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Zongwei Luo
University of Hong Kong, China
Advanced Analytics for
Green and Sustainable
Economic Development:
Supply Chain Models and
Financial Technologies
Advanced analytics for green and sustainable economic development: supply chain models and financial technologies /
Zongwei Luo, editor.
p. cm.
Includes bibliographical references and index.
ISBN 978-1-61350-156-6 (hbk.) -- ISBN 978-1-61350-157-3 (ebook) -- ISBN 978-1-61350-158-0 (print & perpetual
access) 1. Sustainable development--Environmental aspects. 2. Sustainable development--Finance. 3. Economic develop-
ment--Environmental aspects. 4. Industries--Environmental aspects. I. Luo, Zongwei, 1971- II. Title.
HC79.E5A34 2012
338.9’27--dc23
2011027885
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Chapter 6
DOI: 10.4018/978-1-61350-156-6.ch006
Raghu Bir Bista
Tribhuvan University, Nepal
Low Carbon Economy and
Developing Countries:
A Case of Neplese Forest
ABSTRACT
In forest, reduction of emission from deforestation and forest degradation (REDD) is considered as low
carbon instrument. Financial Incentive scheme of this new climate change mitigation approach generates
query about REDD’s economic implication in developing country. This study is to examine empirically
low carbon potential from avoided deforestation in Nepal. The case study is the Kae community forest
of Nepal. We used 10 meter radius circle sample plot for carbon inventory data collection. In addition,
we conducted household survey through 48 households for data set collection.
This study nds that community forest contributes 45 percent livelihood income (re wood, leaf litter,
grass, water) to the forest dependent stakeholder’s total income. This labor incentive based on labor
contribution in forest management is distributed among the member households. This study further nds
huge carbon income potentials. Annually, KCF can earn carbon income Rs. 39, 81,196, if KCF enters
in REDD. It is 41 times higher than the present mean income Rs 24, 549.55 from the forest product sale.
In mixed familiarity about REDD, the study nds only 44 percent households expecting that REDD will
be a better livelihood alternative to the poor. 63 percent responds need and use of carbon income for
livelihood objectives. From estimation, household stakeholders who have good asset holdings (land
and Rlivestock) think that REDD will be not a better livelihood alternative to the poor. However, the
household stakeholders who have literacy, different food sufciency level, land holding (1>), different
earning per day, Rsex, per day earning and age think that REDD will be a better alternative. Thus, the
poor households expects livelihood role from REDD in Nepal. Therefore, REDD should be more ben-
ecial to the poor household stakeholders and their livelihoods.
80
Low Carbon Economy and Developing Countries
INTRODUCTION
Context
It is now well evident that climate change is a major
global threat. If climate change is not mitigated,
there will be a huge damage cost as GDP loss
of developing countries higher than developed
countries (Stern, 2006). Eliasch (2008) estimates
5-20 percent loss of the global GDP. Further, a
large size of the population in developing coun-
tries particularly in African and Asian countries
will suffer from mal nutrition, food deficit, water
scarcity, deaths and diseases in future.
The climate change depends on past and present
emissions of greenhouse gases (IPCC, 2007 and
Royal Haskoning, 2007). Uniformly increasing
GHG stocks are outcomes of population growth
and human activities such as industrial activities,
deforestation etc. Among these drivers, deforesta-
tion is major driver of GHG growth with 18-25
percent GHG emission in recent studies (Fry,
2008, Pagiola & Bosquet, 2009, Ramankutty et
al. 2007, Stern’s Review, 2007, and Sohngena &
Beach, 2006). Fry (2008) argues carbon dioxide
dominant (i.e. 70 percent) in GHG emission. This
context makes low carbon economy relevancy.
Basic idea of low carbon economy is less
carbon emission activities. Stern (2006), IPCC
(2007) and UNFCCC (2007) explain this idea as
climate change mitigation and prospective new
service economy. Developed countries (US, EU
etc) have implemented carbon emission output
compliance policy and carbon intensity input
substitution policy in polluted manufacturing in-
dustries. Clean Development Mechanism (CDM)
as supplementary has been implemented for carbon
emission compliance, carbon input substitution
and developing carbon market. In addition, there
are new economic activities such as development
of efficient technology, alternative energy (wind
energy, solar energy etc.) and service sector. UK
and EU have given top policy preference on wind
energy production as clean energy. In transport,
Norway has tested hydrogen energy public bus.
These countries have focused on service economy
such as education, it etc. There is a claim a huge
big market of efficient technology, alternative
energy and service industry. In addition, there is
a potentiality of carbon market in which carbon
emission reduction activities appear as a big
service trading in the world. The present carbon
market is more than $ 59 billion including CDM
carbon trade. The carbon market will extend after
the implementation of REDD in the post 2012. This
will change life style and consumption pattern if
low carbon economy appears effective. In some
developed countries, we can see it solar vehicle
transportation of household and solar energy cook-
ing stoves and household energy. Besides it, we
can find shifting into energy efficient household
electric appliances (Refrigerator, Television,
Electric heaters, Rice cookers and Bulb). Thus,
the low carbon economy is seen as lower carbon
intensity production and consumption behavior
and also as the market pattern for sustainable
economy and climate change mitigation.
Developing countries such as Asia, Africa and
South America have entertained that economy
as new prospective for economic development.
Simultaneously, these countries are curious about
its relevancy, situation and prospects. Carbon
intensity of consumption and production in these
countries are higher than developed countries. In
rural areas, still large rural population depends on
primitive energy means i.e. agricultural residual
and fuel wood. African and Asian countries con-
sume more than 70 percent fuel wood energy.
This energy dependency and consumption be-
havior leads to deforestation and then to carbon
emission. Stern (2006) considers deforestation as
major driver of carbon emission growth and then
climate change. In this context, REDD as climate
change mitigation is relevant to developing coun-
tries. Some developing countries (Brazil, Bolivia,
Indonesia etc) have already implemented it. Nepal
is in the readiness. This chapter addresses what
will its implication in Nepal.
81
Low Carbon Economy and Developing Countries
Broad objective of this chapter is to study
empirically low carbon potential in Nepal. Its
specific objectives are to estimate carbon emis-
sion reduction potential, to estimate household
expectation on low carbon activities in forest and
to suggest policy implication.
LOW CARBON ACTIVITIES IN NEPAL
REDD is to increase trees density and coverage
particularly for environmental service role of
forest. This carbon is a trans-boundary tradable
service having huge voluntary and non voluntary
markets. This role supplements carbon incentive
to the forest dependent population in developing
countries. The stakeholders of forest expect carbon
incentive and forest products (fuel wood, grass,
litter, fodder, timber etc.) for addressing their
livelihood issues and socio economic improve-
ment. Thus, forest can provide commodity plus
carbon service.
Studies claim giant market prospects of REDD
carbon relative to the carbon of CDM carbon.
Estimation of preliminary survey in developing
countries found huge potentials of REDD carbon.
Inclusion of this carbon may flood carbon in the
global carbon market. Economically, its contri-
bution will be significant in terms of volume,
scale and coverage. However, some literatures
contradict these arguments by saying issue of
opportunity cost. Studies argue a huge opportu-
nity cost of REDD. In addition, there are issues
of financial mechanism, organization, function
and technical sides such as leakages, reference
line and permanence. Despite some extent of
uncertainty, developing countries accept REDD
for meeting livelihood demand of the forest de-
pendent population.
In the context of REDD, Nepal initiated to
internalize it in the forest governance of com-
munity forest and leasehold by signing Readiness
Grant Agreement and committed internationally
its implementation. Now, Nepal has become 14th
RPN country. Nationally, institutional and policy
mechanism required for REDD preparation have
been developed, considering different livelihood
issues such as carbon benefit sharing, right of
livelihood, land right of indigenous people, women
and disadvantaged people, market modality etc.
Addressing these issues, REDD+ approach has
been discussed. Further, some literatures have
focused benefit sharing of the indigenous people,
women and disadvantaged people. Other litera-
tures emphasizes on their land right.
There is a question why Nepal desire to imple-
ment REDD. Nepal claims REDD just like as
community forest management and governance
to avoid deforestation, if environmental service is
included. There may be the inflow of huge carbon
incentive from community forest management in
accordance with national deforestation reference
line. The forest dependent including indigenous
people, women and disadvantageous people will be
livelihood beneficial from carbon incentive. Nepal
wants huge resources, knowledge and technical
support to maintain sustainable forest management
in long run because of growing forest dependent
population and livestock’s population. REDD
incentive and technical support can contribute
in this regard. In Himalaya regions, where both
human and livestock populations are on the rise,
the study concluded that payments for conserv-
ing the carbon in existing community forest are
an important incentive to prevent conversion of
forest for agriculture use offsetting the opportu-
nity costs associated with practicing SFM in the
face of other optimal (www.REDD-net.com). It
is supported by Nepal’s position documents in
which Nepal has focused on
• Enhanced forest protection using partici-
patory approaches
• Better zoning of protected areas
• Expansion of participatory forest arrange-
ment (including community forest and col-
laborative forest management)
• Additional resources for law enforcement
82
Low Carbon Economy and Developing Countries
(www.REDD-net.com)
In addition to carbon incentive, Nepal ex-
pects alternative income generating activities to
the stakeholders, alternative energy service and
social and physical infrastructure development
program and livelihood activities (REDD+) for
addressing livelihood and poverty issues of the
forest dependent population.
These expectations are theoretically argued
by International Lobby groups including CBOS,
INGO and GO. However, these theoretical argu-
ments do not have strong empirical evidence. If
we refer empirical studies in developing countries
like Brazil, Indonesia, Bolivia etc, we can find
some empirical studies using econometric mod-
els. Some studies have found positive impact of
avoided deforestation activities. For example:
Brazil, Indonesia and Bolivia. However, some
studies have found higher opportunity cost of long
term avoided deforestation. From weak empiri-
cal reference of Nepal, the implication of REDD
cannot be concretely concluded.
Regarding recent studies in Nepalese avoided
deforestation; there are two recent papers on
avoided deforestation and climate change. They
are Adhikari (2009) and Staddon (2009). Both
studies are review papers. They are different per-
spectives and areas although they are related to
forest and climate change issues. Adhikari (2009)
focuses on forest and climate change issues but
Staddon (2009) deals on carbon financing and
community forest. Adhikari (2009) argues multiple
outcomes of forest commons in any international
negotiations. The author emphasizes on the evalu-
ation of the contributions of forest commons not
only forest products but also ecological services
such as biodiversity conservation, climate change
mitigation and poverty alleviation. Staddon (2009)
argues carbon offsetting as a solution of three is-
sues: climate change, biodiversity conservation
and socio-economic development. The author
argues the issues of equity, control and power in
community forest under REDD.
This study differs with above these studies
from different respects. The analysis of avoided
deforestation is used at the unit level of districts
including some districts and types of tree level.
A data source of the study is different in terms
of area, coverage, size and method. The method
of analysis is also different including allometric
method and econometric method.
METHODOLOGY
Models
Carbon Accounting from Biomass
There are pre and post REDD initiation: defores-
tation and forestation. Change of biomass gives
carbon stock of the forest between pre and post
REDD. Pre REDD gives baseline carbon stock
and post REDD get the committed period carbon
stock. The difference between two carbon stocks
is REDD tradable carbon credit.
Carbon stock depends on tree biomass (dry
weight of above ground biomass), “Y” which is
determined diameter “X” and height of tree “h”.
It can be expressed into non-linear equation as
follows:
Y = a Xb (1)
Taking log in both sides
LnY= ln a + b ln X (2)
where, “a” and “b” are parameters.
This Equation is called allometric equation
developed by Satoo (1955) in the given dbh (at
1.3m). This equation applied by Ovington and
Madgwick, 1959; Nomoto, 1964; Ogino, Sab-
hasri and Shidei, 1964 in their studies on several
types of forests found reliable and applicable.
The expression of Equation (2) is modified by
Jenkin(2003) to calculate aboveground biomass
83
Low Carbon Economy and Developing Countries
(in kg) and Carbon content (in Kg) by using the
formula
Y= Exp (a + b In (X)) (1)
This study will use Jenkin’s model Equation
(1) to calculate Above Ground Biomass (Tree,
Litter and Shrub) and Below Ground Biomass and
its carbon stock. Based on carbon stock outcome,
carbon credit will be measured by time period
carbon stock change method and carbon benefits
of the carbon producers will be accounted by using
discounting method.
Binary Choices of Households
about REDD
Consider there are heterogeneous socio economic
characters (xi) of nth households in terms of income
level, awareness level, occupation, age, food suffi-
ciency, literacy and organization. These characters
are determinants of nth household’s responses on
dichotomous choices. Different preferences and
choices of the households influence in policy de-
cision making. Such type of issue can be trapped
by using Sequential Model (Greene, 2005; and
Maddala & Lahiri, 2009) for determining the
probability of REDD for alternative livelihood
for the poor in Nepal. Probit Regression Model
will be as follows:
Probit(Yi) = β Xi + ui (2)
where Yotherwise
if Y
= ∫ = >
0
1 0
*
Where, β= vector of regression coefficient
(0<β<1)
xi= vector of predictor variables (e.g. avoided
deforestation, livelihood, REDD etc)
ui= vector of Random variable(error term)
π= probability of an outcome
From probit model, we will get probability of
better alternative livelihood of REDD which will
be dependent variable. The relationship between
dependent variable and independent variables will
be captured by using multiple regression models.
P (better alternative livelihood of REDD=1)
= β0+β1 income+β2 landholding+β3education+β4
caste+β5 household size+β6 occupation+β7 area
+β8organization + ε
Where, β0= intercept ,β1,β2, β3,β4,β5, β6, β7,
β8=regressors, 0<β0,β1,β2, β3,β4,β5, β6, β7, β8<1
ε=error term
Data Sources
Carbon data set is based on secondary and car-
bon inventory method. The Kafle community
forest was selected for the carbon inventory on
the basis of the relevancy to REDD, history and
representative CF and easy accessibility. The
carbon inventory method was used in the sample
plot in the Kafle community forest for collecting
carbon data. The sample plot was made circle by
making a central point from that point there was
made area of 10 meter radius into four directions
east-west-south-north (see Figure 1). In the sample
plot, carbon inventory was used to measure dbh
of trees. In the carbon inventory, approximately
30 tree species were measured (see in appendix).
Average dbh in KCF was 13.56.
Household Data is based on household ques-
tionnaire survey conducted in Lamatar Village in
2010(April-May-June). Out of 63 households, the
48 sample households are selected randomly. It
Figure 1. Sample Plots of KCF
84
Low Carbon Economy and Developing Countries
represents approximately 70 percent of household
of KCF. The pre questionnaire test was con-
ducted. The questionnaire which was used in the
household survey is divided into three sections:
section 1: basic information about household
socio-economic, section 2: household’s participa-
tion and dependency in KCF and section 3: fa-
miliarity about REDD.
Characteristics of Study Area
Characteristics of Kafle
Community Forest (KCF)
Location and Geo-set up: Nepal is the study
country lying between China and India has three
ecological belts: himal, hill and terai. In these
ecological belts, there are divergent forest species
all over the country. Lalitpur District is District
Unit Study Area in which Kafle Commuity Forest
is the case study.
Lalitpur district locates in Kathmandu Valley.
The district has approximately 15,253 ha (FD,
2006). Out of total forest, there was 65 percent
(9,923ha) community forest managed by 162
CFUGs (FD, 2006). One of CF is Kafle Com-
munity forest.
The Kafle Community Forest, areas of 96 ha
managed by 63 households is located in Mathilo
Khoriya Dada in the east, Gumati khola in the
north, Chisapani Peepal Tree to way to Bhihawar
in South and main road to Khatri Bhajho in the
west. Altitude of KCF ranges from 1540 meter to
1970 meter. For forest management and utiliza-
tion, KCF was managed into five blocks such as
A, B, C, D, and E with area of 20, 31,27,6 and
10 hectares respectively. The forest is dominated
mixed type regenerated trees (DFO, 2002)
Characteristics of KCF
Institutional Characters: Collectivism concept
came out in the community level for collective
action for forest conservation, when Kafle forest
had over extraction and free riding under open ac-
cess and public regime in 1980’s. Its consequences
were scarcity of livelihood forest products (fire-
wood, leaf litter, grass, water resources etc). This
forest dependent community was suffering from
livelihood issues. In 1993, the community collec-
tively decided to set up Kafle community forest
user group (KCFUG) in accordance with Forest
Act 1993. In this common property right regime
(CPRR), the community became the owner of the
Kafle forest for conservation, management and
utilization. The institution functions democrati-
cally through General Assembly and Executive
Body. In General Assembly, all general members
of KCF are included to be members of this As-
sembly. Major work is to reach collective decision
on policy, budget and election of executive body
(KCFWP, 2007). Executive body is governing
body having 11 members from the General As-
sembly. It executes the decision of the General
Assembly. Its meeting is held per month. Major
work is to protect the forest, proper utilization of
forest products and other functional activities.
Households were homogeneity of upper caste
Brahmin in caste wise but were heterogeneity in
socio economic level and status, despite upper
caste Brahmin. There were majority households
having less than 12 months food sufficiency.
Kafle Community Forest is used for livelihood
objectives (KCF, 2007).
Self and Collective Governance: KCFUG
has the self governance system. Policy decision
and execution process is collectively done within
the institution for transparency and effective com-
munity participation. Its result is Operating Plan
prepared in 2005 and executed for five years. Col-
lective action is ruled into forest management, pro-
tection and patrolling from illegal extraction and
proper distribution of livelihood forest products.
In forest protection, there is prohibition of graz-
ing, poaching of wild animals and plants, illegal
cutting, mining and encroachment. Violation of
this prohibition will attract fines and punishments.
In distribution of NTFP, there is rule of extracted
85
Low Carbon Economy and Developing Countries
about 1000 kg of green fuel wood, 500 kg of dry
fuel wood, 500 kg of grass fodder, and 1000 kg
of leaf litter and 500 kg of nigalo every year.
On special occasions such a marriage, religious
ceremony or funeral, any member was allowed
to extract 350 kg of fuel wood for the same price.
It is only for 96 hectares of KCF.
Forest Management: Forest management
including cutting, cleaning, thinning, pruning
and plating is a part of collective action. The
KCF land was categorized into five blocks for
these activities in the support of NGO, CBO and
District Office of Forest. KCF using modern
scientific techniques of forest management had
established Demonstration Plot of 0.08625 hect-
ares in 2002 and extended to 1.64 hectares. In the
plot, there were planted with 787 seedlings and
46 plot size NTFPs such as Chialune, Jingaine,
Hinguwa, Angari, Bakle, Laligurans, Lakuri,
Saru, etc. KCF had further extended the size of
model plot by planting different medicinal and
other NTFPS. In addition, KCF has planned to
develop the whole Kafle Community Forest as
the Model Community Forest.
Household Characteristics
of Stakeholders
Household Resource Endowments: There
are two major resource endowments: land and
livestock presented in Table 1. Each Household
holds 0.2 hectare in average irrigated land and
0.17 hectare in average marginal land. Livestock
resource endowments are just conventional. It in-
dicates poor resource endowments of households.
HH size and Composition: The poor house-
holds have generally large family size. However,
family size (4.85) is less than national average
(5.4) (CBS, 2010). Further, the rich family has
less than the poor and medium income group.
Outlier is 9 family members size. So, labor en-
dowments may be less than of large family size.
Family composition by sex is similar (see Table
2).
Household economic condition: In accor-
dance with World Bank’s per day earning pov-
erty reference line, 67.38 percent households are
poor, despite higher literacy level. This is also
supplemented by food sufficiency measurement.
This absolute poverty needs alternative resources
for livelihood (see Tables 3 and 4).
Household Participation: Household’s par-
ticipation in forest protection is 85.3 percent,
followed by forest management at 84 percent,
development activities at 82 percent, resource
Table 1. Household resource endowments
Land Holding Mean Standard
deviation
Min Max
irrigated land 2.7 2.0 0.1 10.0
marginal land 2.3 1.6 0.1 8.0
Livestock
Cow/buffalo 1.57 0.5 1 2
Goat/sheep 2.73 1.5 1 6
Source: Field Survey, 2010
Table 2. Household composition and demography
HH Mean Standard
deviation
Min Max
HH Size 4.85 1.42 2 9
Male 2.48 0.88 1 6
Female 2.46 1.009 1 5
Education
Literate 4.45 1.54 1 9
Illiterate 1.04 0.21 1 2
Source: Field Survey, 2010
Table 3. Poverty scenario
Poverty Relative poor Absolute poor
Mean 5.06 14.17
Standard Error 0.419 1.31
Standard Deviation 1.6 4.18
Population 76 157
% 32.62 67.38
Source: Field Survey, 2010
86
Low Carbon Economy and Developing Countries
utilization at 76.6 percent, decision making 73.0
percent and training at 55.99. These measure
values indicate effective participation of house-
holds in terms of labor contribution and attendance
(see Table 5).
Household Livelihood Dependency: In Ne-
pal, community forest is perceived as alternative
livelihood local resources for the poor (Ninth
Plan, 1997). Each member annually extract in
average 16.4 bhari (656 kg) firewood, 4.4 (176
kg) bhari grass and 7.6 bhari (304 kg) leaf litter.
However, there are extreme extractions: 100 bhari
(4000kg) firewood followed by 40 bhari (1600kg)
grass and 50 bhari (2000kg) leaf litter(see its
details in Table 6 and 7). At nominal charges,
member can extract additional forest product.
Firewood extraction is higher than leaf litter, grass
etc. However, there is not required additional time
allocation for it. Members claim 70 percent less
energy expenditure from firewood.
Similarly, availability of water resources is
positive externality to the community. It is sup-
plied in all member households at free of cost.
KCF earns annually Rs 182,797.9 revenue
from sale of timber and NTFPs. Average share
KCF income is higher than average share income
from service and agriculture sectors (see its details
in Table 8). Thus, KCF is supporting livelihood
of households.
Table 4. Household socio economic condition
HH
categories
No of
HH
Average Average Food
Sufficiency
S i z e o f
HH
12
month
Less than
12 month
Economic
Poor 12 4.9 4 8
Medium 25 4.9 8 16
Rich 11 4.58 4 8
Education
Literate 45 24.35 15 29
Illiterate 3 0.5 3
Sex
Male 45 2.37 12 26
Female 3 2.45 3 6
Source: Field Survey, 2010
Table 5. Household participation in percentage
Participation Higher Medium Lower None
Decision Making 29.5 43.2 25 2.2
Development Activi-
ties
28.8 53.3 17.7
Forest management 27.2 56.8 15.9
Forest Protection 29.2 56.1 14.6
Resource Utilization 16.2 60.46 16.29 6.9
Training 15.9 40.09 34.09 9.09
Source: Field Survey, 2010
Table 6. Statistical descriptive summary of NTFP
extraction
Forest
Product
Minimum Maximum Mean Standard
Deviation
Firewood 0 100 16.4 18.0
Grass 0 40 4.4 5.6
Leaflitter 0 50 7.6 12.9
Source: Field Survey, 2010
Table 7. Annual income of sample households
from different sources (Rs)
Income
Source
Min Max Mean Sta Dev
Service 0 726000 179958.3 133483.1
Agriculture -1000 268800 41122.55 46675.5
CF 73000 328500 182797.9 52003.4
Total 72000 1323300 403878.8 232161.9
Source: Field Survey, 2010
Table 8. Statistical character of biomass and
carbon service
Indicators Min Max Mean Standard
Deviation
Biomass/ha 45.23 90.46 95.95 5.26
Cton/ha 52.22 104.44 47.91 2.67
Source: Field Survey, 2010
87
Low Carbon Economy and Developing Countries
ESTIMATION AND DISCUSSION
Estimation of REDD Potentials
UNFCCC, IPCC and Stern have projected huge
REDD potentials in developing countries. CIFOR
estimates US$17-33 billion per annum in future.
There is still room for query in developing country
like in Nepal about its potentials and implications.
In Nepal, World Bank and UNDP have been engag-
ing in preparation for REDD. In KCF, ICIMOD has
supported for three areas such as sample plotting
development and capacity development for carbon
inventory and carbon inventory for biomass and
carbon data. There were developed eight samples
plotting out of KCF. District Ranger Office had
given carbon inventory training to three members
of KCF in 2005. Since then, there were annual
carbon inventory for biomass and carbon data.
On the basis of these references, this study focus
on assessment of REDD potentials.
Biomass and Carbon Service
Biomass of Tree is storage of carbon. Growth
and size of Biomass are major determinants of
carbon sequestration and stock. Measurement of
Biomass is entry point of carbon accounting. There
are different methods in which carbon inventory
method is one of popular and well practiced. In
carbon inventory, dbh measurement is generally
used. In KCF biomass measurement, carbon inven-
tory method with dbh measurement was applied
in the selected sample plotting of approximately
1 hectare in which 10 meter radius was made for
selecting trees. Allometric method was used to
estimate biomass stock for estimation of carbon
stock. Biomass and carbon stock were found as
follows in Table 9.
Income Potentials of KCF
It is claimed that REDD entry depends on carbon
income potentials. Graph 2 reflects huge carbon
potentials over years, when Rs 12 per ton (i.e.
carbon per ton rate of CDM) is used. Income from
forest product is Rs 24549.55 shown by blue trend
line meanwhile income from carbon service is
Rs. 39, 81,196 shown by red trend line. If carbon
potential is included in KCF income, mean income
from KCF will increase at Rs. 1013711.33. The
income from carbon credit is 41 times more from
than income from NTFP.
Table 9. Model-1 (Familiarity about REDD)
Variable Mean Std. Dev Min Max
HH Size 5.42 2.07 3 9
Food Sufficiency
12> 0.35 0.52 0 2
9> 0.14 0.35 0 1
6> 0.33 0.47 0 1
3> 0.2 0.41 0 1
Land Holding
10> 0.1 0.3 0 1
5> 0.2 0.41 0 1
1> 0.62 0.48 0 1
0.041 0.2 0 1
Earning per day
1> 0.22 0.42 0 1
2> 0.52 0.5 0 1
>2 0.25 0.43 0 1
Rsex 0.79 0.41 0 1
Rlivestock 1.8 1.39 0 1
Literacy
>SLC 0.41 0.49 0 1
SLC> 0.22 0.42 0 1
Literacy 0.27 0.44 0 1
Per day earning 1.68 1.07 0.1 5.2
Age 54.68 90.32 18 66
Source of Infor-
mation
Seminar 0.29 0.45 0 1
Training 0.14 0.35 0 1
Newpaper 0.083 0.279 0 1
Source: Field Survey, 2010
88
Low Carbon Economy and Developing Countries
Estimation and Analysis of
Expectation about REDD
Binary Discrete Choice Questionnaire about
REDD was set up into two levels. They are level
1: familiarity about REDD and level 2: if yes, bet-
ter alternative of REDD to CF. The questionnaire
was surveyed 48 household stakeholders of Kafle
Community Forest. In the household survey, there
was a major concern on awareness level, opinion
and expectation of stakeholders about REDD.
These stakeholders’ character, capacity and de-
cision might show future direction of REDD in
Nepal at stakeholder level in community forest
management.
Descriptive Statistics of
Independent Variables for REDD
Discrete choice of households on REDD is as-
sumed to be influenced from heterogeneous socio
economic household characters when households
response on these choices. These characters includ-
ing such as literacy, poverty level, food sufficiency
level, sex, land holding, family size and income
level are assumed independent variables in the
selected models. Their statistical characters of
Model-1 and 2 are presented in Tables 9 and 10.
In summary, HH size within age group is
measured in terms of number unit. Food suffi-
ciency of households is measured into months.
Landholding of HH is in local unit that is
Ropani(0.07 hectare). In earning per day, there is
used per person per day in terms of dollar. Earn-
ing is considered as exogenous variable. Livestock
of HH is in number unit.
Model-1: Familiarity about REDD
In Model-1: familiarity about REDD, there were
binary choices: Yes and No. These choices reflect
effects of household socio economic characters
and household awareness level. There were used
independent variables: HH size, food sufficiency,
land holding, earning per day, Rsex, Rlivestock,
literacy, per day earning, age and source of infor-
mation surveyed in the 48 households. Statistical
characters of these variables are estimated and
presented in Table 9.
Model-2: Better Alternative of REDD to CF
If the respondents were familiarity with REDD
in Model-1, the question about better alternative
of REDD to CF would be asked. There were only
25 respondents familiar with REDD. With similar
Table 10. Model-2 (if yes, better alternative of CF)
Variable Mean Std. Dev Min Max
HH Size 5.125 1.39 2 9
Food Sufficiency
12> 0.33 0.48 0 1
9> 0.25 0.44 0 1
6> 0.29 0.46 0 1
3> 0.12 0.33 0 1
Land Holding
10> 0.208 0.41 0 1
5> 0.208 0.41 0 1
1> 0.58 0.503 0 1
Earning per day
1> 0.208 0.41 0 1
2> 0.54 0.51 0 1
>2 0.25 0.44 0 1
Rsex 0.916 0.28 0 1
Rlivestock 1.86 1.57 0 1
Literacy
>SLC 0.5 0.51 0 1
SLC> 0.25 0.44 0.4 5.2
Literacy 0.25 0.44 0.4 5.2
Per day earning 1.8 1.11 0.33 2.5
Age 44.62 15.24 19 73
Source of Infor-
mation
Seminar 0.58 0.5 0 1
Training 0.25 0.44 0 1
Newpaper 0.125 0.337 0 1
Source: Field Survey, 2010
89
Low Carbon Economy and Developing Countries
independent variables, the 25 respondents were
surveyed for the model-2: better alternative of
REDD to CF. Statistical characters of these inde-
pendents are estimated and presented in Table 10.
Estimation and Result of Probit Model
In the study, probit model was used for the estima-
tion of parameters. The estimation was extended
from first level to two levels. There were two lev-
els: level-1: familiarity about REDD and level-2:
better alternative of REDD to CF.
Model 1: Familiarity about REDD
In Model-1, the probability of familiarity about
REDD is estimated by using HHsize, food suf-
ficiency, land holding per households, different
earning per day, Rsex, Rlivestock, literacy level,
per day earning, age and training as independent
variables. Familiarity about REDD is the binary
dependent variable having two choices: yes and
no. In the model, yes is coded as one and no is
coded as zero. Positive coefficient of independent
variables implies for increase in the probability
of familiarity about REDD. The estimation of
probit model for level 1: familiarity about REDD
is presented in Table 11.
The higher LRχ2 (16) test of better alternative
of REDD to CF shows that the model has good
explanatory power. The estimated parameters
show that hh size, food sufficiency (12>, 9>, 6>,
3>), land holding (5>) and Rlivestock are sig-
nificant and negative at 95 percent confidence
level. It implies that the probability of familiarity
about REDD decreases, if the households have
large hh size, increasing food sufficiency, greater
land holding than 5 ropani and large number of
Rlivestock. Similarly, the positive parameters
show that age, literacy, per day earning, Rsex and
Training are significant and positive at 95 percent
confidence level. It implies that older people,
increasing literacy, per day earning and male
participation will increase the probability of fa-
miliarity about REDD.
Model-2: Alternative of REDD to CF
The higher LRχ2 (13) test of better alternative
of REDD to CF shows that the model has good
explanatory power in Table 12. The estimated
parameters show that land holding (10>) and
Rlivestock are significant determinant with nega-
tive sign in 95 percent confidence level. It implies
Table 11. Model-1(Familiarity about REDD)
Variable Probit
Coeff St. Err
constant -14.8 5.72
HH size -0.24 0.4
Food Sufficiency
12> -5.01 2.37
9> -3.47 1.85
6> -5.73 1.76
3> -4.41
Land Holding
10> – –
5> -4.54 –
1> 5.38 1.66
Earning per day
1> 4.14 2.59
2> 1.73 1.5
>2 – –
Rsex 2.09 1.55
Rlivestock -0.176 0.358
Literacy
>SLC 9.27 0.91
SLC> 9.01 –
Literacy 8.61 1
Per day earning 1.35 0.89
Age 0.044 0.028
Training 0.14 1.16
Psedo R2 0.48
LR(x2)(16) 27.5
Prob>x2 0.0512
No of observation 48
Source: based on Field Survey, 2010
90
Low Carbon Economy and Developing Countries
that the probability of better alternative of REDD
to CF decreases, if households have large size of
land holding and of livestock. Similarly, positive
value of parameters show that food sufficiency,
land holding (1>), different earning per day, Rsex,
literacy, per day earning and age are significant
determinant with positive sign in 95 percent
confidence level. It implies that the probability
of better alternative of REDD to CF increases if
households have higher literacy, higher age, per
day earning, higher and less than 2 dollar earn-
ing per day. In Rsex, it implies probability of
better alternative of REDD to CF for more male
participation in the respondent. Concerning less
than 1 Ropani land holding, it implies positive on
better alternative of REDD to CF.
CONCLUSION
Collective Action and Decision is used as policy
instrument of avoided deforestation for livelihood
objectives. The poor households are more depen-
dent on the community forest for NTFP. Share
of forest products is approximately 45 percent.
They contribute more labor endowments in forest
management and conservation.
Estimation of biomass and carbon per hect-
are provides REDD potentials, if KCF enters in
REDD. In case of KCF, it has earned Rs 24, 549.55
but potential income is Rs. 39, 81,196, if KCF
enters in REDD. It shows 41 times more carbon
incentive benefit potentials, despite cost of avoided
deforestation. Thus, community forest has huge
carbon service and carbon incentive potential.
The study estimates expectation of households
reflected from familiarity about REDD and better
alternative of REDD to CF. There are found 52
percent household stakeholders having familiarity
about REDD but 48 percent not having familiarity
about REDD. Out of 25 household stakehold-
ers, 44 percent household stakeholders who are
familiar about REDD expects that REDD is a
better alternative livelihood to the poor. Further,
63 percent households expect livelihood from
REDD. Thus, the result is mixed between famil-
iarity and non familiarity about REDD. Large
household respondents don’t believe that REDD
will be a better alternative livelihood to the poor.
Almost households expect REDD for livelihood
objectives.
Table 12. Model-2 (Alternative of REDD to CF)
Variable Probit
Coeff St. Err
constant -93.15 564737
HH size 5.02 6022.2
Food Sufficiency
12> 39.14 3962.4
9> 55.37
6>
3>
Land Holding
10> -12 125906
5>
1> 37.75
Earning per day
1>
2> 6.57 144569
>2 1.57
Rsex 13.53 131869
Rlivestock -11.8 2513.07
Literacy
>SLC
SLC> 10.51 6980.3
Literacy 10.98 7580.8
Per day earning 14.06 8875.52
Age 0.13 211.07
Psedo R2 1
LR(x2)(16) 20.02
Prob>x2 0.09
No of observation 20
Source: based on Field Survey, 2010
91
Low Carbon Economy and Developing Countries
In conclusion, household stakeholders who
have good asset holdings (land and Rlivestock)
think that REDD will not be a better livelihood
alternative to the poor. However, the household
stakeholders who have literacy, different food
sufficiency level, land holding (1>), different
earning per day, Rsex, per day earning and age
think that REDD will be a better alternative. In
other words, rich household characteristics by
asset holding don’t support that REDD will be
a better alternative. However, poor household
characteristics by asset holding support that REDD
will be a better alternative.
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