ANALYSIS OF INDONESIA’S GREEN GDP ACCOUNTING

WITH GREY SYSTEM THEORY TYPE GM (1,1) FOR YEARS

2000 – 2010

Scientific Journal

Andistya Oktaning Listra

0910210022

Submitted As Partial Fulfillment Of Requirements

For The Degree Of Bachelor’s Economics

ECONOMICS DEPARTMENT

FACULTY OF ECONOMICS AND BUSINESS

UNIVERSITY OF BRAWIJAYA

MALANG

2013

LEMBAR PENGESAHAN PENULISAN ARTIKEL JURNAL

Artikel Jurnal dengan judul :

Analysis Of Indonesia’s Green GDP Accounting With Grey System Theory Type GM (1,1)

For Years 2000-2010

Yang disusun oleh :

Nama : Andistya Oktaning Listra

NIM : 0910210022

Fakultas : Ekonomi dan Bisnis

Jurusan : S1 Ilmu Ekonomi

Bahwa artikel Jurnal tersebut dibuat sebagai persyaratan ujian skripsi yang dipertahankan di

depan Dewan Penguji pada tanggal 23 Januari 2013

Malang, 29 Januari 2013

Dosen Pembimbing,

Ferry Prasetyia, SE, MappEc.Int.

NIP.19801228 200501 1 00 2

ANALYSIS OF INDONESIA’S GREEN GDP ACCOUNTING WITH GREY SYSTEM

THEORY TYPE GM (1,1) FOR YEARS 2000 – 2010

Andistya Oktaning Listra

Faculty Of Economics and Business University Of Brawijaya Malang

Email: andistya.ol@gmail.com

ABSTRACT

This research aimed to measure Green GDP accounting with Grey System Theory Type GM (1,1)

with a case study on Indonesia's Green GDP for years 2000 – 2010. The data used in this research is

secondary data obtained from Sucofindo. Based on previous research found that Green GDP accounting

which known as success indicator for green economic still encountered some disadvantages such as

general equilibrium effects which not accompanied with policies and efforts to internalize natural

resources and environmental cost, the difficulties to setting the price of natural resources, and unclear

information about natural resources inventories due to the lack of comprehensive calculations in

estimating data, especially related to the quantity, quality, and changes in natural resources. Therefore,

referring to (Lu and Chiu, 2012) the researchers sought to apply Grey System Theory type GM (1,1) for

Indonesia's Green GDP accounting for years 2000 – 2010 as an attempt to measure Green GDP

accounting. From the results of measurements, the value of MPE and MAPE both showed bias conditions

(15%) and less accurate (36.59%). Indirectly those reflect the weakness of Indonesia's Green GDP

accounting.

Keywords: Green GDP accounting, Grey System Theory Type GM (1,1), Weakness in Indonesia’s Green

GDP accounting

A. INTRODUCTION

Climate change and biodiversity loss is the basis of the Rio +20 Summit held in Rio de Janeiro,

Brazil on 20-22 June 2012. The conference aims to create awareness of various countries about the

importance to care for natural resources and environment sustainability. As a country that participated in

Rio +20 Summit, Indonesia try to actualized both of those issues which correlated to the facts that there

are several natural disaster caused by disruption of ecosystem which bring bad impacts for society and

economic stability in Indonesia. Therefore, strategy to decrease the number of natural disasters caused by

climate change and biodiversity loss is with developing the concept of green economy that theoretically

has ability to minimize environmental risks and ecological scarcity which in the long run can improve

human prosperity and social equity (UNEP, 2011).

Green GDP is one of some indicators that can measure the success of a green economy in a

country. The difference between Brown GDP with Green GDP is a systematic calculation especially for

internalizing the cost of natural resources and the environment. Associated with Green GDP

implementation was found several barriers including general equilibrium effects that are not accompanied

with effort and policies to internalize environmental cost in the production sector. It causes Green GDP

still hard to use as the best substitution for Brown GDP especially to evaluate the economic development

condition in a country. In addition, Green GDP accounting also through difficulty to set the prices of

natural resources and still unclear to determine the number of natural resources inventories because its

calculation less comprehensive in estimating data, especially related to the quantities, quality, and

changes in natural resources (Liu and Guo, 2005).

Therefore, after know the facts of the weakness in Green GDP accounting, the measurement to

know the value of bias and accuracy is a must. According to Lu and Chiu (2012), whom the first researchers who applied

Grey System Theory to measure Taiwan’s Green GDP in 2002-2010 it causes by

the strengths and positive traits that Grey System Theory offer to reduce the weakness in Green GDP

accounting such as limited data and asymmetric information. From their results show that the Green GDP

accounting with Grey System Theory type GM (1,1) can show the status of the environment and give

reference value from forecasting which correlated to create next of government relevant policy with the

linkage of economic and environmental condition. Furthermore, Grey System Theory type GM (1,1)

constructed with eight stages algorithm which can be easier to the calculate if using Matlab software than

calculate it with manual or another software (Wen and Chang, 2005).

By knowing the measurement method of one country in Asia such as Taiwan in applying forecast

of Green GDP accounting with Grey System Theory type GM (1,1), the alternative solution for

Indonesian government's green economy programs especially about the purposes to achieve 7% economic

growth and the REDD program (Reducing Emissions from Deforestation and Forest Degradation) that the

success indicator depend on the result of the Green GDP can be fitted by the measurement method with

Grey System Theory type GM (1,1) through Matlab 5.3 application software. Afterwards, the research

sought to apply Green GDP accounting in Indonesia with Grey System Theory type GM (1,1) for years

2000 – 2010.

B. THEORITICAL STUDY

Global Warming in the Context of Climate Change and Biodiversity Loss

Global warming is increasing the average temperature in the earth's surface and oceans causing

significant loss of biodiversity (Teguh, 2009). In this case, Gore (2006) to explain the specifics that global

warming has increased two-fold risk category 4 and 5 hurricanes, the spread of malaria outbreaks,

increase the flow of ice from glaciers in Greenland, increasing the risk of extinction of more than one

million species of plants and animals, increased mortality up to 300,000 people per year, increasing global

sea level more than 20 feet with the loss of ice shelf in Greenland and Antarctica. According to Gerlach

(2010) in Pappas (2011), human activities have a greater role on the global warming. It is clear that the

amounts of carbon dioxide released volcanic eruptions from 0.13 to 0.44 billion tons of CO2 per year.

While the amount of carbon dioxide from human activities in 2010 estimated at 35 billion tons. For

example, land-uses that release 3.4 gigatons per year, motorbike spewing about 3 gigatons per year, and

the production of cement contaminating about 1.4 gigatons of greenhouse gases into the atmosphere. So

that, global carbon dioxide absorbed nearly 50% in line with the emissions resulting from human

activities.

Picture 1.Accumulated of Carbon Dioxide In The World

Source : NOAA, 2010 The Importance of Implementing Green Economy in Indonesia

Cosmisch-gemeen-schapsgevoel is the dominant perspective of the world as the hallmark of

Easternculture, especially Indonesia which essentially shows the orientation that sees everything as a total

unity (Hidding, 1935 in Soerjani, 1987:148). From this perspective built a subjectivity that views human

beings as part of the cosmos and its related to nature by finding the form of harmonious society through

efforts that compromise to foster environmental awareness and action to avoid bad treatment for nature

through pollution and destruction (Poespowardojo, 1987 in Soerjani et.al, 1987:148). In this regard, the

case of pollution and destruction closely occur in the context of externalities of economic activities.

Negative externalities are reflects the market failure because none of cost over the impact of the economic

activity. In average, negative externalities from economic activities caused by an incompatible of natural

and economic systems that finally impacts the decline of natural resources and environmental stability.

Green economy implementation in Indonesia reflects by the philosophical foundations that refers

to the Pancasila and 1945 Constitution. State philosophy (Pancasila), reflects the concept of a green

economy in Indonesia, namely the precepts of just and civilized humanity, and social welfare for all the

people of Indonesia. Under the 1945 Constitution Chapter XIV of the National Economy and Social

Welfare in Article 33 explicitly states that the structure of Indonesian economy is a joint venture on the

principle of kinship with the organize of people's welfare by the principles of natural resources and

environmental sustainable development (Budimanta, 2010). In an effort to support green economy

implementation, President of Indonesia Republic, Susilo Bambang Yudhoyono (SBY) said that the

government has committed to immediately realize a green economy in accordance with the target of

reducing carbon emissions about 26 percent in 2020 without financial assistance.

The Role of Green GDP As Green Economy Success Indicator

Brown GDP is a indicator for the successful development of a country to become a benchmark in

comparative welfare across different countries and across time. But theoretically proven that Brown GDP

fails as a true indicator of economic prosperity (Stockhammer et al., 1997 in Talberth and Bohara, 2005)

and actually its proved with Global Crisis that started in 2008 until now. Among other shortcomings,

Brown GDP has been criticized for failing to addressthe natural resources depletion, environmental

degradation, inequality of gross income, and profit-oriented economic activity regardless externality

(Castaneda, 1999; Torras, 2003 in Talberth and Bohara, 2005). In addition, Brown GDP ignoring input

and output prices are not bought and sold in the market so there is no range of values of ecosystem

services that must be removed so that the necessary systematic calculation capable of measuring well-

being and meet the context of sustainable development (Hanley, 2000 in Talberth and Bohara, 2005).

Green GDP is an innovative concept of integrating aspects of natural resources and environment

in the context of sustainable economic development. Green GDP calculation variables include natural

resource depletion, environmental degradation, and natural capital depreciation. Natural resources

depletion is a reduced amount of natural resources available in providing environmental goods and

services, while environmental degradation is degradation caused by economic activities. Natural capital

depreciation is the reduction of natural resources in a systematic and rational that measured by the true

potential of natural resources which has been used in creating revenue.

Green GDP formula:

Production value Rp ...................

Intermediate input costs Rp .................. (-)

Brown GDP Rp ....................

Natural resources depletion Rp..................

Environmental degradation Rp ..................

Natural capital depreciation Rp .................. (-)

Green GDP Rp ....................

The Development of Grey System Theory Calculation Method

In 1982 Prof. Deng Ju-Long created the Grey Theory. Grey Theory can process data with

incomplete information or asymmetric information through differential equations of mathematical

calculations on the exponential function (Lu and Chiu, 2012). There are many types of Grey System

Theory but relatively often used in the literature such as, GM (1,1), GM (1, N), and GM (M, N). First, the

type GM (1,1) is one type of Grey System Theory The most widely applied by researchers due to its

efficiency in the calculation of the system than other types of Grey System Theory. First, Grey System

Theory type GM (1,1) is known as the Grey Model First Order One Variable using time series data. In

this case, the type of differential equation of GM (1,1) has a time-varying coefficients where the model

will be updated as new data is available for the forecasting model (Deng, 1989 in Kayacan et.al, 2010).

Second, the type of GM (1, N) for the systematic calculation using two approaches, namely major

sequences factor and influencing sequences factors. This type is a form of Grey System Theory, which

explains the relation for an event that is often called the Grey Relational Analysis. In terms of the

calculation of the systematic use of the two methods in the forecasting considered less efficient even able

to produce a higher accuracy than the GM (1,1) (Lee and Chiu, 2009) and the third, the type of GM (M,

N) where M is the level of grey differential equation and N is the number of variables. The parameter that

used in GM (M, N)is determined with minimum algorithm of differential equation for estimating least

squares. This type has a systematic calculation more complicated due to use high-level multivariable

differential equations. In this case, the type of GM (1,1) is the simplest form of this type of GM (M, N)

(Wang and Hung, 2003). By reviewing several types of Grey System Theory we can concluded that the

majority of previous research has focused on the type of GM (1,1) which consistent with the fact that the

systematic calculation efficiency still a big consideration to determine what kind of method which

suitable to forecast the data such as GM (1, N) and GM (M, N). It should be noted that in the real-time

application, computational load is the most important indicator after the performance (Kayacan et.al,

2010).

C. METHODOLOGY

The method that used in this research is Grey System Theory type GM (1,1). Grey System Theory

type GM (1,1) is the most convenient and efficient method from another method of Grey System Theory

as GM (1, N) and GM (M, N) which built with more complicated algorithms in its calculations. The

formula Grey System Theory Study of GM (1,1) constructing process is described as follows (Lu and

Chiu, 2012):

Step 1: Denote the original data sequence

(0)

= 0

1 , 0

2 , 0

3 ,…, 0

, 4 ≤ ≤ 11 (1)

Step 2 : Use AGO to form a new data series

(1)

= 1

1 , 1

2 , 1

3 ,…, 1

(2)

where 1

1 = 0

1 and 1

= 0

=1 , = 2,3,…, 11

Step 3 : Calculate background values 1

1

= 1 − 1

− 1 + 1

, ∈ 0,1 , = 1, 2, 3,…, 11 (3) Step 4 : Establish grey differential equation

()

+ 1

= (4)

where is the developing coefficient and b is grey input

Step 5 : Solve equation (4) by using the least square method and the forecasting values can be obtained as

B = − 1

2 (1)

− 1

3 (1) (5)

− 1

(1)

Y=[ 0

1 , 0

2 , 0

3 ,…, 0

]

T

, = ()−1 (6)

1

= 0

1 −

−(−1)

+

(7)

0

= 0

1 −

(1 − )(−1)

(8)

where,

1

1 = 0

1 ( = 2,3,…, )

In this case, to determine the bias and accuracy from the measurement results of Indonesia's

Green GDP accounting with Grey System Theory type GM (1,1), it is necessary to measure the results of

forecasting with MPE (Mean Percentage Error) which quiet useful to determine the level of bias

forecasting error using the real value either positive or negative. If the forecasting approach is not biased,

MPE will produce numbers close to zero. While MAPE (Mean Absolute Percentage Error) is useful to

measure the accuracy of forecasting values expressed as mean absolute percentage error, the following

formula is presented below:

MPE =

1

FVk−AV k

AV k

=1

x 100

MAPE =

1

IFV k−AV k I

AV k

=1

x 100

Explanation:

FVk = forecast value

AVk = actual value

IFVk = absolute forecast value

AVkI = absolute actual value

Table 1.Category Of MAPE Accuracy Rate Value

No MAPE Value Category

1 < 10% Very accurate

2 10% - 20% Accurate

3 20% - 50% Less accurate

4 > 50% Inaccurate

Source : Lewis 1982 in Tsai, 2012

Based on the Table 1 it can be seen that for MAPE values <10% categorized as very accurate and

forecast values indicate have nearly tendency to be similar with actual value. For MAPE values ranged

from 10% - 20% is considered accurate and forecast values indicate slightly different from the actual

value. As for the value of MAPE 20% - 50% less accurately categorized so that it has a tendency forecast

values far different from the actual value. As for the MAPE values> 50% then considered inaccurate and

clearly indicate the forecasts value is very much different from the actual value.

D. RESULTS

To demonstrate the application of Grey System Theory type GM (1,1) in Indonesia’s Green GDP

accounting provided by Sucofindo are examined in this research. The historical annual data of Brown

GDP, natural resources depletion is calculating the number of depletion of forest, oil, gas, coal, gold,

silver, tin, bauxite, and nickel, environmental degradation is calculating both total CO2 emissions from

petrol or diesel, while the natural capital depreciation is calculating on natural resources which directly

used in the industries such as oil and gas, food, beverage, and tobacco, etc. The following table of

Indonesia’s Green GDP accounting from 2000 to 2010 that complete it with the forecast by Grey System

Theory type GM (1,1) is presented in Table 2. Tabel 2. Values of the relevant Indonesia’s Green GDP accounting from

2000 – 2010, forecasting values,and errors with Grey System

Theory type GM (1,1)

Based on years 2001 - 2010 can be seen that the GM (1,1) forecast value which obtained from Indonesia’sGreen GDP data

has a tendency

to fluctuate so that the value of error variance will always change every year (heterogeneous). Conditions such as these

indicate the presence of

uncertainty on the Green GDP data, which causes volatility clustering which describe is a huge error with the same

relative gathered in a short

time together.In addition, the MPE of 15% indicates a bias because the value is not close to zero. As for the value of

MAPE of 36.59% also

indicated that the error value is less accurate because it is included in the range 20-50%. Indirectly, it reflects the Green

GDP accounting in

Indonesia still have some weakness though measurements have been made with Grey System Theory type GM (1,1) which

is theoretically able to

minimize the problem of limited data and asymmetric information.

Year Brown GDP Natural resources

Depletion

Environmental

Degradation

Natural capital

Depreciation

Green GDP GM (1,1)

Forecast

Error (%) Absolute

Error (%)

2000 1389.77 107.73 63.93 111.59 1106.52

2001 1684.28 109.91 86.92 140.64 1346.81 2362.0 75.40 75.40

2002 1821.83 105.97 89.66 157.95 1468.25 2424.3 65.10 65.10

2003 2013.67 101.28 98.76 181.23 1632.4 2489.3 52.50 52.50

2004 2295.83 127.02 116.47 217.19 1835.15 2557.2 39.30 39.30

2005 2774.28 199.88 150.73 275.49 2148.18 2629.6 22.40 22.40

2006 3339.22 204.52 187.31 334.26 2613.13 2707.9 3.63 3.63

2007 3950.89 230.75 221.10 398.25 3100.79 2789.5 -10.00 10.00

2008 4948.69 292.31 275.10 502.79 3878.49 2881.1 -25.00 25.00

2009 5603.87 282.63 310.06 573.28 4437.9 2970.7 -33.00 33.00

2010 6422.92 324.48 358.33 662.2 5077.91 3065.7 -39.60 39.60

MPE = 15 MAPE = 36.59

* Number in Trillion Rupiah’s (except the number of error and absolute error)

E. IMPLICATIONS RESULTS

Theoretical Implications

Referring to Lu and Chiu (2012) research about Taiwan’s Green GDP using Grey System Theory

GM type (1,1) in the year 2002 to 2010 with a value of MAPE about 3.25% that categorized as very

accurate. The results of this research across the facts in Indonesia that the results of both MPE and MAPE

indicate bias and less accurate and it reflect that the results can not support Lu and Chiu (2012)research

which states that the measurement of Grey System Theory is the correct method to overcome the

limitations of the data and asymmetric information and it reflected in the acquisition value of MPE which

get bias (15%) and MAPE which also less accurate (36.59%)on Indonesia's Green GDP accounting

forecast with Grey System Theory type GM (1,1).

Therefore, it showed that the Indonesia’s Green GDP accounting still has a weakness though use

similar measurements. Specifically, the high accuracy of Green GDP by Lu and Chiu (2012) research also

backed by the state that the object of research such as Taiwan. Taiwan is a small country where rank in

137thfor the largest area (One World-Nations Online, 2012) in the world, ranking 50th for the highest

population in the world (Geohive, 2012), and is known as a country with relatively minimum of natural

resources inventories.

Another case with Indonesia that categorized as one of large country that ranked 16th in the world

for the largest area (One World-Nations Online, 2012), ranked the 4th for the highest population in the

world (Geohive, 2012), and rank 3rd for the amount of natural resources inventory in the world after

Brazil and Zaire (Indorush, 2012) so the solution to create comprehensive Green GDP accounting will be

through more barriers than in Taiwan. So that, the value of bias and accuracy in Indonesia's Green GDP

accounting could also be caused by the amount of factors that are not expected (outliers).

Policy Implications

Based on the theoretical implications, the policy implications can be drawn as follows:

1. The government needs to internalize environmental costs policies in the production sector in Indonesia,

both at the micro and regional levels to minimize the general equilibrium effects on Green GDP

accounting in Indonesia. By minimizing the effects of general equilibrium, problems of bias and

accuracy from Green GDP results that forecast with Grey System Theory GM type (1.1) can also be

minimized and then the success of planning, decision-making, and the realization of a green economy

will be better in the future.

2. In an effort to avoid ambiguity or asymmetric information related to the price of natural resources in

Indonesia that also affect the level of bias and accuracy in Indonesia’s Green GDP forecasting results

as a solution an inventory report of natural resources and environmental conditions need to be

implemented in the various provinces of Indonesia with a maximum term of calculation approximately

about one year so that the price of resources can be set more relevant.

3. Indonesia’s Green GDP that is known has some weaknesses though in the other hand Green GDP still

bring another advantages as a controlling and comparative tool to Brown GDP which does not reflect

the true value caused by its calculation which not involved the cost of natural resources depletion,

environmental degradation, and natural capital depreciation.

4. The weakness of outcome from Green GDP forecasting with Grey System Theory type GM (1,1) does

not eliminate the strategic benefits to build awareness all stakeholders about the importance of

internalize cost for natural resources and environmental as a responsibility to externalities that come

from economic activities. Therefore, the government as a political authority should try to commit

consistently in the development of natural resources and creating sustainable and equitable

environment in Indonesia.

F. CONCLUSIONS

Based on results some conclusions can be drawn on the issues that have been formulated. Some

conclusions are made as follows:

1. Green GDP data used in forecasting GM (1,1) has a tendency to fluctuate so that the value of error

variance will always change every year (heterogeneous). Conditions such as these indicate the

uncertainty in the Green GDP data resulting large number of errors with the same relative gathered in

several adjacent times (volatility clustering). As it is the cause of the bias in the value of MPE and the

lack of accuracy that reflects the value of MAPE from Indonesia’s Green GDP accounting still has

some flaws though measurements have been made with Grey System Theory GM type (1,1) which is

theoretically able to minimize the problem of limited data and asymmetric information.

2. Unanticipated factors (outliers) as a review of the aspects from the object of research is likely to be one

of the things that influence the level of bias and accuracy for the Indonesia’s Green GDP accounting.

In this case, the emphasis is based outliers objects which previous research that Taiwan is categorized

as a small country than Indonesia is a huge country so it reflected a gap that is known to be one cause

of the high accuracy of the Green GDP accounting in Taiwan, but the bias and lack of accuracy in the

Indonesia’s Green GDP accounting.

3. Indonesia’s Green GDP accounting is known still has some weaknesses though in the other hand Green

GDP still bring another advantages as a controlling and comparative tool to Brown GDP controlling

and comparative tool for GDP as the value of Brown GDP does not reflect its true value. In addition,

the Green GDP is known to have a strategic advantage in raising the awareness about the importance

of internalization cost for natural resources and environmental in economic activities.

G. RECOMMENDATIONS

Based on the conclusions of this research, there are some recommendations that can be presented

as follows:

1. To create the better of Indonesia’s Green GDP accounting in the future it a must to required a

comprehensive, ongoing data collection, and do evaluation for natural resources and environmental

conditions in Indonesia to the availability of physical data for all factors such as natural resources

depletion, environmental degradation, and natural capital depreciation can be fitted so that the

information can be more accurately than ever before.

2. The role of the government and the Ministry of Environment are needed especially to implement

environmental costs policies in the manufacturing sector in Indonesia, both at the micro and regional

levels as well as the reporting of natural resources inventory and environmental conditions in the

various provinces of Indonesia with a maximum term of one year to minimize general equilibrium and

effects, so that the problem of asymmetric information which can influence the bias and accuracy in

Indonesia’s Green GDP accounting with Grey System Theory GM type (1,1) can also be minimized.

As it is correlated with the creation of targeted welfare and sustainable development is the goal of a

green economy program in Indonesia can be actualized.

3. Based on the Indonesian government program to realize the target of economic growth to 7% and the

realization of REDD program Indonesia, Green GDP accounting is correct solution although it still

faced obstacles that economic growth with GDP Greens tend to move more slowly than Brown GDP.

Therefore, efforts to increase the number of Green GDP is the implementation of environmental taxes

(green tax) as well as the optimization of green investment both from domestic and foreign to

stimulate Green GDP growth so that both of targets are more easily realized.

4. Further research is needed to measure Indonesia’s Green GDP but with better proxy, methods and

analysis tools and its expected to applying an improvement of Grey System Theory GM type (1,1) just

to make Indonesia’s Green GDP accounting values that obtained for forecasting is not biased and thus

achieve higher accuracy. Afterwards, it will impact the better of decision-making and policymaking

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