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Energy efficiency and conservation in China's chemical fiber industry
Journal of Cleaner Production xxx (2014) 1e8
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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Energy efficiency and conservation in China's chemical fiber industry Boqiang Lin a, b, *, Hongli Zhao c a
New Huadu Business School, Minjiang University, Fuzhou 350108, China Collaborative Innovation Center for Energy Economics and Energy Policy, Institute for Studies in Energy Policy, Xiamen University, Xiamen, Fujian 361005, PR China c School of Energy Research, Xiamen University, Xiamen 361005, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 30 March 2014 Received in revised form 1 June 2014 Accepted 25 June 2014 Available online xxx
China is currently experiencing rapid economic growth as a result of industrialization and urbanization. In the next 20 years, China's energy demand will increase substantially, and energy conservation will become imperative for the country's low carbon economic transformation. Since 1998, China's chemical fiber production has ranked first in the world. In 2011, China's chemical fiber production accounted for about 70% of the world's total output. Therefore, energy saving is important for China's chemical fiber industry, and can provide immense benefits, and hence the need for thorough and intense research. In this paper, the co-integration method is used to test the relationship between energy consumption and its influencing factors such as GDP, energy prices, population and R&D expenditures. The results show that there is a long-term relationship between the variables. Based on this, we estimate energy demand and predict the energy-saving potential for the industry under different scenarios. We apply the Monte Carlo simulation method to verify the reliability of the prediction. Scenario analysis is adopted to predict energy demand and energy saving in the industry under different scenarios. Finally, we propose some policy suggestions on energy saving based on the research. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Energy saving potential Monte Carlo simulation Scenario analysis approach Chemical fiber industry
1. Introduction Oil, natural gas and other low-molecular weight raw materials are used to synthesize polymers through chemical addition or condensation reactions. The polymers may then be spun into synthetic fibers that are further processed. According to the “China Statistical Yearbook”, the chemical industry mainly consists of two subsectors: cellulose fiber material production and synthetic fiber manufacturing. Chemical fiber production is one of the pillars of China's industry and it plays an important role in creating employment, promoting exports, and increasing farmers' income. China's chemical fiber production has a clear global competitive advantage. Since 1998, China has become the world's largest producer of chemical fiber products (China's Textile Industry Report, 2010a). China's chemical fiber industry has grown rapidly with economic development. As seen in Fig. 1, her share of the world's total chemical fiber output increased from 23.6% in 2002 to 70% in 2012 (China's Textile Industry Report, 2010b), indicating an average annual growth rate of 15%. The demand for chemical fiber products
* Corresponding author. New Huadu Business School, Minjiang University, Fuzhou 350108, China. Tel.: þ86 (0)5922186076; fax: þ86 (0)5922186075. E-mail addresses: [email protected], [email protected] (B. Lin).
will increase in proportion with the increase in the world's population. In the long-run, China's chemical fiber industry has strong international competitiveness. As a resource-intensive industry, the chemical fiber industry consumes 4.3% of total energy consumption and discharges 10% of total wastewater. Therefore, energy utilization and conservation in the industry has great influence on the promotion of overall industrial energy conservation. Currently, most chemical fiber enterprises under-invest in energy conservation, resulting in the low adoption rate of advanced technology and equipment. In addition, the enterprises lack the intrinsic motivation to eliminate backward production capacity and reduce emissions. Besides, their pollution control facilities are obsolete. Therefore, it is difficult to complete the task of energy saving in the chemical fiber industry, and we believe that there is a great opportunity to improve energy saving within the industry. This paper is divided into the following sections: 1) the introduction; 2) literature review; 3) description of the research methods, data, variable indicators, selected data sources and processing methods; 4) results of the co-integration method and stability test; 5) prediction of future energy demands and the energy-saving potential of the industry under different energysaving scenarios; and 6) conclusion and policy recommendations for energy-saving in the industry.
http://dx.doi.org/10.1016/j.jclepro.2014.06.070 0959-6526/© 2014 Elsevier Ltd. All rights reserved.
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Fig. 1. Chemical fiber output in China compared with total world output. Source: China's textile industry development report.
2. Literature review 2.1. Research on energy demand forecast Due to increasing global energy constraints, research into energy demand prediction has attracted wide attention. Silberglitt Richard et al. (2003) used the scenario analysis method to study energy demand and predict future demand for the United States. Investigations into energy demand forecasting are mainly based on econometric and scenario analysis methods. Lin (2003) analyzed the influencing factors of power demand in China and the results showed that power demand, structural changes and efficiency improvements were negatively correlated. Liang (2004) analyzed various socio-economic factors of energy demand and intensity, established a scenario model using a layered energy fuels overlay method and obtained the energy demand under different scenarios. Lin (2005) analyzed and forecasted energy consumption in the Yangtze River Delta region in 2005, 2010 and 2020. Hu (2005) predicted the energy demand in the tertiary industry and electricity demand in Shanghai in 2010 and 2020. Liu (2012) (Liu, 2011) employed scenario analysis to forecast energy demand and carbon emissions in China from 2011 to 2020. The study reflected the trends in energy demand and carbon emissions in China's developed cities and counties. Lin (2013) (Lin and Zhang, 2013) (Lin and Moubarak, 2014) applied the co-integration method to estimate the long-term equilibrium determinants of primary energy demand in China, and predicted its magnitude in the process of urbanization.
2.2. Study on energy-saving potential Currently, research on energy-saving potential mainly focuses on qualitative analysis, while quantitative analysis is rare. Of the few studies that use quantitative analysis, majority are restricted to only one or two aspects of energy consumption, energy efficiency and pollution emissions. There is lack of comprehensive and systemic quantitative analysis on energy consumption. Relevant research on energy-saving potential includes works by Shi (2006) and Lu (2006), which analyzed the energy-saving potential of each province on the condition of energy convergence in different regions in China. Cha et al. (2010) (Cha, 2010) analyzed energy efficiency differences in 28 provinces during 1995e2005 period using an absolute difference method. Zheng (2011) employed a method of cross-provincial comparison and longitudinal structure evolution to analyze energy-saving potential in Hebei Province, and estimated its future energy-saving potential. Huang (2011) adopted the scenario analysis method to analyze the energy-saving potential of the transportation industry in Guangdong Province under different policy choices, and provided energy-saving suggestions
for the transport department in the province. Gu (2012) applied a quantitative method to analyze energy-conservation potential and its technical cost in China's cement industry, and proposed corresponding measures and policy recommendations. Based on qualitative analysis, Ye (2010) (Ye, 2010) pointed out that energy saving and emission reduction were the most urgent requirements, and that there was great potential for energy saving in China's chemical fiber industry. He (2011) constructed an excessive energy input stochastic frontier model to analyze energy-saving potential and its influencing factors in the industrial sector. He found that efficiency improvement in heavy industry has the greatest potential for saving energy. In summary, scholars have conducted many studies on China's energy demand and energy-saving potential (Lin et al., 2012a) (Lin et al., 2012b) (Lin and Xie, 2013), but research into energy demand in the chemical fiber industry is scarce. The available studies are based only on qualitative analysis. Therefore, we used the cointegration model to analyze the long-term relationship between energy demand and variables like GDP and energy prices in China's chemical fiber industry, and further forecast the future energy demand in the industry under different scenarios. Finally, we analyzed energy-saving potential in China's chemical fiber industry under different scenarios based on previous findings. 3. Data and research methods 3.1. Data description The objective of this paper is to examine the long-term relationship between energy consumption and certain influencing variables in China's chemical fiber industry using the co-integration method, as well as predict energy consumption and energy-saving potential in the industry. This paper chooses the following indices as explanatory variables: (1) the retail price index of fuel; (2) GDP; (3) R&D expenditures; and (4) population. Obviously, there are many other factors that influence energy demand in the industry. However, we did not consider other variables as key factors in this study for two reasons. The first is the complexity of China's chemical fiber industry and the lack of data. The second is based on the fact that we are guided by the demand theory and economic implications. 3.1.1. Energy demand in the chemical fiber industry (E) This article takes energy consumption (unit: Mtce) in the chemical fiber industry as the total energy demand in this industry. It is obtained from the “China Energy Statistical Yearbook” (China Energy Statistical, 1990e2011). 3.1.2. Retail price index of fuel (P) According to the definition of demand function, price is an important influencing factor of demand. However, because most energy prices are set by the government, they are generally low and fail to reflect the scarcity of resources. This article adopts the retail price index of fuel as the proxy for energy prices. The retail price index is obtained from the “China Statistical Yearbook” (China Statistical Yearboo, 1990e2011), and is uniformly converted to the fixed-base price index (1990 ¼ 100). 3.1.3. Gross domestic product (GDP) Energy is essential for the society and its main consumers are residents and enterprises. Therefore, the income level of consumers is an important factor affecting energy demand. In this paper, we use GDP which is an important variable affecting energy consumption in the industry to represent consumers' income level. In fact, a number of papers have confirmed that GDP is the most
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important determinant of energy consumption. Kraft J and Kraft A (Kraft and Kraft, 1978) were the first researchers to discover the one-way causal relationship between GDP and energy consumption during the period 1947e1974 in the United States. Apergis et al. (2009) used the panel co-integration and error correction model to test the relationship between energy consumption and economic growth in six Central American countries and concluded that there was a two-way causal relationship between them. In this paper, the GDP data is taken from the “China Statistical Yearbook” and is converted to constant price in 1990. 3.1.4. Population (POP) Consumer population should be taken into account when considering commodity demand. Therefore, population should also be considered when analyzing energy demand in the chemical fiber industry. In this paper, the POP data is obtained from the “China Statistical Yearbook”. 3.1.5. R&D expenditure (RD) Technical progress can improve the operating efficiency of equipment and directly lower energy dissipation per unit of product, thereby reducing energy consumption. Moreover, science and technology can be used to develop new energy sources and exploit high quality energy, which can promote fundamental changes in the energy demand structure. In this paper, the index of R&D expenditure is used to reflect technological progress. In fact, domestic studies have already confirmed this proposition. Liu et al. (2012) employed panel data to conduct empirical analysis on the energy consumption intensity changes of 29 industrial sectors (Liu, 2012). The results show that an increase in R&D expenditure contributes to improving industrial energy efficiency, and consequently reduces industrial energy consumption accordingly. In this paper, the R&D expenditures data is taken from the “China Statistical Yearbook” and the CEIC China Database (China Database and Avai, 2013). To eliminate heteroscedasticity, we take the logarithm of each arbitrary variable, represented by LnX. 3.2. Research methods 3.2.1. Co-integration method Since Engle and Granger (1987) proposed the co-integration method (Engle and Granger, 1987), many scholars have used it to analyze the factors influencing energy demand. However, time series economic variables are often unstable, and a stationary linear combination of economic variables indicates the existence of a long-term equilibrium co-integration relationship. Therefore, we first conduct a stationary test before using the co-integration method. The most widely used methods are the ADF unit root test and the PP test. The ADF unit root test is based on the following OLS regression:
DXt ¼ a þ bt þ dXt1 þ
m X
bi DXti þ εt
i¼1
where Xt is a variable of period t;
stable. In this paper, we adopt the ADF test (Dickey and W. Fuller, 1979) and the pp test (Phillips and Perron, 1988). If all series are integrated in the same order, we can verify the existence of co-integration. The most widely used co-integration tests are the two-step method created by Engle and Granger (EG) and the Johansen method. The EG two-step method can be adopted in a co-integration test with a single equation. The co-integration method has advantages. It enables researchers to study the long-term relationship between energy demand and its determinants. However, it also has a disadvantage. The tests for the existence of unit roots and co-integrated series cannot be considered definitive. In this paper, we use the cointegration model for two reasons. First, during the study period, the relationship between the sectoral energy demand and its determinants is constant. Second, it is easy to illustrate the output of the model. Because we use multiple variables in the study, we employ the Johansen co-integration method to study the relationships between the variables. 3.2.2. Monte Carlo simulation The Monte Carlo method is based on the probability characteristics of the object itself, and can be applied in stochastic simulation and statistical experiment. There is need to select a large random sample for the random variables, calculate the corresponding statistics or parameters according to the sampling data, and simulate their real distribution. The basic idea of this method is to build a probabilistic model, generate a series of random numbers to conduct simulation, and carry out the statistical process. 3.2.3. Scenario analysis As one of the new prediction methods, scenario analysis focuses on uncertainties and combines qualitative and quantitative analysis. It recognizes future developments as diverse; i.e. there are multiple possible trends. The predicted results are multidimensional. The essence of scenario analysis is to identify all possible development trends under different situations. The results comprise three parts: the recognition of possible future development trends, the characteristics of each situation, and the likelihood of occurrence as well as the situation described in the process of path analysis. 4. Co-integration test and corresponding results 4.1. Unit root test As is shown in Table 1, the ADF and PP test indicate that all the five variables are integrated of the same order, satisfying the requirement of co-integration. The first test is to determine the rank and test the number of linearly independent co-integration vectors (Grover and et al., 1998). The rank test results are as follows.
Table 1 Unit root test of variables. Sequence
DXti ¼ Xti Xti1 ; i ¼ 1; 2; :::m; where m is the lag order. The null hypothesis isH0:d ¼ 0; i.e., there is a unit root, and time series are unstable. The alternative hypothesis isH0:d ¼ 0; i.e., there is no unit root, and time series are stable. If the test statistic is less than the critical value, then the null hypothesis should be rejected and the time series is stable. Otherwise, the time sequence is not
3
DLnE DLnGDP DLnpop DLnp DLnRD
ADF
PP
There is no trend
There are trends
There is no trend
There are trends
5.609183*** 3.366495*** 3.229815*** 6.732546*** 7.024622***
5.405798*** 4.170811*** 2.449634** 6.475657*** 6.812935***
5.361226* 3.831511** 2.655194** 5.702290* 5.680036*
5.612675* 4.532598** 3.787302** 4.787302* 4.530618*
Note: *, ** and *** represent the significance level of 10%, 5% and 1%, respectively, under which the null hypothesis is rejected.
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4.2. JohanseneJuselius co-integration rank test
Table 3 VAR lag order selection criteria.
The unrestricted co-integration rank test shows that the null hypothesis of co-integration rank 0 could be rejected under 5% confidence level (110.9872 > 69.81889). There are four uncorrelated linear co-integration vectors, which are marked with asterisks in Table 2. The largest eigenvalue test also shows that we could refuse “the original hypothesis of co-integration rank 0” at 5% confidence level (51.01459 > 33.87687) and there are two co-integration equations. Since the rank test results indicate the existence of linearly independent co-integrating vectors, we could conduct the cointegration analysis. We first established an unrestricted VAR model that consisted of LnE, LnGDP, LnPOP, LnRD, and LnP, and then examined the VAR lag intervals that correspond to the system. The test results are shown below. 4.3. VAR model lag order selection According to the criteria of LogL, LR, FPE, AIC, SC and HQ standard in Table 3, we chose the lag periods of two. Table 4 presents the Johansen co-integration test results based on our assumption. 4.4. Co-integration model results According to the standardized coefficients of the co-integration vector, we establish a corresponding co-integration equation as follows:
LNE ¼ 1:023704LNGDP 0:73119LNP þ 0:429200LNPOP ð0:0457Þ
ð0:12137Þ
ð0:08546Þ
0:756898LNRD ð0:12444Þ
(1) First, Equation (1) indicates that there exists a long-term equilibrium relationship among the variables from 1990 to 2011. Second, the positive coefficients of LNGDP and LNPOP, and the negative coefficients of LNP and LNRD are in accordance with economic theory. Third, with the improvement in people's living standard, GDP growth and high industrial productivity, chemical fiber consumption will increase, and this will stimulate energy consumption in the industrial sector. The elasticity coefficients show that a 1% increase in GDP will result in a 1.024% increase in the energy consumption of the chemical fiber industry. The high elastic coefficient Table 2 Johansen co-integration test. Hypothesized no. of CE(s)
Eigenvalue
Trace statistic
0.05 Critical value
Prob.**
None* At most 1* At most 2* At most 3 At most 4* Hypothesized no. of CE(s)
0.931777 0.790627 0.631148 0.316340 0.193590 Eigenvalue
110.9872 59.97261 30.26352 11.31369 4.088105 Max-Eigen statistic
69.81889 47.85613 29.79707 15.49471 3.841466 0.05 Critical value
0.0000 0.0024 0.0442 0.1929 0.0432 Prob.**
None* At most At most At most At most
0.931777 0.790627 0.631148 0.316340 0.193590
51.01459 29.70909 18.94982 7.225589 4.088105
33.87687 27.58434 21.13162 14.26460 3.841466
0.0002 0.0263 0.0983 0.4628 0.0432
1* 2 3 4*
Trend: trend. Number of obs ¼ 20. Sample: 1992e2011. Lags ¼ 1.
Lag LNL 0 1 2
LR
FPE
AIC
SC
HQ
108.1131 NA 1.33e-11 10.85401 10.60548 10.81195 262.0648 210.6707* 1.89e-17 24.42787 22.93665* 24.17550 298.0850 30.33277 1.20e-17* 25.58789* 22.85399 25.12521*
Note: * indicates lag order selected by the criterion. Sample: 1990e2011. Number of obs ¼ 20.
of GDP indicates that China's rapid economic growth in the last 30 years is the main driving force of the rapid energy demand growth in the industrial sector. Fourth, because the raw materials of chemical products are mostly derived from oil, the fluctuations in energy prices have a direct impact on the chemical fiber market. Compared with the population variable, the elasticity of energy prices is relatively high, which indicates that energy price is a major cause of energy consumption changes in China's chemical fiber industry. The adjustment of energy prices will have a significant impact on energy consumption in the industry. Fifth, compared with other variables, the elasticity coefficient of population is relatively small. The main reason for this is that China has implemented the one-child policy for a long time, leading to a low population growth rate. Consequently, the influence of population on energy consumption in the industry is relatively small. Sixth, the chemical fiber industry is an energy-intensive, largescale and mechanized production industry. The more advanced equipment is used, the higher production efficiency is. Similarly, the lower the specific energy consumption is, the higher the added value of products will be. Technological progress is a key factor in improving energy-saving potential in the chemical fiber industry. The elasticity coefficients show that a 1% increase in R&D expenditures will lead to a 0.76% decline in energy consumption in the industry. It also shows that increases in R&D expenditure can lead to technical advancement, and thus encourage the chemical industry to reduce energy consumption. In summary, we believe that the model results are consistent with existing economic theory, and can reasonably explain the reality of China's economy. The result of the model stability test is presented below. Fig. 2 4.5. Stability test As shown above, all the eigenvalues of the matrix are less than one. There is no characteristic root falling out of the unit circle. The results of the stationary test show that the model satisfies stability condition. 5. Analysis and forecast of energy demand and energy-saving potential in China's chemical fiber industry 5.1. Energy demand forecast of China's chemical fiber industry In this paper, we select annual average growth rate of the variables from 1990 to 2011 as the BAU scenario. Assuming that each Table 4 Johansen co-integration test. LNE
LNGDP
LNP
LNPOP
LNRD
1.000000
1.023704 (0.0457)
0.73119 (0.12137)
0.429200 (0.08546)
0.756898 (0.12444)
Co-integrating Equation(s). Log likelihood: 286.0688. Normalized co-integrating coefficients (standard error in parentheses).
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Based on the Monte Carlo simulation analysis, we produced a distribution histogram, which fits energy consumption in China's chemical fiber industry in 2020 in Fig. 3. According to Fig. 3, the highest probability is that energy consumption in China's chemical fiber industry will fall within 44 and 50 Mtce in 2020. In the preceding part of this paper, we predict that energy consumption in the industry will reach 48.59228 Mtce in 2020, based on the explanatory variables and the historical change tendency. The simulation result verifies our prediction. As shown in Fig. 4, if the various influencing factors of energy consumption in China's chemical fiber industry maintain the same growth trend, the probability that energy consumption in China's chemical fiber industry will be lower than 86 Mtce in 2020 is 100%, and the probability that it will be less than 6 Mtce tends to 0%. In the following part, we use the scenario analysis method to study the effect of energy-saving policy on energy consumption in China's chemical fiber industry, and estimate the corresponding energy saving potential.
Inverse Roots of AR Characteristic Polynomial 1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 -1.5
-1.0
-0.5
0.0
0.5
1.0
5
1.5
Fig. 2. Roots of characteristic polynomial.
variable (2012e2020) will grow at this average rate, we predict China's energy consumption in the chemical fiber industry under the BAU scenario. Under the BAU scenario, the growth rates of China's gross domestic product (GDP), population (POP), fuel retail price index (P) and R&D expenditure (RD) are 9.8%, 1.2%, 8.6% and 16.23% respectively. According to the annual average growth rate of each variable and the co-integration equation, we predict that energy consumption in China's chemical fiber industry will reach 27.5183 Mtce in 2015 and 48.59228 Mtce in 2020. The above result is based on the growth rates of the explanatory variables. However, future influencing factors are uncertain. There are various possibilities, and a more reasonable forecast should have multiple results. Therefore, we employ the Monte Carlo simulation and use the year 2020 as an example. We analyze the highest possible energy consumption in the industry in that year and its probability. The key of Monte Carlo model is in accordance with the distribution of random sampling. According to experience, economic variables follow normal distribution. To determine the distribution of risk variables, we use Matlab software to conduct the distribution test of the growth rate of each explanatory variable during 1990e2011. The test results show that the variables follow normal distribution at 5% confidence levels. Table 5 shows the statistical characteristics of the growth rate for each variable. According to the mean value and standard deviation of each variable, we can get the specific normal distribution that each risk variable should follow. Based on this, we used Matlab software to generate the random values of the risk variables that follow the normal distribution. These values are regarded as the growth rates of the explanatory variables. We obtained 3000 possible values of the explained variables, which were calculated from the abovementioned random values. Thus, the probability distribution of energy consumption in China's chemical fiber industry in 2020 can be obtained.
5.2. Energy-saving potential To predict the energy saving potential, we apply the above standard to set two energy-saving scenarios: the moderate and advanced energy-saving scenarios. The advanced scenario is a situation whereby under the restriction of economic reality, each affecting factor leads to the most active energy-saving effects. The moderate scenario is regarded as intermediate between the BAU and the advanced scenario, based on the reality of the economic development plan. In terms of the growth rate of GDP, the National Economic and Social Development Twelfth Five-Year Plan of the People's Republic of China in 2011 predicted that GDP would grow at an annual rate of 7% in the next 5 years. In this paper, we use 7% as the growth rate of GDP in the advanced scenario. Accordingly, the annual average growth rate of GDP in the moderate scenario is set at 8.3%. Considering the growth rate of POP, the National Economic and Social Development in the Twelfth Five-Year Plan of the People's Republic of China in 2011 predicted that China would maintain a stable fertility level and limit the annual natural population growth rate to within 0.72%. Based on these conditions, we set the annual average population growth rate at 0.6% in the advanced scenario and at 0.8% in the moderate scenario. Technological research and innovation play an important role in improving production efficiency and energy-saving capacity in China's chemical fiber industry. The National Economic and Social Development in the Twelfth Five-Year Plan of the People's Republic of China in 2011 predicted that the level of education in science and technology would improve substantially. R&D expenditure should account for 2.2% of the GDP. Based on the above conditions, we set R&D expenditure growth rate at 18% in the advanced scenario and at 17.11% in the moderate scenario.
Table 5 Statistical characteristics of growth rate for each variable. Variable
Mean
Std. Dev.
d. d. d. d.
. . . .
. . . .
LNGDP LNPOP LNP LNRD
0.0563005 0.0743861 0.042611 0.0421807
0.0137112 0.05022 0.0528586 0.0914857
Fig. 3. Probability distribution histogram of energy consumption in China's chemical fiber industry in 2020 (unit: Mtce).
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B. Lin, H. Zhao / Journal of Cleaner Production xxx (2014) 1e8 Table 7 Forecast of energy consumption in China's chemical fiber industry in 2015 and 2020 (Mtce).
Fig. 4. Cumulative probability distribution of energy consumption in the chemical fiber industry in 2020 (unit:Mtce).
Energy prices can influence energy consumption directly. However, most energy prices in China are undervalued and fail to reflect the scarcity of resources (Liao and Wei, 2010). Based on this, we set the annual average growth rate of energy prices at 10% in the advanced scenario and at 9.3% in the moderate scenario. According to the growth rate of each variable under the different scenarios in Table 6, we predict the energy consumption under the three scenarios in China's chemical fiber industry in 2015 and 2020, and the results are shown in Table 7. From Table 7, it can be seen that under the moderate scenario, energy consumption in China's chemical fiber industry in 2015 will be 20.82479 Mtce, which is 6.69354 Mtce lower than in the BAU scenario. The reduced energy consumption accounts for 24.32% of total energy demand in the BAU scenario. The energy consumption in China's chemical fiber industry will be 35.0284 Mtce in 2020, which is 13.5638 Mtce lower than in the BAU scenario. The reduced energy consumption accounts for 27.91% of total energy consumption in the BAU scenario. From Table 7, it is clear that in the advanced scenario, energy consumption in China's chemical fiber industry in 2015 will be 17.9821 Mtce, which is 9.5362 Mtce lower than in the BAU scenario. The reduced energy consumption makes up 34.65% of total energy demand in the BAU scenario. Similarly, energy consumption will be 29.789 Mtce in 2020, which is 18.8032 Mtce lower than in the BAU scenario. The reduced energy consumption makes up 38.69% of total energy demand in the BAU scenario. When comparing the moderate scenario with the advanced scenario, it is seen that the reduction in energy consumption in China's chemical fiber industry is higher in the latter scenario. This is also the same with its ratio to total energy demand in the BAU scenario within the same time interval. The above comparison shows that there is potential for energy consumption reduction in China's chemical fiber industry. Energy conservation policies under the moderate and advanced scenarios would be highly effective. There is much room for improvement in the energy-saving capacity of the industry. To check whether the energy-saving policy is effective, we compare energy consumption growth rates under the three scenarios with the corresponding GDP growth rates. This paper defines the ratio of energy consumption growth rate to GDP growth rate as the energy consumption index; i.e. growth rate of GDP in energy
Year
2015
2020
BAU scenario Moderate scenario Advanced scenario
27.51833 20.82479 17.98213
48.59228 35.02842 29.78906
consumption index ¼ the growth rate of energy consumption in China's chemical fiber industry/the growth rate of GDP. Table 8 shows that under the moderate scenario, with the slowdown of GDP growth and the transformation of economic development pattern, a combination of various elements has an effective inhibition effect on energy demand in the industry. The annual average growth rate of energy consumption in the industry is 3.4% lower than that of GDP. Under the advanced scenario, owing to the implementation of various policies and technical improvements, the annual average growth rate of energy consumption in the chemical fiber industry is 4% lower than that of GDP. Generally, we can see from Table 8 that as the economy develops, the growth rate of energy consumption in the industry will fall under both the moderate scenario and the advanced energy conservation scenario. This will consequently reduce the GDP growth rate in energy consumption index. Therefore, we can conclude that the implementation of energy saving measures is effective, and the improvement of energy saving-potential is also feasible. After studying the energy-saving potential of China's chemical fiber industry, the following part of the paper analyzes the specific amount of energy-saving potential in the industry and its influence on total national energy demand. To predict the amount of energy conservation, we establish two energy saving scenarios. Scenario I indicates that energy consumption transits from the BAU scenario to the moderate scenario, and scenario II indicates that energy consumption transits from the moderate scenario to the advanced scenario. The two energy-saving scenarios are reasonable because the assumptions are mainly based on historical data and recent development trends of the variables, so they are consistent across variables; in line with the principle of scenarios design. According to the estimation of future energy consumption in China's chemical fiber industry, we calculate the amount of energy conservation under the two scenarios as well as their ratios to China's total energy demand. The results are shown in Table 9. From Table 9, it is seen that if the energy consumption pattern of China's chemical fiber industry transforms from the present situation to Scenario I, the amount of energy saving will be 13.56386 Mtce by 2020, accounting for 0.27% of China's total energy demand. If the energy consumption pattern transforms from the present situation to Scenario II, the energy saving potential will be 18.803 Mtce by 2020, accounting for 0.38% of China's total energy demand.
Table 8 Comparison of growths rates between energy consumption in China's chemical fiber industry and GDP.
Table 6 Growth rates of each explanatory variable under different scenarios. Variables
GDP P POP RD
BAU scenario (%)
Moderate scenario (%)
Advanced scenario (%)
9.8 8.6 1.2 16.23
8.3 9.3 0.8 17.11
7 10 0.6 18
Real data: 1990e2011 BAU scenario: 2013e2020 Moderate scenario: 2013e2020 Advanced scenario: 2013e2020
Average growth rate of energy consumption
Average growth rate of GDP
Growth rate of GDP in energy consumption index
7.50% 6.70% 4.60%
10.50% 9.80% 8%
0.714 0.68 0.5
3.50%
7.50%
0.46
Please cite this article in press as: Lin, B., Zhao, H., Energy efficiency and conservation in China's chemical fiber industry, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.06.070
B. Lin, H. Zhao / Journal of Cleaner Production xxx (2014) 1e8 Table 9 Energy saving amount in China's chemical fiber industry and its ratio in total national energy demand (unit: Mtce). Year
2020
Scenario I
Scenario II
Energy saving of chemical fiber industry
Ratio in total energy demand
Energy saving of chemical fiber industry
Ratio in total energy demand
13.56386
0.27%
18.803
0.38%
At present, China is implementing the Western development strategy, as well as strengthening the infrastructure in the countryside regions. With the development of the economy and the improvement in living standards, the industrial fiber materials market will experience rapid growth. Thus, the energy-saving potential of China's chemical fiber industry is not only tremendous, but also strategically significant.
6. Conclusions and policy implications In this paper, based on time series data from 1990 to 2011, we apply the co-integration model to estimate the factors influencing energy consumption in China's chemical fiber industry. The factors considered in the paper include China's GDP, population, energy price and R&D expenditure. We also predict the energy demand and energy-saving potential in the industry. Monte Carlo simulation analysis verifies the rationality of this model, and indicates that more positive and radical policies are needed to improve energy utilization efficiency and expand the energy-saving potential of the industry. The scenario analysis shows that inhibited by a variety of energy saving policies, the growth of energy consumption in the industry will be slower and the energy-saving potential will be considerable. The main conclusions of this paper are as follows. First, the co-integration equation shows that there is a longterm stable relationship between the selected variables during 1990e2011. As expected, GDP has the largest positive effect on energy consumption in the chemical fiber industry, followed by population. An increase in R&D expenditure and energy prices could reduce energy consumption in the industry. Second, the GDP elasticity coefficient is the largest among all the coefficients. It has the greatest influence on energy consumption in the chemical fiber industry, indicating that the rapid economic growth of China in the last 30 years is a major driving force for the rapid growth of energy demand in the industry. In summary, energy demand is rigid and increases synchronously with rapid economic development, which conforms to China's economic stage characteristics. Third, following GDP, the elastic coefficient of R&D expenditure is the second largest. R&D expenditure is also one of the most important factors influencing energy consumption in the chemical fiber industry, and it illustrates that technology improvement is the key to achieving the energy-saving objectives of the industry. To a large extent, energy saving in the industry can only be realized through technical progress. Fourth, compared with GDP and R&D expenditure, the elasticity of energy price ranks third, indicating that the energy price exerts a great impact on energy consumption in the chemical fiber industry. Therefore, to achieve the energy conservation objective of the industry, energy price is a crucial factor. Increase in energy price has an inhibitory effect on energy demand in the industry. At present, energy prices in China still do not fully reflect the scarcity of resources. Therefore, it is imperative for China to continue reform of energy prices.
7
Fifth, in the long-term equilibrium equation, the elastic coefficient of population is relatively small, which is attributable to the one-child policy that has made the annual average population growth rate relatively low for a long time. Thus, the influence of population on energy consumption in the industry is not significant. The goal of energy conservation and consumption reduction in the industry is not only dependent on technical issues, but also on many other factors such as price and technology level. Based on the conclusions, this paper provides the following suggestions on energy conservation policy in China's chemical fiber industry. First, besides the GDP, R&D expenditure has the greatest influence on energy consumption in the chemical fiber industry. This implies that R&D should be given more priority and attention by the government and the relevant departments concerned with energy conservation and consumption reduction. In other words, the government should increase R&D expenditure as well as improve technology and the capability for scientific innovation in the industry. In this way, energy conservation and sustainable development in the industry could be realized. Second, compared with GDP, the influence of energy price is relatively trivial, but the cumulative effect of price should not be ignored. Economic theory indicates that price reflects the scarcity of resources and has a direct impact on supply and demand. Faced with tight energy constraints, this paper holds that energy prices should be increased, and the efficiency of energy utilization should also be improved. A further reform of China's current energy pricing mechanism is imperative and should be urgently pursued. References Apergis, N., Payne, J.E., 2009. Energy consumption and economic growth in Central America: evidence from a panel co-integration and error correction model. Energy Econ. 31, 211e216. Cha, D.L., 2010. Study on the rebound effect of energy efficiency in China based on CGE model. J. Quant. Tech. Econ. 12. CEIC China Database, 2013. Available at: http://ceicdata.securities.com/cdmWeb/. China Energy Statistical Yearbook, 1990e2011. China Statistics Press, Beijing. China Statistical Yearbook, 1990e2011. China Statistics Press, Beijing. China's Textile Industry Report, 2010a. The Statistic Bureau of China. China's Textile Industry Report, 2010b. The Statistic Bureau of China. Dickey, D.A., W., Fuller A., 1979. Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74 (366), 427e431. Engle, R.F., Granger, C.W.J., 1987. Co-integrationanderrorcorrection:representation, estimationandtesting. Econometrica 55, 251e276. Silberglitt, Richard, Hove, Anders, Shulman, Peter, 2003. Analysis of US energy scenarios: meta-scenarios, pathways, and policy implications. Technol. Forecast. Soc. Change 70 (4), 297e315. Grover, V., et al., 1998. The influence of information technology diffusion and business process change on perceived productivity: the IS executive's perspective. Inf. Manag. 34 (3), 141e159. Gu, A.L., 2012. The Energy conservation and emissions reduction potential and cost analysis of China's cement industry. China Popul. Resour. Environ. 8. He, X.P., 2011. Energy saving potential and influence factors of China's industry. J. Financ. Res. 10. Hu, J.Y., 2005. Shanghai's Total Energy Demand and Long-term Forecasting. Shang Hai Electric Power, p. 6. Huang, Y., 2011. Guangdong transportation's energy saving potential research based on scenario analysis. China Open. Her. 4. Kraft, J., Kraft, A., 1978. On the relationship between Energy and GNP. J. Energy Dev. 3, 401e403. Liang, Q.M., 2004. A model for scenario analysis of energy demand and energy intensity in China and its application. China's J. Manag. 1. Liao, H., Wei, Y.M., 2010. China's energy consumption: a perspective from Divisia aggregation approach. Energy 35 (1), 28e34. Lin, B.Q., 2003. Structure change, efficiency improvement and energy demand forecasting. Econ. Res. 5. Lin, D.R., 2005. The coordinated development of economy and energy of the Yangtze river delta region. J. Nant. Univ. 2. Lin, B.Q., Moubarak, M., 2014. Estimation of energy saving potential in China's paper industry. Energy 65, 182e189. Lin, B.Q., Xie, C.P., 2013. Estimation on oil demand and oil saving potential of China's road transport sector. Energy Policy 61, 472e482.
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Lin, B.Q., Zhang, G.L., 2013. Estimates of electricity saving potential in Chinese nonferrous metals industry. Energy Policy 60, 558e568. Lin, B., Zhang, L., Wu, Y., 2012. Evaluation of electricity saving potential in China's chemical industry based on cointegration. Energy Policy 44 (0), 320e330. Lin, B., Wu, Y., Zhang, L., 2012. Electricity saving potential of the power generation industry in China. Energy 40 (1), 307e316. Liu, J.J., Li, S.L., Fan, H.J., Lin, Q., 2012. Scenario analysis method is applied to forecast energy demand and carbon emissions. Energy Conserv. Technol. 30 (1), 70e75. Lu, E.P., 2006. Combined model in China's energy demand forecasting. Math. Stat. Manag. 9.
Phillips, P.C.B., Perron, P., 1988. Testing for a unit root in time series regression. Biometrica 75 (2), 335e346. Shi, D., 2006. Analysis of regional difference of China's Energy efficiency and the energy saving potential. China Ind. Econ. 2. Ye, Y.M., 2010. Energy saving and emission reduction is the most urgent task in chemical fiber industry. Hi-Tech Fiber Appl. 6. Zheng, M.H., 2011. Energy consumption demand forecasting of Hebei province based on grey system theory. China Econ. Trade Her. 19.
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Contents lists available at ScienceDirect
Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Energy efficiency and conservation in China's chemical fiber industry Boqiang Lin a, b, *, Hongli Zhao c a
New Huadu Business School, Minjiang University, Fuzhou 350108, China Collaborative Innovation Center for Energy Economics and Energy Policy, Institute for Studies in Energy Policy, Xiamen University, Xiamen, Fujian 361005, PR China c School of Energy Research, Xiamen University, Xiamen 361005, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 30 March 2014 Received in revised form 1 June 2014 Accepted 25 June 2014 Available online xxx
China is currently experiencing rapid economic growth as a result of industrialization and urbanization. In the next 20 years, China's energy demand will increase substantially, and energy conservation will become imperative for the country's low carbon economic transformation. Since 1998, China's chemical fiber production has ranked first in the world. In 2011, China's chemical fiber production accounted for about 70% of the world's total output. Therefore, energy saving is important for China's chemical fiber industry, and can provide immense benefits, and hence the need for thorough and intense research. In this paper, the co-integration method is used to test the relationship between energy consumption and its influencing factors such as GDP, energy prices, population and R&D expenditures. The results show that there is a long-term relationship between the variables. Based on this, we estimate energy demand and predict the energy-saving potential for the industry under different scenarios. We apply the Monte Carlo simulation method to verify the reliability of the prediction. Scenario analysis is adopted to predict energy demand and energy saving in the industry under different scenarios. Finally, we propose some policy suggestions on energy saving based on the research. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Energy saving potential Monte Carlo simulation Scenario analysis approach Chemical fiber industry
1. Introduction Oil, natural gas and other low-molecular weight raw materials are used to synthesize polymers through chemical addition or condensation reactions. The polymers may then be spun into synthetic fibers that are further processed. According to the “China Statistical Yearbook”, the chemical industry mainly consists of two subsectors: cellulose fiber material production and synthetic fiber manufacturing. Chemical fiber production is one of the pillars of China's industry and it plays an important role in creating employment, promoting exports, and increasing farmers' income. China's chemical fiber production has a clear global competitive advantage. Since 1998, China has become the world's largest producer of chemical fiber products (China's Textile Industry Report, 2010a). China's chemical fiber industry has grown rapidly with economic development. As seen in Fig. 1, her share of the world's total chemical fiber output increased from 23.6% in 2002 to 70% in 2012 (China's Textile Industry Report, 2010b), indicating an average annual growth rate of 15%. The demand for chemical fiber products
* Corresponding author. New Huadu Business School, Minjiang University, Fuzhou 350108, China. Tel.: þ86 (0)5922186076; fax: þ86 (0)5922186075. E-mail addresses: [email protected], [email protected] (B. Lin).
will increase in proportion with the increase in the world's population. In the long-run, China's chemical fiber industry has strong international competitiveness. As a resource-intensive industry, the chemical fiber industry consumes 4.3% of total energy consumption and discharges 10% of total wastewater. Therefore, energy utilization and conservation in the industry has great influence on the promotion of overall industrial energy conservation. Currently, most chemical fiber enterprises under-invest in energy conservation, resulting in the low adoption rate of advanced technology and equipment. In addition, the enterprises lack the intrinsic motivation to eliminate backward production capacity and reduce emissions. Besides, their pollution control facilities are obsolete. Therefore, it is difficult to complete the task of energy saving in the chemical fiber industry, and we believe that there is a great opportunity to improve energy saving within the industry. This paper is divided into the following sections: 1) the introduction; 2) literature review; 3) description of the research methods, data, variable indicators, selected data sources and processing methods; 4) results of the co-integration method and stability test; 5) prediction of future energy demands and the energy-saving potential of the industry under different energysaving scenarios; and 6) conclusion and policy recommendations for energy-saving in the industry.
http://dx.doi.org/10.1016/j.jclepro.2014.06.070 0959-6526/© 2014 Elsevier Ltd. All rights reserved.
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Fig. 1. Chemical fiber output in China compared with total world output. Source: China's textile industry development report.
2. Literature review 2.1. Research on energy demand forecast Due to increasing global energy constraints, research into energy demand prediction has attracted wide attention. Silberglitt Richard et al. (2003) used the scenario analysis method to study energy demand and predict future demand for the United States. Investigations into energy demand forecasting are mainly based on econometric and scenario analysis methods. Lin (2003) analyzed the influencing factors of power demand in China and the results showed that power demand, structural changes and efficiency improvements were negatively correlated. Liang (2004) analyzed various socio-economic factors of energy demand and intensity, established a scenario model using a layered energy fuels overlay method and obtained the energy demand under different scenarios. Lin (2005) analyzed and forecasted energy consumption in the Yangtze River Delta region in 2005, 2010 and 2020. Hu (2005) predicted the energy demand in the tertiary industry and electricity demand in Shanghai in 2010 and 2020. Liu (2012) (Liu, 2011) employed scenario analysis to forecast energy demand and carbon emissions in China from 2011 to 2020. The study reflected the trends in energy demand and carbon emissions in China's developed cities and counties. Lin (2013) (Lin and Zhang, 2013) (Lin and Moubarak, 2014) applied the co-integration method to estimate the long-term equilibrium determinants of primary energy demand in China, and predicted its magnitude in the process of urbanization.
2.2. Study on energy-saving potential Currently, research on energy-saving potential mainly focuses on qualitative analysis, while quantitative analysis is rare. Of the few studies that use quantitative analysis, majority are restricted to only one or two aspects of energy consumption, energy efficiency and pollution emissions. There is lack of comprehensive and systemic quantitative analysis on energy consumption. Relevant research on energy-saving potential includes works by Shi (2006) and Lu (2006), which analyzed the energy-saving potential of each province on the condition of energy convergence in different regions in China. Cha et al. (2010) (Cha, 2010) analyzed energy efficiency differences in 28 provinces during 1995e2005 period using an absolute difference method. Zheng (2011) employed a method of cross-provincial comparison and longitudinal structure evolution to analyze energy-saving potential in Hebei Province, and estimated its future energy-saving potential. Huang (2011) adopted the scenario analysis method to analyze the energy-saving potential of the transportation industry in Guangdong Province under different policy choices, and provided energy-saving suggestions
for the transport department in the province. Gu (2012) applied a quantitative method to analyze energy-conservation potential and its technical cost in China's cement industry, and proposed corresponding measures and policy recommendations. Based on qualitative analysis, Ye (2010) (Ye, 2010) pointed out that energy saving and emission reduction were the most urgent requirements, and that there was great potential for energy saving in China's chemical fiber industry. He (2011) constructed an excessive energy input stochastic frontier model to analyze energy-saving potential and its influencing factors in the industrial sector. He found that efficiency improvement in heavy industry has the greatest potential for saving energy. In summary, scholars have conducted many studies on China's energy demand and energy-saving potential (Lin et al., 2012a) (Lin et al., 2012b) (Lin and Xie, 2013), but research into energy demand in the chemical fiber industry is scarce. The available studies are based only on qualitative analysis. Therefore, we used the cointegration model to analyze the long-term relationship between energy demand and variables like GDP and energy prices in China's chemical fiber industry, and further forecast the future energy demand in the industry under different scenarios. Finally, we analyzed energy-saving potential in China's chemical fiber industry under different scenarios based on previous findings. 3. Data and research methods 3.1. Data description The objective of this paper is to examine the long-term relationship between energy consumption and certain influencing variables in China's chemical fiber industry using the co-integration method, as well as predict energy consumption and energy-saving potential in the industry. This paper chooses the following indices as explanatory variables: (1) the retail price index of fuel; (2) GDP; (3) R&D expenditures; and (4) population. Obviously, there are many other factors that influence energy demand in the industry. However, we did not consider other variables as key factors in this study for two reasons. The first is the complexity of China's chemical fiber industry and the lack of data. The second is based on the fact that we are guided by the demand theory and economic implications. 3.1.1. Energy demand in the chemical fiber industry (E) This article takes energy consumption (unit: Mtce) in the chemical fiber industry as the total energy demand in this industry. It is obtained from the “China Energy Statistical Yearbook” (China Energy Statistical, 1990e2011). 3.1.2. Retail price index of fuel (P) According to the definition of demand function, price is an important influencing factor of demand. However, because most energy prices are set by the government, they are generally low and fail to reflect the scarcity of resources. This article adopts the retail price index of fuel as the proxy for energy prices. The retail price index is obtained from the “China Statistical Yearbook” (China Statistical Yearboo, 1990e2011), and is uniformly converted to the fixed-base price index (1990 ¼ 100). 3.1.3. Gross domestic product (GDP) Energy is essential for the society and its main consumers are residents and enterprises. Therefore, the income level of consumers is an important factor affecting energy demand. In this paper, we use GDP which is an important variable affecting energy consumption in the industry to represent consumers' income level. In fact, a number of papers have confirmed that GDP is the most
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B. Lin, H. Zhao / Journal of Cleaner Production xxx (2014) 1e8
important determinant of energy consumption. Kraft J and Kraft A (Kraft and Kraft, 1978) were the first researchers to discover the one-way causal relationship between GDP and energy consumption during the period 1947e1974 in the United States. Apergis et al. (2009) used the panel co-integration and error correction model to test the relationship between energy consumption and economic growth in six Central American countries and concluded that there was a two-way causal relationship between them. In this paper, the GDP data is taken from the “China Statistical Yearbook” and is converted to constant price in 1990. 3.1.4. Population (POP) Consumer population should be taken into account when considering commodity demand. Therefore, population should also be considered when analyzing energy demand in the chemical fiber industry. In this paper, the POP data is obtained from the “China Statistical Yearbook”. 3.1.5. R&D expenditure (RD) Technical progress can improve the operating efficiency of equipment and directly lower energy dissipation per unit of product, thereby reducing energy consumption. Moreover, science and technology can be used to develop new energy sources and exploit high quality energy, which can promote fundamental changes in the energy demand structure. In this paper, the index of R&D expenditure is used to reflect technological progress. In fact, domestic studies have already confirmed this proposition. Liu et al. (2012) employed panel data to conduct empirical analysis on the energy consumption intensity changes of 29 industrial sectors (Liu, 2012). The results show that an increase in R&D expenditure contributes to improving industrial energy efficiency, and consequently reduces industrial energy consumption accordingly. In this paper, the R&D expenditures data is taken from the “China Statistical Yearbook” and the CEIC China Database (China Database and Avai, 2013). To eliminate heteroscedasticity, we take the logarithm of each arbitrary variable, represented by LnX. 3.2. Research methods 3.2.1. Co-integration method Since Engle and Granger (1987) proposed the co-integration method (Engle and Granger, 1987), many scholars have used it to analyze the factors influencing energy demand. However, time series economic variables are often unstable, and a stationary linear combination of economic variables indicates the existence of a long-term equilibrium co-integration relationship. Therefore, we first conduct a stationary test before using the co-integration method. The most widely used methods are the ADF unit root test and the PP test. The ADF unit root test is based on the following OLS regression:
DXt ¼ a þ bt þ dXt1 þ
m X
bi DXti þ εt
i¼1
where Xt is a variable of period t;
stable. In this paper, we adopt the ADF test (Dickey and W. Fuller, 1979) and the pp test (Phillips and Perron, 1988). If all series are integrated in the same order, we can verify the existence of co-integration. The most widely used co-integration tests are the two-step method created by Engle and Granger (EG) and the Johansen method. The EG two-step method can be adopted in a co-integration test with a single equation. The co-integration method has advantages. It enables researchers to study the long-term relationship between energy demand and its determinants. However, it also has a disadvantage. The tests for the existence of unit roots and co-integrated series cannot be considered definitive. In this paper, we use the cointegration model for two reasons. First, during the study period, the relationship between the sectoral energy demand and its determinants is constant. Second, it is easy to illustrate the output of the model. Because we use multiple variables in the study, we employ the Johansen co-integration method to study the relationships between the variables. 3.2.2. Monte Carlo simulation The Monte Carlo method is based on the probability characteristics of the object itself, and can be applied in stochastic simulation and statistical experiment. There is need to select a large random sample for the random variables, calculate the corresponding statistics or parameters according to the sampling data, and simulate their real distribution. The basic idea of this method is to build a probabilistic model, generate a series of random numbers to conduct simulation, and carry out the statistical process. 3.2.3. Scenario analysis As one of the new prediction methods, scenario analysis focuses on uncertainties and combines qualitative and quantitative analysis. It recognizes future developments as diverse; i.e. there are multiple possible trends. The predicted results are multidimensional. The essence of scenario analysis is to identify all possible development trends under different situations. The results comprise three parts: the recognition of possible future development trends, the characteristics of each situation, and the likelihood of occurrence as well as the situation described in the process of path analysis. 4. Co-integration test and corresponding results 4.1. Unit root test As is shown in Table 1, the ADF and PP test indicate that all the five variables are integrated of the same order, satisfying the requirement of co-integration. The first test is to determine the rank and test the number of linearly independent co-integration vectors (Grover and et al., 1998). The rank test results are as follows.
Table 1 Unit root test of variables. Sequence
DXti ¼ Xti Xti1 ; i ¼ 1; 2; :::m; where m is the lag order. The null hypothesis isH0:d ¼ 0; i.e., there is a unit root, and time series are unstable. The alternative hypothesis isH0:d ¼ 0; i.e., there is no unit root, and time series are stable. If the test statistic is less than the critical value, then the null hypothesis should be rejected and the time series is stable. Otherwise, the time sequence is not
3
DLnE DLnGDP DLnpop DLnp DLnRD
ADF
PP
There is no trend
There are trends
There is no trend
There are trends
5.609183*** 3.366495*** 3.229815*** 6.732546*** 7.024622***
5.405798*** 4.170811*** 2.449634** 6.475657*** 6.812935***
5.361226* 3.831511** 2.655194** 5.702290* 5.680036*
5.612675* 4.532598** 3.787302** 4.787302* 4.530618*
Note: *, ** and *** represent the significance level of 10%, 5% and 1%, respectively, under which the null hypothesis is rejected.
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4.2. JohanseneJuselius co-integration rank test
Table 3 VAR lag order selection criteria.
The unrestricted co-integration rank test shows that the null hypothesis of co-integration rank 0 could be rejected under 5% confidence level (110.9872 > 69.81889). There are four uncorrelated linear co-integration vectors, which are marked with asterisks in Table 2. The largest eigenvalue test also shows that we could refuse “the original hypothesis of co-integration rank 0” at 5% confidence level (51.01459 > 33.87687) and there are two co-integration equations. Since the rank test results indicate the existence of linearly independent co-integrating vectors, we could conduct the cointegration analysis. We first established an unrestricted VAR model that consisted of LnE, LnGDP, LnPOP, LnRD, and LnP, and then examined the VAR lag intervals that correspond to the system. The test results are shown below. 4.3. VAR model lag order selection According to the criteria of LogL, LR, FPE, AIC, SC and HQ standard in Table 3, we chose the lag periods of two. Table 4 presents the Johansen co-integration test results based on our assumption. 4.4. Co-integration model results According to the standardized coefficients of the co-integration vector, we establish a corresponding co-integration equation as follows:
LNE ¼ 1:023704LNGDP 0:73119LNP þ 0:429200LNPOP ð0:0457Þ
ð0:12137Þ
ð0:08546Þ
0:756898LNRD ð0:12444Þ
(1) First, Equation (1) indicates that there exists a long-term equilibrium relationship among the variables from 1990 to 2011. Second, the positive coefficients of LNGDP and LNPOP, and the negative coefficients of LNP and LNRD are in accordance with economic theory. Third, with the improvement in people's living standard, GDP growth and high industrial productivity, chemical fiber consumption will increase, and this will stimulate energy consumption in the industrial sector. The elasticity coefficients show that a 1% increase in GDP will result in a 1.024% increase in the energy consumption of the chemical fiber industry. The high elastic coefficient Table 2 Johansen co-integration test. Hypothesized no. of CE(s)
Eigenvalue
Trace statistic
0.05 Critical value
Prob.**
None* At most 1* At most 2* At most 3 At most 4* Hypothesized no. of CE(s)
0.931777 0.790627 0.631148 0.316340 0.193590 Eigenvalue
110.9872 59.97261 30.26352 11.31369 4.088105 Max-Eigen statistic
69.81889 47.85613 29.79707 15.49471 3.841466 0.05 Critical value
0.0000 0.0024 0.0442 0.1929 0.0432 Prob.**
None* At most At most At most At most
0.931777 0.790627 0.631148 0.316340 0.193590
51.01459 29.70909 18.94982 7.225589 4.088105
33.87687 27.58434 21.13162 14.26460 3.841466
0.0002 0.0263 0.0983 0.4628 0.0432
1* 2 3 4*
Trend: trend. Number of obs ¼ 20. Sample: 1992e2011. Lags ¼ 1.
Lag LNL 0 1 2
LR
FPE
AIC
SC
HQ
108.1131 NA 1.33e-11 10.85401 10.60548 10.81195 262.0648 210.6707* 1.89e-17 24.42787 22.93665* 24.17550 298.0850 30.33277 1.20e-17* 25.58789* 22.85399 25.12521*
Note: * indicates lag order selected by the criterion. Sample: 1990e2011. Number of obs ¼ 20.
of GDP indicates that China's rapid economic growth in the last 30 years is the main driving force of the rapid energy demand growth in the industrial sector. Fourth, because the raw materials of chemical products are mostly derived from oil, the fluctuations in energy prices have a direct impact on the chemical fiber market. Compared with the population variable, the elasticity of energy prices is relatively high, which indicates that energy price is a major cause of energy consumption changes in China's chemical fiber industry. The adjustment of energy prices will have a significant impact on energy consumption in the industry. Fifth, compared with other variables, the elasticity coefficient of population is relatively small. The main reason for this is that China has implemented the one-child policy for a long time, leading to a low population growth rate. Consequently, the influence of population on energy consumption in the industry is relatively small. Sixth, the chemical fiber industry is an energy-intensive, largescale and mechanized production industry. The more advanced equipment is used, the higher production efficiency is. Similarly, the lower the specific energy consumption is, the higher the added value of products will be. Technological progress is a key factor in improving energy-saving potential in the chemical fiber industry. The elasticity coefficients show that a 1% increase in R&D expenditures will lead to a 0.76% decline in energy consumption in the industry. It also shows that increases in R&D expenditure can lead to technical advancement, and thus encourage the chemical industry to reduce energy consumption. In summary, we believe that the model results are consistent with existing economic theory, and can reasonably explain the reality of China's economy. The result of the model stability test is presented below. Fig. 2 4.5. Stability test As shown above, all the eigenvalues of the matrix are less than one. There is no characteristic root falling out of the unit circle. The results of the stationary test show that the model satisfies stability condition. 5. Analysis and forecast of energy demand and energy-saving potential in China's chemical fiber industry 5.1. Energy demand forecast of China's chemical fiber industry In this paper, we select annual average growth rate of the variables from 1990 to 2011 as the BAU scenario. Assuming that each Table 4 Johansen co-integration test. LNE
LNGDP
LNP
LNPOP
LNRD
1.000000
1.023704 (0.0457)
0.73119 (0.12137)
0.429200 (0.08546)
0.756898 (0.12444)
Co-integrating Equation(s). Log likelihood: 286.0688. Normalized co-integrating coefficients (standard error in parentheses).
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Based on the Monte Carlo simulation analysis, we produced a distribution histogram, which fits energy consumption in China's chemical fiber industry in 2020 in Fig. 3. According to Fig. 3, the highest probability is that energy consumption in China's chemical fiber industry will fall within 44 and 50 Mtce in 2020. In the preceding part of this paper, we predict that energy consumption in the industry will reach 48.59228 Mtce in 2020, based on the explanatory variables and the historical change tendency. The simulation result verifies our prediction. As shown in Fig. 4, if the various influencing factors of energy consumption in China's chemical fiber industry maintain the same growth trend, the probability that energy consumption in China's chemical fiber industry will be lower than 86 Mtce in 2020 is 100%, and the probability that it will be less than 6 Mtce tends to 0%. In the following part, we use the scenario analysis method to study the effect of energy-saving policy on energy consumption in China's chemical fiber industry, and estimate the corresponding energy saving potential.
Inverse Roots of AR Characteristic Polynomial 1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 -1.5
-1.0
-0.5
0.0
0.5
1.0
5
1.5
Fig. 2. Roots of characteristic polynomial.
variable (2012e2020) will grow at this average rate, we predict China's energy consumption in the chemical fiber industry under the BAU scenario. Under the BAU scenario, the growth rates of China's gross domestic product (GDP), population (POP), fuel retail price index (P) and R&D expenditure (RD) are 9.8%, 1.2%, 8.6% and 16.23% respectively. According to the annual average growth rate of each variable and the co-integration equation, we predict that energy consumption in China's chemical fiber industry will reach 27.5183 Mtce in 2015 and 48.59228 Mtce in 2020. The above result is based on the growth rates of the explanatory variables. However, future influencing factors are uncertain. There are various possibilities, and a more reasonable forecast should have multiple results. Therefore, we employ the Monte Carlo simulation and use the year 2020 as an example. We analyze the highest possible energy consumption in the industry in that year and its probability. The key of Monte Carlo model is in accordance with the distribution of random sampling. According to experience, economic variables follow normal distribution. To determine the distribution of risk variables, we use Matlab software to conduct the distribution test of the growth rate of each explanatory variable during 1990e2011. The test results show that the variables follow normal distribution at 5% confidence levels. Table 5 shows the statistical characteristics of the growth rate for each variable. According to the mean value and standard deviation of each variable, we can get the specific normal distribution that each risk variable should follow. Based on this, we used Matlab software to generate the random values of the risk variables that follow the normal distribution. These values are regarded as the growth rates of the explanatory variables. We obtained 3000 possible values of the explained variables, which were calculated from the abovementioned random values. Thus, the probability distribution of energy consumption in China's chemical fiber industry in 2020 can be obtained.
5.2. Energy-saving potential To predict the energy saving potential, we apply the above standard to set two energy-saving scenarios: the moderate and advanced energy-saving scenarios. The advanced scenario is a situation whereby under the restriction of economic reality, each affecting factor leads to the most active energy-saving effects. The moderate scenario is regarded as intermediate between the BAU and the advanced scenario, based on the reality of the economic development plan. In terms of the growth rate of GDP, the National Economic and Social Development Twelfth Five-Year Plan of the People's Republic of China in 2011 predicted that GDP would grow at an annual rate of 7% in the next 5 years. In this paper, we use 7% as the growth rate of GDP in the advanced scenario. Accordingly, the annual average growth rate of GDP in the moderate scenario is set at 8.3%. Considering the growth rate of POP, the National Economic and Social Development in the Twelfth Five-Year Plan of the People's Republic of China in 2011 predicted that China would maintain a stable fertility level and limit the annual natural population growth rate to within 0.72%. Based on these conditions, we set the annual average population growth rate at 0.6% in the advanced scenario and at 0.8% in the moderate scenario. Technological research and innovation play an important role in improving production efficiency and energy-saving capacity in China's chemical fiber industry. The National Economic and Social Development in the Twelfth Five-Year Plan of the People's Republic of China in 2011 predicted that the level of education in science and technology would improve substantially. R&D expenditure should account for 2.2% of the GDP. Based on the above conditions, we set R&D expenditure growth rate at 18% in the advanced scenario and at 17.11% in the moderate scenario.
Table 5 Statistical characteristics of growth rate for each variable. Variable
Mean
Std. Dev.
d. d. d. d.
. . . .
. . . .
LNGDP LNPOP LNP LNRD
0.0563005 0.0743861 0.042611 0.0421807
0.0137112 0.05022 0.0528586 0.0914857
Fig. 3. Probability distribution histogram of energy consumption in China's chemical fiber industry in 2020 (unit: Mtce).
Please cite this article in press as: Lin, B., Zhao, H., Energy efficiency and conservation in China's chemical fiber industry, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.06.070
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B. Lin, H. Zhao / Journal of Cleaner Production xxx (2014) 1e8 Table 7 Forecast of energy consumption in China's chemical fiber industry in 2015 and 2020 (Mtce).
Fig. 4. Cumulative probability distribution of energy consumption in the chemical fiber industry in 2020 (unit:Mtce).
Energy prices can influence energy consumption directly. However, most energy prices in China are undervalued and fail to reflect the scarcity of resources (Liao and Wei, 2010). Based on this, we set the annual average growth rate of energy prices at 10% in the advanced scenario and at 9.3% in the moderate scenario. According to the growth rate of each variable under the different scenarios in Table 6, we predict the energy consumption under the three scenarios in China's chemical fiber industry in 2015 and 2020, and the results are shown in Table 7. From Table 7, it can be seen that under the moderate scenario, energy consumption in China's chemical fiber industry in 2015 will be 20.82479 Mtce, which is 6.69354 Mtce lower than in the BAU scenario. The reduced energy consumption accounts for 24.32% of total energy demand in the BAU scenario. The energy consumption in China's chemical fiber industry will be 35.0284 Mtce in 2020, which is 13.5638 Mtce lower than in the BAU scenario. The reduced energy consumption accounts for 27.91% of total energy consumption in the BAU scenario. From Table 7, it is clear that in the advanced scenario, energy consumption in China's chemical fiber industry in 2015 will be 17.9821 Mtce, which is 9.5362 Mtce lower than in the BAU scenario. The reduced energy consumption makes up 34.65% of total energy demand in the BAU scenario. Similarly, energy consumption will be 29.789 Mtce in 2020, which is 18.8032 Mtce lower than in the BAU scenario. The reduced energy consumption makes up 38.69% of total energy demand in the BAU scenario. When comparing the moderate scenario with the advanced scenario, it is seen that the reduction in energy consumption in China's chemical fiber industry is higher in the latter scenario. This is also the same with its ratio to total energy demand in the BAU scenario within the same time interval. The above comparison shows that there is potential for energy consumption reduction in China's chemical fiber industry. Energy conservation policies under the moderate and advanced scenarios would be highly effective. There is much room for improvement in the energy-saving capacity of the industry. To check whether the energy-saving policy is effective, we compare energy consumption growth rates under the three scenarios with the corresponding GDP growth rates. This paper defines the ratio of energy consumption growth rate to GDP growth rate as the energy consumption index; i.e. growth rate of GDP in energy
Year
2015
2020
BAU scenario Moderate scenario Advanced scenario
27.51833 20.82479 17.98213
48.59228 35.02842 29.78906
consumption index ¼ the growth rate of energy consumption in China's chemical fiber industry/the growth rate of GDP. Table 8 shows that under the moderate scenario, with the slowdown of GDP growth and the transformation of economic development pattern, a combination of various elements has an effective inhibition effect on energy demand in the industry. The annual average growth rate of energy consumption in the industry is 3.4% lower than that of GDP. Under the advanced scenario, owing to the implementation of various policies and technical improvements, the annual average growth rate of energy consumption in the chemical fiber industry is 4% lower than that of GDP. Generally, we can see from Table 8 that as the economy develops, the growth rate of energy consumption in the industry will fall under both the moderate scenario and the advanced energy conservation scenario. This will consequently reduce the GDP growth rate in energy consumption index. Therefore, we can conclude that the implementation of energy saving measures is effective, and the improvement of energy saving-potential is also feasible. After studying the energy-saving potential of China's chemical fiber industry, the following part of the paper analyzes the specific amount of energy-saving potential in the industry and its influence on total national energy demand. To predict the amount of energy conservation, we establish two energy saving scenarios. Scenario I indicates that energy consumption transits from the BAU scenario to the moderate scenario, and scenario II indicates that energy consumption transits from the moderate scenario to the advanced scenario. The two energy-saving scenarios are reasonable because the assumptions are mainly based on historical data and recent development trends of the variables, so they are consistent across variables; in line with the principle of scenarios design. According to the estimation of future energy consumption in China's chemical fiber industry, we calculate the amount of energy conservation under the two scenarios as well as their ratios to China's total energy demand. The results are shown in Table 9. From Table 9, it is seen that if the energy consumption pattern of China's chemical fiber industry transforms from the present situation to Scenario I, the amount of energy saving will be 13.56386 Mtce by 2020, accounting for 0.27% of China's total energy demand. If the energy consumption pattern transforms from the present situation to Scenario II, the energy saving potential will be 18.803 Mtce by 2020, accounting for 0.38% of China's total energy demand.
Table 8 Comparison of growths rates between energy consumption in China's chemical fiber industry and GDP.
Table 6 Growth rates of each explanatory variable under different scenarios. Variables
GDP P POP RD
BAU scenario (%)
Moderate scenario (%)
Advanced scenario (%)
9.8 8.6 1.2 16.23
8.3 9.3 0.8 17.11
7 10 0.6 18
Real data: 1990e2011 BAU scenario: 2013e2020 Moderate scenario: 2013e2020 Advanced scenario: 2013e2020
Average growth rate of energy consumption
Average growth rate of GDP
Growth rate of GDP in energy consumption index
7.50% 6.70% 4.60%
10.50% 9.80% 8%
0.714 0.68 0.5
3.50%
7.50%
0.46
Please cite this article in press as: Lin, B., Zhao, H., Energy efficiency and conservation in China's chemical fiber industry, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.06.070
B. Lin, H. Zhao / Journal of Cleaner Production xxx (2014) 1e8 Table 9 Energy saving amount in China's chemical fiber industry and its ratio in total national energy demand (unit: Mtce). Year
2020
Scenario I
Scenario II
Energy saving of chemical fiber industry
Ratio in total energy demand
Energy saving of chemical fiber industry
Ratio in total energy demand
13.56386
0.27%
18.803
0.38%
At present, China is implementing the Western development strategy, as well as strengthening the infrastructure in the countryside regions. With the development of the economy and the improvement in living standards, the industrial fiber materials market will experience rapid growth. Thus, the energy-saving potential of China's chemical fiber industry is not only tremendous, but also strategically significant.
6. Conclusions and policy implications In this paper, based on time series data from 1990 to 2011, we apply the co-integration model to estimate the factors influencing energy consumption in China's chemical fiber industry. The factors considered in the paper include China's GDP, population, energy price and R&D expenditure. We also predict the energy demand and energy-saving potential in the industry. Monte Carlo simulation analysis verifies the rationality of this model, and indicates that more positive and radical policies are needed to improve energy utilization efficiency and expand the energy-saving potential of the industry. The scenario analysis shows that inhibited by a variety of energy saving policies, the growth of energy consumption in the industry will be slower and the energy-saving potential will be considerable. The main conclusions of this paper are as follows. First, the co-integration equation shows that there is a longterm stable relationship between the selected variables during 1990e2011. As expected, GDP has the largest positive effect on energy consumption in the chemical fiber industry, followed by population. An increase in R&D expenditure and energy prices could reduce energy consumption in the industry. Second, the GDP elasticity coefficient is the largest among all the coefficients. It has the greatest influence on energy consumption in the chemical fiber industry, indicating that the rapid economic growth of China in the last 30 years is a major driving force for the rapid growth of energy demand in the industry. In summary, energy demand is rigid and increases synchronously with rapid economic development, which conforms to China's economic stage characteristics. Third, following GDP, the elastic coefficient of R&D expenditure is the second largest. R&D expenditure is also one of the most important factors influencing energy consumption in the chemical fiber industry, and it illustrates that technology improvement is the key to achieving the energy-saving objectives of the industry. To a large extent, energy saving in the industry can only be realized through technical progress. Fourth, compared with GDP and R&D expenditure, the elasticity of energy price ranks third, indicating that the energy price exerts a great impact on energy consumption in the chemical fiber industry. Therefore, to achieve the energy conservation objective of the industry, energy price is a crucial factor. Increase in energy price has an inhibitory effect on energy demand in the industry. At present, energy prices in China still do not fully reflect the scarcity of resources. Therefore, it is imperative for China to continue reform of energy prices.
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Fifth, in the long-term equilibrium equation, the elastic coefficient of population is relatively small, which is attributable to the one-child policy that has made the annual average population growth rate relatively low for a long time. Thus, the influence of population on energy consumption in the industry is not significant. The goal of energy conservation and consumption reduction in the industry is not only dependent on technical issues, but also on many other factors such as price and technology level. Based on the conclusions, this paper provides the following suggestions on energy conservation policy in China's chemical fiber industry. First, besides the GDP, R&D expenditure has the greatest influence on energy consumption in the chemical fiber industry. This implies that R&D should be given more priority and attention by the government and the relevant departments concerned with energy conservation and consumption reduction. In other words, the government should increase R&D expenditure as well as improve technology and the capability for scientific innovation in the industry. In this way, energy conservation and sustainable development in the industry could be realized. Second, compared with GDP, the influence of energy price is relatively trivial, but the cumulative effect of price should not be ignored. Economic theory indicates that price reflects the scarcity of resources and has a direct impact on supply and demand. Faced with tight energy constraints, this paper holds that energy prices should be increased, and the efficiency of energy utilization should also be improved. A further reform of China's current energy pricing mechanism is imperative and should be urgently pursued. References Apergis, N., Payne, J.E., 2009. Energy consumption and economic growth in Central America: evidence from a panel co-integration and error correction model. Energy Econ. 31, 211e216. Cha, D.L., 2010. Study on the rebound effect of energy efficiency in China based on CGE model. J. Quant. Tech. Econ. 12. CEIC China Database, 2013. Available at: http://ceicdata.securities.com/cdmWeb/. China Energy Statistical Yearbook, 1990e2011. China Statistics Press, Beijing. China Statistical Yearbook, 1990e2011. China Statistics Press, Beijing. China's Textile Industry Report, 2010a. The Statistic Bureau of China. China's Textile Industry Report, 2010b. The Statistic Bureau of China. Dickey, D.A., W., Fuller A., 1979. Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74 (366), 427e431. Engle, R.F., Granger, C.W.J., 1987. Co-integrationanderrorcorrection:representation, estimationandtesting. Econometrica 55, 251e276. Silberglitt, Richard, Hove, Anders, Shulman, Peter, 2003. Analysis of US energy scenarios: meta-scenarios, pathways, and policy implications. Technol. Forecast. Soc. Change 70 (4), 297e315. Grover, V., et al., 1998. The influence of information technology diffusion and business process change on perceived productivity: the IS executive's perspective. Inf. Manag. 34 (3), 141e159. Gu, A.L., 2012. The Energy conservation and emissions reduction potential and cost analysis of China's cement industry. China Popul. Resour. Environ. 8. He, X.P., 2011. Energy saving potential and influence factors of China's industry. J. Financ. Res. 10. Hu, J.Y., 2005. Shanghai's Total Energy Demand and Long-term Forecasting. Shang Hai Electric Power, p. 6. Huang, Y., 2011. Guangdong transportation's energy saving potential research based on scenario analysis. China Open. Her. 4. Kraft, J., Kraft, A., 1978. On the relationship between Energy and GNP. J. Energy Dev. 3, 401e403. Liang, Q.M., 2004. A model for scenario analysis of energy demand and energy intensity in China and its application. China's J. Manag. 1. Liao, H., Wei, Y.M., 2010. China's energy consumption: a perspective from Divisia aggregation approach. Energy 35 (1), 28e34. Lin, B.Q., 2003. Structure change, efficiency improvement and energy demand forecasting. Econ. Res. 5. Lin, D.R., 2005. The coordinated development of economy and energy of the Yangtze river delta region. J. Nant. Univ. 2. Lin, B.Q., Moubarak, M., 2014. Estimation of energy saving potential in China's paper industry. Energy 65, 182e189. Lin, B.Q., Xie, C.P., 2013. Estimation on oil demand and oil saving potential of China's road transport sector. Energy Policy 61, 472e482.
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Please cite this article in press as: Lin, B., Zhao, H., Energy efficiency and conservation in China's chemical fiber industry, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.06.070