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Carbon emissions reduction in China's food industry
Energy Policy 86 (2015) 483–492
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Carbon emissions reduction in China's food industry Boqiang Lin a,n, Xiaojing Lei b a Collaborative Innovation Center for Energy Economics and Energy Policy, China Institute for Studies in Energy Policy, Xiamen University, Fujian, 361005, PR China b The School of Economics, China Center for Energy Economics Research, Xiamen University, Xiamen, Fujian, 361005, PR China
H I G H L I G H T S
We analyze the energy consumption and CO2 emissions in China's food industry. LMDI decomposition analysis is conducted for finding out the driving forces. Industrial activity is the main driving force of CO2 emissions in this industry. Energy intensity is the main factor mitigating carbon emissions in this industry. Main advice: improving energy efficiency, optimizing industrial scale.
art ic l e i nf o
a b s t r a c t
Article history: Received 19 March 2015 Received in revised form 22 May 2015 Accepted 29 July 2015
In this paper, we evaluate the changes in carbon dioxide emissions from energy consumption in China's food industry from 1986 to 2010 based on the Logarithmic Mean Divisia Index (LMDI) method. The results show that energy intensity (EI) and industrial activity (IA) are the main determinants of the changes in carbon dioxide. Energy intensity (EI) contributes to decrease in emissions within 25 years while industrial activity (IA) acts in a positive way to increase the emissions level. Industry scale (IS) mostly contributes to increase in emissions except for the time interval 1996–2000. However, for both carbon intensity (CI) and energy structure (ES), they have a volatile but not significant influence on emissions in the different time intervals. To further understand the effects, we analyze the cumulative emission during the whole period 1986–2010. The results further testify that energy intensity and industrial activity are the most important factors affecting reduction and growth of carbon emissions. The results indicate that efforts to reduce emission in China's food industry should focus on the enhancement of energy efficiency, the optimization of industrial scale and the restructuring energy use. Finally, recommendations are provided for the reduction of carbon dioxide in China's food industry. & 2015 Published by Elsevier Ltd.
Keywords: Carbon dioxide emissions China's food industry
1. Introduction It is generally believed that the widespread use of coal and steel as well as the improvement in steam engines are important factors contributing to the accelerated development of industrial technology. After the industrial revolution, coal became a major power resource. The world's coal, oil and natural gas reserves decreased rapidly while there is a rapid increase in emissions such as carbon dioxide and carbon monoxide. At the same time, the globalization of the economy led to the expansion of industrialization by means of trade and capital investment, and subsequently to transfer the n Corresponding author at: Collaborative Innovation Center for Energy Economics and Energy Policy, China Institute for Studies in Energy Policy, Xiamen University, Fujian, 361005, PR China. Tel.: þ 86 5922186076; fax: þ86 5922186075. E-mail addresses: [email protected], [email protected] (B. Lin).
http://dx.doi.org/10.1016/j.enpol.2015.07.030 0301-4215/& 2015 Published by Elsevier Ltd.
energy intensive and highly polluting industries from developed regions to developing regions. Compared with other industries such as chemical fiber industry or mining and quarrying, the food industry is not an energy- and pollution-intensive industry, but it has a greatly significant impact on the stability of the national economy and people's livelihood, economic development and the stimulation of domestic demand. The food industry mainly refers to the industrial system with continuous and organized economic activities, and the raw materials are the products or semi-products of agriculture, forestry, animal husbandry, fishery and chemical industry. There is a long history in the development of the food industry, and the emergence of the modern food industry can be dated back to the early eighteenth century. With the development of technology and the application of scientific and technological achievements in the food industry, modern food industry has developed rapidly. Food is a primary need of human
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Fig. 1. The industrial value added and energy consumption in China's food industry. Data resources: China Statistical Yearbook (1986-2011), Statistical Review of World Energy Full Report 2013, CEIC China Database.
Fig. 2. The energy consumption ratio of China's food industry. Data resources: China Statistical Yearbook (1986-2011).
existence. Therefore, the food industry is of great importance. According to the Bureau of China, the food industry is a comprehensive industrial sector and it can be divided into four categories: agricultural food processing industry, food manufacturing industry, beverages and tobacco products manufacturing industry. The food industry is one of the most important industries related to the national economy and people's livelihood. In the 21st century, especially in the tenth Five-year Plan, China's food industry developed rapidly and stably. In 2013, the number of enterprises above designated size was 36,275, an increase of 52.2% compared with 2005 and an annual average growth rate of 5.39%. The added value of the food industry accounted for 11.2% of China's total industrial added value, which made an contribution rate of 12.6% to the growth rate of China's whole industry, thereby raising national industrial growth by 1.3 percentage points. Additionally, the number of employees surged to 7.07 million, an increase of 0.39 million compared with previous year. It can be seen that the prominent role of the food industry has strengthened continuously. As a country with the largest population in the world, China is also the biggest food producing and consuming country. As shown in Fig. 1 (BP, 2013), from 1986 to 2010, the value added of China's food industry increased from 26.115 billion yuan to 2071.162 billion yuan, with an average annual growth rate of 20.63 percent, and eighty times as in 1986. Meanwhile, large amounts of electricity, heat and some other kinds of energy systems are needed in the food production process, making the food industry a key energy-consuming industry. Energy consumption in the food industry increased from 25.7 million ton of coal equivalent1 in 1986 1
Tons of coal equivalent: which is short as tce in the paper.
to 55.121 million tce in 2010, with an annual average growth rate of 3.35 percent. Before 1996, its share of the total energy consumption was 3 percent, but in 2006, the figure was between 1.7% and 2%. The total amount of energy consumption in the food industry is equivalent to total amount of energy consumed in Algeria or half of Argentina's total energy consumption. In 2012, the total amount of energy consumed in the food industry is 57.99 million tce, which consist of 38.5 million tce of coal, 0.14 million ton of coke, 80 t of crude oil, 0.51 million ton of gasoline, 16 ton of kerosene; 0.91 million ton of diesel oil, 0.14 million ton of fuel oil, 1.29 billion cubic meters of natural gas, and 95.4 billion kW h of electricity. Of the total energy consumption, fossil energy accounted for up to 95%. As shown in the figures from 1986 to 2010, coal is the primary energy consumed in the food industry, followed by electricity and diesel fuel. According to the estimation of the power balance sheet, 80% of electricity is from thermal power plants. Therefore, the energy consumed in the food industry is not much, but for the high proportion of fossil energy, which leads to carbon dioxide emissions. Thus the fossil-based energy structure, especially coaldominant energy structure has led to huge carbon dioxide emissions in China's food industry. Based on the data from 1986 to 2010, we calculated the carbon dioxide emission in China's food industry. As shown in Fig. 2, the carbon dioxide emission in China's food industry in 1986 was about 18.46 million ton while it was 39.25 million in 2010, with an annual average growth rate of 3.6%. According to CO2 Emission from Fuel Combustion Highlights published by the International Energy Agency, the amount of China's carbon dioxide emissions in the food industry is equivalent to the entire emissions of Norway in 2010.
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China has the largest population in the world. Therefore, China's food industry is also largest in the world. The sector is one of the fast growing sectors in China, as China's personal income increases quickly. The government considers the sector is very important, as the sector has a significant impact on people's livelihood, social stability, and economic development. A good understanding of the sector is also very important for energy and CO2 emissions reduction policies in China. The main contributions of the paper include three areas: (i) so far there are very few studies on this subject for the China's food industry and our study provides a good understanding of the sector's energy and CO2 emission related issues; (ii) different from the previous studies, the paper not only discusses the accumulative effects, but also the factors' effects in different stages in according with China's “fiveyear development plans”, which provide a good analysis from policy perspectives; (iii) based on the research results, the paper also provides policy recommendations, which are well-matched with the current government policies on the energy and CO2 emission reduction in the food sector. This paper is organized as follows. The first part is introduction which captures carbon dioxide emission and energy consumption in China's food industry. Furthermore, we summarize the major research methods and the major research results in different industries at home and abroad that is relevant to our paper. The second part is the methodology. For the purpose of simplicity, we use LMDI method to analyze the influential elements in the carbon dioxide emission of China's food industry. In the third and fourth part, we present the results of the decomposition and discuss the results. The last part is the conclusion and policy recommendations.
2. Methods and data 2.1. Methodology As China is currently in a rapid industrialization process, the energy conservation situation is severe. With the decomposition analysis of carbon dioxide emissions, we can quantitatively analyze the factors that contribute to carbon dioxide emissions. Researchers have used different decomposition methods. One way to quantitatively analyze carbon dioxide emissions is to make econometric analysis with carbon dioxide emissions based on the Environmental Kuznets Curve theory. Another way is the Factor Analysis Approach. Currently the main decomposition analysis methods used internationally are Structural Decomposition Analysis (SDA) and Index Decomposition Analysis (IDA). For SDA, the analysis process is more complex and requires data on Input– output Table, and the latest Input–output Table of China in 2007 is outdated. For IDA, it only needs the industry data which is particularly suitable for the model, including less factors and timeseries data (Guo, 2010). IDA can be divided into two categories: Laspeyres IDA and Divisia IDA. Log Mean Divisia Index method (LMDI) is preferred in this paper due to its adaptability, ease of use, absence of unexplained residual term and full resolution. It is widely used in analyzing carbon dioxide in different industries. In 1925, Divisia came up with Divisia Index Decomposition. Then Boyd et al. used the arithmetic mean function to weigh and apply it to the research of American industry energy consumption in 1987. This was called Arithmetic Mean Divisia Index (AMDI). Later, Ang and Choi (1997) substituted arithmetic mean weigh with Log Mean Divisia Index to improve AMDI, and Ang (Ang and Liu., 2001; Ang, 2004,, 2005; Ang and Liu, 2007; Ang et al., 2009) expanded and demonstrated it extensively. The LMDI have been widely used in the literature to conduct research on environmental issues such as energy intensity. In terms of the studies conducted on other countries, Kyonghwa and Kimb
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(2013) decomposed Korean industrial manufacturing greenhouse gas (GHG) emissions from 1991 to 2009 using the Log Mean Divisia Index (LMDI). The results show that five different factors can be divided: the activity effect denoted by overall industrial activity, the structure effect denoted by industrial activity mix, the intensity effect denoted by sectoral energy intensity, the mixed energy effect denoted by sectoral energy mix and the emission-factor effect denoted by CO2 emission factors. The results also show that the structure effect and intensity effect play important roles in reducing GHG emissions, with the structure effect playing a larger role. The energy-mix effect increased GHG emissions, and the emission-factor effect decreased GHG emissions. Tadhg (2013) uses an extended Kaya identity and the log mean Divisia index (LMDI) to study the changes in carbon emissions from 1990 to 2010. The results indicate that the main driving factor which increases emissions are scale effects of affluence and population growth which are countered primarily by energy intensity and fossil fuel substitution. Besides, renewable energy penetration has a low impact but it has been increasing in recent years. For China, Tang et al. (2011) constructed carbon dioxide emissions drivers nationwide in a superimposed decomposition model based on extended Kaya identity and LMDI analysis. The empirical results show that the scales of the economy and energy intensity are the strongest and most robust driving factors of carbon dioxide emissions. Guo (2010) used the LMDI method to construct a carbon emissions identity based on economic aggregate, economic structure, energy efficiency, energy consumption structure, and the carbon dioxide emissions coefficient. The results show that the main element for the rapid increase in China's carbon emissions was the expansion of the economy scale, while energy efficiency was the major factor inhibiting the growth of carbon emissions. Apart from studying carbon dioxide decomposition nationwide, current researches also study it in different industries in China. Lin and Ouyang (2014) analyzed CO2 emissions changes by factor decomposition from energy consumption in China's non-metallic mineral products industry using LMDI. Lin and Moubarak (2013) employ the decomposition analysis of carbon dioxide emissions change in China's textile industry over the period 1986–2010. The results showed that industrial activity and energy intensity were the main determinants that influenced the emissions change. Industrial activity mostly contributed to increase in emissions while energy intensity contributed to decrease in emissions. In 1989, the Japanese economist Yoichi (1989) came up with C E GDP the famous Kaya Identity: that is C = E × GDP × P × P , where the left side of the identity represents the total amount of carbon emission, and on the right side carbon emissions can be decomposed into the product of several affecting variables. C means the E
carbon intensity of energy consumption;
E GDP
represents the en-
ergy consumption intensity, that is the energy amount per GDP consumes which shows the economic efficiency of energy consumption; GDP is the GDP per capita and P is the population. P Based on China's food industry's characteristics, in this paper we refine the Kaya Identity. We first introduce fossil energy factor E
Ef to construct f , which means the ratio of fossil energy conE sumption in total energy consumption. Then we replace the GDP in Kaya Identity with the value added of the food industry, which is denoted as y . Finally, the population denoted by P is replaced by the number of employees in the food industry, and is denoted as w . In this way, the Kaya Identity can be rewritten as:
C=
Ef C E y × × × ×w Ef E y w
(1)
In the equation above, C represents the total carbon emission of China's food industry; Ef represents the fossil fuel consumed
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during food processing, which includes the coal used by thermal power plants; E represents the total energy consumption of China's food industry; y is the value added of the food industry and w is the number of employees of the industry. The economic meaning of each factor in Eq. (1) can be expressed as follows: C is the effect of carbon intensity, which is abbreviated
∆ES = L ( C0, Ct ) × ln
0
∆EI = L ( C0, Ct ) × ln
sumption will lead to differences in carbon dioxide emissions.
Ef E
y
which is abbreviated as EI. It calculates the energy consumption per value added, reflecting the efficiency of energy use in the food iny dustry, and also indicating the effect of technology. w is the effect of industry activity which is abbreviated as IA. It is the output per unit of labor. The effect of industrial activities on carbon dioxide emissions depends on the way it improves labor productivity, and it may be positive (that is increasing the carbon dioxide emissions) or negative (that is decreasing the carbon dioxide emissions). w is the number of employees in the food industry which means the effect of industry scale which is abbreviated as IS. All the abbreviations of the factors are listed in Table 1. We can re-write Eq. (1) as follows:
(2)
C = CI × ES × EI × IA × IS
Let us set the carbon dioxide emission of the period t to be Ct , and that of the base period to be C0 . Then we can get:
Ct = CIt × ESt × EIt × IA t × ISt
(3)
C0 = CI0 × ES0 × EI0 × IA 0 × IS0
(4)
With the Log Mean Divisia Index method, we can divide the cumulative changes in carbon dioxide in year t into the summation of five different factors' cumulative changes
∆C = Ct − C0 = ∆CI + ∆ES + ∆EI + ∆IA + ∆I
(5)
Based on Eq. (5), we can either treat the first year of the Fiveyear Plan or the year 1986 as the base year in order to calculate the influence of carbon dioxide emissions, to make analysis in a more macro perspective, and to provide reference for energy saving in the food industry during the Five-year Plan period. Each part of Eq. (5) can be re-written as follows:
∆CI = L ( C0, Ct ) × ln
CIt C −C CI = t C 0 × ln t t CI0 CI 0 ln C 0
Table 1 The abbreviations of the factors. Abbreviations
Factors
CI ES EI IA IS
The The The The The
effect effect effect effect effect
of of of of of
carbon intensity energy structure energy intensity industry activity industry scale
∆IA = L ( C0, Ct ) × ln
IA t C −C IA = t C 0 × ln t IA 0 IA 0 ln C t 0
is
the effect of energy structure which is abbreviated as ES. It is the ratio of fossil fuel consumption and total energy consumption. As electricity is a kind of clean energy, it will not produce carbon dioxide when it is in use but it will produce carbon dioxide when it is generated. There are both fossil fuel and clean energy during power generation, and the different use of fossil fuel will lead to the different emission of carbon dioxide. E is the effect of energy intensity
EIt C −C EI = t C 0 × ln t EI0 EI0 ln C t 0
Ef
as CI. It is the ratio of the total carbon dioxide emission and the consumption of fossil fuel. Due to different emission factor of fossil energy, the differences in the proportion of fossil energy con-
ES ESt C −C = t C 0 × ln t ES0 ES0 ln C t
∆IS = L ( C0, Ct ) × ln
ISt C −C IS = t C 0 × ln t t IS0 IS 0 ln C 0
L (C0, Ct ) =
Ct − C0 C ln t
is the logarithmic average weight. We can
C0
make analysis of the carbon dioxide emissions in China's food industry to reveal the influence of the different factors using data on energy consumption, value added, average employees from 1986 to 2010. 2.2. Data source In order to maintain continuity, and consistency of the statistical data in this paper, we select the sample data from 1986 to 2010. China's food industry consists of food manufacturing industry, beverages and tobacco processing industry before 1993, and after that time, the food manufacturing industry was subdivided into agricultural food processing industry and food manufacturing industry. Besides, the name of the tobacco processing industry was changed to tobacco products processing manufacturing industry. In this way, China's food industry is finally settled into four different industries: agricultural food processing industry, food manufacturing industry, beverages and tobacco products manufacturing industry. In this paper, energy consumption data are mainly from China Statistical Yearbook (China’s National Bureau of Statistics, 1986), and the data for 1993 is from China Energy Statistical Yearbook (China’s National Bureau of Statistics, 1994). As shown in Fig. 2, coal occupies an absolutely important position with the highest level in 1992, when the proportion is as high as 92.88%. However, it fell as the share of electricity consumption began to rise. Given that 80% of electricity in China is from thermal power plants, the summation of direct and indirect consumption of coal in China's food industry is still huge, and it has kept steady at around 85% since 2000. In addition to the rising trend of electricity consumption, the other forms of energy consumption are more stable. The industrial added value data are mainly obtained from China's Industrial Statistics Yearbook (China’s National Bureau of Statistics, 1986–2011). For some of the data that cannot be obtained directly, we derived them using some calculations. We extract the industrial gross value from China Statistical Yearbook, and estimate the ratio of industrial value added and industrial gross value according to the previous ratio. Then we calculate the industrial value added by multiplying the industrial gross value by the ratio. The data on employees of the food industry come from China's Industrial Statistical Yearbook, while electricity composition comes from China Statistical Yearbook. As shown in Fig. 3, we can see that the average growth rate of the ratio of value added of China's food industry and the GDP is 20.63%, and the highest increase is seen in 1993, when the growth rate was 62.88%. The carbon dioxide emissions of China's food industry are calculated as follows. First, we convert the different forms of energy consumption into standard statistics (tons of coal equivalent) as a
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Fig. 3. The value added and ratio of China's food industry. Data resources: China Statistical Yearbook, China's Industrial Statistics Yearbook, CEIC.
Fig. 4. The carbon dioxide emissions of China's food industry. Data resources: Data resources: China Statistical Yearbook, China's Energy Statistics Yearbook.
unified unit of measurement2, according to China Statistical Yearbook. Then we make use of the research results of Xu et al. (2006). According to the calculations of energy coefficient of carbon emissions,3 we calculate the carbon emissions of the various forms of energy. In this paper, we exclude thermal power from total electricity. Hydropower, nuclear power, wind power are categorised as clean energy, and are also not included in the calculations of carbon dioxide emissions.
3. Empirical results As shown in Fig. 4, carbon dioxide emissions in China's food industry shows an increasing trend year by year, and the average annual growth rate is 2.43% with the highest growth level of 15.96% in 1990. This is mainly due to a drop in carbon dioxide emissions in 1989. In 1995, the carbon dioxide emissions level reached the peak, while during the period of 1996 to 2000, it showed a downward trend at an average of 5.71% per year. From 2001 to 2004, the emission levels were at a steady growth except in 2005, when it jumped from 24.5372 million ton to 27.0727 million ton. After that it maintained an average annual 2 Electricity: 1.229 tce/ton thousand kW, coal: 0.713 kce/kilo, coke: 0.974 kce/ kilo, crude oil: 1.42 kce/kilo, fuel oil: 1.428 kce/kilo, gasoline: 1.4714 kce/kilo, kerosene: 1.4714 kce/kilo, diesel: 1.4571 kce/kilo, petroleum: 1.7143 kce/kilo, natural gas: 13.300 tce/ten thousand cubic meters. 3 Coal: 0.7476 ton coal/tce, crude oil: 0.5532 ton coal/tce, diesel: 0.5913 ton coal/tce, natural gas: 0.4479 ton coal/tce, kerosene: 0.3416 ton coal/tce, fuel oil: 0.6176 ton coal/tce, crude oil: 0.5854 ton coal/tce, petroleum: 0.5042 ton coal/tce, electricity: 2.2132 ton coal/tce, coke: 0.1128 ton coal/tce.
growth rate of 7.08%. In order to analyze the changes in different stages of China's food industry carbon dioxide emissions in this paper, we first divide the samples into five intervals based on the Five-year Plan implemented by the government, which is the guide for China's economic development. The intervals are respectively the periods of 1986–1990, 1986–1995, 1996–1990, 2001–2005, and 2005– 2010. In each interval, we choose the first year of the Five-year Plan as the base year to calculate the carbon dioxide emissions and the influence of various factors. In Fig. 5, we show the decomposition of carbon dioxide emissions in China's food industry in the different intervals. As shown, carbon dioxide emissions are not always showing a rising tendency. During the period of 1996–2000, carbon dioxide emissions reduced by 15.3163 million ton. This was mainly due to the internal rectification and the elimination of backward production capacity within the food industry. Apart from that, the reduction of employees could also be responsible for the large scale reduction of carbon dioxide emissions in the food industry. From 1986 to 1990, there was an increase of 8.6759 million ton of carbon dioxide emissions in the food industry. An increase of 0.625 million ton in carbon dioxide emissions was due to energy structure, a reduction of 40.0355 million ton was due to energy intensity, an increase of 44.3465 million ton was due to industrial activity and an increase of 7.6667 million ton of was due to the industry scale. Statistically, the expansion of industrial activity was the most important factor driving carbon emissions increase in this period, while the decrease in emission over the period is as a result of decrease in energy intensity. The changes resulting from industry scale and carbon intensity were also significant, but energy structure has less effect
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Fig. 5. The decomposition of China's food industry.
From 1991 to 1995, there was an increase of 11.4411 million ton of carbon dioxide emissions. 2.5075 million ton of carbon dioxide emissions was due to carbon intensity, a decrease of 0.4095 ton was due to energy structure, a decrease of 59.5164 million ton was due to energy intensity, an increase of 61.0832 million ton was due to industrial activity and an increase of 7.7764 million ton was due to industry scale. Statistically, the main reason for carbon dioxide emissions increase is industrial activity while the main reason for the decrease is energy intensity. Industry scale and carbon intensity also played a significant role in the changes in carbon dioxide emissions while energy structure had a little impact. From 1996 to 2000, there was a decrease of 15.3163 million ton of carbon dioxide emissions: an increase of 5.6471 million ton was due to carbon intensity, a decrease of 0.6213 million ton was due to energy structure, a decrease of 37.2018 million ton was due to energy intensity, an increase of 40.9131 million ton was due to industrial activity and a decrease of 24.0534 million ton was due to the industry scale. Compared with the previous years, carbon dioxide emissions reduced in this period. Statistically, industrial activity still contributed to increase in carbon emissions while energy intensity and industry scale were the main forces that reduce emissions. Meanwhile, industry scale became the important driver of carbon dioxide emissions reduction. Apart from that, carbon intensity had a significant effect while energy structure had a little effect on changes in carbon dioxide emissions. From 2001 to 2005, there was an increase of 4.21 million ton of carbon dioxide emissions: an increase of 3.9355 million ton of
carbon dioxide emissions due to carbon intensity, a decrease of 0.1588 million ton due to energy structure, a decrease of 48.1054 million ton due to energy intensity, an increase of 40.1225 million ton due to industrial activity and an increase of 8.4162 million ton due to industry scale. The decomposition results showed similar effects of the driving forces of the different factors over the period of 1991–1995. From 2006 to 2010, there was an increase of 28.5243 million ton of carbon dioxide emissions: a decrease of 0. 2474 million ton of carbon dioxide emissions due to carbon intensity, a decrease of 0.6922 ton due to energy structure, a decrease of 44.9453 million ton due to energy intensity, an increase of 63.7009 million ton as a result of industrial activity and an increase of 10.7084 million ton due to industry scale. In this period, there are three major factors that affect emissions: energy intensity, industrial activities and industrial scale. Among these factors, energy intensity is the main reason for emission reduction, while industrial activities and industrial scale drive increase in carbon dioxide emissions. Carbon intensity and energy structure have rather little impact on carbon dioxide emissions.
4. Discussion From the comparison of the influences of the different factors over the different periods, a number of findings were made. Firstly, the emissions impacts of energy intensity and industrial activities
Fig. 6. The energy intensity comparison of China’s all industry and food industry. Data source: China Statistical Yearbook, Calculated by the authors.
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are same and significant over the five periods. Energy intensity is the main driving force of emission reduction while industrial activity is the main driving force of the increase in emissions. Energy intensity reflects the economic efficiency of energy consumption. As shown in Fig. 6, the energy intensity of China's food industry shows a declining trend, thus, it fell from 8.48 tce per 10,000 yuan in 1986 to 0.2 tce per 10,000 yuan in 2010, which is far below the average level of China's energy intensity of 0.81 tce per 10,000 yuan. The decrease in energy intensity implies that the same level of output can be achieved with less energy, which results in decrease of carbon emissions. Industrial activities mainly reflect the impacts of labor productivity. Labor productivity will increase with the improvement in technology, and the energy rebound effect may follows. On one hand, the improvement in labor productivity will lead to reduction in energy consumption. On the other hand, the factories may expand production, thereby consuming more energy. If the consumption increases by the latter effect which is greater than the former, it means the energy rebound effect would lead to higher energy consumption and carbon emissions. Secondly, generally speaking, energy intensity, industrial activities and industry scale have more significant impacts on carbon emissions while carbon intensity and energy structure have little impacts. This is mainly due to the fact that the proportion of all kinds of energy consumed in the food industry in the sample period basically remains unchanged. Generally, industry scale plays a positive role in emissions except in the period 1996–2000. The effect of industry scale on carbon dioxide emissions is largely negative, which makes the overall carbon dioxide emissions to reduce. Jump-points also appear in the carbon intensity and energy structure. In order to further study carbon dioxide emissions in the food industry, as well as various jump-points, we choose 1986 as the base year and calculate the cumulative contributions of the different factors in 25 years to reach the conclusion shown in Fig. 7. First, carbon intensity and energy structure have little but fluctuating effects on emissions. This means the cumulative carbon dioxide emission curve of carbon intensity and energy structure fluctuates up and down around the axis. This is because the proportion of the different kinds of fossil energy fluctuates. Meanwhile, as shown in Fig. 8, energy structure denoted by the ratio of fossil energy consumption to total energy consumption declined, but it is not significant. Energy structure affects emissions in a positive way from 1986 to 1990 and afterward in a negative way, but it is not obvious. It can be inferred that the coal-oriented energy consumption structure in China has not changed. Secondly, energy intensity in China's food industry has negative impacts on the cumulative carbon dioxide emissions over the whole period. As shown in Fig. 6, energy intensity presents a decreasing trend from 1986 to 2010. Therefore energy intensity and its cumulative effect are positively related. In other words, we can effectively reduce carbon dioxide emissions by reducing energy intensity.
Fig. 7. The accumulative contributions of all factors.
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Thirdly, as shown in Fig. 9, the cumulative effects of industrial activities in the food industry show a gradually increasing trend. This indicates there exists a positive correlation between industrial activity and carbon dioxide emission. Besides, from 1986 to 2010, industrial activity factor shows an increasing trend (except for 2005 when there was a slight decline compared with 2004), which is the result of the energy rebound effect mentioned above. The improvement in industrial activity will increase energy efficiency, and factories may expand their production, leading to increase in energy consumption. When the amount of the increase in energy consumption which results from the expansion of production is larger than the amount of energy reduction from energy efficiency, there will be increase in emission level. At present, the supportive evidences of the rebound effect from the perspective of macroeconomic models or economic history proposes the “suggestive” evidence. Therefore, the enhancement of industrial activity energy efficiency leads to the increase of carbon dioxide emissions, which is more obvious in the long run than in the short run. Fourthly, industry scale generally has positive impacts on carbon dioxide emissions, which is consistent with other research findings. It however caused the decrease in emissions from 1999 to 2005. China has the largest population in the world, and the food industry is a labor-intensive industry which can absorb a large number of labor, especially the rural surplus labor, as shown in Fig. 10. However during the period of 1997–2004, employees in the food industry was below the 1986 level. The reasons may be as follows. First, the tertiary industry started to absorb more labor. The ninth Five-year Plan called for transfering the older policy that only focus on the increase of proportion of a certain industry or the number of products, to concentrate on the flow and migration of people in order to solve the full employment and structure adjustment. In this way, we can form the appropriate agriculture, effective manufacturing, labor-intensive tertiary industry and construction industry. As labor-intensive manufacturing is in a large scale, it is hard to continue the old way that only depends on cheap surplus labor. However, most of the third industries are labor-intensive, faced with smaller international competition. So they should be vigorously absorbing rural surplus labor and surplus personnels from manufacturing (Yang, 1996). Second, the improvement in technology and labor efficiency, as well as the elimination of backward technology and equipment also helped to reduce emissions by improving energy efficiency and reducing carbon dioxide emissions per unit of output.
5. Conclusions and policy implications This paper employs the Kaya Identity and LMDI method to decompose carbon dioxide emissions in China's food industry from 1986 to 2010. Then we calculate the influence of emission quantity from five aspects: carbon intensity, energy structure, energy intensity, industrial activities and industry scale. Our conclusion from the findings is that industry activity always plays a role in increasing carbon dioxide emissions while energy intensity remains the main driver of the reduction in carbon dioxide emissions. Besides, carbon intensity and industry scale are also important to the carbon dioxide emissions but they play different roles (sometimes positive and sometimes negative) in carbon dioxide emissions due to the ratio of the different forms of energy consumption and different government policies. Energy structure is the least important factor. This is because the energy structure in the food industry which is fossil-based or in particular coalbased has not been changed in the past 25 years. From the perspective of cumulative effects, they contribute 4.9159, 0.8157, 103.8258, 81.57, 109.5438 and 10.9653 million ton of carbon dioxide respectively for the five-year period intervals. The results of
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Fig. 8. The ratio of fossil energy consumption and the total energy. Data resources: China Statistical Yearbook, China's Energy Statistics Yearbook, calculated by the authors.
the empirical analysis indicates that both industrial activity and energy intensity are significant, industrial activity and carbon dioxide emissions present a positive correlation, and energy intensity poses a negative correlation with emissions. Interestingly, these findings are consistent with Lin and Ouyang (2014) research for the non-metallic minerals industry and Lin and Moubarak (2013) research for China's textile industry. At the same time, during the period of the ninth Five-year Plan, due to the loss of labor and the elimination of backward technology and equipment, emissions were effectively reduced. Finally, we observed that the coal-oriented energy structure in China's food industry is still unchanged. Energy conservation and emissions reduction is the long-term goal of economic development in China. However if energy consumption is reduced at the cost of economic growth, there is no doubt that it will be effective. Therefore, to ensure a balance in economic growth and energy consumption reduction in China's food industry, we propose the following suggestions based on the research results. Firstly, the government should adjust the internal structure of the food industry. This can be achieved by ensuring efficiency and effectiveness at the processing part of the internal structure of the food industry. In the food industry, the internal structure focuses mostly on the raw materials and transforming them into finished food products, with little particular attention for the production of food for special groups such as infants and pregnant women. Moreover, the internal structure also involves fixed costs such as depreciation expense among others as well as variable cost which include but not limited to, water consumption and energy consumption From our studies we observed for instance that, labor costs accounts for about 35% and energy costs accounts for about 25% of the total cost. By replacing old technology and equipment, training of employees to improve on their skills and abilities, and
reducing energy consumption per unit of output, we will not only reduce energy intensity to further reduce carbon dioxide emissions, but we will as well be reducing costs to increase economic efficiency. In addition to the adjustment of the internal structure of the food industry, the layout of the industry nationwide should also be further rationalized. With the guidance of Western development and Revitalization of Old Industry Zone of Northeast China, the food industry in China could transfer from the east to the mid-west and northeast, and gradually turn the agricultural advantage of the mid-west and northeast into the industrial advantage of the food industry. Moreover, food companies should continue to move to the main raw material production areas, key markets locations and important transportation hubs (National Development and Reform Commission, 2011). Secondly, the enterprise scale in the food industry is generally small to accelerate the industrial cluster, therefore, policy actions should be taken to reduce little workshops and enterprises with high cost, and to make use of the large scale advantage to improve energy efficiency. It is imperative however to note that, the food industry is made up of farming, forestry, animal husbandry and fishery which are prune to natural disasters. As a result, a stable supply of raw materials will help ensure the full use of the fixed assets so as to improve labor productivity and reduce energy consumption. Furthermore, to ensure acceleration of large scale production in food, a number of enterprise groups with high market share and good driving ability can be restructured through mergers and acquisitions. Thirdly, it can be seen from the energy consumption in the food industry that power consumption presents an increasing trend year by year while the direct use of coal is declining. Therefore, in order to achieve energy conservation and emissions reduction, it should be advocated using electricity instead of coal to reduce carbon emissions. It is however noteworthy that, the
Fig. 9. The industrial activity index in food industry. Data resources: China Industrial Statistical Yearbook, calculated by the authors.
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Fig. 10. The number of employees in food industry. Data resource: CEIC China Database.
implementation of the coal substitution needs to be based on the enterprises' technical progress, such as the technical renovation of the existing equipments and technologies, and the optimization of the manufacturing process. According to the requirements of the 12th five-year plan of the food industry the promotion of energy conservation and emissions reduction focused on industries such as fermentation, brewing, sugar refinery, starch, quick-frozen food and meat slaughtering and processing. In addition, enterprises also can improve the development and utilization of the by-products of the food industry by taking advantage of new technology and equipment. At the same time, detailed development goals have been put forward in the 12th five-year plan of the food industry. The target industries are grain processing industry, edible vegetable oil, meat processing industry, dairy industry, and marine seafood products industry. By 2015, the output value of the grain processing industry should reach 3.9 trillion yuan, with an average annual growth of 12%. Edible vegetable oil production should be 24.4 million ton, of which production of domestic oil plants should be increased to 12.6 million ton. Meat production should be 85 million ton, and the capacity of backward manual and semimechanization should eliminate 50%. Dairy production should be 27 million ton, with an increase of 15%, and the idle rate of dairy products processing capacity should be controlled within 25%. The processing rate of marine sea food products should be increased to more than 45%, and frozen-prepared food and small packaged food should account for over 30% of frozen processed products (Yang, 1996). Finally, from the perspective of reducing carbon dioxide emissions, improving the efficiency of energy use or production efficiency can only be a short-term policy. With the energy rebound effect, the improvement in production efficiency is not as effective as expected. To achieve the goal of Energy conservation and emissions reduction, the government should play a key role in the pricing of energy, taxation and influencing people's behavior. For the central government, there is a need to implement energy pricing reforms to reverse the current situation where fossil energy prices are undervalued. Only when the prices of fossil energy are well reflected then energy price can be used to influence the enterprises to use clean energy, and thereby transform the energy consumption structure in the food industry. Besides, most of the enterprises in China's food industry are small and medium-sized enterprises, due to the price discrimination of banks against small and medium-sized private enterprises. This serves as a disincentive to these enterprises to access capital for maintenance and upgrade of their equipments. Therefore, the central government should formulate relevant policies to reduce the financing costs for small and medium-sized enterprise, which will enable them upgrade their equipments to reduce emissions. For the local
government, there is need to strengthen guidance and support for the food industry to encourage technological innovation. Local finance department should also strengthen the special fund support for small and medium-sized enterprises. Besides, a policy of comprehensive utilization of waste food should be implemented to actively support the comprehensive utilization of the by-products of the food industry. State Administration of Grain claims that China's waste food in a year has reached more than 120 billion catties, which is enough for two hundred million people. This includes, about 50 billion catties of food wastes, and about 70 billion catties of waste during the storage, processing, and transport. The food processing is public-demand-oriented, some consumption habit errors caused by the higher standards of living, such as excessive pursuit of fine grain, which makes the grain manufactures go for brighter, purer and more exquisite grain during the processing, which leads to as many as more than 13 billion catties grain each year. Thus, Government's publicity of good dietary habit, and promotion of Clean Plate Campaign are great steps to reduce waste, especially the Eight Honors and Eight Shames which forces civil servants to take initiative to crack down on corruption. Reducing food waste promotes energy conservation and emissions reduction, and should be encouraged among the citizenry.
Acknowledgements The paper is supported by National Social Science Foundation (Grant No. 12&ZD059, and 14JZD031), and Ministry of Education (Grant No. 10JBG013).
References Ang, B.W., Choi, K.H., 1997. Decomposition of aggregate energy and gas emission intensities for industry: A refined Divisia. Energy 18 (3), 59. Ang, B.W., 2004. Decomposition analysis for policymaking in energy: which is the preferred method? Energy Policy 32 (9), 1131–1139. Ang, B.W., 2005. The LMDI approach to decomposition analysis: a practical guide. Energy Policy 33 (7), 867–871. Ang, B.W., Liu., F.L., 2001. A new energy decomposition method: perfect in decomposition and consistent in aggregation. Energy 26 (6), 537–548. Ang, B.W., Huang, H.C., Mu, A.R., 2009. Properties and linkages of some index decomposition analysis methods. Energy Policy 37 (11), 4624–4632. Ang, B.W., Liu, N., 2007. Handling zero values in the logarithmic mean Divisia index decomposition approach. Energy Policy 35 (1), 238–246. BP, Statistical Review of World Energy Full Report 2013. BP: London; 2013. and CEIC China Database. Available from: 〈http://webcdm.ceicdata.com/cdmWeb/data Manager.html?languageCode ¼ zh). China’s National Bureau of Statistics, 1986&;2011. China Statistical Yearbook. China Statistics Press, Beijing. China’s National Bureau of Statistics, 1986&;2011. China's Industrial Statistical Yearbook. China Statistics Press, Beijing.
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China’s National Bureau of Statistics, 1994. China Energy Statistical Yearbook. China Statistics Press, Beijing. China’s National Bureau of Statistics, 2005. China Food Industry Statistical Yearbook. China Statistics Press, Beijing. Guo, C., 2010. Decomposition of China's carbon emissions: based on LMDI method. China Popul. Resour. Environ. 12, P4–P9. Kyonghwa, Jeonga, Kimb, Suyi, 2013. LMDI decomposition analysis of greenhouse gas emissions in the Korean manufacturing sector. Energy Policy 62, 1245–1253. Lin, B.Q., Moubarak, Mohamed, 2013. Decomposition analysis: change of carbon dioxide emissions in the Chinese textile industry. Renew. Sustain. Energy Rev. 26, 389–396. Lin, B., Ouyang, X., 2014. Analysis of energy-related CO2 (carbon dioxide) emissions and reduction potential in the Chinese non-metallic mineral products industry [J]. Energy 68 (8), 688–697. National Development and Reform Commission, 2011–12. Twelfth Five-Year Plan of Food Industry.
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Contents lists available at ScienceDirect
Energy Policy journal homepage: www.elsevier.com/locate/enpol
Carbon emissions reduction in China's food industry Boqiang Lin a,n, Xiaojing Lei b a Collaborative Innovation Center for Energy Economics and Energy Policy, China Institute for Studies in Energy Policy, Xiamen University, Fujian, 361005, PR China b The School of Economics, China Center for Energy Economics Research, Xiamen University, Xiamen, Fujian, 361005, PR China
H I G H L I G H T S
We analyze the energy consumption and CO2 emissions in China's food industry. LMDI decomposition analysis is conducted for finding out the driving forces. Industrial activity is the main driving force of CO2 emissions in this industry. Energy intensity is the main factor mitigating carbon emissions in this industry. Main advice: improving energy efficiency, optimizing industrial scale.
art ic l e i nf o
a b s t r a c t
Article history: Received 19 March 2015 Received in revised form 22 May 2015 Accepted 29 July 2015
In this paper, we evaluate the changes in carbon dioxide emissions from energy consumption in China's food industry from 1986 to 2010 based on the Logarithmic Mean Divisia Index (LMDI) method. The results show that energy intensity (EI) and industrial activity (IA) are the main determinants of the changes in carbon dioxide. Energy intensity (EI) contributes to decrease in emissions within 25 years while industrial activity (IA) acts in a positive way to increase the emissions level. Industry scale (IS) mostly contributes to increase in emissions except for the time interval 1996–2000. However, for both carbon intensity (CI) and energy structure (ES), they have a volatile but not significant influence on emissions in the different time intervals. To further understand the effects, we analyze the cumulative emission during the whole period 1986–2010. The results further testify that energy intensity and industrial activity are the most important factors affecting reduction and growth of carbon emissions. The results indicate that efforts to reduce emission in China's food industry should focus on the enhancement of energy efficiency, the optimization of industrial scale and the restructuring energy use. Finally, recommendations are provided for the reduction of carbon dioxide in China's food industry. & 2015 Published by Elsevier Ltd.
Keywords: Carbon dioxide emissions China's food industry
1. Introduction It is generally believed that the widespread use of coal and steel as well as the improvement in steam engines are important factors contributing to the accelerated development of industrial technology. After the industrial revolution, coal became a major power resource. The world's coal, oil and natural gas reserves decreased rapidly while there is a rapid increase in emissions such as carbon dioxide and carbon monoxide. At the same time, the globalization of the economy led to the expansion of industrialization by means of trade and capital investment, and subsequently to transfer the n Corresponding author at: Collaborative Innovation Center for Energy Economics and Energy Policy, China Institute for Studies in Energy Policy, Xiamen University, Fujian, 361005, PR China. Tel.: þ 86 5922186076; fax: þ86 5922186075. E-mail addresses: [email protected], [email protected] (B. Lin).
http://dx.doi.org/10.1016/j.enpol.2015.07.030 0301-4215/& 2015 Published by Elsevier Ltd.
energy intensive and highly polluting industries from developed regions to developing regions. Compared with other industries such as chemical fiber industry or mining and quarrying, the food industry is not an energy- and pollution-intensive industry, but it has a greatly significant impact on the stability of the national economy and people's livelihood, economic development and the stimulation of domestic demand. The food industry mainly refers to the industrial system with continuous and organized economic activities, and the raw materials are the products or semi-products of agriculture, forestry, animal husbandry, fishery and chemical industry. There is a long history in the development of the food industry, and the emergence of the modern food industry can be dated back to the early eighteenth century. With the development of technology and the application of scientific and technological achievements in the food industry, modern food industry has developed rapidly. Food is a primary need of human
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Fig. 1. The industrial value added and energy consumption in China's food industry. Data resources: China Statistical Yearbook (1986-2011), Statistical Review of World Energy Full Report 2013, CEIC China Database.
Fig. 2. The energy consumption ratio of China's food industry. Data resources: China Statistical Yearbook (1986-2011).
existence. Therefore, the food industry is of great importance. According to the Bureau of China, the food industry is a comprehensive industrial sector and it can be divided into four categories: agricultural food processing industry, food manufacturing industry, beverages and tobacco products manufacturing industry. The food industry is one of the most important industries related to the national economy and people's livelihood. In the 21st century, especially in the tenth Five-year Plan, China's food industry developed rapidly and stably. In 2013, the number of enterprises above designated size was 36,275, an increase of 52.2% compared with 2005 and an annual average growth rate of 5.39%. The added value of the food industry accounted for 11.2% of China's total industrial added value, which made an contribution rate of 12.6% to the growth rate of China's whole industry, thereby raising national industrial growth by 1.3 percentage points. Additionally, the number of employees surged to 7.07 million, an increase of 0.39 million compared with previous year. It can be seen that the prominent role of the food industry has strengthened continuously. As a country with the largest population in the world, China is also the biggest food producing and consuming country. As shown in Fig. 1 (BP, 2013), from 1986 to 2010, the value added of China's food industry increased from 26.115 billion yuan to 2071.162 billion yuan, with an average annual growth rate of 20.63 percent, and eighty times as in 1986. Meanwhile, large amounts of electricity, heat and some other kinds of energy systems are needed in the food production process, making the food industry a key energy-consuming industry. Energy consumption in the food industry increased from 25.7 million ton of coal equivalent1 in 1986 1
Tons of coal equivalent: which is short as tce in the paper.
to 55.121 million tce in 2010, with an annual average growth rate of 3.35 percent. Before 1996, its share of the total energy consumption was 3 percent, but in 2006, the figure was between 1.7% and 2%. The total amount of energy consumption in the food industry is equivalent to total amount of energy consumed in Algeria or half of Argentina's total energy consumption. In 2012, the total amount of energy consumed in the food industry is 57.99 million tce, which consist of 38.5 million tce of coal, 0.14 million ton of coke, 80 t of crude oil, 0.51 million ton of gasoline, 16 ton of kerosene; 0.91 million ton of diesel oil, 0.14 million ton of fuel oil, 1.29 billion cubic meters of natural gas, and 95.4 billion kW h of electricity. Of the total energy consumption, fossil energy accounted for up to 95%. As shown in the figures from 1986 to 2010, coal is the primary energy consumed in the food industry, followed by electricity and diesel fuel. According to the estimation of the power balance sheet, 80% of electricity is from thermal power plants. Therefore, the energy consumed in the food industry is not much, but for the high proportion of fossil energy, which leads to carbon dioxide emissions. Thus the fossil-based energy structure, especially coaldominant energy structure has led to huge carbon dioxide emissions in China's food industry. Based on the data from 1986 to 2010, we calculated the carbon dioxide emission in China's food industry. As shown in Fig. 2, the carbon dioxide emission in China's food industry in 1986 was about 18.46 million ton while it was 39.25 million in 2010, with an annual average growth rate of 3.6%. According to CO2 Emission from Fuel Combustion Highlights published by the International Energy Agency, the amount of China's carbon dioxide emissions in the food industry is equivalent to the entire emissions of Norway in 2010.
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China has the largest population in the world. Therefore, China's food industry is also largest in the world. The sector is one of the fast growing sectors in China, as China's personal income increases quickly. The government considers the sector is very important, as the sector has a significant impact on people's livelihood, social stability, and economic development. A good understanding of the sector is also very important for energy and CO2 emissions reduction policies in China. The main contributions of the paper include three areas: (i) so far there are very few studies on this subject for the China's food industry and our study provides a good understanding of the sector's energy and CO2 emission related issues; (ii) different from the previous studies, the paper not only discusses the accumulative effects, but also the factors' effects in different stages in according with China's “fiveyear development plans”, which provide a good analysis from policy perspectives; (iii) based on the research results, the paper also provides policy recommendations, which are well-matched with the current government policies on the energy and CO2 emission reduction in the food sector. This paper is organized as follows. The first part is introduction which captures carbon dioxide emission and energy consumption in China's food industry. Furthermore, we summarize the major research methods and the major research results in different industries at home and abroad that is relevant to our paper. The second part is the methodology. For the purpose of simplicity, we use LMDI method to analyze the influential elements in the carbon dioxide emission of China's food industry. In the third and fourth part, we present the results of the decomposition and discuss the results. The last part is the conclusion and policy recommendations.
2. Methods and data 2.1. Methodology As China is currently in a rapid industrialization process, the energy conservation situation is severe. With the decomposition analysis of carbon dioxide emissions, we can quantitatively analyze the factors that contribute to carbon dioxide emissions. Researchers have used different decomposition methods. One way to quantitatively analyze carbon dioxide emissions is to make econometric analysis with carbon dioxide emissions based on the Environmental Kuznets Curve theory. Another way is the Factor Analysis Approach. Currently the main decomposition analysis methods used internationally are Structural Decomposition Analysis (SDA) and Index Decomposition Analysis (IDA). For SDA, the analysis process is more complex and requires data on Input– output Table, and the latest Input–output Table of China in 2007 is outdated. For IDA, it only needs the industry data which is particularly suitable for the model, including less factors and timeseries data (Guo, 2010). IDA can be divided into two categories: Laspeyres IDA and Divisia IDA. Log Mean Divisia Index method (LMDI) is preferred in this paper due to its adaptability, ease of use, absence of unexplained residual term and full resolution. It is widely used in analyzing carbon dioxide in different industries. In 1925, Divisia came up with Divisia Index Decomposition. Then Boyd et al. used the arithmetic mean function to weigh and apply it to the research of American industry energy consumption in 1987. This was called Arithmetic Mean Divisia Index (AMDI). Later, Ang and Choi (1997) substituted arithmetic mean weigh with Log Mean Divisia Index to improve AMDI, and Ang (Ang and Liu., 2001; Ang, 2004,, 2005; Ang and Liu, 2007; Ang et al., 2009) expanded and demonstrated it extensively. The LMDI have been widely used in the literature to conduct research on environmental issues such as energy intensity. In terms of the studies conducted on other countries, Kyonghwa and Kimb
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(2013) decomposed Korean industrial manufacturing greenhouse gas (GHG) emissions from 1991 to 2009 using the Log Mean Divisia Index (LMDI). The results show that five different factors can be divided: the activity effect denoted by overall industrial activity, the structure effect denoted by industrial activity mix, the intensity effect denoted by sectoral energy intensity, the mixed energy effect denoted by sectoral energy mix and the emission-factor effect denoted by CO2 emission factors. The results also show that the structure effect and intensity effect play important roles in reducing GHG emissions, with the structure effect playing a larger role. The energy-mix effect increased GHG emissions, and the emission-factor effect decreased GHG emissions. Tadhg (2013) uses an extended Kaya identity and the log mean Divisia index (LMDI) to study the changes in carbon emissions from 1990 to 2010. The results indicate that the main driving factor which increases emissions are scale effects of affluence and population growth which are countered primarily by energy intensity and fossil fuel substitution. Besides, renewable energy penetration has a low impact but it has been increasing in recent years. For China, Tang et al. (2011) constructed carbon dioxide emissions drivers nationwide in a superimposed decomposition model based on extended Kaya identity and LMDI analysis. The empirical results show that the scales of the economy and energy intensity are the strongest and most robust driving factors of carbon dioxide emissions. Guo (2010) used the LMDI method to construct a carbon emissions identity based on economic aggregate, economic structure, energy efficiency, energy consumption structure, and the carbon dioxide emissions coefficient. The results show that the main element for the rapid increase in China's carbon emissions was the expansion of the economy scale, while energy efficiency was the major factor inhibiting the growth of carbon emissions. Apart from studying carbon dioxide decomposition nationwide, current researches also study it in different industries in China. Lin and Ouyang (2014) analyzed CO2 emissions changes by factor decomposition from energy consumption in China's non-metallic mineral products industry using LMDI. Lin and Moubarak (2013) employ the decomposition analysis of carbon dioxide emissions change in China's textile industry over the period 1986–2010. The results showed that industrial activity and energy intensity were the main determinants that influenced the emissions change. Industrial activity mostly contributed to increase in emissions while energy intensity contributed to decrease in emissions. In 1989, the Japanese economist Yoichi (1989) came up with C E GDP the famous Kaya Identity: that is C = E × GDP × P × P , where the left side of the identity represents the total amount of carbon emission, and on the right side carbon emissions can be decomposed into the product of several affecting variables. C means the E
carbon intensity of energy consumption;
E GDP
represents the en-
ergy consumption intensity, that is the energy amount per GDP consumes which shows the economic efficiency of energy consumption; GDP is the GDP per capita and P is the population. P Based on China's food industry's characteristics, in this paper we refine the Kaya Identity. We first introduce fossil energy factor E
Ef to construct f , which means the ratio of fossil energy conE sumption in total energy consumption. Then we replace the GDP in Kaya Identity with the value added of the food industry, which is denoted as y . Finally, the population denoted by P is replaced by the number of employees in the food industry, and is denoted as w . In this way, the Kaya Identity can be rewritten as:
C=
Ef C E y × × × ×w Ef E y w
(1)
In the equation above, C represents the total carbon emission of China's food industry; Ef represents the fossil fuel consumed
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during food processing, which includes the coal used by thermal power plants; E represents the total energy consumption of China's food industry; y is the value added of the food industry and w is the number of employees of the industry. The economic meaning of each factor in Eq. (1) can be expressed as follows: C is the effect of carbon intensity, which is abbreviated
∆ES = L ( C0, Ct ) × ln
0
∆EI = L ( C0, Ct ) × ln
sumption will lead to differences in carbon dioxide emissions.
Ef E
y
which is abbreviated as EI. It calculates the energy consumption per value added, reflecting the efficiency of energy use in the food iny dustry, and also indicating the effect of technology. w is the effect of industry activity which is abbreviated as IA. It is the output per unit of labor. The effect of industrial activities on carbon dioxide emissions depends on the way it improves labor productivity, and it may be positive (that is increasing the carbon dioxide emissions) or negative (that is decreasing the carbon dioxide emissions). w is the number of employees in the food industry which means the effect of industry scale which is abbreviated as IS. All the abbreviations of the factors are listed in Table 1. We can re-write Eq. (1) as follows:
(2)
C = CI × ES × EI × IA × IS
Let us set the carbon dioxide emission of the period t to be Ct , and that of the base period to be C0 . Then we can get:
Ct = CIt × ESt × EIt × IA t × ISt
(3)
C0 = CI0 × ES0 × EI0 × IA 0 × IS0
(4)
With the Log Mean Divisia Index method, we can divide the cumulative changes in carbon dioxide in year t into the summation of five different factors' cumulative changes
∆C = Ct − C0 = ∆CI + ∆ES + ∆EI + ∆IA + ∆I
(5)
Based on Eq. (5), we can either treat the first year of the Fiveyear Plan or the year 1986 as the base year in order to calculate the influence of carbon dioxide emissions, to make analysis in a more macro perspective, and to provide reference for energy saving in the food industry during the Five-year Plan period. Each part of Eq. (5) can be re-written as follows:
∆CI = L ( C0, Ct ) × ln
CIt C −C CI = t C 0 × ln t t CI0 CI 0 ln C 0
Table 1 The abbreviations of the factors. Abbreviations
Factors
CI ES EI IA IS
The The The The The
effect effect effect effect effect
of of of of of
carbon intensity energy structure energy intensity industry activity industry scale
∆IA = L ( C0, Ct ) × ln
IA t C −C IA = t C 0 × ln t IA 0 IA 0 ln C t 0
is
the effect of energy structure which is abbreviated as ES. It is the ratio of fossil fuel consumption and total energy consumption. As electricity is a kind of clean energy, it will not produce carbon dioxide when it is in use but it will produce carbon dioxide when it is generated. There are both fossil fuel and clean energy during power generation, and the different use of fossil fuel will lead to the different emission of carbon dioxide. E is the effect of energy intensity
EIt C −C EI = t C 0 × ln t EI0 EI0 ln C t 0
Ef
as CI. It is the ratio of the total carbon dioxide emission and the consumption of fossil fuel. Due to different emission factor of fossil energy, the differences in the proportion of fossil energy con-
ES ESt C −C = t C 0 × ln t ES0 ES0 ln C t
∆IS = L ( C0, Ct ) × ln
ISt C −C IS = t C 0 × ln t t IS0 IS 0 ln C 0
L (C0, Ct ) =
Ct − C0 C ln t
is the logarithmic average weight. We can
C0
make analysis of the carbon dioxide emissions in China's food industry to reveal the influence of the different factors using data on energy consumption, value added, average employees from 1986 to 2010. 2.2. Data source In order to maintain continuity, and consistency of the statistical data in this paper, we select the sample data from 1986 to 2010. China's food industry consists of food manufacturing industry, beverages and tobacco processing industry before 1993, and after that time, the food manufacturing industry was subdivided into agricultural food processing industry and food manufacturing industry. Besides, the name of the tobacco processing industry was changed to tobacco products processing manufacturing industry. In this way, China's food industry is finally settled into four different industries: agricultural food processing industry, food manufacturing industry, beverages and tobacco products manufacturing industry. In this paper, energy consumption data are mainly from China Statistical Yearbook (China’s National Bureau of Statistics, 1986), and the data for 1993 is from China Energy Statistical Yearbook (China’s National Bureau of Statistics, 1994). As shown in Fig. 2, coal occupies an absolutely important position with the highest level in 1992, when the proportion is as high as 92.88%. However, it fell as the share of electricity consumption began to rise. Given that 80% of electricity in China is from thermal power plants, the summation of direct and indirect consumption of coal in China's food industry is still huge, and it has kept steady at around 85% since 2000. In addition to the rising trend of electricity consumption, the other forms of energy consumption are more stable. The industrial added value data are mainly obtained from China's Industrial Statistics Yearbook (China’s National Bureau of Statistics, 1986–2011). For some of the data that cannot be obtained directly, we derived them using some calculations. We extract the industrial gross value from China Statistical Yearbook, and estimate the ratio of industrial value added and industrial gross value according to the previous ratio. Then we calculate the industrial value added by multiplying the industrial gross value by the ratio. The data on employees of the food industry come from China's Industrial Statistical Yearbook, while electricity composition comes from China Statistical Yearbook. As shown in Fig. 3, we can see that the average growth rate of the ratio of value added of China's food industry and the GDP is 20.63%, and the highest increase is seen in 1993, when the growth rate was 62.88%. The carbon dioxide emissions of China's food industry are calculated as follows. First, we convert the different forms of energy consumption into standard statistics (tons of coal equivalent) as a
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Fig. 3. The value added and ratio of China's food industry. Data resources: China Statistical Yearbook, China's Industrial Statistics Yearbook, CEIC.
Fig. 4. The carbon dioxide emissions of China's food industry. Data resources: Data resources: China Statistical Yearbook, China's Energy Statistics Yearbook.
unified unit of measurement2, according to China Statistical Yearbook. Then we make use of the research results of Xu et al. (2006). According to the calculations of energy coefficient of carbon emissions,3 we calculate the carbon emissions of the various forms of energy. In this paper, we exclude thermal power from total electricity. Hydropower, nuclear power, wind power are categorised as clean energy, and are also not included in the calculations of carbon dioxide emissions.
3. Empirical results As shown in Fig. 4, carbon dioxide emissions in China's food industry shows an increasing trend year by year, and the average annual growth rate is 2.43% with the highest growth level of 15.96% in 1990. This is mainly due to a drop in carbon dioxide emissions in 1989. In 1995, the carbon dioxide emissions level reached the peak, while during the period of 1996 to 2000, it showed a downward trend at an average of 5.71% per year. From 2001 to 2004, the emission levels were at a steady growth except in 2005, when it jumped from 24.5372 million ton to 27.0727 million ton. After that it maintained an average annual 2 Electricity: 1.229 tce/ton thousand kW, coal: 0.713 kce/kilo, coke: 0.974 kce/ kilo, crude oil: 1.42 kce/kilo, fuel oil: 1.428 kce/kilo, gasoline: 1.4714 kce/kilo, kerosene: 1.4714 kce/kilo, diesel: 1.4571 kce/kilo, petroleum: 1.7143 kce/kilo, natural gas: 13.300 tce/ten thousand cubic meters. 3 Coal: 0.7476 ton coal/tce, crude oil: 0.5532 ton coal/tce, diesel: 0.5913 ton coal/tce, natural gas: 0.4479 ton coal/tce, kerosene: 0.3416 ton coal/tce, fuel oil: 0.6176 ton coal/tce, crude oil: 0.5854 ton coal/tce, petroleum: 0.5042 ton coal/tce, electricity: 2.2132 ton coal/tce, coke: 0.1128 ton coal/tce.
growth rate of 7.08%. In order to analyze the changes in different stages of China's food industry carbon dioxide emissions in this paper, we first divide the samples into five intervals based on the Five-year Plan implemented by the government, which is the guide for China's economic development. The intervals are respectively the periods of 1986–1990, 1986–1995, 1996–1990, 2001–2005, and 2005– 2010. In each interval, we choose the first year of the Five-year Plan as the base year to calculate the carbon dioxide emissions and the influence of various factors. In Fig. 5, we show the decomposition of carbon dioxide emissions in China's food industry in the different intervals. As shown, carbon dioxide emissions are not always showing a rising tendency. During the period of 1996–2000, carbon dioxide emissions reduced by 15.3163 million ton. This was mainly due to the internal rectification and the elimination of backward production capacity within the food industry. Apart from that, the reduction of employees could also be responsible for the large scale reduction of carbon dioxide emissions in the food industry. From 1986 to 1990, there was an increase of 8.6759 million ton of carbon dioxide emissions in the food industry. An increase of 0.625 million ton in carbon dioxide emissions was due to energy structure, a reduction of 40.0355 million ton was due to energy intensity, an increase of 44.3465 million ton was due to industrial activity and an increase of 7.6667 million ton of was due to the industry scale. Statistically, the expansion of industrial activity was the most important factor driving carbon emissions increase in this period, while the decrease in emission over the period is as a result of decrease in energy intensity. The changes resulting from industry scale and carbon intensity were also significant, but energy structure has less effect
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Fig. 5. The decomposition of China's food industry.
From 1991 to 1995, there was an increase of 11.4411 million ton of carbon dioxide emissions. 2.5075 million ton of carbon dioxide emissions was due to carbon intensity, a decrease of 0.4095 ton was due to energy structure, a decrease of 59.5164 million ton was due to energy intensity, an increase of 61.0832 million ton was due to industrial activity and an increase of 7.7764 million ton was due to industry scale. Statistically, the main reason for carbon dioxide emissions increase is industrial activity while the main reason for the decrease is energy intensity. Industry scale and carbon intensity also played a significant role in the changes in carbon dioxide emissions while energy structure had a little impact. From 1996 to 2000, there was a decrease of 15.3163 million ton of carbon dioxide emissions: an increase of 5.6471 million ton was due to carbon intensity, a decrease of 0.6213 million ton was due to energy structure, a decrease of 37.2018 million ton was due to energy intensity, an increase of 40.9131 million ton was due to industrial activity and a decrease of 24.0534 million ton was due to the industry scale. Compared with the previous years, carbon dioxide emissions reduced in this period. Statistically, industrial activity still contributed to increase in carbon emissions while energy intensity and industry scale were the main forces that reduce emissions. Meanwhile, industry scale became the important driver of carbon dioxide emissions reduction. Apart from that, carbon intensity had a significant effect while energy structure had a little effect on changes in carbon dioxide emissions. From 2001 to 2005, there was an increase of 4.21 million ton of carbon dioxide emissions: an increase of 3.9355 million ton of
carbon dioxide emissions due to carbon intensity, a decrease of 0.1588 million ton due to energy structure, a decrease of 48.1054 million ton due to energy intensity, an increase of 40.1225 million ton due to industrial activity and an increase of 8.4162 million ton due to industry scale. The decomposition results showed similar effects of the driving forces of the different factors over the period of 1991–1995. From 2006 to 2010, there was an increase of 28.5243 million ton of carbon dioxide emissions: a decrease of 0. 2474 million ton of carbon dioxide emissions due to carbon intensity, a decrease of 0.6922 ton due to energy structure, a decrease of 44.9453 million ton due to energy intensity, an increase of 63.7009 million ton as a result of industrial activity and an increase of 10.7084 million ton due to industry scale. In this period, there are three major factors that affect emissions: energy intensity, industrial activities and industrial scale. Among these factors, energy intensity is the main reason for emission reduction, while industrial activities and industrial scale drive increase in carbon dioxide emissions. Carbon intensity and energy structure have rather little impact on carbon dioxide emissions.
4. Discussion From the comparison of the influences of the different factors over the different periods, a number of findings were made. Firstly, the emissions impacts of energy intensity and industrial activities
Fig. 6. The energy intensity comparison of China’s all industry and food industry. Data source: China Statistical Yearbook, Calculated by the authors.
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are same and significant over the five periods. Energy intensity is the main driving force of emission reduction while industrial activity is the main driving force of the increase in emissions. Energy intensity reflects the economic efficiency of energy consumption. As shown in Fig. 6, the energy intensity of China's food industry shows a declining trend, thus, it fell from 8.48 tce per 10,000 yuan in 1986 to 0.2 tce per 10,000 yuan in 2010, which is far below the average level of China's energy intensity of 0.81 tce per 10,000 yuan. The decrease in energy intensity implies that the same level of output can be achieved with less energy, which results in decrease of carbon emissions. Industrial activities mainly reflect the impacts of labor productivity. Labor productivity will increase with the improvement in technology, and the energy rebound effect may follows. On one hand, the improvement in labor productivity will lead to reduction in energy consumption. On the other hand, the factories may expand production, thereby consuming more energy. If the consumption increases by the latter effect which is greater than the former, it means the energy rebound effect would lead to higher energy consumption and carbon emissions. Secondly, generally speaking, energy intensity, industrial activities and industry scale have more significant impacts on carbon emissions while carbon intensity and energy structure have little impacts. This is mainly due to the fact that the proportion of all kinds of energy consumed in the food industry in the sample period basically remains unchanged. Generally, industry scale plays a positive role in emissions except in the period 1996–2000. The effect of industry scale on carbon dioxide emissions is largely negative, which makes the overall carbon dioxide emissions to reduce. Jump-points also appear in the carbon intensity and energy structure. In order to further study carbon dioxide emissions in the food industry, as well as various jump-points, we choose 1986 as the base year and calculate the cumulative contributions of the different factors in 25 years to reach the conclusion shown in Fig. 7. First, carbon intensity and energy structure have little but fluctuating effects on emissions. This means the cumulative carbon dioxide emission curve of carbon intensity and energy structure fluctuates up and down around the axis. This is because the proportion of the different kinds of fossil energy fluctuates. Meanwhile, as shown in Fig. 8, energy structure denoted by the ratio of fossil energy consumption to total energy consumption declined, but it is not significant. Energy structure affects emissions in a positive way from 1986 to 1990 and afterward in a negative way, but it is not obvious. It can be inferred that the coal-oriented energy consumption structure in China has not changed. Secondly, energy intensity in China's food industry has negative impacts on the cumulative carbon dioxide emissions over the whole period. As shown in Fig. 6, energy intensity presents a decreasing trend from 1986 to 2010. Therefore energy intensity and its cumulative effect are positively related. In other words, we can effectively reduce carbon dioxide emissions by reducing energy intensity.
Fig. 7. The accumulative contributions of all factors.
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Thirdly, as shown in Fig. 9, the cumulative effects of industrial activities in the food industry show a gradually increasing trend. This indicates there exists a positive correlation between industrial activity and carbon dioxide emission. Besides, from 1986 to 2010, industrial activity factor shows an increasing trend (except for 2005 when there was a slight decline compared with 2004), which is the result of the energy rebound effect mentioned above. The improvement in industrial activity will increase energy efficiency, and factories may expand their production, leading to increase in energy consumption. When the amount of the increase in energy consumption which results from the expansion of production is larger than the amount of energy reduction from energy efficiency, there will be increase in emission level. At present, the supportive evidences of the rebound effect from the perspective of macroeconomic models or economic history proposes the “suggestive” evidence. Therefore, the enhancement of industrial activity energy efficiency leads to the increase of carbon dioxide emissions, which is more obvious in the long run than in the short run. Fourthly, industry scale generally has positive impacts on carbon dioxide emissions, which is consistent with other research findings. It however caused the decrease in emissions from 1999 to 2005. China has the largest population in the world, and the food industry is a labor-intensive industry which can absorb a large number of labor, especially the rural surplus labor, as shown in Fig. 10. However during the period of 1997–2004, employees in the food industry was below the 1986 level. The reasons may be as follows. First, the tertiary industry started to absorb more labor. The ninth Five-year Plan called for transfering the older policy that only focus on the increase of proportion of a certain industry or the number of products, to concentrate on the flow and migration of people in order to solve the full employment and structure adjustment. In this way, we can form the appropriate agriculture, effective manufacturing, labor-intensive tertiary industry and construction industry. As labor-intensive manufacturing is in a large scale, it is hard to continue the old way that only depends on cheap surplus labor. However, most of the third industries are labor-intensive, faced with smaller international competition. So they should be vigorously absorbing rural surplus labor and surplus personnels from manufacturing (Yang, 1996). Second, the improvement in technology and labor efficiency, as well as the elimination of backward technology and equipment also helped to reduce emissions by improving energy efficiency and reducing carbon dioxide emissions per unit of output.
5. Conclusions and policy implications This paper employs the Kaya Identity and LMDI method to decompose carbon dioxide emissions in China's food industry from 1986 to 2010. Then we calculate the influence of emission quantity from five aspects: carbon intensity, energy structure, energy intensity, industrial activities and industry scale. Our conclusion from the findings is that industry activity always plays a role in increasing carbon dioxide emissions while energy intensity remains the main driver of the reduction in carbon dioxide emissions. Besides, carbon intensity and industry scale are also important to the carbon dioxide emissions but they play different roles (sometimes positive and sometimes negative) in carbon dioxide emissions due to the ratio of the different forms of energy consumption and different government policies. Energy structure is the least important factor. This is because the energy structure in the food industry which is fossil-based or in particular coalbased has not been changed in the past 25 years. From the perspective of cumulative effects, they contribute 4.9159, 0.8157, 103.8258, 81.57, 109.5438 and 10.9653 million ton of carbon dioxide respectively for the five-year period intervals. The results of
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Fig. 8. The ratio of fossil energy consumption and the total energy. Data resources: China Statistical Yearbook, China's Energy Statistics Yearbook, calculated by the authors.
the empirical analysis indicates that both industrial activity and energy intensity are significant, industrial activity and carbon dioxide emissions present a positive correlation, and energy intensity poses a negative correlation with emissions. Interestingly, these findings are consistent with Lin and Ouyang (2014) research for the non-metallic minerals industry and Lin and Moubarak (2013) research for China's textile industry. At the same time, during the period of the ninth Five-year Plan, due to the loss of labor and the elimination of backward technology and equipment, emissions were effectively reduced. Finally, we observed that the coal-oriented energy structure in China's food industry is still unchanged. Energy conservation and emissions reduction is the long-term goal of economic development in China. However if energy consumption is reduced at the cost of economic growth, there is no doubt that it will be effective. Therefore, to ensure a balance in economic growth and energy consumption reduction in China's food industry, we propose the following suggestions based on the research results. Firstly, the government should adjust the internal structure of the food industry. This can be achieved by ensuring efficiency and effectiveness at the processing part of the internal structure of the food industry. In the food industry, the internal structure focuses mostly on the raw materials and transforming them into finished food products, with little particular attention for the production of food for special groups such as infants and pregnant women. Moreover, the internal structure also involves fixed costs such as depreciation expense among others as well as variable cost which include but not limited to, water consumption and energy consumption From our studies we observed for instance that, labor costs accounts for about 35% and energy costs accounts for about 25% of the total cost. By replacing old technology and equipment, training of employees to improve on their skills and abilities, and
reducing energy consumption per unit of output, we will not only reduce energy intensity to further reduce carbon dioxide emissions, but we will as well be reducing costs to increase economic efficiency. In addition to the adjustment of the internal structure of the food industry, the layout of the industry nationwide should also be further rationalized. With the guidance of Western development and Revitalization of Old Industry Zone of Northeast China, the food industry in China could transfer from the east to the mid-west and northeast, and gradually turn the agricultural advantage of the mid-west and northeast into the industrial advantage of the food industry. Moreover, food companies should continue to move to the main raw material production areas, key markets locations and important transportation hubs (National Development and Reform Commission, 2011). Secondly, the enterprise scale in the food industry is generally small to accelerate the industrial cluster, therefore, policy actions should be taken to reduce little workshops and enterprises with high cost, and to make use of the large scale advantage to improve energy efficiency. It is imperative however to note that, the food industry is made up of farming, forestry, animal husbandry and fishery which are prune to natural disasters. As a result, a stable supply of raw materials will help ensure the full use of the fixed assets so as to improve labor productivity and reduce energy consumption. Furthermore, to ensure acceleration of large scale production in food, a number of enterprise groups with high market share and good driving ability can be restructured through mergers and acquisitions. Thirdly, it can be seen from the energy consumption in the food industry that power consumption presents an increasing trend year by year while the direct use of coal is declining. Therefore, in order to achieve energy conservation and emissions reduction, it should be advocated using electricity instead of coal to reduce carbon emissions. It is however noteworthy that, the
Fig. 9. The industrial activity index in food industry. Data resources: China Industrial Statistical Yearbook, calculated by the authors.
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Fig. 10. The number of employees in food industry. Data resource: CEIC China Database.
implementation of the coal substitution needs to be based on the enterprises' technical progress, such as the technical renovation of the existing equipments and technologies, and the optimization of the manufacturing process. According to the requirements of the 12th five-year plan of the food industry the promotion of energy conservation and emissions reduction focused on industries such as fermentation, brewing, sugar refinery, starch, quick-frozen food and meat slaughtering and processing. In addition, enterprises also can improve the development and utilization of the by-products of the food industry by taking advantage of new technology and equipment. At the same time, detailed development goals have been put forward in the 12th five-year plan of the food industry. The target industries are grain processing industry, edible vegetable oil, meat processing industry, dairy industry, and marine seafood products industry. By 2015, the output value of the grain processing industry should reach 3.9 trillion yuan, with an average annual growth of 12%. Edible vegetable oil production should be 24.4 million ton, of which production of domestic oil plants should be increased to 12.6 million ton. Meat production should be 85 million ton, and the capacity of backward manual and semimechanization should eliminate 50%. Dairy production should be 27 million ton, with an increase of 15%, and the idle rate of dairy products processing capacity should be controlled within 25%. The processing rate of marine sea food products should be increased to more than 45%, and frozen-prepared food and small packaged food should account for over 30% of frozen processed products (Yang, 1996). Finally, from the perspective of reducing carbon dioxide emissions, improving the efficiency of energy use or production efficiency can only be a short-term policy. With the energy rebound effect, the improvement in production efficiency is not as effective as expected. To achieve the goal of Energy conservation and emissions reduction, the government should play a key role in the pricing of energy, taxation and influencing people's behavior. For the central government, there is a need to implement energy pricing reforms to reverse the current situation where fossil energy prices are undervalued. Only when the prices of fossil energy are well reflected then energy price can be used to influence the enterprises to use clean energy, and thereby transform the energy consumption structure in the food industry. Besides, most of the enterprises in China's food industry are small and medium-sized enterprises, due to the price discrimination of banks against small and medium-sized private enterprises. This serves as a disincentive to these enterprises to access capital for maintenance and upgrade of their equipments. Therefore, the central government should formulate relevant policies to reduce the financing costs for small and medium-sized enterprise, which will enable them upgrade their equipments to reduce emissions. For the local
government, there is need to strengthen guidance and support for the food industry to encourage technological innovation. Local finance department should also strengthen the special fund support for small and medium-sized enterprises. Besides, a policy of comprehensive utilization of waste food should be implemented to actively support the comprehensive utilization of the by-products of the food industry. State Administration of Grain claims that China's waste food in a year has reached more than 120 billion catties, which is enough for two hundred million people. This includes, about 50 billion catties of food wastes, and about 70 billion catties of waste during the storage, processing, and transport. The food processing is public-demand-oriented, some consumption habit errors caused by the higher standards of living, such as excessive pursuit of fine grain, which makes the grain manufactures go for brighter, purer and more exquisite grain during the processing, which leads to as many as more than 13 billion catties grain each year. Thus, Government's publicity of good dietary habit, and promotion of Clean Plate Campaign are great steps to reduce waste, especially the Eight Honors and Eight Shames which forces civil servants to take initiative to crack down on corruption. Reducing food waste promotes energy conservation and emissions reduction, and should be encouraged among the citizenry.
Acknowledgements The paper is supported by National Social Science Foundation (Grant No. 12&ZD059, and 14JZD031), and Ministry of Education (Grant No. 10JBG013).
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