The green behavioral effect of clean coal technology on China's power generation industry

Science of the Total Environment 675 (2019) 286–294

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

The green behavioral effect of clean coal technology on China's power generation industry Yuan Zhao a, Zhen Cui b, Lei Wu c, Wei Gao d,⁎ a

School of Economics and Finance of Xi'an Jiaotong University, Xi'an, 710061, China Industrial Securities CO., LTD, China Haitong Securities CO., LTD, China d Beijing University of Technology, Higher Education Research Institute, Management Science and Engineering Post-doctoral Mobile Station, Beijing, 100124, China b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• The coal-fired electricity demand will reach 5070.1 billion kWh in 2020. • Carbon emissions reduction will be 0.233 billion tons in 2020. • Traditional clean coal technology has limited emission reduction capacity.

a r t i c l e

i n f o

Article history: Received 23 December 2018 Received in revised form 5 April 2019 Accepted 9 April 2019 Available online 12 April 2019 Editor: Deyi Hou Keywords: Clean coal technology Carbon emission reduction Ultra-supercritical power generation Support vector machine

⁎ Corresponding author. E-mail address: [email protected] (W. Gao).

https://doi.org/10.1016/j.scitotenv.2019.04.132 0048-9697/© 2019 Elsevier B.V. All rights reserved.

a b s t r a c t Clean coal technology is a green technology and it can improve the efficiency of thermal coal usage and helps to speed up the green development of the power generation industry. To better understand the actual effect of clean coal generating technology on CO2 emission reduction, we take the ultra-supercritical power generation technology as an example to illustrate the positive impact on the environment. Since support vector machine has widely been used in the field of time series prediction due to its advantages compared with other prediction models, in this paper we will use support vector machine to build a prediction model and estimate the electricity demand from 2020 to 2040 in China. Then, based on the future electricity demand with ultra-supercritical power generation technology, we calculate the amount of carbon emission reduction which uses the carbon emission calculation method of UNFCCC. Our results show that: 1. The coal-fired electricity demand will reach 5070.1 billion kWh in 2020. 2. The amount of carbon emissions reduction due to the application of ultra-supercritical power generation technology in coal-fired electricity will be 0.233 billion tons. 3. Carbon emissions will only reduce by 2.1%–3.0%, which means that traditional clean coal generation technology can only decrease CO2 emissions to a certain extent, and the achievement of larger reductions may have to rely on carbon capture and storage and renewable energy. © 2019 Elsevier B.V. All rights reserved.

Y. Zhao et al. / Science of the Total Environment 675 (2019) 286–294

1. Introduction Green growth needs to coordinate the relationship between economic growth and environmental protection. Climate change is recognized as one of the most serious environmental problems all around the world (Wen et al., 2018; Chen et al., 2017). The largest contributor to climate change is greenhouse gas, which is mainly generated by fossil fuel combustion (Mardani et al., 2019). China, the world biggest emitter (Wang and Song, 2017), has emitted 28% of the global carbon emissions with 80% came from fuel coal. As the largest developing country, China's energy structure has been dominated by coal. Both in consumption and production, the proportions of coal in energy are extremely high, which are 63% and 77% respectively in 2015. For the coal use, thermal coal, which is mostly used for power generation, accounts for about half of the whole coal consumption in China and produces a lot of greenhouse gas. Therefore, mass consumption of thermal coal leads to climate change and serious environmental pollution, which makes a great challenge to sustainable development in China. China promised at Copenhagen meeting that by 2020, carbon emissions per unit of GDP would be cut by 40%–50% compared to 2005, and this goal has also been integrated into mid-long development plans for society and national economy. Besides, “U.S.-China Joint Announcement on Climate Change”, which was signed in 2014, points out that China plans to reach its peak of CO2 emissions around the year 2030 and try to realize earlier. Moreover, it sets to raise the proportion of non-fossil fuels in primary energy consumption to 20% by 2030. How to control and reduce carbon emissions has become a pressing issue that needs to be addressed for the Chinese government. According to IEA in 2015, in light of the industry of global emissions distribution, electricity and thermal industry account for 42% of total emissions, and 70% of which are from fuel coal. To deal with climate change, electricity industry has to bear the heavy responsibility in emissions reduction (Liu et al., 2017; Ma et al., 2017; Song and Wang, 2018), and the most important part is to find the huge potential for reducing emissions through steam coal. In the government working report 2015, it is pointed out that coal must be utilized cleanly and efficiently and it is necessary to promote the ultra-low emission transformation of coal power plants. Therefore, China has begun to promote clean coal-fired power generation technology, and one of the representatives is the ultra-low pollutant emissions technology of coal-combustion flue gas which has attracted much attention (Wang et al., 2015; Cen et al., 2015). Besides, clean coal technology is of great significance to promoting China's green economic growth (Chen and Xu, 2010). The study of “clean-coal” technology is growing globally, while previous researches about it are most concentrated on electric power engineering and environmental engineering to analyze new techniques of clean coal. Based on the calculation of return of investment on the clean coal technology in the US, Bezdek and Wendling (2013) found that the clean coal program could bring huge profit in many fields such as environmental protection and energy use. Oboirien et al. (2018) compared four kinds of clean coal technology with traditional techniques to explore the potential energy recoveries of clean coal technologies in Nigeria. On the basis of systematic analysis of clean coal technology of coal-fired power plant, Franco and Diaz (2009) analyzed the alternative paths to reduce carbon emissions. Wilberforce et al. (2019) made a comparative analysis of different technologies for carbon capture and storage, and studied various separation techniques of CO2. There are also many scholars studying CO2 emissions reduction effect of different clean coal technologies. Through simulating calculation, Topper et al. (1994) and Falcke et al. (2011) found that the introduction of clean coal technology to thermal power generation would reduce CO2 emissions effectively. Prabu and Mallick (2015) estimated the reduction of CO2 through carbon sequestration based on the coalbed methane technology in India. Zhang et al. (2018) studied the reduction effect of coal by constructing a coal input-output table.

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What's more, some researches discuss about the implementation of related policy. Tang et al. (2015) thought that the two developmental problems of the clean coal technology in China are incomplete environmental laws and structures for governance. Xiao et al. (2005) compared different clean coal generating technologies under different life-cycle models in China, which provided technical support for project decision theory and clean coal development policies. Wang and Nakata (2009) built multi-period models for market equilibrium and electricity to investigate the policy instruments to promote marketization application of clean coal in the electric power sector. Malen and Marcus (2017) found that government support for clean energy technologies could have a positive impact on clean energy technology startups. Lu et al. (2008) evaluated China's current laws, regulations and policies to analyze the main obstacles to the development of clean coal technology. Support vector machine is one of the most important methods in the field of machine learning (Wang and Xing, 2019). Based on the support vector machine, Papadimitriou et al. (2014) studied the forecast accuracy of next-day directional change in the electricity markets. Zendehboudi et al. (2018) forecasted the solar and wind energy resources by applying different methods and found that the support vector machine performed better than other models. With the least squares support vector machine method, Lv et al. (2017) predicted the bed temperature of circulating fluidized bed (CFB) boilers with the actual data. Besides, Zhu et al. (2019) used least squares support vector machine to predict the energy consumption in China, and the optimal model parameters are determined by particle swarm optimization. Similarly, Wang et al. (2018) forecasted the primary energy consumption in China, and used the self-adaptive multi-verse optimizer to make the parameters in support vector machine optimized. Even though clean coal has become a hot issue for academic scholars, few researches are specialized in the carbon emission reduction effect of clean coal technology used in China's electric power sector. Therefore, in this paper, we build the prediction model by using the support vector machine to predict power demand and supply in China, and based on the method of carbon emission calculation of UNFCCC,1 we calculate carbon emission reduction under the situation that clean-coal technology will be applied producing electricity. Since ultra-supercritical coalfired power generation technology is popular in China, we will discuss the reduction effect of this clean coal technology. The structure of this paper is as follows: Section 2 is the calculation method and variables selection. Section 3 presents the calculation results and finally, Section 4 provides conclusions and research prospects. 2. Calculation method and variables selection 2.1. Calculation method of carbon emissions from coal power CO2 emission reduction of electricity generation under the coalbased energy structure mainly depends on the efficiency promotion, which means to provide the same amounts of power with less coal. This reduction comes from the change of production efficiency and it does not lead to any change in the production structure of power generation, namely electric quantities of capability do not change.2 The carbon emissions reduction ECTmn caused by efficiency improvement of coal-fired power generation could be calculated using the following formula: ECTmn ¼ P CTmn  H CT  ðMm −M n Þ  C COAL

ð1Þ

1 In 2006, IPCC (Inter-governmental Panel on Climate Change) compiled IPCC Guidelines for National Greenhouse Gas in 2006, which was invited by UN Framework Convention on Climate Change (UNFCCC), thus we calculate carbon emissions based on this guidelines. 2 This idea is mainly found in carbon dioxide reduction coefficient caused by the substitution of non-coal based clean energy. The specific way can refer to carbon reduction method ACM002 which is developed by UNFCCC for CDM program.

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where ECTmn represents the CO2 emissions reduction caused by the efficiency improvement of new coal-fired power generation from year m to n, and the unit of this parameter is 0.1 billion tons; PCTmn is the new capability of coal-fired power generation from year m to n, and the unit of this parameter is billion kW; HCT is the average annual operating hours of coal-fired power generator set, and the unit of this parameter is thousand hours; Mm and Mn are coal consumption in power generation at year m and n respectively, and the unit of this parameter is gce/kWh; and CCOAL is the CO2 emission coefficient of coal, which is generally 2.66tCO2/tce according to the report of UNFCCC. For simplicity, we transform Eq. (1) into the following one: ECTmn ¼ Q CTmn  ðM m −Mn Þ  C COAL

ð2Þ

where QCTmn is the increasing demand of coal-fired power generation from year m to n; and Mm and Mn are coal consumption in power generation at year m and n respectively. 2.2. Prediction method of coal power demand and variables selection 2.2.1. Prediction method of coal power demand There are generally two kinds of prediction methods for the demand of energy or electricity. One is time series method, e.g., auto-regressive (AR), moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average model (ARIMA), general index smoothness and so on. The advantage of time series approach is that there doesn't exist model dependency. This method uses historical data and predicts the energy demand through examining the volatility of disturbance term, thus it does not depend on the economic variable of energy or electricity demand. However, if this method is used to explain energy demand in the long term, it lacks proper explanatory power since it does not consider the error term in the economic system. Therefore, this approach is applicable to the prediction of demand volatility in the short term. Besides, co-integration method is also widely applied in many researches to predict electricity demand. Since there are many factors that can influence the electricity demand, it's hard to define the changes and relationships among these factors which are characterized by stochastically changeable and complex nonlinearity. Power prediction always needs multi-variate time series analysis, in which the dynamic process of a set (multi-dimension) of correlated random variables should be observed simultaneously and studied as a whole. What's more, traditional linear prediction models, e.g., multi-statistical regression prediction model, are very hard to resolve complex nonlinear time series relationships. Many different artificial intelligence forecasting models become popular now in making the prediction. For example, artificial neural network (ANN) has been widely used in many fields, but it has some defects with slow training process, easily getting into local minimization, low global searching and so on. Multi-layer perceptron (MLP) has always been used for non-linear fitting, but it needs to choose an optimal number of layers and neurons (Zarei and Behyad, 2019) and the performance of the final effect depends on its network architecture (Fan et al., 2016). Adaptive neuro-fuzzy inference system (ANFIS), which is based on the combination of ANN and FIS, can learn by itself through a set of fuzzy rules, but it may lead to trapping in local optimum and a lot of calculations (Azad et al., 2019). In contrast, with limited samples, support vector machine (SVM) could get a global optimum solution through the linearly constrained quadratic programming (Tang et al., 2019). It can keep algorithm complexity appropriate, and have stronger robustness than many other machine learning methods (Liu and Zio, 2019). In this paper, we use SVM to build the electricity demand prediction model and take the results of SVM as the predicted value of future electricity demand. SVM are machine learning algorithms. They are proposed by Vapnik and Lerner (1963), and the core of this method is to improve the generalization ability as much as possible on the basis of structural risk minimization principle. SVM can be transformed into

the problem of solving convex quadratic programming, making the extreme we get is a globally optimal solution. The basic idea of SVM is to map each training sample into a high dimensional feature space F through a non-linear function. And then linear regression is carried on in that space and we will get the non-linear regression result in previous space. Assume the linear regression is carried on in high dimensional feature space, and we can get the following formula: f ðxÞ ¼ ω  ΦðxÞ þ b;

x∈Rn ;

ω∈ F;

Φ : Rn → F

ð3Þ

The loss function ε is used to calculate prediction error in this paper. Given the training sets (x1, y1), (x2,y2), … , (xl, yl), empirical risk and Vapnik-Chervonenkis (VC) dimension should be minimized according to structural risk minimization principle, and moreover the objective function is shown as follows. l l X X
 1 minΦ ω; ζ i ; ζ i ¼ ω  ω þ C ζi þ ζ i 2 i¼1 i¼1

! ð4Þ

Constrains: 8 ω  Φðxi Þ þ b ≥ y − ε −ζ i > > < ω  Φðxi Þ þ b ≤ y þ ε þ ζ ; ζ ≥ 0 > > : i ζi ≥ 0

i ¼ 1; 2; …; l

To solve the extremum problems with constrains, the Lagrange function is as follows: l l X X
 1 L ω; b; ζ i ; ζ i ; α  ; α; γ; γ ¼ ω  ω þ C ζi þ ζ i 2 i¼1 i¼1

− −

l X

l X

!

α i ½yi −ω  Φðxi Þ−b þ ε þ ζ i 

i¼1

  α i ω  Φðxi Þ þ b−yi þ ε þ ζ i

i¼1

−

l
X  γ i ζ i þ γi ζ i i¼1

where

α i ≥0; α i ≥0; γ i ≥0; γi ≥0; i ¼ 1; 2; ⋯; l:

ð5Þ In fact, using Lagrange function to solve extremum problem is the process of finding saddle point, which tries to make Lagrange function minimum through the variables ω, b, ζi, ζi∗ or Lagrange function maximum with Lagrange polynomial multiplier under constraints. We can P find that Lagrange function will be minimum when ω ¼ li≠1 ðα i −α i ÞΦ ðxi Þ. Plug this value into the above function, and the minimum problem can be transformed into finding the maximum: l
l l
X
  X
 1X  α i þ α i þ yi α i −α i − α i −α i W α i ; α i ¼ −ε 2 i¼1 i; j¼1  i¼1 
  α j −α j K xi ; x j

ð6Þ

where K(xi, xj) = Φ(xi) ⋅ Φ(xj) is a kernel function, and the constraint conditions are: 8 l l X X > > > < α i ¼ αi i¼1

> > 0 > : 0

≤ ≤

i¼1

α i ≤C α i ≤C

;

i ¼ 1; 2; ⋯; l:

ð7Þ

Y. Zhao et al. / Science of the Total Environment 675 (2019) 286–294

We can obtain all values of αi∗, αi by solving quadratic programming, and get the value of b through supporting vectors in training set. 0 1 l l

X 

  1X  @y − b¼ α i −α i K x j ; xi þ ε  sign α i −α i A l i∈SV i j¼1

ð8Þ

Finally, plug ω and b into Eq. (3), and we will get the prediction function: f ðxÞ ¼

l
X

 α i −α i K ðxi ; xÞ i¼1 0 1 l l

X 

  1X  @y − α i −α i K x j ; xi þ ε  sign α i −α i A þ l i∈SV i j¼1

ð9Þ

2.2.2. The selection of influence factors of electricity consumption It is essential to choose the proper influence factors to predict electricity consumption. There are many factors that affect electricity consumption, such as the development of national economy, industry structure, the level and form of electricity price, resident income, consumer preference, climate change and so on. Based on the previous researches, we select the following influence variables. 2.2.2.1. Gross domestic product. China is one of the fastest-growing economies in the world. With the steady growth of the economy, electricity consumption also increases steadily. In 1977, China achieved the balance between electricity supply and demand for the first time. However, power shortage has become more common in many regions from 2011. Power shortage influences economic growth to a certain extent. The growth of electricity consumption is affected by different kinds of factors, but in the long term, electricity demand change is determined by the development of the national economy. In this paper, GDP is taken into the model to explain the change in electricity demand. 2.2.2.2. Total population. Lin (2003) pointed out that the growth of population was one of the important reasons for electricity consumption growth. Due to the policy of birth control in China, the absolute number of increasing population declines basically since the late 1980s, and the annual growth rate decreases from 1% to 0.6%. However, annual average population growth still reaches 1258 ten thousand since 1978 because of the huge population base in China, and thus we can conclude that even smaller growth rate of population will lead to more population growth and large electricity demand. 2.2.2.3. Urbanization level. Compared to population growth, the improvement of urbanization has much more significant influences. China's urbanization level has increased from 17.9% in 1978 to 43.9% in 2006, and it still keeps rising. Urbanization has triggered a boom in electricity consumption. On the one hand, energy consumption per capita in urban residents are greatly higher than rural residents, which leads to the massive increase in electricity consumption of residents. On the other hand, the demand for massive urban infrastructure and housing constructions are increasing during the process of urbanization, which leads to the rapid growth of energy-intensive industries, such as building material, metallurgy and so on. Therefore, the development of urbanization could also lead to high energy demand for the economy. In this paper, the urbanization level (the ratio of urban population to total population) is introduced into the equation as a variable to represent the change of urbanization level. 2.2.2.4. Industry structure. In the process of economic development, the leading industry will change from the primary industry to the tertiary industry. Today, the secondary industry still accounts for a large proportion in China. In addition, secondary industry accounts for the largest

289

share of electricity consumption in the whole society, and industrial growth is the main force for the growth of electricity consumption. Therefore, this paper introduces the changes in the industrial structure into the model to predict increment in electricity consumption. 2.2.2.5. Electricity price. Price is one of the most important factors that influence demand, and many scholars select price as an explanatory variable to analyze electricity demand. The price elasticity and relative price have also been selected in some researches. For example, Holtedahl and Joutz (2004) used the ratio of electricity price in Taiwan to world oil price as an index and found that electricity price has a negative impact on power demand. Due to the incomplete marketization reform of the energy industry, the electricity price is controlled by the government in China. Therefore, electricity price is hard to reflect the market fluctuation in demand. The fuel prices are more affected by the market, and coal-fired power accounts for more than 70% in China's electricity generation. In this paper, we choose the retail price index of fuels to represent the changes in electricity price, and the increase in fuel price will make electricity demand decline. 2.2.2.6. Income level. In general, household income levels are positively correlated with electrification levels, and higher incomes tend to result in more electricity demand and consumption of electrical products. Therefore, this paper introduces urban per capita disposable income and rural residents' net income as explanatory variables. 3. Data processing and calculation results 3.1. Data and standardization of data processing The data used in this paper is China national data from 1980 to 2012, which comes from the China Statistical Yearbook and China Energy Statistical Yearbook. We take the electricity consumption as decision attribute, and all the influencing factors (such as GDP, population, urbanization level, added value of primary, secondary and tertiary industry, retail price index of fuels, urban per capita disposable income and net income of rural residents) as condition attributes. All these data are normalized by the minimum-maximum standardization method and mapped to the interval [0,1]. Therefore, we can get the normalized values of electricity consumption and the main influencing factors. This standardization method is helpful to avoid the magnitude difference between each factor and eliminate the influence due to different dimensions and units. The specific calculation formula is: 0

xi ¼

xi − minfxi g maxfxi g− minfxi g

ð10Þ

where x'i is the normalized value of factor i; xi is the original value; max {xi} represents the maximum value of factor i, and min{xi} is the minimum value of factor i. The data processing software we use is Matlab2011b. 3.2. Calculating the optimal set of factors by correlation coefficient method For stochastic vectors, in addition to mathematical expectation and variance, we also need to study the relationship between each component. Therefore, the correlation coefficient is introduced to select the optimal set of factors. The specific formula is: CovðX; YÞ ρXY ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi DðXÞDðYÞ

ð11Þ

where Y represents power consumption; X represents the factors that influence power consumption. ρXY is the correlation coefficient between stochastic vector X and Y; Cov(X, Y) represents the covariance between stochastic vector X and Y; D(X)andD(Y) are the variance of X and Y

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respectively. Finally, the most important influence factors we select, namely the optimal set affecting electricity consumption in China, are GDP, added value of secondary industry, added value of tertiary industry, urbanization level, retail price index of fuels, urban per capita disposable income and net income of rural residents. The normalized values of attributes we finally selected are shown in Table 1. 3.3. The prediction results of coal power Before the calculation, we need to choose kernel function. There are different kinds of core functions in the SVM model, e.g., linear kernel function, radial base kernel function, sigmoid kernel function and Fourier kernel function. According to their characteristics, we select radial base kernel function, linear kernel function and polynomial kernel function to construct the prediction model respectively, and then compare the different results with the original data. 3.3.1. Radial base kernel function Its expression is:   0
k ¼ exp −ðu−vÞ  ðu−vÞ = 2  σ 2

ð12Þ

where u represents influencing factor matrix; v represents power consumption vector, and σ is the parameters estimated. In actual calculation, we find that the value of σ is important to the fitting effect of data prediction. By using the method of cross validation, we let the parameter of kernel function σ = 6.6. The parameter C(90) of objective function and the parameter ε(0.01) of loss function also need be assigned values through grid search. Finally, we construct and solve the prediction model, and the fitting results are shown in Table 2.

3.3.2. Linear kernel function Using linear kernel function k = u × v' in SVM we can get linear regression prediction model, the parameter C(82) in objective function, and parameter ε(0.01) in loss function. Fitting results are shown in Table 2. 3.3.3. Polynomial kernel function In SVM model, the representation of polynomial kernel function is k = (u × v ' + 1)d. Let d = 2 through calculation, we get the parameter C(95) in objective function and parameter ε(0.01) in loss function. Similarly, fitting results are shown in Table 2. What's more, the statistical results, e.g., mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R2), of different kernel functions are shown in Table 3. As can be seen from Table 3, the MAPE of radial basis kernel function is smaller than that of other two functions. Figs. 1–3 and the MAPE show that the fitting effect of radial basis kernel function is better than the linear kernel function and polynomial kernel function. However, based on the performances of RMSE and R2, we find that the forecasting effect of radial basis kernel function is still unsatisfactory. Therefore, we choose the linear kernel function to forecast the electricity consumption. For the SVM model, the development trend of variables on the right hand side of the equation should be determined before prediction. As shown in Section 3.2, the optimal sets which affect electricity consumption are GDP, the added value of secondary and tertiary industry, urbanization level, the retail price index of fuels, urban resident disposable income and net income of rural residents. Among them, some factors will affect GDP growth. Therefore, we set three different growth situations, which are low, medium and high growth rates, to analyze the influence of the above factors on electricity demand more comprehensively.

Table 1 Electricity consumption and the main influencing factors after normalization. Year

Power consumption

GDP

Added value of secondary industry

Added value of tertiary industry

Urbanization level

Retail price index of fuels

Urban per capita disposable income

Net income of rural residents

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

0.0000 0.0019 0.0059 0.0110 0.0165 0.0238 0.0321 0.0423 0.0526 0.0611 0.0690 0.0812 0.0980 0.1159 0.1338 0.1501 0.1659 0.1770 0.1838 0.1989 0.2238 0.2506 0.2879 0.3427 0.4056 0.4691 0.5471 0.6353 0.6745 0.7277 0.8326 0.9409 1.0000

0.0000 0.0007 0.0015 0.0028 0.0052 0.0087 0.0111 0.0146 0.0204 0.0242 0.0274 0.0335 0.0435 0.0598 0.0848 0.1092 0.1294 0.1445 0.1551 0.1653 0.1839 0.2041 0.2249 0.2549 0.3017 0.3503 0.4113 0.5074 0.6011 0.6532 0.7709 0.9100 1.0000

0.0000 0.0003 0.0008 0.0019 0.0039 0.0072 0.0099 0.0131 0.0189 0.0218 0.0237 0.0297 0.0408 0.0612 0.0869 0.1137 0.1358 0.1517 0.1580 0.1667 0.1861 0.2031 0.2219 0.2586 0.3078 0.3666 0.4358 0.5307 0.6302 0.6672 0.7949 0.9367 1.0000

0.0000 0.0004 0.0008 0.0015 0.0035 0.0069 0.0087 0.0112 0.0156 0.0193 0.0212 0.0275 0.0363 0.0473 0.0658 0.0823 0.0967 0.1126 0.1282 0.1424 0.1634 0.1878 0.2118 0.2382 0.2753 0.3201 0.3792 0.4779 0.5644 0.6367 0.7474 0.8843 1.0000

0.0000 0.0231 0.0524 0.0673 0.1092 0.1301 0.1547 0.1787 0.1936 0.2055 0.2115 0.2275 0.2432 0.2592 0.2748 0.2908 0.3342 0.3773 0.4207 0.4638 0.5072 0.5506 0.5937 0.6371 0.6742 0.7113 0.7520 0.7986 0.8318 0.8726 0.9210 0.9608 1.0000

0.0000 0.0005 0.0012 0.0021 0.0040 0.0076 0.0112 0.0147 0.0309 0.0628 0.0750 0.1000 0.1271 0.2016 0.2450 0.2688 0.2865 0.3136 0.2981 0.2996 0.3678 0.3787 0.3880 0.4320 0.4962 0.5858 0.6691 0.7008 0.8266 0.7600 0.8640 0.9694 1.0000

0.0000 0.0009 0.0024 0.0036 0.0072 0.0109 0.0176 0.0218 0.0292 0.0372 0.0429 0.0508 0.0643 0.0872 0.1253 0.1580 0.1811 0.1944 0.2054 0.2232 0.2409 0.2650 0.3000 0.3319 0.3713 0.4158 0.4684 0.5525 0.6353 0.6932 0.7735 0.8856 1.0000

0.0000 0.0042 0.0102 0.0153 0.0212 0.0267 0.0301 0.0351 0.0458 0.0531 0.0641 0.0670 0.0767 0.0945 0.1333 0.1795 0.2246 0.2458 0.2551 0.2613 0.2669 0.2816 0.2957 0.3147 0.3553 0.3966 0.4396 0.5112 0.5915 0.6423 0.7414 0.8784 1.0000

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Table 2 Comparison between fitting values of SVM and real data during 1980–2012. Year

Real values

Fitted values of radial basis kernel function

Fitted values of linear kernel function

Fitted values of polynomial kernel function

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

0.0000 0.0019 0.0059 0.0110 0.0165 0.0238 0.0321 0.0423 0.0526 0.0611 0.0690 0.0812 0.0980 0.1159 0.1338 0.1501 0.1659 0.1770 0.1838 0.1989 0.2238 0.2506 0.2879 0.3427 0.4056 0.4691 0.5471 0.6353 0.6745 0.7277 0.8326 0.9409 1.0000

0.0097 0.0058 0.0052 0.0065 0.0169 0.0245 0.0361 0.0497 0.0568 0.0627 0.0674 0.0791 0.0924 0.1192 0.1403 0.1566 0.1716 0.1836 0.1899 0.2035 0.2303 0.2587 0.2939 0.3427 0.4068 0.4798 0.5544 0.6300 0.6801 0.7198 0.8202 0.9401 0.9902

0.0075 0.0104 0.0139 0.0154 0.0235 0.0292 0.0366 0.0431 0.0500 0.0591 0.0596 0.0734 0.0889 0.1179 0.1381 0.1493 0.1597 0.1792 0.1903 0.2085 0.2495 0.2750 0.3033 0.3479 0.3978 0.4595 0.5326 0.6163 0.7106 0.7327 0.8404 0.9483 0.9900

0.0104 0.0110 0.0137 0.0156 0.0248 0.0303 0.0377 0.0471 0.0518 0.0593 0.0602 0.0720 0.0813 0.1116 0.1237 0.1402 0.1665 0.1818 0.1742 0.1901 0.2303 0.2435 0.2777 0.3497 0.4204 0.4800 0.5391 0.6074 0.6778 0.7395 0.8408 0.9484 0.9866

Fig. 2. The fitting of linear kernel function.

Fig. 3. The fitting of Polynomial kernel function.

Table 3 Statistical parameters of the fitted values in different kernel functions.

MAPE RMSE R2

Radial basis kernel function

Linear kernel function

Polynomial kernel function

0.1115 0.0369 0.9836

0.2531 0.0094 0.9989

0.2700 0.0143 0.9975

Based on historical experience in many developed countries, urbanization level will go through a process of “low speed - high speed - low speed - stagnation”. Where, urbanization level which is below 30% is defined as low-speed growth stage; between 30% and 70% is defined as

high-speed growth stage; over 70% is defined as the slow-speed growth and gradually enter the stagnation stage (Song, 2007). In 1996, China's urbanization level exceeded 30%, which has entered into the highspeed growth stage. In 2011, the urban population proportion in China was 49.68%. So this paper assumes that the urbanization level will reach 60% in 2020, 70% in 2040, and then enter the stage of steady growth. For the rest variables, we adjust and estimate their growth rates on the basis of the average over the past decade. The growth rates of the main variables are summarized in Table 4. And the prediction results are summarized in Table 5 and Fig. 4.

Table 4 The growth rate estimations of influence factors of power demand (unit:%). Year

2016–2020

2021–2030

2031–2040

GDP

8.0 7.0 6.0 −0.1 0.7 2.3 7.0 7.5 8.0

7.0 6.0 5.0 −0.1 0.5 0.8 6.0 6.5 7.0

6.0 5.0 4.0 −0.1 0.5 0.8 5.0 5.5 6.0

Sec/GDP Ter/GDP UR P EU ER

Fig. 1. The fitting of radial basis kernel function.

High Medium Low – – – – – –

Note: Sec/GDP represents the proportion of the added value of secondary industry to GDP. Ter/GDP represents the proportion of the added value of tertiary industry to GDP. UR represents urbanization rate. P represents fuel price. EU represents disposable income of urban residents, and ER represents net income of rural residents.

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Table 5 The prediction results of power demand. Year

2020 2025 2030 2035 2040

Normalized values

Table 6 China's power generation capacity prediction in 2020 and 2040 (unit:hour, 0.1 billion kW).

Absolute values (0.1 billion kWh)

High speed growth

Medium speed growth

Low speed growth

High speed growth

Medium speed growth

Low speed growth

1.7350 2.3115 3.1033 4.0141 5.2123

1.5976 1.9980 2.5018 3.0009 3.5903

1.4690 1.7292 2.0191 2.2343 2.4295

89,343.92 118,033.96 157,433.01 202,758.40 262,383.05

82,508.88 102,432.99 127,503.44 152,337.27 181,668.56

76,105.20 89,053.62 103,483.05 114,190.38 123,904.46

According to the prediction results of Table 5, the electricity demand of China will reach 7.61052 trillion kWh in 2020, and 12.390446 trillion kWh in 2040 (according to the situation of low-speed growth situation). Under three different kinds of GDP growth rates, the prediction of China's power generation capacity in 2020 and 2040 is shown in Table 6. According to China Electricity Council, China's power generation capacity reached 1.25 billion kilowatts in 2013, which has become the largest one in the world. The calculation results in Table 6 show that the power generation capacity in China will increase from 20% to 40% in 2020, and power generation capacity in 2040 would be more than 2 to 4 times than the current level. If the average annual power generation operation hours are 4500, the capacity requirements will be much larger. China's electricity consumption is dominated by coal-fired production, so there is a high correlation between coal demand and electricity demand (see Fig. 5). This correlation can be explained by China's resource endowment and the low cost of coal. Since China's energy reserves are mainly coal, and oil and natural gas consumption is limited by high import prices, the cost of coal power is much lower than that of natural gas or oil. At the same time, it is difficult for clean energy power generation to replace thermal power on a large scale. For example, hydropower, wind power, and solar power are limited by factors such as resource reserves, transmission capacity, and instability. Nuclear power is limited by safety and cost. Therefore, coal demand is highly correlated with power demand to some extent. Based on the prediction of China's primary energy structure of Lin and Jiang (2009), we forecast the power generation structure. At the same time, according to China's renewable energy development planning in the medium and long term, the proportion of China's renewable energy to primary energy will reach 16% in 2020. With these constraints, the prediction of primary energy structure is shown in

Power generation hours per year

Year

high growth rate of GDP

medium growth rate of GDP

low growth rate of GDP

5000

2020 2040 2020 2040

17.87 52.48 19.85 58.31

16.50 36.33 18.34 40.37

15.22 24.78 16.91 27.53

4500

Table 7. Some of the coal consumption is used for electricity generation production, and the other is used for heating, coal chemical industry, metallurgy and so on. The ratio of power production is close to 50%. In this paper, we assume that the ratio of coal consumption to electricity production will maintain 50% in 2020. Combined with the prediction of primary energy consumption structure, the proportion of coal power generation to total power generation will reach 66.62% in 2020. The value of the proportion predicted in this paper is similar to Zhang (2011), which used CSGM model to predict this ratio (62% in 2020). Take the proportion of coal power generation into power demand prediction in 2020, and we can calculate that coal demand is 5.0701 trillion kWh in 2020. With the development of technology and efficiency, China's thermal coal consumption levels dropped year by year. In 2011, the thermal coal consumption reduced about 100 gce/kWh compared to 1985, reducing with an annual rate of 2% during the period of 11th “five-year plan”. According to the history of the thermal coal consumption descent rate and ultra-supercritical units of which consumption level is 230 gce/kWh on average in 2030, we expect that the average coal consumption will fall by 5gce/kWh annually, and reach 280 gce/kWh in 2020. 3.4. Carbon emissions prediction results Taking the coal power demand 5070.1 billion kWh, coal consumption 280gce/kWh in 2020, thermal coal demand 3416.8 billion kWh and coal consumption 333gec/kWh in 2010 into Eq. (2), we can calculate the CO2 emission reduction due to the improvement of clean coal electricity efficiency as following: ECT2020 ¼ ð50701−34168Þ  ð333−280Þ  2:66  10−6 ¼ 2:33ð0:1 billion tonsÞ

ð13Þ

According to the study of carbon emissions in China's urbanization stage by Lin and Liu (2010), China's carbon dioxide emissions in 2020

Fig. 4. The prediction values of power demand.

Y. Zhao et al. / Science of the Total Environment 675 (2019) 286–294

293

Fig. 5. Installed electricity capacity and power consumption in China. Data resource: CEIC database.

Table 7 Primary energy consumption structure prediction (Unit:%).

4. Conclusions

Year

Coal

Oil

Natural gas

Hydropower nuclear power, wind power

2020

63.86

14.67

5.47

16.00

Data resource: Lin and Jiang (2009). Table 8 Calculation of CO2 emissions reduction in 2020.

electricity coal consumption+10% Basis of electricity coal consumption electricity coal consumption-10%

CO2 emission reduction (0.1 billion tons)

Minimum proportion

Maximum proportion

1.91

1.7%

2.4%

2.25

2.0%

2.9%

2.59

2.3%

3.3%

is expected to reach 7.743 to 11.414 billion tons. Therefore, the reduction of carbon emissions caused by clean coal power generation is about 2.1% to 3.0% of the total emissions. When electricity coal consumption increases or decreases by 10%, we can calculate the corresponding emissions fluctuations, and the calculation results are show in Table 8. As can be seen from Table 8, the development of clean coal power generation can reduce CO2 emissions to a certain extent in the future, and increasing proportion of clean coal power generation3 will be conducive to CO2 emissions reductions. But it is worth noting that conventional clean coal technologies such as USC, IGCC (without CCS) and PFBC, can only alleviate CO2 emissions to a certain extent. They have little effect on solving the CO2 emissions problems thoroughly. For example, 10% reduction of coal consumption for power supply brings only 0.3%–0.4% reduction of carbon emissions. Therefore, the conventional clean coal technologies have not achieved the desired results in terms of CO2 emission reduction. However, this paper is limited to calculate the emission reductions of new coal demand caused by using clean coal technology rather than the current average level. Therefore, the results of this paper and other researches may be different.

3

The coal consumption for power supply is assumed to be lower than the base level.

According to the calculation results of this paper, carbon dioxide emissions will reduce by 2.1% to 3.0% in 2020 due to the use of conventional clean coal power generation technologies to meet the demand for coal-fired electricity. Compared to clean energy, carbon emission reduction effect of clean coal power generation is limited. In addition, clean coal power generation, such as the application of ultra-supercritical units, will lead to a large demand for coal and reduce the consumption of renewable energy. The efficiency improvement of coal-fired electricity will weaken the carbon emission reduction effect as we expected. Thus, in the long run, CCS technology or renewable energy may be a more preferred way of technological development to reduce carbon emissions. Two other effects of clean coal power generation technology on carbon emissions are not introduced in the model of this paper. The first is the substitution for the existing power plants with relatively outdated technology and more carbon emissions. The renewal of power generation equipment will have positive effects on emission reduction. Second, the development of clean coal technology will promote the use of coal, which may increase emissions of carbon dioxide and thus have a negative effect on reducing emissions. Taking the above factors into consideration, we may have a more comprehensive analysis of the emission reduction of clean coal technology in the following studies. Acknowledgements The work in this paper was supported by Open Fund of Operation and Control of Renewable Energy & Storage Systems (NYB51201801579). References Azad, A., Manoochehri, M., Kashi, H., Farzin, S., Karami, H., Nourani, V., Shiri, J., 2019. Comparative evaluation of intelligent algorithms to improve adaptive neurofuzzy inference system performance in precipitation modelling. J. Hydrol. 571, 214–224. Bezdek, R.H., Wendling, R.M., 2013. The return on investment of the clean coal technology program in the USA. Energy Policy 54, 104–112. Cen, K.F., Ni, M.J., Gao, X., Luo, Z.Y., Wang, Z.H., Zheng, C.H., 2015. Progress and prospects on clean coal technology for power generation. Eng. Sci. 17 (9), 49–55 (In Chinese). Chen, W.Y., Xu, R.N., 2010. Clean coal technology development in China. Energy Policy 38 (5), 2123–2130. Chen, W.D., Wu, F.Y., Geng, W.X., Yu, G.Y., 2017. Carbon emissions in China's industrial sectors. Resour. Conserv. Recycl. 117, 264–273. Falcke, T.J., Hoadley, A.F.A., Brennan, D.J., Sinclair, S.E., 2011. The sustainability of clean coal technology: IGCC with/without CCS. Process Saf. Environ. Prot. 89 (1), 41–52.

294

Y. Zhao et al. / Science of the Total Environment 675 (2019) 286–294

Fan, X.H., Wang, L., Li, S.S., 2016. Predicting chaotic coal prices using a multi-layer perceptron network model. Resour. Policy 50, 86–92. Franco, A., Diaz, A.R., 2009. The future challenges for clean coal technologies? Joining efficiency increase and pollutant emission control. Energy 34, 348–354. Holtedahl, P., Joutz, F.L., 2004. Residential electricity demand in Taiwan. Energy Econ. 26 (2), 201–224. Lin, B.Q., 2003. Electricity consumption and the growth of Chinese economy: based on the study of product function. Manage. World 11, 18–27 (In Chinese). Lin, B.Q., Jiang, Z.J., 2009. Prediction and factors analysis of environmental Kuznets curve of carbon dioxide in China. Manage. World 4, 27–36 (In Chinese). Lin, B.Q., Liu, X.Y., 2010. China's carbon dioxide emissions under the urbanization process: influence factors and abatement policies. Econ. Res. J. 8, 66–78 (In Chinese). Liu, J., Zio, E., 2019. Integration of feature vector selection and support vector machine for classification of imbalanced data. Appl. Soft Comput. J. 75, 702–711. Liu, Y., Hu, X.H., Feng, K.S., 2017. Economic and environmental implications of raising China's emission standard for thermal power plants: an environmentally extended CGE analysis. Resour. Conserv. Recycl. 121, 64–72. Lu, X., Yu, Z.F., Wu, L.X., Yu, J., Chen, G.F., Fan, M.H., 2008. Policy study on development and utilization of clean coal technology in China. Fuel Process. Technol. 89, 475–484. Lv, Y., Hong, F., Yang, T.T., Fang, F., Liu, J.Z., 2017. A dynamic model for the bed temperature prediction of circulating fluidized bed boilers based on least squares support vector machine with real operational data. Energy 124, 284–294. Ma, X.J., Wang, Y., Wang, C., 2017. Low-carbon development of China's thermal power industry based on an international comparison: review, analysis and forecast. Renew. Sust. Energ. Rev. 80, 942–970. Malen, J., Marcus, A.A., 2017. Promoting clean energy technology entrepreneurship: the role of external context. Energy Policy 102, 7–15. Mardani, A., Streimikiene, D., Cavallaro, F., Loganathan, N., Khoshnoudi, M., 2019. Carbon dioxide (CO2) emissions and economic growth: a systematic review of two decades of research from 1995 to 2017. Sci. Total Environ. 649, 31–49. Oboirien, B.O., North, B.C., Obayopo, S.O., Odusote, J.K., Sadiku, E.R., 2018. Analysis of clean coal technology in Nigeria for energy generation. Energ. Strat. Rev. 20, 64–70. Papadimitriou, T., Gogas, P., Stathakis, E., 2014. Forecasting energy markets using support vector machines. Energy Econ. 44, 135–142. Prabu, V., Mallick, N., 2015. Coalbed methane with CO2 sequestration: an emerging clean coal technology in India. Renew. Sust. Energ. Rev. 50, 229–244. Song, L.M., 2007. An analysis of the level of China's urbanization. J. Liaoning Univ. (Philosophy and Social Science Edition) 3, 115–119 (In Chinese). Song, M.L., Wang, J.L., 2018. Environmental efficiency evaluation of thermal power generation in China based on a slack-based endogenous directional distance function model. Energy 161, 325–336.

Tang, X., Snowden, S., Mclellan, B.C., Höök, M., 2015. Clean coal use in China: challenges and policy implications. Energy Policy 87, 517–523. Tang, L., Tian, Y.J., Pardalos, P.M., 2019. A novel perspective on multiclass classification: regular simplex support vector machine. Inf. Sci. 480, 324–338. Topper, J., Cross, M., Goldthorpe, P.J.I., 1994. Clean coal technology for power and cogeneration. Fuel 73 (7), 1056–1063. Vapnik, V.N., Lerner, A.Y., 1963. Pattern recognition using generalized portraits. Avtomatika i Telemekhanika 24 (6), 774–780. Wang, H., Nakata, T., 2009. Analysis of the market penetration of clean coal technologies and its impacts in China's electricity sector. Energy Policy 37 (1), 338–351. Wang, S.H., Song, M.L., 2017. Influences of reverse outsourcing on green technological progress from the perspective of a global supply chain. Sci. Total Environ. 595, 201–208. Wang, X., Xing, Y.L., 2019. An online support vector machine for the open-ended environment. Expert Syst. Appl. 120, 72–86. Wang, S.M., Song, C., Chen, Y.B., Sun, P., 2015. Technology research and engineering applications of near-zero air pollutant emission coal-fired power plants. Res. Environ. Sci. 28 (4), 487–494 (In Chinese). Wang, X.Y., Luo, D.K., Zhao, X., Sun, Z., 2018. Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation. Energy 152, 539–548. Wen, W., Zhou, P., Zhang, F.Q., 2018. Carbon emissions abatement: emissions trading vs consumer awareness. Energy Econ. 76, 34–47. Wilberforce, T., Baroutaji, A., Soudan, B., Al-Alami, A.H., Olabi, A.G., 2019. Outlook of carbon capture technology and challenges. Sci. Total Environ. 657, 56–72. Xiao, B., Zhang, A.L., Chen, G.F., 2005. Life cycle inventory of clean coal-fired power generation in China. Clean Coal Technol. 22 (2), 1–4. Zarei, T., Behyad, R., 2019. Predicting the water production of a solar seawater greenhouse desalination unit using multi-layer perceptron model. Sol. Energy 177, 595–603. Zendehboudi, A., Baseer, M.A., Saidur, R., 2018. Application of support vector machine models for forecasting solar and wind energy resources: a review. J. Clean. Prod. 199, 272–285. Zhang, S.W., 2011. Impacts of carbon tax on the power sector-modeling with CSGM. Energy Technol. Econ. 23 (3), 11–15 (In Chinese). Zhang, L., He, C.N., Yang, A.Q., Yang, Q., Han, J.S., 2018. Modeling and implication of coal physical input-output table in China—based on clean coal concept. Resour. Conserv. Recycl. 129, 355–365. Zhu, B.Z., Ye, S.X., Jiang, M.X., Wang, P., Wu, Z.C., Xie, R., Chevallier, J., Wei, Y.M., 2019. Achieving the carbon intensity target of China: a least squares support vector machine with mixture kernel function approach. Appl. Energy 233-234, 196–207.