How to promote energy conservation in China’s chemical industry

Energy Policy 73 (2014) 93–102

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Energy Policy journal homepage: www.elsevier.com/locate/enpol

How to promote energy conservation in China’s chemical industry Boqiang Lin a,b,n, Houyin Long a a b

The School of Economics, China Center for Energy Economics Research, Xiamen University, Xiamen, Fujian, 361005, PR China New Huadu Business School, Minjiang University, Fuzhou 350108, PR China

H I G H L I G H T S








Labor productivity and energy intensity are crucial driving factors. The relationship among variables is co-integrated. The result of the EG co-integration is the same as that of LMDI. ECM displays the short-term fluctuation of fossil fuel consumption. Under the scenario analysis, there is a huge energy saving potential.

art ic l e i nf o

a b s t r a c t

Article history: Received 5 February 2014 Received in revised form 27 April 2014 Accepted 30 May 2014 Available online 1 July 2014

Fossil fuel consumption in China’s chemical industry accounted for 19.7% of the total industrial fossil fuel consumption, and the industry has become the second highest energy intensive sector in the country. Therefore, it is extremely urgent and important to study the problems related to fossil fuel consumption in the industry. This paper adopts the factor decomposition and the EG co-integration methods to investigate the influencing factors of fossil energy consumption and measure the saving potential of fossil fuel. The paper concludes that the influencing factors can be divided into positive driving factors (labor productivity effect and sector scale effect) and negative driving factors (energy intensity effect and energy structure effect). Among them, labor productivity and energy intensity are the main factors affecting fossil fuel demand. The largest saving potentials of fossil fuels are predicted to be 23.3 Mtce in 2015 and 70.6 Mtce in 2020 under the middle scenario and 46.8 Mtce in 2015 and 100.5 Mtce in 2020 under the ideal scenario, respectively. Finally, this paper provides some policy implications on fossil fuel conservation. & 2014 Elsevier Ltd. All rights reserved.

Keywords: LMDI Co-integration Fossil fuel conservation

1

1. Introduction China is the world’s fastest growing economy in the past several decades. According to data from China Bureau of Statistics, the real gross domestic product of China grew at an average annual rate of 9.3% from 1980 to 2011 and 7.8% in the first half of 2012. Over the period 1980–2011, the massive fossil fuel consumption, which supported the economic development in China, increases at a growth rate of 5.5% every year. According to the International Energy Agency (IEA), China has become the largest oil importer since September 2013 after surpassing the USA.

n Corresponding author at: Newhuadu Business School, Minjiang University, Fuzhou, Fujian, 350108, PR China. Tel.: þ 86 5922186076; fax: þ 86 5922186075. E-mail addresses: [email protected], [email protected] (B. Lin).

http://dx.doi.org/10.1016/j.enpol.2014.05.056 0301-4215/& 2014 Elsevier Ltd. All rights reserved.

The main kinds of energy consumed in China are coal, oil, natural gas and hydropower. The energy structure in China is dominated by fossil fuels. Because 70% of fossil fuel is consumed in the industrial sectors, it is necessary to study energy problems in the sector. This paper studies the problems related to fossil fuel consumption in the chemical industry. Broadly speaking, the chemical industry adopts chemical process to produce chemical products, including steel, building materials and so on. In a narrow sense, the chemical industry is a manufacturing industry producing chemical raw materials and chemical products which consists of basic chemical raw materials manufacturing industry, fertilizer manufacturing industry pesticide manufacturing industry, coatings, inks, paints and similar products manufacturing industry, synthetic material manufacturing industry, special chemical product manufacturing industry and daily-chemical products manufacturing industry. This paper focuses on the chemical raw materials and chemical products manufacturing industry. In other words, in this paper, chemical

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industry refers to chemical raw materials and chemical products manufacturing industry. The chemical industry is a significant industry in China’s economy. In 1949, there were only 100 types of chemical products (Mao, 1998). However, after the reform and opening up in China, the contribution of the chemical industry to the GDP increased from 7% in 1981 to 20% in 2011. As can be seen in Fig. 1, the total value of output of the chemical industry increased from 300 billion Yuan to 1761 billion Yuan in the same period, indicating an average growth rate of 14.1%. In particular, at the beginning of this century, the industry started to grow at an annual average rate of 18.9%. By the end of 2010, the chemical industry in China has ranked first in the world. In 2011, the main business income of enterprises above designated size was 6010 billion Yuan (1761 billion Yuan at the constant prices in 1981). The chemical industry has developed rapidly over the past 30 years. However, it has also resulted in new problems, such as overconsumption of fossil fuel, heavy pollution and unsustainable means of production (Gong, 2003). As the second highest energy-consuming industry, the demand for fossil fuels in the chemical industry has increased considerably. Fig. 2 signifies that fossil consumption increased from 52.5 Mtce (million tons coal equivalent) in 1981 to 204.6 Mtce in 2011, which matched the development of the chemical industry. From 1998 to 2000, fossil energy demand decreased due to the reform of statedowned enterprises and transition to a market economy. In 2011, the overall energy consumption in Spain and Italy were 146 Mtce and 168.5 Mtce, respectively. The total fossil consumption of China’s chemical industry in 2010 at 169.8 Mtce was slightly and considerably higher than the primary energy consumption in Italy and Spain, respectively. As a result, it is absolutely necessary to study the driving factors and saving potential of fossil fuel.

Total value of output

Rate

25 20 15 10 5 0

Fig. 1. The total value of output and the growth rate (at the constant price of 1981).

250 Mtce 200 150 100 50 0

Fig. 2. The total of fossil fuel consumption.

In the past few years, many advanced technologies and equipment have been introduced in China in order to reduce fossil energy consumption. However, energy-consumption problems in the chemical industry still persist due to the lower starting point. Based on the 12th Five-year plan of the chemical industry, fossil fuel demand is expected to reduce by 20%. Similarly, the average energy consumption of synthetic ammonia is expected to reduce to 1350 kg coal equivalent per ton. Economic models have been widely used to study and address issues related to energy. These models, which are used to analyze energy saving and emissions reduction, are called top-down models and include econometric and CGE models.. In other words, economic models are employed to solve problems related to energy in order to realize sustainable development vis-a-vis energy, the economy and the society (Zhang, 2010). Since there are several economic models being used to study energy problems, we introduce a few models. Li and Zhang (2012) explored carbon motivated border tax adjustments influence on China and suggested that CBTA was an inefficient policy instrument to mitigate carbon emissions. Li and Lin (2013) adopted CGE model to provide empirical contributions by analyzing the potential effects of several policies implemented to mitigate China’s carbon emissions, and found that the government would have to make tradeoffs among different objectives when designing and implementing climate policies. Lin and Li (2012) measured the effect of removing fossil fuel subsidies and found that subsidy removal would influence the competitiveness, output, welfare and emissions of different countries. The adopted IOA approaches mainly include single-regional input–output models and multi-regional input–output models. Sanchez Choliz and Duate (2004) adopted a single-regional model to estimate the emissions associated with international trade in Spain. Su et al. (2010) used the sector aggregation effect to estimate embodied carbon emissions in China. Lin and Sun (2010) analyzed embodied carbon emissions in international trade in China based on the input–output table of 2005. Chen et al. (2008) researched embodied energy in international trade in China from 2002 to 2006 quantitatively. Weber and Matthews (2007) adopted a multiregional IO model to analyze the trade between USA and its seven largest trading partners (Canada, China, Mexico, Japan, Germany, the UK, and Korea), and estimate the environmental effects of US trade structure and volume variation over 1997–2004. Meier and Rosenfeld (1982) first adopted conservation supply curve to analyze energy saving in California. Worrell et al. (2001, 2008) employed the method to investigate energy saving of the cement and steel industry, respectively in the USA, and Hasanbeigi et al. (2013) used it to study energy saving in the cement industry in Thailand. Charnes et al. (1978) proposed the data envelopment analysis theory. Wang et al. (2013) used DEA method to study the gap in technology in China. Biana et al. (2013) adopted non-radial DEA to analyze China’s energy saving potential. Zou et al. (2013) used SFA model to analyze energy efficiency in China. The co-integration method has been widely used to analyze energy demand. Galindo (2005) employed it to predict energy demand in Mexico, while Turkekul and Unakıtan (2011) used it in Turkey. Kulshreshtha and Parikh (2000) adopted it to estimate coal demand in India, Park and Zhao (2010) employed it to forecast gasoline demand in the United States, and Amarawickrama and Hunt (2010) used it to investigate electricity demand in Sri Lanka. Considering the important role that China plays in the international energy market and carbon emissions reductions, a lot of local and foreign researchers and analysts have been more interested in studying industrial energy and the saving potential of fossil fuel in China. The co-integration method has long been used to analyze energy demand and energy saving. Lin (2003) found

B. Lin, H. Long / Energy Policy 73 (2014) 93–102

that the main factor affecting electricity demand was economic growth, and the relationship between them is 1 over 1. Xue and Wang (2006) predicted petroleum demand in China. Lin et al. (2012a,b) estimated China’s energy demand in the process of urbanization and industrialization. Lin et al. (2011) found that R&D, energy saving investment, labor, and structure effect of energy intensity are the determinants of energy conservation potential in the China’s steel industry. Using the co-integration approach, Lin et al. (2012a) evaluated the electricity saving potential in China’s chemical industry and found that more pragmatic electricity saving policies are objectively required in order to reduce electricity intensity in China’s chemical industry and shrink future electricity saving potential. In terms of the electricity intensity of the chemical industry, China is approaching the level of Japan, with the gap expected to narrow significantly by the year 2020. Lin et al. (2012b) showed that the amount of electricity saving in China’s power industry will be 41.3 TW h in 2020, which is higher than the total electricity consumption of New Zealand in 2010. Lin and Guoliang (2013) evaluated the electricity saving potential in China’s nonferrous metals industry and found that more vigorous electricity conservation policies are needed in order to reduce the electricity intensity of the industry. They also find that the electricity efficiency gap could be significantly narrowed by 2020 if proper electricity conservation policy is adopted. Lin and Chunping (2013) established a long-run equilibrium relationship between oil consumption, GDP, road condition, labor productivity and oil price, and predicted oil saving potential in China’s transport sector. The results showed that oil saving potential would be 86 MTOE and 131 MTOE under moderate oil-saving scenario and advanced oil-saving scenario in 2020, respectively. Divisia index method is also widely adopted to analyze energy and environmental problems (Ang, 2004). Divisia index method is made up of AMDI and LMDI (Zhang and Zhu, 2012). LMDI was put forward by Sun (1998), Ang and Choi (1997), Ang and Liu (2001) in the 1970s. It never results in a residual and allows data to take zero and negative values. In 2005, Ang and Liu (2001) described all index decomposition analysis methods and concluded that the logarithmic mean Divisia index method is the best method. LMDI was employed to analyze energy intensity in China’s industry. Sinton and Levine (1994) analyzed energy intensity in China’s industry during 1980–1990 and found that energy efficiency was the most important factor affecting energy intensity. Zhang (2003) found that energy efficiency and structure led to the downward trend of energy intensity. Qi and Zhixin (2006) reached the conclusion that energy efficiency depended on technical progress. Wu and Cheng (2006) decomposed energy intensity into structure effect and efficiency effect over 1980–2003. Zhang and Guo (2013) adopted LMDI to explore the factors that influence rural residential and commercial energy consumption in China, and suggested that income and energy intensity effects were the critical factors. Zhang et al. (2013) examined the driving factors of carbon emissions in the China’s electricity generation over the period 1991–2009, and found that economic activity effect was the most important factor influencing the increase in carbon emissions in electricity generation. Li and Wang (2008) analyzed energy intensity from 1995 to 2003, and found that the change in energy intensity was based on cumulative energy intensity within the region. LMDI was also adopted to study the change in carbon emissions (Greening et al., 1998, 1999, 2001; Greening, 2004; Bhattacharyya and Ussanarassamee, 2004; Kyonghwa and Suyi, 2013; Wang et al., 2010). In light of the advantages and disadvantages of the above models and the availability of data, this paper employs two models: LMDI and the EG co-integration to explore the influencing factors and the saving potential of fossil fuel in the chemical industry in China.

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The structure of this paper is arranged as follows: Section 1 briefly introduces the basic situation of the chemical industry. Section 2 explains the methodology and discusses data sources. Section 3 gives out the results and Section 4 provides the final conclusions and some policy implications.

2. Material and models 2.1. The co-integration Although some economic variables are non-stationary time series, their linear combination may be stationary. In other words, some economical indexes stem from the same economic system and there are the long-run equilibrium relationships among them. Co-integration is thought as the equilibrium relationship. Engle and Granger (1987) provided the definition of cointegration. “All components of xt ¼ ðx1t ; x2t ; …; xnt Þ0 are integrated of order d, there exists a vector α ¼ ðα1 ; α2 ; …; αn Þ, so that αxt ¼ α1 x1t þ α2 x2t þ ⋯ þ αn xnt  I(db), b40. So the vector α ¼ ðα1 ; α2 ; …; αn Þ is called the co-integrating vector.” In other words, if two or more series are individually integrated (in the time series sense), but some linear combination of them has a lower order of integration, then the series are said to be co-integrated. A common example is presented to explain the definition. First, we establish a linear combination between two time sequences of xt and yt , yt  αxt ¼ μt . The residual μt is obtained generally by using ordinary least squares. Second, we test the residual μt for stationarity using the Augmented Dickey–Fuller test (Dickey and Fuller, 1979) and Phillips–Perron test (Phillips and Perron, 1998). If the residual is the stationary sequence, this is EG two-step method. The two main methods that are used to test co-integration are the Engle–Granger two-step method and the Johansen test. Unlike the Engle–Granger method, the Johansen test allows for more than one co-integrating relationship. However, in this paper, we use the EG two-step method. 2.2. ECM ECM, also called DHSY model, is introduced by Davidson et al. (1978). The co-integration describes the long-run equilibrium relationship, but the real economic data is generated by nonequilibrium process. As a result, we need to approximate the longrun process by using the data of dynamic non-equilibrium process, which is called autoregressive distributed lag model. In this paper, we roughly introduce the ADL (1.1). yt ¼ β0 þ β1 yt  1 þβ2 xt þβ3 xt  1 þ μt ;

t ¼ 1; 2; …; T

ð1Þ

Minus yt  1 on both sides of the equation and plus and minus β2 xt  1 on the right side, we get:
 β β þ β3 Δyt ¼ ðβ1  1Þ yt  1  0  2 xt  1 þ β2 Δxt þ μt ð2Þ 1  β1 1  β1 This equation is called error correction model. The error     correction term isyt  1  β0 =1  β1  β2 þ β3 =1  β1 xt  1 . An error correction model is a dynamic system with the characteristic that the deviation of the current state from its long-run relationship will be fed into its short-run dynamics. An error correction model is not a model that corrects the error in another model. There are a category of multiple time series models that directly estimate the speed at which a dependent variable – Y – returns to the equilibrium state after a change in an independent variable—X. ECM is a theoretically-driven approach

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that is useful to estimate both short term and long term effects of one time series on another. Thus, they often coincide well with theories of political and social processes. ECM is useful models when dealing with integrated data, but can also be used for stationary data. 2.3. LMDI This paper employs LMDI method to explore the factors influencing fossil fuel consumption in China’s chemical industry. Based on the Kaya identity, the dependent variable can be decomposed into several explanatory variables (Kaya, 1990). In this paper, we adopt this approach to identify and estimate the factors that influence fossil fuel consumption. Now, we establish the LMDI of fossil fuel with the following equation: Ef ¼

Ef E Y    P ¼ ES  EI  LP  SS E Y P

ð3Þ

where, Ef denotes the total fossil fuel consumption; E represents the total fossil fuel consumption of the chemical sector; Y refers to the value added of industry in the sector; P stands for the number of employees in the sector. In Eq. (3), the variables signify different effects. The meanings and abbreviations of each multiplier are as following: ES represents energy structure factor in the chemical industry sector. It measures the share of fossil fuel consumption in total energy consumption, and is usually influenced by fossil fuel price and other energy prices. EI signifies energy intensity factor in the sector. It describes how much fossil fuel is consumed to produce an average value added of output. The lower the energy intensity is, the lower the energy consumed given the same level of output. This index signifies whether there is improvement in technology or not. When the energy structure is constant, energy intensity is the reciprocal of energy efficiency.LP represents labor productivity in the sector. SS represents the expansion of a single enterprise. Relatively, large-scale enterprises contribute to the rational utilization of energy and improve equipment utilization efficiency (Lin et al. 2011). Compared with larger companies, for instance, Du Pont and BASF SE, and other well-known chemical factories in the world, there are relatively large number of medium and small enterprises in China. Based on the LMDI, fossil energy consumption is decomposed into four parts. This paper chooses the year 1981 as a base year. The total change in year t in comparison with its level in a base year is estimated and decomposed as follows: ΔEC ¼ EC t  EC 0 ¼ ΔEC ES þ ΔEC EI þ ΔEC LP þ ΔEC SS

ð4Þ

So we will obtain:  t t 0 ES ΔEES ¼ ðEt  E Þ 0 ln ES 0 ðlnE  lnE Þ

ΔEEI ¼

ðln Et  ln E0 Þ t

ΔELP ¼ ΔESS ¼




ðEt  E0 Þ




0

ðE E Þ t

ln

0

ðlnE lnE Þ

ln

ðEt  E0 Þ ðln Et  ln E0 Þ

ln

EI t



EI 0  LP t LP 0 SSt

!

SS0

By Eq. (4), the total change in fossil fuel consumption is obtained by the sum of the effects. 2.4. Data and variable Fossil fuel consumption (E): the data on fossil fuel consumption in seven sectors are obtained from China Statistical Yearbook, China

Chemical Industry Statistical Yearbook and China Energy Statistical Yearbook. The total energy consumption in chemical industry (E): the data on total energy consumption in seven sectors are obtained from China Statistical Yearbook, China Chemical Industry Statistical Yearbook and China Energy Statistical Yearbook. Based on the data on fossil fuel consumption and total energy consumption, we calculate the energy structure and energy intensity as follows: Energy structure ¼

Fossil fuel consumption The total energy consumption

The added value of industry (AV): the value added of industry represents the total value of sales by enterprises (their turnover) in an accounting period. We adjust the value added of industry based on the constant prices in 1981. The data are from China Statistical Yearbook and China Industrial Economy Statistical Yearbook. Based on data on total energy consumption and value added of industry, we calculate energy intensity as follows: Energy intensity ¼

The total energy consumption The added value of output

The number of employees (SS): Generally speaking, the larger the number of workers is, the more the value added of industry would be. This situation would bring about more fossil fuel consumption. There are many variables being used to proxy the development of the chemical industry. Such variables include the total value of industry and the number of workers, etc. We choose employment figures to represent the scale of the industry when conducting the LMDI analysis. Data on the number of enterprises are from China Statistical Yearbook and China Labor Statistical Yearbook. Based on data on the number of workers and the value added of output, we calculate labor productivity as follows: Labor productivity ¼

The added value of output The number of workers

3. Results 3.1. LMDI As has been mentioned above, we adopt Eq. (4) to analyze the growth of fossil fuel consumption in the China’s chemical industry and reach the following conclusions: In Fig. 3, energy intensity effect causes decline in fossil fuel consumption. Decrease in energy intensity indicates that technology and management have improved. In the same way, the increase in energy intensity indicates that fossil fuel consumption per unit output increases. As a result, energy intensity is a negative factor. Energy structure effect is another negative factor. The main reason is that more power is used and substituted for fossil fuel in the chemical industry over the period 1981–2011. This situation makes the industry cleaner. Comparing the two negative effects, energy intensity has the larger effect leading to the decline in fossil fuel consumption. Labor productivity effect is the most important variable which results in the increase in fossil fuel demand. The increase in labor productivity means that the demand for fossil fuel would increase. The main reason is that with the development of the chemical sector, advanced equipments enhanced the output per unit worker, and consumed more fossil fuels during 1981–2010. In other words, more capital and energy replaced labor as technology improved.

B. Lin, H. Long / Energy Policy 73 (2014) 93–102

ES

EI

LP

SS

40000 30000 20000 10000

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1982

-10000

1984

0

-20000 -30000 Fig. 3. The tendency of every variable.

Sector scale effect (SS) was the second factor leading to the growth of fossil fuel consumption. In Fig. 3, the curve of the sector scale declines in the year 1998. This is because China further reformed state-owned firms and shut down some unviable enterprises. After that, the curve rose again. As China develops, the scale of the sector expands, and the sector’s demand for fossil fuel increases. Comparing the two positive effects, sectoral scale has the larger effect resulting in the increase in fossil fuel consumption. In all, we divide the total factors into positive driving factors (labor productivity effect and sector scale effect) and negative driving factors (energy intensity effect and energy structure effect). Labor productivity effect and energy intensity effect are the main factors affecting fossil fuel demand.

There are lots of other factors affecting energy consumption in the sector, such as energy conservation investment and chemical product structure (Lin et al. 2011). Developing advanced energysaving technology needs investment. Thus, we seek data on energy conservation investment, which is an important influencing factor of energy intensity. The bioconversion processes are widely used in the developed countries, and reasonable and proper chemical product structure is a major way to save energy and reduce carbon emissions. Until 2000, chemical product structure in Japan was about 70% while that in China was only 45%. However, because statistical data on energy conservation investment and chemical product structure cannot be obtained, it was excluded. Many factors affect fossil fuel demand in the chemical industry. Based on the LMDI model, we choose the total value of industry, fossil fuel price, energy efficiency, energy structure and labor productivity as explanatory variables to analyze fossil energy demand in China’s chemical industry during 1981–2010. The data in 2011 is retained to examine and test the prediction accuracy of the co-integration result. We set up the function of fossil fuel consumption as follows: E ¼ f ðES; EE; LP; TVP; PÞ In this paper, we use the same function as Lin et al. (2011). To neutralize the influence of dimension, every variable takes the form of logarithm.

12 10 8

3.2. The co-integration test 3.2.1. The adjustment of variables LMDI shows the factors that affect the growth of fossil fuel demand, but do not reveal the extent to which the factors affect fossil fuel demand. Before employing the co-integration to address this problem, we adjust some of the above variables. We substitute the total value of industry for the number of employees for two reasons. First, since statistical category of employees has been changed several times, the quality of the total value data is better than that of enterprises. Thus, when conducting the co-integration and ECM models, we choose the total value of output. Data on the total value of industry are from China Statistical Yearbook. Second, the total value of industry denotes not only sectoral scale but also other economic meanings. Energy intensity has the same meaning as energy efficiency, and they are reciprocal. In the co-integration and ECM models, we use energy efficiency. Price is a crucial variable affecting supply and demand for any commodities, including fossil fuel. The different prices of energy determine energy consumption in the chemical industry. Therefore, we introduce fossil fuel price into the energy structure. However, energy price in China is still regulated and controlled by the government, and there are large amounts of subsidies (Lin and Jiang, 2012). Elkhafif (1992) found that a substantial part of energy conservation is attributed to the higher aggregate price of energy. In the case of China, Yuan et al. (2010) posited that higher energy price would cause decline in energy consumption in China’s industrial sectors. Given that there is no data on fossil fuel price, we use Purchased Price Indices of raw materials, fuel and fossil fuel obtained from China Statistical Yearbook.

97

E

6

EE

4

ES

2

TVP LP

0

P

-2 -4 -6 Fig. 4. The picture of every variable.

Table 1 Unit root test.

n

Sequence

ADF With trend and intercept

PP With trend and intercept

ln E Δ ln E ln EE Δ ln EE ln TV P Δ ln TV P ln P Δ ln P ln ES Δ ln ES lnLP Δ ln LP

 3.096374  4.364470nnn  0.917349  5.019807nnn 0.600894  4.164293nn(p¼ 0.0143)  1.495113  3.811978nn (p¼ 0.0309)  1.572319  5.698053nnn  1.501195  4.874630nnn

 2.088117  4.420050nnn  1.060268  5.019807nnn 0.168841  4.158238nn(p¼ 0.0145)  1.935847  3.777623nn (p¼ 0.0332)  1.501794  5.718784nnn  1.520200  5.018904nnn

Significant at 1%, respectively. nn

Significant at 5%, respectively. Significant at 10%, respectively.

nnn

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B. Lin, H. Long / Energy Policy 73 (2014) 93–102

Based on the above analysis, we display the function of fossil fuel demand as follows: ln E ¼ θ1 þ θ2 ln ES þ θ3 ln EE þ θ4 ln LP þ θ5 ln TVP þ θ6 ln P þ εt From Fig. 4, we find that every variable has the growth trend and intercept. However, energy structure does not show trend and intercept, as the values of other variables are far larger than it. From 1981 to 2011, energy structure declined from  0.357 (71%) to  0.529 (52%). Therefore, energy structure also has trend and intercept.

3.2.2. Unit root test We apply the ADF test and the PP test to investigate the existence of unit root in every variable and acquire the following result in Table 1. In summary, at the 5% confidence level, six variables are significant.

3.2.3. EG two-step co-integration test Based on Fig. 5, since the variables appear to have the same growth tendency, there may exist a co-integration relationship among them. Then, we employ EG co-integration to test the existence of the relationship. First, we regress the above six variables with the ordinary least squares regression and obtain the following results. In Table 2, at the 5% confidence level, P value implies that five parameters are significant. The R-squared value is 0.9946, which indicates that the fitting equation is good. The sign of all the parameter is in line with the expectation and the actual situation. Second, we test whether the residual sequence is stationary.

According to Fig. 5, there exists no trend and intercept in the residual sequence. They fluctuate around zero. Thus, we test it with PP test and ADF test. Based on the ADF test and the PP test in Table 3, the residual sequence is significant and stationary. Therefore, the relationship among the six variables is co-integrated. From the table, we obtain the co-integration equation: ln E ¼ 1:076493 þ 2:006558 ln ES  0:995935 ln EE þ 0:309893 ln LP þ 0:819195 ln TVP 0:12207 ln P

ð5Þ

This equation indicates that there is a long-term equilibrium relationship among E, EE, ES, LP, TVP and P. On the right side of the equation, the coefficients of ln ES; ln TVP and ln LP are positive while the others are negative. The parameter value of ln ES is the largest, which implies that energy structure is the most important factor of energy consumption. The coefficient of ln TVP is the second largest, which indicates that the development of the chemical process industry is an important factor that causes growth of fossil fuel demand. When ln TVP changes 1%, lnEC will change 0.82%, indicating that the relationship between fossil fuels and the total value of output is almost 0.8:1. The coefficient of ln EE is negative, which implies that the higher the energy efficiency is, the lesser the fossil fuel consumed. The coefficient of ln LP shows that the larger labor productivity is, the more fossil fuel will be consumed. Generally, the coefficient of ln P is negative but very small. It is in line with economic theory and reflects the real situation in China. The energy market in China is regulated by the government and energy price is distorted. In other words, the energy price is undervalued, which indicates that the government subsidizes the enterprises and stimulates them to over-use fossil fuel. This situation provides an avenue for energy saving policies. This result is consistent with Elkhafif (1992) and Yuan et al. (2010).

Residual

0.1

3.2.4. ECM The EG co-integration shows the long-run equilibrium. But the change in ln E is determined by long-run equilibrium and the short-term fluctuations. Now, we adopt the error correction model

0.08 0.06 0.04 0.02

Table 3 ADF test and PP test.

0 -0.02 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Test critical values (%)

-0.04

PP t-Statistic  5.713154

-0.06 -0.08

1 5 10

Fig. 5. The residual sequence.

ADF Prob.n 0.0000

 2.647120  1.952910  1.610011

t-Statistic  5.623392

Prob.n 0.0000

 2.647120  1.952910  1.610011

Table 2 The result of regression. Variable

Coefficient

Std. error

t-Statistic

C EE ES LE P TVP R-squared Adjusted R-squared S.E. of regression Sum squared resid. Log likelihood F-statistic Prob. (F-statistic)

1.0765  0.9959 2.0066 0.3099  0.1221 0.8192 0.9946 0.9935 0.0324 0.0252 63.6514 885.287 0.0000

0.075286  13.2287 0.244535 8.205606 0.045168 6.86089 0.035091  3.47865 0.051628 15.86722 Mean dependent var. S.D. dependent var. Akaike info criterion Schwarz criterion Hannan–Quinn criter. Durbin–Watson stat.

Prob.

0.0000 0.0000 0.0000 0.0019 0.0000 9.1334 0.4016  3.8434  3.5632  3.7538 2.1096

Table 4 the result of ECM. Variable

Coefficient

Std. error

t-Statistic

D (ES) D (LE) D (TVP) RESIDUAL (  1) D (EE) R-squared Adjusted R-squared S.E. of regression Sum squared resid. Log likelihood Durbin–Watson stat.

1.9847 0.3315 0.7146  0.9938  0.9130 0.9414 0.9316 0.0309 0.0229 62.4206 1.7972

0.223677 8.872984 0.070551 4.698087 0.065648 10.88547 0.240479  4.13254 0.077525  11.7764 Mean dependent var. S.D. dependent var. Akaike info criterion Schwarz criterion Hannan–Quinn criter.

Prob. 0.0000 0.0001 0.0000 0.0004 0.0000 0.0424 0.1182  3.9600  3.7243  3.8862

B. Lin, H. Long / Energy Policy 73 (2014) 93–102

to study the short-term fluctuations. Deleting the non-significant variable and intercept, we get the results in Table 4. At the 5% confidence level, the coefficient of every variable is significant. The R-squared is 0.973, which implies that the fitting equation of ECM meets the requirement. The ECM equation is presented as follows: Δ ln E ¼  0:91296Δ ln EE þ 1:984682Δ ln ES þ 0:331453Δ ln LP þ 0:714611Δ ln TVP 0:99379 Residual: ð6Þ Substitute Eqs. (3) into (4): Δ ln E ¼  0:91296Δ ln EE þ 1:984682Δ ln ES þ 0:331453Δ ln LP þ 0:714611Δ ln TVP  0:99379 ðln EC  1:076493 þ 0:995935 ln EE  2:006558 ln ES  0:309893 ln LP  0:819195 ln TVP þ0:12207 ln PÞ:

ð7Þ

3.2.5. The fitting effect In this section, we investigate the fitting effect between the predictions and real data. The real data during 1981–2010 are substituted into Eq. (5) to obtain the fitting values. The red line is obtained from the real data and the blue line is from the prediction value. The blue line is the same as the red line. The red line hardly covers the blue line, which indicates that the fitting effect is good. By comparing the fitted value with real data, Eq. (5) meets the requirement (Fig. 6). To further prove the reasonability of the result, we examine the prediction in 2011. The prediction shown in Table 5 is 202.2 Mtce, which is almost as much as the actual data (204.6 Mtce). This further illustrates that Eq. (5) has high degree of accuracy. 3.3. Fossil fuel saving potential

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The ideal scenario is a special scenario. It is the maximum or minimum of the BAU and the ideal scenario. In other words, to get the maximal value of energy saving, we obtain the growth rate of every variable in terms of the parameter value of Eq. (5). The middle scenario: under the 12th five-year plan and the restriction of economic reality, the growth rate of each variable could lead to the largest fossil-fuel saving potential. The growth rate of TVP comes from the 12th five-year plan of the chemical industry. It is known that there is a gap in the chemical industry of China and Japan. In this paper, we assume that the level of energy efficiency in the chemical industry in China will attain that of Japan by the end of 2025. The growth rate of labor productivity comes from Lin et al. (2011). The growth rate of energy structure is the maximum of the historical growth rate. As mentioned earlier, we calculate the growth rate of EE in Table 6. Looking at the historical trend of every variable, the above growth rate can be reached.

3.3.2. Fossil fuel saving potential In the light of Eq. (5) and the three scenarios, we are able to forecast the fossil fuel consumption in the future. The blue line is the real data, and the red line is the prediction under BAU. The green line is the prediction under the ideal scenario, and the rest is the prediction under the middle scenario. From Fig. 7 and Table 7, the fuel demand in the chemical industry will increase over time by the end of 2020. The predictions under BAU are 212.8 Mtce in 2012, 263.4 Mtce in 2015 and 376 Mtce in 2020. The predictions under the middle scenario are bigger than those of the ideal scenario. For example, the predictions under the middle scenario are 240.2 Mtce in 2015 and Table 6 The growth rate of every variable.

3.3.1. Set of scenarios The targets for energy-saving and emission-reduction should be set based on the real situation of enterprises and sectors. We establish three scenarios to analyze the fossil fuel saving potential: business as usual (BAU), the middle scenario and the ideal scenario. Similar scenario analysis has been used by IEA (2008), Dowling and Russ (2012), Roinioti et al. (2012). Business as usual (BAU): this paper supposes that every variable grows at the speed of the average annual growth rate over 1981–2010.

Variables

BAU (%)

Middle scenario (%)

Ideal scenario (%)

TVP EE LE ES P

14 7.5 11 0.7 9

13 18 6 5.7 20

13 18 6 0.7 20

400 350

Prediction

Actual data

300

20000 18000 16000 14000 12000 10000 8000 6000 4000 2000 0

250

Real data

200

BAU

150

Ideal

100

Middle

50 2020

2017

2014

2011

2005

2008

2002

1999

1996

1993

1990

1984

1987

1981

0

Fig. 7. The prediction under three scenarios (Mtce). Fig. 6. The fitting picture (million tons coal equivalent). Table 7 Prediction of the fossil fuel demand (Mtce). Table 5 The fossil fuel demand in 2011. Mtce

Prediction

Actual data

2011

202.2

204.58

Year

2012

2015

2020

BAU Middle scenario Ideal scenario

212.8 208 187.6

263.4 240.2 216

376 305.3 275.4

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B. Lin, H. Long / Energy Policy 73 (2014) 93–102

305.3 Mtce in 2020 while those of the ideal scenario are 216.7 Mtce in 2015 and 275.4 Mtce in 2020. Based on Fig. 7 and Table 7, we estimate the amount of fossil fuel saving under the middle scenario and the ideal scenario. From Fig. 8 and Table 8, the amount of fossil fuel saving under the ideal scenario is more than that under the middle scenario. Specifically, the amount of fossil fuel saving under the ideal scenario is predicted to be 100.5 Mtce in 2020 and that under middle scenario is predicted to be 70.6 Mtce in 2020. From Fig. 8 and Table 8, we find that the chemical industry has a huge energy saving potential. However, the potential will not be achieved without incentives provided by the government. In other words, energy pricing must be reformed and made to be marketoriented. Based on the energy structure in 2011 and energy saving, we can evaluate the emissions reduction, as shown in Table 9. Table 9 shows that the emissions reduction in 2020 would be 172 million tons under the middle scenario and 245 million tons under the ideal scenario. The carbon emissions of Portugal showed in Table 10 almost equal the reduction in emissions in 2015 under the middle scenario. The reduction in 2020 is more than the carbon emissions in Kazakhstan. As shown in Table 10, the emissions in Czech Republic are a bit larger than the emissions reductions in 2015 under the ideal scenario. The emissions reductions in 2020 are a little less than the carbon emissions in The Netherlands. 120 100 80 Ideal

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Middle 40 20 0 2012 2013 2014 2015 2016 2017 2018 2019 2020 Fig. 8. The number of fossil fuel saving in the future.

Table 8 The number of fossil fuel saving (Mtce). Year Middle scenario Ideal scenario

The number of fossil fuel saving

2012

2015

2020

4.9 25.2

23.3 46.8

70.6 100.5

Table 9 The number of emission reduction (million tons). Year

2012

2015

2020

Middle scenario Ideal scenario

11.94 61.41

56.78 114.04

172.04 244.90

Table 10 Carbon emissions in two countries. Million tons

Portugal

Kazakhstan

Czech Republic

The Netherlands

2011

56.6

170.3

121.6

265.0

4. Conclusions and policy implications The chemical industry, which is the second largest energy intensive sector in China, has developed rapidly in the past 30 years. At the same time, it has resulted in excessive use of fossil fuel, which increased from 52.5 Mtce (million tons coal equivalent) in 1981 to 204.6 Mtce in 2011. The total level of fossil fuel consumption in the sector in 2010 almost equals the primary energy consumption in Spain or Italy. This paper applies the index decomposition method, the EG two-step co-integration and the ECM to study the driving factors of fossil fuel demand, and test their long and short-term relationships. First, we employ the LMDI method to analyze the total driving factors and conclude that the total factors were divided into positive driving factors (labor productivity effect and sector scale effect) and negative driving factors (energy intensity effect and energy structure effect). Labor productivity effect and energy intensity effect were the main factors affecting fossil fuel demand. Second, we adjust some variables and test for unit root in six variables (fossil fuel consumption, energy efficiency, labor productivity, energy structure, energy price and the total value of output) and they are all integrated of order one. Then, we use the EG co-integration method to test the cointegration relationship among the six variables and obtain the cointegration equation, which indicates that there exists a long-term equilibrium relationship. In this equation, the coefficient of all the variables on the right-hand side matches China’s economic situation. The coefficients of ln EE and ln P are negative, and coefficients of the rest variables are positive. The coefficient of ln ES is the largest and positive and the coefficient value of ln P is not only in line with economic theory in Eq. (5) but also fits the real situation in China. Fourth, the change in ln E is determined by the long-run equilibrium and the short-term fluctuation. We adopt error correction model to study the short-term fluctuations, and obtain the result after deleting the non-significant variable and intercept. Lastly, we measure the amount of fossil fuel that could be conserved in the China’s chemical process industry in the future. We predict that fossil fuel demand would be 263.4 Mtce in 2015 and 376 Mtce in 2020 under the BAU, 240 Mtce in 2015 and 305.3 Mtce in 2020 under the middle scenario, and 216 Mtce in 2015 and 275.4 Mtce in 2020 under the ideal scenario. Under the middle scenario, energy saving potential would be 23.3 Mtce in 2015 and 70.6 Mtce in 2020, and the corresponding emissions reductions would be 56.8 million tons and 172 million tons in the respective years. Under the ideal scenario, energy saving potentials would be 46.8 Mtce in 2015 and 100.5 Mtce in 2020, and the corresponding emissions reductions would be 114 million tons and 245 million tons in the respective years. The chemical industry has a huge energy saving potential, but this cannot be achieved without incentives from the government. In other words, energy pricing must be reformed and made more market-oriented. The coefficient of ln EE is the smallest negative value, which implies that energy efficiency is the most effective measure to save fossil fuel. Introducing advanced technology is the most important means to increase efficiency. First, compared with developed countries, China’s chemical process technology has a huge gap. There is need to attract foreign capital and equipment to improve the technology level. Second, government may stimulate enterprises to research and develop new technology and equipment using financial policy implements such as low interest or no interest loans and R&D subsidies. Third, the government could also encourage chemical factories to cooperate with universities in the area of research.

B. Lin, H. Long / Energy Policy 73 (2014) 93–102

Technology is necessary for energy saving, but rebound effect shows that it is insufficient, and needs to be complemented with price reforms. From Eq. (5), although the coefficient of ln P is negative, it is very small. Thus, energy price plays a limited role in energy saving. The main reason is that it is controlled and regulated by the government and does not reflect supply and demand and the scarcity of resources. Compared with the energy price of other countries, the price of coal, electricity and gas in China are relatively lower. Thus, the energy price promotes overuse of energy and inefficiency. Therefore, it is extremely urgent and significant to set up a transparent and reasonable energy pricing mechanism. 1) Coal: the central government liberalized the key contract coal price and canceled the annual coordination meeting for coal suppliers, users and transporters this year. This indicates that the price of coal would reflect market supply and demand. 2) Product oil: At present, the factory-gate prices of oil products are fixed by the government. Although the price of oil products is adjusted based on the international oil price, the adjustment time always lags behind, and the adjustment ranges are always less than the fluctuation ranges. The key to reform the current pricing mechanism is to reduce the adjustment period. Theoretically, if price is adjusted daily and the adjustment range is reasonable, market pricing will be achieved. 3) Gas: the reform of gas pricing is still ongoing. China’s gas market is always far from the international market, and the self-sufficient model of gas leads to the isolated gas pricing mechanism in China. The price of gas is divided into the factory-gated price, the pipeline transmission price and the city gas distribution price. The factorygated price is a national uniform pricing using cost plus method, which is supervised by government. The pipeline transmission price employs the government-guided price. The city gas distribution price is set by the local government. This pricing mechanism does not reflect resource and environmental cost, and gas price is relatively low. Moreover, gas price has a positive correlation with oil price. With more imported gas, the government has to establish a fair and effective pricing mechanism. Although the government would substitute the netback pricing for cost plus pricing, gas pricing reform requires two steps in the long run. On the supply side, the government should separate pipeline transmission and distribution and enables the factory-gated price and the pipeline transmission price to freely fluctuate. On the demand side, allow the price to vary in different provinces using the weighted average method. Last year, China conducted an industrial gas pricing reform. The residential gas pricing need to catch up with the reform process and multi-tier pricing is a relatively effective measure. Considering the relatively low income, the benchmark tier could cover majority of the residents in China. 4) Because the electricity market influences the coal market indirectly, it is necessary to analyze the reform on the electricity market. The reform on electricity market basically implies power price reform. The goal of power price reform is to set up a transparent and reasonable pricing mechanism, which allows power price to fluctuate within a certain range. This can promote the development of electricity market and optimize power investment. Power price must reflect the cost of generation and stimulate consumers to conserve electricity. A transparent and reasonable pricing mechanism can display the structure of power price and reflect the cost of supply and subsidy.

Acknowledgements The paper is supported by Newhuadu Business School Research Fund, Ministry of Education (Grant No. 10BG013), National Social

101

Science Foundation of China (Grant No. 71203186), and the National Science Foundation for Distinguished Young Scholars of China (Grant No. 71203187), National Natural Science Foundation of China (Grant No. 71173170).

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