Estimation Of Energy And Co2 Emission Biology Essay

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The GDP of Malaysia in 1991 is about RM205 billion with energy consumption about 14million TOE, while the GDP in 2000 is about RM356 billion with energy consumption is about 29 million TOE for the whole Malaysia economy sector. It shows that the percentage increases for GDP from 1991 to 2000 is almost 74%, while energy consumption changes for almost 107%. From the energy consumption, this study will figure out which sector produced more CO2 emission. Energy consumption by Malaysian economy sector grew at 7.55 percent over the period 1991 -2000 while CO2 emission grew at 8.05 percent. From this growth rate it shows that energy consumption by each economy sector will produce a greater growth rate of CO2 emission. Therefore this study attempts to identify the energy intensive sectors due to more energy use will produce more CO2 emission. Moreover, the government policy seen recently in the 10th Malaysian Plan will highly promote to energy efficiency and focus on high value added sector that produce less CO2 emission. This study is very important because recently the government, economist and policy makers have discussed the best strategies to protect the environment particularly in regarding to reduce the CO2 emission. Using hybrid input output table, the amount of the energy intensities and CO2 emission intensities, caused by energy consumption were estimated for every aggregated 40 sectors including three energy sectors. From this analysis, some sectors have reduced their CO2 emission over 80 percent by comparing year 1991 and 2000. In future, in order to reduce CO2 emission, the energy intensive sectors will also have to reduce energy consumption by applying the energy efficiency technology.

Introduction

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The environmental problems are the biggest issues faced by Malaysia. Malaysia has experienced one of the least environmental problems in Asia. However, with the enormous of Malaysian structural change of recent years through the industrialization, agriculture, tourism, and export activities indicated the positive economic growth of Malaysia over the years. Due to this growth has caused air pollution from industrial activities and motor vehicles emissions as well as water pollution from raw sewage. In terms of well-being and health, the continuous rising of CO2 emissions could have many other damaging effects. It might harm plants and animals living in the sea or land due to extremely heat. It could also change the world weather patterns, causing floods, drought, and an increase in damaging storms. Global warming due to CO2 emissions could melt enough polar ice to raise the sea level. In certain parts of the world, human disease could spread such as malaria and dengue, and crop yields could decline. Longer-lasting and more intense heat waves could cause more deaths and illnesses as well as increase hunger and malnutrition.

There are many activities that cause the increase of CO2 emission. One of the activities is from energy consumption by households and producers such as electricity; gas, petroleum product, coal and crude oil. Energy is one of the main source of country's economic and development as well as social progress. Energy has changed the level of value added through the production activities as well as changes the lifestyle of households all over the world. Energy also gives the terrible impact to environment through the direct and indirect energy consumption, with direct and indirect impact on CO2 emission. The consequences of energy consumption in Malaysia to the CO2 emissions are not the new issues to discuss but this issue has been growing for the last two decades. Due to these conditions, government has promoted the strategy to reduce the amount of energy consumption as well as to reduce CO2 emission through energy efficiency in order to protect environmental issues as stated in the 10th Malaysia Plan. Therefore, Malaysia has agreed to reduce the carbon dioxide by up to 40 percent by year 2020 in comparison to 2005 level.

As represented by the Kyoto Protocol, energy and the environment are interrelated issues which both must be taken into consideration. This issue is fast becoming the focus of extensive concentration as global main concern issues. It is very important for consumers to manage their efficiency of energy use while for industries to increase their efficiency of energy consumption from the point of view of environmental preservation.

Figure 1: GDP and CO2 emission growth rate from 1991 to 2000

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Figure 1 shows the comparison of GDP and CO2emission growth rate from 1991 to 2000. Most sectors grew at positive growth rate of GDP with decreases in CO2 emission growth rate, but some sectors such as mining, yarn and cloth, paints and lacquers, drugs and medicines, other non metallic and electrical appliances grew at negative growth rate of GDP and CO2 emission. In order to promote energy efficiency by country, the sector that contributed the highest growth rate of CO2 emission must be taken into consideration. However, in this case study, all sectors has reduced their CO2 emission in the period of study.

This study will start by presenting a review of literature from previous studies. Next, the step in measuring the energy intensity and CO2 emission intensity. Then, the results and findings regarding direct and total energy intensities and estimated CO2 emission by sector will be presented. Lastly, conclusions and recommendation will be discussed. The main objectives of this study are to estimate CO2emissions associated with energy consumption and to identify the sector contributes high value added but less contribute on CO2 emission.

Empirical studies

This study use the hybrid analysis in order to seek the flow of energy consumes by every sectors in the economy. In other words, the process analysis is used to calculate energy requirement of energy intensive products and the input-output analysis is applied to calculate that of other products. Suh et al. (2004) classifies hybrid approaches in three groups, e.g. tiered hybrid analysis, input-output based analysis and integrated hybrid analysis. Vringer and Blok (1995a, 2000) used the so-called tiered hybrid energy analysis to calculate the total energy requirement of Dutch households. They determined the energy intensities of about 350 basic consumption categories using the expenditure of 2767 representative households from The Netherlands Household Expenditure Survey of 1990. Although they analyzed changes in consumption patterns of Dutch households in the period from 1948 to 1996, information on energy intensities of only one year (1990) was available. This method is also working intensive and requires detailed data (van Engelenberg et al., 1994; Vringer and Blok, 1995a, 2000; Vringer et al., 2006; Park and Heo, 2007; Whan et al., 2009).

By using an IO table constructed by DOS, this study is divided into energy sector that consists of 3 energy sector and 37 non energy sectors of Malaysian economy sector. The results of this study will facilitate energy policy makers to investigate the sector consumes high energy and contributed high CO2 emission, and encouraged the high value added sectors that produce less CO2 emission. This study use top-down approach (IO analysis) to quantify the relations of economy, energy and CO2 emissions in detail and to get complete information for energy and environment in Malaysia.

There are several studies associated with energy consumption in Malaysia such as Jafar et al. (2008) applied an input output analysis in their study on electricity generation and it impact to the environment in Malaysia. However, there are some studies done by a few researchers for developed and developing economies in term of energy and growth, such as Ramcharran (1990), Wu (1997), Huang (1993), Paul and Bhattacharya (2004), Shiu and Lam (2004), Yoo (2005) and Montalvo and Marta (2005). Except for Wu (1997) and Montalvo and Marta (2005), the other researchers focused on the relationship between aggregated energy consumption and economic growth. Ramcharran (1990) and Huang (1993) examined the relationship between electricity consumption and economic growth. The results showed that the aggregate demand for electricity is slightly income elastic, implying that electricity has a significant impact on economic growth.

While Paul and Bhattacharya (2004) focused on energy consumption, Shiu and Lam (2004) concentrated on electricity consumption in examining the causal relationship between the independent variables and GDP using econometric techniques. Paul and Bhattacharya (2004) found that there exists bi-directional causality between electricity consumption and economic growth, implying that an increase in electricity consumption directly affects economic growth and that economic growth also stimulates further electricity consumption.

In term of CO2 emission, Cruz (2002) suggested that such an approach provides a consistent and systematic tool to appraise impacts of measures regarding the achievement of both pollution control and sustainable development for Portugal, regarding the energy intensities and CO2 emissions derived from fossil fuels use and CO2 emissions are reported, Alcantara and Padilla (2006) presented an approach that allows the identification of the "key" productive sectors responsible for CO2 emission. Tunc et al. (2006) estimated the CO2 emissions for the Turkish economy using an extended I-O model using 1996 data in order to identify the sources of CO2 emissions. Lise (2006) stated that the emission growth in Turkey, over the period 1980-2003, was almost 80% as a result of the growing economy, 13% as a result of structural change towards more energy-intensive sectors and 13% as a result of an increase in the carbon intensity of energy, while decreasing energy intensity offset these increases by 7%.

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Mongelli et al. (2006) suggested that developing countries may become a shelter for the production of not environmental-friendly commodities. In this case, the so-called Pollution Haven Hypothesis, due to freer international trade the comparative advantage may change the economic structure and consequently the trade patterns of the countries linked by trade relationships, may occur. Munksgaard et al. (2006) showed how the input-output approach can be used to specify the problem of sustainable consumption. The measures of carbon dioxide emissions at different spatial levels: nation, city, and household were undertaken. Chung et al. (2009) estimate the energy and GHG emission intensity in Korea and concluded that energy consumption and environment counter measure not to slow down the activity of economic as well as achieve GHG reduction.

Methodology

3.1 Energy intensity

This study converted the monetary unit input-output tables into energy input-output tables with the help of energy prices (Miller and Blair, 2009). First, average energy prices are calculated using information on energy use and expenditure by fuel of the input-output tables. Average energy prices are calculated as ratios of energy use (inputs) to the total output (intermediate plus final demand) by fuel, expressed in TOE/M-RM, same as energy intensities as shown in Equation 1. The reciprocal numbers of the energy intensities are more commonly used prices expressed in M-RM/TOE. Thus, higher TOE/M-RM values or higher energy intensities mean lower energy prices.

Pi = ei/Xi-mi (TOE/M-RM) (1)

where ei is energy use. P1, the price of energy sector 1, e.g. price of petroleum product, is used to quantify 40 intermediate inputs of petroleum product to produce goods of 40 sectors. Industries (40 sectors) will pay much lower prices than households (final expenditure) for the same fuel. The price differential exists within the intermediate demand for fuels. For more discussions see Lenzen (1998)

t1,j x P1 = te1,j (2)

Once intermediate energy inputs (energy input-output tables) are computed as in Equation 4, it is easy to estimate direct energy intensities of individual sectors. Direct energy intensities are calculated as ratios of direct energy expenditure converted in energy terms to total inputs (intermediate inputs and value added inputs), also expressed in TOE/M-RM in

d1 = ei, 1/Xi (TOE/M-RM) (3)

where d1 (direct) is the direct energy intensity of sector 1. Total or cumulative energy intensities can be then computed by multiplying them with the Leontief inverse (1-A)-1 of the corresponding input-output table as expressed in

di,j x (1-A)-1 = Ti,j (4)

The indirect energy intensities are the differences between total Equation 6 and direct energy intensities Equation 5 equal to Equation 7.

Tij -dij =Indij (5)

Sectoral total or cumulative energy consumption can be computed by multiplying total energy intensity with sectoral household expenditure. Indirect household energy consumption is then the sum of sectoral cumulative energy consumptions of 37. Direct use of petroleum products, coal and electricity in primary energy terms by households is considered as direct household energy consumption. Total household energy consumption is the sum of direct and indirect energy requirement.

CO2 emission factor and intensity

This study focus on CO2 emission only because this emission is the most listed in the IPCC (revised in 1996) and most effect to climate changes. This study used the emission factor recommended in IPCC guidelines for the assessment of the amount of CO2 emission caused by energy consumption. However, this factor was partly modified in order to reflect the condition in Malaysia. For the electricity emission factor calculated from previously primary emission to the energy use by products. While concerning to another CO2 emission factor were calculated based on models below:

fi = Ci/ ei (6)

where fi is the CO2 emission factor of energy type 1 i.e. petroleum product. Ci is the CO2 emission from energy type 1 and ei is the energy consumption by sector type 1.

In order to estimate generation of CO2 emission in Malaysia, the models used as below:

Ei = (mi#ri) f (I-A)-1 (7)

Model (7) estimates the indirect CO2 emissions from household consumption by using the extended input-output model introduced by Leontief and Ford (1972). Where Ei is denotes a vector of total indirect CO2 emissions in the production sectors as a consequence of production of goods for household consumption; f is a 11x1 vector of CO2 emissions per unit of consumption of each of the 5 energy types; m is a 11x40 matrix of fuel mix in the production sectors, i.e. demand for 11 energy types per unit of total demand for energy for all production sectors; r is a 1x40 vector of energy intensities, i.e. total energy consumption per unit of production in all 40 sectors; (I-A)-1 is the 40 x 40 Leontief inverse matrix. According to model (6), CO2 emissions change as a consequence of changes in four factors: f, mi, ri, and (I-A-)-1. Whereas f, mi, ri, (I-A-)-1 are factors of behavior of the sector in the economy, i.e. demand for inputs in the energy supply sector and other production sectors.

Data

This study used the 40 sector classification input-output tables for Malaysia for the years 1991 and 2000 published by the Department of Statistic (DOS), in current prices. The 40 sectors consist of 37 non-energy sectors and 3 energy sectors which are petroleum products (motor petrol, gasoline, diesel, kerosene, LPG, refinery gas, non energy, aviation fuel and fuel oil), coal and natural gas and electricity. Energy consumption data for 1991 and 2000 are taken from the National Energy Balance of Malaysia Energy Centre (PTM, 2000) and the Ministry of Energy, Water and Communications Malaysia. While the data concerning to CO2 emission factor by calculating the energy consumption and CO2 emission in Malaysia recommended by IPCC (revised in 1996)

Results and findings

4.1. Energy intensity analysis by sector

This study estimates two energy intensity: the direct and total. Direct energy intensity calculated by using the input or technical coefficient matrix and the total energy intensity calculated from the Leontief inverse coefficient matrix. The indirect energy intensity for each sector can be obtained by subtracting the direct energy intensity from the total energy intensity. The average values of the direct and total energy intensity were found to be 51.9 and 79.8 (TOE/M-RM) respectively in 1991, while 57 and 70.3 (TOE/M-RM) respectively in 2000.

In terms of direct energy use, sector with high energy intensities in energy sector are petroleum product, 55 (TOE/M-RM) in 2000 and 56 (TOE/M-RM) in 1991. In 1991 transportation (508 TOE/M-RM), irons and steels industries (263 TOE/M-RM) and others chemical products (257 TOE/M-RM) while the sector with high total energy intensity in the group of non energy sectors were transportation (543 TOE/M-RM), iron and steels industries (475 TOE/M-RM), and manufactured of soap etc (233 TOE/M-RM) in 2000. In both cases, the sectors with the highest direct and total energy intensity were almost the same in 2000 and 1991, as can been seen in Table 1.

Table 1: The sectors with the highest energy saving in the energy intensity in 1991 and 2000 (TOE/M-RM)

Direct energy intensity

1991

2000

% energy saving

 

Total energy intensity

1991

2000

% energy saving

Energy sector group

 

 

 

 

Energy sector group

 

 

 

Petroleum products

183

80

(56)

Petroleum products

214

96

(55)

Electricity

60

46

(22)

Electricity

103

62

(40)

Crude petrol, natural gas and coal

4

2

(46)

Crude petrol, natural gas and coal

15

11

(28)

Non energy sector group

 

 

 

 

Non energy sector group

 

 

 

Agriculture

12

3

(74)

Wholesale and retail trade

46

16

(64)

Mining

210

74

(65)

Other chemical industries

257

122

(53)

Other chemical industries

227

94

(58)

Mining

249

124

(50)

Wholesale and retail trade

13

6

(53)

Private non-profit institution

54

27

(50)

Other non-metallic manufacture

56

30

(47)

Manufacture of household machinery

36

20

(44)

Construction

66

39

(41)

Business services

20

15

(25)

Private non-profit institution

12

8

(35)

Other non-metallic manufacture

133

104

(22)

Manufacture of motor vehicle

17

11

(33)

Manufacture of wood product

81

66

(18)

Manufacture of industries chemical

25

19

(23)

Agriculture

28

23

(18)

Manufacture of wood product

40

32

(20)

Manufacture of motor vehicle

39

32

(16)

Communication

7

6

(16)

Structural metal industries

78

67

(14)

Manufacture of others products

50

46

(8)

Construction

121

106

(12)

Manufacture of wearing apparels

11

10

(6)

Other metal industries

89

81

(9)

Other metal industries

28

26

(6)

Manufacture of others products

80

75

(7)

Manufacture of household machinery

10

10

(3)

Communication

21

21

1

Transportation

447

466

4

Education

17

17

1

Manufacture of yarns and cloth

45

49

9

Others services

39

39

2

Structural metal industries

19

22

14

Transportation

508

543

7

Manufacture of soap etc.

163

190

17

Manufacture of other electric machinery

34

37

8

Business services

4

5

21

Manufacture of yarns and cloth

68

76

11

Manufacture of paints and lacquers

22

27

23

Manufacture of radio, television etc.

21

25

18

Recycling

21

29

34

Manufacture of other textiles

46

54

19

Manufacture of cement etc.

91

137

50

Recycling

60

73

22

Manufacture of other textiles

20

31

53

Manufacture of cement etc.

164

200

22

Manufacture of oils and fats

32

52

61

Manufacture of wearing apparels

29

36

23

Manufacture of other electric machinery

12

20

62

Manufacture of soap etc.

188

233

24

Manufacture of radio, television etc.

8

13

65

Manufacture of other foods

66

83

26

Manufacture of other foods

30

49

65

Real estate

11

13

26

Others services

8

15

75

Manufacture of industries chemical

45

63

40

Education

4

7

83

Manufacture of oils and fats

98

139

42

Iron and steel industries

200

390

95

Manufacture of paints and lacquers

44

64

44

Recreation

3

6

126

Manufacture of electric appliances etc.

40

61

54

Manufacture of industries machinery

22

62

188

Manufacture of industries machinery

49

78

59

Manufacture of electric appliances etc.

11

33

202

Manufacture of non-ferrous metals

103

166

61

Manufacture of non-ferrous metals

39

122

215

Iron and steel industries

263

475

81

Manufacture of drugs and medicines

15

67

339

Manufacture of drugs and medicines

41

92

124

Real estate

1

6

612

Recreation

11

33

185

Average of 40 sectors

56

59

 

 

Average of 40 sectors

90

92

 

Table 1 has shown the energy saving by sectors of energy and non energy groups. The sectors such as wholesale and trade, other chemical industries, mining and private non-profit institution were characterized by the highest share of saving in energy intensity over 50 percent of total energy intensity. While the sectors such as recreational, manufactured of drugs and medicine, iron and steels products, manufactured non-ferrous metals were considered as the highest share of energy surplus in energy intensity which more than 60 percent of total energy intensity. This implies that in the case of sectors with such high energy saving will reduce the generation of CO2 emission.

4.2 Estimating the generation of CO2 emission in Malaysia.

The sectors with the highest CO2 emission intensity in the energy sector was petroleum products for 1991 and 2000, 0.22 (T-CO2/M-RM) and 0.44 (T-CO2/M-RM), respectively. The sector with the highest CO2 emission intensity in the non energy sector was transportation in 1991 and 2000 about 1.02 and 1.40 (T-CO2/M-RM) respectively as shown in table 2. By using the hybrid IO table, average value of the total CO2 emissions intensity caused by energy consumption in Malaysia in 1991 and 2000 were found to be 0.255 (T-CO2/M-RM) and 0.265 (T-CO2/M-RM), respectively.

The results from the analysis showed that the highest CO2 emission was from petroleum product 0.438 (T-CO2/M-RM) and electricity 0.291 (T-CO2/M-RM) from energy sector in 1991 compared to 0.22 (T-CO2/M-RM) and 0.139 (T-CO2/M-RM) in 2000, while the transportation and irons and steels products contributes the highest CO2 in the non energy sector, 1.02 (T-CO2/M-RM) and 0.58 (T-CO2/M-RM) in 1991 and the sector contributed the highest CO2 were transportation, 1.40 (T-CO2/M-RM) and iron and steels industries 0.97 (T-CO2/M-RM). From 1991 to 2000, electricity had reduced about 52 percent of the total CO2 emission, while petroleum products has reduced about 50 percent of the total CO2 emission and primary energy sector (crude oil, natural gas and coal) has reduced about 22 percent of the total CO2 emission. This amount showed that energy sectors have reduced their energy use more 30 percent in the period of the study.

Table 2: The sector with the highest ranking of energy saving and CO2 emission reduction in 1991 - 2000

TOTAL ENERGY INTENSITY

(TOE/M-RM)

CO2 EMISSION INTENSITY

(T-CO2/M-RM)

% of energy saving (1991-2000)

Energy sector group

Rank

1991

2000

1991

2000

 

Electricity

1

103

62

0.291

0.139

(40)

Petroleum products

2

214

96

0.438

0.220

(55)

Crude petrol, natural gas and coal

3

15

11

0.053

0.041

(28)

Non energy sector group

Wholesale and retail trade

1

46

16

0.173

0.057

(64)

Manufacture of household machinery

2

36

20

0.141

0.068

(44)

Private non-profit institution

3

54

27

0.221

0.119

(50)

Other chemical industries

4

257

122

0.508

0.301

(53)

Mining

5

249

124

0.534

0.332

(50)

Business services

6

20

15

0.089

0.062

(25)

Manufacture of wood product

7

81

66

0.254

0.204

(18)

Other metal industries

8

89

81

0.318

0.259

(9)

Other non-metallic manufacture

9

133

104

0.473

0.386

(22)

Structural metal industries

10

78

67

0.291

0.241

(14)

Others services

11

39

39

0.166

0.141

2

Manufacture of motor vehicle

12

39

32

0.133

0.115

(16)

Education

13

17

17

0.074

0.066

1

Manufacture of others products

14

80

75

0.227

0.212

(7)

Manufacture of radio, television etc.

15

21

25

0.083

0.078

18

Manufacture of other electric machinery

16

34

37

0.128

0.123

8

Real estate

17

11

13

0.051

0.049

26

Agriculture

18

28

23

0.095

0.095

(18)

Manufacture of cement etc.

19

164

200

0.475

0.484

22

Communication

20

21

21

0.082

0.084

1

Manufacture of other textiles

21

46

54

0.175

0.183

19

Construction

22

121

106

0.357

0.392

(12)

Manufacture of electric appliances etc.

23

40

61

0.175

0.195

54

Manufacture of industries machinery

24

49

78

0.166

0.186

59

Manufacture of yarns and cloth

25

68

76

0.201

0.233

11

Manufacture of non-ferrous metals

26

103

166

0.362

0.426

61

Manufacture of other foods

27

66

83

0.224

0.276

26

Recycling

28

60

73

0.220

0.275

22

Manufacture of wearing apparels

29

29

36

0.112

0.143

23

Manufacture of drugs and medicines

30

41

92

0.167

0.218

124

Manufacture of soap etc.

31

188

233

0.377

0.509

24

Transportation

32

508

543

1.024

1.396

7

Manufacture of paints and lacquers

33

44

64

0.162

0.244

44

Manufacture of oils and fats

34

98

139

0.416

0.658

42

Iron and steel industries

35

263

475

0.581

0.968

81

Manufacture of industries chemical

36

45

63

0.137

0.269

40

Recreation

37

11

33

0.053

0.141

185

In non energy sectors, most of sectors have reduced their CO2 emission more than 1 percent of the total CO2 emission particularly on wholesale and trade, manufactured of household machinery, others chemical industries and mining.

4.3 The relationship between GDP by economy sector and CO2 emission intensity.

In the case of total energy use in 1991, the average values of GDP by sector and CO2 emission intensity were estimated RM14, 850 (million) and 0.255 (T-CO2/M-RM). In the case of total energy use in 2000, the average values of GDP by sector and CO2 emission intensity of the 40 economic sector including energy and non energy sector were RM22,505 (million) and 0.265 (T-CO2/M-RM).

Figure 4: Distribution of 40 sectors from total energy use in 1991

Figure 4 and 5 shows the relationship between GDP by sector and CO2 emission intensity for 1991 and 2000. The scatter plot in figure 4 and 5 have divided into four quadrants i.e. quadrant I, II, III and IV. Most of sector lays in quadrant III and IV compared to quadrant I and II. The sector that lies in quadrant III indicates that this sector produced low GDP with low CO2 emission while the sector that lies in quadrant IV indicates that this sector produced high GDP but lower in CO2 emission. The sectors lie in quadrant I and II are energy intensive sector. The sectors with high GDP with less CO2 emission in 1991 and 2000 are manufactured of radio, television, and etc. However, there were significant changes from 1991 to 2000 particularly on manufacturing sector and wholesale and trade.

Figure 5: Distribution of 40 sectors from total energy use in 2000.

CO2 emissions are in proportion to GDP by sectors that are located in quadrant I in figure 4 and 5, sector with lower value added and higher CO2 emission intensities such as petroleum product and iron and steel industries in 1991, and manufactured of cement and iron and steel industries in 2000. Such sectors are typically characterized by industries that use environmental friendly process in terms of energy use. On the other hand, sectors with higher GDP with lower CO2 emission intensities than the CO2 average value are located in quadrant IV. Most of sectors are located in quadrant III and IV in figure 4 and 5 indicates that this sector low dependence on energy source because this sector remains below the horizontal average value line for CO2 emission intensity for 1991 and 2000.

Conclusions

This study uses the hybrid IO analysis to speculate information on the energy use and CO2 emission and analyze some information on every sector. This analysis was performed on 40 aggregated non energy sectors including 3 energy sectors, and calculates the energy intensities and CO2 emission intensities for every sector.

This study has explained the structure of energy used and CO2 emission in each economy sector in Malaysia. In the energy sector, petroleum products has contributed the highest CO2 emission, while in non energy sector, transportation has contributed the highest CO2 emission in 2000 and manufactured of wood products in 2000. A few number of sector from manufacturing have reduced about over 80 percent of CO2 emission compared to another sector that has reduced less than 80 percent of total CO2 emission.

Malaysia's effort to protect the environmental issue based on energy use by not encouraging new energy intensive sector but will promote the energy efficiency and high production industries. Based on estimation of 40 sectors, plotted in figure 4 and 5, the sector uses more energy and produced more CO2 emission must be taken into consideration at the same time particularly the sector located in quadrant II. For example, the sector lies over average value of GDP by sector and CO2 emission as shown in figure 4 and 5, have to reduce their energy use and also should focus on primarily on energy conservation and efficiency improvement rather than environment friendly energy use. In contrast, the sectors lies on less than average value of GDP by sector and CO2 emission should be supported as strategic industries since they have comparative advantage from the perspective of Malaysian energy and environment.

Analyzing in energy intensities and CO2 emission intensities is becoming essential step in correct understanding the structure of energy use. Moreover, nowadays, global warming has become the issues of concern particularly those countries with high growth rate in energy consumption and CO2 emission such as Malaysia. Hence energy use and CO2 emission structure should be taken into account for policy makers. Intensity for each sector should be clearly analyzed and understood so that policy of environment friendly can be recognized.