Journal of Economic Cooperation and Development, 37, 3 (2016), 87-108

Environmental Kuznets Curve for Deforestation in Indonesia:
An ARDL Bounds Testing Approach
Efendi Agus Waluyo1 and Taku Terawaki2
The main objective of this study is to empirically demonstrate the inverse
U-shaped relationship, which is generally called the environmental Kuznets
curve (EKC), between economic development and deforestation rate inIndonesia. For this purpose, we analyzed time-series data for Indonesia over 46
years from 1962 to 2007 with the autoregressive distributed lag (ARDL)
bounds testing approach to cointegration. Results support the long-run
inverted-U relationship, which implies that, while the deforestation rateincreases at the initial stage of economic growth, it declines after a thresholdpoint. The income turning point of the EKC was calculated to be US$ 990.4.
These findings derived solely from the time-series data for Indonesia provide
helpful information for the Indonesian government and policy-makers in thesense that it explicitly indicates the specific tendency for that country.

1. Introduction
A “grow first, clean up later” approach, which means that only the
economic growth is targeted with little regard for its environmental
impact, is the basic strategy that have been taken by many developing
countries. Unfortunately, the rapid economic growth in this strategy hasoften caused unprecedented environmental degradation in an early stageof the growth especially. Tropical deforestation is one of the examples.
Since the forestry sector is a major contributor to the economy in thedeveloping countries most part of whose land is covered in forest, theinitial economic growth in those countries have naturally a direct andnegative impact on the forest ecosystem. Flood damage occurring in allparts of the world will be one piece of clear evidence showing the fact.
There is no doubt that it is one of the greatest concerns of many
1 Forestry Research Institute, Forestry Research and Development Agency (FORDA),

The Ministry of Forestry Republic of Indonesia E-mail address: ndi_arla@yahoo.com
2 College of Economics, Ritsumeikan University, Japan

88

Environmental Kuznets Curve for Deforestation in Indonesia:
An ARDL Bounds Testing Approachdeveloping countries to know whether “grow first, clean up later” is acostly strategy in a long-run view, and whether it can be a threat against
the sustainability of growth itself.
Indonesia, which has the most extensive forest area in the ASEAN
nations, has also suffered from massive and rapid destruction of theforests for the last few decades, while the country has experienced theeconomic growth acceleration by extracting natural resources in its“grow first, clean up later” strategy. The Food and Agriculture
Organization of the United Nations (FAO) shows that about 30 % of theforest cover area in Indonesia had been lost during the period 1962-2007,
although the GDP per capita had increased by five times and moreduring the same time. However, such a negative correlation between
these two economic and environmental indicators is not always applied.
The inverse U-shaped relationship between them, which is generally
called “environmental Kuznets curve (EKC)”, is a key concept inunderstanding the impact of economic growth on deforestation. The
evidence of the existence of EKC for deforestation would encourage
developing countries facing the problem of serious forest loss toadvance their economic development.
The main objective of this study is to empirically demonstrate theinverse U-shaped relationship between economic development anddeforestation rate in Indonesia. We also figure out the turning point at
which the increase in income level does not lead to the increase in
deforestation rate. For these purposes, time-series data for Indonesia
over 46 years from 1962 to 2007 was analyzed with the autoregressive
distributed lag (ARDL) bounds testing approach to cointegration,
developed by Pesaran and Shin (1998) recently. Although many relevant
previous studies have so far tested the EKC hypothesis for deforestation
with cross-country or panel data (Shafik and Bandyopadhyay, 1992;
Koop and Tole, 1999; Bhattarai and Hammig, 2001; Culas, 2007), to ourknowledge, no study exists as yet that has shown the existence of the
EKC on deforestation by using the data for a single country. The
findings derived solely from the time-series data for Indonesia would
provide helpful information for the Indonesian government andpolicy-makers in the sense that it explicitly indicates the specifictendency for that country.

Journal of Economic Cooperation and Development

89

The paper consists of six sections. Following this introduction, thesecond section provides the literature review on previous EKC studies.
The third section explains the empirical model specified in this study
and the data employed for the analysis. The ARDL bounds testingprocedure will be described in the forth section. The fifth section
discusses the results and discussion. The key findings are summarized in
the sixth section, and then we conclude this paper with further
discussion.
2. Literature review on the EKC
The environmental Kuznets curve is a theoretical concept that describes
the relationship between income growth and environmental degradation.
The term is named for Simon Kuznets (1955) who proposed that aconnection between economic growth and income equality is shaped as
an inverted U. This inverted U-shape hypothesis for environmental
indicators were first examined by Grossman and Krueger (1991). They
found in their study that the concentrations of two air pollutants out of
three (sulfur dioxide and “smoke”) increases at a low level of national
income and decreases at a higher level of income by analyzing the datafor 42 countries. Studies on the EKC following Grossman and Krueger(1991) have exhibited an inconsistent tendency. Panayotou (1995) andSong et al. (2008) showed that the EKC hypothesis was supported for all
the pollutants employed in those studies in common, while Grossman
and Krueger (1995), Akbostanchi et al. (2009), Shaw et al. (2010)
demonstrated that the inverted U is not necessarily described for all theenvironmental indicators. More recently, it is reported that the inverse
U-shaped relationship regarding carbon dioxide was accepted for China(Jalil and Mahmud, 2009; Jalil and Feridun, 2011) and for France (Iwata
et al., 2010), but it was rejected for Turkey (Akbostanchi et al., 2009;
Ozturk and Acaravci, 2010) and for Russia (Pao et al., 2011). We also
have to note that these inconsistent results could be caused by the otherfactors, such as model specification and employed variables (Stern,2004).
The EKC studies regarding deforestation have also produced various
findings. In the two pioneering papers, mixed results are reported.
Shafik and Bandyopadhyay (1992) failed to explain the EKC

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Environmental Kuznets Curve for Deforestation in Indonesia:
An ARDL Bounds Testing Approach

relationship between income and two types of deforestation indicators(annual deforestation and total deforestation). In Panayotou (1995)1, on
the contrary, an inverse U-shaped relationship appeared to hold between
forest area and GDP per capita from the cross-sectional data covering 68
countries. The income turning point was estimated to be about US$ 800
in his research. Several studies that investigated the existence of the
EKC for each continent also do not show regular patterns. While
Bhattarai and Hammig (2001) suggested that there was a strong
evidence of the EKC relationship between income and deforestation for
all the three continents of Latin America, Africa, and Asia, Cropper &Griffiths (1994) indicated that the EKC hypothesis was supported for
Latin America and Africa, but not for Asia. In the more recent researchby Culas (2007), the inverted-U shape was statistically accepted only for
Latin America. The existence of the EKC for Africa or Asia did not
result in being significant in that study. In addition, Koop and Tole
(1999) were unable to reject the hypothesis that the country-specificcoefficients of GDP and GDP squared were vary across the countries foreach of Latin America, Africa, and Asia. This implies the necessity of
estimating an individual EKC with the data for each single country, as
well as the difficulty of obtaining a single EKC relationship among all
the countries in a region.
3. Model specification and data
This study estimates the deforestation equation that describes the factors
affecting deforestation in Indonesia, by using time-series data for thatcountry over 46 years from 1962 to 2007. The empirical model is
specified as(1)

𝐷𝐸𝐹 = 𝛼 + 𝛽1 𝐺𝐷𝑃𝑡 + 𝛽2 (𝐺𝐷𝑃𝑡 )2 + 𝛽3 𝑃𝑂𝑃𝐺𝑅𝑊𝑡+ 𝛽4 𝑅𝑃𝑂𝑃𝑡 + 𝛽5 𝐴𝐺𝐼𝑡 + 𝛽6 𝐴𝐺𝐿𝑡 + 𝛽7 𝑅𝑊𝑂𝑂𝐷𝑡+ 𝛽8 𝐹𝑂𝑅𝐸𝑋𝑃𝑡 + 𝑢𝑡 ,where DEF is the annual rate of deforestation, GDP is gross domestic
product per capita, POPGRW is population growth, RPOP is rural
population, AGI is agricultural index, AGL is agricultural land area,
RWOOD is roundwood production, FOREXP is forest products export,
1

The original paper was published in 1993 (Panayotou, 1993).

Journal of Economic Cooperation and Development

91

and ut is a stochastic error term. The subscript t refers to year t. The DEF,
which is the dependent variable in equation (1), is calculated as(2) DEF = (Ft-1 – Ft) / Ft-1
where F is forest cover area. The data on forest cover area comes from
FAOSTAT released by FAO. For the GDP, which is the most importantdata in the explanatory variables, we employ the real GDP per capita
converted into US dollars that is obtained from World Bank. The EKC
hypothesis for deforestation in Indonesia would be accepted, when thecoefficient of GDP is positive and the coefficient of GDP2 is negative inequation (1).
As carried out by many previous EKC studies on deforestation, we also
include variables other than income as explanatory variables, because
the causes of deforestation are considered to be complex and interlinked.
In our analysis, the significances of population, agricultural, and forestry
factors, as well as income, are inspected. First, the variables of
population growth (POPGRW) and rural population (RPOP) are
included in the model to examine the impact of population pressure on
deforestation. Those variables have been widely used in previous
empirical studies (Cropper and Griffiths, 1994; Bhattari and Hammig,
2001; Barbier and Burgess, 2001; Culas, 2007). We obtained the data on
population growth from World Bank, and the data on rural populationfrom FAOSTAT. Population pressure can increase the demand for forest
products or alternative land uses that causes deforestation, but it mightalso work so as to reduce the deforestation, inducing technologicalprogress or institutional changes in agricultural or forestry sector (Culas,2007). Second, we add the variables of agricultural land area (AGL) andagricultural production index2 (AGI) to the list of explanatory variables.
The purpose of adding them is to illustrate how the deforestation inIndonesia is connected with the increase in agricultural production. The
two major strategies to promote agricultural production are theexpansion of agricultural land into forests and technologicalimprovement in agriculture. The AGL and AGI are employed as proxyvariables for them, respectively. Third, the model comprises roundwood
2This index shows the relative level of the aggregate volume of agricultural
production for each year in comparison with that of the base period of 1999-2001.

92

Environmental Kuznets Curve for Deforestation in Indonesia:
An ARDL Bounds Testing Approachproduction (RWOOD) and forest products export (FOREXP) as thevariables expressing the forestry factors of deforestation. These can be
the direct determinants that raise the deforestation rate. The sources ofdata on agricultural and forestry variables are all FAOSTAT.
4. The ARDL bounds testing procedure
The ARDL bounds testing approach to cointegration developed by
Pesaran and Shin (1998) is often applied by EKC studies in recent years(Jalil and Mahmud, 2009; Shahbaz et al., 2010; Iwata et al., 2010; Jalil
and Feridun, 2011). While many macroeconomic variables areintegrated of order zero (I(0)) or one (I(1)), this approach is applicable,
even in the case that explanatory variables have different orders ofintegration, as long as it is less than two. In addition, it is argued that the
ARDL approach to cointegration gives better results for small sample
data, as compared to other techniques, such as Engle and Granger (1987)
and Johansen and Juselius (1990) (Haug, 2002).
The first step of this ARDL approach is to establish the long-run
relationship among variables by estimating an unrestricted error
correction model. In this study, the model is specified as
(3)

∆𝐷𝐸𝐹𝑡
= 𝛼 + 𝛽0 𝐷𝐸𝐹𝑡−1 + 𝛽1 𝐺𝐷𝑃𝑡−1 + 𝛽2 (𝐺𝐷𝑃𝑡−1 )2+ 𝛽3 𝑃𝑂𝑃𝐺𝑅𝑊𝑡−1 + 𝛽4 𝑅𝑃𝑂𝑃𝑡−1 + 𝛽5 𝐴𝐺𝐼𝑡−1+ 𝛽6 𝐴𝐺𝐿𝑡−1 + 𝛽7 𝑅𝑊𝑂𝑂𝐷𝑡−1 + 𝛽8 𝐹𝑂𝑅𝐸𝑋𝑃𝑡−1
+∑
+∑
+∑
+∑
+∑

𝑝

𝛿𝑖 𝛥𝐷𝐸𝐹𝑡−𝑖 + ∑

𝑖=1
𝑝

𝑝

𝜃𝑖 𝛥𝐺𝐷𝑃𝑡−𝑖𝑖=1𝜇𝑖 𝛥(𝐺𝐷𝑃𝑡−𝑖 )2 + ∑𝑝

𝜋𝑖 𝛥𝑃𝑂𝑃𝐺𝑅𝑊𝑡−𝑖

𝑖=1
𝑝

𝑝

𝑖=1
𝑝

𝑝

𝑖=1

𝑖=1
𝑝

𝑖=1

𝜌𝑖 𝛥𝑅𝑃𝑂𝑃𝑡−𝑖 + ∑𝜏𝑖 𝛥𝐴𝐺𝐿𝑡−𝑖 + ∑𝑖=1

𝜎𝑖 𝛥𝐴𝐺𝐼𝑡−𝑖

𝜑𝑖 𝛥𝑅𝑊𝑂𝑂𝐷𝑡−𝑖

𝜔𝑖 𝛥𝐹𝑂𝑅𝐸𝑋𝑃𝑡−𝑖 + 𝑢𝑡 ,𝑖=1

where α is the drift component, and ut is the white noise error
component. The null hypothesis that there is no cointegration among theJournal of Economic Cooperation and Development

93

variables is expressed as β0=β1= β 2=β 3= β 4=β 5=β 6= β 7=β 8.
We can conclude that there is a cointegration relationship among them,
if the calculated F-statistics is more than the upper critical bound given
by Pesaran et al., (2001). If the F-statistics is lower than the lower
critical bound, then it is judged that there is no cointegration. The
decision regarding cointegration will be inconclusive, when theF-statistic lies within the upper and lower critical bounds.
Once a cointegration relationship among the variables is established, thenext step is to obtain the long-run equilibrium equation for deforestationand its determinants. We can derive the reduced-form solution fromequation (3) as(4)

𝐷𝐸𝐹𝑡 = 𝜆0 + 𝜆1 𝐺𝐷𝑃𝑡 + 𝜆2 (𝐺𝐷𝑃𝑡 )2 + 𝜆3 𝑃𝑂𝑃𝐺𝑅𝑊𝑡+ 𝜆4 𝑅𝑃𝑂𝑃𝑡 + 𝜆5 𝐴𝐺𝐼𝑡 + 𝜆6 𝐴𝐺𝐿𝑡 + 𝜆7 𝑅𝑊𝑂𝑂𝐷𝑡+ 𝜆8 𝐹𝑂𝑅𝐸𝑋𝑃𝑡 + 𝑢𝑡 ,where 𝜆0 = − 𝛼 ⁄𝛽0 , 𝜆1 = − 𝛽1⁄𝛽0 , 𝜆2 = − 𝛽2⁄𝛽0 , 𝜆3 = − 𝛽3⁄𝛽0 ,𝜆4 = − 𝛽4 ⁄𝛽0 , 𝜆5 = − 𝛽5⁄𝛽0 , 𝜆6 = − 𝛽6⁄𝛽0 , 𝜆7 = − 𝛽7⁄𝛽0 , and
𝜆8 = − 𝛽8⁄𝛽0 . On the other hand, the short-run dynamics is described in
the form of an error correction model (ECM) as
(5)

∆𝐷𝐸𝐹𝑡
=∑
+∑
+∑
+∑
+∑

𝑝

𝛿𝑖 𝛥𝐷𝐸𝐹𝑡−𝑖 + ∑

𝑖=1
𝑝

𝑝

𝜃𝑖 𝛥𝐺𝐷𝑃𝑡−𝑖𝑖=1𝜇𝑖 𝛥(𝐺𝐷𝑃𝑡−𝑖 )2 + ∑𝑝

𝜋𝑖 𝛥𝑃𝑂𝑃𝐺𝑅𝑊𝑡−𝑖

𝑖=1
𝑝

𝑝

𝑖=1
𝑝

𝑝

𝑖=1

𝑖=1
𝑝

𝑖=1

𝜌𝑖 𝛥𝑅𝑃𝑂𝑃𝑡−𝑖 + ∑𝜏𝑖 𝛥𝐴𝐺𝐿𝑡−𝑖 + ∑𝑖=1

𝜎𝑖 𝛥𝐴𝐺𝐼𝑡−𝑖

𝜑𝑖 𝛥𝑅𝑊𝑂𝑂𝐷𝑡−𝑖

𝜔𝑖 𝛥𝐹𝑂𝑅𝐸𝑋𝑃𝑡−𝑖 + ψ 𝐸𝐶𝑇𝑡−1 + 𝑢𝑡 ,𝑖=1

where the ECT is an error correction term.
To evaluate the goodness of fit of the model, we use several criteria.
These include classical assumption test, R-squared and adjusted

94

Environmental Kuznets Curve for Deforestation in Indonesia:
An ARDL Bounds Testing ApproachR-squared, lowest standard error of regression, lowest AIC, lowest SIC,
and model stability test. The technique employed to test model stability
is cumulative sum (CUSUM) and cumulative sum of squares(CUSUMSQ). If the plots of CUSUM and CUSUMSQ statistics staywithin the critical bounds of 5 % level of significance, the null
hypothesis that all of the coefficients in the given regression are stablecannot be rejected.
5. Results and discussion
5.1. Preliminary Examination
This study performs the conventional Augmented Dicky Fuller (ADF)
test and the Philip-Perron (PP) test to ensure that none of the variables
are I(2) or beyond. Table 1 shows the results of these unit root tests foreach variable. In the ADF test, the Swarchz Baysian Criterion (SBC)
was used to determine the optimal lag length. As shown in Table 1, theresults of ADF tests indicate that most of the variables are non-stationary
and have a unit root, while only the population growth (POPGRW) andrural population (RPOP) are stationary at level. The results of PP tests
are also consistent with those of ADF tests. We can conclude from theseresults that there is only a mixture of I(0) and I(1) among underlying
regressors. Hence, the ARDL bounds testing approach to cointegration
can be applied in this analysis (Duasa, 2007).

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Journal of Economic Cooperation and Development

Table 1. Results of Unit Root Tests
Variable

DEF
GDP
GDP

2

ADF test at level
None

Intercept

0.8275

ADF test at first difference
NoneIntercept

Trend andintercept

0.8311

Trend
andintercept
0.08250.0001***0.0008***0.0046 ***1.00000.99840.30020.0008 ***0.0003 ***0.0012 ***0.99990.99980.82060.0004 ***0.0008 ***0.0011 ***POPGWR

0.0003***0.29210.38930.24430.0672 *0.6523

RPOP

0.0001***0.0014***0.52240.16730.9972

0.2186AGI

1.00001.00000.88960.35030.0000 ***0.0000 ***AGL

0.9606

0.95750.64020.0000 ***0.0000 ***0.0002 ***RWOOD

0.0000***0.98680.75430.0155 **0.0000 ***0.0000 ***FOREXP

0.96250.9396

0.29400.0000 ***0.0000 ***0.0000 ***PP test at level

PP test at first difference

DEF

0.91840.90250.45850.0001 ***0.0016 ***0.0096 ***GDP

1.00000.99840.44180.0010 ***0.0004 ***0.0015 ***GDP2

0.99970.99950.89030.0004 ***0.0007 ***0.0014 ***POPGWR

0.0372 **0.99160.11500.35690.52620.8978RPOP

0.94290.0342**1.00000.33580.99250.1833AGI

1.00001.00000.87010.0012 ***0.0000 ***0.0000 ***AGL

0.95050.94100.64020.0000 ***0.0000 ***0.0003 ***RWOOD

0.0000 ***0.9906

0.76260.0001 ***0.0000 ***0.0000 ***FOREXP

0.97690.95900.30340.0000 ***0.0000 ***0.0000 ***Note1: Reported values are p-values for testing the null hypothesis that the variable has unit root.
Note 2: The symbols ***, **, and * indicate 1, 5, and 10 percent of significance, respectively.

The step of discovering the long-run relationship among explanatory
variables requires an adequate lag length of them in order to remove any
serial correlation. The optimum lag length of vector autoregressive(VAR) is usually selected based on AIC, SBC, and likelihood ratio (LR)test statistic. From the values of each criterion presented in Table 2, we
can choose order 2 in this study. Pesaran and Shin (1998) and Narayan(2005) also have suggested that we should choose 2 as the maximum
order of lags for annual data in the ARDL.

96

Environmental Kuznets Curve for Deforestation in Indonesia:
An ARDL Bounds Testing Approach

Table 2. Selection Criteria for Lag Length
Lag

LogL

LR

FPE

AIC

SC

HQ

0

-2847.4NA

4.04E+46132.8558

133.2244

132.9918

1-2445.04

617.5763

1.41E+40

117.9088

21.5951*119.2682

2-2307.94

3.0454*1.73e+39*

5.2994*122.3032

17.8822*

Note 1: The symbol * indicates the lag order selected by the criterion.
Note 2: LR: Sequential modified LR test statistic (each test at 5% level); FPE: Final
prediction error; AIC: Akaike information criterion; SC : Schwarz information
criterion; HQ: Hannan-Quinn information criterion

Table 3. Test Results of Granger Causality
Null Hypothesis
GDP does not Granger Cause DEF
DEF does not Granger Cause GDP
GDP2 does not Granger Cause DEF
DEF does not Granger Cause GDP2

F-Statistic

Prob.

6.45599

0.00380.317880.72963.804360.03090.790560.4607A major concern in the analysis of EKC hypothesis is whether the GDP
has an impact on the deforestation or the contrary. We therefore
conducted the Granger causality tests to ascertain the direction of
causality, before testing the cointegration. As presented in Table 3, theresults indicate that GDP per capita Granger causes deforestation rate in
the long run at the 1 % level of significance. The same results were also
observed for GDP per capita squared at the 5 % level.
5.2. Results of bounds testing
The F-statistic calculated under equation (3) for testing whether thevariables are cointegrated or not was 1.515 as shown in Table 4. Thisvalue is lower than the lower bound critical value at the 10 % level ofsignificance given by both Pesaran et al. (2001) and Narayan (2005),
which implies that null hypothesis of no cointegration cannot be rejected.
Hence, it is concluded that DEF is not cointegrated with GDP, GDP2,
POPGRW, POPDEN, AGI, AGL, RWOOD, FOREXP.

Journal of Economic Cooperation and Development

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Table 4. Result of Cointegration Test (including all the variables)
Equation

DEF = f (GDP, GDP2, POPGRW, RPOP, AGI, AGL, RWOOD, FOREXP)

Lag structure

ARDL (2,2,2,2,2,2,2,2,2)

F-statistic

1.515425

Significancelevel

Bound critical values of Case III (Unrestricted intercept and no trend)
Pesaran et al. (2001)

Narayan (2005)

I(0)

I(1)

I(0)

I(1)

1%

3.41

4.68

4.030

5.598

5%

2.62

3.79

2.922

4.268

10%

2.26

3.35

2.458

3.647

Since no evidence of cointegration was detected among all the variables,
we next attempted to narrow the variables down.We finally selected thevariables of RPOP, AGI, and RWOOD, as well as GDP and GDP2,
based on the statistical significance of those variables. The F-statistic
calculated again (=3.564) was higher than the upper bound critical value
provided by Pesaran et al. (2001) at the 10 % level of significance. Thisimplies that the null hypothesis of no cointegration can be rejected at the
10 % level, but it has not been supported yet according to the Narayan’s
critical values.
Then, we eliminated insignificant variables, except for the levelvariables and the intercept, with the general-to-specific approach(Krolzig and Hendry, 2001). The results are displayed in Table 5. The
F-statistic (=4.183) is higher than the upper critical value at the 5 %level of significance given by Pesaran et al. (2001), and at the 10 %level by Narayan (2005). It suggests that the variables are cointegrated,and confirms the existence of long-run relationship among them.

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Environmental Kuznets Curve for Deforestation in Indonesia:
An ARDL Bounds Testing Approach

Table 5. Result of Cointegration Test (excluding POPGRW, AGL, FOREXP)
Equation

DEF = f (GDP, GDP2, RPOP, AGI, RWOOD)

Lag structure

ARDL (2,1,1,1,1,0)

F-statistic

4.182707

Significancelevel

Bound critical values of Case III (Unrestricted intercept and no trend)
Pesaran et al. (2001)

Narayan (2005)

I(0)

I(1)

I(0)

I(1)

1%

3.41

4.68

4.030

5.598

5%

2.62

3.79

2.922

4.268

10%

2.26

3.35

2.458

3.647

5.3. Long-run and short-run coefficient estimates
The estimated long-run coefficients are presented in Table 6. All the
explanatory variables included in this equation significantly affect the
deforestation rate. The positive coefficient of GDP and the negative
coefficient of GDP2 support the existence of the inverse U-shaped
relationship between economic growth and deforestation rate. Thisfinding is consistent with the empirical evidence of Panayotou (1995)
and Bhattarai and Hammig (2001). The income turning point (ITP) is
calculated to be US$ 990.4 from these coefficient estimates. This value
lies within the range of the GDP data set employed in this analysis,
which suggests that the ITP has already been reached in Indonesia. This
result is in line with several previous studies showing that ITP for
deforestation is placed within the sample range (Panayotou, 1995;
Kallbekken, 2000; Bhattarai and Hammig, 2001). Panayotou (1995) also
found that the ITP for deforestation in developing countries was
US$ 823. The value of ITP obtained in this research is extremely close
to the Panayotou’s finding.
The coefficient relating rural population to deforestation rate wasnegative and significant at the 1% level. This suggests that an increase inrural population in Indonesia tends to decrease the deforestation rate.
The same tendency was also found in several previous studies. In
Cropper and Griffiths (1994), and Bhattarai and Hammig (2001), rural
population density had a negative effect on the deforestation in AsianJournal of Economic Cooperation and Development

99

region. In addition, Reis and Guzman (1994) obtained the negative signof the coefficient of rural population in the case of Amazon deforestation.
Culas (2007) also detected a negative coefficient of population density.
Templeton and Scherr (1999) noted that population pressure on forest
resources will increase at first, but it will change along with efficiency inproduction processes into the direction of the conservation of theremaining forest resources. This result might be related to thetechnological or institutional innovation induced from populationpressure.
As to the agricultural indicators, the AGI significantly affects thedeforestation rate in a negative way. This implies that an increase inagricultural production does not promote the conversion of forest lands
to agricultural lands, and that the increase has been led by improving
technology in agriculture. Technological progress in agriculture must
reduce the pressure on land demand and slow down the speed ofdeforestation.
Roundwood production was also significantly connected to thedeforestation rate in Indonesia. This negative coefficient of RWOOD
indicates that the deforestation rate decreases with increasing log
production. Allen and Barnes (1985) that examined the effect of wooduse on forest area change over 1968-78 in developing countries, also
found a negative coefficient of wood use variable. This result may beclosely associated with that the data on roundwood production used inthis study is legally reported one. The roundwood products reported
legally are probably the ones that come from the forest managed
sustainably, and thus they cannot be a cause of deforestation.
Unfortunately, many log products have not been officially reported andsome of them are illegally produced. There is also evidence that large
amounts of timber traded in the world market are harvested illegally(Hembery et al., 2007). The increase in illegal logs may decrease theproduction of legal logs, and may cause higher deforestation at the same
time.

100

Environmental Kuznets Curve for Deforestation in Indonesia:
An ARDL Bounds Testing Approach

Table 6. Estimation Results of Long-run Model
Dependent Variable = DEF
Variable

CoefficientT-statistic

Prob.

Intercept

8.7418016

4.010585***0.0003GDP

0.0266419

4.066877***0.0003GDP2-1.345E-05-3.404278***0.0018RPOP-0.0001057-3.582405***

0.0011AGI-0.0332558-2.893453***

0.0068RWOOD-2.092E-08-3.141136***0.0036

Diagnostic Checks
Jarque-Bera1.2114 (0.5457)

Serial Correlation LM

0.5612 (0.5764)

Heterocedasticity Test

0.9185 (0.5349)

Note1: ARDL (2,1,1,1,1,0) was selected on the basis of AIC.
Note2: The symbol *** indicates 1 percent of significance.

The three diagnostic tests of LM test, normality test of residual term,and White heteroscedasticity test was also conducted in this step. The
results show that the long-run model has passed all the diagnostic tests
successfully. This indicates that there is no serial correlation, the residual
term is normally distributed, and there is no evidence of White
heteroscedasticity.
The results of short-run dynamics are presented in Table 7. The signs ofcoefficients of GDP and GDP2 support the EKC hypothesis at the 1 %level of significance. Only roundwood production variable wasinsignificant, which implies that the change in log production does notaffect the change in deforestation in the short run. The coefficient oflagged ECT is statistically highly significant, and its sign and size arealso reasonable, since it is generally required to be greater than -1 andless than 0. The coefficient estimate of that variable suggests thatdeviation from long-run equilibrium is corrected by nearly 57 % within
a year.

Journal of Economic Cooperation and Development

101

Table 7. Estimation Results of Short-run Model
Dependent Variable = ΔDEF
Variable

T-statistic

Prob.

Intercept

Coefficie
nt
0.463402

4.046573***0.0002ΔGDP

0.0141943.554423***0.0010-7.52E-06-3.168890***0.0030

ΔRPOP

0.000505

5.236923***0.0000

ΔAGI-0.029510-3.919570***0.0004ECT(-1)-0.568800-5.676490***0.0000

ΔGDP

2

Diagnostic Checks
Jarque-Bera0.8867 (0.6418)

Serial Correlation LM

0.7740 (0.4689)

Heterocedasticity test

0.9760 (0.4634)

Note1: ARDL (2,1,1,1,1,0) was selected on the basis of AIC.
Note2: The symbol *** indicates 1 percent of significance.
Note3: ECT = DEF - 0.0266419*GDP + 1.345E-05*GDP2 + 0.0001057*RPOP +0.0332558*AGI + 2.092E-08*RWOOD - 8.7418016

The last stage of ARDL bounds testing approach is to check the stabilityof parameter estimates included in the model. In order to test thestability, cumulative sum (CUSUM) and cumulative sum of squares(CUSUMSQ) tests are generally performed. Figure 1 exhibits the plotsof CUSUM and CUSUMSQ, respectively. We can see from this figurethat the statistics are well within the critical bounds, which means that
all the parameter estimates in the model are stable.

102

Environmental Kuznets Curve for Deforestation in Indonesia:
An ARDL Bounds Testing Approach

Figure 1. Plots of Cumulative Sum (CUSUM) and Cumulative Sum of Squares(CUSUMSQ) of Recursive Residuals
20

1.4

15

1.2
1.0

10

0.8

5

0.6
0

0.4
-5

0.2

-10

0.0

-15

-0.2
-0.4

-20
76

78

80

82

84

86

88

90

CUSUM

92

94

96

98

5% Significance

00

02

04

06

76

78

80

82

84

86

88

90

92

CUSUM of Squares

94

96

98

00

02

04

5% Significance6. Conclusion
Out results support the long-run inverted-U relationship between
economic growth and deforestation rate in Indonesia. It implies that,while the deforestation rate increases at the initial stage of economic
growth, it declines after a threshold point. Regarding previous study
conducted by several researchers (Shafik & Bandyopadhyay, 1992;
Cropper & Griffiths, 1994; Koop & Tole, 1999; Bhattarai & Hammig,
2001; Barbier & Burgess, 2001; Culas, 2007), the results of EKC for
deforestation in Asia are still debatable due to variety of data andmethodology. All of them used cross-country analyses and panel data
analyses. In addition, some studies used relatively small sample sizethat might made the EKC hypothesis was not supported in somestudies in Asia. The income turning point of the EKC was calculated
to be US$ 990.4. This estimated ITP lies within the range of the data
on GDP employed in this analysis, which means that the ITP has
already been reached in Indonesia. In addition, we found that rural
population, agricultural index, and roundwood production have anegative and significant impact on the deforestation. These results
suggest in order that (1) the deforestation might be restrained bytechnological or institutional innovation in agricultural or forestry
sectors induced from population pressure in rural area, (2)technological progress in agriculture reduce the pressure on landdemand, and then would slow down the speed of deforestation, and(3) there is a possibility that the increase in “illegal” logs, which are
not reported officially, cause higher deforestation. The analysis of06

Journal of Economic Cooperation and Development

103

short-run dynamics also reveals that the deviation from the long-run
equilibrium is quickly adjusted.
While the EKC results obtained from cross-country information
clearly shows an inverse U-shaped relationship as a whole between
economic development and environmental degradation, they would beinsufficient for each developing country facing the issue of whether
the development of the country is sustainable or not to be
optimistically confident that “grow first, clean up later” strategy will
work well in that country. As described above, several previous
studies analyzing these data, in fact, have revealed that there exist
different EKCs among continents. In terms of practical policy-making
on sustainable development, it would be necessary to test theexistence of the specific EKC for each country. This study definitely
demonstrates the usefulness of adopting the ARDL approach in theevaluation of EKC hypothesis for a single country.
Due to data restriction, it was not possible to use provincial data inthis study. Using provincial data would provide a more valuable
insight into policy making, because they can introduce the effect ofregion to the model. Another weakness of this study is that there is no
policy variable in the empirical model. Policy variables that could be
included are, for example, international environmental agreement,
enforcement of environmental legislation, and green project policy(e.g. forest and land rehabilitation movement). The addition of thesevariables would facilitate explaining what kinds of policies can
reduce deforestation.

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Environmental Kuznets Curve for Deforestation in Indonesia:
An ARDL Bounds Testing Approach

References
Akbostanci, E., Türüt-Asik, S., Tunc, G. I., 2009. The relationshipbetween income and environment in Turkey: Is there an environmental
Kuznets curve? Energy Policy, 37 (2), 861-867.
Allen, J.C., Barnes, D.F., 1985. The causes of deforestation indeveloping countries. Annals of the Association of American
Geographers, 75 (2), 163-184.
Barbier, E.B., Burgess, J.C., 2001. The economics of tropicaldeforestation. Journal of Economic Survey, 15 (3), 413-433.
Bhattarai, M., Hammig, M,. 2001. Institutions and the environmental
Kuznets Curve for deforestation: A cross-country analysis for Latin
America, Africa, and Asia. World Development, 29 (6), 995-1010.
Cropper, M., Griffits, C., 1994. The interaction of population growth andenvironmental quality. The American Economic Review, 84 (2), 250-254.
Culas, R.J., 2007. Deforestation and the environmental Kuznets curve:
An institutional perspective. Ecological Economics, 61 (2-3), 429-437.
Duasa, J., 2007. Determinants of Malaysian trade balance: An ARDL
bound testing approach. Journal of Economic Cooperation, 28 (3),21-50.
Engle, R. F., Granger, C. W. J., 1987. Cointegration and error correction
representation: estimation and testing. Econometrica, 55 (2), 251-276.
Grossman, G. M, Krueger, A. B., 1991. Environmental impacts of a
North American free trade agreement. NBER Working Paper 3914. MA:
National Bureau of Economic Research, Cambridge.
Grossman, G.M., Krueger, A.B., 1995. Economic growth and theenvironment. Quarterly Journal of Economics, 110 (2), 353–377.

Journal of Economic Cooperation and Development

105

Haug, A.A., 2002. Temporal aggregation and the power of cointegration
test: A Monte Carlo study. Oxford Bulletin of Economics and Statistic,
64 (4), 399-412.
Hembery, R., Jenkins, A., White, G., Richards B., 2007. Illegal logging:Cut it out! The UK’s role in the trade in illegal timber and wood product.
WWF UK Illegal logging report.
Iwata H., Okada, K., Samreth, S., 2010. Empirical study on theenvironmental Kuznets curve for CO2 in France: The role of nuclear
energy. Energy Policy, 38 (8), 4057–4063.
Jalil, A., Mahmud, S.F., 2009. Environmental Kuznets curve for CO2
emissions: A cointegration analysis for China. Energy Policy, 37 (12),5167-5172.
Jalil, A., Feridun, M., 2011. The impact of growth, energy and financial
development on the environment in China: A cointegration analysis.
Energy Economics, 33 (2), 284-291.
Johansen, S., Juselius, K., 1990. Maximum likelihood estimation andinference on cointegration with applications to the demand for money.
Oxford Bulletin of Economics and Statistics, 52 (2), 169-210.
Kallbekken, S., 2000. An alternative environmental Kuznets curve
approach to deforestation. Disertation in Environmental Economics andEnvironmental Management. University of York, UK.
Koop, G., Tole, L., 1999. Is the an environmental Kuznets Curve for
deforestation? Journal of Development Economics, 58 (1), 231-244.
Krolzig, H-M., Hendry, D.F., 2001. Computer automation ofgeneral-to-specific model selection procedures. Journal of Economic
Dynamics and Control, 25 (6-7), 831-866.
Kuznets, S., 1955. Economic growth and income inequality. The
American Economic Review 45 (1), 1-28.

106

Environmental Kuznets Curve for Deforestation in Indonesia:
An ARDL Bounds Testing Approach

Narayan, P.K., 2005. The saving and investment nexus for China:
Evidence from cointegration tests. Applied Economics, 37 (17),1979-1990.
Ozturk, I., Acaravci, A., 2010. CO2 emissions, energy consumption andeconomic growth in Turkey. Renewable and Sustainable Energy Reviews,
14 (9), 3008-3014.
Panayotou, T., 1993. Empirical Tests and Policy Analysis ofEnvironmental Degradation at Different Stages of Economic
Development. Working Paper WP238: Technology and Employment
Programme, ILO.
Panayotou, T., 1995. Environmental degradation at different stages of
economic development. In: Ahmed, I. and J.A. Doeleman (Eds.),
Beyond Rio: The environmental crisis and sustainable livelihoods in thethird world. ILO, MacMillan Press Ltd., London, pp. 13-36.
Pao, H.-T., Yu, H.-C., Yang, Y.-H., 2011. Modelling the CO2 emissions,energy use, and economic growth in Russia. Energy, 36 (8), 5094-5100.
Pesaran, M.H., Shin, Y., 1998. An autoregressive distributed lag
modeling approach to cointegration analysis. A revised version of apaper presented at the Symposium at the Centennial of Ragnar Frisch,
The Norwegian Academy of Science and Letters, Oslo, March 3-5,
1995. In: Strom, S. (Eds.), Econometrics and Economic Theory in the20th Century. Cambridge University Press, UK, pp. 371-413.
Pesaran, M.H., Shin, Y., Smith, R.J., 2001. Bounds testing approaches toanalysis of level relationship. Journal of Applied Econometrics, 16 (3),289-326.
Reis, E., Guzman, R., 1994. An econometric model of Amazon
deforestation. In: Brown, K., Pearce, D.W. (Eds.), The Causes of
Tropical Deforestation: The economic and statistical analysis of factors
giving rise to the loss of the tropical. UBC Press, Vancouver, Canada, pp.
172-191.

Journal of Economic Cooperation and Development

107

Shafik, N., Bandyopadhyay, S., 1992. Economic growth andenvironmental quality: Time-series and cross-country evidence. Policy
Research Working Paper. World Development Report.
Shahbaz, M., Jamil, A., Dube, S., 2010. Environmental Kuznets curve(EKC): Time series evidence from Portugal. MPRA Paper No.27443, 15
October.
Shaw, D., Pang, A., Lin, C-C., Hung., M-F., 2010. Economic growth andair quality in China. Environmental Economics and Policy Studies, 12(3), 179-196.
Song, T., Zheng, T., Tong, L., 2008. An empirical test of theenvironmental Kuznets curve in China: A panel cointegration approach.
China Economic Review, 19 (3), 381-392.
Stern, D.I., 2004. The rise and fall of the environmental Kuznets curve.
World Development, 32 (8), 1419–1439.
Templeton, S.R., Scherr, S.J., 1999. Effect of demographic and related
microeconomic change on land quality in hills and mountains ofdeveloping countries. World Development, 27 (6), 903-918.