Memoire ics christine

Thesis

Presented by
Christine Langevin

To pass the degree of
Engineer in Agronomics - Montpellier SUPAGRO

Subject:
Evaluation of the suitability of a micro-scale improved cook-
stoves project within the voluntary carbon finance scheme
(Case study in Andhra Pradesh, India)

Defended in public (13 november 2009)

at AgroParisTech-ENGREF
Centre de Montpellier

Board of examiners:

Tiberghien Matthieu Placement supervisor
Titre Nom Prénom Examinateur
Titre Nom Prénom Examinateur
Manlay Raphaël Internship supervisor
ENGREF

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ACKNOWLEDGEMENTS
I would like to express my gratitude to all those who gave me the possibility to complete this
thesis. I thank the GoodPlanet foundation and, in particular, Matthieu Tiberghien and Nitin Pagare,
respectively responsible and project manager of Action Carbon Program, for their help with the
methodology. I would like to thank also the GEO team with Dr N. Sai Bhaskar Reddy, chief executive
officer, Ajai, Ramech, Naresh and Sandeep for their reception in India and help for the field work. I
would like to express a special thank to Minh Cuong Le Quan from GERES (Groupe Energies
Renouvelables Environnement et Solidarités, a french NGO for sustainable development and
international solidarity) and Marion Chesnes from the ONF international (Office National des Forêts,
french International environmental consulting and expertise cabinet) for theirs valuable advices.

Christine LANGEVIN
Thesis GEEFT-FRT 2008-2009
Evaluation of the suitability of a micro-scale improved cook-stoves project within the voluntary carbon finance scheme

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FOREWORD
This work was realized in the framework of a Voluntary Service International (VSI) of one
year issued from a tripartite partnership between the French Association of Volunteers of Progress
(AFVP), the Good Planet Association and a local Indian organization, GEO (Geoecology Energy
Organization). Created in 1963, the AFVP is recognized as acting in the name of the French
government and gives the opportunity to spend two years working on volunteering missions in
developing countries with their partners. This thesis corresponds to the first 6 months of the voluntary
service carried out in Hyderabad, India. .

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ABSTRACT
The purpose of this thesis is to demonstrate in terms of Emissions Reduction (ER) the interest
of a micro-scale Improved Cook Stoves (ICS) activity in rural community following a methodology
developed by the Gold Standard: Methodology for Improved Cook-Stoves and Kitchen Regime (2007). The
surveys and experimentations have been held in a village located in Andhra Pradesh, India. It shows a
total calculated emissions reduction on a 3 years period (life time estimated of the ICS) of 1445.7
tCO2e. This result dissuade the project developer of generating Voluntary Emissions Reduction
(VERs) through a Gold Standard certification process which will induce costs higher to benefices
obtained from VERs trading. Thereby, the study underlines the difficulties due to the local conditions
and the application of the methodology. Also, it gives suggestions to improve the calculation of ER
and, therefore, the amount of VERs that can be produced, using the same methodology or another
approved methodology for a similar activity.

RESUME
L’objectif de ce mémoire est de démontrer en termes de réduction d’émissions, l’intérêt d’un
microprojet d’introduction de foyers améliorés dans des communautés rurales à l’aide d’une
méthodologie développée par le Gold Standard. Les enquêtes et expérimentations ont eu lieu dans un
village situé en Andhra Pradesh, Inde. On estime alors des réductions d’émissions, sur une période de
3 ans correspondant à la durée de vie potentielle du foyer améliorés, égales à 1445,7 tCO2e. Ce résultat
dissuade le développeur de projet de produire des unités de réduction d’émissions volontaires via une
certification Gold Standard qui impliquerait des coûts supérieurs aux bénéfices apportés par la
commercialisation de ces unités. L’étude met en évidence les difficultés occasionnées par les conditions
locales et l’application de la méthodologie. De plus, elle donne des propositions pour améliorer le
calcul des réductions d’émissions et, par conséquent, des unités de réduction d’émissions volontaires
via la méthodologie Gold Standard et une autre méthodologie approuvée pour une même activité.

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SUMMARY
ACKNOWLEDGEMENTS .......................................................... 1

FOREWORD .................................................................... 2

ABSTRACT ..................................................................... 3

RESUME ....................................................................... 3

SUMMARY...................................................................... 4

1. INTRODUCTION ............................................................. 6

2. MATERIAL AND METHODS .................................................... 9
2.1 Study area 9

2.2 Acquisition of data 10

2.2.1 The Gold Standard methodology for ICS 10

2.2.2 Description of the baseline scenario 11

2.2.3 Households characteristics, fuel consumption and kitchen regimes 11

2.2.4 Fuel consumption reduction 12

2.2.5 Analysis of the renewability status of wood-fuels 13

2.3 Analysis of the data 16

2.3.1 Kitchen surveys 16

2.3.2 Kitchen performance tests 17

2.3.3 Emissions reduction calculation 17

3. RESULTS ................................................................. 18
3.1 Households characteristics, fuel consumption and kitchen regimes 18

3.1.1 Representativeness of the data collected 18

3.1.2 Households and kitchen characteristics 18

3.1.3 Households fuel consumption 19

3.1.4 Patterns of Emissions Reduction performances 20

3.1.5 Households satisfaction and willingness to adopt a Magh CM ICS 22

3.2 Fuel consumption reduction 23

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3.3 Analysis of the renewability status of wood fuels 24

3.3.1 Fuel Collection Area 24

3.3.2 Growth of woody biomass 25

3.3.3 Global dry biomass extraction per year 26

3.3.4 NRB Rate calculation 26

3.4 Emissions reduction calculation 27

3.4.1 Baseline emissions calculation 27

3.4.2 Project emissions calculation 27


4. DISCUSSION............................................................... 29
4.1 Difficulties encountered 29

4.2 Criticism of the results 29

4.2.1 Criticism of the data obtained from the surveys 29

4.2.2 Calculation of the fuel consumption reduction (quantitative tests) 30

4.2.3 Estimation of the renewability of the biomass 31


4.3 Propositions to maximize the ER calculated 32

4.3.1 Fuel consumption reduction 32

4.3.2 NRB rate 33

4.3.3 Emission factors 33

5. CONCLUSION ............................................................. 34

6. BIBLIOGRAPHY ............................................................ 35

7. ABBREVIATIONS ........................................................... 37

8. TABLES LIST .............................................................. 38

9. FIGURES LIST ............................................................. 39

10. APPENDIX ............................................................... 40

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1. INTRODUCTION
Climate change, a consequence of the accumulation of greenhouse gases (GHG) in the
atmosphere is today scientifically proved. The United Nations agreed on the United Nation
Framework Convention about Climate Change (UNFCCC) which recognizes the necessity to initiate
concrete actions to reduce GHG emissions. In 1997, the Kyoto Protocol adopted by the UNFCCC in
1992 opened to ratification and committed industrialized countries listed in Protocol’s Annex 1 to
reduce their GHG emissions to an average of five per cent against 1990 levels over the period 2008-
2012 (United Nations, 1997).
In order to meet their targets, committed countries have to take national measures and can appeal to
market-based mechanisms defined by the protocol. Described in the Article 12 of the protocol, the
Clean Development mechanism (CDM) permit to committed countries listed in the Annex B to
implement emission-reduction projects in developing countries. Such projects can generate saleable
certified emission reduction (CER) credits (equivalent to one tonne of CO2) which can be counted
towards meeting the protocol targets (United Nations, 1997).
Facing, notably, the constraints of the costs and procedures required for the validation of projects
under the CDM, a voluntary carbon market has been developed for the institutions, enterprises and
individual that would like to act against climate change on a voluntary basis for their conviction and
image. The Voluntary Carbon Market allow the project developers to create voluntary emission
reduction (VER) certified by label acknowledged like Gold Standard, Voluntary carbon standard
(VCS), VER+, etc.

The program Action Carbone has been created in 2006 within the association Good Planet of Yann
Arthus Bertrand, a well known French photographer offsetting voluntarily emissions of GHG caused
by his activity. It is a non-profit responsible and solidarity program, which proposes to companies,
institutions and individuals to participate to energy efficiency and renewable energy projects, developed
by NGOs in Southern countries. Those project follow the Code of Best Practices for the Voluntary
Carbon Market developed by the French Environment and Energy Management Agency (ADEME,
Agence de l'Environnement et de la Maîtrise de l'Energie), which give a guarantee for the quality of
voluntary carbon offsetting (ADEME, 2008).

Efficient pyrolysis of biomass to produce renewable energy for cooking constitutes an offset activity
which fit into the trading scheme of certified emission reduction. A cook stove is a basic stove heated
by burning biomass and fossil fuel commonly used in developing countries. It has been estimated that
2 billion people in the developing countries rely on biomass for cooking and are depending on fuel
renewability (Jonhson, 2007). Since the 1940s, the governments, international development
organizations, and Non-Governmental Organizations (NGOs) made efforts to improve the efficiency
of cook stoves with the introduction of improved biomass cook stoves (ICS). Moreover, still many
households in rural settings in developing countries (around 1.2 billion extremely poor people) didn’t
benefit from those programs (HEDON, 2003). They suffer from low efficiency stoves, indoor air
pollution and spend more time collecting firewood. According to Jonhson and all (2007),
implementing improved cook stoves in households around the world could save up to the equivalent
of 10 tonnes of carbon dioxide per year. On top of that, the implementation of ICS program can
allow the reduction of the smoke emissions and indoor air pollution; the pressure on forest and energy
resources; saving money and time to acquire fuel; the development of skills and creation of job in the
rural communities (HEDON, 2003).

Framework of the internship
The GSBC project (Good Stoves and Biochar Communities Project) is issued from a
partnership between Action Carbone and GEO (Geoecology Energy Organization). GEO is a NGO
based at Hyderabad (Andhra Pradesh, India) that works on various issues as environment, energy,
agriculture and climate change. GEO has a wide experience in the field of ICS and till date GEO has
designed around 30 different models of ICS named as Good Stoves.

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The GSBC project started in May 2009. It is a three years project implemented in Andhra
Pradesh, India integrating three components.
- The first part of the project is the dissemination of 5,000 improved cook stove in 50 villages
distributed in 3 districts of the state of Andhra Pradesh. The users would be households who
previously used inefficient, traditional stoves.
- The second part of the project will be the promotion of biochar as soil amendments. The term
of biochar is the product of pyrolysis of biomass at low temperature destined to be
incorporated into the soils. This process has been proved to be useful to enhance soil fertility
and sequestrated carbon (Lehmann, 2009).
- The third part of the project will consist by designing and promoting new technology more
efficient and less polluting for the charcoal production.
The Good Stoves part of the project is aiming the creation of VER certified by the Gold Standard. It is a
micro-scale project since the emission reductions will not exceed the limit of 5 000 tCO2 equivalent per
annum (The Gold Standard, 2009).

Technology to be employed

The improved cook stove Magh CM (Common Man) is a portable biomass stove designed by
GEO in august 2009 and constructed locally. The design has been especially made so it can be
produced by the local communities for a low cost (less than 8 $ USD). Also, all the material necessary
to build the ICS are available locally. The Magh CM has it has the options to run on forced air thanks
to an electric fan a 12 VDC (Volts Direct Current) or to be use without power with an additional
window for secondary air (natural draft). This design has been selected because of the particular local
conditions where power is not available all day long. Most importantly, there is a charcoal and ash
removal facility at the bottom of the stove, the grate can be lifted using a wire and immediately refilled
for re-use. The stove has been built with the most commonly available oil tin can of 12 x 9 x 9 inches
with a combustion chamber of 6 inches diameter and 9 inches height for the convenience of adoption
for a family of 5 members cooking needs and weight around 15 kg (cf. figure 1).

Source: http://www.e-geo.org

Figure 1: Picture and schematic representation of the Magh CM

The types of biomass that can be used in the stove are wood shavings, leaves, crop residues, pieces of
sticks, cowdung cakes, etc.

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Sustainable development
In the framework of a project to be certified by a voluntary label, the environmental and social
dimensions are very important issues. The implementation of the Good Stoves part of the project will
contribute to:
- Bring wood consumption down so as to enhance a better natural recovery of the woody
biomass,
- Reduce indoor air pollution caused from wood smoke and avoid its harmful health
consequences,
- Reduce the fuel wood expenditures and time spent harvesting for households,
- Decrease pressure on resources and biodiversity,
- Create local employment opportunities for enterprises, manufacturing, distributing, retailing,
and maintaining the stoves.

Assignment of the volunteer
In the framework of the GSBC project, the volunteer was in charge of the carbon expertise of
the Good Stoves part of the project. More exactly, the volunteer had to built a protocol and carry out
field surveys in order to estimate the emissions reduction arising from the project activity
implementation with the Methodology for Improved Cook-Stoves and Kitchen Regime developed for the label
Gold Standard (The Gold Standard, 2008). For the first stage of the project, the target was to
implement 100 ICS. As a case study, the assignment of the volunteer will be focusing on the first year
stage of the project activity to evaluate if it is suitable for the project developer to invest in a
certification process to get VER with the label Gold Standard.

Approach and research question
The main question of this work is: Is the voluntary Carbon Finance suitable for a micro-scale
biomass improved cook-stoves project?
To answer to that question, we have first to determinate: at what level do we contribute mitigating
climate change in term of emissions reduction using a biomass ICS micro-project? A biomass ICS
program contributes to the reduction of emissions arising from households by changing their fuel
consumption pattern and of pressure on available wood fuel resources in the project area. Therefore,
to estimate the ER, three questions need to be answered:
- What are the fuel consumption patterns of the beneficiaries?
- What are the quantitative differences in fuel consumption created by the project activity?
- What are the current state of the wood fuel resources (renewable or not) and the impacts of
the ICS program implementation on them?
In this thesis, we will explore those different questions using the Gold Standard methodology; discuss
the reliability of the calculation and of the methodology; suggest ways to improve the results and
conclude on the suitability of engaging a certification process of the activity.

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2. MATERIAL AND METHODS
2.1 STUDY AREA
The project is taking place in Andhra Pradesh in India. The state of Andhra Pradesh of 274
400 km2, is located in the south-east of India between 12°41' and 22°N latitude and 77° and 84°40'E
longitude. It is the fourth largest state in term of area and the fifth largest in term of population with
76.2 million people in 2001 with an annual growth rate of 1.37% (Center for Economic and Social
Studies, 2007). Andhra Pradesh is divided into 23 districts included in 3 geographical areas: the Coastal
Andhra, the Rayalaseema (in the south) and the Telangana (from west to north-west). The districts are
themselves divided in smaller administrative units specific to India called mandal or thesil.
The study is taking place in the village of Peddamadur in Devarruppula mandal (Warangal district) in
the Telangana region (see figure 2). The local language spoken in the area is the Telugu.

Legend

Peddamadur

Source : http://www.mapsofindia.com/maps/andhrapradesh/andhrapradesh-district.htm

Figure 2: Localization of the study area

The total geographical area of the district of Warangal is 12 846 km2 with a population of 3 200 000 of
which 19.2% were urban according to the 2001 census (Center for Economic and Social Studies,
2007). The district is located in a semi-arid region in a transition area between the tropical and sub-
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tropical climates. It receives South West and North East monsoon rains with clear rainfall zonation
from West East (764 mm) to North East (1096 mm). The maximum and minimum temperatures
recorded are respectively 42.9 and 16.2oC (Reddy C.S., 2008). Warangal is predominantly an agricultural
district with an important number of artificial lakes and the Godavari River. The crops grown are
Paddy (which accounts for 34% of the total cropped area), cotton, maize, chili, groundnut, green
grams, castor, etc (Reddy C.S., 2008) and the farming activity is concerning 68.1% of the total workers
(Center for Economic and Social Studies, 2007). According to the data obtained at the Devaruppula
mandal office, the village of Peddamadur, of 21.7 km2 and a population of 4400 is also mainly
agricultural. With 67% of the workers that are farmers, the net area sown corresponds to 57% of the
total area and a total cultivable area to 88% (cf. Appendix I). The population is living under poor
condition with an average income 15 000 Rs/yr (equivalent to 325 $USD) which is below the poverty
line fixed by the Asian Bank (1.35$/day).

In Andhra Pradesh, around 87 % of the rural households rely on firewood and chips for fuel against
77% in all India (Center for Economic and Social Studies, 2007). This has multiple effects including
local deforestation and diseases link to indoor pollution. In India, we estimate that indoor pollution
causes 500 000 deaths of children under 5 years, 340 000 cases of chronic respiratory diseases in
women under 45 and 800 cases of lung cancer (Center for Economic and Social Studies, 2007). Along
with the changes of land use in Andhra Pradesh, the heavy demands of fuel wood by households and
timber production (with 70% for fuel wood and 30% for timber) leads to deforestation and forest
degradation (Singhal R.M., 2003). In India, around 41 % of the forests have been degraded since the
1970s. In 1996, a net deficit of 86 million tonnes of fuel wood has been recorded, which as a
compulsion, has been removed from the forests. Forests have five times more pressure than they can
support (Meenakshi J., 2003). Thus, those reasons motivated GEO and Good Planet to start
implementing the project in a rural area in Andhra Pradesh where the majority of the population is
relying on woody biomass to cook and uses traditional low efficient stoves. The choice of the village of
Peddamadur was also convenient for the other parts of the project since no other similar project were
implemented, we noticed the presence of Terra Preta (black anthropogenic soil where charcoal has been
incorporated) and the development of charcoal production activities using traditional, low efficient
technology.

2.2 ACQUISITION OF DATA
2.2.1 THE GOLD STANDARD METHODOLOGY FOR ICS
The Methodology for Improved Cook-Stoves and Kitchen Regime (The Gold Standard, 2008) is used to
estimate the emissions reduction (ER) arising from the Good Stoves part of the project. The
methodology has been specifically designed for programs and activities introducing low-emissions
cook-stoves and kitchen regimes (range of practices which evolve GHG emission arising from energy
use in the kitchen) in institutions and communities that replace relatively high-emission baseline
scenarios within a distinct geographical area (Gold Standard, 2008). The Baseline means the amount of
GHG emissions that would be produced in the absence of the project (Gold Standard, 2008). The
methodology requires qualitative and quantitative surveys in the households of the stove users. Fuel
consumption reductions, directly link to ER, are directly calculated from the surveys. Moreover, the
methodology requires quantifying GHG emissions from non-renewable biomass (NRB), and applies to
projects where the use of woody biomass as cooking fuel is not balanced by re-growth in the collection
area.
To calculate ER due to the project, the methodology is proposing an approach per clusters. Clusters
are groups of beneficiaries that are going to present distinct pattern of ER performances. If there are
significant difference in fuel types, fuels mix and kitchen regime used by the households or in the type
of ICS they are willing to acquire (if different types of stoves are proposed through the project), the
beneficiaries will be divided accordingly into different clusters. Those clusters will be determined
starting from the data available and then, refine thanks to a qualitative survey (Kitchen Survey).
Afterward, quantitative measurements of fuel consumption in households (Kitchen Tests) will be carried
out in each cluster using their traditional stove and the ICS to quantify the fuel consumption reduction
per cluster.
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The reason for choosing this methodology which comes first was that it was the only methodology on
ICS approved by the Gold Standard giving detailed guidelines. The others methodologies eligible for the
Gold Standard label are the approved UNFCCC CDM methodologies (The Gold Standard, 2009) and,
according to Action Carbone, the project activities that use them face numerous difficulties during the
verification stage, especially caused by the lack of guidelines. Most of ICS activities following an
UNFCCC CDM methodology did not completed the validation due to complicated issues in the
process.

2.2.2 DESCRIPTION OF THE BASELINE SCENARIO
According to the Gold Standard methodology, the baseline scenario is the Business as usual'
scenario (The Gold Standard, 2009). For the baseline scenario, the methodology gives the possibility to
choose a fixed baseline or an evolving baseline depending on the implementation process of the project. The
baseline is fixed if the totality of the ICS is implemented in the same time in households and evolving if
they are introduced in a progressive way. In the framework of the Good Stoves part of the GSBC project,
5 000 ICS will be introduced in a progressive way on a 3 years period.
Consequently, the baseline scenario chosen will be evolving and, for the first year, will take into
consideration only 100 ICS introduced in the village of Peddamadur.
The village of Peddamadur is made of 1012 households according to the census of 2001. At the
beginning of the project implementation, no pilot sales record with names of beneficiaries was
established, this will be done afterword, while monitoring of the ICS selling.
During the first visits of the village and after discussing with a local NGO, Women and Rural
Development society (WORDS) which helps the farmers getting access to micro-credit, it appears that
the only fuels used in the village were mostly woody biomass and liquefied petroleum gas (LPG). From
the discussion with the farmers and the women, we determinate that around 100-120 households were
using LPG and were not willing to change. Therefore, the ER will be arising only from the households
that replaced traditional biomass stoves with biomass ICS.

2.2.3 HOUSEHOLDS CHARACTERISTICS, FUEL CONSUMPTION AND KITCHEN REGIMES
The goal of the Kitchen Survey (KS) is to make a qualitative assessment of the fuel types, fuel mix
and kitchen regime: factors that affect fuel consumption and GHG emissions through the year. No data
on theses aspects were available through the local NGO or the local government representative (mandal
office). The KS main goal is to indicate if it is necessary to divide the targeted population by the project
activity into clusters which present significant differences in terms of fuel consumption and GHG
emissions. In order to justify the need of making cluster, a statistical analysis will have to be conducted.
The questionnaire template was elaborated in collaboration with GEO (see appendix II). It is important
to note that the ICS was under designing phase and yet to be finalised.
One other main goal of the questionnaire was to determinate the needs of the beneficiaries and
understand how the ICS will be adopted.
The questionnaire has been divided into 6 sections:
- Household information
This section’s purpose is to identify the responder and inhabitants of the household.
- Household characteristics
The purpose is to obtain information on annual income and belonging of the households.
- House and kitchen characteristics;
It permits to get a better understanding on the cooking place and the evacuation of the smoke.
- Fuel use and acquisition
This section aims to identify the fuels used, the consumption through the year and the methods of
acquisitions (purchased or harvested) of fuels. This section helps also to determinate factors useful for
the NRB rate calculation like the fuel collection area and the total extraction of biomass by the
households (cf. Part 2.2.3);
- Cook stove(s)
The objective is to characterise stoves used for cooking, their purposes and the willingness of the
households to adopt an ICS;

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- Food cooking and processing
This section aims a better understanding on cooking practices and emissions of smoke.
The questionnaire was finalized after the first 16 surveys in order to become more appropriate and
understandable by the responders.
According to the methodology Gold Standard on ICS, the KS has to be carried out in each cluster of
customers following those guidelines:
- Cluster size<300 households: minimum sample size 30
- Cluster size of 300 to 1000 households: minimum sample size 10% of the cluster size
- Cluster size>1000 households: minimum sample size 100
The 100 households targeted by this part of the project are the one that are using traditional biomass
stoves as the main cook stove. Since no cluster has been defined previously to the surveys because of the
lack of data on fuel use and kitchen regimes, as a first approach, we consider a single cluster. Therefore,
the minimum sample size retained is 30.
The KS was carried out in 48 random households located in Peddamaddur between the 29/07 and the
27/08/2009 in the local language. It was translated and documented in English. The surveyors were
Christine Langevin from Good Planet, Ajay and Sandeep from GEO who spoke telugu.

2.2.4 FUEL CONSUMPTION REDUCTION
The purpose of the Kitchen Tests is to make quantitative measurements of factors that affect the
quantity of GHG emissions. The methodology employed has been proposed by Rob Ballis: Kitchen
Performance Tests: KPT (2007b). It has been developed to demonstrate differences in consumption of fuels
between traditional cooking technologies and ICS technologies. Those tests consist basically to measure
the exact quantity of fuel consumed per day, household and standard adult for a period of 4 days
consecutives. The Standard adult equivalence factor in terms of sex and age, defined by Keith Openshaw
(FAO, 1983), correspond to:
Table 1: Standard adult equivalence factors in terms of age and sex
Fraction of standard adult
Child (0-14) 0,5
Female (>14) 0,8
Male (15-59) 1
Male (>59) 0,8

The methodology is proposing two different approaches to conduct those tests. The first one is a cross
sectional study which consists of carrying out tests in households that are using the traditional stove and
in other households that are using the ICS at the same time. The second, the paired sample study consists
of conducting the tests on the traditional stove and ICS in the same households for the same period at
different moments. The choice between those two has to be made according the local circumstances.
Since ICS weren’t implemented in households by the time the KPT started; the paired sample study was
selected.
In the framework of a paired sample study, the KPT methodology recommends to testers to try to detect
a reasonable fuel reduction of 30% (the ICS wasn’t design and, therefore, we couldn’t estimate its
efficiency). According to Rob Bailis (2007b), the sample size required to show a statistically significant
reduction in fuel consumption per capita (for an interval of confidence of 95%) is 14.
The KPT has been conducted during the rainy season between the 2/09-6/09 and the 21/09 and
24/09/2009 in households that use mainly traditional biomass stoves.
On September 2nd, the selection of the 14 households was made randomly with voluntary participants.
The participants who agreed to take part to the survey were given an ICS for a free testing period of
one month and also free wood shaving to be used during the second testing period.
The households surveyed used wood logs and branches in traditional biomass stoves during the first
stage and wood branches and shaving during the second stage of the test.
In order to determinate the fuel consumption, the KPT was conducted two times for 4 days between
6:00 to 11:00 am at the same time in each household. The households were asked to keep pre-weighted
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bundles of wood and wood shaving to be used for each 4 days period. Every day, the quantity and
moisture of the wood remaining were measured. Also, the surveyors asked the number of members
who ate since the past day and theirs ages (cf. appendix III). The equipment used was a scale (accuracy:
0.5kg) and a wood moisture meter that measure the moisture content on a dry basis (accuracy: 0.1 %).

Figure 3: Wood moisture meter (left) and scale (right) used for the KPT
To determine the moisture content (MC) of the woody biomass used, we used two methods proposed
by Rob Bailis (2007a):
- For the wood logs and branches, the wood moisture meter was used. As suggested by Rob
Bailis (2007a), three piece of wood were taken randomly from the bundle and the moisture
was measured at three places. The MC retained is an average of the 9 measurements.
This operation was done every day in each household.
- For the wood shaving, two samples of 200g were taken randomly every day in the households,
kept in hermetic bags, weighted then dried in oven at 100 °C and weighted again. The MC
retained is calculated with the equation 1.
MCwet(%)= [(Mass of fuel)wet-(Mass of fuel)dry] / (Mass of fuel)wet (equation 1)
Those data permits to calculate an average consumption of dry wood per day, per household and per
standard adult using a traditional biomass stove and an ICS and, therefore, an average reduction of fuel
wood per household and per standard adult.

2.2.5 ANALYSIS OF THE RENEWABILITY STATUS OF WOOD-FUELS
The NRB status is the extent to which the amount of wood harvested is not balanced by re-
growth in the collection area (FAO, 2007). In the good stove part of GSBC project, woody biomass is a
component of the baseline and the project scenario. Therefore, the presence of NRB is a condition
necessary for the suitability of the Gold Standard methodology and the extent to which CO2 emissions
of that biomass are not offset by re-growth in the collection area has to be specify and then, calculate
the reduction in NRB rate thanks to the project implementation.
In Methodology for Renewability Studies – The Case of Miao Villages in Danzhai District – Guizhou Province –
China (2008), Becuwe and Zhang identify two different approaches for calculating the NRB fraction:
- A global approach in which “the scale is the population (a village, a city, a district, a
province…). The biomass extractions may for instance include all types of biomass extractions
of the populations, such as wood fuel consumptions of many populations and timber
production. The NRB rate will be an average NRB rate for all the extractions and may be
applied for a single sub-extraction as long as this sub-extraction is a significant share”;
- A local approach in which “every single biomass extraction of an area is considered separately.
While the scale of the global approach is the population (villages, districts, etc), the scale of the
local approach is the single biomass extractor (a villager, a charcoal producer, a timber plant,
etc). A local NRB rate is then calculated for each biomass extractor and the NRB rate for a
population (population of villagers, of professional wood fuel producers…) will be the average
on a representative sample of the NRB rates of this population”.

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FAO (2007) defines a significant share when:
- The share of wood fuel is greater than the natural production of wood of the forest;
- The share of wood fuel is the largest woody biomass consumption sector;
- The share of wood fuel is greater than 20% or 30% of the total woody biomass consumption.
Theoretically, the local approach is more accurate but demands to identify the biomass source
management system for each single biomass extractor which can lead to significant statistical errors. It
is also time consuming. As Becuwe and Zhang show in the review, “the local approach fits better to
professional such as timber producers or charcoal producers, rather than to villagers who extract
biomass randomly in their surrounding forest”. Therefore, in the case of the GSBC project, since it is
the villagers who extract randomly biomass, a global approach will be preferred to the local approach.
In the global approach, the NRB rate (XNRB) is then given through the comparison between the global
sustainable yield of the biomass source (A×Y, where A is the size of the biomass source and Y the
biomass produced annually/unit of surface) and the global dry biomass extraction per year (C)
(Becuwe and Zhang, 2008).

- If C < A × Y, the extraction is totally renewable, then:
XNRB = 0 (equation 2)
- If C > A × Y, the extraction is partly non-renewable. It can be considered as a cause of local
deforestation, then:
XNRB = (C – A × Y) / C (equation 3)

Fuel Collection Area (A)
The Fuel Collection Area or Reachable Collection Area is the geographic area where the
targeted population by the project is extracting wood fuel (The Gold Standard, 2008). It can be forest,
woodland, grassland and/or cropland.
The GSBC project is taking place, the first year, in the village of Peddamadur. The State or the district
cannot be chosen as the Fuel Collection Area because areas where wood fuel can be extracted are too
heterogeneous as well as the accessibility to these places (cf. figure 4).

Moist deciduous forest
Tropical dry forest

Plantation
Scrub
Agriculture area
Barren land
Water
Orchard
Settlement
Source: Reddy,2008

Figure 4: Vegetation and land use map of Warangal district (Andhra Pradesh)
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Moreover, a reforestation program of 354 600 hectares is taking place in Andhra Pradesh (Rao K., no
date) which has to be taken into account in the NRB rate calculation. Therefore, the NRB that can be
defined considering the state or district as the fuel collection area will not be representative.
However, according to the mandal officer of Devarruppula Mandal, the reforestation project does not
concern the mandal area.
Today, there is no data available on the harvesting methods employed by the population. Therefore,
the methods proposed by Becume and Zhang (2008) to assess them are the use of GIS (Geographic
Information System), a field study (to provide accurate data on the size of the area) or a specific survey
among the population. In the case of Peddamadur,mthere is no forest (cf. Appendix I) and the
population harvest at random places wherever they find woody biomass. Therefore, the method
selected to define the fuel collection area is a specific survey among the population. Some questions on
the localization of the fuel collection area and harvesting methods have been added to the
questionnaire for Kitchen Surveys (cf. Appendix II). Also, the others sub-flows of woody biomass within
the fuel collection area defined will have to be identified and, if they cannot be ignored in the NRB rate
calculation (if they represent a significant share), they may change the fuel collection area.
According to the meeting with the representatives of the farmers and the WORDS organization, there
are three types of biomass extraction in the area: extraction by households mainly for cooking, by the
local furniture factory and by charcoal producers. Therefore, a specific survey will have to be
conducted to define the localization of the wood collection area and the global extraction per year.

Growth of woody biomass (Y)
The growth of woody biomass in the fuel collection area has to be determined preferentially
from the data and studies available. Whenever such data aren’t available, the methodology approves
the use of the Intergovernmental Panel on Climate Change (IPCC) indicators on above-ground net
biomass growth (tons/year/hectare) (IPCC, 2006a). Those indicators have been determined only for
forests. If we assimilate the fuel collection area to a natural forest under the same conditions climatic
and geographic, the calculation of the NRB rate will be conservative (that means underestimated
compare to the actual situation) and be acceptable.

Global dry biomass extraction per year (C)
To start, an assessment of the data available on the global biomass extraction per year has to
be made. According to Ingmar J. (2007), in order to calculate the NRB rate, we can consider all the
extractions or a single sub-extraction as long as this sub-extraction represents a significant share.
Depending on the flows selected for the calculation of the NRB rate, we will need data on the amount
of woody biomass consumed per household and per year, the number of households and the amount
of woody biomass extracted by the others sub-flows selected.
Since only the data on the number of households were available at the local government office, the
average consumption by households will be obtained through the KS (cf. Appendix II) and for other
activities, by independents surveys among the population.
According to the guide for wood fuel survey, made by the FAO that aims to “propose a uniform
methodological basis to obtain data for user, producer and supplier of wood fuels” (FAO, 2002), if we
consider that there are 900 to 1000 households using traditional biomass, a sample of 42 households
would be sufficient to estimate the total consumption of woody biomass within a confidence interval
of 95% and a standard error of 15% (assuming a coefficient of variation of 50%).

The NRB fraction has to be estimated for both baseline and project scenarios.

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2.3 ANALYSIS OF THE DATA
All the data collected from the Kitchen Survey and Kitchen Tests have been entered and analysed
on Microsoft Excel 2007.

2.3.1 KITCHEN SURVEYS
Data obtained from the KS must give a better understanding on the community targeted; their
willingness to adopt an ICS and reveal factors that could affect the fuel consumption between groups
inside the population.
A first step of the analysis will be to determinate if the sample taken is representative of the population
of Peddamadur. The only data available on Peddamadur population nearby local agencies were the
average income. Relatively to the variable “income”, a first statistical analysis will to be done using a
Pearson's chi-square test (cf. Equation 4)

X2= ∑ [(ni-npi)2/npi] < t5%,df (equation 4)
Where,
X2= value calculated of the test;
ni= absolute frequency observed in the sample;
npi= absolute frequency theoretical of the sample;
t5%,df= value of the X2 table given for a given degree of freedom with a confidence interval of 95%

The second step of the analysis is to identify factors that can influence the consumption of fuel and to
distinguish within the population clusters that would show significant differences in emissions
reductions performances.
The survey gives three types of variables: categorical with a ticked box reply, categorical with an open
reply and numerical data. As suggested by the methodology proposed by the guide for wood fuel
survey (FAO, 2002), the absolute and relative frequency of the answers were extracted from the
categorical variables and, for the numerical data, were extracted the mean, standard deviation and
standard error. This step permits revealing factors that may affect fuel consumption and differences
within the population concerning fuel mix and kitchen regimes.
Once those differences identified, in order to see if a particular factor generates a significant difference
in fuel consumption, a statistical analysis will be required. A bilateral student t-test made on two
normally distributed and independent sub-samples that will compare the means of the wood
consumption per capita and per day will be used to determinate if the difference is significant or not. If
we consider two sub-sample A and B and the hypothesis H0: A- B = 0 (the difference in wood
consumption between the two groups is not significant), then, H0 is accepted at an interval of
confidence of 95% if the equation 5 is verified:

t=
µA -µB
[ ]
∈ − t 0 ,95; df ; t 0 ,95;df (equation 5)
(∑ X A ) 2
(∑ X B ) 2
− + ∑ XB −
2 2
∑X A
nA nB 1 1
×( + )
n A + nB − 2 nB n A

where:
t = t-score of the test;
µ = mean;
n = size of the sample;
X = value of the observation;
t0,95;df = critical value of the test given by the Student t-test table.

If the number of sub-sample is superior to two, an ANOVA (Analysis of variances) will be required.

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The data issued from the KS questionnaire must also permits to estimate the NRB rate with the
determination of the fuel collection area and the average consumption of woody biomass by the
households through the year (considering the seasonal variations). We note that an average
consumption of woody biomass can also be estimate thanks to the KPT results.

2.3.2 KITCHEN PERFORMANCE TESTS
The goal of those tests is to estimate from data measured directly within the households of the
beneficiaries the reduction of dry woody biomass consumption through the year thanks to the project
activity.
In the framework of a paired sample study, the KPT tests were conducted twice in the same 14
households using a traditional biomass stove during the first stage and a Magh CM during the second.
Unfortunately, during the second stage, 6 households forsook the testing. Reasons were that they were
either not interested anymore (for 3 of them) or didn’t respect the guidelines of the test. Therefore,
the results for the first testing period concern 14 families and 8 families for the second. To compare
the fuel consumed using the traditional and improved cook stove, two results will be evaluated: the
difference of the average consumption/day/standard adult of dry wood fuel considering all the
participants and considering only participants to both testing stage.
To calculate the emissions from the baseline and project scenarios, the average consumption of woody
biomass per day will be extrapolated to a year period taking into consideration the possible variations
of consumption due to seasons.

2.3.3 EMISSIONS REDUCTION CALCULATION
The methodology Gold Standard is proposing two different approaches to calculate emissions
arising from the baseline and project scenarios. Both approaches are specific to each cluster (ER
arising from the project will be the sum of those arising from each cluster). The first approach is
considering all the fuel types used in the kitchen, while the second quantifies only one major fuel used
and attribute fractions of energy delivered by the other fuels (Gold Standard, 2008). In the case of the
Good Stoves part of the GSBC project, only woody biomass is considered in the baseline and project
scenario. Therefore, the two approaches are the same. The equations, parameters and source of the
data proposed by the methodology are proposed in the appendix IV and V.
Equations involve emissions factors (EF) that estimate the amount of a specific pollutant discharged into
the atmosphere by a specific source (process, fuel, equipment, etc). Those emissions factors can be
determined by a specific study. If no such study can be conducted, the IPCC guidelines provide default
EF in kg of GHG/ TJ on a Net Calorific Basis, country specific or more general that can be used.
There is no country specific EF for fuel wood and stationary combustion in residential area available
on the Emission Factor Database (IPCC, 2005). Therefore, if we consider that the net calorific value of
wood fuel is 15.6 TJ/Gg and a Global Warming Potential (GWP) of CH4 and NO2 are respectively
21tCO2/tCH4 and 310tCO2/tNO2 (IPCC, 2006b), the EF that will be used for the baseline and the
project scenarios are given by the table 2.
Table 2: Default emission factors for Solid biofuel wood and stationary combustion in residential area
Gases EF (tCO2e/twoodfuel)
CO2 1.75
CH4 0.098
NO2 0.019

The annual amount of ER is the difference between the emissions arising from the baseline and
project scenarios taking into account leakages that occurs as a consequence of the project activity's
implementation and increase the emissions of GHG outside of the project boundaries. The 6 different
forms of leakages that have to be estimated and justify while calculating the emission reduction are
given by the Gold Standard methodology (cf. Appendix VI).

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3. RESULTS
3.1 HOUSEHOLDS CHARACTERISTICS, FUEL CONSUMPTION
AND KITCHEN REGIMES

3.1.1 REPRESENTATIVENESS OF THE DATA COLLECTED
The KS were conducted in 48 households of Peddamadur. To determinate if the sample taken
is representative of the population; the only data available and comparable on the project area were the
annual income of the households. The table 3 shows a comparison between data issued from the
census 2001 obtained at the local government office.

Table 3: Comparison between the data from the census 2001 and the KS on the variable “income”
Origin frequency very poor poor families middle class Above TOTAL
data families families

below 10 000Rs/y below 20 000Rs/y below 50 000Rs/y above 50000 Rs/y

Survey Absolute 6 27 12 2 47

Relative 12.8 57.4 25.5 4.3 100

Census Absolute 41 532 307 28 908

Relative 4.5 58.6 33.8 3.1 100

To appreciate the representativeness of the sample, we use a Pearson's chi-square test. As a first
hypothesis (H0), we consider that the sample is representative considering the variable “income class”.
Table 4: Observed and theriocal absolute frequency within each class of wealth
very poor poor families middle class Above
families families

ni 6 27 12 2

npi 2.1 27.5 15.9 1.5

In order for the test to be acceptable, the theoretical absolute frequency observed in each group has to
be superior to 5. The 4 classes have to be assembled into two groups: “poor families” (below 20 000
Rs/year and “middle class families” (above 20 000 Rs/year). Then, we have X2 = 1.02 and t5%,1 =
3.841. The hypothesis is acceptable until further notice at an interval of confidence of 5%.

3.1.2 HOUSEHOLDS AND KITCHEN CHARACTERISTICS
Most of people surveyed were women (43 out of 48 responders) aged between 18 and 80 years
old (in average, 40 years old) who always cook meals. The little number men surveyed did say that they
use sometimes the stove to cook. Also, 64.6% of the responders were illiterate.

The households interrogated were composed in average of 4.25 people with 1 women and 1 child
(equivalent to 3.3 standard adult). This result is in accordance with the census of 2001 where the
households are composed, in average, of 4.3 persons.
The main activity in Peddamadur is farming, where 75% of the people interviewed possessed their own
land of, in average, 2.8 acres. The common assets are, for 24% of the beneficiaries, poultry, 41%
livestock and 70% have a vehicle (usually a bicycle and sometimes an oxcart or a bike).

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The families cook usually in a kitchen located outside the house in an open space (the stove is not
protected by a roof) for 44 % or in a semi-open space for 50% where the stove is usually against a wall
of the house, under a roof.
Only two households have a special room dedicated to the kitchen (for one, it was outside the house
and, for the other, inside with a partial separation wall) and one had a kitchen in the bedroom with a
chimney.
The main stoves used to cook are unimproved biomass stove for 100% of the families interrogated. All
the families paid for it themselves (with no governmental help) for, in average 40 Rs (equivalent to 0.9$
USD). Pictures, in figure 5, show some examples of those stoves (were taken during the survey):

Figure 5: Traditional stoves used in Peddamadur

12.5 % had a 3 stones stove and 77.5% have the traditional fix model made in clay. The purposes of
the stoves are cooking family meals and heating/boiling water. None of them use it to cook for
commercial purposed but those who possess livestock sometimes use it to cook animal feeds (13
households out of 19). The main food they cook is rice and dal (Indian dish made by simmering in
water spices, herbs, oil, onions…). However, 77% of them said that they sometimes cook roti. This
type of food which is prepared like pancakes takes more time to cook.
Since most of them cook in semi-open or open space, when it’s raining (around 15days/y according to
them), 41 % use another stove. Sometimes, they just put bricks inside; use a temporary unimproved
stove or a kerosene/LPG stove. Around 12% of the households interrogated have kerosene stove and
15% LPG stove, but as they all said, they use it rarely, only for emergency and when it is raining.

3.1.3 HOUSEHOLDS FUEL CONSUMPTION
The type of biomass used in the traditional stoves is woody biomass (branches and logs).
According to the survey, none of them use crop residues which are burned on fields after the harvest.
Concerning the type of wood used, it was difficult for them to tell exact types since it is a mix of
whatever they can find and sometimes, they did not know what it was. However, some names came up
often: Sarkar, Tuma, Kampa, Neem and modugu. Sarkar, Tuma and Kampa stand for prosopis juliflora
(Swartz) DC., Neem for Melia azadirachta, Linn. (meliaceae) and Modugu Butea frondosa, Roxb.
(leguminosae).

To estimate the woody biomass consumption by households through the year, the interviewer asked
the amount in kilograms consumed per day according to the season. Each results have been multiply
by 30 days then by 4 (number of months in each season) then divided by 365 and by the number of
standard adults living in the house. The table 5 shows the woody biomass consumed in kilograms per
day and per household or per standard adult. After removing one aberrant value, most probably due to
the difficulty encountered by the responder for estimating the total amount of biomass consumed per
day, the size of the sample came down to 46.
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Table 5: Average consumption of woody biomass per day, per household and standard adult
Size of the Average ( -kg) Standard Standard error
sample (n) deviation (σ)

Consumption of woody 45 4.64 ± 1.75 ± 0.26
biomass per day and per
household

Consumption of woody 45 1.44 ± 0.52 ± 0.08
biomass per day and per
standard adult

As noticed previously, the consumption of woody biomass is changing accordingly to the seasons. The
main reason for the difference of consumption between rainy/winter seasons and the summer is that
families are boiling water for taking bath. The table 5 shows the relative and absolute differences in
biomass consumption per day and per standard adult between the rainy season (when the KS were
conducted) and the summer/winter seasons.
Table 6: Difference in biomass consumption/day/standard adult in winter and summer compared to the rainy
season
Absolute Relative
difference (kg) difference (%)

Winter 1.07 - 2.3

Summer 9.93 -20.9

The difference between the consumption of biomass between the winter and rainy seasons can be
ignored, but difference between summer and rainy seasons is significant and cannot be ignored in
baseline and project emissions calculation.
Considering that, in Peddamadur, there are, at least, 892 households composed, in average, of 3.3
standard adult, using mainly traditional biomass stoves, therefore, the total wet woody biomass
consumption by the population through the year is estimated to 1 419t/yr.

3.1.4 PATTERNS OF EMISSIONS REDUCTION PERFORMANCES
It is important to remind that the beneficiaries should be divided into clusters that reflect
differences in patterns of ER performances. Therefore, between the clusters, we should be able to
identify significant differences in average fuel consumption. The variables selected to suggest clustering
options concern fuel types, fuel mix, kitchen regimes employed.
Firstly, we have to look at the type of traditional used by the family which might have different
efficiencies and, therefore, the average wood consumption could be different. During the survey, we
encounter two types of traditional biomass stoves: a 3 stones stove (for 12.5% of the households
surveyed) and a traditional fix model made in clay (for 77.5% of them). The table 7 shows the
difference in the average wood fuel consumption/day/standard adult according to the stove’s type.

Table 7: Difference in the wood-fuel consumption/day/stadult according to the type of traditional biomass stove
used
Size of the sample Average ( -kg) Standard Standard error
(n) deviation (σ)

3 stones stove (A) 6 1.41 ± 0.65 ± 0.27

Clay stove (B) 40 1.44 ± 0.51 ± 0.08

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According to the Fisher-Snedecor test, the variances are homogenous:

σ A2
= 1.62 < F0,95 (6;40) = 2.32
σ B2

Therefore a bilateral Student t-test (comparison of the means) can be conducted. We consider the
hypothesis H0: A- B = 0. The Student t-test table gives a critical value of t(0,95;44) equal to 2.0154 and,
according to the calculation, t is equal to 0.18. Since -t(0,95;44) < t < t(0,95;44), the hypothesis H0 is accepted
within an confidence level of 95 %. There is no need to divide the beneficiaries into clusters according
to the type of traditional biomass stove used.

Concerning the fuel types and mix used in the kitchen, the main one is woody biomass. Although,
some families use occasionally LPJ or kerosene stove. Those practices can affect the wood fuel
consumption through the year and may be seen as a clustering option. The table 8 shows the
difference in the average wood fuel consumption/day/standard adult according to that option.

Table 8: Difference in the wood-fuel consumption/day/stadult according to the fuel mix used
(n) deviation (σ)

Use only traditional 33 1.47 ± 0.58 ± 0.10
biomass stove (A)

Use occasionally a 13 1.38 ± 0.38 ± 0.11
kerosene/LPJ stove(B)


σ A2
= 2.26 < F0,95 (33;13) = 2.38
σ B2

We consider the hypothesis H0: A- B = 0. According to the calculation, t is equal to 0.53. Since -t(0,95;44)
< t < t(0,95;44), the hypothesis H0 is accepted within an confidence level of 95 %. There is no need to
divide the beneficiaries into two clusters according to the variable “use occasionally a kerosene/LPJ
stove or not”.
Concerning the kitchen regimes, variables that can be interesting to look at are:
- The purposes of the main stove used (only for cooking family meals, commercial purposes,
heating/boiling water, cooking for animals feed);
- The type of food they use to cook (since some food can take more time to cook).
According to the survey, all the households interrogated use their traditional stove to cook for family
meals, heating, boiling water and none of them for commercial purposes. Moreover, some family who
possess livestock use it to prepare animals feed. The table 9 shows the difference in wood fuel
consumption/day/standard adult between the families who prepare animal feed and those who don’t.
Table 9: Difference in the wood-fuel consumption/day/stadult between the families who prepare animals feed
and those who don’t
(n) deviation (σ)

Cook for family 32 1.42 ± 0.52 ± 0.09
only (A)

Cook for family and 14 1.50 ± 0.54 ± 0.14
livestock (B)

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σ B2
= 1.06 < F0,95 (14;32) = 2.01
σ A2

We consider the hypothesis H0: A- B = 0. According to the calculation, t is equal to -0.51. Since -
t(0,95;44) < t < t(0,95;44), the hypothesis H0 is accepted within an confidence level of 95 %. There is no need
to divide the beneficiaries into two clusters according to the variable « prepare animals feed or not ».
For all the families surveyed, the main food is rice and dal. However, 77% of them cook rotis which
take more time to cook. The table 10 shows the difference in wood fuel consumption/day/standard
adult in between the families who prepare rotis and those who don’t.

Table 10: Difference in the wood-fuel consumption/day/stadult between the families who prepare rotis and
those who don’t
(n) deviation (σ)

Cook only rice and 10 1.47 ± 0.58 ± 0.18
dal (A)

Cook rotis (B) 36 1.43 ± 0.51 ± 0.09

The hypothesis that the families who cook rotis use more woody biomass can be already rejected since
we can see a difference of fuel consumption/day/standard adult of -2.5% compare to those who are
not preparing rotis. However, since the variances are homogenous, we can still conduct the Student t-
test.
σ A2
= 1.27 < F0,95 (10;36) = 2.11
σ B2

We consider the hypothesis H0: A- B = 0. According to the calculation, t is equal to -0.19. Since -
t(0,95;44) < t < t(0,95;44), the hypothesis H0 is accepted within an confidence level of 95 %. There is no need
to divide the beneficiaries into two clusters according to the variable « prepare rotis or not ».
Others analysis that will cross the relevant variables cited before could be conducted, however the size
ni of the i samples will be to small and the tests will be distorted.
In conclusion, we can say that there are no significant differences in fuel consumption within
traditional biomass stoves users in Peddamadur and, therefore, in patterns of emissions reduction
performances among the beneficiaries of the ICS activity. There will be only one cluster: households of
Peddamadur switching from a traditional biomass stoves to a Magh CM.

3.1.5 HOUSEHOLDS SATISFACTION AND WILLINGNESS TO ADOPT A MAGH CM ICS
The table 11 shows the satisfaction and the willingness of the 48 households interrogated to improve
their traditional biomass stove.
Table 11: Households satisfaction and willingness to improve their traditional biomass stove
Satisfaction (% of Willingness to improve
households) (% of households)

Yes 39.6 95.8

No 47.9 0

Indifferent/Don’t know 12.5 4.1

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The results shows that the great majority (96%) of responders are willing to improve their traditional
biomass stove even if 40 % of them said that they were satisfied with it. We will note that 92% of the
responders said that they are always choking and feel their eyes burning while cooking (against 8% for
whom it’s only sometimes); 73% realized that it could be harmful for health and 68 % of the
responders were ready to change the fuel they used if there was less smoke while cooking. Also, 75%
would like to reduce the time spent cooking, while the others don’t want to or don’t care. Therefore,
we can already say that the community will welcome the implementation of an ICS program.
Moreover, we asked what price they will be ready to pay in order to improve their traditional stove (cf.
figure 6).
40
30
% of households
20
10
0
0 <100 100-200 200-500 >500
Price (Rs)

Figure 6: Price range that the families are ready to accept to improve their cooking stove

More than 45% of the households are ready to pay more than 200 Rs (equivalent to 4.2 $ USD) to
improve their traditional stove. One of the main objective of GEO was to design an ICS for a very low
cost (less than 8$ USD which correspond to around 369 Rs). The KS has been conducted before the
demonstrations of the Magh CM to the communities. During those demonstrations, the participants
(around 100-150 people) said that they will be ready to pay more to get the stove.

3.2 FUEL CONSUMPTION REDUCTION
The KPT has been conducted in order to calculate the consumption of woody biomass per
households using their traditional stove and the ICS, and then to deduce the efficiency of the ICS
compare to the traditional stove in houses of beneficiaries. The table 12 shows the average wood
consumption per day calculated from the field survey with the traditional stove.
Table 12: Average fuel wood consumption before intervention calculated from the KPT
Household daily Average wood Household daily Adult
consumption of moisture (% wet consumption of equivalent daily
fuelwood (wet basis) fuelwood (dry consumption of
weight-kg) weight-kg) fuelwood (dry
weight-kg/cap)

Average 2.60 16 2.18 0.7
All
St deviation ± 0.90 ±2 ± 0.76 ± 0.2
participants
CV (%) ± 34.45 ± 12 ± 34.99 ± 23.1

Participants Average 2.76 16 2.31 0.7
to both
St deviation ± 0.90 ±2 ± 0.76 ± 0.1
testing
stages CV (%) ± 32.73 ± 11 ± 32.95 ± 20.9

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Considering that in Peddamadur, there are, at least, 892 households of, in average, 3.3 standard adults
using mainly traditional biomass stoves and that in summer, they are using 21% less woody biomass
(cf. Part 3.2.1), therefore, the total dry woody biomass consumption by the population through one
year is estimated to 689t/yr.
Tables 13 and 14 give the average wood consumption per day calculated from the field survey with the
ICS and the reduction of wood consumed using the ICS in comparison to the traditional stove.
Table 13: Average fuel wood consumption after intervention calculated from the KPT
Household Average Household Average Household Adult
daily wood daily wood daily equivalent
consumption moisture (% consumption shaving consumption daily
of fuel wood wet basis) of fuel wood moisture of fuel wood consumption
(logs, (wood (% wet (dry weight- of fuel wood
branches-wet shaving-wet basis) kg) (dry weight-
weight-kg) weight-kg) kg/cap)

Average 14.63 0.54 15.33 2.19 0.58 0.6

St deviation ± 1.66 ± 0.51 ± 1.53 ± 0.61 ± 0.18 ± 0.2

CV (%) ± 11.32 ± 93.97 ± 9.96 ± 27.69 ± 31.15 ± 31.1

Table 14: Fuel wood consumption reduction obtained from the ICS calculated from the KPT
All participants Participants to both testing stages

Absolute Relative Absolute Relative
difference (kg) difference (%) difference (kg) difference (%)

Wood consumption /cap -0.1 -14.0 -0.1 -15.2

In conclusion, we can say that the ICS activity permits to the population to save up 15% of wood used
for cooking. Considering that ICS would be introduced in 100 households by the end of the first year
of the project, the average wood consumption of the population is estimated to 670t/yr.
We remind that the Magh CM can be used in two ways, with the powered fan or not. In Peddamadur,
there is courant only during one half the days, the morning or the afternoon (change every week).
During the week when the KPT was conducted, there has been courant only the afternoon. In general,
the families cook longer the morning (for breakfast and lunch) than at night. Also, the second stage of
the KPT was conducted just after the installation of ICS. Many families were did not get used enough
to the new stove and damaged the fan or just refused to use it. Therefore, only two families used the
ICS with the fan on for 30 minutes every night on a total time spent cooking of 2 hours/day.
Moreover, two families used their traditional stove to heat water for taking shower. The wood used in
the traditional stove was taken into account in the calculation. In conclusion, we can say that the
estimation of wood reduction consumption is under estimate but stays conservative.

3.3 ANALYSIS OF THE RENEWABILITY STATUS OF WOOD
FUELS
There are three types of biomass extraction in the village of Peddamadur: the extraction by
households, by the local furniture factory and by charcoal producers.

3.3.1 FUEL COLLECTION AREA
The fuel collection area has to be determined from local population’s statements. According to
the survey, the households are using only wood logs and branches in their traditional biomass stoves
that they buy and/or harvest, we have:
Christine LANGEVIN

25

Table 15: Distribution of the households surveyed according to their woody biomass supply practices
Frequency Households that Households that only Households that only
harvest and purchase harvest wood purchase wood
wood

Absolute 14 24 10

Relative (%) 29.2 50.0 20.8

As a first approach (qualitative), according to the 38 households that harvest woody biomass, for 52 %
of them, the distance to their collection area increased since the past 5 year. It proves the presence of
non-renewable biomass in the area.
The woody biomass is harvested in a random way: in the border of the roads, near the lakes, in the
fallows or anywhere else where it is present. There are two methods of harvesting wood. Some
dedicated full days to go harvest an important amount of wood during the summer (1 person or in
groups) and use an oxcart or a truck (renting price: 311 Rs/y in average). In average, they spend 5.7
days harvesting wood around 1.7 km away from the habitations (A).
Others harvest wood every week for 1-2h (1 to 4 times/week) by foot or bicycle at around 1.9 km
away from habitations. They can bring back only little amount. If we consider that a working day is
made of 8 hours, the average of time spend to harvest is 25 days (B).
According to the survey, the population harvests only within the village administrative boundaries. As
a first approach, the fuel collection area chosen could be the village.
The table 16 shows the distance from the habitation covered by the population to harvest wood.
Table 16: Average distance covered by the population in order to harvest wood according to their biomass supply
practices
Size of the Average (km) Standard Standard Maximum Minimum
sample deviation error distance distance
(km) (km)

A+B 38 1.70 ± 0.87 ± 0.14 3 0.5

A 17 1.74 ± 0.97 ± 0.23 3 0.5

B 21 1.67 ± 0.91 ± 0.20 3 0.5

If we look at a scaled map of Peddamadur (not available in this thesis because of the restrictive sharing
rights) and draw a circle of 3 km of radius with habitations as the centre, the circle is located within the
village boundaries except the north part. According to the answers given, nobody seems to go harvest
in the north. Also, 29.2 % of the households surveyed purchase wood. They either pay local people to
go harvest for them or buy it at a small furniture factory in the village. According to the discussion
with the owner of the factory, they employ the same local people than the population to harvest wood.
The distance covered by them to harvest was varying and difficult to estimate. However, he could
certify that they were harvesting only within the village boundaries, as well as for charcoal makers.
The appendix I shows the land utilization in Peddamadur (data from 2008-09 obtained from an
interview with the mandal officer). The table shows that 56.8% of the total area is under current
agricultural use, sown with crops. The main crops cultivated in Peddamadur are cotton, paddy,
sesamum and redgram. Since there is no woody biomass present on the fields currently in use, we can
exclude them from the fuel collection area. Therefore, the fuel collection area chosen corresponds to
the village of Peddamadur without the net area sown and is restraint to 938 hectares.

3.3.2 GROWTH OF WOODY BIOMASS
Data on growth of woody biomass in the fuel collection area wasn’t available. Therefore, the
fuel collection area was assimilated to a natural forest under the same conditions climatic and
geographic. The Patersons index, based on rainfall, soil condition and length of growth period, estimates
Christine LANGEVIN

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