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Journal of Energy
Volume 2013 (2013), Article ID 747516, 14 pages
Empirical Study of Decomposition of Emission Factors in China
1School of Energy and Power Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
2Faculty of Economics, Saitama University, Saitama 338-0875, Japan
Received 30 June 2013; Revised 20 September 2013; Accepted 21 September 2013
Academic Editor: Mattheos Santamouris
Copyright © 2013 Yadong Ning et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
China’s CO2 emissions increase has attracted world’s attention. It is of great importance to analyze China’s CO2 emission factors to restrain the CO2 rapid growing. The CO2 emissions of industrial and residential consumption sectors in China during 1980–2010 were calculated in this paper. The expanded decomposition model of CO2 emissions was set up by adopting factor-separating method based on the basic principle of the Kaya identities. The results showed that CO2 emissions of industrial and residential consumption sectors increase year after year, and the scale effect of GDP is the most important factor affecting CO2 emissions of industrial sector. Decreasing the specific gravity of secondary industry and energy intensity is more effective than decreasing the primary industry and tertiary industry. The emissions reduction effect of structure factor is better than the efficiency factor. For residential consumption sector, CO2 emissions increase rapidly year after year, and the economy factor (the increase of wealthy degree or income) is the most important factor. In order to slow down the growth of CO2 emissions, it is an important way to change the economic growth mode, and the structure factor will become a crucial factor.
At Copenhagen Conference in December 2009, China’s CO2 emissions attracted the world’s attention, not only the rapid increase of CO2 emissions but also the leading position of China’s CO2 emissions in the world. This has exerted considerable pressure upon China in CO2 emissions reduction. It is becoming more and more significant to acquire quantity and feature of CO2 emissions by all sectors and regions in China. As the first major carbon emitters and the largest developing country in the world, China’s CO2 emissions will increase in a long period. The Chinese State Council or cabinet said that China would aim to cut carbon intensity—the amount of carbon dioxide emissions per unit of gross domestic product—by a range of 40% to 45% by 2020. However, the acceleration of China’s urbanization process and the improvement of the living standard contribute to the rapid increase of CO2 emissions. As a result, analysis should be made on the causes for CO2 emissions in China.
CO2 emissions are determined by the economic development, technical level, energy structure, economic structure, population structure, and many other factors. However, their contributions to CO2 emissions are not of the same significance, and many types of models have been used to acquire quantity and feature of CO2 emissions. The economic development factor is generally acknowledged as the major factor of CO2 emissions by some studies. The study conducted by Fan et al.  showed that the economic development is the biggest factor to CO2 emissions on the global perspective. A survey had been done by Li and Wang , and the results showed that in the long term, a 1% increase in real GDP per capita increases the consumption of energy by approximately 0.48%–0.50% and accordingly increases the carbon dioxide emissions by about 0.41%–0.43% in China. Based on dynamic general equilibrium model, macroeconomic data, and CO2 emissions, the paper made by Fu and Pei  indicated that, with further effective emission control measures, China’s economic growth over the next thirty years (2010–2040) will not lead to significant CO2 emissions increasing. Scale effect and technical effect of Divisia index decomposition results showed that CO2 emissions can be realized by focused abatement activities, cleaner production, advances in cleaner fuel products and their use of technologies. A logarithmic mean Divisia index (LMDI) model has been used by Wang et al.  to study the carbon emission of China during 1957 to 2000. The study showed that China has achieved a considerable decrease in its CO2 emissions mainly due to the energy intensity improved. In addition, fuel switching and renewable energy penetration also exhibit positive effect on the CO2 decrease. Lin and Jiang  verified the influence of industrial structure and energy structure change to the CO2 emissions. The cointegration analysis indicated that the relationship between industrial structure and CO2 emission is balanced and steady over a long period of time. Liou and Wu  employed data envelopment analysis approach to construct the metafrontier global technical efficiency of energy use index and global technical efficiency of CO2 emissions control index to measure the energy use efficiency and CO2 emissions control efficiency at country level. The results indicated that, for developed countries, the enhancement of the pure technical efficiency in the energy use and the scale efficiency of CO2 emissions control are important tasks to pursue. On the contrary, developing countries have to seek the improvement of the pure technical efficiency of CO2 emissions control and scale efficiency of energy use. Knapp and Mookerjee  made a study on the relationship between CO2 emissions and population. The paper indicated that there is no cointegration relationship between them. Based on a data for 93 countries over the period 1975–1996, the study made by Shi  found that global population change over the last two decades is proportionally associated with growth in carbon dioxide emissions and that the impact of population change on emissions is more pronounced in developing countries than in developed countries. York et al.  discussed the relationship of these three formulations, their similar conceptual underpinnings, and their divergent uses. They refined the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model by developing the concept of ecological elasticity. Their findings suggested that population has a proportional effect (unitary elasticity) on CO2 emissions and the energy footprint. The research made by Lin et al.  showed that the family planning policy has a positive effect on CO2 emissions. On the contrary, some scholars considered that population will lead to technical reform and ultimately has a negative effect. For example, Bin and Dowlatabadi  argued that population growth will slow down the CO2 emissions to a certain extent. Besides, Liu , Poumanyvong and Kaneko , and Martínez-Zarzoso and Maruotti  et al. studied the relationship between city urbanization and CO2 emissions to test and verify the Cruz Nez effect between them.
Since there are many factors which affect CO2 emissions, the decomposition method is widely used to study the CO2 emission. The relationship between these factors and CO2 emissions, which is studied by many authors using various methodologies for different time periods including some pioneering work of Ehrlich and Holdren , becomes key and hot topic in environmental science, climatology, and other relative academic fields. The two scholars developed a model named IPAT equation which considered the environmental impact (), population (), per capita wealth (), and technical level (); that is, . Kaya  formulated the Kaya Identity in Intergovernmental Panel on Climate Change (IPCC) based on ; that is, where is the carbon intensity, represents energy intensity of GDP, is the per capita GDP or income, and represents population. The identity contains population, GDP, carbon intensity, and energy intensity factors which affect the CO2 emissions.
With the improvement of the Kaya identity, this decomposition method was widely used in the field of CO2 emissions. A research based on the STIRPAT model was formulated by York et al.  to analyze the relationship of CO2 emissions, population, rich degree, and urbanization. The results showed that the elasticity coefficients of population, the per capita GDP, and urbanization to CO2 emissions are 1, 0.82–1.48, and 0.62–0.70. Using the STIRPAT model, Fan et al.  analyzed the impact of population, affluence, and technology on the total CO2 emissions of countries at different income levels over the period of 1975–2000. The results showed at the global level that economic growth has the greatest impact on CO2 emissions, and the proportion of the population between ages 15 and 64 has the least impact. The proportion of the population between 15 and 64 has a negative impact on the total CO2 emissions of countries at the high income level, but the impact is positive at other income levels. Diakoulaki et al.  presented a decomposition analysis of CO2 emissions in Greece for the period of 1990–2002 split into two equal time intervals. The proposed analytical procedure relies on the refined Laspeyres model and follows a bottom-up approach starting from the major energy consuming sectors and aggregating the obtained effects for estimating their relative impact at the country level. Xu et al.  and Li and Wang  studied the contribution of energy structure, energy efficiency, and economic growth to the CO2 emissions. With the development of research on the characteristics of carbon emissions, the particularities of industrial sector and residential consumption sector are remarkable gradually. A survey on the characteristics of carbon emissions made by Wei et al.  indicated that the CO2 emissions were mainly caused by secondary industry, and at the same time, the secondary industry made great contribution to economy growth. Based on the STIRPAT model, a research was made by Lin et al. . The paper analyzed the contribution of population, urbanization, per capita GDP, and industrialization level. The results showed that the contribution degrees of per capita GDP and population are 38% and 32%. The decrease of energy intensity is the main reason. Using the LMDI decomposition technique, Pani and Mukhopadhyay  examined the contribution of the major factors in changing the level of emissions. The effect of GDP on emissions is found to be substantially more than that of population. However, the income effect shows high fluctuation over time, while the population effect has been roughly constant. Wang et al.  adopted LMDI method based on the basic principle of Kaya identities and set up the expand decomposition model of CO2 emissions. The results showed that CO2 emissions increase year after year, and the changes in economic growth and energy intensity are important factors for affecting CO2 emissions. Zha et al.  estimated and compared the energy related CO2 emissions from urban and rural residential energy consumption from 1991 to 2004. It is found that energy intensity and the income effects, respectively, contributed most to the decline and the increase of residential CO2 emissions for both urban and rural China. In urban China, the population effect was found to contribute to the increase of residential CO2 emissions with arising tendency. However, in rural China, the population effect on residential CO2 emissions is kept decreasing since 1998. The study made by Shi and Zhang  indicated that economy scale and energy efficiency are the main factors of the CO2 emission. The effects of energy transform and population are small relatively. In addition, the paper predicted that energy efficiency will become the most important factors.
However, there are still some aspects that need to improve when calculating the CO2 emissions and discussing the factors affecting the CO2 emissions. Firstly, some scholars use the primary energy consumption when calculating the CO2 emissions. However, some energy is used as raw material and dose not release CO2. Some papers divide the energy into coal, oil, and natural gas which may make the results do not reflect the CO2 emissions accurately. Secondary, the CO2 emission is divided into direct emission and indirect emission of terminal energy consumption. The former means the CO2 caused by energy combustion directly, while the latter one means the CO2 emissions when the primary energy is transformed into secondary energy, like electric and heat. Some research works take the CO2 emissions made from electric and heat energies as the CO2 emissions of secondary industry. As a result, the CO2 emissions of secondary industry are larger than the real amount, while the CO2 emissions of residential consumption sector are too small. This makes the decomposition results not accurate. Finally, the energy consumption of China is divided into industrial sector (material production department and service department) and residential consumption sector. The latter does not make a contribution to the GDP, and its position in the energy consumption system is relatively independent. Residential consumption sector should be analyzed individually, while most papers did not separate it from the industrial sector.
To solve the issues mentioned above, this paper calculates the CO2 emissions of industrial sector and residential consumption sector. Based on the Kaya identity, decomposition of CO2 emissions factors in China in the period of 1980–2010 is discussed in the paper. The remainder of the paper is organized as follows. Section 2 describes an overview of decomposition analysis. Sources of data of this study are included in Section 3. Section 4 reports the results. Section 5 provides the conclusions.
2.1. CO2 Emissions Calculation
In this paper, we assumed complete combustion of energy sources to generate carbon emissions. The calculation equation of CO2 emissions is shown as where is the CO2 emissions, means the energy consumptions of sector or trade , and refers to CO2 emission factor of energy .
2.2. Completed Decomposition Model of Industrial Sector
The factors affecting CO2 emissions can be divided into 4 kinds as follows: (1) economy factor, which represents the effect caused by scale of economy, (2) structure factor, which represents the effect caused by industrial structure, (3) efficiency factor, which represents the effect caused by the change of energy intensity, and (4) emission factor, which represents the CO2 emission per unit of energy consumption. The model is shown in where is the CO2 emissions of industrial sector, represents gross domestic product (GDP), is the product of the industry sector , means the energy consumption of industrial sector , and is the CO2 emissions of the industry sector . , , , and are the economy factor, structure factor, efficiency factor, and emission factor, respectively.
As is shown in (3), the change of CO2 emissions can be attributed to the four factors: where is the difference between the current year and the reference which is the last year in this paper. On account of the annual GDP change of China being very large, the residual term would lead the results of the formula to large errors. In order to improve the precision of the model analysis, in this study, the residual is decomposed according to the principle of “jointly created and equally distributed.” In the time interval , let where means three industry sectors and the complete decomposition model can be written as follows.
Economy factor ():
Structure factor ():
Efficiency factor ():
Emission factor ():
The change of CO2 emissions of industrial sector between the current year and the reference can be written as
2.3. Completed Decomposition Model of Residential Consumption Sectors
There is difference between urban and rural in the life style and living standard, so separating them into urban and rural parts can make the results more accurate. The factors affected CO2 emissions can be described as follows: (1) population factor, the effect caused by change of population, (2) economy factor, which represents the effect caused by per capita income, (3) efficiency factor, and (4) emission factor. The model is shown in where is the population, means the residents income (disposable income is used in urban model, while rural model uses pure income according to the initial data of yearbook), represents energy consumption, and is CO2 emissions of residential consumption sector. , , , and are the population factor, economy factor, efficiency factor, and emission factor, respectively. The decomposition equations of CO2 emission of residential consumption sector are obtained in (12)–(15).
Population factor (PF):
Economy factor ():
Efficiency factor ():
Emission factor ():
The CO2 emissions of current and base year can be calculated and written as follows:
3. Data Management
The GDP, population by residence, annual per capita income, and energy consumption in this study are statistical data from 1981 to 2010 from China Statistical Yearbook , and China Energy Statistical Yearbook  respectively. Taking into account the comparability between variables, GDP of industrial sector is calculated at 2005 constant price by the GDP indices to eliminate the price impact before the data analysis. Annual per capita income is different because it is disposable income in urban residential consumption but net income in rural residential consumption. And annual per capita income has been calculated at the base year 2005 according to Engel’s coefficient. Table 1 shows China’s GDP and energy consumption by industrial sector, while Table 2 indicates China’s population, annual per capita income, and energy consumption by residence.
CO2 emissions have been divided into three distinct sectors consistent with GDP according to the data. The primary sector includes agriculture, forestry, animal husbandry, fishery, and water conservancy; the secondary sector includes industry and construction; the tertiary sector includes transport, storage and post, wholesale and retail trades, hotels and catering services, and other sectors. This paper adopts the final energy consumption from the energy balance tables of China. To improve the resolution of the energy analysis, we define 17 kinds of energy: raw coal, washed coal, other washed coal, coal briquettes, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, LPG, other petroleum products, other coking products, natural gas, coke oven gas, refinery dry gas, and other gas. The carbon-emission coefficients for the 17 kinds of energy are obtained from 2006 Inventories Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories . Based on the data from energy balance tables and the carbon-emission coefficients shown in Table 3, this paper calculates the CO2 emissions of industrial sector and residence. It should be noted that the direct and indirect CO2 emissions are calculated in this paper. That is to say, CO2 emissions of power and heat are considered. The indirect CO2 emissions of per unit power and heat are calculated based on the data from energy balance tables in China Energy Statistical Yearbook . Table 4 indicates CO2 emissions of per unit power and heat.
4.1. Calculated CO2 Emissions
According to (2), CO2 emissions of industrial and residential consumption sectors are shown in Figure 1. From the figure, several conclusions can be summarized as follows.(1)The stage of CO2 emission: the CO2 emissions have an obvious stage from 1980 to 2010. It is a rapid growth phase from 1980 to 1996 during which CO2 emissions grew from 1.36 Gt to 3.23 Gt. After the rapid growth phase, the CO2 emissions came into a stable stage that they grew only 0.08 Gt. Another rapid growth phase is from 2000 to 2010, the net increased amount of CO2 emissions is 4.29 Gt, and in 2010, CO2 emissions reached 7.58 Gt.(2)There is an obvious difference for CO2 emission among the sectors. The CO2 emissions of primary, secondary, and tertiary industry are from 0.11 Gt (1980) to 0.16 Gt (2010), 0.84 Gt (1980) to 5.54 Gt (2010), and 0.11 Gt (1980) to 1.05 Gt (2010), respectively. At the same time, the proportion of primary industry is from 8.20% to 2.07%, while it increases from 8.05% to 13.80% for tertiary industry. The secondary industry has a heavy proportion which is between 59.59% and 73.34%. On the other hand, CO2 emissions of urban consumption increase from 0.17 Gt in 1980 to 0.50 Gt in 2010, while the CO2 emissions of rural consumption increase from 0.13 Gt in 1980 to 0.34 Gt in 2010. The growth rate is lower than that in industry sector. Besides, the proportions of urban and rural CO2 emissions decrease from 12.29% to 6.50% and from 9.39% to 4.52%, respectively. As is shown in Figure 1, the difference between industrial sector and residential consumption sector is becoming large. It is 0.77 Gt in 1980, while it turns to 5.91 Gt in 2010. The proportion of industrial sector is nearly 90% which is as 8.08 times as residential consumption sector. The CO2 emissions caused by secondary industry make great contribution.
4.2. Industrial Sector
Based on (9)–(12), CO2 emissions of industrial sector in China are decomposed from 1980 to 2010. The results are shown in Table 5 and Figure 2. It should be noted that the positive means increase of CO2 emissions, while the negative indicates decrease. The actual CO2 emissions change is calculated with annual energy consumption in the energy balance sheets. The differences between the calculated CO2 emissions change in (13) and the actual change are, respectively, small, about 0.01%, which means that the model is accurate.
Table 5 and Figure 2 indicate that CO2 emissions of industrial sector are mainly caused by economy factor. Efficiency factor slows down the speed of CO2 emissions increase. Efficiency factor is always negative except 2003 and 2004. The effect of the industry structure is positive to the change of CO2 emissions. The accumulated values of decomposition factors as the reference year of 1980 are shown in Figure 3. During the 30 years, CO2 emissions increase caused by economy factor was 8.68 Gt, while the amount caused by structure was 0.93 Gt. The main factor to decrease the CO2 emission was efficiency factor, and it was about 4.00 Gt. The effect of emission factor was not obvious.
Decomposition results of economy and structure factor are shown in Table 6. Column represents the change of CO2 emission when the GDP changes one percent. Similarly, columns , , and represent the change of CO2 emission when the structure of three industries changes one percent. For example, when the GDP increased 1% in 2010, the CO2 emissions increased 61.7 Mt. The corresponding values for primary industry, secondary industry, and tertiary industry are 16.8 Mt, 109.2 Mt, and 24.0 Mt. The secondary sector value is much higher than the others. Table 6 indicates that the CO2 emission of secondary sector is mainly caused by economy factor. If the mode of economic growth and economy structure does not change, the rapid growth trend will not change in a long period of time.
Table 7 is the decomposition results of efficiency factor. Columns , , and represent the change of CO2 emissions when the energy intensity of primary industry, secondary industry, and tertiary industry changes one percent. Just the same as structure factor, the secondary sector value is much higher than the others. Columns , , and are 1.5 Mt, 55.3 Mt, and 10.2 Mt in 2010. That is to say, it is very effective to reduce the ratio of secondary sector and energy intensity for CO2 emissions reduction. Efficiency factor value is always negative in most years which means that efficiency factor mitigates the pressure of CO2 emissions reduction. Tables 6 and 7 indicate that the contribution made by structure factor is larger than efficiency factor when they change 1%. However, the CO2 emissions reduction in the 30 years is mainly caused by efficiency factor. Structure factor increases the CO2 emissions, which is due to the enhancement of secondary sector proportion. That is to say, the CO2 emissions reduction of structure adjustment is obvious but hard to achieve.
Through the same manner, the decomposition results of emission factor can be obtained. As shown in Table 8, the values of columns , , and represent the change of CO2 emissions when the CO2 emissions intensity of primary industry, secondary industry, and tertiary industry changes 1%. Just as the same as the former two factors, the secondary sector value is much higher than the others. The CO2 emissions reduction contribution made from structure adjustment is outstanding. As a result, changing the energy structure can make great contribution to CO2 emission reduction. However, the situation that coal is as the main energy will not change in a short period of time in China.
4.3. Urban Residential Consumption
Based on (15)–(16), CO2 emissions of urban residential consumption in China are decomposed from 1980 to 2010. The results are shown in Table 9 and Figure 4. Columns , , , and represent the change of CO2 emissions when population, per capita income, energy intensity, and CO2 emission intensity change 1%, respectively. Obviously, the contributions are nearly the same.
The accumulated decomposition values of urban residential consumption CO2 emissions as the reference year of 1980 are shown in Figure 5. The accumulated changes of population, economy, efficiency, and emission factors were 313.2 Mt, 557.9 Mt, −500.6 Mt, and −45.0 Mt from 1981 to 2010. The CO2 emissions reduction of urban residential consumption is mainly caused by efficiency factor, while population and economy factors have a positive function to increase the CO2 emissions. Emission factor has a weak function of CO2 emissions reduction which means that the energy structure of urban residential consumption is more reasonable. With the enhancement of live level and the transformation of consumption structure, the CO2 emission will expand further.
4.4. Rural Residential Consumption
Through the same manner, the decomposition results of rural residential consumption CO2 emission can be gotten. Table 10 and Figure 6 show the decomposition results of rural residential consumption CO2 emission. Columns , , , and represent the change of CO2 emissions when population, economy, efficiency, and emission factors change one percent, respectively. Obviously, the contributions are nearly the same. Figure 7 indicates the accumulated values of CO2 emissions change in rural residential consumption. The efficiency factor is the main reason for the decrease of rural residential consumption CO2 emissions. Since China begins to increase the speed of urbanization, the positive effect of population is becoming more and more obvious. Above all, the main reason for the increase of rural residential consumption CO2 emission is economy factor. With the commercial energy being popularized step by step and residential consumption appliances becoming universal, the rural residential consumption CO2 emission will increase in the future.
(1)The CO2 emissions of industry sector are mainly caused by economy factor. Efficiency factor slowed down the speed of CO2 emission increase. The effect of the industry structure is positive to the change of total CO2 emission, while the effect of emission factor is not obvious.(2)The CO2 emissions of secondary sector are mainly caused by economy factor. It is very effective to reduce the ratio of secondary sector and energy intensity for CO2 emissions reduction. Efficiency factor value is always negative in most years which means that efficiency factor mitigates the pressure of CO2 emissions reduction. Changing the structure of energy can make great contribution to CO2 emissions reduction. However, the situation that coal is the main energy will not change in a short period of time. (3)The CO2 emission of residential consumption sector is in a rapid growth phase. Economy factor is the main aspect of increase of CO2 emissions not only in urban area but also in rural village. With the commercial energy being popularized step by step and residential consumption appliances becoming universal, the CO2 emissions of residential consumption sector will increase in the future. It is very important to lead the consumers to choose sustainable live style.(4)It is of great importance to transform the economic growth mode to control the CO2 emissions. Structure factor and efficiency factor can make great contribution to the CO2 emissions reduction. The former has a more obvious effect and will be the key point of CO2 emission reduction in the future.
This work is sponsored by the Technology Research Project of Ministry of Education: “Investigation of Energy-Economy-Environment System Based on the Population Geography” in China from 2011.
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