Grey Relevance Analysis of Major Factors of Energy-Related CO2 Emissions in Ti

Frontier of Environmental Science December 2015, Volume 4, Issue 4, PP.104-108

Grey Relevance Analysis of Major Factors of Energy-Related CO2 Emissions in Tianjin, China Zhe Wang1, Ben Wu 2†, Jianan Wang1, Liyan Zheng 1 1. Department of Environmental Science and Engineering, Nankai University Binhai College, Tianjin, 300270, China 2. Tianjin Academy of Environmental Sciences, Tianjin, 300191, China †

Email: wb_0118@126.com

Abstract Energy-related CO2 emissions from Tianjin’s production and household sectors during 2000–2012 were calculated based on the default carbon-emission coefficients provided by the Intergovernmental Panel on Climate Change. Grey relational analysis was used in this study to capture the dynamic characteristics of 12 different factors related to CO2 emissions. The results indicated that population scale and structure, industrial structure, per capita disposable income, energy consumption and structure appeared as the main drivers related to the CO2 emissions increase during the study period. Based on the research, we make the policy recommendations including optimizing the industrial structure and energy structure, improving energy efficiency and promoting low-carbon consumption.

Keywords: CO2 Emissions; Grey Relational Analysis (GRA); Tianjin

1 INTRODUCTION China has become the top primary energy consumer as well as the top carbon emitter in the world [1, 2]. The combustion of fossil fuels contributes to the emissions not only of CO2, but also of air pollutants such as SO2, NOx and Particulate Matter[3]. About half of the Chinese population now lives in cities, especially in metropolises such as Tianjin, one of the four municipalities directly under the Central Government of China, which is not only an economic center in the north of China but also a well-known international harbor[4]. Tianjin has experienced a sharp increase in carbon emissions in association with its rapid economic development. As one of China’s pilot low-carbon cities, Tianjin is facing major pressure to discover new ways in which to reduce CO2 emissions. The objectives of this study are to analyze the driving forces, i.e., population, economic growth, energy structure, and energy intensity behind CO2 emissions in Tianjin by applying grey relational analysis (GRA) and based on this, to make policy recommendations to help achieve the stated emission reduction targets.

The rest of this paper is organized as follows. In Section 2, the GRA approach and the data used in our analysis are described. In Section 3, we present the results of the GRA analysis, conclude the paper and make our policy suggestions.

2 METHODOLOGY AND DATA 2.1 Grey Relational Analysis (GRA) Grey system theory, which was formulated by Professor Julong Deng in the 1970s and is characterized by “less data modeling”, considers as the research object a “small sample” uncertainty system in which part of the information is known and part of the information is unknown and focuses on the study of “less data” and “poor information” uncertainty problems [5]. GRA is an active branch of grey system theory that compares geometric relationships between time series data in relational space and represents the relative variations between one major factor and all - 104 http://www.ivypub.org/fes

other factors in a given system by the grey relational grade (GRG). If the relative variations between two factors are consistent during their development process, i.e., the geometry sequence curves of different sequences are similar, then the GRG is large, and vice versa [6]. The specific calculation steps in determining the GRG are as follows: (1) Normalization of the reference series and compared series. There are two common normalization methods: initial value method, as shown in Equation (1) and average value method, as shown in Equation (2), in which the value in the reference series and compared series is normalized by dividing the respective data of the original series by its value in the base year or by the average value of all years. In this study, the initial value method is used to obtain the comparable series.

( x ′ (1) , x ′ ( 2) , ⋅ ⋅ ⋅, x ′ ( n )) ,=i

= X i′ X i / x= i (1)

i

Xi =

i

0,1, 2, ⋅ ⋅ ⋅, m

( x ′ (1) , x ′ ( 2) , ⋅ ⋅ ⋅, x ′ ( n )) ,

= X i′ X= i / Xi 1

i

i

i

(1)

i

n

∑ xi ( k ) , i= 0,1, 2, , m, k = i= 0,1, 2, , n

.

(2)

i = 1, 2, ⋅ ⋅ ⋅, m .

(3)

n k =1

(2) Obtaining the absolute difference between the two series, represented by ∆i ( k ) : ∆i ( k ) = x0′ ( k ) − xi′ ( k ) , ∆i =

( ∆i (1) , ∆i ( 2 ) , ⋅ ⋅ ⋅, ∆i ( n ) ) ,

(3) Seeking the maximum value M and minimum value m in Equation (3):

M = max max ∆i ( k ) , m = min min ∆i ( k ) . i

k

i

k

(4)

(4) The GRG between two series at a certain point is called the grey relational coefficient. Consequently, the grey relational coefficient γ 0i ( k ) of the compared series xi (t) to reference series x0 (t) at time t = k can be expressed as:

γ 0i ( k= )

m +ξM k 1, 2, ⋅ ⋅ ⋅, n;= i 1, 2, ⋅ ⋅ ⋅, m , , ξ ∈ ( 0,1) , = ∆ i (k ) + ξ M

(5)

where ξ is the distinguishing coefficient used to adjust the difference of the relational coefficient; usually, ξ ∈ [ 0,1] .

According to the sensitivity analysis by Chang and Lin, the suggested value for the distinguishing coefficient ξ is 0.5, because of the moderate distinguishing effects and good stability of outcomes; therefore, we adopted ξ = 0.5 for further analysis [7].

(5) The GRG between two sequences can be expressed by dividing the relational coefficient by its average value, to show the relationship for the entire system[5]:

γ= 0i

1

n

, i 1, 2, ⋅ ⋅ ⋅, m . ∑ γ 0i ( k ) =

n k =1

(6)

It is not easy to recognize the developing trends between the two series from the above formula, because the GRG is an absolute value that lacks the directional influence of elements that affect the objective factor. To this end, we introduced a factor “α” into the GRG to reflect the relative trends between the various factors [7][8]. During the study period, when the growth rate of the compared series is faster than the reference series α = 1; conversely, when the developing trend of the compared series is lower than the reference series α= -1. Thus, the modified GRG is expressed as:

γ 0i ′ = γ 0i ⋅ α .

(7)

Methodological and theoretical advances in grey theory have been developed in recent years. In addition, some studies have attempted to explore the relationship between GDP, population, energy consumption, fuel mix, and CO2 emissions. Based on the results of existing research, GRA was performed to calculate the GRG between 12 indices (X2-X13) and the CO2 emissions (X1) of Tianjin from 2000–2012 (Table 1), and the trends of their changes are shown in Figure 1.

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2.2 Data Sources With regard to the availability and quality of data, local sources were used to obtain the data required for this analysis. The data spanning 2000–2012 used in the analysis were collected from various issues of the Tianjin Statistical Yearbook, China Energy Statistical Yearbook, and China Statistical Yearbook on the Environment published annually by the National Bureau of Statistics (NBS). The volume of energy sources consumed by agriculture, industry, construction, services, and residents in Tianjin was obtained directly from Tianjin’s Energy Balance Table in the China Energy Statistical Yearbooks (NBSC, 2001–2013). The GDP and energy consumption data are given as 108 yuan in constant 2000 price and 104 tons of coal equivalents (tce) in the calorific value calculation, respectively. Besides, all the calculations are conducted in the Excel software.

TABLE 1GREY RELATIONAL ANALYSIS INDEX

No

Item

Unit

Interpretation

1

Total CO2 emission /X1

104 tons

CO2 emission scale

4

2

Total population /X2

10 persons

Permanent residents, reflecting population size

3

Urbanization rate /X3

%

Urban population to total population

4

Total energy consumption /X4

4

10 tce

Total energy consumption

5

Proportion of fossil energy /X5

%

Energy consumption structure

6

GDP /X6

108 yuan

Economic growth

7

Proportion of secondary industry /X7

%

Industry structure

8

Proportion of tertiary industry /X8

%

Industry structure

4

9

Energy consumption per unit of GDP /X9

tce /10 yuan

Energy consumption intensity

10

Per capita disposable income of urban residents /X10

yuan

Consumption level of urban residents

11

Per capita disposable income of rural residents /X11

yuan

Consumption level of rural residents

10 vehicles

Traffic scale

12 13

Total number of vehicles /X12 CO2 emissions per unit of GDP /X13

4

4

ton/10 yuan

Carbon intensity, reflecting technological progress

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11

FIG. 1 CHANGE TRENDS OF FACTORS

3 CONCLUSIONS The GRA results shown in Table 2 revealed that the GRGs of the above indices are >0.60, which indicate that each - 106 http://www.ivypub.org/fes

of the 12 studied driving factors has strong correlation with CO2 emissions in Tianjin and several findings are identified as follows.

Total population (γ12′ = -0.8732) and urbanization rate (γ13′ = -0.8400) rank first and fourth, respectively, indicating that both population scale and population structure (urbanization) play an important role in the increase of CO2 emissions. The proportions of tertiary industry (γ18′) and secondary industry (γ17′) are listed second and third, respectively, implying that industrial structure factors also have very strong effects on CO2 emissions. Negative relations occur where CO2 emissions grew at a higher rate than the compared series (α = -1).

Disposable income per capita of rural residents (γ111′ = 0.8357) and urban residents (γ110′ = 0.7705) rank fifth and eighth, respectively, showing that the current consumption levels of urban and rural residents are the main factors influencing carbon emissions. The proportions of fossil energy (γ15′ = -0.8249) and total energy consumption (γ16′ = 0.8222) are ranked sixth and seventh, respectively, suggesting that the contributions from energy consumption structure, dominated by fossil energy, and the increasing scale of energy consumption are relatively large. Because of the close relationship between energy consumption and carbon emissions, emphasis should be placed on measures for energy conservation and clean energy transition in order to reduce emissions.

The GRG ranked ninth is energy consumption per unit of GDP (γ19′ = -0.7422), which represents energy intensity, and that ranked tenth is CO2 emissions per unit of GDP (γ113′ = -0.7084), which represents carbon intensity; both of which followed a declining trend throughout the study periods. GRA shows that energy efficiency improvements and technological progress have some effect on the reduction of CO2 emissions in Tianjin. The final two driving factors are GDP (γ16′ = 0.6560) and total number of vehicles (γ112′ = 0.6189). Although they rank last, their GRGs verify their strong correlation with CO2 emissions and their higher rates of growth.

TABLE 2 GREY RELATIONAL ANALYSIS RESULTS

Item

Symbol

Sort

Modified GRG

Total population /X2

γ12′

1

−0.8732

Urbanization rate /X3

γ13′

4

−0.8400

Total energy consumption /X4

γ14′

7

0.8222

Proportion of fossil energy /X5

γ15′

6

−0.8249

GDP/X6

γ16′

11

0.6560

Proportion of secondary industry /X7

γ17′

3

−0.8439

Proportion of tertiary industry /X8

γ18′

2

−0.8459

Energy consumption per unit of GDP /X9

γ19′

9

−0.7422

Per capita disposable income of urban residents /X10

γ110′

8

0.7705

Per capita disposable income of rural residents /X11

γ111′

5

0.8357

Total number of vehicles /X12

γ112′

12

0.6189

CO2 emissions per unit of GDP /X13

γ113′

10

−0.7084

It is generally unacceptable to limit CO2 emissions through the control of population growth and the constraint of the ever-increasing economic growth and per capita demand for goods and services, particularly material and energy needs. A number of policies should be implemented to optimize the energy structure and increase energy efficiency, although the share of coal in primary energy consumption and its dominant position as energy carrier in Tianjin cannot be changed in the near future. Government should prioritize the development and use of non-fossil-fuel energy sources such as solar, geothermal, and biomass. In the future, Tianjin should continue to ensure industrial development, integrate low-carbon development as part of its overall industrial development strategy, and attempt to build a low-carbon industrial system. Attention should also be given to improving the consciousness of residents to green transportation and low-carbon lifestyles, which would promote patterns of low-carbon consumption.

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REFERENCES [1]

BP. BP Statistical Review of World Energy 2013. 2013

[2]

Boden, T.A., Marland, G., Andres, R.J.Global. Global, regional, and national fossil-fuel CO2 emissions. Carbon Dioxide

[3]

Mark D. Agee, S.E.A., Thomas D.Crocker, Jonathan W. Williams. Non-separable pollution control: Implications for a CO2

[4]

NBSC. China Statistical Yearbook 2013. China Statistics Press, Beijing, 2013

[5]

Julong, D. Introduction to grey system theory. The Journal of Grey System, 1989, 1: 1-24

[6]

Sifeng, L., Y. Yingjie, and W. Lifeng. Grey system theory and its application [M]. Beijing: Science Press, 2014

[7]

Chang, T.C., Lin, S.J. Grey relation analysis of carbon dioxide emissions from industrial production and energy uses in Taiwan.

Information Analysis Center., 2012 emissions cap and trade system. Resource and Energy Economics, 2014, 36(1): 64-82

Journal of Environmental Management, 1999, 56: 10

AUTHORS Zhe Wang (1981- ), master degree of environmental science,

Ben Wu(1981- ), bachelor degree of land resource management,

lecturer

engineer of Tianjin Academy of Environmental Sciences,

of

Department

of

Environmental

Science

and

Engineering, Nankai University Binhai College, majoring in low

majoring in environmental planning and management.

carbon

Email: wb_0118@126.com

economy,

environmental

impact

assessment

and

environmental planning. Email: wmomo1981@126.com

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