Getting the numbers right: Revisiting woodfuel sustainability in the developing world

Abstract

The United Nations' Sustainable Development Goals encourage a transition to 'affordable, reliable, sustainable and modern energy for all'. To be successful, the transition requires billions of people to adopt cleaner, more efficient cooking technologies that contribute to sustainability through multiple pathways: improved air quality, reduced emissions of short-lived climate pollutants, and reduced deforestation or forest degradation. However, the latter depends entirely on the extent to which people rely on 'non-renewable biomass' (NRB). This paper compares NRB estimates from 286 carbon-offset projects in 51 countries to a recently published spatial assessment of pan-tropical woodfuel demand and supply. The existing projects expect to produce offsets equivalent to ~138 MtCO2e. However, when we apply NRB values derived from spatially explicit woodfuel demand and supply imbalances in the region of each offset project, we find that emission reductions are between 57 and 81 MtCO2e: 41%–59% lower than expected. We suggest that project developers and financiers recalibrate their expectations of the mitigation potential of woodfuel projects. Spatial approaches like the one utilized here indicate regions where interventions are more (and less) likely to reduce deforestation or degradation: for example, in woodfuel 'hotspots' in East, West, and Southern Africa as well as South Asia, where nearly 300 million people live with acute woodfuel scarcity.

Full text

Environ. Res. Lett. 12 (2017) 115002 https://doi.org/10.1088/1748-9326/aa83ed
LETTER
Getting the numbers right: revisiting woodfuel
sustainability in the developing world
Rob Bailis1,6, Yiting Wang2,RudiDrigo
3, Adrian Ghilardi4and Omar Masera5
1Stockholm Environment Institute—US Center, Somerville, MA, United States of America
2WWF China, Beijing, People’sRepublicofChina
3Independent consultant, Poggibonsi (SI), Italy
4UNAM-CIGA, Antigua Carretera a Pátzcuaro 8701, Col. Ex Hacienda de Sán José de la Huerta, 58190 Morelia, Mich., Mexico
5UNAM-IIES, Antigua Carretera a Pátzcuaro 8701, Col. Ex Hacienda de Sán José de la Huerta, 58190 Morelia, Mich., Mexico
6Author to whom any correspondence should be addressed.
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9 February 2017
REVISED
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27 October 2017
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E-mail: rob.bailis@sei-us.org
Keywords: woodfuels, carbon offsets, deforestation, forest degradation, energy transitions
Supplementary material for this article is available online
Abstract
The United Nations’Sustainable Development Goals encourage a transition to ‘affordable, reliable,
sustainable and modern energy for all’. To be successful, the transition requires billions of people to
adopt cleaner, more efficient cooking technologies that contribute to sustainability through multiple
pathways: improved air quality, reduced emissions of short-lived climate pollutants, and reduced
deforestation or forest degradation. However, the latter depends entirely on the extent to which
people rely on ‘non-renewable biomass’(NRB). This paper compares NRB estimates from 286
carbon-offset projects in 51 countries to a recently published spatial assessment of pan-tropical
woodfuel demand and supply. The existing projects expect to produce offsets equivalent to ∼138
MtCO2e. However, when we apply NRB values derived from spatially explicit woodfuel demand and
supply imbalances in the region of each offset project, we find that emission reductions are between
57 and 81 MtCO2e: 41%–59% lower than expected. We suggest that project developers and financiers
recalibrate their expectations of the mitigation potential of woodfuel projects. Spatial approaches like
the one utilized here indicate regions where interventions are more (and less) likely to reduce
deforestation or degradation: for example, in woodfuel ‘hotspots’in East, West, and Southern Africa
as well as South Asia, where nearly 300 million people live with acute woodfuel scarcity.
1. Introduction
Traditional woodfuels, specifically firewood and char-
coal used for cooking, water treatment, and space
heating, represent approximately 55% of global wood
harvest and 9% of primary energy supply [1,2].
The current extent and future evolution of traditional
woodfuel consumption are closely related to several
key challenges in achieving sustainable development.
Roughly 2.8 billion people worldwide, including the
world’s poorest and most marginalized, burn wood to
satisfy their basic energy needs [3], which has profound
impacts on public health [4] and also contributes to
forest degradation, deforestation, and climate change
[5–8]. The Sustainable Development Goals encour-
age a transition to ‘affordable, reliable, sustainable and
modern energy for all’(SDG7) [9]. To be successful, this
transition requires billions of people to adopt cleaner,
more efficient cooking technologies, which are part icu-
larly attractive because they potentially deliver multiple
benefits: improved air quality and climate change miti-
gation as well as reduced fuel collection or expenditure
for poor households, and potential employment for
stove manufacturers [10,11].
The climate change impacts of traditional woodfu-
els depend on several independent factors. One is the
combustion process, which releases CO2and short-
lived climate forcers (SLCFs) like black and organic
carbon (BC and OC) aerosols and methane (CH4). In
the case of charcoal, carbonization also releases CH4
and numerous OC compounds [6]. Some commer-
cial woodfuels are transported long-distances, which
© 2017 IOP Publishing Ltd

Environ. Res. Lett. 12 (2017) 115002
also results in emissions. Finally, the sustainability of
wood harvesting, the main focus of this analysis, plays
an important role. Nearly all tropical landscapes pro-
duce a measurable increment of woody biomass over
time, either as new growth, or as re-growth from pre-
vious harvesting. If woodfuels are harvested below
this increment, then the harvest is considered sus-
tainable and the CO2emitted during combustion is
sequestered by biomass growth. However, if the rate
of harvest exceeds the rate of growth, then harvest-
ing will cause biomass stocks to decline, contributing
to degradation or, in extreme cases, deforestation7.
Such harvesting is unsustainable and a fraction of
the CO2emitted during combustion remains in the
atmosphere.
Household energy interventions that promote
clean and efficient stoves or fuel switching can miti-
gate climate change both by reducing SLCF emissions
and reducing woodfuel consumption. However, the
latter only mitigates climate change if woodfuel is har-
vested unsustainably. Working from the assumption
that unsustainable harvesting is widespread, house-
hold energy interventions have been enlisted in climate
change mitigation efforts throughout the developing
world. At the time of writing, over 300 carbon-
offset projects are in various stages of implementation
to reduce emissions from woodfuels by disseminat-
ing more efficient stoves or promoting alternative
technologies like biogas and solar cookers. Many
Nationally Determined Contributions (NDCs) indi-
cate plans to reduce non-renewable biomass (NRB).
However, evidence was recently presented question-
ing the assumption that woodfuel harvesting is highly
unsustainable [13]. Here we extend that analysis by
comparing the findings from that study with claims
from several hundred existing projects. We find that, if
they are successfully implemented, the existing projects
expect to reduce emissions by ∼138 MtCO2e. How-
ever, using the ‘Woodfuels Integrated Supply/Demand
Overview Mapping’(WISDOM) methodology, which
an analytic tool that quantifies spatially explicit imbal-
ances between supply and demand for woody biomass
and has been applied in over 25 countries, we find that
these projects will likely reduce emissions by 57–81
MtCO2e accounting for the same suite of climate forc-
ing pollutants that the projects use to calculate their
impact.
Thus, the carbon offsets generated by house-
hold energy interventions may be 41%–59% lower
than expected. This is not to say that scarcity of
woody biomass is not problematic, or that woodfuels
consumption does not contribute to climate forc-
ing. Woodfuel scarcity is most certainly a challenge
throughout the global south, and woodfuel demand
can contribute to degradation or deforestation under
certain conditions. However, the problem is both
7There are multiple definitions of degradation or deforestation; for
a review of the terminology see [12].
less severe and more heterogeneous than is generally
acknowledged.
Moreover, we stress that clean-burning stoves and
fuels are worth promoting for reasons unrelated to for-
est conservation and climate change mitigation. We
bring these results to the attention of researchers,
development practitioners, and donors not because
we advocate a halt to these types of interventions, but
rather to recalibrate expectations. Woodfuel projects
are unlikely to deliver the magnitude of em ission reduc-
tions that project documents imply, but this analysis
highlights specific geographic regions where interven-
tions are more (and less) likely to be effective. There
are woodfuel ‘hotspots’in parts of East, West, and
Southern Africa as well as South Asia, where nearly 300
million people struggle with acute woodfuel scarcity
and substantial emission reductions (ERs) are achiev-
able. Finally, we wish to raise the profile of this issue, by
stressing that these results are estimations that require
further examination and validation. In the sections
that follow, we briefly review the competing narratives
of woodfuel sustainability, examine the methods that
researchers and analysts have developed to estimate
changes in climate forcing that result from interven-
tions, explain the methods that we utilized to carry out
this analysis, review the results in detail, and discuss the
policy implications of our findings.
2. Woodfuel sustainability: competing
narratives
To understand the mitigation potential of clean-
burning stoves and fuels, we must understand the
sustainability of woodfuel extraction. Many studies
have focused on woodfuel emissions, driven largely by
a concern for public health and the close association
between emissions of climate forcers and health dam-
aging pollutants (see [14] for a review). Although the
overall impact of aerosol emissions is still uncertain, a
clear picture of the climate change impact from biomass
combustion is emerging [7,15,16]. In contrast, few
studies of carbon flows related to woodfuel harvest-
ing have been conducted [16]. Historically, woodfuel
demand was considered a major driver of deforesta-
tion [17,18], but this position was challenged decades
ago [19,20]. More recent local or regional assessments
find conflicting results [21–25], suggesting that geog-
raphy is an important factor in determining woodfuel
sustainability. The IPCC’s Fourth Assessment claimed
that 10% of global woodfuel is harvested unsustainably,
[26,27] but the Fifth Assessment stressed that net
emissions from woodfuels are still unknown [25].
Despite many years of interventions in the house-
hold energy sector in the developing world, we have
a fairly limited understanding of the environmental
implications of woodfuel demand. On one hand, there
is a compelling narrative of environmental destruction
in which demand from impoverished woodfuel users
2

Environ. Res. Lett. 12 (2017) 115002
outstrips nature’s supply, leading to deforestation and
degradation [28,29]. On the other hand, we have a
more tempered story of resilient forests, ‘trees on farms’
[30], and woodfuel-users who respond to scarcity by
switching to lower quality fuels or augmenting wood
supplies through agroforestry [19].
These conflicting narratives have developed over
the past few decades against a backdrop of alarming
tropical deforestation. Between 2001 and 2013, over 110
million ha of tropical forest were cleared [31]. There is
evidence suggesting that woodfuels play a role in degra-
dation or deforestation in specific locations [24,32],
typically working in conjunction with other pressures
like such as livestock production or the expansion of
agriculture and transportation networks [30,33–35].
However, quantifying the impact of woodfuel harvest-
ing on deforestation and forest degradation is difficult.
Woodfuel extraction occurs in diverse social and eco-
logical settings. Demand depends on local cooking
practices, species composition, and the availability of
alternative fuels. Few countries collect regular data, and
there is some seasonality in demand that can make one-
time surveys misleading. Woodfuel supplies vary with
stock, productivity, and accessibility of diverse types of
woody biomass. Woodfuels are extracted from many
types of land cover: forests, agricultural lands, and other
‘trees outside forests,’such as live fences, home gar-
dens, and roadside commons. Moreover, landscapes
utilized for woodfuel extraction are rarely isolated
from other pressures. They may be subject to tim-
ber extraction, agricultural expansion, grazing, or other
uses.
Supply systems vary greatly in length, from those
involving individual users who extract material from
within a few kilometers of their homes for their own
use to complex networks consisting of thousands of
harvesters moving tons of wood through middle-
men to commercial markets hundreds of kilometers
away. Finally, the landscape’sresponsetoharvestingis
dynamic and non-linear. The structure of tree stands
may change over time, driving people to adjust the
quantity they harvest, or shift extraction sitesaltogether
[36–38].
These complexities raise challenges for policy mak-
ers and researchers, which are reflected in the lack
of clarity in current methodologies designed to esti-
mate carbon savings from cookstove projects described
below [39–42]. As our assessment demonstrates, most
woodfuel interventions appear to be overstating the
extent to which woodfuels contribute to deforestation,
and, by extension, their mitigation benefits. We expand
on this below.
3. Review of existing woodfuel-based
mitigation projects
We identified woodfuel-based carbon-offset projects
using several publically accessible databases including
the Gold Standard (GS) Foundation project reg-
istry [43], the UNEP DTU Partnership’s listing of
Clean Development Mechanism (CDM) and Pro-
gramme of Activities (PoA) projects [44], and the
United Nations Framework Convention on Climate
Change (UNFCCC)’s CDM project database [45].
These projects generate offsets by reducing the use of
NRB, which is the term used by carbon market practi-
tioners to define unsustainably harvested woodfuels.
At the end of 2014, we found 286 woodfuel-based
offset projects at various stages of development world-
wide consisting of a mix of individual project activities
(PAs) and ‘component project activities’(CPAs) within
distinct PoAs8. Of these, 75 were voluntary projects
registered with the GS Foundation, two with the Vol-
untary Carbon Standard (VCS), and one with the
American Carbon Registry (ACR) [43]. The remain-
ing 211 projects were developed either as PAs or CPAs
under the UNFCCC’s Clean Development Mechanism
(CDM) and 41 of these have GS certification [44]9.
To put this in perspective, these projects represent less
than 1% of carbon offset projects in the CDM, which
was always dominated by large-scale projects target-
ing industrial gases [44]. However, woodfuel-based
carbon-offset projects make a substantial contribu-
tion to voluntary credits, generating over $160 million
in trade between 2007 and 2014, which was nearly
10% of the cumulative value in the voluntary market
[48].
We include all projects that are registered in the
pipeline and exclude projects that had been withdrawn
or rejected. We found projects from 51 different coun-
tries. The regional breakdown by number of projects,
expected ERs during the project lifetime, and ERs issued
by December 2014, is shown in figure 1. Project loca-
tions are shown in figure 2.
To quantify the offsets generated by reducing
NRB consumption, project developers follow specific
methodologies. The methodologies undergo expert
review and public comment prior to acceptance by the
UNFCCC and voluntary registries. After methodolo-
gies are accepted, project developers submit project
‘design documents’(PDDs, PoA-DDs and CPA-DDs)
describing the methods they utilize, and third par-
ties audit every project to ensure adherence to the
specific methodological requirements. Despite numer-
ous checks on the system, the methodologies to
quantify ERs and NRB are vague and subject to
multiple interpretations, which leads to inconsistent
applications and results in large systematic over-
estimations of NRB. In the following section, we
briefly review the approaches used to determine
NRB.
8A PoA can encompass one or more CPAs. For a full explanation of
UNFCCC regulations, readers may refer to the UNFC CC’s‘Glossary
of CDM terms’[46].
9For a detailed exploration of carbon markets and types of offset
projects readers can refer to [47].
3

Environ. Res. Lett. 12 (2017) 115002
0
80
160
Africa Asia &
Pacific
Latin
America
No. of Projects
-
40
80
Africa Asia &
Pacific
Latin
America
ERs Expected (x 106)
Voluntary
CDM & CPA with GS
CDM & CPA
0
4
8
Africa Asia &
Pacific
Latin
America
ERs Issued (x 106)
Figure 1. Woodfuel-related carbon offset projects registered or in the ‘pipeline’in December 2014.
Figure 2. Siting of projects generating carbon offsets by reducing consumption of NRB; points are sized proportionally to the volume
of C/VERs expected during the lifetime of the projects.
4. Assessing NRB in carbon offset projects
There are two main categories of projects using NRB
reduction to generate carbon offsets: those producing
offsets for the UNFCCC’s CDM and those producing
offsets for voluntary markets. Both CDM and volun-
tary markets have developed detailed methodologies to
define and calculate NRB, which we examine in this
section.
4.1. UNFCCC
The UNFCCC first introduced a CDM methodol-
ogy to quantify ERs from NRB in 2005, soon after
the CDM itself went into effect. The methodology,
AMS-I.C, was a generic methodology introduced for
projects displacing fossil fuels with renewable energy in
thermal applications (supplementary table 2, available
at stacks.iop.org/ERL/12/115002/mmedia). The sixth
version of this methodology permitted displacement
ofNRBaswellasfossilfuels,butlackeddetailsabout
how to calculate NRB, and it was dropped. In 2008,
two new NRB methodologies were introduced (AMS-
I.E and AMS.II.G), which allowed woodfuel projects
to gain a stronger foothold in the CDM. In 2011 two
additional methodologies were added: one accounted
for methane ERs, which catered to biogas projects that
reduce NRB consumption and a second that promotes
water filters to reduce NRB consumption by assuming
that filter adoption reduces reliance on woodfuels to
boil water.
AMS-I.E and AMS.II.G are the main methodolo-
gies used with NRB projects in the CDM. Each is
based on a ruling from the 23rd meeting of the CDM
Executive Board (EB) [49] relying on differentiation
between NRB and demonstrably renewable biomass
(DRB). Under this approach, DRB is defined by one
of several conditions (described in the supplementary
material).
Once DRB is identified, NRB is defined as what-
ever biomass is not DRB. However, realizing that this
may not be sufficiently conservative, the methodologies
include additional considerations. Biomass that is not
DRB can be considered NRB when two of these three
trends are observed:
•Time or distance required to gather woodfuel is
increasing;
•Prices are increasing
•Biomass is declining in quality.
4

Environ. Res. Lett. 12 (2017) 115002
Once NRB and DRB are distinguished, the frac-
tion of NRB, which indicates the percentage of woody
biomass that is unsustainable, is defined as:
fNRB = NRB
NRB + DRB .(1)
4.2. UNFCCC default values
More recently, in order to encourage more projects
in countries that did not attract much CDM activ-
ity, the CDM EB suggested fNRB default values be
used for least developed countries (LDCs), small island
developing states (SIDs), and other countries with few
projects [39]. Currently, the EB has defined 58 national
default values ranging from 40% in Bhutan and Cuba
to 100% in Mauritius, Bahrain, Comoros, and Djibouti
(see supplementary table 1 for all values). The default
calculation utilizes equation (1), and assumes DRB can
only originate from protected forest areas as defined by
the Food and Agriculture Organization (FAO)’s 2010
Forest Resource Assessment [50].
4.3. Gold Standard (GS)
Woodfuel projects made a rapid entry into the vol-
untary carbon market through the GS certification
scheme. The GS methodology was released in 2008,
a few months ahead of the CDM methodologies
described above. Like the CDM, the GS methodol-
ogy allowed for multiple approaches to assess NRB and
evolved over time to allow for the use of default values
in some cases. The first version of the GS methodology
allowed NRB assessment by following UNFCCC ’sDRB
approach, supplemented by data from field surveys,
literature, and mapping. However, the methodology
makes provisions for alternative approaches depend-
ing on information available in the project area. They
suggest a quantitative approach if there is sufficient
data, and allow project developers to adopt a qualita-
tive approach if data is lacking (both approaches are
described in the supplementary material).
The qualitative approach suggests the use of
‘Satellite imagery, combined with field surveys, per-
tinent literature reviews, and expert consultations’to
determine the NRB fraction [51,p.29].Subsequent
versions of the GS methodology, which were released
in 2010 and 2011, introduced small changes, but the
overarching approach remains the same [40]. In 2013,
the GS released a Simplified Methodology for Effi-
cient Cookstoves [52], which allows projects to use
the UNFCCC default values. Active projects submit-
ted under various GS methodologies are listed in the
supplementary material.
4.4. Methodological shortcomings
Both UNFCCC and GS methodologies have sev-
eral shortcomings. First, the UNFCCC and early GS
methodologies require land to be under ‘sustainable
management practices’in order for harvesting to be
demonstrably renewable, but they offer no guidance
about what constitutes sustainable practices. Manage-
ment of land from which woodfuels are extracted
is rarely formalized [19,53,54]. However, lack
of formal management does not necessarily mean
that biomass resources are exploited unsustainably.
Informal rules governing access may be in place to
discourage unsustainable exploitation, but such rules
are often unrecognized by outsiders [54]. The quali-
tative option in the GS methodology is equally vague.
Similarly, the UNFCCC’s default values equate sustain-
able management with national parks, game reserves,
wilderness areas, and other legally established pro-
tected areas, making an implicit assumption that
land without these designations cannot be sustainably
managed.
In addition, the suggested approach allows for no
middle ground. In areas that are not demonstrably
renewable, it implicitly assumes wood extraction is
completely non-renewable. This is equivalent to a ssum-
ing that the land has no regenerative capacity,whichis
a gross overstatement of land degradation. In most
cases, when woody biomass is removed from a given
area of land, that land can continue producing woody
biomass. If it has been overexploited or degraded, it
may produce woody biomass at a slower rate than in
the past, or with a different species composition, but it
is unlikely to remain barren in perpetuity. An except ion
of course, is if forests or woodlands are cleared to meet
woodfuel demand, but subsequently conv erted to crop-
land. However, under those circumstances, reduced
NRB consumption will not result in emission reduc-
tions because other pressures are preventing regrowth
of the wood that is harvested.
The GS quantitative approach is somewhat more
realistic. It accounts for the productivity of woody
biomass by including a mean annual increment (MAI)
in the assessment of NRB. However, the assessments
typically apply a single MAI across large regions, up
to, in many cases, entire countries. These average val-
ues fail to account for heterogeneity in growth rates
and assume that the people harvesting wood exploit
all regions equally. In reality, people are more likely
to exploit areas with more abundant woody biomass,
which would have higher growth rates than surround-
ing areas with low stocks of woody biomass.
Finally, trends of increasing time, distance, or
prices, may indeed be indicative of scarcity. How-
ever, this scarcity may be induced by factors that are
independent of woodfuel demand. Urbanization, crop
expansion, and grazing pressure are all recognized
drivers of land cover change [24,33]thatcandecrease
access to woodfuels, increase collection times, or raise
prices and drive users to opt for lower quality fuels.
However, these are not necessarily indications that
woodfuel extraction itself is unsustainable. In addition,
price trends may simply reflect inflation. For exam-
ple, in Kenya, the average nominal price of a 4 kg tin
of charcoal increased three-fold over the past decade,
but adjusting for inflation shows that the price has not
5

Environ. Res. Lett. 12 (2017) 115002
changed in real terms [55] . Thus, evidence of increasing
prices should be treated with care.
5. An alternative approach
Here we present an alternative method to assess
woodfuel sustainability. The ‘Woodfuels Integrated
Supply/Demand Overview Mapping’(WISDOM),
accounts for some of the complexities of balancing
traditional wood harvest for energy with associated
CO2emissions. For example, it uses spatial analyses
to account for woody biomass supplied by various land
cover classes as well as landscape features, deforesta-
tion patterns, and other factors that affect accessibility
to woody biomass resources. The methodology has
been described in numerous peer-reviewed articles
[13,56–59] and applied in over 25 countries [60]. The
recent pan-tropical analysis [13,59] produced a range
of estimates dependent on several key assumptions such
as whether by-products of deforestation caused by agri-
cultural expansion were used as fuel and the degree to
which people optimize woodfuel harvesting based on
the sustainable yield of woody biomass.
The pan-tropical analysis was particularly sen-
sitive to the utilization of accessible deforestation
by-products. When deforestation driven by agricul-
tural expansion occurs in regions that are accessible
to woodfuel-dependent populations, there is strong
evidence that some cleared biomass is utilized as fire-
wood or to make charcoal [13,16], but the actual
quantity of material utilized in a given location is
not known. Lacking data, the pan-tropical assessment
considered two scenarios. In Scenario A, deforesta-
tion by-products generated in accessible regions are
not used and woodfuels are harvested entirely from
other sources. NRBAdefines the quantity of non-
renewable biomass when deforestation by-products are
not used at all. In Scenario B, accessible deforestation
by-products are utilized as fuel. This scenario results
in two components of woodfuel supply: one compo-
nent (B1) consists of the deforestation by-products used
to meet a fraction of woodfuel demand and the other
component (B2) consists of wood harvested from other
sources required to fully satisfy demand after defor-
estation by-products are exhausted. Component B1is
non-renewable by definition, defined as NRBB1.IfB
2
exceeds woody biomass growth in a given region, the
excess quantity is non-renewable, defined as NRBB2 .In
each case, fNRB is defined as the ratio of NRB to total
consumption.
6. Quantifying overestimations of CERs and
VERs from woodfuel-based carbon offset
projects
To compare ERs claimed by carbon-offset projects to
ERs that would be generated from alternative fNRB
estimates, we reviewed design documents from 287
projects. The documents indicate a total 138 MtCO2e
of emissions reductions are expected. Of this, 12.1
MtCO2e had been verified and issued (figure 1). Some
GS methodologies include CH4and N2O in their offset
calculations; however, only 6% of projects utilize these
methodologies and the total reductions expected from
CH4and N2O are less than 1% of the total. Thus, the
vast majority of ERs result from CO2emissions reduc-
tions linked directly to reduced consumption of NRB.
Figure 3shows the average of fNRB values (±standard
deviation) used in carbon-offset projects wit h the num-
berofprojectsineachcountrygivenonthehorizontal
axis. It also shows the range of geographically specific
fNRB estimates derived from the pan-tropical analysis
(black dashes and gray bars)10 . The lower bound shows
the results of Scenario B1, the middle value shows the
results of Scenario A, and the upper bound shows the
netresultofB
1+B2. In many cases, fNRBAis equivalent
to fNRBB1+B2.
The fNRB values used in project design documents
to calculate ERs range from 43% to 100% with a media n
of 88%. The values derived from the pan-tropical WIS-
DOM analysis are much lower, with a median value
of 24%–29%. There are just three countries in which
the upper bound of fNRB derived from WISDOM
match or exceed fNRB used in project documents: Pak-
istan, Cameroon and Nicaragua. In addition, in some
countries, WISDOM-derived fNRB values vary widely.
This is largest in parts of Central America and West
Africa, which both experience high rates in deforesta-
tion in accessible regions. If deforestation by-products
are not used as woodfuel (Scenario A), fNRB is relatively
low because high woody biomass productivity ensures
woodfuel demand can be met sustainably. However,
if people do use deforestation by-products (Scenario
B), which are unsustainable by definition, then fNRB
is quite high because there are sufficient forest clear-
ance by-products to satisfy much or all of demand11 .
It is difficult to reduce emissions with woodfuel
interventions alone under these conditions because
deforestation is likely driven by demand for cropland
and pasture [24]. In such cases, reducing woodfuel
demand without addressing other drivers would have
minimal impact on forest loss.
In other countries, the range of uncertaint y in fNRB
is narrower. For example, some countries have very
little deforestation and no available by-products (e.g.
India and China), resulting in similar outcomes for
both Scenarios A and B. Other countries experience a
lot of deforestation (e.g. Brazil, Democratic Republic of
Congo, and Indonesia), but the by-products are inac-
cessible to the majority of woodfuel users. Finally, in
10 We used fNRB from the subnational unit (state or province)
where projects were located. If projects were national in scope or
documents did not specifythe sub-national unit(s), we used national
fNRB.
11 Cameroon is an extreme example: fNRB derived from Scenario
A is zero, while fNRB from Scenario B is 100%.
6

Environ. Res. Lett. 12 (2017) 115002
Figure 3. Mean (±standard deviation) fNRB values used in offset projects compared to range of values derived from WISDOM at the
same locations.
1.E+03
1.E+05
1.E+07
Expected ERs using project fNRB (tCO2e)
Estimated ERs using WISDOM fNRB (tCO2e)
ERs assuming LCC by-products are not used
ERs assuming LCC by-products are used
n=76 n=7
n=39
n=39
n=7 n=92
n=6
n=8 n=2
1.E+05
1.E+06
1.E+07
1.E+08
tCO2e
Figure 4. Expected vs. estimated ERs by project (left) and aggregate overestimations of ERs by region (right). Note that both plots use
logarithmic scales.
some places, deforestation occurs in accessible regions,
but the volume of by-products is lower than overall
woodfuel demand, so people must rely on direct har-
vesting regardless of whether by-products are utilized
(e.g.muchofEastandSouthernAfrica).
Overestimation of fNRB shown in figure 3results
in considerable overestimation of ERs from offset
projects. To calculate the extent, we replaced the val-
ues of fNRB used to estimate ERs (described in the
supplementary material), with geographically specific
values derived from the pan-tropical WISDOM analysis
(fNRB values from regions where projects are imple-
mented are provided in the supplementary material).
Theresultsareshowninfigure4. The plot on the left
shows expected ERs reported in each project plotted
against the ERs calculated by substituting fNRB with
the estimate from the pan-tropical NRB assessment.
Thelineindicateswhereequalestimateswouldfall.In
the majority (98%) of cases, the ERs expect ed by project
implementers are larger than the ERs that are likely to
be generated based on the WISDOM assessment. The
magnitude of overestimates is shown in the plot on the
7

Environ. Res. Lett. 12 (2017) 115002
right. The bars show the upper and lower range of val-
ues derived from Scenario A and B respectively. Taken
together, PDDs overestimate ERs expected from NRB
demand reduction by 57–81 MtCO2e (41%–59%).
We also note that thereis a negative correlation between
the WISDOM-based fNRB value and the relative mag-
nitude of the ER overestimation. This is a logical
outcome of the analysis; when WISDOM-based fNRB
values are high, differences between the ERs derived
from WISDOM-based fNRB values and ERs reported
in the PDDs are smaller than when with WISDOM-
based fNRB estimates are low.
7. Conclusion
Nearly 300 woodfuel projects have been implemented
in an effort to mitigate climate change while also deliv-
ering co-benefits like reduced household air pollution.
These projects could be viewed as forerunners of more
ambitious activities that will be implemented to achieve
SDG7 as well as many NDCs. However, we find that
∼80% of these projects are likely overestimating the
mitigation potential of their activities by using exces-
sively negative assumptions about the sustainability
of woodfuel harvesting. The projects are unlikely to
reduce deforestation and/or forest degradation to the
degree promised. We suggest that project developers
and investors recalibrate their expectations by adopting
more conservative values of fNRB, based on spatially
explicit estimates of woody biomass productivity and
accessibility. While this will reduce revenue available
from carbon offsets, it will also avoid disappoint-
ment and disillusionment from under-performing
projects. In addition, the spatially explicit approach
provides project developers with a useful tool to
identify locations where interventions are likely to
achieve the largest emission reductions. For exam-
ple, there are woodfuel ‘hotspots’in parts of East,
West, and Southern Africa as well as South Asia, where
nearly 300 million people struggle with acute woodfuel
scarcity.
We also acknowledge some limitations to the spa-
tial approach. First, the methodology requires expertise
in geo-spatial analysis, and it would not be practical
to incorporate into carbon-offset methodologies like
those described in the appendices to this paper. In
addition, the processes that are modeled, summarized
in section 2, require supply and demand data that is
often highly uncertain. Moreover, the model should
undergo calibration and testing in order to develop
greater confidence [61],buttodosoonaglobalscaleis
very difficult. Nevertheless, some straightforward pro-
cedures can help. First, fNRB estimates can be used to
project biomass decay over time. If high fNRB values
were as widespread as registered carbon-offset projects
imply, we would observe extensive woodfuel-driven
land cover change. Second, we could use coarse spa-
tial resolution imagery (e.g. MODIS) and search for
temporal trends in vegetation cover over global wood-
fuel hot spots and look for correlations between high
NRB and land cover change. However, with multiple
drivers of land cover change present in most locations,
linking land cover change specifically to woodfuel
demand remains open to debate.
Finally, we acknowledge our suggested recalibra-
tion of expectations from woodfuel interv entions might
result in lower carbon revenues flowing to clean
and efficient stoves or fuel switching projects. How-
ever, rather than choking off activity, we hope this
research serves to boost the growing debate about how
to bring more robust finance to clean and efficient
stoves or fuel switching interventions. We should not
rely too heavily on forest conservation and associated
emission reductions as a source of finance for tradi-
tional energy interventions12 . Such interventions carry
many potential benefits including reduced emissions of
household air pollution and short-lived climate forcers
and reduced fuel collection or expenditure for poor
households, as well as employment for stove producers.
While these benefits are not monetized like carbon off-
sets, there are potential pathways for revenues to flow
that would replace, if not exceed, the loss in carbon
revenue if more realistic fNRB values are adopted.
ORCID iDS
Rob Bailis https://orcid.org/0000-0002-4111-3760
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