Decomposition for influence factors of carbon emissions in the region of beijing, tianjin and hebei—

Frontier of Environmental Science June 2014, Volume 3, Issue 2, PP.75-83

Decomposition for Influence Factors of Carbon Emissions in the Region of Beijing, Tianjin and Hebei ——Based on the Perspective of Terminal Energy Xiaohong Yu#, Mengsi Zhang Management and Economics Institute/Beijing Institute of Technology, Beijing 100081, China #

Email: yuxiaohong@bit.edu.cn

Abstract In order to analyze the influence factors of carbon emissions in the region of Beijing, Tianjin and Hebei, and explore the path to develop low-carbon economy, this paper starts with the terminal energy consumption of three industries and residential consumption, and constructs an identical equation, which is composed of the population size, level of economic development, energy intensity, the proportion of energy consumption, energy structure and the coefficient of carbon emission. Based on the data of terminal energy consumption during 2000-2012, various factors are analyzed and their contribution is measured by LMDI (Logarithmic Mean Divisia Index). The result shows that, the scale of population and economy make positive driving effect, energy intensity, energy structure and carbon intensity make negative driving effect, and the proportion of energy consumption makes negative driving effect before 2006, and then changes into positive driving effect. Some suggestions of low-carbon economy are listed: to control the population size and improve the quality of economic development, to support the research of new energy technology, to accelerate regional integration and optimize the industrial structure, to enhance environmental protection and spread the concept of low-carbon.

Keywords: Carbon Emission; LMDI; the Region of Beijing-Tianjin-Hebei; Low-carbon Economy

1 INTRODUCTION In 2009, on the 15th Meeting of the Parties of “the United Nations Framework Convention on Climate Change”—the conference held at Copenhagen, China has made a promise that, comparing to 2005, carbon emissions of per unit GDP would be reduced by 40%-45% in 2020. The economic circle of Beijing, Tianjin and Hebei is the important growth pole of Chinese economy. There are many problems that having a dense population, rapid economic growth, higher development level of heavy chemical industry. To achieve this reduction target, this region should be focused on. On February 26, 2014, Chinese President (Xi 2014) hosted a forum in Beijing. He listened to the thematic work reports about the joint development of Beijing, Tianjin and Hebei. He emphasized the significance of coordinated development of Beijing, Tianjin and Hebei, and made seven requirements, wishing to explore an effective path of ecological civilization to promote population, economy, resources and environment in harmony. Therefore, how to obtain the maximum economic output in the entire region of Beijing, Tianjin and Hebei, while reducing carbon dioxide emissions (hereinafter referred to as carbon emissions) as much as possible, must be paid attention by scholars.

2 LITERATURE REVIEW At present, Kaya identity is the basic formula in the study of carbon emissions. This identity was first proposed by Yoichi Kaya on a seminar of the IPCC (Intergovernmental Panel on Climate Change). Based on the idea of identity, 

Fund: Research on social sciences from department of education (13YJAH122) - 75 http://www.ivypub.org/fes

Most of scholars introduced different factors in the formula to extend the LDMI (Logarithmic Mean Divisia Index) decomposition method, in order to explore the factors affecting carbon emissions. B.W. Ang (2004) made a comparative analysis about different index decomposition methods and practical application. The study showed LMDI method is the best of all methods. Of course, LMDI method has its limitations. It can’t deal with the data of zero and negative values. But B.W. Ang et al. (1998) solved this problem skillfully by AL (analytical limit).

Can Wang et al. (2005) studied the change of carbon emissions by LMDI method during 1957-2000, in China. It found that 95% of China's carbon emissions reduction was caused by the decline in energy intensity, while only a small part thanks to the energy restructuring. Katerina Papagiannaki & Danae Diakoulak (2009) made factor decomposition for the change of carbon emission by LMDI method during 1990-2005, in Denmark and Greece. It introduced factors that vehicle ownership, mixed fuel, an annual mileage, engine displacement and vehicle technology, and revealed how these factors affected trends of carbon dioxide emissions. Claudia Sheinbaum et al.

(2010) analyzed trends of carbon emissions from Mexican steel industry by LMDI method during 1970-2006. This study showed that the energy structure and energy efficiency promoted carbon emission, and energy efficiency made the highest contribution. Xu Guoquan et al. (2006) studied the change of per capita carbon emissions in China by LDMI method during 1995-2004. It found that the positive contribution rate of economic development presented exponential growth, while the negative contribution rate of energy efficiency and energy structure showed an inverted U type. Wang Jia & Yu Weiyang (2012) extended the Kaya formula, introduced the carbon emissions of primary, secondary and tertiary industries in the formula, and made decomposition for influence factors of carbon emissions by LMDI method in the region of Beijing, Tianjin and Hebei during 2000-2009. It showed that Energy intensity, energy structure and industrial structure were negative driving factors, the level of economic development, population size were positive driving factors. Among these factors, the adjustment and optimization of industrial structure played a key role in the reduction of carbon dioxide emissions. Yang Rong & Chang Xuanyu (2012) analyzed five influence factors of carbon emissions in the western region of china by LMDI method during 19952009. These factors were economic scale, economic structure, energy intensity, energy structure and the coefficient of carbon emission. The result showed that the major factor in increasing carbon emissions was the rapid growth of economic scale. The potential factors of carbon reduction were optimization of energy structure. Zhang Jilu (2012) constructed four factors of economic growth, technological progress, energy consumption structure, the coefficient of energy carbon emission, and analyzed carbon emissions in central region of China by LMDI method during 19952010. It showed that the determinant factor of per capita carbon emissions in the central region was the growth of economic scale, whose cumulative effect of carbon emissions was much higher than the reduction effect of energy efficiency and energy structure.

3 MODEL BUILDING AND DATA SOURCES At present, many scholars analyze and demonstrate the influence factors of carbon emissions in China or some provinces by LMDI method. However, the research on carbon emissions in the region of Beijing-Tianjin-Hebei is involved rarely. In view of this situation, the influence factors of carbon emission in the region of Beijing-TianjinHebei are analyzed, and the contribution of each factor are estimated in order to explore a feasible path of lowcarbon economy for the region of Beijing-Tianjin-Hebei, and to make theoretical basis for the realization of regional integration.

The terminal energy consumption is used to calculate CO2 emission in this paper. Therefore, the value of energy emission intensity is changing. It is affected by the structure of terminal energy and combustion efficiency, and can reflect the impact of the terminal energy consumption habits on carbon emission. It is also an innovation of this paper.

3.1 Model Building For carbon emissions, a Japanese scholar Yoyichi Kaya proposed the famous Kaya identity. Combining with factors of carbon emissions, the Kaya identity is extended. The influence factors of carbon emission in the region of BeijingTianjin-Hebei are decomposed by the following identity.

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C   Cij   P ij

ij

G E Ei Eij Cij P G E Ei Eij

  PRIAi SijU ij

(1)

ij

Where, i is whereabouts of the terminal energy consumption, including three industries and residential consumption, j is the three kinds of energy, including Coal, oil and natural gas, C is the total carbon emissions, Cij is the CO2 emissions from energy j in the consumption whereabouts i , P is the scale of population, G is GDP, E is the total of terminal energy consumption, Ei is the energy consumption in whereabouts i , Eij is the consumption of energy j in consumption whereabouts i , R  G P , I  E G and Ai  Ei E , are, respectively, the level of economic development, energy intensity and the proportion of energy consumption. The structure of energy consumption is given by Sij  Eij Ei and the coefficient of carbon emission is given by Uij  Cij Eij .

The change in emission level from year 0 to year T ( Ctot ) is decomposed into effects associated with the following factors: population size ( C pop ), level of economic development ( Cact ), energy intensity ( Cint ), proportion of energy consumption ( Cstr ), structure of energy consumption ( C pro ), carbon intensity ( Cemf ): Ctot  CT  C 0  C pop  Cact  Cint  Cstr  C pro  Cemf

(2)

The LMDI formulas are as follows where decomposition is for changes between year 0 and year T:

 PT  0  P 

(3)

 RT  0  R 

(4)

 IT  0  I 

(5)

 AiT  0   Ai 

(6)

C pop   ij ln  ij

Cact   ij ln  ij

Cint   ij ln  ij

Cstr   ij ln  ij

C pro

 SijT   ij ln  0 S ij  ij

 U ijT U0  ij

Cemf   ij ln  ij

   

(7)    

(8)

3.2 Sources and Instructions of Data Based on the data from IPCC (2006) and Chinese low calorific value of various energy sources, the Coefficients of carbon emission are calculated. In the Calculation process, since only the calculation of primary energy, it is assumed that the carbon in the fuel is completely oxidized. Because of Chinese energy classification and IPCC calibre are inconsistent; some energy coefficients are estimated by similar energy’s value. Conversion coefficients of standard coal are from GB/T 2589-2008 General rule of energy consumption calculation in China. When the coefficient value is in an interval, the intermediate value is adopted. The adopted coefficients are shown in Table1.

In the statistics of Chinese energy balance table, the value of final energy consumption is calculated by deducting losses only in coal, coke, oil, refining, transmission and distribution, not deducting losses in electricity generation and energy used in energy industry. Therefore, in order to avoid issues of double counting and inter-provincial power scheduling, only the carbon emissions from primary terminal energy consumptions are calculated in the energy balance tables of Beijing, Tianjin and Hebei. The consumptions of raw coal, washed coal, briquettes, coke and coke oven gas are added up to the consumption of coal. The consumptions of crude oil, gasoline, kerosene, diesel oil, fuel oil, liquefied petroleum gas and refinery gas are added up to the consumption of oil. The consumption of natural gas is kept.

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TABLE 1

THE COEFFICIENTS OF CO2 EMISSION AND STANDARD COAL CONVERSION OF PRIMARY ENERGY

Name of energy

Coefficient of CO2 emission

Unit

Conversion coefficient of standard coal

Unit

Total coal raw coal

2.69

KgCO2/Kg

0.7143

Kgce/Kg

cleaned coal

2.69

KgCO2/Kg

0.9000

Kgce/Kg

briquettes

2.69

KgCO2Kg

0.9714

Kgce/Kg

coke

3.14

KgCO2/Kg

1.4286

Kgce/Kg

coke oven gas

0.93

KgCO2/M3

0.5929

Kg/M3

crude oil

3.07

KgCO2/Kg

1.4286

Kgce/Kg

Total oil gasoline

3.02

KgCO2/Kg

1.4714

Kgce/Kg

kerosene

3.10

KgCO2/Kg

1.4714

Kgce/Kg

diesel oil

3.17

KgCO2/Kg

1.4571

Kgce/Kg

fuel oil

3.24

KgCO2/Kg

1.4286

Kgce/Kg

LPG

3.17

KgCO2/Kg

1.7143

Kgce/Kg

refinery gas

2.65

KgCO2/Kg

1.5714

Kgce/Kg

natural gas

2.09

KgCO2/M3

1.2721

Kg/M3

TABLE 2 AL STRATEGY PROCESSING METHOD

X ijT

Cij0

CijT

CX ,ij  L(CijT ,Cij0 ) ln(XTij / Xij0 )

0

+

0

+

CX ,ij  CijT

+

0

+

0

CX ,ij  Cij0

case

X

1 2

0 ij

6

0

0

0

0

0

3

+

+

0

+

0

4

+

+

+

0

0

5

+

+

0

0

0

7

+

0

0

0

0

8

0

+

0

0

0

Consumption of terminal energy is divided into four parts, including the primary, secondary and tertiary industry and residential consumption. The primary industry includes agriculture, forestry, animal husbandry, fisheries and water conservancy. The secondary industry includes industrial and construction. The tertiary industry includes transportation, warehousing and postal service, wholesale, retail, hotel etc.

In addition, for zero value in the decomposition by LMDI method, AL strategy is adopted. The handling method of different cases as shown in Table 2. The year 2000 as the base year, all data of GDP is converted at constant prices.

Annual population data is the number of the resident population in Statistical Yearbook of Beijing, Tianjin and Hebei.

4 DECOMPOSITION AND ANALYSIS FOR THE INFLUENCE FACTORS OF CARBON EMISSIONS IN THE REGION OF BEIJING-TIANJIN-HEBEI 4.1 Source Constitution of Carbon Emission During 2000-2012, the carbon emissions in the region of Beijing-Tianjin-Hebei have been in a gradually increasing trend, and the momentum is fierce. In the perspective of terminal consumption as shown in figure 1, the carbon emissions from the primary industry make minimum contribution, the contribution of the secondary industry has been high, and the contribution of the tertiary industry begins to have a larger increase after 2004. The contribution of residential consumption shows a gradual decline. Changes in the contribution of the secondary and tertiary industries, is closely related to well-developed transportation industry and rapid development of Beijing-Tianjin- 78 http://www.ivypub.org/fes

Hebei. At the same time, it shows that the impact of residents living habits and cooking methods on carbon emission is relatively small.

FIG. 1 ANALYSIS OF CARBON EMISSION IN THE VIEW OF TERMINAL CONSUMPTION

In the perspective of energy structure as shown in figure 2, although the contributions of carbon emissions from coal decrease slightly, but still are dominant. So reducing the use of coal can reduce carbon emissions effectively.

Meanwhile, it can be seen, the contributions of oil and natural gas increase yearly, which are closely related with the rapid development of transportation industry in the region of Beijing-Tianjin-Hebei. It is imperative to Change the consumption structure of energy, especially a reduction in coal consumption. In addition, economic development is bound to increase energy consumption. Therefore, in order to reduce carbon emission radically, it is an effective way to promote technological progress, and to improve energy efficiency.

FIG. 2 ANALYSIS OF CARBON EMISSION IN THE VIEW OF ENERGY STRUCTURE

4.2 Decomposition for Influence Factors of Carbon Emission by LMDI According to the formula (1) - (8) and the collected data, the contribution value C pop , Cact , Cint , Cstr , C pro and Cemf of population size P , economic scale R , energy intensity I , the proportion of energy consumption Ai , energy consumption structure Sij and energy emission intensity U ij are calculated. These are six influence factors of carbon emissions in the region of Beijing-Tianjin-Hebei. The practical contribution of various factors on the total contribution is calculated. The results are shown in table 3 and figure 3.

Obviously, among the six factors: the scale of population and economy has positive correlation with carbon emissions. They generate positive driving effect, and make higher contribution, especially economic scale. Energy intensity, energy consumption structure and energy emission intensity have negative correlation with carbon emissions. They generate negative driving effect. Among these, the contributions of energy intensity and energy emission intensity are slightly higher. The proportion of energy consumption makes negative driving effect before 2006, and then changes into positive driving effect. But, on the whole, the positive driving effect of six factors is - 79 http://www.ivypub.org/fes

significantly greater than the negative driving effect. So carbon emissions keep an increasing trend in the region of Beijing-Tianjin-Hebei.

TABLE 3 DECOMPOSITION FOR THE INFLUENCE FACTORS OF CARBON EMISSION IN THE REGION OF BEIJING-TIANJIN-HEBEI

total effect year

Ctot 2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

population scale

C pop

economy scale

energy energy intensity consumption proportion

energy consumption structure

energy emission intensity

Cact

Cint

Cstr

C pro

Cemf

1056.40

164.29

2768.69

-1831.12

-9.16

-213.45

177.14

contribution

15.55%

262.09%

-173.34%

-0.87%

-20.21%

16.77%

2622.57

429.43

5731.28

-2980.53

-39.60

-250.18

-267.83

contribution

16.37%

218.54%

-113.65%

-1.51%

-9.54%

-10.21%

6393.33

706.63

9500.23

-2687.49

-24.32

-150.15

-951.56

contribution

11.05%

148.60%

-42.04%

-0.38%

-2.35%

-14.88%

10513.41

1076.02

14170.27

-3007.88

-108.96

-189.90

-1426.14

contribution

10.23%

134.78%

-28.61%

-1.04%

-1.81%

-13.56%

19513.46

1631.53

20083.91

166.32

-88.67

-270.12

-2009.52

contribution

8.36%

102.92%

0.85%

-0.45%

-1.38%

-10.30%

23754.33

2299.76

25427.21

-748.38

-35.82

-347.58

-2840.86

contribution

9.68%

107.04%

-3.15%

-0.15%

-1.46%

-11.96%

26583.53

3044.14

30779.89

-4150.32

35.56

-624.90

-2500.83

contribution

11.45%

115.79%

-15.61%

0.13%

-2.35%

-9.41%

31294.14

4068.24

35819.22

-4978.44

22.38

-565.45

-3071.80

contribution

13.00%

114.46%

-15.91%

0.07%

-1.81%

-9.82%

33343.89

4952.00

40353.44

-8138.03

58.86

-633.61

-3248.79

contribution

14.85%

121.02%

-24.41%

0.18%

-1.90%

-9.74%

35043.93

6468.40

44868.09

-11109.74

16.02

-887.71

-4311.13

contribution

18.46%

128.03%

-31.70%

0.05%

-2.53%

-12.30%

40679.51

7477.35

51335.73

-12427.65

82.64

-1022.16

-4766.41

contribution

18.38%

126.20%

-30.55%

0.20%

-2.51%

-11.72%

43395.72

8324.23

56219.18

-15431.48

108.08

-1126.17

-4698.13

contribution

19.18%

129.5%

-35.56%

0.25%

-2.60%

-10.83%

2012 Note: Based on 2000 as the base period, all the data in the table is evaluated.

FIG. 3 DECOMPOSITION RESULTS FOR INFLUENCE FACTORS OF CARBON EMISSION BY LMDI DURING 2000-2012

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4.3 Analysis of empirical results: 1) Positive Driving Effect of Population Size During 2000-2012, the average growth of population in the region of Beijing-Tianjin-Hebei is 1.47%. The positive driving effect of population increase on the carbon emission is reflected in every aspect. For example, communication and transportation, household energy consumption etc. Among the six factors, although, the contribution of population size is not high, it shows an increasing trend. Therefore, it should be focused on controlling population growth to reduce carbon emissions.

2) Positive Driving Effect of Economic Scale As shown in figure 3, it is obvious that economic scale made positive driving effect on carbon emission. Moreover, among all the factors, the contribution of economic scale for carbon emissions is the highest. From 2005, the contribution decreased slightly, but it is still dominant. Rapid economic growth is accompanied by an increase in energy consumption. It is the most important reason of causing the continued growth of carbon emissions. As an important growth pole of Chinese economy, the annual average growth rate of GDP in the region of Beijing-TianjinHebei remains at 12.18%, which is higher than the national average. It is a significant challenge to reduce carbon emission, while maintaining sustained and rapid economic development.

3) Negative Driving Effect of Energy Intensity Energy intensity makes carbon negative driving effect on carbon emissions. And it makes the biggest contribution, making negative driving effect among factors. Because energy intensity represents energy efficiency. If increasing the negative driving effect of energy intensity was intended, promoting technological progress and developing clean energy is needed. Then the improvement of energy efficiency can be achieved. Relying on advanced technology in the region of Beijing- Tianjin-Hebei, energy intensity is from 10,300 tons per million in 2000 down to 7,900 tons / million in 2012. However, the negative driving effect is far less than the positive driving effect.

4) Negative Driving Effect of Energy Consumption Structure Because the proportions of consumption of coal and oil decline, and the proportion of natural gas increases. Energy consumption structure makes negative driving effect on carbon emission. Although the contribution of this effect is relatively low, in the long run, it is imperative to reduce the consumption of coal and oil and make effort to manufacture "low-carbon" structure of energy consumption.

5) Negative Driving Effect of Energy Emission Intensity The data of primary energy consumption is adopted in most studies, so the driving effect of energy emission intensity is zero. But the data of terminal energy is calculated in this paper, the effect of energy emission intensity exists. Energy emission intensity stand for the coefficient of carbon dioxide emission. The essence of improving energy emission intensity is to improve the combustion efficiency of energy, make full use of energy. The negative driving effect of energy emission intensity ranks only second to energy intensity. Therefore, it is greater important to make scientific and technological progress, in order to reduce carbon emission.

4.4 Analysis for Driving Effect of the Proportion of Energy Consumption The proportion of energy consumption stand for the impact of three industries and residential consumption on carbon emissions. It reflects the industrial structure in the view of energy. The result shows that it makes negative driving effect before 2006, then changes into positive driving effect. The reason is industrial restructuring in each province during the "Eleventh Five-Year". This shows that, in order to reduce carbon emission, it is required to restructure and upgrade industrial structure from the overall situation of regional integration, not just to consider this issue from the province own interests.

5 COUNTERMEASURE AND RECOMMENDATIONS FOR DEVELOPMENT OF LOWCARBON ECONOMY IN THE REGION OF BEIJING-TIANJIN-HEBEI - 81 http://www.ivypub.org/fes

5.1 Control the Population Size and Improve the Quality of Economic Development Increasing population makes tremendous pressure on energy, environment. Low-carbon economy requires population, resource and environment in harmony, and ultimately to achieve an equilibrium. Based on long-term goal, the region of Beijing-Tianjin-Hebei need to balance population size, economic development, resource and environment, ensuring sustainable development.

5.2 Vigorously Support Research of New Energy Technology Improving energy efficiency is an important way to reduce carbon emissions. There are basic conditions for improvement of new energy technology in the region of Beijing-Tianjin-Hebei. Power of science and technology is developed. New energy technology is in lead. Development of industry is mature. The government should vigorously support research of new energy technology, improves energy efficiency, ultimately achieves the purpose of reducing carbon emission.

5.3 Accelerate the Implementation of Regional Integration, Optimize the Industrial Structure After 2006, in spite of that many heavy industrial factories are moved from Beijing to Tianjin and Hebei. But because of their own policies of Beijing, Tianjin and Hebei, carbon emissions in the region increased rather than decreased, and the industrial structure also made the positive driving effect. The reason is that the industrial structure adjustment and optimization is not implemented in the entire region. To implement regional integration, the geographical, administrative, technical and other obstacles should be broken. The top-level design of BeijingTianjin-Hebei should be all-round cooperation. The optimization and upgrading of industrial structure is an effective way to reduce carbon emission. The regional integration of Beijing, Tianjin and Hebei, is not just the integration of economy, but an all-round integration of industry, ecology, environment, spatial function, society and public services.

5.4 Increase Efforts on Environmental Protection, Popularize the Concept of Low-carbon Carbon dioxide emission is closely related to production modes and living habits. In various industries, especially heavy industry, it can largely reduce carbon dioxide emission to strengthen efforts on environmental protection and reduce energy consumption of per unit output. Popularizing the concept of low-carbon can make producers and residents gradually establish an awareness of low-carbon environmental in production and daily life. Starting from the minor matter and scratch, but all add up, this will surely make greater contribution to reduction of carbon dioxide emission.

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AUTHORS 1

Xiaohong Yu, Born in 1972, PHD,

2

Mengsi Zhang, Born in 1986, graduate of Management and

associate professor of Management and

Economics, Beijing Institute of Technology, research for the

Economics Institute, Beijing Institute of

industrial economy.

Technology, research for the industrial economic theory and policy, industrial ecology management.

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