in Kenya. In many African countries,

charcoal constitutes a major source of greenhouse gas

(GHG) emissions [1]. Like in any bioenergy system, carbon

dynamics from charcoal are linked to two distinct

processes: terrestrial carbon dynamics and emissions from

fuel processing and combustion. Consequently, the degree

to which the charcoal industry can contribute to climate

2; fax: þ1 203 436 9158.

er Ltd. All rights reserved

conversion technologies. The dual components of the

baseline complicate assessments of emissions reductions

(ERs) that result from changing production practices.

I analyze charcoal as it is current production in one part of

Kenya and model several alternative production methods

that have been proposed in order to promote sustainable

production [2–5].

Estimates of Kenya’s charcoal consumption vary widely

(Fig. 1), but it is likely one of the largest charcoal consumers

globally in absolute and per capita terms [1,6,7].

.

mailto:[email protected]

http://www.elsevier.com/locate/biombioe

0

1

2

3

1993 1996

IEA

IEA

FAOSTAT*

Nyang

Kituyi

MoE

Millio

n m

etric to

ns

60% upward revision of IEA data based on 2000 data from the MoE.

200520021999

Fig. 1 – Point and time-series estimates of annual charcoal

consumption in Kenya between 1994 and 2004

[4,6,7,43,66,67]. * FAOSTAT includes charcoal consumption

data up to 2005, but the data abruptly drops from 650

thousand tons per year in 2000, which is already the

lowest of any published estimate, to just 16,500 t yL1 in

2001 and remains constant at that level until 2005 [6]. This

is an obvious error and has therefore been omitted from

the figure.


The majority of the country’s charcoal originates in woody

savannah that constitutes over two-thirds of the country’s

area [5,8,9]. Woody savannah is a resilient ecosystem [10,11],

which can recover from charcoal production under some

management regimes [12–14]. When charcoal production

contributes to long-term deforestation, it is typically the result

of post-harvest management decisions. If trees cover is rees-

tablished, emissions are reduced relative to the deforestation

scenario. However, emissions are not entirely negated

because charcoal production releases 2–3% of the tree’s

carbon as CH4, which has nine times the warming impact of

a molar equivalent quantity of CO2, and a small quantity of

N2O, an even more potent GHG [15–19].

The degree of post-harvest tree regeneration depends on

environmental as well as socioeconomic factors, which are

mediated by environmental regulations that might be set at

a national or sub-national level. In Kenya, such regulations do

not favor sustainable charcoal production [20].

The Clean Development Mechanism (CDM) of the Kyoto

Protocol is the primary vehicle through which climate change

mitigation projects are developed. ‘‘Traditional’’ forms of

bioenergy are largely absent from the CDM. To date, two

methodologies involving charcoal production have been

approved by the CDM Executive Board (the approved method-

ologies are AM41 and AMS-III.K: see [21,22]). Each one calcu-

lates emission’s reductions based on reductions in CH4

induced by changing charcoal production technology. Neither

makes an attempt to assess carbon flux resulting from LUC [23].

However, methodological difficulties arise as a result of the

need to account for land-use change (LUC) and energy-related

emission reductions within a single project. To reduce

uncertainties, the CDM-EB defines biomass as either renew-

able or non-renewable. Their classification includes woody

and non-woody biomass from forested and non-forested land

as well as biomass wastes. Woody biomass originating from

forestland is the most appropriate for analysis of existing

charcoal production systems. In this context, biomass is

renewable if [24]:

1) the land remains a forest;

2) sustainable management practices are in place to ensure

that the level of carbon stocks does not systematically

decrease over time (stocks may temporarily decrease due to

harvesting); and

3) all national or regional forestry and nature conservation

regulations are complied with.

Biomass that fails to meet all three conditions is ‘‘non-

renewable biomass’’ (NRB) by default.

This paper proceeds as follows. Section 2 examines some of

the methodological challenges that arise when analyzing the

carbon implications of a shift from NRB to sustainable biomass.

Section 3 describes the charcoal production systems currently

used in Kenya and several alternatives to this system. Section 4

introduces the model used to estimate GHG emissions from

different charcoal production systems. Section 5 presents the

results and Section 6 discusses the policy implications and

suggests some possible paths for future research.

2. Methodological issues in LUC andbioenergy

The analysis relies on CO2Fix (version 3), a ‘‘carbon book-

keeping’’ model (additional details are provided below, but

also see [25–27]).

Greenhouse gas (GHG) emissions from charcoal result from

three distinct processes: LUC processes induced by wood

harvest, pyrolysis of the woody feedstock; and the combustion

of the charcoal itself. Several studies have quantified emis-

sions from both pyrolysis and end-use [18,28–31]. However,

there have been few analyses of the impact of the charcoal

economy on land cover, nor the effect of changes in charcoal

production technology. Others have assumed a fixed

percentage of biomass regenerates, but with little empirical

basis [1]. These approaches do not accurately assess carbon

dynamics at the stand level. In addition, none of the studies

conducted to date have considered soil carbon, which is likely

to change in response to LUC.

Additional approaches carbon dynamics in wood fuel

systems have utilized GIS models to identify imbalances

between wood fuel supply and demand [32,33]. At the stand

level, several empirical studies have measured biomass

regeneration in stands of trees harvested for charcoal

production [13,14,34,35]. Other researchers have modeled

stand-dynamics in fuel wood systems to estimate carbon flux,

including changes in carbon stored in the soil and in forest

products [26,36,37]. However, to date none have modeled

carbon dynamics in charcoal production systems.

In any bioenergy system, emission reductions are sensitive

to the choice of the ‘‘functional unit’’, a concept commonly

deployed in life-cycle assessment in order to systematically

define the performance of a good or service [38]. Characterizing

the functional unit in bioenergy systems in the context of GHG

abatement is challenging because LUC must be accounted for

b i o m a s s a n d b i o e n e r g y 3 3 ( 2 0 0 9 ) 1 4 9 1 – 1 5 0 2 1493

in parallel with emissions from processing and end-use. For

LUC activities, it is logical to assess changes in carbon stocks in

biomass and soil from a fixed area of land (e.g. 1 ha). Emissions

that result from processing harvested woodfrom that land area

are also accounted for. Thus, emissions from LUC and pro-

cessing attributable to the ‘‘functional unit’’ are summed and

compared to the ‘‘business-as-usual’’ (BAU) scenario,

accounting for ‘‘leakage’’ if applicable (see [39] and the

discussion in Section 6 below). However, when an activity

induces a shift away from NRB, it is logical to account for

emissions reductions based on a ‘‘functional unit’’ of energy

delivered to consumers. Indeed, the methodologies that the

CDM-EB approved for NRB substitution bases its assessment of

emission reductions on ‘‘the fuel consumption of the tech-

nologies that would have been used in the absence of the

project activity times an emission coefficient for the fossil fuel

displaced,’’ [39]. This raises challenging questions because

different emission reductions result from the choice of func-

tional unit as unit of land or energy.

EM kilns in Rwanda

Cassamance kilns in Rwanda

EM kiln in Zambia

Small EM kilns in Kenya

Medium EM kilns in Kenya

Round brick kiln in Brazil

Metal drum kiln in Thailand

Rice husk EM kiln in Thailand

EM kiln in Thailand

Mud beehive kiln in Thailand

Large EM kilns in Kenya EM kilns

Brick beehive kiln in Thailand

Hot tail kiln in Brazil

Rectangular metal kiln in Brazil

0

Fig. 2 – Charcoal and carbon yield from earthmound (EM) and im

is defined as the mass of charcoal to the mass of dry wood used

in the charcoal to the initial mass of carbon in the wood. * (* Sou

Medium kilns in Kenya [20]; Thailand [31]; Zambia [47]; and Rwa

there is only moderate correlation between the two variables (r

differed).

3. Charcoal production systems

3.1. Charcoal production technology

Charcoal can be made from many forms of biomass, including

agricultural residues and timber waste [40]. However, nearly

all charcoal in Kenya is made from native woody vegetation

[4,8]. Survey results show over 200 different tree species are

used [5,9,20]. Indigenous trees are favored, though in certain

areas exotic trees are also used [41–43].

Pyrolysis takes place in an enclosed space, ranging from

a simple pit or earthmound (EM) kiln to brick or metal kilns.

In industrialized settings, retorts may be used, which utilize

combustible compounds in the wood to generate heat for

pyrolysis [44,45], but retorts have not been used in Kenya.

EM kilns are typically thought to have low yields, although

empirical measurements show that yields, while variable,

can overlap with yields from improved kilns. For example,

40 80

Charc yield - EM kiln Charc yield - Imp kiln Carbon yield - EM kiln Carbon yield - Imp kiln St. Deviations

Yield (in percent)

proved kilns reported in published literature. Charcoal yield

as feedstock. Carbon yield is defined as the mass of carbon

rces: kilns in Brazil [17]; Large and small kilns in Kenya [17];

nda [49]. In studies reporting both mass and carbon yields,2 [ 0.64), indicating that the degree of pyrolyzation likely

300 600 900

EM kiln in Thailand

EM kiln in W. Africa

EM kiln in Kenya

EM kiln in Zambia

Rectangular metal kiln in Brazil

Brick beehive kiln in Thailand

Mud beehive kiln in Thailand

Beehive kiln in Brazil

Single drum kiln in Thailand

Round brick kiln in Brazil

EM

kiln

sIm

prov

ed k

ilns

Kilograms of carbon in CO2 equivalent units (using 100 yr GWP) per ton of charcoal produced

CO2

CH4

N2O

Fig. 3 – Emissions measured in traditional earthmound (EM) kilns and a variety of improved kilns reported in the literature

showing emissions of each GHG weighted by its 100-year global warming potential. Arrows indicate the emissions factors

used in this analysis. * (* Non-CO2 gases are expressed in CO2-equivalent units using 100-year global warming potential

(GWP), a dimensionless factor that expresses the warming impact of GHGs relative to the warming impact of CO2 [68].

Sources: emissions from Brazil and Kenya [17]; emissions from Thailand [31]; emissions from Zambia [28]; and emissions

from W. Africa [29]. Reference [28] found N2O levels very close to detection limits and do not report an emission factor).


in Kenya, government literature cites yields of just 10%

(mass basis) [4]. However field work by this author and

others finds mass-based yields from EM kilns as high as

20–30% [30,46–49].

Yield is typically defined as the ratio of charcoal produced

to dry wood input. However, comparisons between different

studies should be made with caution as methodologies vary.

In addition, this definition of yield doesn’t account for the

degree of carbonization that the wood has undergone. High

temperatures and/or long residence times in the kiln yield

charcoal with a higher fraction of fixed carbon and lower

content of volatile matter, which lowers the mass of the end

product, but does not necessarily reduce the thermodynamic

efficiency of the process [50]. Thus, conversion efficiencies are

hard to interpret without information about the extent of

carbonization. Nevertheless, Fig. 2 is included here to show

Litter

Humification

BiomassIncrement

Litter fall

Tree biomass Stemwood- Foliage - Branches - Roots

Intermediatehumus

Harvestresidues and treemortality

Decomposition

Timberharvesting

Biomassmodule

Carbon in the atm

Fig. 4 – Land-use change model for estimating emiss

the range of efficiencies reported in the literature. Notably,

two studies measured carbon yield and both mass and carbon

yields from these studies are shown.

Traditional kilns are favored across sub-Saharan Africa

because they require very little capital investment, are flexible

in size and shape, and are well-matched to the dispersed

nature of the charcoal trade [5]. Measurements from charcoal

production show overlap in emissions from EM and improved

kilns. EM kiln emissions range from 0.514 to 0.840 t C (in CO2

equivalent units weighted by100 y global warming potentials)

per ton charcoal. Emissions from improved kiln technologies

vary from 0.400 to 0.820 t C per ton charcoal (Fig. 3).

This analysis relies on data from Kenya to estimate EM

emissions [30]. For the improved kiln, emissions from a Bra-

zilian metal kiln are used [30]. This is the cleanest production

technology reported and was chosen in order to show the

Stablehumus

Emissions from burning of wood and charcoal

Humification

Woodfuels- Firewood - Charcoal

Soil module

Productsmodule

osphere

ions from wood harvesting - adapted from [25].

Table 1 – Baseline and alternative models of charcoal production.

Model name Land management model Initial harvest Key assumptions

Baseline-EM Clearance of native vegetation (AG and BG)

via one-time charcoal production in a traditional

EM or improved kiln followed by wheat cultivation

All AG and 50% of

BG biomass is cleared

Initial soil carbon is lost due to annual

tillage so that 36% of initial soil-C is lost

after 20 years.aBaseline-IK

Tarch5-EM

Tarch10-EM

Tarch15-EM

Clearance of native vegetation (AG only) for

charcoal production in an EM kiln followed by

coppice management of native tree cover on

5, 10, and 15-year coppice cycles with harvested

wood processed in EM or improved kilns,

depending on the scenario.

All AG biomass.

BG biomass is left

intact

Coppiced stands regenerate without loss

for entire modeling period. Growth of AG

biomass during coppice cycles is boosted

by 5% relative to growth from seed due to

intact root structure.

Tarch5-IK

Tarch10-IK

Tarch15-IK

Euc10-EM Clearance of native vegetation (AG and BG) for

charcoal production in an EM kiln followed by

coppice management of E. grandis on a 10-year

cycle with harvested wood processed in EM

or improved kilns, depending on

the scenario.

All AG and 50% of

BG biomass is cleared

Coppiced stands regenerate without loss for

entire modeling period. Growth of AG biomass

during coppice cycles is boosted by 5% relative

to growth from seed germination due to large

intact root structure.

Euc10-IK

a This matches the IPCC’s default assumptions for conversion of woodlands to croplands [57, Ch. 3].


largest possible contrast between technical options. In reality,

this option would likely be too costly for local production,

although it is possible that a scaled-down more locally

appropriate version could be developed.

3.2. Land management

There are two dominant modes of charcoal production in

Kenya and a third mode that is proposed, but not practiced: 1)

charcoal as a by-product of land conversion from woodland to

cropland, 2) charcoal produced on Kenya’s semiarid range-

lands without conversion to cropland and 3) management of

either natural woodlands or plantations for sustained char-

coal production.

Historically, much of Kenya’s charcoal production has

been linked to agricultural expansion [4]. When woodlands

are converted to croplands, there is a long-term loss of

terrestrial carbon and a reduction in soil carbon, as inputs of

carbon from leaf litter and decaying biomass are reduced and

soils are subject to annual tillage. This loss occurs whether or

not charcoal is produced because in the absence of charcoal

production trees are cleared and probably burned in situ.

However, the warming impact may be greater with charcoal

production because the GHG emission factors per ton of

standing biomass are greater when charcoal is produced than

when woodlands are burned [28].

In other modes of charcoal production, the loss of terres-

trial carbon may be temporary. In Kenya’s rangelands, trees

are harvested with little post-harvest management. Many

species can coppice and, left undisturbed, cleared woody

savannah typically regenerates [13,14]. However, regeneration

is contingent on natural and anthropogenic factors including

the presence of wildlife and livestock, precipitation patterns,

soil qualities, and natural or anthropogenic burning [11,51].

The net change in terrestrial carbon stocks on woodlands

composed of either exotic or indigenous tree species, when

managed for charcoal will depend on both the frequency of

harvest, and the management applied. This study analyzes

six charcoal production scenarios drawn from the three

modes of production. The business-as-usual (BAU) scenario

simulates current charcoal production in Narok District

(1�050South, 35�520East), where the author conducted exten-

sive field work in 2004 and 2005. At the time, Narok was

a major charcoal supply zone, providing roughly 30% of

Nairobi’s charcoal [20]. Charcoal in Narok originates in

woodlands dominated by Tarchonanthus camphoratus,

a common dioecious evergreen shrub. It is usually multi-

stemmed, growing up to 9 m, with a sparse crown and

a trunk up to 40 cm in diameter. It favors dry forest margins

or secondary woodlands and is often dominant or co-domi-

nant in association with Acacia spp. [52,53]. In the BAU

scenario, a full-grown stand of native vegetation is

completely cleared (including all aboveground (AG) and

a large portion of belowground (BG) biomass). Charcoal is

produced from the cleared biomass and wheat is planted on

the cleared land. A variation of BAU with an improved kiln is

included to demonstrate the effect of a technological shift

with no change in land management.

Additional scenarios are explored in which improved kilns

and coppice management of either native vegetation

(assumed to be pure stands of T. camphoratus) or a common

exotic species (Eucalyptus grandis) are introduced. Table 1

describes each scenario in more detail.

4. Estimating emissions from charcoalproduction

4.1. Modeling terrestrial carbon dynamics in charcoalproduction systems

CO2FIX (Version 3.1) was used to estimate terrestrial carbon

dynamics in each wood fuel production system. The total

stock of carbon at time t is the sum of carbon in three pools:

CTt ¼ Cbt þ Cst þ Cpt (1)

t defines an increment of time, CTt is the total stock of carbon,

Cbt is the carbon stored in AG and BG biomass, Cst is the carbon

stored in soil organic matter, and Cpt is the carbon stored in

Table 2 – Initial carbon stocks in stand of nativevegetation (T. camphoratus).

Component Carbon content (t ha�1)

Soil (carbon) 29.5

Biomass (carbon)

Stems 10.5

Foliage 0.3

Branches 9.6

Roots 9.1

Total 59.0


forest products. In this case, charcoal is the only product, and

it is consumed in the same period as the harvest. All carbon

stocks are measured in tonne per hectare. The model treats

each component as a separate module that is parameterized

independently. A diagram of stocks and flows of carbon is

shown in Fig. 4; modules are distinguished by broken gray

lines.

From one time period to the next, carbon flows within and

between modules as depicted by the arrows in Fig. 4. After

a time period of n years, the net flux of carbon can be deter-

mined by summing the difference in each module between

t¼ n and t¼ 0:

DCT ¼ CTn � CT0 ¼ DCb þ DCs þ DCp

¼ ðCbn � CboÞ þ ðCsn � CsoÞ þ�Cpn � Cpo

�(2)

To simulate carbon dynamics for a given stand of trees,

each module must be parameterized according to the physical

properties of the stand. Parameterization is described below.

4.2. Biomass

The biomass module is parameterized with a biomass growth

function with branches, foliage and roots defined relative to

the stem. In addition, the turnover of each component must

be defined as well as wood density, carbon content, mortality,

and harvest-induced losses.

0

CAI

0

10

20

30

years

Bio

mass g

ro

wth

(d

ry to

ns · h

a-1 · y

-1) Euc (moderate)

Euc (slow) Tarch

252015105

Fig. 5 – Current and Mean Annual Increments (CAI and MAIs) f

slow-growth stands of E. grandis. Initial years of T. camphoratus

estimated in order to give maximum AG carbon of w25 t haL1. G

South Africa [55].

Each scenario has identical initial conditions, defined as

a mature stand of T. camphoratus, which is assumed to have

a total terrestrial carbon content of w60 t ha�1 apportioned

with roughly 35% in AG biomass, 20% in BG biomass, and 45%

in the soil. Table 2 shows estimated carbon allocation in the

initial plot. This allocation of soil and biomass carbon matches

the typical range for savannah woodlands where ‘‘root-to-

shoot ratios’’ of 0.5 are common [11,54]. Model parameters

also match empirical data for T. camphoratus measured by

Young and Francombe [35] in coppiced stands. That study

found a mean annual increment (MAI) of 2.3 dry t ha�1 after 6

years of regeneration. The growth function used in this

analysis simulates their findings: the MAI is 2.3 t ha�1 after six

years, increasing to 3 t ha�1 at 20 years and then declining to

simulate slowing growth of a mature stand.

To simulate growth of E. grandis, a growth function based

on in an ecological zone similar to the study area in Kenya was

used [55]. Two site quality (SQ) indices were chosen in order to

simulate two rates of tree growth (moderate and slow). Both

are managed on a 10-year rotation. The growth functions

(current annual increments (CAI) and mean annual incre-

ment) used for both species are shown in Fig. 5.

4.3. Soil

The soil module incorporates a soil carbon model that has

been shown to robustly estimate decomposition rates for

different types of litter across a range of climates [56]. The

model assumes carbon inputs to the soil via three litter

compartments: coarse consisting of stemwood, fine consisting

of branches and coarse roots, and non-woody consisting of

foliage and fine roots. The rate of carbon input into each

compartment is determined by the growth and turnover rates,

as well as inputs arising from mortality and slash (defined in

the biomass module). Depending on its chemical composition,

litter is partitioned into one of three subsequent compart-

ments: soluble compounds, holocellulose, and lignin-like

compounds. In addition to these compartments, there are two

0

10

20

30

MAI

years

Euc (moderate)

Euc (slow) Tarch

0 252015105

or a pure stand of T. camphoratus as well as moderate and

are based on empirical observations [35]; the later years are

rowth curves for E. grandis are based on empirical data from


humus compartments, which receive inputs from the coarse

woody litter as well as the faster decomposing compounds.

The model applies temperature- and moisture-dependent

rates of partitioning and decomposition that have been cali-

brated across a range of temperatures [56].

For this analysis, the soil module was parameterized such

that initial soil carbon is roughly 30 t ha�1, which is the low

end for the range observed in savannah woodlands but is well-

matched to the steady state of carbon that would result from

leaf litter and root turnover of T. camphoratus as well as local

temperature and moisture conditions. The IPCC lists approx-

imate soil organic carbon under native vegetation in dry

tropical regions (rainfall less than 1000 mm y�1) between 35

and 60 t y�1 [54,57]. Ringius [58] notes soil carbon ranged

between 30 and 44 t ha�1 in several different soil types in

different locations in Kenya prior to crop cultivation [59].

4.4. Forest products

The product module tracks the carbon in the harvested wood. In

the case of stands that are managed strictly for woodfuels, there

are no long-lived products. All fuel-bound carbon extracted in

a given period is released to the atmosphere immediately.

Baseline

0

40

80

T. Camphoratus - 10 year coppice

0

40

80

t • h

a-1

E. Grandis (slow) - 10 year coppice

0

40

80

120

160

0 10 20 30

0 10 20 30

0 10 20 30Years

t • h

a-1

AG carbon

BG carbonSoil carbon

Total carbon

t • h

a-1

a

c

e

Fig. 6 – Carbon stocks in each stand o

4.5. Adjusting the model for coppice systems

CO2Fix is not designed to calculate carbon dynamics under

coppice management. The model assumes that biomass

either enters the pool of wood products or litter upon harvest

whereas under coppice management, BG biomass remains in

the pool of living biomass. Moreover, because there is already

a well-established root system, early stem growth in subse-

quent generations is typically faster than stem growth in trees

planted from seed or seedlings [60,61].

Coppicing was simulated by using BG biomass and soil-C at

end of the nth coppice cycle to define the initial BG and soil

conditions for the (nþ 1)th cycle. Lettens and colleagues

describe an alternate approach for dealing with coppicing

within CO2Fix [37].

5. Results

5.1. Emissions reductions from carbon storage

In each scenario, a mature stand of T. camphoratus is cleared

for charcoal production in order to reflect current practices in


0

40

80

b

t • h

a-1


0

40

80

t • h

a-1

E. Grandis (moderate) - 10 year coppice

0

40

80

120

160

Years

t • h

a-1

0 10 20 30

0 10 20 30

0 10 20 30

AG* = above-ground; BG* = below-ground

b

d

f

f T. camphoratus and E. grandis*.

Table 3 – Carbon emissions from pyrolysis after 30 yearsof charcoal production on each hectare of land (t haL1 inCO2 equivalent units using 100-year GWPs).

Baseline

Tarch5

Tarch10

Tarch15 Euc(slow)

Euc(moderate)

Traditional

kilns

12 17 18 15 53 74

Improved

kilns

8 12 13 11 38 53

Tarch10 Euc (slow)a


the study area. This initial clearance is defined as Year-1.

Subsequently, management practices are defined as in Table 1.

Each simulation was run for 30 years (the maximum lifetime of

an A/R project in the CDM). The evolution of carbon stocks is

shown in Fig. 6a–f.

The two E. grandis scenarios, which have peak growth

rates four and seven times larger than the peak growth rate

of T. camphoratus respectively, have vertical scales that are

double the stands of T. camphoratus. Fig. 6a shows the

baseline scenario, in which initial AG and BG biomass are

harvested. After harvest, stocks of living biomass drop to

near zero and the soil receives a pulse of carbon in the form

of fine roots and leaf litter. Soil carbon decays rapidly at first,

then more slowly as carbon is shifted to longer-lived pools in

the soil. Wheat is cultivated during the entire period,

creating small stocks of biomass carbon during each growing

season. This is estimated at roughly 2 t ha�1 based on

average wheat yields in Narok district (w3.3 t ha�1 [62]) and

published estimates of stalk to grain and root-to-shoot ratios

in wheat [57, Ch. 4].

Fig. 6b–d show the simulations for each stand of T. cam-

phoratus. Carbon is lost relative to Year-0 because the trees

never reach the size of the mature stand. The models also

show gradual declines in BG and soil carbon during the

modeling period, because natural turnover of the fully

formed roots outpaces the ability of the tree, with smaller

quantities of AG biomass, to allocate BG carbon. Inputs of leaf

litter are also lower than in the mature stand of trees. These

declines are most pronounced in the 5-year coppice cycle.

Fig. 6e and f show slow and moderate rates of growth for E.

grandis under a 10-year coppice cycle. Stocks of carbon in AG

and BG biomass are far larger than in T. camphoratus

scenarios. In addition, soil carbon increases over time

because leaf litter and root turnover are larger in stands of

E. grandis.

-50

0

50

100

150

to

ns o

f carb

on

Euc (moderate)

0 10 20 30

5.2. Emissions from charcoal production

Total quantities of charcoal produced in each scenario are

shown in Fig. 7. Emission factors given in Fig. 3 are multiplied

by charcoal production in each scenario to define emissions

from pyrolysis (shown in Table 3).

0

50

100

150

Baseli

ne

Baseli

ne-IK

Tarch5

Tarch1

0

Tarch1

5

Euc (m

odera

te)

Euc (s

low)

to

ns o

f ch

arco

al p

er h

a Traditional kilnsImproved kilns

Fig. 7 – Total charcoal production after 30 years of

management.

5.3. Assessing changes in carbon stocks relative to thebaseline

Each emissions profile can be assessed at the stand level

assuming a fixed land area. Carbon stocks at each point in time

are subtracted from the carbon stocks in the baseline. This

yields time-dependent emission reductions for each scenario,

shown in Fig. 8a and b. The plots extend to year-31 to show the

effect of a final harvest on carbon dynamics in each scenario.

The net change in carbon closely mirrors the coppice cycle.

However, peak carbon storage declines over time as a result of

successive cycles of charcoal production. Each time charcoal

is produced, CH4 and N2O are released, which are not fully

offset by the next cycle of biomass growth. Thus, with each

successive cycle of charcoal production, the net sequestration

relative to the baseline is diminished. This effect is more

pronounced in Fig. 8a, which shows the results for EM kilns,

because more CH4 and N2O are released per unit of charcoal

produced.

Fig. 9 shows changes in carbon stocks and emissions from

charcoal production relative to the baseline scenario after 30

-50

0

50

100

150

0 10 20 30to

ns o

f carb

on

Baseline w/IK Tarch10Euc (slow) Euc (moderate)

Note: Tarch5 and Tarch15 have been omitted for clarity.

b

Fig. 8 – a. Changes in carbon stocks relative to the baseline

scenario, including atmospheric emissions from charcoal

production using traditional EM kilns. b. Changes in carbon

stocks relative to the baseline scenario, including

atmospheric emissions from charcoal production using

improved kilns.

Euc (slow)

AG BG

Soil Charc(IK)

Charc(EM)

-150

-100

-50

0

50

Baseline-IK

Carb

on

em

issio

n red

uctio

ns ( t • h

a-1)

Tarch5

Tarch10

Tarch15

Euc (moderate)

AG BG

Soil Charc(IK)

Charc(EM)AG BG

Soil Charc (IK)Charc (EM)

Fig. 9 – Net carbon emissions per hectare relative to the

baseline scenario in aboveground (AG) pools, belowground

(BG) pools, and soil, as well as emissions from charcoal

production (Charc EM or IK) in year-30 of each scenario.

Positive changes represent carbon emissions; negative

changes represent carbon sequestration. Emissions are

shown for charcoal production from EM kilns (dashed

lines) and from improved kilns (solid lines).

0

200

400

600

Baseline-IK

Tarch5

Tarch10

Tarch15

Euc (slow)

Euc (moderate)

To

ns o

f carb

on

em

issio

n red

uctio

ns

an

d seq

uestratio

n in

year-30

AG CBG CSoil CCharc C

AG CBG CSoil C

EM kiln scenario Improved kiln scenario

Fig. 10 – Net carbon emission reductions and sequestration

relative to the BAU scenario, demonstrating the impact of

increasing the land area affected by BAU land clearance so

that the same quantity of charcoal is produced during the

30-year project activity. Each scenario includes charcoal

production from EM kilns (shaded entries) and from

improved kilns (solid entries).

Table 4 – Average annual carbon emissions reductionsusing Baseline-1, a stand-level assessment with fixedland area, and Basline-2, an assessment of NRBsubstitution (t haL1).

Scenario EM kiln Improved kiln


years of management. Stock changes are shown for each pool

(AG, BG, and soil). Emissions are shown for both EM and IK

scenarios. Simply shifting to an IK with no change in post-

harvest management results in carbon emission reductions of

3.4 t ha�1. Shifting to coppice management and using an EM

kiln reduces carbon emissions by 20–105 t ha�1. Shifting to IK

production yields reduces carbon emissions by an additional

4.4–21.4 t ha�1. Averaged over the 30-year project lifetime,

these scenarios generate annual carbon offsets ranging from

0.1 to 3.5 t ha�1. For comparison, the median annual carbon

offsets among the 30 afforestation/reforestation projects

currently in the CDM pipeline is w3 t ha�1 [23].

As discussed above, quantifying emission reductions

based on the amount of energy from NRB that is displaced

gives a different picture. Coppice management yields 1.6–14

times the amount of deliverable energy per hectare relative to

the baseline scenario. Thus, to deliver the same quantity of

energy from the BAU scenario as in the coppice management

scenarios, additional land must be cleared. Fig. 10 shows the

results of adding additional land. Here, net emission reduc-

tions and carbon sequestered increase by 150–500% relative to

the fixed land scenario. Further, shifting from EM to improved

kilns increases the net emission reductions and sequestered

carbon by an additional 60–120%. Table 4 summarizes the

average annual carbon savings achieved by each scenario

under the two baselines.

Baseline-1 Baseline-2 Baseline-1 Baseline-2

Baseline-IK – – 0.1 1.2

Tarch5 0.5 1.4 0.7 3.2

Tarch10 0.8 1.8 1.0 3.6

Tarch15 1.0 1.5 1.2 3.1

Euc (slow) 1.7 8.3 2.2 13.6

Euc (moderate) 2.8 12.9 3.5 20.4

6. Discussion

This analysis demonstrates that GHG emissions reductions

can be realized by shifting from one-time charcoal

production as a by-product of land clearance to a dedicated

coppice-management system in which either native vegeta-

tion or exotic species are periodically harvested and carbon-

ized. The magnitude of carbon emissions reductions that

may be achieved varies depending on the coppice cycle as well

as the choice of tree species and carbonization technology.

However, these variations are smaller than the variations that

arise from the choice of baseline methodology.

The large differences in ERs associated with different

baseline scenarios raise questions about how to best assess

changes in GHG emissions associated with activities involving

fuel substitution and land cover change. Calculating emissions

reductions relative to the baseline scenario based on a single

unit of land leads to systematically lower emissions reduc-

tions than calculating emissions reductions based on the

amount of NRB displaced.

Recent analyses have shown that similar complications

arise in the modern energy sector. When biofuels replace

fossil fuels, losses in biomass and soil carbon can result in

a ‘‘carbon debt’’ that takes decades or centuries to repay

[63]. However, as this analysis shows, when the conversion


of natural ecosystems defines the BAU scenario, alternate

post-clearance land management options may have the

opposite effect: leading to fewer emissions than would have

otherwise occurred. For land managers, deviations from

BAU behavior must bring tangible benefits. This is not

possible under Kenya’s current market conditions; land

managers see much greater returns from one-time charcoal

production followed by intensive grain cultivation, than they

would see from any of the charcoal production scenarios

explored here [20].

6.1. Charcoal and food production

The scenarios explored here shift land out of wheat produc-

tion. This is not a threat to local food security as wheat is

a negligible part of the local diet. However, wheat from Narok

is processed for consumption in urban and peri-urban

markets. Thus, shifting land that was destined for wheat

production into less carbon-intensive uses may create some

unmet food demand. If this were to occur on a large scale,

a segment of the population could be adversely affected.

Alternatively, it may induce other land clearance to meet

demand, which could emit additional GHGs. These indirect

effects have been estimated to have substantial impacts when

biofuel production occurs at very large scales [64], but such

effects are difficult to characterize in small markets.

6.2. Assessing the choice of baseline

The disparity between emission reductions arising from

different choices of baseline methodology reflect the difficulty

in combining emissions reductions derived from LUC and

energy-based activities. One might simply ask ‘‘which base-

line most accurately reflects reality?’’ The problem is that both

can be justified with reasonable arguments. For example, land

planted with E. grandis managed on a 10-year coppice cycle

over 30 years would produce 10 times more charcoal than is

produced from one-time clearance of native vegetation. In

theory, providing that charcoal to the market would lessen

demand for additional virgin land to be cleared for charcoal

production. The problem with this logic is that land clearance

is only partially driven by demand for charcoal. It is also

driven by a desire to expand crop cultivation. Thus, the

clearance of additional land also depends on the extent to

which cultivation presents an attractive land use option.

This argument may make the assessment based on fixed

land area seem more justifiable. However, wheat is not

consumed locally. Like charcoal, wheat is simply a commodity

sold to non-local markets. As was mentioned above, current

policies in Kenya are not amenable to sustainable land

management for charcoal production [20]. However, this may

change as a result of recent legislation [2,3]. If sustainable land

management for charcoal production were a viable option,

then it is possible that fewer land owners would opt to plant

wheat, making the assessment of emission reductions based

on energy the more realistic option.

It should also be noted that the methodologies that have

been developed to calculate emission reductions in existing

carbon offset projects are not necessarily realistic. In some

cases, realism is traded for simplicity and replicability. For

example, the methodologies that have been approved for

reducing or substituting the use of NRB estimate emission

reductions by defining a baseline in which users switch to

fossil fuels like LPG or kerosene, an unlikely scenario in most

settings where NRB is common [65]. Thus, while assessment

of net carbon savings based on fixed land area is certainly

a more conservative assessment method, in the absence of

genuine experience with land management for sustainable

charcoal production, we can not determine which method

better reflects reality. In any case, careful monitoring would be

required to elucidate how Kenya’s charcoal market would

respond to more sustainable production practices.

Acknowledgements

The author acknowledges the support of the Link Foundation

Energy Fellowship which supported field work that provided

vital insights into this analysis. In addition, Professor Omar

Masera of the National Autonomous University of Mexico

provided useful advice on how to model NRB and Murefu

Barasa provided research and editorial assistance, but any

errors or omissions are the sole responsibility of the author.

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Abbreviations used in the text

A/R: afforestation and reforestationAG: abovegroundBG: belowgroundBAU: business-as-usualCDM: Clean Development MechanismCDM-EB: CDM Executive BoardEM: earthmound (kiln)ER: emissions reductionEUC: eucalyptus (modeling scenario)GIS: Geographic Information SystemGHG: greenhouse gasGWP: global warming potentialIK: improved kilnIPCC: Intergovernmental Panel on Climate ChangeLUC: land-use changeLPG: liquid petroleum gasNRB: non-renewable biomassTARCH: Tarchonanthus camphoratus (modeling scenario)

http://www.ipcc-nggip.iges.jp/public/gl/invs6.htm

http://www.fao.org/docrep/004/AC459S/AC459S00.HTM%20(web%20version%20is%20only%20available%20in%20Spanish)





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