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Illegal Logging and Wood Consumption: Estimation and Projection of Illegal Wood Harvesting in Pakistan through System Dynamics

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The scale and impacts of the illegal logging economy are an important and interesting area of research, which suffers from data deficiency that makes the analysis a challenging task, especially in developing countries. The present study is an attempt to estimate the illegal wood harvest from State forests in Pakistan, using a system dynamics model to simulate time series data from 1990-2010. Projections of illegal logging were made up to 2029-30. Depending on the global estimation criteria for illegal logging, the study incorporated legal harvest from the State forests in a system dynamics model with population growth as a driving force of wood consumption. The supply contribution of State forests to total wood consumption and wood actually harvested from State forests is set as a base to estimate the level of illegal wood harvest. The monetary value of illegally harvested wood is determined on the basis of forestry sector contribution to GDP. The results show that illegal wood harvest is 4 times more than the legal wood harvest, and after the harvesting ban this ratio has decreased over time with the increasing share of wood supply from farmlands.
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Pakistan Journal of Commerce and Social Sciences
2017, Vol. 11 (2), 406-427
Pak J Commer Soc Sci
Illegal Logging and Wood Consumption: Estimation
and Projection of Illegal Wood Harvesting in
Pakistan through System Dynamics
Naila Nazir (Corresponding author)
Department of Economics, University of Peshawar, Pakistan
Email: nailauom@gmail.com
Laura Schmitt Olabisi
Department of Community Sustainability and Environmental Science and Policy
Program Michigan State University, USA
Email: schmi420@msu.edu
Abstract
The scale and impacts of the illegal logging economy are an important and interesting area
of research, which suffers from data deficiency that makes the analysis a challenging task,
especially in developing countries. The present study is an attempt to estimate the illegal
wood harvest from State forests in Pakistan, using a system dynamics model to simulate
time series data from 1990-2010. Projections of illegal logging were made up to 2029-30.
Depending on the global estimation criteria for illegal logging, the study incorporated legal
harvest from the State forests in a system dynamics model with population growth as a
driving force of wood consumption. The supply contribution of State forests to total wood
consumption and wood actually harvested from State forests is set as a base to estimate the
level of illegal wood harvest. The monetary value of illegally harvested wood is determined
on the basis of forestry sector contribution to GDP. The results show that illegal wood
harvest is 4 times more than the legal wood harvest, and after the harvesting ban this ratio
has decreased over time with the increasing share of wood supply from farmlands.
Keywords: illegal wood harvest, wood consumption, state forests, GDP, Pakistan.
1. Introduction
Illegal logging is a significant part of the black market. The illegal logging economy
involves a set of actors that are complex and diverse both in nature and operation (Brown,
2011). Different definitions of illegal logging lead to different conclusions on the
magnitude of the problem (Miller et al., 2006). Illegal logging usually refers to one or more
of the following malpractices: logging of protected or endangered species, including those
listed under the Convention on International Trade in Endangered Species (CITES);
logging in protected areas; logging in violation of permits, violating rules related to size,
area of logging and volume of logging, or other official requirements; logging with fake or
illegally obtained permits; damaging trees to make them vulnerable to fell legally;
processing timber without p e r m i t s a n d documentation; practices to avoid taxation;
Nazir & Olabisi
407
redefinition of forest classification; overvaluing and buying timber above market price
(Brack, 2003; Brack et al., 2002; Callister, 1999; Nellemann, 2012; Contreras-Hermosilla,
2000; UNODC 2013). Illegal logging results in deforestation, deprives local communities
of natural resource endowments, causes environmental degradation, costs governments in
lost revenue (Koyunen & Yilmaz 2009) and promotes corruption (Mendes, 2011).
Data on illicit trade of goods and other illegally produced flows including natural resources
is scarce (Haken, 2011). Chaudhary et al. (2017) while facing the problem of data
availability. Finer et al. (2014) and Kleinschmit et al. (2016) state that even the
FAOSTAT trade database does not have data on illegal wood being traded.
Illegal timber harvesting has connections with militancy and this conflict timber has
accelerated the pace of illegal logging. Forests in Kashmir (disputed area between Pakistan
and India) and areas in Khyber Pakhtunkhwa remain vulnerable to illegal harvest by
militant groups (Forester et al., 2003). Illegal wood is smuggled between Afghanistan and
Pakistan (Giordono, 2009; Peters, 2010; The News, 2010). The timber smuggled from
Pakistan is re-exported back to Pakistan to declare it as duty-free Afghan timber (WCS.
2008). Once the wood i s smuggled to Pakistan it is destined to Karachi and onward to the
Gulf States (Bader et al., 2013). However, there is no data showing the scale of such
timber. Further, there is no data showing the classification of consumers with respect to the
consumption of illegal wood. Overall wood supply and demand analysis in the country
shows that the household sector is the largest consumer mainly for fuel-wood and the
construction sector is the largest consumer (20%) of timber (Zaman and Ahmad, 2012). A
study conducted by UNOCD & SDPI (2011) mentioned that in terms of quality, the timber
trade data of Pakistan is poor. There is a need to conduct more studies on estimating
forestry data to develop a strong data-base for analysis.
Considering the fact that forest area in Pakistan is already very low (5.1%) (Bukhari et al.,
2012) and the data base is weak, the present study addresses the question of illegal wood
harvest from State owned forests of Pakistan. The present study is a first attempt to use
system dynamics methodology to estimate macro level data on wood consumption, wood
supply and illegal logging in the country. The purpose of using systems methodology is to
derive the system components from time series data. In our case, we are concerned with
the estimation of the illegal wood trade in Pakistan. Once the systems components are
developed and values are estimated, the time series data generated through system
dynamics then helps to estimate illegal wood harvest in the country. The present model is
developed in such a way that it has incorporated the sources of wood supply and total wood
consumption in the country. One of the main difficulties in developing the model was that
the time series data on wood supply was not available. For this purpose, the information
has been retrieved from the literature. This information was available in reports (see
methodology), and later converted into equations to incorporate in the model. Thus, the
model results would add to the data-base of the forestry sector and would help to estimate
wood extraction by different interest groups and illegal wood being consumed by different
consumer groups in the country. The present study will help the policy makers and
environmentalists to know the scale of the illegal logging economy in Pakistan. The model
developed by the present study would be a sample model for other developing countries
where there is not a complete set of data to estimate systems variables for the forestry sector
(discussed in section 3).
Wood Consumption and Illegal Wood Harvest in Pakistan
408
State-owned forests in Pakistan are vulnerable to illegal logging as farmlands in the country
are the property of individuals and families, and are therefore being protected by families
themselves. State-owned forests’ contribution to total wood consumption in the country
may or may not be equal to the wood officially harvested from State-owned forests. Wood
officially harvested from the State-owned forest as highlighted in official documents is far
less than wood supply contribution from State-owned forest to total wood consumption in
the country (see for example PFI, 2004; Clark, 1990; GOP, 2005). The gap between the
two may be considered illegal wood harvest from the State-owned forest, considering the
fact that wood imports have declined sharply as discussed below in the methodology
section.
The primary objective of the present study is to review the estimated data and methods
describing the scale of illegal logging in Pakistan. The focus of the study is to figure out
the key dynamics of illegal wood harvest in Pakistan. The difference between the volume
of wood from State-owned forest that contributes to wood consumption, and the official
data showing wood extraction from State-owned forest, serves as an estimate of illegal
harvesting. The sources of wood supply contributing to wood consumption in the country
are analyzed for comparison with the officially harvested wood from State-owned forest.
The value of illegal wood harvest is estimated and added to the total wood consumption in
the country. The share of illegal wood to the Gross Domestic Product is also calculated.
The study concludes with some policy suggestions.
The paper is structured as follows. Following the introduction and the objectives of the
research, a review is given highlighting the literature on the volume of illegally logged
wood. The methods used to estimate illegal logging are also discussed. The methodology
based on system dynamics model is elaborated by developing a graphic model with its
mathematical equations. The model results are validated in the light of official data and
conclusions are drawn with some policy suggestions.
2. Review of Volume and Methods of Estimating Illegal Logging
At a global level, the value of the black-market economy is about $1.81 trillion (UNEP &
INTERPOL, 2012) and the value of illegal logging is about $7 b (Haken, 2011). The value
of the black market in Pakistan is about $6.53 b, and the value of illegal logging is $782 m
(Roul, 2009). Some other estimates show this value as Rs. 835 million per annum. This
was based on the average annual recorded illegal cutting of about 50,000 and an average
timber price in 2005/06 of Rs. 16,700 (US$ 196.5 approximately) per (UNOCD &
SDPI, 2011). A forest harvesting ban has been imposed since 1993. It is believed that the
illegal logging continued even after the ban and the volume of illegally logged wood may
be ten times more than the legal timber harvest (Hausler et al., 2000) as cited in (Fischer et
al., 2010). This ban has had the effect of driving timber harvesting into illegal markets
(Suleri, 2002). The Royal Institute of International Affairs mentioned that the estimated
value of global trade in wood products is $150 billion and illegal global forest activity
accounts for more than 10 times that figure (RIIA, 2003).
At the time of independence, Pakistan had 7% of its land area under forests. This declined
to less than 5% after the separation of Bangladesh in 1971 (Fischer et al., 2010). The
deforestation in the country is about 2% (Ma &Broadhead, 2002) and the net area under
forest in the country is 4.55m hectares (Bukhari et al., 2012).
Nazir & Olabisi
409
Globally, the estimates on illegal logging are based on different methodologies, including
wood flow analysis (Contreras- Hermosilla et al., 2007; World Bank 2006; Contreras-
Hermosilla, 2000); interview based information (WWF Latvia, 2003; World Bank, 2005;
Rhodes, Allen & Callahan, 2006); comparing import and export statistics (Birikorang,
2001; Blaser et al., 2005; Lawson & MacFaul, 2010) and the difference between the prices
paid to the loggers and the final market prices of wood (Solinge & Boekhout, 2008). These
estimates are expressed in terms of percentage share to GDP (Solinge & Boekhout, 2008).
Supply-demand gap has been used as a proxy for the analysis of illegal logging in many
studies (Manurung et al., 2007; Tacconi, 2007; Harwell, 2009). The level of illegality
varies with changes in demand and supply of wood and is affected by multiple factors like
implementation of laws related to certification, level of sustainable harvesting, trade flows
from one country matching with the recipient country, etc. (Wellesley, 2014; Hoare, 2014;
Wakker, 2014; Lawson, 2014; Barr et al., 2010). Game Models are being used which are
a helpful tool to identify the channels of illegal logging (Lee et al., 2015). Songchoo and
Suriya (2012) also discussed game theory as helping to make policy decisions to control
illegal logging. Lack of identification information is also a problem in the way of curbing
the crime. A range of scientific forensic methods; visual identification methods, chemical
methods, and genetic methods, have been developed to provide identification information
as criminal evidence (Dormontt et al., 2015).
Haken (2011) cited the Seneca Creek and Wood Resources International report estimates,
that show illegal forest activity as representing 5% to 10 % of global industrial production,
and volume of illegal logged round wood that enters international trade represents one
percent of global production for both softwood and hardwood. Haken (2011) further cited
estimates of a 2007 report that the global forestry sector accounts for about one percent of
world Gross Domestic Product. The CIA World Factbook estimated the 2009 world GDP
at $70.17 trillion, which would set the 2009 value of global wood production at $701.7
billion. Based on the Seneca Creek and Wood Resources International estimate of one
percent, the said report calculates the value of suspicious wood in the international market
to be around $7 billion in 2009. This is consistent with the Seneca Creek and Wood
Resources International estimate of $4.9 billion in 2004 when world GDP was $42 trillion.
Contreras- Hermosilla et al. (2007) also mentioned that the forest products’ sector is
contributing about 1% of world GDP and stands at 3% of international merchandise trade.
3. System Dynamics Methodology
System dynamics helps in understanding the time varying behavior of complex systems
(Musango et al., 2012). System dynamics models are developed by constructing stocks and
flows of information, data as sets of differential equations linked through intermediary
functions and data structures (Gilbertand Troitzsch, 1999). Human and ecological
interactions can be represented within these models (Baker 1989; Sklar and Costanza,
1991). In the present study, the stocks and flows of wood and forest area in the country
with population dynamics and wood consumption helped to derive information on wood
supply and wood consumption by constructing functional relationships between legal and
illegal wood harvest with respect to time.
Wood Consumption and Illegal Wood Harvest in Pakistan
410
3.1 A System Dynamics Model for Evaluating Illegal Wood Harvest
In order to explain the methodology a conceptual figure (1) is depicted to explain the key
variables of the model. The figure shows the basic conceptual design of wood supply and
wood harvesting on which the system dynamics model has been built. Our study is based
on the hypothesis that the legal wood harvest from State-owned forest land and wood
supply contribution from State-owned forest to total wood consumption are not equal;
therefore, the gap between the two is counted towards illegal wood harvest.
Figure 1: Structural Concept of the Sources of Wood Supply and Wood Consumption
In Pakistan, the official sources do not provide time series data on wood supply. A thorough
process of information collection from the literature has been done for the present study.
The information was available in statement forms. This information on the contribution of
each source of wood supply in terms of its share to total wood consumption in the country
is sorted out. The statements have been put together with respect to time (section B) and
converted into model equations (see appendix). Other variables, including the consumption
of wood in the country and wood harvest from State-owned forest have been incorporated
in the model. Further, the model highlighted population growth as well. Since consumption
of wood in the country is increasing with population growth, power shortages and the lack
of alternative energy resources (electricity, gas, nuclear energy), are pushing up the wood
consumption. In the case of Pakistan, in the pre-harvesting ban period, the supply share of
wood from State-owned forests was higher than (legal) wood extraction from State- owned
forests. After the ban, official information stated that the supply share from State-owned
forest declined (see FAO., 2009; UNDP-ECC, undated; Clark, 1990; GOP., 2005), but
according to some studies, the illegal wood supply had not declined after the ban (Fischer
et al., 2010; Shahbaz & Suleri, 2009). There was a wood shortage of 29.361 million m³ in
the country that has grown at 2.1% annually from 1992 to 2003 (UNDP-PK-ECC,
undated). Thus, the volume of pre-ban period wood supply from State-owned forests
helped us to check the trends in wood supply after the ban from State owned forests, which
in turn provides a base to determine the trend of wood illegally harvested from these forests.
Studies show that after the harvesting ban, the illegal wood supply from State owned-
forests has increased (see Fischer et al., 2010; Shahbaz and Suleri, 2009; Hausler et al.,
2000), which is contrary to the official claim that after the ban the supply share of State
Wood Consumption
Official Wood
Extraction
from State
Forests
Gap in Wood Officially
Extracted and Wood Supply
Contribution of State
Forests in Total Wood
Consumption
Wood Imports
Illegal Wood
Harvest
Wood Supply
Contribution
from Farmlands
Wood Supply
Contribution
from State
Forests
Nazir & Olabisi
411
owned forests has declined. This leads us to design the present study to compare the data
on wood supply share of State owned forests in wood consumption and wood officially
harvested from State owned forests. Any gap between the two sets of the estimated values
is considered as illegal wood harvest. How much is the “gap”, is a task being solved below
with the help of a model built in Stella to develop time series data on the wood supply share
of State forests and wood legally harvested from State owned forests to determine the value
of illegal wood harvest over time simulating up to the year 2029-30. Once the level of
illegal wood harvest is estimated, it is added with legal wood harvest thus estimating the
total wood consumption in the country over time. The methodology of estimating illegal
wood harvest is based on the idea taken from the work of Manurung et al. (2007), Tacconi
(2007), and Harwell (2009), in which they used the gap between demand and supply of
wood as a proxy for illegal wood. However, the present study considers the gap between
official wood extraction from State forests and wood actually supplied and consumed from
the State- owned forests in Pakistan.
Based on the structure described above, a model was built in Stella 10.1(Figure 2) to
generate time series data on wood supply. The share of each source of wood supply depends
on how much each source is contributing to total wood consumption. The information has
been collected in statement form (see below sector frame B with italic statements).
The model is divided into four sectoral frames: human population; sources of wood supply;
national wood stock availability; and illegal wood extraction. Sources of wood supply are
set on the basis of the share of each supply source in wood consumption. The model is
based on time series data from 1990-2010 and projection estimates are highlighted for
2029-30.
Wood Consumption and Illegal Wood Harvest in Pakistan
412
Figure 2: Model Showing Wood Supply, Wood Consumption and Wood Harvest
Nazir & Olabisi
413
3.1.1 Sector Frame “People”
The population growth is presented in sector frame “People”. The data on population is
given in equations below. Based on the growing population, using per capita timber and
firewood consumption, the growing trends in wood consumption are calculated over time.
3.1.2 Sector Frame “Wood Supply”
There are three sources of wood supply in the country: State owned forests, farmlands and
imports. The values of per capita firewood consumption and per capita timber consumption
in the country are taken from FBS (2010), GOP (2005) and Zaman & Ahmad (2012). The
information to calculate percentage share of wood supply contribution to total wood
consumption from all three sources and wood supply from State owned forests, farmlands
and imports are taken from FAO (2009), UNDP-ECC (undated), Clark (1990) and GOP
(2005), and are summarized below. This information is available in statement forms. With
the help of the systems model, the information is then converted into equations (see model
equations) to generate time series data given in table 2 (appendix).
Imports during the 1990’s were 41% of the total timber consumption, later decreased to
20% in 2000's and then to 5% during 2005-2010. Out of total firewood consumption, from
1990 till 1996, 10% of the firewood consumption was supplied by State-owned forest.
After 1996, the figure dropped to 0.91%. Out of the total timber consumption, from 1990
to 1995, timber consumption from State-owned forest was 18%, in 1996 it became 10%
and from 1997 onward it dropped to 8%. From 1990 to 1995, timber supply from farmlands
was 41%, for 1996 it became 63% and from 1997 onward it increased to 72% of the total
timber consumption. Out of total firewood consumption, from 1990 till 1996, 90% of the
fuel- wood was supplied by farmlands and the remaining 10% by the State-owned forest.
After 1996, this ratio changed to 99.09% and 0.91% respectively”.
3.1.3 Sector Frame “Illegal Wood Harvest”
Wood consumption from State owned forests has been explained above. Total wood,
legally harvested from State owned forests are the summation of timber and firewood
extraction from state owned forests. The data on official wood harvest/extraction
(including timber and firewood) represents government statistics on wood harvest from
State-owned forest, given in table (2) in appendix. Illegal wood harvest is calculated on the
basis of the difference between total wood consumption from State owned forests and wood
officially (legally) harvested from State owned forests. Principally, the wood supplied from
the State-owned forest and official recorded wood harvest from State-owned forests should
be equal. Any discrepancy in these figures should give an estimate of an illegal wood
harvest.
3.1.4 Sector Frame “Wood Stock”
Wood stock in the country is taken as wood stock from State-owned forest and from
farmlands. In 1992, the Forestry Sector Master Plan estimated a total national standing
volume of wood as 368 million m³; farmland standing stock as 70.3mm³ and farmland
stock growth per annum as 7.7 million m³ per year. These estimates are based on the data
from Forest Department working plans, the farmland tree survey and the Household Energy
Strategy Study (HESS). The total wood yield per annum was estimated as 10.9 percent of
the standing stock (EC-FAO, 2002). Based on 10.9% value, the national wood yield growth
Wood Consumption and Illegal Wood Harvest in Pakistan
414
per year is calculated as 40.112 million cubic meters. Change in wood stock also comes
from change in forest area. Therefore, the net change in total forest area (forest area growth)
in the country is also incorporated in the model to add per hectare yield growth in the wood
stock. The forest area was 3.46 million hectares in 1990 that increased to 4.26 m hectares
in 2010-11, showing an increase of 1.2% per annum. This increase is mainly attributed to
farmland growth as mentioned by FAO (2007). Wood extraction is the result of wood
harvest from State owned forests, from farmlands and wood illegally extracted, thus
affecting wood stock availability. Wood stock, forest area and other auxiliary variables
shown in Fig. 2 are expressed in model equations. The estimated model data is given in
table 1 & 2.
3.2 Estimating Illegal Wood Harvest to Gross Domestic Product (GDP) Of Pakistan
In our model, GDP based calculations are not part of the Stella built model but have been
used to determine the monetary value of illegal wood estimates. The model value of
illegally harvested wood has been used to estimate its monetary value and to find out its
percentage contribution to GDP.
To estimate the monetary value of illegal wood harvest, wood prices given by UNODC &
SDPI (2011) are used, where the average price of wood is Rs. 16700 per m³ in 2005-06.
Keeping in mind the non-availability of time series data for the prices of wood, this value
(average price per m³) is then used to determine the monetary value of illegal logged wood
for the period under study. The estimated monetary value of illegal wood is used to
determine its contribution to GDP of the country. World GDP to illegal logging
relationship estimates by Haken (2011) and Seneca Creek Associates, LLC, and Wood
Resources International (November 2004; November 2009) show that in 2009, global GDP
was $ 70.17 trillion, global wood production was 1% of the GDP ($ 701 b), and global
suspicious wood production was 1% of global wood production ($ 7 b). FAO (2012) and
Agrawal et al. (2013) also mentioned the same criteria that forestry is contributing nearly
1 percent of the global GDP. Based on this information, the estimates of the present study
are then compared with the global standards.
4. Results and Analysis
4.1 Model Validation Based on Past Data from 1990-2009
Model results are checked in the light of data taken from official sources. Three key
variables are selected for comparing model data and official data. The three key variables
are population, which is the driving force of wood consumption; forest area as the main
source of wood stock in the country; and wood consumption. These data are graphically
presented with results from the present model to compare the two. There is no significant
difference between the trends of growth found in official data and model data on
population, forest area and also on wood consumption, thus validating the results of the
model (Table 1, Fig.3 a &b). Therefore, the projection based on this model is reliable.
Nazir & Olabisi
415
Table 1: Estimated Model Data and Official Data for Model Validation
*Official
data-
Population
(m)
Model
Data-
Population
(m)
Years
**Official
data- Wood
Consumption
(mm3)
Model data-
Wood
Consumption
(mm3)
Years
***Official
data-
Forest
Area
(m. ha)
Model
data-
Forest
Area
(m.
ha.)
112.27
112.27
1990
25.38
25.726
1990-91
3.46
3.46
112.61
114.3
1991
27.523
26.191
1991-92
3.47
3.5
115.54
116.36
1992
27.08
26.663
1992-93
3.48
3.54
118.5
118.46
1993
29.815
27.145
1993-94
3.45
3.59
121.48
120.6
1994
30.53
27.635
1994-95
3.6
3.63
124.49
122.77
1995
31.243
28.134
1995-96
3.61
3.67
127.51
124.99
1996
31.955
29.454
1996-97
3.58
3.72
130.56
127.25
1997
32.576
30.399
1997-98
3.6
3.76
132.25
129.54
1998
33.425
30.948
1998-99
3.6
3.81
136.69
131.88
1999
34.298
31.506
1999-00
3.78
3.85
139.96
134.26
2000
35.192
32.075
2000-01
3.77
3.9
142.86
136.69
2001
35.57
32.654
2001-02
3.8
3.95
146.02
139.15
2002
59.716
33.243
2002-03
4.04
3.99
149.32
141.66
2003
30.141
33.843
2003-04
4.01
4.04
152.66
144.22
2004
30.994
34.454
2004-05
4.02
4.09
156.04
146.83
2005
31.649
35.076
2005-06
4.03
4.14
159.46
149.48
2006
31.762
35.71
2006-07
4.21
4.19
162.91
152.17
2007
34.98
36.354
2007-08
4.21
4.24
166.41
154.92
2008
35.274
37.01
2008-09
4.24
4.29
169.94
157.72
2009
36.615
37.678
2009-10
4.23
4.34
Source: *Supply and demand of fuel wood and timber for household and industrial sectors and
consumption pattern of wood and wood products in Pakistan, Government of Pakistan 2005, Ministry
of Environment, Government of Pakistan. ** Compendium on Environment Statistics of Pakistan
2010, Federal Bureau of Statistics, Islamabad- values are rounded off. ***Land Cover Atlas of
Pakistan, 2012
Wood Consumption and Illegal Wood Harvest in Pakistan
416
Figure 3: Comparing Model Data and Official Data on Forest Area (M. Hec.)
Figure 4: Comparing Model Data and Official Data on Population (M)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
1990-91
1991-92
1992-93
1993-94
1994-95
1995-96
1996-97
1997-98
1998-99
1999-2000
2000-01
2001-02
2002-03
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
Years
million ha.
Forest Area
**Official data-Forest Area (m. heatc)
Model data-Forest Area (m. heatc)
0
20
40
60
80
100
120
140
160
180
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Years
million
Population Data
Model Data- Population (m) Official data- Popuation (m)
Nazir & Olabisi
417
Figure 5: Comparing Model Data and Official Data on Wood Consumption
(Wood Imports and Illegal Wood Excluded)
The official available data on wood consumption covers the consumption of timber and
firewood from state owned forests and from farmlands. The data on imported wood is not
included in the official data on wood consumption. Therefore, for validation, we also
selected the same two classes of model generated data for wood consumption; consumption
of timber and firewood from state owned forests and from farmlands (Table 1). The results
show that the official data and estimated model data on wood consumption from these two
sources are not significantly different from each other (Figure 5). Thus, based on the
uniformity between the model data and official data on wood consumption in two wood
consumption categories, we can proceed with the rest of the estimation i.e. estimating
illegal wood and estimating time series data on imported wood to calculate “Total wood
consumption”. If model-estimated data on illegal wood harvest shows a rising trend over
time, then we will accept the claim that illegal logging increased even after the harvesting
ban. If the trend is declining, it would mean that the official claim of decreasing supply
share of State owned forests and increasing contribution of farmlands, is more reliable.
4.2 Projection based on Past Trends
Since the model data has been validated on the basis of official data, the model results
allow us to make a projection for the period 2029-30. The projection is made for the key
variables: forest area of Pakistan; national wood stock availability; official wood
extraction; and illegal wood harvest from state forests.
4.2.1 Forest area and National Wood Stock
The forest area in the country was 3.46 million hectares in 1990, having a wood stock of
368 million with the estimated growing stock of 40.112 million per annum. The
results show that wood stock has increased to 1441.3 million in 2010. Simulating on
0
10
20
30
40
50
60
70
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Years
Million cubic meters
Wood consumption data
Official data- Wood Consumption (mm3)
Model data- Wood Consumption (mm3)
Wood Consumption and Illegal Wood Harvest in Pakistan
418
the basis of estimated growth, the wood stock availability in the country would be 4255.3
million m³ in 2029-30. This growth in wood stock is net growth i.e. after deducting legal
and illegal wood extraction from State-owned forests and wood harvest from farmlands.
The model results show that the forest area would be 5.5 million hectares in 2029-30.
4.2.2 Official Recorded Wood Extraction and Illegal Wood Harvest from State-Owned Forests
On the basis of official firewood harvest and timber harvest from 1990-2010, the total
official recorded wood harvest (legal) in terms of volume has increased from 0.616 million
m³ in 1990 to 0.719 m³ in 2010 with projected estimate of 0.543 million m³ for 2029-30.
Here one thing is important to note that in terms of volume, the officially harvested wood
from State-owned forests has increased in later years as compared to 1990 levels, but in
terms of share contribution to wood consumption, after the timber harvesting ban in 1993,
the State-owned forests supply share has declined as mentioned by official data. There is a
gap between wood supply share of State-owned forests as shown by official data and wood
legally harvested from State-owned forests. The value of illegal wood harvest from State-
owned forests is estimated approximately 2.6 million m³ in 1990 that has decreased to
0.172 million in 2010 and is projected at 0.708 million m³ in 2029-30. Model data shows
that the total wood consumption of farmland wood, imported wood, legal and illegal wood
consumption from State-owned forest stands at 30.4 million m³ in 1990 and is projected to
increase to 55 million m³ by 2029-30.
4.2.3 Estimates of Illegal Wood and Ratio to GDP in Pakistan
From 1990 to 2010, the average contribution of forestry in Pakistan was 0.33% to the GDP.
The model value shows that in 2005, the volume of illegal wood harvest was 0.475 million
m³. The GDP value Rs. 7158527m at constant factor costs for the year 2005 is used to
calculate percentage share of illegally harvested wood. The year 2005 is selected for
comparison only because the prices of wood and comparative estimates were available for
that year. On the basis of prices of wood in 2005-06 (Rs. 16700 per cubic meter) the value
stands as Rs.7932 m. This estimated monetary value of illegally logged wood shows that
illegal wood is 0.11% of the GDP of the country. If we add the value of illegal wood share
to GDP to the value of 0.33%; an average share of forestry to GDP, the actual share of
forestry becomes 0.44 % to the GDP. On the basis of the data from 1990 to 2010, a net area
growth of 1.2 % has been observed. The growth in forest area resulted in additional growth
in wood stock which was already growing at 40.112 million m³ per annum. However, the
wood stock is being depleted by wood cutting in the country. The wood is being cut from
State owned forests and from farmlands. The wood stock is also affected by illegal wood
cutting. We added the estimated data on illegal wood harvest to the wood being depleted
over time. Thus, total wood depletion increased at 2.3% per year, however stands lower
than the growth of wood stock (11.7%) thus making wood stock available for wood
consumption in the country. The illegal wood harvest is found at points where there is a
gap between wood legally harvested from State owned forests and supply share of these
forests to total wood consumption. After the harvesting ban in 1993, the supply share of
State owned forests has decreased. The estimated value of illegal wood cutting shows that
after the ban, its consumption from State owned forests has declined. The decline in the
supply share of State owned forests to total wood consumption has been decreasing and
has been compensated by increase in the supply share of farmlands. Since the farmlands
are private lands and are not under threat of illegal wood cutting, we thus conclude that
illegal wood cutting decreased after the ban and the wood supply share of farmlands has
Nazir & Olabisi
419
increased. Farmlands are now providing 72% of timber and 99.09% of the firewood in the
country. Using global standards to compare illegal wood contribution, our estimates for
illegal wood harvest stands at 1.5% of the total wood production of the country in 2010,
which is higher than the global suspicious wood share to the global wood production i.e.
1%. Further, average share of forestry to GDP (including estimated illegal wood) in
Pakistan is 0.44% that is less than the global average of 1%.
On the whole, it is found that the official data on wood harvest does not match the supply
share of wood from each source. For example, going back to the literature reported level
of illegal wood extraction which is said to be 10 times more than the legal wood harvest
from state owned forests, the official value for illegal wood may be equal to 6.16 m. cubic
meters for the year 1990 (calculated on the basis of officially harvested wood value for the
year 1990). By adding this value to the rest of wood supplied by other official sources--
Imports, farmlands wood supply and wood supplied by state owned forests--the total
officially declared wood supply may be equal to 33.6 mm3, which is more than 25 mm3;
the official estimate of wood consumption (FBS, 2010) for the year 1990, thus invalidating
the claim that the illegal wood is 10 times more than legal wood harvest.
On the other hand, model data calculates the total wood supplied by three sources; imports,
state owned forests, farmlands and illegal wood to arrive at a total level of wood
consumption in the country of 30.4 mm3 for the year 1990. On the basis of the model
values, the value for illegal wood stands at 4 times the legal wood harvest from State owned
forests for the said year. The trend in illegal wood harvest is declining after 1990, mainly
because the share of farmlands’ wood in the total wood consumption has increased
overtime.
5. Findings and Conclusion
Illegal logging is a threat to forest resource management. Pakistan, already having low
forest area, is facing the challenge of illegal wood cutting from State-owned forests. How
much illegal wood is being extracted from these forests, was a question that has been
addressed in the present study. An effort has been made to estimate illegal wood harvest
at the macro level in Pakistan. The official data on wood consumption is the sum of timber
and firewood consumption in the country. This data has been checked and compared with
the available information on supply of wood from three sources: wood imports, wood
supply from State forests, and wood from farmland. The estimates on wood officially
extracted from State forests have been checked and compared with the wood supply from
state forests. These two estimates are found to be significantly different from each other,
thus providing a base for estimating illegal wood extraction from State owned forests. A
review of the literature showed that there is no time series data on wood imports. Time
series data on wood supply from each source-- imported wood, supply of wood from state
forests and from farmlands-- was not complete, thus leaving a big challenge for the present
research to build time series data by converting statements into mathematical equations,
and incorporating it into a system dynamics model that has an inbuilt capability to generate
time series output. The present study thus has used these estimates of wood supply to
determine the total wood consumption by including all categories of wood: wood from
imports, from State owned forests, from farmlands and illegal wood consumption. The
basic argument was that since farmlands are under private ownership, State- owned forests
are vulnerable to illegal wood harvest in the country. The study by Badshah et al. (2014)
also showed that a significant difference has been found between the average annual
Wood Consumption and Illegal Wood Harvest in Pakistan
420
removal of wood and wood consumption in hilly areas of Pakistan. Using a system
dynamics model, we found that there is a gap between estimates on wood officially
harvested and consumed in the country, which represents illegally harvested wood. The
results further show that the illegal wood harvest was 4 times the official wood harvest
during the pre-harvesting ban year i.e. 1990. The contribution of illegal wood to GDP for
the year 2005-06 is estimated at 0.11%, making the average share of forestry to GDP 0.44%
i.e. lower than the global average of 1%. The model results have been validated based on
the official data on forest area, population growth and wood consumption.
Based on the above estimation and analysis, it is imperative to devise policies to control
illegal logging. Wood consumption in third world countries, especially where law
enforcement is weak, results in illegal cutting and bribery. It is suggested by Songchoo and
Suriya (2012) that this situation can be controlled by giving high rewards for arresting
criminals. Game Models are also a useful tool to identify the channels of illegal logging.
Lee et al. (2015) used this technique and found that the education of law enforcement
officers and information on corrupt officials have significant effects in controlling
corruption. In the presence of week institutions, natural resources should be managed by
communal management groups as suggested by Pellegrini (2007).
Access to natural resources is another impediment resulting in ruthless use of the resource
and making its control difficult. The theory of access helps to identify how much the
stakeholders have access to natural resources (see for example Ribot and Peluso, 2003).
This notion can be used to find the reasons for the gap between the consumption and
production of wood. Forensic methods have also been devised to identify the illegal wood
and the source from which it has been brought. These measures not only help to address
the menace of illegal logging but also help to identify the factors and variables that can be
used to estimate the volume of wood illegally extracted.
6. Contribution of the Study
The model developed under the present study is the first of its kind that has generated
results on key variables for which the time series data was not available. The model
includes wood stock, wood imports, and total supply of wood from farmlands and from
national forests (Table 2, appendix). The results generated by the model would add to the
data-base of the forestry sector of the country. Data on the key supporting variables--forest
area, population growth and the past trends of wood consumption-- validate the model
results. We also used projection by the model to estimate the demand supply gap of wood
in the country. The results are helpful in analyzing energy policies, sustainable natural
resource management and controlling illegal wood harvesting in the country. Since
population growth is driving the wood consumption mainly because of a need for fuel
wood, it is imperative to work on fuel wood substitutes. There is a need to work on
estimating other key variables that contribute to illegal timber harvesting, like timber
harvested by militants and other illegal timber consumption.
Nazir & Olabisi
421
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Appendix
Data and Model Equations
Forest area and wood growth
INITIAL Forest area = 3460000 ha.
Forest area (t) = Forest area (t - dt) + (Net area growth) * dt
National wood stock (t) = National wood stock (t - dt) + (Wood growth Wood depletion)
* dt
INITIAL National wood stock = 368000000 million m³
Per hectare wood stock = 106.4 m³
Wood Consumption and Illegal Wood Harvest in Pakistan
426
Wood stock growth (m³) = 40112000+ Increase in wood production per hectare
Wood stock depletion (m³) = Illegal wood extraction from State-owned forest + Official
Recorded Wood extraction from State-owned forest + Wood Supply and extraction from
Farmlands
Population Growth
Population (t) = Population (t - dt) + (Births - Deaths) * dt
INITIAL Population = 112270000
Births = Population*(Birth rate/1000)
Deaths = Population*(Death rate/1000)
Birth rate = 25.45
Death rate = 7.4
Wood Consumption and wood supply
Average Per capita firewood consumption = 0.201754698 m³
Average Per capita timber consumption = 0.046429402 m³
Domestic Wood Supply = wood consumption from State-owned forest + Wood Supply and
extraction from Farmlands
Total national wood consumption legal and illegal = Domestic Wood SS +Imports of
timber + illegal wood
Official Recorded Wood extraction from State-owned forest = Timber extraction +
Firewood extraction
Wood Consumption from State-owned forest = Timber Consumption from State-owned
forest + Firewood Consumption from State-owned forest
Wood Supply and extraction from Farmlands = Firewood from Farmland + Timber from
farmland
Wood Supply and wood Consumption variation with respect to time
Timber from farmland = IF TIME >= 1990 AND TIME <= 1995 then 0.41*Timber
Consumption ELSE IF
TIME =1996 then 0.63*Timber Consumption ELSE 0.72*Timber Consumption
Timber Consumption from State-owned forest = IF TIME >= 1990 AND TIME <= 1995
then 0.18*Timber Consumption ELSE IF TIME = 1996 then 0.10*Timber Consumption
ELSE 0.08*Timber Consumption
Firewood from Farmland = IF TIME<=1996 THEN 0.90*Firewood Consumption ELSE
0.9909 *Firewood Consumption
Firewood Consumption from State-owned forest = if time <= 1996 then 0.1 *Firewood
Consumption else .0091 * Firewood Consumption
Nazir & Olabisi
427
Table: 2 Official Data and Estimated Model Data
Yea
r
Populatio
n (m)
Nationa
l wood
stock
(m m3)
Forest
area
(m.
ha.)
Official
Recorded
Wood
extractio
n from
state
forests (m
m3)
Imports
of
Timber
and other
wood
products
(m m3)
wood
Supply
share
from
state
forests
(m m3)
Wood
Supply
from
Farmlan
ds (m
m3)
Illega
l
wood
(m
m3)
Total
national
wood
Consumptio
n including
illegal wood
(m m3) (sum
of column 6
to 9)
1990
112.27
368
3.46
0.616
2.137
3.203
22.523
2.587
30.451
1991
114.3
382.39
3.5
0.612
2.176
3.261
22.93
2.649
31.016
1992
116.36
400.72
3.54
0.564
2.215
3.32
23.343
2.756
31.635
1993
118.46
423.06
3.59
0.569
2.255
3.38
23.765
2.811
32.211
1994
120.6
449.44
3.63
0.813
2.296
3.441
24.194
2.628
32.559
1995
122.77
479.91
3.67
0.792
2.337
3.503
24.631
2.711
33.182
1996
124.99
514.51
3.72
0.62
1.567
3.102
26.352
2.482
33.503
1997
127.25
552.49
3.76
0.569
1.182
0.706
29.693
0.138
31.718
1998
129.54
594.26
3.81
0.55
1.203
0.719
30.229
0.169
32.32
1999
131.88
640.29
3.85
0.579
1.225
0.732
30.774
0.153
32.884
2000
134.26
690.61
3.9
0.562
1.247
0.745
31.33
0.184
33.505
2001
136.69
745.29
3.95
0.458
1.269
0.759
31.895
0.301
34.224
2002
139.15
804.37
3.99
0.25
1.292
0.772
32.471
0.522
35.058
2003
141.66
867.89
4.04
0.328
1.315
0.786
33.057
0.459
35.618
2004
144.22
935.91
4.09
0.305
1.339
0.8
33.654
0.496
36.289
2005
146.83
1008.48
4.14
0.34
0.341
0.815
34.261
0.475
35.892
2006
149.48
1085.65
4.19
0.365
0.347
0.83
34.88
0.465
36.521
2007
152.17
1167.47
4.24
0.228
0.353
0.845
35.509
0.617
37.324
2008
154.92
1253.99
4.29
0.232
0.36
0.86
36.15
0.628
37.997
2009
157.72
1345.26
4.34
0.294
0.366
0.875
36.803
0.581
38.626
2010
160.56
1441.35
4.39
0.719
0.373
0.891
37.467
0.172
38.903
2011
163.46
1542.29
4.44
0.731
0.379
0.907
38.143
0.176
39.606
2012
166.41
1648.15
4.5
0.757
0.386
0.924
38.832
0.166
40.308
2013
169.42
1758.98
4.55
0.794
0.393
0.94
39.533
0.146
41.012
2014
172.47
1874.84
4.61
0.779
0.4
0.957
40.246
0.179
41.783
2015
175.59
1995.78
4.66
0.728
0.408
0.975
40.973
0.247
42.602
2016
178.76
2121.85
4.72
0.686
0.415
0.992
41.712
0.306
43.425
2017
181.98
2253.13
4.77
0.65
0.422
1.01
42.465
0.36
44.258
2018
185.27
2389.65
4.83
0.628
0.43
1.028
43.232
0.4
45.09
2019
188.61
2531.49
4.89
0.613
0.438
1.047
44.012
0.434
45.931
2020
192.02
2678.7
4.95
0.639
0.446
1.066
44.806
0.427
46.745
2021
195.48
2831.33
5.01
0.679
0.454
1.085
45.615
0.406
47.56
2022
199.01
2989.46
5.07
0.655
0.462
1.105
46.439
0.45
48.455
2023
202.6
3153.14
5.13
0.615
0.47
1.125
47.277
0.51
49.381
2024
206.26
3322.43
5.19
0.619
0.479
1.145
48.13
0.525
50.279
2025
209.98
3497.4
5.25
0.632
0.487
1.165
48.999
0.534
51.186
2026
213.77
3678.11
5.32
0.636
0.496
1.187
49.883
0.55
52.116
2027
217.63
3864.61
5.38
0.64
0.505
1.208
50.784
0.568
53.065
2028
221.56
4056.99
5.44
0.593
0.514
1.23
51.7
0.637
54.081
2029
225.56
4255.29
5.51
0.543
0.524
1.252
52.634
0.709
55.118
Source: Estimated model data to build System Dynamics Model is based on the information taken from FAO (2009), UNDP-ECC
undated), Clark (1990), EC-FAO (2002) and GOP (2005).
... In the protected areas illegal logging practices can cause the rare plant and animal species to become threatened Reboredo [20]. ...
... Pakistan contributes about $6.53 billion in the underground market economy including $782 million for illegal logging. These values are based on annual illegal wood harvesting Nazir et al.[20]. ...
... In Indonesia conflicts related to timber may cause damage to property, injuries and even deaths. The fastgrowing economy of China is imposing great pressure on wood consumption by means of the huge amount of wood being imported and increasing plantation Reboredo[20].The economic impacts are that when trees are cut down without permits and then smuggled abroad the financial loss is faced by government in different ways.These losses are in the form of taxes and duties that are not paid by smugglers or in another word through corruption (TEFSO). The illegal logging also disturbs the timber markets in a way by lowering down the timber prices which leads to unsustainable forest management (TEFSO). ...
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