irena-dbfz_biomass potential in africa
TRANSCRIPT
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International Renewable Energy Agency
IRENA
BIOMASS
POTENTIAL
IN AFRICA
K. Stecher
A. Brosowski
D. Thrn
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2 IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA
Copyright IRENA 2013
DISCLAIMER
The designations employed and the presentation o materials herein do not imply the expression
o any opinion whatsoever on the part o the International Renewable Energy Agency or
German Biomass Research Centre (DBFZ) concerning the legal status o any country, territory,city or area, or concerning their authorities or the delimitation o their rontiers or boundaries.
About IRENA
The International Renewable Energy Agency (IRENA) is an intergovernmentalorganisation that supports countries in their transition to a sustainable energyuture, and serves as the principal platorm or international cooperation, a centre oexcellence, and a repository o policy, technology, resource and inancial knowledgeon renewable energy. IRENA promotes the widespread adoption and sustainable useo all orms o renewable energy, including bioenergy, geothermal, hydropower, ocean,solar and wind energy in the pursuit o sustainable development, energy access,energy security and low-carbon economic growth and prosperity.
www.irena.org
About DBFZ
The mission o the German Biomass Research Centre (DBFZ) is to support the eectiveintegration o biomass as a valuable resource or a sustainable energy supply in thecontext o applied scientiic research - including technical, environmental, economic,social and energy economics issues along the entire chain o exploitation.
www.dbz.de
Unless otherwise indicated, material in this publication may be used reely, sharedor reprinted, but acknowledgement is requested. IRENA and DBFZ would appreciatereceiving a copy o any publication that uses this publication as a source.
No use o this publication may be made or resale or or any other commercial purposewhatsoever without prior permission in writing rom IRENA/DBFZ. Any omissions anderrors are solely the responsibility o the authors.
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IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA 3
Acknowledgement
This report was prepared by Deutsches Biomasseorschungszentrum gGmbH (DBFZ) or the International RenewableEnergy Agency (IRENA).
Questions or comments about this report can be directed to IRENA or to the authors at DBFZ:
IRENA
E-mail: [email protected]
DBFZ
Kitty Stecher (Dipl.-Ing. agr.)
Tel.: +49-341-2434-580
E-Mail: [email protected]
Andr Brosowski (Dipl. Geogr.)
Tel.: +49-341-2434-718
E-Mail: [email protected]
Daniela Thrn (Pro. Dr.-Ing.)
Tel.: +49-341-2434-578
E-Mail: [email protected]
C67 Oce Building
Khalidiyah (32nd) Street
P.O. Box 236
Abu DhabiUnited Arab Emirates
Internet: www.irena.org
International Renewable Energy Agency
IRENA
Torgauer Strae 116
D-04347 Leipzig
Tel.:+49-341-2434-112
Fax:+49-341-2434-133E-Mail: [email protected]
Internet: www.dbz.de
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4 IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA
Contents
Abbreviations 7
Executive Summary 9
1. Introduction 13
2. Literature Review 152.1 Approach 15
2.2 Overview o ormer reviews and studies analysed in the report 16
2.3 Results 18
2.3.1 Findings rom comparisons o the studies 18
2.3.2 Other important driving actors 20
2.3.3 Results o the studies 24
2.4 Discussion 31
2.4.1 Area potential 31
2.4.2 Energy crops 32
2.4.3 Forestry biomass 33
2.4.4 Residues and waste 34
2.5 Conclusion 38
3. Summary 41
References 42
4
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Figures
Figure 1: Based on International Energy Agency (IEA) data or 2009 10
Figure 2: Total primary energy demand or energy sources in Arica (IEA, 2010) 13
Figure 3: Structure o the analysis ollowed in this report 15
Figure 4: Important driving actors to consider when assessing biomass potential 20
Figure 5: Total technical biomass potential or dierent Arican regions and time periods 26
Figure 6: Area potential as dependent on time-period, Arican region and study 27
Figure 7: Energy crops potential as dependent on time-period, Arican region and study 28
Figure 8: Forestry biomass potential as dependent on time-period, Arican region and study 29
Figure 9: Residues and waste potential as dependent on time-period, Arican region and study 30
Tables
Table 1: An overview o the reviewed studies 17
Table 2: Dierences in timerame, geographical coverage and type o potential 19
Table 3: The biomass resources in each study, and its classication 21
Table 4: Other important driving actors 22
Table 5: Residue and waste biomass potential energy data or Arica (PJ/yr.) 25
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Sugarcane FieldHywit Dimyadi/Shutterstock
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IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA 7
AbbreviationsCEMA Conerence o Energy Ministers o Arica
CGPM Crop and Grass Production Model
DBFZ Deutsches Biomasseorschungszentrum gemeinntzige GmbH
EJ Exajoule: 1018J
EU European Union
FAO Food and Agriculture Organization (o the United Nations)
FRA Global Forest Resources Assessment
GAPP Global Agriculture Production Potential Model
GJ Gigajoule : 109 J
GLC Global Land Cover
GLUE Global Land Use and Energy Model
ha Hectare
IEA International Energy Agency
IIASA International Institute or Applied Systems Analysis
IMAGE Integrated Model to Assess the Greenhouse Eect
IPCC Intergovernmental Panel on Climate Change
IRENA International Renewable Energy Agency
LPJml Lund-Potsdam-Jena managed Land Dynamic Global Vegetation and Water Balance Model
Mtoe Million tonnes o oil equivalent
NGO Non-Governmental Organisation
OECD Organisation or Economic Co-operation and Development
PJ Petajoule: 1015 Joule
PBL,NEAA PBL Netherlands Environmental Assessment Agency
SRC Short Rotation Coppice
SSA Sub-Saharan Arica
TPES Total Primary Energy Supply
UN United Nations
UNDP United Nations Development Programme
UNO United Nations Organization
UNSD United Nations Statistical Division
WBGU Wissenschatlicher Beirat der Bundesregierung Globale Umweltvernderungen
yr Year
Conversion factors: 1 Mtoe = 41.868 PJ = 0.041868 EJ1 EJ = 23.88 Mtoe
1 PJ = 0.02388 Mtoe
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Ngorongoro Crater,TanzaniaGary C. Tognoni/Shutterstock
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IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA 9
Arica is currently experiencing strong
economic growth and showing positive
trends in human development indicators.
Access to modern energy will be critical
to sustain these positive signals. The
continent possesses abundant renewable resources
that could spur continued economic growth, accelerate
social development and help the transition to a
sustainable energy system that can provide universal
energy access.
The Arican Union Assembly o Heads o State and
Government in February 2009 decided to develop
renewable energy resources to provide clean, reliable,
aordable and environmentally riendly energy.
The political will to promote renewable energy
was reairmed in the 2010 Arican Union Maputo
Declaration, which established the Conerence o
Energy Ministers o Arica (CEMA).
Ministers o energy and heads o delegations romArican countries, along with the Arican Union
Commission and CEMA, met at the invitation o the
International Renewable Energy Agency (IRENA) in
Abu Dhabi, United Arab Emirates, in July 2011, hosted
by the UAE Government. The subsequent Communiqu
on Renewable Energy or Accelerating Aricas
Development, endorsed by 46 Arican countries
and 25 Arican energy ministers, called or the
promotion o increased utilisation o the continents
vast renewable energy resources to accelerate
development. In the communiqu, the Arican Energy
Ministers also agreed to urther engage with IRENA as
a key intergovernmental orum on renewable energy.
They urged IRENA to provide strategic support or
renewable energy in its message to the international
community at Rio+20-United Nations Conerence on
Sustainable Development.
Arican countries seek to attract large investments in
renewable energy technologies. A thorough planning
phase is necessary to estimate the energy share that can
be supplied by renewable energy sources; to compare
energy costs with that o conventional solutions; and
to identiy the most eective technologies that are
adaptable to local conditions. This enables countries
to develop adequate policy rameworks and inancing
instruments, to nurture human capacity and to create
the necessary inrastructure to ulil their sustainable
energy aspirations.
For countries to properly analyse and plan the use orenewable energy, they need accurate and documented
estimates o their renewable energy potential, as well
as to identiy the most suitable locations or investment
and deployment in renewable energy technologies. The
accuracy o this inormation correlates directly with
the risk taken in the decision-making process . Accurate
data strengthens each countrys national strategy to
deploy renewable energy technologies.
Perorming an accurate estimate o the potential
requires a high level o technical knowledge, extensiveconsultations and large upront investments in
measurement campaigns. This situation is set
to improve as IRENA increasingly provides ree
inormation and tools to assess the renewable energy
potential within a country. The recent release o the
solar and wind component o the Global Renewable
Energy Atlas, intended to expand to other resources,
has initiated a valuable process o knowledge-sharing
among countries and expert institutions on renewable
energy resource data.
In the meantime, however, no detailed standard
methodology exists to estimate the theoretical,
technical, and economic potential or renewable energy
development. An analysis o the available literature on
each renewable energy resource inds discrepancies in
the deinitions used, even or undamental terms such
as renewable and potential 1.
Each study in the literature investigated uses a
signiicantly dierent approach to estimate renewable
Executive Summary
1 Aaiabe te Gba Ata ebite .iena.g/GbaAta
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Figure 1: Based on International Energy Agency (IEA) data or 2009. The charts show the shares o bioenergy in
the total primary energy supply. (A) Arican continent. TPES: 673 Mtoe (28 177 PJ). (B) Sub-saharan Arica. TPES: 517
Mtoe (21 646 PJ) (77% o Arican TPES). (C) Sub-saharan Arica, excl. South Arica. TPES:372 Mtoe (15 575 PJ) (55%
o Arican TPES. 72% o Sub-saharan TPES)
A) AFRICAN CONTINENT
Bioenergy Shares
Solid biouels 47.6%Other renewables 0.2%
Coal and peat 15.7%
Oil 22.4%
Natural Gas 12.4%
Nuclear 0.5%
Hydro 1.3%
B) SUB-SAHARAN AFRICA
Bioenergy Shares
Solid biouels 61.2%
Other renewables 0.2%
Coal and peat 19.7%
Oil 14.1%
Natural Gas 2.7%
Nuclear 0.6%
Hydro 1.4%
C) SUB-SAHARAN AFRICA
(excl. SOUTH AFRICA)
Bioenergy Shares
Solid biouels 81.2%
Other renewables 0.3%
Coal and peat 1.0%
Oil 13%
Natural Gas 2.7%
Hydro 1.9%
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energy potential. With variations in the input datasets
and the main parameters considered, the results o the
various analyses are hardly comparable. Consequently,
decision-makers are aced with a number o
assessments, all o which use dierent methods and
provide diverse results. There is a need or clarity on
the key actors decision-makers must consider about
renewable energy. IRENA, with its ability to convene
countries and stakeholders in the ield, is well placed
to help determine suitable methods and the best
practices to ollow.
This report ocuses on bioenergy in Arica , as this orm
o renewable energy represents almost 50% o the total
primary energy supply (TPES) or the Arican continent
(International Energy Agency (IEA), 2009a), and more
than 60% o the Sub-Saharan TPES. Bioenergy is astrategic asset or Aricas energy uture and needs to
be assessed in a transparent manner (Figure 1).
At IRENAs behest, the German Biomass Research
Centre (Deutsches Biomasseorschungszentrum
gGmbH - DBFZ) has collected recent studies assessing
bioenergy potential in Arica, compared their various
methodologies, benchmarked the results, and identiied
the key dimensioning elements or those assessments.
The outcomes o the analysis are as ollows:
1. The studies show an enormous range o calculated
biomass potentials;
2. The calculated area potential or energy crops
ranges between 1.5 million hectares (ha) and 150
million ha;
3. The various assessments indicate a potential or
energy crops rom 0 PJ/yr to 13 900 PJ/yr, between
0 PJ/yr and 5 400 PJ/yr or orestry biomass and 10
PJ/yr to 5 254 PJ/yr or residues and waste in Arica
by 2020;
Dried cornluckypic/Shutterstock
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12 IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA
4. Signiicant variations ound between the studies
are primarily due to the selected timerame, the
geographical coverage, the type o potential and
biomass resources analysed. Other important
reasons or deviations are the underlying
assumptions, the so-called driving actors, and the
accuracy o the input databases;
5. All the biomass calculations rom today through to
the year 2100 show a considerable range in potential.
For the year 2050, a potential range between 2
360PJ/yr and 337 000 PJ/yr is revealed or Arica
(energy crops: 0 PJ/yr to 317 000 PJ/yr; orestry
biomass: 14 820 PJ/yr to 18 810 PJ/yr; residues and
waste: 2 190 PJ/yr to 20 000 PJ/yr). The potentials
or the year 2100 determined in two studies, vary
between 74 000 PJ/yr and 181 000 PJ/yr;
6. The calculated area potentials and the assumed
production yields have a signiicant impact on the
potential amount o energy crops, orestry biomass,
and residues and waste that are available or energy
provision;
7. In the studies, both the area potentials and
production yields are analysed at dierent levels o
detail and ound generally to have a high degree o
uncertainty;
8. A precondition or achieving high energy crop
potential is an increase in productivity to a level
similar to that o industrialised countries, or a
stronger ocus on energy crop production, e.g.
through the cultivation o energy crop rotations;
9. In contrast studies showing little or no potential
o energy crops in Arica assume that the enormous
population growth and the associated increase
in per capita consumption especially o animal
products will prevent an energy-related use obiomass;
10. One o the key parameters to determine the
potential o orestry biomass, or residues and waste,
is the competitive use o materials;
11. In general, climate change impacts, biodiversity
and social criteria, e.g. land ownership, remain
insuiciently considered in the studies reviewed;
12. A large problem in determining biomass
potentials or Arica is the poor data availability.
Inaccurate and obsoleted databases oten provide
the initial values or the estimations o the current
and uture potentials.
Due to the large range in results presented by the
reviewed studies, no deinite igures regarding the
availability o biomass in Arica can be provided. Anyanalysis based on these studies needs to account
or their underlying assumptions. The existing data,
methodological approach and results, can however be
used to identiy areas or urther research.
This report is a irst step towards bringing clarity
to decision makers on the inormation available
on bioenergy potentials. By understanding the
discrepancies between the existing approaches and
visualising the variability o results between the
dierent methods, decision makers can evaluate
the impact dierent assessments can have on their
strategic decisions.
This report highlights the need for
d ev e l o p i n g r eco m m en d a t i o n s
and standard methods to provide
accurate estimates of bioenergy
potentials.
Bioenergy resource assessment is a particularly
complex subject. The assessment needs to actor
possible conlicts with other land uses, orecast
increases in agricultural productivity, and project
ood demand over time (Belward, et al., 2011). Since
the assessment is extremely sensitive to the local
context and political choices, assessing the bioenergypotentials is a country-driven exercise.
The Global Renewable Energy Atlas initiative began by
oering ree and open access to data on wind and solar
energy. IRENA will progressively expand this initiative,
with the objective o providing relevant inormation,
access to datasets and tools or decision-makers to
assess all types o renewable energy potential, including
biomass. IRENA will continue to work with the bioenergy
community to identiy the relevant inormation, methods
and tools that can help countries wanting to assess their
national potential or biomass development in detail.
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IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA 13
1. Introduction
Bioenergy is currently the primary energy
resource or about 2.7 billion people
worldwide (Wicke, et al., 2011), playing a
traditional role in Arica. The total primary
energy demand (TPED) or Arica is predominantly
determined by biomass demand (Figure 2), with almost
hal o the energy demand (47.9%) being covered by
biomass and waste. The International Energy Agency
(IEA) projects a decline in the total energy share o
biomass and wastes by 20352, but biomass will stillcontinue to remain an important energy resource or
Arica in the uture (IEA, 2010).
As well as the TPED, the total inal consumption (TFC)
or Arica is also predominately composed o biomass
and wastes. The IEA estimates that the total inal
consumption biomass/waste share will lie between
51% and 57% by 2035 depending on which scenarios
are assumed (IEA, 2010). However, there are enormous
regional dierences within Arica. Particularly those
countries characterised by high poverty, the proportion
o biomass is much higher. In some countries, such as
Burundi, Rwanda and the Central Arican Republic,
the energy provision rom biomass is 90% or greater
(Dasappa, 2011). In Sub-Saharan Arica (SSA) people
are more dependent on traditional biomass energycompared to other regions (Eleri and Eleri, 2009). In
this report traditional biomass reers to the unsuitable
use o uel wood, charcoal, tree leaves, animal dung and
agricultural residues or cooking, lighting and space
heating. In comparison to a modern use o biomass,
which normally includes an eective technical utilisation
2 Te jetin ae bae n te Ne iie enai. Ti enai take ant te ba i itent an an tat ae annne b ntie an
te , t take eite eninenta eneg eit nen, een ee te eae t ieent tee itent ae et t be ientiie annne
(IEA, 2010).
Figure 2: Total primary energy demand or energy sources in Arica (IEA, 2010)
40 000
35 000
30 000
25 000
20 000
15 000
10 000
5 000
0
1990 2008 2015 2020 2025 2030 2035
J
Other renewables
Biomass and waste
Hydro
Nuclear
Natural Gas
Oil
Coal
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14 IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA
to produce energy eiciently, traditional biomass is used
with very low energy eiciencies. Moreover, traditional
biomass use can have serious negative impacts on
health and living conditions, e.g. pneumonia, chronic
obstructive pulmonary diseases or lung cancer (IEA,
2009b; Chum, et al., 2011; Legros, et al., 2009).
Despite an increase in energy use, many poor households
in Arica have no access to modern energy sources.
Worldwide, SSA and India have the greatest proportion
o population dependent on traditional biomass use.
In Arica, a total o 657 million people (80% o the
population) rely on the traditional use o biomass or
cooking (IEA, 2010). It is important given this scale o
biomass use that modern bioenergy systems are able
to provide an important contribution to uture energy
systems and to the development o sustainable energy
supplies (Berndes, Hoogwijk and Broek, 2003).
It is thereore crucial to use the potential renewable
energy resources eectively. In addition, against
the background o ood security, i biomass energy
is developed in a sustainable manner, this creates
opportunities or increased ood production, especially
in rural areas (Eleri and Eleri, 2009).
In general, the availability and quality o biomass energy
data in Arica is scarce. There are some country-speciic
studies, as well as various international research projects
that ocus on the estimation o global biomass potentials,
which provide some inormation about Arica. Although
a ew publications engage with this ield o research,
there is no scientiic literature review or Arica to date.
It is important that in making statements about the
prospects or development o sustainable bioenergy
rom biomass sources in Arica, that a realistic order o
magnitude or biomass potentials is assessed. This project
report aims to provide an overview and comparisono the available studies relating to biomass potentials
in Arica. The considerable discrepancies among the
studies results will be illustrated and explained, and the
requirements concerned with determining parameters
and the other challenges associated with potential
estimates will be discussed.
This report contains an overview o the selected studies.
The indings are presented and discussed; they concern
the methodology and analysis o results or dierent
biomass categories, including the uncertainties in the
database and the broad deviations o the study results.
The report closes with conclusions and remarks.Food on market placeZurijeta/Shutterstock
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IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA 15
2. Literature Review
2.1 ApproAchThe approach in this study (Figure 3) rst
evaluates existing literature reviews on
global biomass potentials. In particular,
attention is given to those biomass studies published
ater 2005 that have included inormation about Arica.
The number o studies ound within this timerame is
small and the search expanded to include those studies
published ater 2000. Additional studies ocus on specic
Arican countries. By including some country-specicstudies it became possible to compare the national
biomass potentials and to make comparisons with larger-
scale continental and global studies. A total o 14 studies
are used or the analysis.
The study analysis is divided in two sections (Figure 3). In
the rst section, the methodology and assumptions used in
the selected studies are analysed. To enable a comparison,
several criteria are established, which include the denition
o the biomass potential, the timerame, the geographic
coverage and the underlying biomass resources considered
in the studies. Another criteria used to compare studies
is the group o other important driving actors, which
combines all parameters that impacted on the amount o
biomass potential. These parameters are both listed and
classied in a database, with special attention paid to their
source and data quality.
In the second section o the analysis, study results anddeclared potentials are evaluated, creating a comparative
basis where the results are grouped by the our categories:
available land or energy production; energy crops; orestry
biomass; and residues and waste.
The discussion identied the main causes or discrepancies
in the results. All results rom the our biomass potential
categories are discussed, and the main parameters
compared. The question o where to ocus urther research
is highlighted in the conclusion.
Figure 3: Structure o the analysis ollowed in this report
Literature research
Existing reviews
Studies on a
global scale
Studies on a
continental scale
Selection
of
relevant
studies
Conclusion
Analysis
Timeframe
Geographical
coverage
Biomass
resources
Other important
driving factors
Methodology
used in the studies
Area potential
Energy crops
Forestry biomass
Residues & waste
Biomass
potentials Discussion
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16 IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA
2.2 ovErvIEw of formEr rEvIEwsANd sTudIEs ANAlysEd IN ThE rEporTThree literature reviews are presented hereunder,
comparing dierent methodologies used to assess
global biomass potentials, the inal results ound and
the input databases used.
In Berndes, Hoogwijk and Broek (2003), 17 studies are
analysed. The conclusions o the reviewed studies are
discussed with regard to the underlying assumptions and
methodology (timerame, geographical aggregation and
bioenergy resources).
Moreover, the review concentrates on the classiication
o studies in demand-driven3 and resource-ocused4
assessments. The possible contribution o biomass touture global energy supply ranges rom below 100
exajoules per year (EJ/yr) to over 400 EJ/yr by 2050.
As a matter o comparison, the global energy demand
in 2010 reached 12 380 Mtoe in 2010, equivalent to 518
EJ (IEA, 2012).
Most o the studies consider biomass plantations as the
most important source o biomass or energy (e.g. biomass
plantation supply is orecast to be between 47 EJ/yr and
238 EJ/yr by the year 2050). Berndes, Hoogwijk andBroek (2003), ound two major parameters explaining
the discrepancies among the literature reviewed: land
availability; and yield levels in energy crop production.
Both parameters although uncertain, are crucial to
estimating uture biomass potentials.
In a comparison o 19 studies by Thrn, et al. (2010a),
there was ound to be a large range in results or
biomass ractions o energy crops, organic residues
and waste. The largest dierence between the results inthis literature review is ound in the potential o energy
crops. While some studies indicate an energy crop
potential o 0 EJ/yr by 2050, optimistic estimations o
values reach 1 272 EJ/yr or this time period. The global
potential o orest residues ranges rom 0 EJ/yr to 150
EJ/yr in 2050. In addition to analysing results, Thrn, et
al. (2010a) highlights the dominant inluencing actors
on the amount o biomass potential, with the most
important being global population growth; ollowed
by uture per capita consumption and development o
speciic yields or ood, including odder and biomass
production (Thrn, et al., 2010a).
The third literature review, by Chum, et al., (2011),
analyses 15 scientiic studies in which global biomass
potentials are calculated. Although the various actors
inluencing the amount o the potential are named, there
is no precise speciication attached to these actors.
The literature review comes to the conclusion that the
global potential o biomass or dierent categories5
in the studies ranges rom less than 50 EJ/yr to more
than 1 000 EJ/yr by 2050 (Chum, et al., 2011).
All the literature reviews highlight the enormous
range in global biomass potential studies. Although
some reviews analyse the regional dierentiation o
global biomass potentials, no igures on Arica are
reported, or example, Berndes, Hoogwijk and Broek
(2003), distinguished between the calculated biomass
potentials o developed and developing countries.
According to the estimates from mostof the reviewed studies, developing
countries will account for the largest share
of global energy supply in the longer term
(Berndes, Hoogwijk and Broek, 2003).
Fourteen studies analysed in the ollowing section
(Table 1), make more precise statements about thebiomass potential in Arica. Those ourteen studies
include nine global, three continental and two country-
speciic studies. All studies indicate numerical estimates
o the biomass potentials or dierent biomass ractions.
The exact geographical coverage or the calculated
potentials can be taken rom Section 2.3.1.
3 dean-ien aeent anae te etitiene bia-bae eetiit an bie, etiate te eqie bia t eet exgen taget a
iate-neta eneg (ean ie ) (Bene, hgijk an Bek, 2003).
4 In ee-e aeent te tta bieneg ee bae an te etitin bet een ieent ee e ( i e) ae anae (Bene, hgijk an Bek, 2003).
5 Te ategie ine eie agite (15 EJ/ t 70 EJ/.), eiate bia tin n agita an (0 EJ/ t 700 EJ/.), eiate bia
tin n agina an (0 EJ/ t 110 EJ/.), et bia (0 EJ/ t 110 EJ/.), ng (5 EJ/ t 50 EJ/.) an gani ate (5 EJ/ t e 50 EJ/.).
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IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA 17
Table 1: An overview o the reviewed studies.
Acronym 6 Reference
BATIDZIRAIBatidzirai, B., A.P.C. Faaij and E.Smeets (2006), Biomass and bioenergy supply rom Mozambique, Energy or
Sustainable Development, Vol. 10, No. 1, pp. 54-81 (doi:10.1016/S0973-0826(08)60507-4).
BMVBS
BMVBS (2010), Globale und regionale rumliche verteilung von biomassepotenzialen - status quo und mglichkeit
der przisierung; Endbericht, BMVBS-Online-Publikation, (www.bbsr.bund.de/cln_032/nn_629248/BBSR/
DE/Veroeentlichungen/BMVBS/Online/2010/DL_ _ON272010,templateId=raw,property=publicationFile.pd/
DL_ON272010.pd).
COOPERCooper, C.J. and C.A. Laing (2007), A macro analysis o crop residue and animal wastes as a potential energy
source in Arica, Journal o Energy in Southern Arica, Vol. 18, No. 1, pp. 10-19.
DASAPPA
Dasappa, S. (2011), Potential o biomass energy or electricity generation in Sub-Saharan Arica, Energy or
Sustainable Development, Vol. 15, No. 3, pp. 203-213 (doi:10.1016/j.esd.2011.07.006).
DUKUDuku, M.H., S. Gu, and E.B. Hagan (2011), A comprehensive review o biomass resources and biouels potential
in Ghana, Renewable and Sustainable Energy Reviews, Vol. 15, No. 1, pp. 404-415.
FISCHERFischer, G. and L. Schrattenholzer (2001), Global bioenergy potentials through 2050, Biomass and Bioenergy,
Vol. 20, No. 3, pp. 151-159, (doi:10.1016/S0961-9534(00)00074-X).
HABERLHaberl, H., et al. (2011), Global bioenergy potentials rom agricultural land in 2050: Sensitivity to climate change,
diets and yields, Biomass and Bioenergy, Vol. 35, No. 12, pp. 4753-4769 (doi:10.1016/j.biombioe.2011.04.035).
HAKALAHakala, K., Kontturi, M., and Pahkala, K (2009), Field o biomass as global energy source, Agricultural and Food
Science, Vol. 18, No. 3-4, pp. 347-365.
HOOGWIJKHoogwijk, M. (2004), On the global and regional potential o renewable energy sources, PhD Thesis, Utrecht
University, Utrecht.
KALTSCHMITTKaltschmitt, M.,H. Hartmann, and H. Hobauer (2009), Energie aus biomasse: grundlagen, techniken und
verahren, Springer, pp 1032(ISBN: 9783540850946).
SMEETS
Smeets, E.M.W., et al. (2007), A bottom-up assessment and review o global bio-energy potentials to 2050,
Progress in Energy and Combustion Science, Vol. 33, No. 1, pp. 56-106 (doi:10.1016/j.pecs.2006.08.001).
WECWorld Energy Council (2010), 2010 Survey o energy resources, London, pp. 618, www.worldenergy.org/
publications/3040.asp
WICKE Wicke, B., et al. (2011), The current bioenergy production potential o semi-arid and arid regions in sub-Saharan
Arica, Biomass and Bioenergy, Vol. 35, No. 7, pp. 2773-2786 (doi:10.1016/j.biombioe.2011.03.010).
YAMAMOTO
Yamamoto, H., J. Fujino, and K. Yamaji (2001), Evaluation o bioenergy potential with a multi-regional
global-land-use-and-energy model, Biomass and Bioenergy, Vol. 21, No. 3, pp. 185-203 (doi:10.1016/S0961-
9534(01)00025-3).
6 An ae e in Tabe 1 t itingi te tie te iteate eeene ae tgt ti et.
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18 IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA
2.3 rEsulTs
The substantial dierences ound between the studies are
due to complex reasons. The main dierences between
the various methodologies and assumptions used are:
Timerame
Geographical coverage
Denition o potential
Biomass resources
Other important driving actors
Section 2.3.1 includes all results rom the calculated
potentials in the reviewed studies. The biomass potentials
are classied into the ollowing categories: area potentialand energy crops, orestry biomass, and residues and
waste.
2.3.1 Findings rom comparisons o thestudies
The irst signiicant dierence between the studies is
the selected timerame (Table 2). The majority o the
studies show values or the present-day situation. In
order to project uture energy potentials in Arica,
most studies calculate up to the year 2050. Very little
inormation is available or the short- and mid-term
orecasts (2020-2030), or or the longer-term (year
2100). In the studies, various parameters such as
uture ood and eed demand, yields rom crop species,
environmental issues, etc. (see section 2.3.2) are taken
into account. Their signiicance in assessing the uture
biomass potential depends strongly on the time period
selected.
Secondly, the studies dier in the geographical area
covered. With the exception o country-speciic
studies, all other studies calculate and summarise
biomass potentials or a number o Arican countries
that are then reerred to as overall Arica or SSA
(Table 2). In the SSA studies, the analysis o potentials
in North Arica is combined with West Asia, Middle
East or Near East. This geographical delimitation
means it is not possible to use these studies to
make a statement on the potential or the whole o
the Arican continent. Furthermore, the studies also
varied in the number o countries considered, i this
value was given at all. These variations in area need
to be considered when interpreting the results o the
studies.
Thirdly, the heterogeneous use o the term potential in
the studies diers, but can generally be distinguished as
theoretical, technical or economic potential.
The theoretical potential describes the theoretical
maximum energy supply that is physically available in
a given region within a speciied period o time, (e.g.
energy saved in the entire crop mass). It is determined
by its limits o physical utilisation and thereore is
the upper limit to the provision o energy that can
be theoretically realised. As not all o the theoretical
energy potential can be realised it is o no practical
relevance in assessments o actual biomass availability.
The technical potential describes the theoreticalpotential that can be used ater accounting or energy
losses through the technical conversion process. The
calculation o the technical potential oten includes
the structural, ecological, administrative and social
limitations as well as legal requirements, since they
ultimately can be regarded as insurmountable, in
a way that is similar to the technically determined
limitations.
The economic potential is the share o the technical
potential which is economically proitable in given
conditions (Rettenmaier, et al., 2008).
To clariy urther the available level o potential,
additional terms are used besides the theoretical,
technical and economic potential. For example,
the implementation potential describes the actual
contribution to energy supply. This potential depends
on a variety o other socio-political and practical
constraints (Thrn, et al., 2010b). An estimation o the
implementation potential can only be assessed at aregional level, requiring an enormous eort, because
o the number o restrictions that have to be taken into
account (e.g. considering the willingness o the biomass
producers to sell the eedstock). An assessment o the
implementation potential or the whole o Arica is
currently not practical.
All the studies reviewed evaluate the technical potential
(Table 2) and in three studies, the economic potential
is also determined. Although the same term is used in
the studies, the underlying assessment actors o the
potentials vary considerably. Technical, structural and
environmental constraints, as well as legal requirements
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IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA 19
Table 2: Dierences in timerame, geographical coverage and type o potential
Acronym Time period Geographical coverage
Type of potential
Technical Economical
BATIDZIRAI 2015 Mozambique X X
BMVBS2007; 2010; 2015;
2020; 2050Arica X
COOPER Current Arica X
DASAPPA Current SSA X
DUKU Current Ghana X
FISCHER 2050 SSA X X
HABERL 2050 SSA X
HAKALA 2006; 2050 Arica X
HOOGWIJK 2050; 2100 Arica X
KALTSCHMITT Current Arica X
SMEETS 2050 SSA X
WEC Current Arica X
WICKE Current
Botswana, Burkina Faso,
Kenya, Mali, Senegal, South
Arica, Tanzania, Zambia
X X
YAMAMOTO 2100 SSA X
are accounted or as each author sets out a range
o actors which maniest themselves to dierent
extents, depending on the system boundary and the
biomass raction considered. The biomass potential is
thereore not strictly deined, but is related to numerous
assumptions and boundary conditions (BMVBS, 2010;
Kaltschmitt, Hartmann and Hobauer, 2009). Anoverview o the driving actors considered in the studies
is given in Section 2.3.2.
Finally, there is no uniorm classiication or the
description o types and ractions o biomass resources.
Biomass being deined as the biodegradable raction o
products , waste and residues rom biological origin rom
agriculture (including vegetal and animal substances),
orestry and related industries including isheries and
aquaculture, as well as the biodegradable raction o
industrial and municipal waste (European Union (EU),
2009).
To compare the amount o potentials calculated in thereviewed studies, the dierent biomass resources are
divided into three biomass ractions:
Energy crops
Forestry biomass
Residues and waste
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20 IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA
Table 3 shows the biomass resources considered in the
studies and their allocation within the three biomass
ractions. The resources included in Table 3 identiy
the biomass ractions used exclusively in the Arican
studies. All o the studies provide an estimate o the
potential or the biomass raction rom residues and
waste, whereas the eedstock type is dierent orevery study. In ten studies, the energy crop potential
is determined, while the orestry biomass potential is
only calculated in six o the studies.
The type o biomass ound in the dierent biomass
ractions varies greatly. While some studies calculated
only the potential o a particular crop type (e.g.
eucalyptus), other studies reer to a large number
o crop species. Some studies ail to identiy the
eedstock composition or its raw materials raction,
instead providing a general description o the biomassresources (e.g. energy crops on grassland or crop
residues).
Moreover, a stringent classiication o eedstock is not
always possible, because the biomass ractions overlap
each other (e.g. orest products and wood residues).
Not all studies are clear about the exact eedstock they
consider, which may inluence greatly the biomass
potential.
2.3.2 Other important driving actors
The biomass potential is largely determined by the actors
shown in Figure 4. The demand or ood grows with an
increasing population; and the per-capita consumption is
also dependent on a countrys economic development.
With limited potential area, the increased demand may beoset by increases in crop yield. Otherwise, the agricultural
acreage needs to expand (e.g. through deorestation) or
ood imports need to be increased to meet the populations
demand.
These actors can all operate as driving actors that cover
certain social, environmental or economic dimensions, or
can be seen as restrictions that directly limit the biomass
availability. The driving actors in the selected studies are
determined by parameters that are considered by the
authors o each study, dierently.
Table 4 presents the associated databases or the driving
actors used by authors in their studies. These include
or example, areas o land with special protection status
(e.g. biodiversity, conservation areas) that can reduce
the potential area or bioenergy production, and climate
change with its eect on production yields. Yields are
also infuenced by both economic development and
agricultural productivity o a country. Besides these
broader dierences between the models used in the
Figure 4: Important driving actors to consider when assessing biomass potential
Population
FOOD DEMAND
Yields Area potential
Per-capita
consumption
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IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA 21
Table 3: The biomass resources in each study, and its classifcation.
Acronym
Biomass fraction
Energy crops Forestry biomass Residues and waste
BATIDZIRAI Eucalyptus Logging residues, industrial waste,bagasse
BMVBS
Cereals, maize, rape, sunfower, soya,
sugar beet, sugarcane, oil palm,
grassland
Fuel-wood
Straw, municipal waste (bio-waste
and wood residues), animal residues,
industrial wood waste, logging
residues, coconut, oil palm, bagasse
COOPERCrop residues, animal waste,
bagasse, coconut shells
DASAPPA Fuel-wood Crop residues, logging residues,industrial waste wood
DUKU Crop residues, coconut, oil palm
FISCHERBiomass (e.g. energy crops) on
grasslandForest products Crop residues
HABERLEnergy crops on cropland and on
other land (i.e. grazing land)
Residues on cropland
HAKALA Not claried Crop residues
HOOGWIJK Energy crops
KALTSCHMITT Not claried Fuel-wood Crop residues, animal residues
SMEETS
Woody energy crops (e.g.
eucalyptus, willow), conventionalcrops (e.g. sugarcane, wheat, maize)
and grasses (e.g.miscanthus)
Crop harvest residues, crop process
residues, wood harvest residues,wood process residues, wood waste
WEC Bagasse
WICKEAtrophy, cassava, short rotation
coppice
YAMAMOTO Not claried Fuel-wood
Human aeces, kitchen reuse,
animal dung, harvesting residues,
timber scrap, paper scrap, sawmillresidues, black liquor, uel wood/
industrial elling residues
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22 IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA
analysis, variations occur with regard to the application
and combination o data sources. Sometimes, very
complex phenomena (e.g. climate change and the issue
o sustainability) are excluded or not dealt with to the
correct level o detail in the studies, and yet some o the
reviewed studies use complex data models to simulatecurrent and uture material fows. Variations also extend to
the database chosen and the time period considered, and
the dierences may signicantly infuence the extent and
the range in results.
In the ollowing box, inormation about the individual
databases is compiled. Complex models tend to use
statistical data rom the FAO and other UN entities.
Unortunately there are no real alternatives to these
datasets and the data quality or Arica is poor. For
example, the data used rom remote sensing, e.g. GlobalLand Cover (GLC) oten has no reerence to any reporting
requirements rom the country concerned. The GLC
database was produced in 2000, and is now out o date,
because o the dynamic land use in Arica.
Table 4: Other important driving actors
Driving factor Database used
Social dimension
Food demand
- Population
- Per capita consumption
United Nations Development Programme (UNDP), United Nations (UN), Global Land
Use and Energy Model (GLUE), Integrated Model to Assess the Greenhouse Eect
(IMAGE), International Institute or Applied Systems Analysis (IIASA), Global Agricultural
Production Potential Model (GAPP)
Food and Agriculture Organization (FAO), GLUE, UN, GAPP
Environmental dimension
Biodiversitywww.biodiversityhotspots.org, Wissenschatlicher Beirat der Bundesregierung Globale
Umweltvernderungen (WBGU)
Conservation areas World Database on Protected Areas (WDPA), GLUE
Climate change/
environmental conditions
like deorestation
Lund-Potsdam-Jena managed Land Dynamic Global Vegetation and Water Balance
Model (LPJml), Intergovernmental Panel on Climate Change (IPCC), WBGU, IMAGE, FAO,
GLUE, Literature
Economic dimension
Economic growth FAO, World Bank, Quickscan Model, IMAGE
Costs (production,
transport, conversion)FAO, World Bank, Literature, Expert decision
Eciency o agricultural
production system
Crop and Grass Production Model (CGPM), IMAGE, FAO, IIASA, GLUE, IPCC, GAPP
Literature
General
Land use Global Land Cover(GLC); FAO, GLUE, IMAGE, IIASA, GAPP, Literature
Biomass demand/material
utilisationIMAGE, GLUE, Literature
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IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA 23
FAO: The FAO is a UN specialised agency that manages a comprehensive database o reported national data. In
general, data provision is based on the reporting o governments (ministries), non-governmental organisations(NGOs) or private, as well as international authorities (FAO, 2011a). The FAO Statistics Division is responsible or the
management o the data pool e.g. FAO holds statistics about ood, agriculture, orestry and sheries in FAOSTAT,
a statistical databases collection (FAO, 2011b). The database itsel is connected with national or regional statistical
inormation systems (e.g. CountrySTATS; FAO, 2011c). Additionally, the FAO uses databases rom other international
organisations, e.g. United Nations (UN) in order to carry out secondary statistical research (United Nations Statistics
Division, 2011a).
UN: The United Nations Statistical Division (UNSD) perorms, or example, energy statistics through national surveys
within the member countries (Energy Statistics Database; UNSD, 2011). The UNSD manages the data pool o the UN
(web based inormation systems UNDATA) and is supervised by the United Nations Statistical Commission (UNSC).
UNDP: As a member within the United Nation system, the UNDP is part o the UNO data pool. The organization also uses
surveys rom other international institutions, such as the Organisation or Economic Co-operation and Development
(OECD), World Bank, etc. (UNDP, 2011).
GLC: The Global Land Cover, produced in 2000 provides global land use data. The data on global spatial inormation
with a resolution o 1 km were recorded during a 14 month period (November 1999 to December 2000) using SPOT4
satellites (European Commission, Joint Research Centre 2011).
IMAGE: This database was initiated by the Environmental Assessment Agency o the Netherlands (PBL, NEAA, 2011a).
The model simulates the environmental impacts o human activities and processes demographical, technological,
economic, social, cultural and political data (Bouwman, Kram and Klein Goldewijk, 2006). Secondary statistical data
sources are used, including values rom the World Bank, UN, FAO, etc.
IIASA: This database relates to the development o orestry and above-ground biomass, the IIASA used the results o
the Global Forest Resources Assessment 2005 (FRA 2005) (FAO, 2011d). This biomass data has been extrapolated
with a linear inter-relationship to country level on a reerence area with a 0.5 grade (longitude/latitude) (IIASA, 2011).
The report provides an estimation o the global orestry inventory or 1990, 2000 and 2005. It comprises reports rom
229 countries and results developed in regional workshops (FAO, 2011d).
CGPM: The CGPM ollows the approach developed by the FAO (between 1978 and 1981) using agro-ecological zones
and is based on the world soil map o the FAO (1991). It considers in particular, three energy crops; sugarcane, corn andwood. Input data or this model include temperature, rainall, soil data, carbon dioxide concentrations, plant specic
growth parameters and yields. Furthermore, the model results serve as the data source or other models like IMAGE
or the LCM2000 (PBL, NEAA, 2011b).
GLUE: This Model developed by Yamamoto, Junichi and Kenji (2001), considers land use competition and overall
biomass fow. The model is based on data rom FAO 1992, World Bank 1993, Intergovernmental Panel on Climate
Change (IPCC) 1990/1992 and also data collected rom the literature (Yamamoto, Junichi and Kenji, 2001).
GAPP:The Model developed by the University o Hohenheim, calculates the area potential. Several structural parameters,
e.g.areas under cultivation, animal livestock, yields, population and per-capita consumption, are considered (BMVBS,
2010).
dETAIls of ThE mAJor dATABAsEs usEd By ThE sTudIEs
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24 IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA
2.3.3 Results o the studies
Figure 5 presents the overall results categorised by time
period and region as a logarithmic interpolation. The range
o all study results lies between 242 PJ/yr (WEC) and
337 000 PJ/yr (SMEETS) or the entire period under
review. The results or the Arican continent up to 2020vary between 242 PJ/yr (WEC) and 6 679 PJ/yr (BMVBS).
Looking long-term (2050 to 2100) the technical potential
is expected to lie between 2 360 PJ/yr (HAKALA) and
337 000 PJ/yr (SMEETS). A detailed presentation o
results according to dierent biomass categories area
potential and energy crops, orestry biomass, and residues
and waste can be ound in the ollowing sections.
Area potential and energy crops
The area available or the biomass production is a crucialparameter to calculate the quantity o raw material and total
energy crop potential. Figure 6 summarizes the values o
areas potentials calculated by ve o the reviewed studies.
According to these ve studies, SSA has an area potential
o at least 17.6 million ha, with Arica reaching a maximum
area potential o 150 million ha. In the long term (2050), an
area potential up to 717 million ha is assumed.
Over the short- and mid-term time period (2015-2020)
the area potential calculated by BATIDZIRAI provides
the highest values ound in any paper, albeit only or
Mozambique. Other high values ound include those rom
KALTSCHMITT (Figure 6). For the time period leading
to 2020, BMVBS orecasts the largest range o values
or a number o dierent scenarios (1.56 million ha to
20.6 million ha).
According to Figure 7, the studies that show a high area
potential over a specic time also have the highest amounts
o energy crop potential (see KALTSCHMITT, BATIDZIRAI and
SMEETS). However, in general, the energy crops potential,or the period up to 2020, ranges rom 0 PJ/yr (HAKALA)
to 13 900 PJ/yr (KALTSCHMITT). The high energy crop
potential value or Arica o 13 900 PJ/yr determined by
KALTSCHMITT, is in sharp contrast to the average value or
Arica o 992 PJ/yr, rom studies or the same time period.
More values are ound rom studies or the year 2050,
with a low energy crops potential o 0 PJ/yr calculated by
HAKALA and BMVBS, which is in contrast to the value o
317 000 PJ/yr produced by SMEETS. Finally, two o the
evaluated studies provide gures or the year 2100. While in
YAMAMOTO no energy crop potential is expected in 2100,
HOOGWIJK assumes a potential between 74 000 PJ/yr and
279 000 PJ/yr.
Forestry biomass
Five studies present results or the category orestry
biomass (Table 3). The authors use the term uel wood
potential with the exception o FISCHER who reers to
the data with the expression orestry products; a term,
which is not clearly deined. An overview on the resultso the orestry potential is shown in Figure 8.
A direct comparison o results is not possible, because o
variations between the studies. The studies o DASAPPA,
FISCHER and YAMAMOTO all reer to SSA, but relate
to dierent time periods. They present a potential or
SSA increasing rom 0 PJ/yr or the present-day time to
75 000 PJ/yr or the year 2100.
Two o the studies (FISCHER and BMVBS) represent
several scenario values or dierent time periods. TheBMVBS report speciies three scenarios or 2020 and
quantiies uel wood potential between 949 PJ/yr and
2 459 PJ/yr, although these values reer only to our
Arican countries (South Arica, Nigeria, Democratic
Republic o Congo and Ethiopia).
The uel wood potential o Nigeria by 2020 is predicted
to be 0 PJ/yr, whereas South Arica (242 PJ/yr to 1 200
PJ/yr) and the Democratic Republic Congo (558 PJ/yr to 1
123 PJ/yr) provide the highest percentage o the overall
values. The results or dierent scenarios presented by
FISCHER range rom 14 820 PJ/yr to 18 810 PJ/yr. Whereas,
KALTSCHMITT indicates a current orest biomass potential
o 5 400 PJ/yr or the whole Arican continent.
Residues and waste
Nearly all studies used in this report present data or
waste and residual potentials; an overview is shown in
Figure 9. In comparison to the wide range o results in
the previous ields o energy crops and orestry biomass,
the values or waste and residues show only moderatevariation lying between 2 100 PJ/yr and 5 200 PJ/yr. The
mean value rom the studies o KALTSCHMITT, HAKALA,
BMVBS, COOPER, HABERL and FISCHER is calculated
as 2 900 PJ/yr. Even accounting or the dierent time
periods, the deviation rom the calculated average value
is small.
Exceptional low values or waste and residual potentials
can be ound in the studies covering larger areas (WEC,
242 PJ/yr and DASAPPA, 585 PJ/yr), as well as in the
country-specic studies or Ghana (DUKU, 0.1 PJ/yr, data
not presented in Figure 9) and Mozambique (BATIDZIRAI,
10 PJ/yr). Comparatively high gures or the year 2050 are
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IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA 25
ound by SMEETS (12 000 PJ/yr to 20 000 PJ/yr) and or
2100 by YAMAMOTO (25 000 PJ/yr).
The reviewed studies consider dierent biomass categories
in their analysis. In Table 5, the results or each biomass
category are presented. The crop residues raction has been
analysed in the majority o studies in this report; in total 10studies present results including this agricultural by-product.
Comparing these studies in Figure 9, it can be noted that
HAKALA (2 380 PJ/yr to 2 600 PJ/yr), BMVBS (1 161 PJ/yr)
and COOPER (1 089 PJ/yr to 3 588 PJ/yr) report similar
results or the Arican continent or the current time
period. Yet in contrast to these ndings, DASAPPA states
a comparatively low yield o only 135 PJ/yr or the SSA
countries. In the longer-term (2050) HAKALA expects a
potential similar, i slightly lower than that ound today.
Signicant dierences occur in a detailed comparison o
crop residue data rom the SSA. The results range rom
between 2 190 PJ/yr (HABERL) and 20 000 PJ/yr (SMEETS).
A crop residue potential o approximately 10 500 PJ/yr,
which is comparatively high or the year 2100, is calculated
by YAMAMOTO.
A large range in results also occurs or animal residues.
COOPER (1 450 PJ/yr) and KALTSCHMITT (1 200 PJ/yr)
present similar ndings; with the BMVBS study indicating
a low yield o 28 PJ/yr or the same region and time
period. Signiicant dierences are also shown or
industrial wood waste (0 PJ/yr to 356 PJ/yr) and or
logging residues (0 PJ/yr to 822 PJ/yr). The estimated
potential or bagasse (201 PJ/yr to 242 PJ/yr) and
coconut shells (11 PJ/yr to 15 PJ/yr) are almost identical
in the two studies that included them.
Table 5: Residue and waste biomass potential energy data or Arica (PJ/yr.)
StudyCrop
residues
Animal
residues
Municipal
waste
Industrial
wood waste
Logging
residuesBagasse
Coconut
shellsOil palm
KALT-
SCHMITT900 1 200 - - - - - -
HAKALA
2 380-2 600
(current)
2 360-2 370(2050)
- - - - - - -
BMVBS 1 161 28 353 4 822 208 11 88
COOPER 1 089-3 588 1 450 - - - 86-201 15 -
WEC - - - - 242 - -
DASAPPA 135 - - 356 94 - 0.001 0.007
DUKU 0.07 - - - - - - -
BATIDZIRAI 2 1 8 - -
HABERL 2 190 - - - - - - -
FISCHER 4 884 - - - - - - -
SMEETS12 000-
20 000- - 0 0 - - -
YAMAMOTO42 %
(10 500)not specied - not specied not specied - - -
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26 IrENA-dBfZ: BIomAss poTENTIAls IN AfrIcA
Figure 5: Total technical biomass potential or dierent Arican regions and time periods
]ry/JP[laitnetopssamoiB
21400
2380
6
096
2626
242
5223
585
6147
6307
6680
7686
134000
2360
23440
46000
279000
100000
2600
6679
4845
5239
4588
4195
4
162
51000 2
370
122000
125000
5728
4
450
4020
3671
3624
91000
280000
181000
5169
15000
337000
74000
110
100
1000
1
0000
10
0000
100
0000
KALTSCHMITT
HAKALA
BMVBS
2003-2007
BMVBS
2010
COOPER
WEC
WICKE
DASAPPA
BMVBS
2015
BMVBS
2020
BATIDZIRAI
BMVBS
HOOGWIJK
HAKALA
HABERL
SMEETS
HOOGWIJK
YAMAMOTO
SSA
Mozambique
SSA
SSA
2015-2020
2100
2050
currently
Study
Region
Timeframe
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IrENA-dBfZ: BIomAss poTENTIAls IN AfrIcA 27
Figure 6: Area potential as dependent on time-period, Arican region and study
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28 IrENA-dBfZ: BIomAss poTENTIAls IN AfrIcA
Figure 7: Energy crops potential as dependent on time-period, Arican region and study
]ry/JP[laitnetopssamoiB
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IrENA-dBfZ: BIomAss poTENTIAls IN AfrIcA 29
Figure 8: Forestry biomass potential as dependent on time-period, Arican region and study
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30 IrENA-dBfZ: BIomAss poTENTIAls IN AfrIcA
Figure 9: Residues and waste potential as dependent on time-period, Arican region and study
]ry/JP[laitnetopssamoiB
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IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA 31
2.4 dIscussIoN
The potentials calculated by the reviewed studies reer
to dierent time periods, geographical regions and
biomass ractions. The studies thereore require careul
consideration beore comparing their results. The
comparisons are hampered by a dierent denition orthe technical potentials (see Section 2.3.1). Ultimately only
a ew parameters are incorporated into the calculations.
This report nds major dierences between the reviewed
studies, applied methodology, underlying databases and
the assumptions made.
In the ollowing, the results or the area potential, energy
crops, orestry biomass and residues and waste are
discussed. Signicant dierences are expounded.
2.4.1 Area potential
The area potential is ound to be a critical variable
in determining the quantity o raw material. Most o
the studies evaluated ail to mention the calculated
area potential. The results o the remaining studies
demonstrate that large area potentials can lead to high
potentials o energy crops. The availability o land and
the production yield o the energy crop are inherently
uncertain parameters and both tend to be estimated
dierently (Berndes, Hoogwijk and Broek, 2003). For
assessing the uture area potential, the studies estimated
the available land, which depends on population growth,
eating habits, increased urbanisation, and so on. In
theory an increase in yield could lead to a release o land
or energy crop cultivation.
O all the studies reviewed, only the BMVBS study shows
the development o the area potential or dierent time
periods. In two scenarios, a reduction in the area potential
is predicted. The area potential is assumed to rangerom between 1.5 million ha and 1.7 million ha by 2020,
and 0 million ha in 2050. These calculations are greatly
aected by the increased demand or ood. An increased
potential may only be realised (25.6 million ha in 2050)
i biomass energy use is to be supported, or example,
by government policies that may incentivise armers to
cultivate the most ecient crops (e.g. Short Rotation
Coppice (SRC), corn silage). Policies are not explored
urther in this report. The assessment o the current area
potential by BMVBS assumes that land set aside rom
ood production can be used and that additional land
will be available i agricultural overproduction is used or
bioenergy production.
All other studies indicate the area potential or a given
year, without depicting the development o this potential.
From the literature examined, by ar the highest area
potential was determined by KALTSCHMITT (150 million
ha). Generally the authors assumed that 10% o agricultural
area could be available or energy crop cultivation inthe world. In contrast, a more explicit determination o
area potentials was conducted by WICKE with a GIS-
approach. The available land (34.4 million ha) or energy
crop production resulted rom the dierence between the
total area and the non-usable area. The latter included
unsuitable areas (e.g. cities, sandy desert and dunes), high
biodiversity areas and agricultural land.
The uture area potentials assessment is carried out in only
three o the studies reviewed. In BATIDZIRAI, the calculated
area potential or Mozambique (45 million ha) is highmainly due to the increased eciency in ood production.
Despite the increasing demand or ood, more area is
made available through ecient crop production. It must
be noted that this potential can only be achieved i in 2015
the average crop yields increases by more than our times
(rom 4.9 to 8.9) and the average eed conversion ratio
more than doubles (rom 1.3 to 4.5). The resulting yields
and the eed conversion eciency in Mozambique would
then be equivalent to an industrialised countrys highly
ecient production system.
Similar to the assumptions used in BATIDZIRAI, SMEETS
calculates an area potential o 717 million ha or SSA in 2050
that is dependent on the agricultural production system.
The precondition is also very high levels o technology
or crop production and eed conversion eciencies in
Arica; this includes an increased use o ertilisers and
agrochemicals. To date, globally, SSA has one o the lowest
ertiliser usages in the world (Hakala and Pahkala, 2009).
Another point raised is that ood cultivation is projected
onto areas that achieved the highest yields per hectare ora particular crop. Only through keeping ood cultivation
areas to a minimum and optimising production will there
be more area available or energy crop production.
Due to the small number o studies available, it is dicult to
make any general statements about the major causes o the
signicant dierence ound between the area potentials.
Although it is possible to determine the area potential,
using a general calculation (x% o total agricultural area),
which is the simplest approach compared to those ound
in the studies; however, the assessment o uture area
potentials depends largely on the underlying assumptions.
According to the results analysed in this report, the high
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32 IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA
area potentials required or Arica can only be realised i
ecient production systems are assumed, or i a ocus is
made on biomass use or energy production (e.g. increasing
support). In summary, only by transorming the current
inecient and low-intensive agricultural management
systems into state o the art production systems using theappropriate technologies can the area potential illustrated
in most o these studies be achieved.
2.4.2 Energy crops
The most dominant parameters used to determine the
potential o energy crops are land and yield. All results are
considered with regard to, timerame; geographical coverage;
the type o potential; and the biomass eedstock (see Section
2.3.1). There are undamental dierences between thecalculated potential amounts in the reviewed studies.
For the period up to 2020, the amount o energy
crop potential shows range between 13 900 PJ/yr
(KALTSCHMITT) and 0 PJ/yr (HAKALA). HAKALA
justied this value by reasoning that Arica will continue to
have ood production problems or a signicantly growing
population in the uture. According to the authors, a decit
in ood production also indicates there is no land available
or energy crop production.
When interpreting the results, or example, KALTSCHMITT
used a general estimation that the potential area or
energy crop cultivation was equivalent to 10% o the total
agricultural area. Compared to other studies, this simple
assumption leads to the largest potential o energy crops
ound in any study. Whereas, the calculated value or
Mozambique in BATIDZIRAI (6 670 PJ/yr) can only be
achieved i there is to be a large increase in ood production
eciency that corresponds to a level ound in industrialised
countries (see Section 2.4.1).
The values presented in WICKE indicate area potentials
that exclude other competitive land uses, such as land
or hunting or livestock, due to the chosen methodology.
Excluding these areas leads to a reduction in available land
and a reduction o the calculated energy crop potential. A
dierent picture emerged in the BMVBS study, in which the
calculated values vary according to the chosen scenario. It
should be noted that the highest values are only realised
in the bioenergy scenario, which assumes an increasing
ocus on biomass use, e.g. through a cultivation o SRC
or sugarcane. I previous trends regarding agricultural
development, land use change and population growth
continue, or environmental and conservation restrictions
increase, the calculations show that there can be a decrease
in the energy crop potential (33 PJ/yr to 47 PJ/yr in 2020
and 0 PJ/yr in 2050).
The highest value o 317 000 PJ/yr or the energy
crop potential is determined by SMEETS with the key
parameter in this study being the eciency o ood
production. However, only with very high crop production
eciency and through improved agricultural technology
or geographical optimisation (see Section 2.3.3) can it be
possible to realise this potential by 2025.
Six studies estimate the potential o energy crops in 2050;
the average value ound or Arica is 76 508 PJ/yr. Two
o these studies (HAKALA, BMVBS) indicate that thereis no potential or energy crops by 2050, mostly due to
the high uture population demand or ood. In BMVBS an
energy crop potential o 2 552 PJ/yr is only achieved with a
scenario that has a ocus on energy crop cultivation.
The values or 2050 determined by HABERL (21 250 PJ/yr)
and FISCHER (54 510 PJ/yr to 68 310 PJ/yr) lie in the lower
to middle range o the calculated values. The increase
in biomass potential ound by HABERL, are primarily
explained by the assumption o a global increase o 54%
in yields and also by a 9% expansion in cropland. Although
improvements to the current productivity levels are
implied in this study, there is no massive intensication o
land management assumed.
A wide range o results or the year 2050 is shown in
HOOGWIJK (15 000 PJ/yr to 134 000 PJ/yr). It is assumed
that abandoned agricultural land, low productive land and
rest land (mainly savannah, scrub and grassland/steppe)
are available or energy crop production. The area potential
is determined with the model IMAGE (see Section 2.3.2).An area reduction is achieved by the so-called land-claim
exclusion actor. This actor indicates the percentage o
land that is not available or biomass production, e.g. claims
or nature development, urbanisation, or cattle grazing on
extensive grassland. The authors note that this actor or the
competing land-use claims is associated with a high degree o
inaccuracy. The selection o the land-claim exclusion actors
or rest land area is arbitrary, but has an important infuence
on the total estimated potential. It should also be stated that
on a global scale the potential are assumed to be available or
energy crops corresponds to 30%-40% o the total land area.
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IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA 33
Only two studies assess the potential or 2100. HOOGWIJK,
calculates a potential or energy crops ranging rom
74 000 PJ/yr to 279 000 PJ/yr, depending on the scenario7,
as opposed to YAMAMOTO who assumes that there is
absolutely no potential rom energy crops in 2100. In the
latter study, this result is explained by a high demand orland due to the rising per capita consumption o meat and
despite an increasing availability o land (through the use
o allow and degraded land) and increasing productivity in
developing countries. This demand or meat counteracted
the growth in area, so that nally no areas are available
or energy crop cultivation. In YAMAMOTO, these material
fows are simulated using the model GLUE (see Section
2.3.2).
2.4.3 Forestry biomass
Only ve o the reviewed studies provide values or orestry
biomass potentials. The results present a very wide
range rom 0 PJ/yr to 75 000 PJ/yr (see Section 2.3.3). A
direct comparison o the published results is impractical,
because the ndings are based on dierent regions and
time periods. Yet it is possible to discuss the applied
methodology and the assumed ramework conditions.
For the Arican continent today, KALTSCHMITT derives
a potential o 5 400 PJ/yr using a calculation based on
FAO numbers or raw wood harvesting. The theoretical
increments o timber account or approximately hal o the
calculated potential.
DASAPPA, FISCHER and YAMAMOTO provide inormation
on the SSA region. Although these studies partly use the
same data set, the results are very divergent (Figure 8).
DASAPPA calculates a potential o 0 PJ/yr or the current
time, using a gure o 600 million people living in the
region without access to energy; a per capita consumptiono 0.89 m3/yr o uelwood is calculated or cooking and
heating that eaves no wood resources or other energy
requirements . FISCHER calculates that orestry production
potentials range rom 14 820 PJ/yr to 18 810 PJ/yr or the
year 2050. In this study the evaluation data or yield and
availability are collected rom the literature up to the year
1992 and then combined with FAO land use data rom 1999.
This data is computed with the IIASA-model. YAMAMOTO
uses their sel-developed model GLUE to predict biomass
potentials up to the year 2100. The calculated orestry
production potential or SSA reaches 75 000 PJ/yr (Figure
8). This calculation is based on the average development
rom 1961 to 1990 and then uses a linear projection annually
to 2010. Their basic assumptions include aorestation
and uture biomass demand. For developing countries
YAMAMOTO assumes a perect reorestation until the year
2025 and a constant biomass demand based on the 1990slevel.
The BMVBS calculations o 949 PJ/yr to 2 459 PJ/yr
biomass potential, use the countrys average annual
change to orest and plantation areas since 1990, which in
combination with a countrys specic annual net increase,
can be used to derive the sustainable extractable raw wood
potential. The calculations link a time series o production
and consumption gures, as well as the gross domestic
product and population data. The main data sources are
the FAO databases. The potentials are calculated or threescenarios with varying ramework conditions.
The highest potential can only be
achieved through the ambitious
implementation of forest plantations.
The lowest potentials can be obtained
in a scenario where 50% of the
primary forest in the tropics and 10%
of commercial forest in the temperate
zones are put under protection.
In this global approach it is impossible to account or all
relevant actors infuencing the raw wood potential orevery country and as such the ndings o the BMVBS
study rely on partial rough estimates and assumptions.
Considering the inconsistent orest types and tree stands
worldwide, net increases can also only be estimated.
Possible developments o inrastructure and technology
are not taken into account.
The studies analysed have a number o points concerning
the data quality, e.g. production and consumption
parameters, as well as the lack o data or the estimation o
the orest biomass potential. Despite the act that almost all
the studies are based on FAO data, the results dier greatly.
7 Te enai e in te t en t te enai bie b te Ipcc.
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34 IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA
This is explained through the use o dierent timelines, as
well as urther data processing using other datasets or
models. In addition, the calculated potentials are infuenced
by the assumptions concerning several determinants, e.g.
the actors or reorestation and biomass demand set by
YAMAMOTO are simplied and do not capture the wholepicture. Another critical actor is the data itsel, which
being generated in the 1990s, is considered rom todays
perspective fawed, i.e. the productivity yield calculation.
Additionally the lack o ocial statistics and the material
use o timber are not considered suciently in the models
used. The dynamic demographic development, especially
in the very poor regions o Arica, is assumed to cause an
increasing demand or uelwood or cooking and heating.
Furthermore a rising share o material use and export o
timber can be assumed.
In light o this discussion, the orest biomass potentials can
only be considered as rough estimates or the assessment
o the respective regions. Regional analyses based on
current data are a precondition to allow sustainable use o
orest resources or energy production.
2.4.4 Residues and waste
In the analysed studies, dierent residual ractions (Table
3) are considered. Although the overall results are mostly
similar, the individual results are compared (Table 5).
Most studies have ndings or crop residues. For the current
investigated period HAKALA (2 380 PJ/yr to 2 600 PJ/yr),
BMVBS (1 161 PJ/yr) and COOPER (1 089 PJ/yr to 3 588 PJ/yr)
provide broadly similar values or the entire continent. In all
three studies the potentials are determined by using crop
yield actors, such as soil quality, crop heating value, crop
straw ratios and the corresponding crop residue ractions.For this purpose HAKALA uses literature data and their
own calculations. The BMVBS calculations are based on
a region-and crop-specic grain-to-straw ratio. Whereas,
COOPER uses actors that are based on literature data or
Asia.
To protect the soil quality only a small part o the total
potential should be used or energy generation. This part
depends on diverse actors (e.g. crop rotation, ertiliser
use) and is estimated in the studies by a (sustainable)
recovery rate. HAKALA estimates this rate to be 50% to
70% while the BMVBS study uses conservative 20% and
COOPER uses 35%.
With the help o specic heating values, the remaining
residue potential is converted into units o energy.
HAKALA and COOPER use a uniorm number o 18 GJ/t,
or all crops. BMVBS calculations utilise a slightly lower
value o 17 GJ/t. KALTSCHMITT evaluates a crop residue
potential o 900 PJ/yr, but gives no explanation on howthis is reached. Nevertheless, this value is still o the same
order o magnitude as those described in the other studies.
Despite a similar methodological approach, the respective
residue and waste energy potential results calculated or
the Sub-Saharan region dier considerably. DASAPPA
calculates a very low 135 PJ/yr or the present-day. For the
year 2050, the results range rom 2 190 PJ/yr (HABERL)
to 20 000 PJ/yr (SMEETS). The amount o crop residue
can be estimated rom the assumptions made or area,
energy crops potential and yield development (Section2.4.2). High energy crop potentials are linked to high crop
residue potentials. In particular, the high yield expectations
ound by SMEETS leads to high values or the estimated
crop residues. This relationship is also evident in FISCHER.
YAMAMOTO indicates no specic value or crop residues
or the year 2100, but estimated a proportion o 42%. As
this value is not mentioned in a global context it is taken to
only be conditionally related to Arica.
The quantity o crop residue is determined by the
parameters such as area, and crop productivity, including
the amount o raw material and the dierent recovery
rates or the crops produced. The results or Arica at
present vary only slightly. The dierences arise rom the
actors used to determine the raction o residue, the
(sustainable) recovery rates, and the varying sample site
conditions. Taking into consideration the methodology and
the relatively small deviations in the results, the potential
o crop residues would range between 1 000 PJ/yr and
3 000 PJ/yr. Further analyses with actual and updated
regional databases are necessary to speciy andcorroborate these igures, given the importance
attached to yield and its role in uture potential energy
evaluations.
In three studies the potentials o animal residues
are presented or the whole continent. The values o
KALTSCHMITT (1 200 PJ/yr) and COOPER (1 450 PJ/yr)
are very similar. In contrast, the potentials calculated
by BMVBS are very low (28 PJ/yr). The calculations are
based on FAO animal data and subsequently multiplied
by speciic energy content. COOPERs calculations
assume 223 million cattle, and each animal is assumed
to produce 50 gallons (gal) o animal residue, with an
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IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA 35
Wooden pelletsJiri Hera/Shutterstock
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36 IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA
energy content o 130 MJ/gal. However, the authors
stress that the results reers to the traditional use o
dried excrement and draws attention to one the biggest
challenges acing Arica, which is the ability to create
an appropriate inrastructure or large-scale excrement
use. KALTSCHMITT also assumes an energy-related use
o a dried solid uel.
The calculations include an additional actor o 50% or
the development o excrement amounts o cattle and
pigs. In the BMVBS calculations, the FAO average values
used are rom 2003-2007 (237 million cattle, 21.6 million
pigs, 1.2 billion chickens). All countries investigated by
the studies are characterised by a poor inrastructure.
Thereore, additional country-specic actors o the uture
development o excreta use are applied. These actorsare assumed to be at a very low level or animal residue
potential: or pigs between 15 to 30% and or chickens 30
to 70%. For bee a development actor o 0% is expected
and could explain the signicant dierence in the results o
COOPER and KALTSCHMITT.
The demand or milk and meat is expected to increase in
the uture with an increasing population. The production o
animal residues is expected to increase. The FAO livestock
data are not suciently detailed to make an estimate,
since the individual stocks (e.g. bee cattle, dairy cows and
calves) are not separately listed. Animal species dierence
will also have a signicant impact on the amount o the
excrements and the possible associated energy yields.
Results using the FAO database must be interpreted with
caution. Moving orward, the improved capture o animal
residue potential in Arica lies in the development o an
appropriate inrastructure.
Only the BMVBS study calculates the municipal waste
potential; 353 PJ/yr. To determine this potential, the FAO
population data and IPCC inormation on the per capita
biomass resources in year 2000 are used. The technical
uel potential is then calculated with a development actor
o 75%. I the population rises, the volume o municipal
waste will increase, especially in major cities. Inaccuracies
in the data arise rom the inormation aabout theoretical
potentials per capita and other dierences in the regional
development actors.
Data or industrial wood waste potential ranges rom 0 PJ/yr
(SMEETS) to 356 PJ/yr (DASAPPA). All authors describe
the FAO database as limited, with ragmented data.
BATIDZIRAI (2 PJ/yr) provide calculations or Mozambique
based on orest area and identied industrial wood waste
potential or sustainable logging. The analysis derives a
potential with regard to the wood processing industry that
takes into account an additional eciency actor o 55%.
BMVBS (4 PJ/yr) and SMEETS (0 PJ/yr) also use this actor,
but assume 75%. DASAPPA assumes a relatively small
actor o 30% or energy production (356 PJ/yr). However,
in general the calculations reveal a high potential and
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IRENA-DBFZ: BIOMASS POTENTIALS IN AFRICA 37
such numbers possibly result rom the assumptions being
made, the theoretical potential and the underlying wood
yields being used. In the aorementioned studies, only the
most essential actors are considered. In many cases, they
are only partially taken into account. For example, dierent
wood types cannot be distinguished. Also the material use
can be estimated only very roughly. The results thereore
provide very approximate values
Equally, the data concerning the logging residues is o poor
quality. The BMVBS study calculates a potential o 822
PJ/yr. It is assumed that the logging