Good subsidies or bad subsidies? Evidence from low-carbon transition in China's metallurgical industry

Energy Economics 83 (2019) 52–60

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Good subsidies or bad subsidies? Evidence from low-carbon transition in China's metallurgical industry Boqiang Lin ⁎, Mengmeng Xu School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Fujian 361005, PR China

a r t i c l e

i n f o

Article history: Received 28 January 2019 Received in revised form 2 June 2019 Accepted 22 June 2019 Available online 25 June 2019 JEL classifications: Q01 Q54 Q56 Q58 H23 L72 Keywords: China's metallurgical industry Fossil fuel subsidies Low-carbon transition

a b s t r a c t Since the metallurgical industry has become the main source of China's carbon dioxide emissions and energy consumption in recent years, low-carbon transition in that industry is of great significance for achieving China's carbon reduction targets. It is generally believed that phasing out fossil fuel subsidies is an effective way to reduce energy-related CO2 emissions since it can increase the energy prices and lower its consumption. This paper aims to investigate whether the energy subsidy removal can promote the low-carbon transition of China's metallurgical industry. Taking inter-fuel and inter-factor substitution effects as the link, we calculate the CO2 mitigation potential on the assumption that the subsidies for each category of fossil energy were eliminated. We find that the metallurgical industry has a sluggish reaction to the changes in energy price. Supposing eliminating the energy subsidies in the period of 2003–2015, the amount of reduced CO2 would be 487.286 million tons, accounting for a slight proportion of the total emissions in the industry. But it is meaningful for the global CO2 mitigation since it approximates the whole CO2 emissions in Norway during the same period. These findings can provide some new insights for the energy subsidy issue and suggest that the additional measures are required to promote the low-carbon transition in China's metallurgical industry rather than just relying on the removal of fossil fuel subsidies. © 2019 Elsevier B.V. All rights reserved.

1. Introduction 1.1. Background As the major part of the total energy subsidies, Fossil fuel subsidies have become a major challenge in tackling global climate change (Von Moltke et al., 2017). This is mainly reflected in two aspects: one is that subsidies for fossil fuel would depress the actual price paid by energy users. And then according to economic principle, more consumption of energy will be resulted in, and consequently increasing the CO2 emission and aggravating the environmental pollution. The other is that the large scale of subsidies not only increases the governments' financial burden, it also distorts the energy market mechanism thereby threatening the sustainable economic and social development. Therefore, it is imperative to promote the reforms of energy subsidies. At the 2009 Pittsburgh Summit, the G20 countries proposed “to phase out and rationalize the inefficient fossil fuel subsidies over the medium term”. Besides, the International Energy Agency (IEA), OPEC and the World Bank called for a systematic research on the fossil ⁎ Corresponding author at: School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Fujian 361005, PR China. E-mail address: [email protected] (B. Lin).

https://doi.org/10.1016/j.eneco.2019.06.015 0140-9883/© 2019 Elsevier B.V. All rights reserved.

subsidies to minimize the negative impact of removing energy subsidies on the economy, thereby promoting energy conservation and environmental sustainability. Since then, an increasing attention has been paid to the issues of fossil fuel subsidies worldwide. Related studies are enormous, and they are conducted for the scale of fossil fuel subsidies, (Coady et al., 2017; Clements et al., 2014; Lin et al., 2015), the economic effect of fossil fuel subsidy removal (Rentschler et al., 2017; Dennis, 2016; Rentschler, 2016; Li et al., 2017b) and the environmental effect of fossil fuel subsidy removal (Wesseh Jr and Lin, 2017; Mundaca, 2017; Wesseh Jr et al., 2016; Nwachukwu and Chike, 2011). Studies on CO2 and energy consumption in China have increased substantially in recent years since China is now the largest CO2 emitter in the world. The total amount of CO2 emissions due to China's energy consumption reached above 960 million tons in 2015, accounting for nearly 27% of global CO2 emissions (Ma et al., 2019). In general, the existing literatures in this field can be roughly divided into two categories: The first category estimates the amount and efficiency of energy consumption and CO2 emissions in China. For instance, Zhang et al. (2014) adopted the directional distance functions (DDF) model to investigate the energy efficiency of the power industry in China and suggested that the deregulation of state-owned firms would enhance the energy efficiency. Wang and Zhao (2017) calculated the eco-efficiency of China's non-ferrous metal industry based on provincial-level panel data. Feng and Wang (2018) adopted a global meta-frontier data

B. Lin, M. Xu / Energy Economics 83 (2019) 52–60

envelopment analysis (DEA) to estimate the energy efficiency in China's transportation sector between 2006 and 2014. Focusing on CO2 emissions, Zhao et al. (2015) conducted an empirical analysis on 137 power plants to examine the influence of environmental regulations on CO2 reduction and discovered that the market-based regulations and government subsidies were effective for CO2 reduction in power industry. An et al. (2018) calculated the potential of CO2 mitigations in China iron and steel industry and the results showed that over 810 Mt. CO2 could be reduced during 2015–2030. From the perspective of Chinese cities, Zhou et al. (2018) estimated that the CO2 emission performance and carbon allocation in 2005–2012. Additionally, Hasanbeigi et al. (2013), Meng et al. (2016), Seo et al. (2018) also conducted empirical research on energy and CO2 efficiency in China. Another category investigates the influence of the economic policy on the CO2 emissions, and most of them concentrate on carbon tax and carbon trading scheme. For instance, Dong et al. (2017) adopted a computable general equilibrium (CGE) model to measure the CO2 mitigating policies across 30 Chinese provinces using different carbon tax scenarios. Lu et al. (2010) also analyzed the impact of carbon tax on gross domestic product and carbon mitigation effect. The authors proved that carbon tax was an effective policy instrument as it can curb carbon emissions effectively with little adverse impact on the economy. A study by Liu et al. (2017) calculated the environmental impact of emission trading schemes in China, with a focus on Hubei province. Li et al. (2018) analyzed the impact of carbon trading market on electric power industry in China and the results showed that the carbon market would have greater emission reduction potential under the scenario of fully auctioning quotas. However, studies on the environmental effect of energy subsidies are scarce. Most of the literature related to energy subsidies in China focus on measuring the subsidy scale and the economic impact of energy subsidies. For example, Lin et al. (2015) used the price-gas approach to calculate the natural gas subsidies in China between 2010 and 2012. Taking energy subsidies into account, Li and Jiang (2016) and Li et al. (2017a) analyzed the rebound effects on China using the input-output model and CGE model respectively. Ouyang and Lin (2014) adopted scenario analysis method to investigate the variation of economic growth after increasing the renewable energy subsidies and removing the subsidies on fossil fuel in 2010. Compared with developed countries, the subsidies in developing countries (including China) are regarded as necessary if considering the social inequality which may be resulted in by economic transformation (Yao et al., 2011). China is now facing great pressures of both energy scarcity and CO2 emission reduction, and the role of energy subsidy cannot be neglected. In order to build a low carbon economy and realize the goal of environment protection and sustainable development in China, it is necessary to promote the reform of fossil fuel subsidies. However, regarding some crucial sectors in China, it is still uncertain what extent the fossil fuel subsidy is and what impact will the fossil fuel subsidy reform have on the carbon emissions. These problems will directly affect the reform process of fossil fuel subsidy and need to be urgently solved. Thus it is of great significance to conduct further studies on fossil fuel subsidies in some crucial sectors of China. 1.2. Objectives of studying the metallurgical industry As an important sector of the energy-intensive industries in China, the metallurgical industry has a significant impact on the energysaving and low carbon transition. The metallurgical industry in China comprises of two main sub-sectors: smelting and pressing of ferrous metals sector, and smelting and pressing of the non-ferrous metals sector. For efficient operation, the metallurgical industry required largescale energy especially the fossil fuel in the production process. In 2000–2015, the energy consumption of the metallurgical industry reached 9881.15 million tons of standard coal, accounting for 21.45% of China's total industrial energy consumption (CNBS, 2016). The

53

massive energy consumption in the metallurgical industry inevitably leads to huge CO2 emissions in China. According to Lin and Xu (2018), China's metallurgical industry witnessed a growing trend annually of the CO2 emissions. This trend increased by approximately 4 times in 2015 compared with the year 2000. The characteristics of high energy consumption and high carbon emissions also make the metallurgical industry be the key area for achieving the goal of carbon emission reduction in the 13th Five-Year plan. Therefore, in view of the slather of the fossil fuel used in China's metallurgical industry, the subsidy reform of fossil fuel in China would have a certain impact on the energy consumption as well as CO2 emissions in the metallurgical industry. The reason is that conducting fossil fuel subsidy reform will cause the relative price changes among different categories of energy, and because of the existence of substitution among different energy categories, their consumptions will be changed consequently, so the CO2 emitted by energy consumption would ultimately be affected. To be more specific, since coal plays a dominant role in the energy mix and has the highest carbon efficient, if coal could be substituted by a low-carbon energy such as oil and gas, removing the subsidy for coal would stimulate the consumption of low carbon energy and this would be helpful in curbing carbon emissions in the metallurgical industry. Based on the above discussions, this paper contributes to the existing studies in the following three aspects: (1) In policy debates, it is generally believed that phasing out fossil fuel subsidies can effectively reduce energy-related CO2 emissions. However, whether it is the real case for the metallurgical industry is still uncertain. This paper gives an answer to this critical issue. (2) As discussed in Section 1.1, many previous studies only estimate the scale of fossil fuel subsidies with individual years. For instance, Lin and Jiang (2011), Liu and Li (2011) provide an estimation of only 2007. Jiang and Lin (2014) calculate the fossil fuel subsidies in 2008. By contrast, this study uses the provincial panel data between 2003 and 2015 to study the fossil fuel subsidies in China. A collection of a large amount of data will help improve the correctness of the estimation results and support formulating appropriate policy for the implementation of the fossil fuel subsidies in China. (3) The existing studies referring to China's metallurgical industry mainly estimate the carbon emission efficiency (Lin and Xu, 2018) or the efficiency of energy conservation (Lin and Du, 2017). To the best of our knowledge, none has studied the CO2 mitigation effect of removing energy subsidies from the perspective of metallurgical industry. It should be noted that the high-energy consumption and high-carbon emission industry would play a key role in achieving the carbonreduction targets in China's 13th Five-Year plan, an in-depth investigation on the crucial sector in China would help to make more targeted policy recommendations. Therefore, this paper intends to solve this problem by utilizing the substitution elasticity as a link to investigate the CO2 mitigation potential by removing fossil fuel subsidies in the metallurgical industry. To sum up, we seek to overcome the above limitations and provide fresh insights into the following research questions: • How large is the scale of fossil fuel subsidies in China's metallurgical industry? • What are the influences of changes in the energy prices on the consumption of each individual energy? • How much is the impact of fossil fuel subsidies removal on CO2 mitigation in China's metallurgical industry?

The rest of the paper is organized as follows. Section 2 introduces the research methodology and data we used in this paper. Section 3 presents the empirical results and discussions. In Section 4, the impact of levying additional energy tax is added in the discussion. The main findings and policy suggestions are provided in Section 5.

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B. Lin, M. Xu / Energy Economics 83 (2019) 52–60

The cost-share function, Si, is then given by Eq. (7):

2. Methods and data 2.1. The price-gap approach

Si ¼

Price-gap approach is the most popular method in calculating energy subsidies. The theoretical framework of the price-gap approach is first established by Corden (1957). Afterwards, the method is widely adopted in many studies to calculate the scale of energy subsidies. Representative examples include Yao et al. (2011), Jiang and Tan (2013), and Peltovuori (2017). Following the common steps, we first calculate the price gap of the i-th energy (PGi) by Eq. (1): PGi ¼ P ri −P ei

ði ¼ coal; oil; gasÞ

ð1Þ

where, Pri and Pei represent the reference price and end-user price of the i-th energy respectively. In this paper, the subscript i concludes three types of energy (coal, oil, gas). It will be stated in detail in Section 2.4. Referring to the method used in Li and Sun (2018), we can calculate the reference price for each individual energy. Then, as described by Eq. (2), we can further estimate the scale of fossil fuel subsidies of each individual energy (SCi) in China's metallurgical industry. SC i ¼ C i  PGi

ði ¼ coal; oil; gasÞ

ð2Þ

where, Ci represents the energy consumption for the i-th fuel.

xi  P i P i ∂C ∂ lnC ¼  ¼ C C ∂P i ∂ ln P i

ð7Þ

Eq. (8) is derived from Eqs. (3) and (7) by taking the partial derivative of lnC with respect to ln Pi. Thus: Si ¼

3 X ∂ lnC ¼ βi þ ϕi ln Y þ βij ln P j þ βit t ði; j ¼ K; L; EÞ ∂ ln P i j¼1

ð8Þ

Assuming that the total cost function has the trans-log form, the same assumption is applied to the aggregate energy price function, i.e.: ln P E ¼ γ 0 þ

X i

γ i ln P i þ

X 1XX γij ln P i ln P j þ γit t ln P i 2 i j i

ð9Þ

where i and j are the energy types. In this paper, the considered energy types are categorized as coal (CO), oil (OI), and gas (GA). Pi is the price for each energy type and PE is the aggregate energy price. The energy-share function for the ith energy, denoted by SFi , is given by the derivative of ln PE with respect to ln Pi, i.e.: SiF ¼

X ∂ lnP E ¼ γi þ γ ij ln P j þ γit t ∂ ln P i j

ð10Þ

2.2. Estimating the inter-fuel/inter-factor substitution effects The application of Trans-log cost function to estimate the inter-fuel/ inter-factor substitution effects has gained great popularity, and examples of recent studies are Xie and Hawkes (2015), Lundmark and Olsson (2015), Wesseh Jr et al. (2013). Also, Coelli et al. (2005) describe the trans-log cost function as a representation of duality theory which provides a second-order approximation for any given function of indeterminate form. Taking energy, labor and capital as inputs, the gross industrial output can be expressed by the following function: X X 1 2 βi ln P i þ ϕi ln Y ln P i ln C ¼ β0 þβy lnY þ βyy ð ln Y Þ þ 2 i i X 1XX 1 þ βij ln P i ln P j þ βt t þ βtt t 2 þ βit t lnP i 2 i 2 j i þβyt t lnY

where C is the total cost, Y is the gross output, P is the input price and technological change is indicated by t. β is the coefficient waiting to be estimated. Eq. (3) must be subjected to the symmetry restrictions defined in Eq. (4) and the homogeneity restrictions defined in Eq. (5):

X

βi ¼ 1;

i

X

βij ¼

i

ð4Þ X i

βji ¼ 0;

X i

ϕi ¼

X

βit ¼ 0 ði; j ¼ K; L; EÞ ð5Þ

i

Shephard's Lemma indicates that the demand for any one of the production factors equals the derivative of the total cost with respect to the corresponding price, since this corresponds to the cost-minimizing point. Hence: xi ¼

∂C ði ¼ K; L; EÞ ∂P i

Sit ¼

3 X ∂ lnC ¼ βi þ ϕi ln Y t þ βij ln P jt þ βit t þ λi Si;t−1 ði; j ¼ K; L; EÞ ∂ ln P it j¼1

ð11Þ Sfuel it ¼

X ∂ ln P E ¼ γi þ γij ln P jt þ γ it t þ λi Sfuel i;t−1 ði; j ¼ CO; OI; GAÞ ∂ lnP it j

ð3Þ

ði ¼ K; L; EÞ

βij ¼ βji for all i≠j

If considering the dynamic adjustment to price change. Eqs. (8) and (10) can be written as:

ð6Þ

where the demand for the ith factor is given by xi; the total cost is given by C and the price of the ith factor is given by Pi.

ð12Þ The left hand side of factor-share of the factor-share equations and the energy-share equations both sum to 1 in the approximation process, hence one of them must be dropped in the estimation to eliminate the singularity problem. The parameters are estimated via the following three-step process: (i) Eq. (12) is estimated by omitting CO and adding error terms to the equations for the two remaining energy types; (ii) the parameters obtained in step (i) are inserted into Eq. (9) to obtain the aggregate energy price; (iii) the energy price obtained in step (ii) is used to estimate Eq. (12) while omitting the labour equation and adding residual terms to the energy and capital equations. In accordance with Pindyck (1979) and Cho et al. (2004), it must be stressed that the constant γ0 in step (ii) is determined when ln PE = 1 during the first period of study. Once all the parameters have been estimated, Eq. (13) can be used to calculate the own-price elasticities (ηii) and cross-price (ηij) elasticities of the factors or energy:   β^ij γ^ij β^ii ðγ^ii Þ þ Si −1ηij ¼ þ S j ði≠jÞ ηii ¼ Si Si

ð13Þ

where the ith and jth factor or energy shares are denoted by Si and Sj; β^ij is the parameter βij estimated from Eq. (11) and γ^ij is the parameter estimated from Eq. (12). Following the examples of Ma et al. (2008), the inter-fuel and interfactor feedback effect should be further accounted for, so that the own-

B. Lin, M. Xu / Energy Economics 83 (2019) 52–60

considered together as coal (CO) while diesel and gasoline are considered together as oil (OI). The prices for each type of energy are obtained from China Premium Database. For the absence of energy price data at the provincial level before 2003, the research period in this paper was from 2003 to 2015.

price and cross-price elasticities of the energy are given by Eq. (14). ηii ¼

  γ^ii þ Si −1 þ ηEE Si ; ηij ¼ Si

γ^ij þ Sj Si

! þ ηEE S j ði≠jÞ

55

ð14Þ

where, the own-price elasticity of energy is denoted by ηEE. 2.3. The CO2 mitigation effect by removing fossil fuel subsidies Based on Section 2.1, the percentage change of energy price by removing fossil fuel subsidies (PCFP) can be expressed by: ð15Þ

PCFP ¼ PG=P e

Then, we can calculate the total amount of CO2 mitigation potential (CMP) by removing fossil fuel subsidies in China's metallurgical industry using Eq. (16): CMP ¼

XX i

ai  ηij  PCFPj

ði; j ¼ CO; OI; GAÞ

ð16Þ

j

where, ai represents the CO2 emission factors for the ith energy, if removing the subsidy for the jth energy, the CO2 mitigation potential of the ith energy could be expressed as ai × ηij∗ × PCFPj. 2.4. Data and resources The data used in this paper include gross industrial output along with the quantities and prices of capital stock, labour, and energy. The present section describes each of these variables in detail. (1) The output is measured by the gross industrial output value of metallurgical industry in China, which is obtained directly from the China Industrial Economic Statistical Yearbook for each province. (2) The perpetual inventory method described by Goldsmith (1951) and Chen (2011) is adopted in this paper to calculate the capital stock. The depreciation rate in year t is first obtained by dividing the depreciation in year t by the original value of the fixed asset in year t − 1. The first difference in the original value of the fixed assets is then taken as the investment for year t, and it is converted into the constant price in 2000 using the price index of investment in fixed assets.

The net value of fixed assets in 2000 is then taken as the capital stock for the initial year. Eq. (17) is then used to obtain the capital stock for the subsequent years: K t ¼ It þ ð1−δt ÞK t−1

ð17Þ

where Kt is the capital stock in year t. Kt−1 is the capital stock in year t − 1. It is the investment in year t and δt is the depreciation rate in year t. Eq. (18) is used to estimate the capital price as follows: P K ðt Þ ¼ r ðt Þ þ δðt Þ−πðt Þ

ð18Þ

where r(t) is the loan rate obtained from the Almanac of China's Finance and Banking, π(t) is the rate of inflation and δ(t) is the depreciation rate. (3) The labour data are obtained from the China Industrial Economic Statistical Yearbook, and the price of labour can be obtained from the China Labour Statistical Yearbook. (4) The types of energy consumed (coke, coal, gasoline, diesel and natural gas) are selected on the basis of Li and Sun (2018) and Lin and Liu (2016). These are combined into three groups for simplicity and convenience. Among them, coke and coal are

Table 1 presents the statistical characteristics of the variables used in this paper. The panel data contain 29 Chinese provinces between 2003 and 2015. Hainan and Tibet were excluded because of low output and unavailability of data. Lack of available data also led to the exclusion of Taiwan, Macao and Hong Kong. 3. Results and discussions 3.1. Estimation results for fossil fuel subsidies in China's metallurgical industry Based on the price-gap approach discussed in Section 2, we measure the scale of subsidies for oil, gas, and coal in the period of 2003–2015. The corresponding results are presented in Table 2. The results showed that coal recorded the biggest subsidies which valued 229.969 billion CNY over the period of 2003–2015, this is because coal is the main energy source in the metallurgical industry, accounting for more than 80% of the total energy consumption. It should be noted that after years of market-oriented reform, the coal price has realized all-round marketization since 2013. Therefore, this paper assumed that since 2013, the subsidies for China's coal sector have been eliminated. For oil and gas, the scale of subsidies is affected by the fluctuation of international oil and gas price. A high international energy price usually leads to a low price of oil and gas to ease the downside shock of energy price to the economy. When the international energy price shows a downward trend, the National Development and Reform Commission in China would raise a consumption tax on domestic energy products to narrow the gap between end-user price and reference price to promote the energy conservation. The rate of fossil fuel subsidies for a period of 2003–2015 is presented in Fig. 1. The highest subsidy rate is for gas with the average value of 20.09%, which is followed by oil (9.87%), and coal (6.49%). This implies that the subsidy for gas was the main part of China's fossil fuel subsidies. The lowest average value of subsidy rate for coal indicates that the price of coal would have a dominant position than that of gas and oil when removing fossil fuel subsidies in China. Assuming that a lower energy price would lead to an increased energy consumption, it is noteworthy that the transition of consumption from oil and gas to coal would inevitably bring more carbon emissions. Moreover, it is notable that since 2013, both the scale and the rate of fossil fuel subsidies have dropped sharply. Specifically, the scales of the total subsidies for the three categories of energy declined from 6.254 Table 1 Descriptive statistics of variables. Variable

Obs Mean

Std. Dev.

Min

Max

Unit

k l ccoal coil cgas pcoal poil pgas pl pk

377 377 377 377 377 377 377 377 377 377

691.312 13.261 2047.681 16.750 1.656 151.405 1801.655 5843.293 10,616.360 0.014

36.949 1.120 0.910 0.000 0.000 330.000 3428.590 19,590.900 7249.208 0.093

4573.229 65.270 12,662.200 256.537 13.432 871.178 9975.370 37,158.300 76,306.860 0.144

109 yuan 104 persons 104 ton 104 ton 108 m3 Yuan/ton Yuan/ton Yuan/104 m3 Yuan/person

683.259 17.306 1648.615 8.127 1.101 585.188 6645.299 27,517.890 28,554.520 0.114

Note: ccoal, coil, cgas mean the consumptions of coal, oil and natural gas, respectively. pcoal, poil, pgas, pl and pk indicate the price of coal, oil, natural gas, labor and capital, respectively.

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B. Lin, M. Xu / Energy Economics 83 (2019) 52–60

international oil prices under the guidance of the government. In the period of low oil prices, the government would raise the consumption tax of refined oil products to prevent excessive energy consumption. For example, international oil prices began to plunge in the second half of 2014. In 2015, the prices of international crude oil prices have fallen to 52 USD/barrel. In the circumstance of low oil price after 2013, the government has raised the consumption tax on oil products several times to promote energy conservation as well as control air pollution (Lin and Liu, 2016). Specifically, the consumption tax of gasoline and diesel has risen by 1.32 CNY/l and 1.1 CNY/l from 2009 to 2015. It is notable that the consumption tax accounts for nearly half of the retail prices of oil products (Li and Sun, 2018). Thus, the adjustment of consumption tax promoted the reduction of oil subsidies to a certain extent, and even led to negative subsidies. Third, China has accelerated the natural gas subsidy reform and canceled the subsidy for increment gas since 2013, thereby making the scale of subsidy for gas fall from 5.645 billion CNY in 2013 to 2.185 billion CNY in 2015. To sum up, China's subsidy reform for each individual energy all contribute to a decline in the fossil fuel subsidies, the negative values of the total subsidy scale in 2015 indicated that China might have fulfilled its commitment to phase out fossil subsidies in the G20 summit held in Pittsburgh, which is consistent to the finding of Lin and Liu (2016).

Table 2 The scale of fossil fuel subsidies in China's metallurgical industry during 2003–2015 (Units: billion CNY). Year

S_oil

S_gas

S_coal

total

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Total

1.529 2.105 4.706 4.626 4.218 4.770 −1.320 −0.176 1.103 1.001 0.609 −0.382 −2.980 19.809

0.064 0.311 0.942 1.531 1.696 2.993 1.117 2.857 4.742 5.359 5.645 3.902 2.185 33.344

1.206 24.698 15.281 5.196 24.130 102.000 19.422 19.156 4.250 14.629 0.000 0.000 0.000 229.969

2.799 27.115 20.929 11.353 30.044 109.763 19.219 21.838 10.095 20.989 6.254 3.520 −0.795 283.122

Note: S_oil, S_gas, S_coal represent the scale of subsidies of oil, gas and coal, respectively.

billion CNY to −0.795 billion CNY within 3 years, showing a reduction of more than 70% between 2013 and 2014. It is also the first time in China to record a negative value of fossil fuel subsidy in 2015. For individual energy, the following factors are responsible for this phenomenon: First, the government annulled the key coal contract and the double track system of coal price in the year 2013. Moreover, the government has mandated all the coal enterprises and the electric power enterprises to sign the contract to set up the independent price of coal. This means that the coal subsidy is basically eliminated since 2013. As the coal is the dominant source of energy in China's metallurgical industry, the elimination of coal subsidies would unexceptionally slash the total scale of fossil fuel subsidies. Second, as for oil, the subsidy rates in China were only 1.67% during the period of high oil prices in 2013. While along with the further improvement of the pricing mechanism of refined oil as well as the sustained downturn in international crude oil prices since 2013, the subsidy rate of oil has declined year by year, and even the situation of negative subsidies has appeared in the year of 2014 and 2015. This phenomenon is not surprising and the explanations are as follows. The price of oil products in China is indirectly linked to the fluctuation of

3.2. Estimation results for inter-fuel/inter-factor substitution effects Referring to Section 2.2, we adopt the seemingly unrelated regression (SUR) method to estimate the parameters of energy share equations. Since the model has satisfied the constraint of homogeneous and symmetric. We drop one of the share equations and estimate the share equation of oil and gas. The estimation results are listed in Table 3. The estimation results for factor share equations are shown in Table 4 and we can see that most of parameters are significant at 1% significance level, so we can further calculate the own-price elasticity and cross-price elasticity of each energy based on Eq. (14). In Table 5 we provided the results of the total own-price elasticities and cross-price elasticities of each individual energy. The results in Table 5 provide several implications.

40.00%

30.00%

20.00%

10.00%

0.00% 2003

2004

2005

2006

2007

2008

2009

2010

2011

-10.00%

-20.00%

-30.00% oil

coal

gas

Fig. 1. The rate of fossil fuel subsidies (2003–2015).

2012

2013

2014

2015

B. Lin, M. Xu / Energy Economics 83 (2019) 52–60 Table 3 The estimation results for fuel share equations. Variable

L.SEgas lnpgas lnpcoal lnpoil t L.SEoil Constant Observations R-squared

(1)

Table 5 Results for the elasticities of individual fuels. (2)

SEgas

Std Err.

0.6900⁎⁎⁎ 0.0686⁎⁎⁎ −0.0846⁎⁎⁎ 0.0160⁎ 0.00222⁎⁎

0.0405 0.0239 0.0240 0.0095 0.0010

−0.243⁎⁎⁎ 348 0.804

0.0922

Own-price elasticity

SEoil

Std Err.

0.0160⁎ −0.0201⁎⁎ 0.00407 −0.00175⁎⁎⁎ 0.375⁎⁎⁎

0.0095 0.0101 0.0109 0.0004 0.0434 0.0319

−0.0401 348 0.934

Note: L.SEgas and L.SEoil stand for the lagged variable of gas ratio and oil ratio respectively. ⁎⁎⁎ suggests significance level at 1%. ⁎⁎ suggests significance level at 5%. ⁎ suggests significance level at 10%.

First, all the energy have recorded the negative values for own-price elasticity thus providing the similar findings by Li and Sun (2018), Li and Lin (2016) and so on. This implies that an increase in price will depress a demand for energy. The result is consistent with the microeconomic theory, revealing that an increase in price will depress the quantity demanded. Among them, the own-price elasticity of oil has the largest absolute value of 0.8925, indicating that the demand for oil is most sensitive to the price changes. When the price of oil increases by 1%, the demand for oil would drop by 0.8925%. As the dominant energy type in the metallurgical industry of China, the own-price elasticity of coal shows a relatively smaller absolute value of 0.3776, followed by gas. The reason is that coal and gas have long been in the mode of governmental pricing system, which impedes the impact of price mechanism on energy products. While the price of oil products in China is indirectly linked with international oil price. So the change of oil price can better convey to market demand (Wang and Lin, 2017). Second, the negative crossprice elasticity of gas and coal means that there is no substitution relationship between these two types of energy, that is to say, no matter an increase in the price of gas or coal, it would not bring out the ascending demand for another energy in the metallurgical industry. Taken the cross-price elasticity of coal to gas (ηCO-GA) as an example, when the price of gas shows a 1% increase, the demand of coal in the metallurgical industry would only jump by 0.0576%, showing that the demand for coal is less sensitive to the price change of gas. Third, the positive value of cross-price elasticity among other energy implies that these energy can substitute each other in the manufacturing sector but at a low level. Notably, all the cross-price elasticities show an absolute value of less than 1, indicating that there is inelasticity among the three energy. Thus, the metallurgical industry is slowly in response to the price changes of energy.

Table 4 The estimation results for factor share equations. Variables

L.SE lnPE lnpk lnpl lny t L.SK Constant Observations R-squared

(1)

(2)

SE

Std Err.

0.8345⁎⁎⁎ 0.0606⁎⁎⁎ −0.0736⁎⁎⁎ 0.0130⁎⁎⁎ −0.0095⁎⁎⁎ −0.0050⁎⁎⁎

0.1592 0.0717 0.0772 0.0024 0.0024 0.0007

−0.1649⁎⁎⁎ 348 0.914

0.3308

57

SK

Std Err.

−0.0736⁎⁎⁎ 0.1048⁎⁎⁎ −0.0312⁎⁎⁎ 0.0122⁎⁎⁎ 0.0070⁎⁎⁎ 0.7986⁎⁎⁎ 0.5718⁎⁎⁎

0.0077 0.0091 0.0031 0.0027 0.0008 0.0181 0.0479

348 0.886

Note: L.SE and L.SK stand for the lagged variable of energy ratio and capital ratio respectively. ⁎⁎⁎ suggests significance level at 1%.

ηGA-GA ηco-co ηOI-OI

Cross-price elasticity −0.1376 −0.3776 −0.8925

ηGA-OI ηGA-CO ηCO-GA ηCO-OI ηOI-GA ηOI-CO

0.2523 −0.6658 −0.0576 0.0080 0.0108 0.1246

3.3. The impact of fossil fuel subsidies removal on CO2 mitigation in the metallurgical industry Based on Eqs. (15) and (16), we can further estimate the potential of CO2 emissions mitigation in China's metallurgical industry. The results of CO2 mitigation potential and its proportion by removing the subsidy for each type of energy are presented in Table 6. The negative value indicates that the price change of energy would lead to a reduction in CO2 in China's metallurgical industry. In total, removing fossil fuel subsidies reduced the 487.286 million tons of CO2 emissions during 2003–2015, accounting for 4.063% of the total emissions in the metallurgical industry. Although it accounts for a slight proportion of the total emissions in the industry, it is meaningful for the global CO2 mitigation since it approximates the whole CO2 emissions in Norway in that period. The impact of removing the subsidy for coal on CO2 emissions reduction was the strongest, which could mitigate CO2 emissions by 295.582 million tons. The proportion of CO2 mitigation showed a value of −2.456%. It means CO2 emissions would have been 2.456% lower without coal subsidies during 2003–2015. The CO2 mitigation effect of removing gas subsidy ranked the second, with a physical quantity of 192.018 million tons, accounting for 1.602% of the total emissions in China's metallurgical industry. By contrast, the impact of removing the subsidy for oil on CO2 mitigation is not obvious and even shows a positive total value during the research period. This is because even though the related CO2 from oil has declined, removing the subsidy for oil would stimulate the growing consumption of gas and coal (high carbon energy) as substitutes, so the CO2 emissions from gas and coal may offset the CO2 mitigation effect by removing the subsidy for oil.

4. More discussions: comparing with the situations of levying additional energy tax In this section, we calculate the impact of levying additional energy tax on CO2 mitigation in the metallurgical industry and compare it with that of energy subsidy removal. Energy tax will increase the price of energy and make the end users to switch to cleaner energy types and in this way, the greenhouse gas emission will be reduced (Rentschler and Kornejew, 2017). In 1993, the State Council of China issued the Provisional Regulations on Resource Tax of the People's Republic of China in which the taxes on coal, oil and gas are set. They are 0.3–5 yuan/ton for coal, 8–30 yuan/ton for oil and 2–15 yuan/thousand cubic meter for natural gas. This version of regulation is implemented until November 2011. And from 2012 to 2015, the new version of regulation is implemented in which the tax for coal is 0.3–20 yuan/ton, 5%–10% of the sales for oil and 5%–10% of the sales for natural gas. For the discussion, we set the additional energy tax as the average one of each energy type and recalculate the possible CO2 emissions and energy consumptions. Table 7 lists the results of possible changes of CO2 emissions and energy consumption if levying additional energy tax using inter-fuel substitution as the link. Compared with the results of energy subsidy removal, the results after levying energy tax are smaller. The total possible decrease of CO2 emissions is 67.017 million tons, lower than that of energy subsidy removal (487.286 million tons). The reason is that the energy tax is

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B. Lin, M. Xu / Energy Economics 83 (2019) 52–60

Table 6 CO2 mitigation due to the price change of individual energy types. Year

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Total

Coal

Oil

Gas

Total

Emission reductions (million tons)

Proportion (%)

Emission reductions (million tons)

Proportion (%)

Emission reductions (million tons)

Proportion (%)

Emission reductions (million tons)

Proportion (%)

−2.636 −47.898 −24.414 −7.869 −34.506 −118.789 −23.203 −17.931 −3.538 −14.798 0.000 0.000 0.000 −295.582

−0.600% −8.576% −3.699% −1.058% −4.107% −13.759% −2.577% −1.868% −0.332% −1.440% 0.000% 0.000% 0.000% −2.465%

−0.083 0.347 0.037 0.333 0.364 0.747 −0.356 0.011 0.329 0.209 0.044 −0.194 −1.409 0.378

−0.019% 0.062% 0.006% 0.045% 0.043% 0.087% −0.040% 0.001% 0.031% 0.020% 0.003% −0.015% −0.110% 0.003%

−0.806 −2.776 −10.513 −12.188 −14.443 −24.984 −6.475 −16.488 −26.835 −27.190 −26.851 −16.180 −6.352 −192.081

−0.183% −0.497% −1.593% −1.639% −1.719% −2.894% −0.719% −1.718% −2.521% −2.646% −1.935% −1.271% −0.498% −1.602%

−3.525 −50.327 −34.890 −19.725 −48.586 −143.026 −30.034 −34.407 −30.044 −41.779 −26.808 −16.374 −7.761 −487.286

−0.802% −9.011% −5.286% −2.653% −5.783% −16.566% −3.335% −3.584% −2.823% −4.066% −1.932% −1.286% −0.609% −4.063%

smaller than the subsidy. During 2003–2015, the total decreased energy consumptions are 25.40 million tons of standard coal, accounting for only 0.56% of total energy consumption of the metallurgical industry. These findings imply that only levying additional energy tax has little effect on energy saving and emission reduction in China's metallurgical industry. If removing the energy subsidy and levying additional energy tax simultaneously, the potential CO2 mitigation and energy savings are present in Table 8. If implementing the two polices at the same time, the effects on CO2 mitigation and energy conservation are significant. The decreased CO2 emissions will be 554.303 million tons and the energy consumption will be 210.022 million tons of standard coal. The findings provide new sight for achieving low carbon transition in China's metallurgical industry. In the future, policymakers can formulate targeted policy combinations to address energy consumption and environmental issues. 5. Main findings and policy suggestions Taking China's metallurgical industry as the research objective, this paper first measures the scale of fossil fuel subsidies, and then takes the inter-fuel substitution elasticities as the link to examine whether phasing out fossil fuel subsidy could effectively promote the lowcarbon transition. The major findings are summarized as follows: First, with the acceleration process of fossil fuel subsidy reform, the scale of fossil fuel subsidy represented an obvious downward trend since 2013, and the total scale of fossil fuel subsidy even appeared as a negative value of −0.795 billion CNY in the year of 2015, which may

become a possible evidence that China had basically removed the fossil fuel subsidies in a financial sense for the metallurgical industry. Moreover, among the three types of energy, the subsidy rate of gas was the largest with an average value of 20.09%, followed by oil and coal. This reveals that the subsidy for gas was the chief component of China's fossil fuel subsidies over the period. Another notable finding is that when removing the fossil fuel subsidies, the price of coal would have a relatively dominant price than that of gas and oil. Third, regarding the substitutions, the own-price elasticities of oil, gas and coal are −0.8925, −0.3776, −0.1376 respectively. The negative values indicate that the demand for each individual energy in the metallurgical industry would decrease with the rise of their prices. The cross-price elasticities among individual energy all show an absolute value less than 1, implying that all the three energy factors are very inelastic. The metallurgical enterprises have a sluggish reaction to the relative price changes of energy. Finally, removing fossil fuel subsidies to a certain extent can sharpen the CO2 emissions of China's metallurgical industry. The reduced amount accounts for a slight proportion of the total CO2 emissions in the industry. But it is meaningful for the global CO2 mitigation since the reduced amount approximates the whole CO2 emissions in Norway. Among them, the CO2 mitigating effect of removing the subsidy for coal was strongest, followed by gas and oil. Thus, on the one hand, as the reduction in fossil fuel subsidies in recent years has largely benefited from the plunged prices of oil as well as the slowing downward growth of energy demand. The government should grasp the opportunity of reform and lock in the achievements of reducing fossil fuel subsidies in recent years. Besides, it is necessary

Table 7 The possible changes of CO2 emissions and energy consumption if levying additional energy tax. Year

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Total

Changes of CO2 emissions (million tons)

Changes of energy consumption (10 thousand tons of standard coal)

Coal

Oil

Gas

Total

Coal

Oil

Gas

Total

−1.317 −1.487 −1.447 −1.544 −1.651 −1.383 −1.479 −1.227 −1.220 −5.473 −8.412 −8.798 −10.805 −46.244

−0.002 0.005 0.000 0.003 0.004 0.006 0.010 −0.005 0.011 0.300 0.283 0.589 0.554 1.760

−0.108 −0.136 −0.160 −0.159 −0.164 −0.160 −0.159 −0.160 −0.166 −4.382 −5.907 −5.442 −5.429 −22.533

−1.427 −1.618 −1.607 −1.700 −1.811 −1.537 −1.627 −1.392 −1.375 −9.555 −14.036 −13.651 −15.681 −67.017

−52.632 −59.685 −59.278 −62.738 −66.951 −56.902 −60.144 −51.042 −50.905 −345.216 −503.873 −502.338 −574.272 −2445.976

−0.880 −0.798 −0.946 −0.823 −0.825 −0.616 −0.584 −1.083 −0.445 −21.336 −34.593 −16.985 −21.788 −101.702

−0.502 −0.783 −0.604 −0.813 −0.745 −0.595 −0.869 −0.670 −0.634 3.710 4.334 3.406 2.416 7.651

−54.014 −61.267 −60.828 −64.374 −68.522 −58.112 −61.597 −52.795 −51.984 −362.842 −534.132 −515.917 −593.644 −2540.027

B. Lin, M. Xu / Energy Economics 83 (2019) 52–60

59

Table 8 The possible changes of CO2 emissions and energy consumption if removing energy subsidy and levying additional energy tax simultaneously. Year

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Total

Changes of energy consumption (10 thousand tons of standard coal)

Changes of CO2 (million tons) Coal

Oil

Gas

Total

Coal

Oil

Gas

Total

−3.953 −49.385 −25.861 −9.414 −36.157 −120.173 −24.682 −19.158 −4.758 −20.271 −8.412 −8.798 −10.805 −341.827

−0.085 0.352 0.037 0.336 0.367 0.753 −0.346 0.007 0.340 0.509 0.327 0.395 −0.855 2.139

−0.914 −2.912 −10.673 −12.347 −14.607 −25.144 −6.634 −16.648 −27.001 −31.572 −32.759 −21.622 −11.781 −214.615

−4.952 −51.945 −36.497 −21.425 −50.397 −144.563 −31.662 −35.799 −31.419 −51.334 −40.843 −30.025 −23.441 −554.303

−146.900 −1893.068 −1278.465 −732.136 −1822.212 −5343.970 −1192.442 −1340.927 −1169.878 −1901.093 −1504.959 −1119.188 −883.619 −20,328.856

−56.739 −63.519 −128.769 −112.199 −92.920 −76.557 28.645 7.840 −15.521 −33.290 −43.022 −10.494 34.672 −561.873

6.587 −15.899 8.413 19.558 −1.045 −46.503 −29.470 −16.079 1.238 −4.319 4.180 −2.037 −36.144 −111.520

−197.052 −1972.486 −1398.821 −824.776 −1916.177 −5467.031 −1193.268 −1349.166 −1184.161 −1938.702 −1543.801 −1131.719 −885.091 −21,002.249

for the government to further promote the market-oriented reform of the energy price mechanism and prevent the rebound of fossil energy subsidies for oil products. On the other hand, accelerate the reform of fossil fuel subsidies is necessary for the CO2 mitigation of China's metallurgical industry, but it is not enough to achieve the low carbon economy in the metallurgical industry in China just by removing fossil fuel subsidies. The coal-based energy structure in the metallurgical industry is still hard to change at present. Therefore, additional measures must be taken to promote the low carbon production in China's metallurgical industries, such as increase R&D spending to promote low-carbon technologies (e.g. Koster Technology, Insoluble anode tin plating technology, packing technique), consummate the related policies of phasing out the backward production capacities, or accelerate the implementation of pilot projects for low-carbon industrial parks (Yan et al., 2019). In the future, the policy-makers can also develop targeted policy mix to cope with the environmental concerns. It is worth noting that as the developing and emerging countries have similar conditions in fossil fuel subsidies, the policy suggestions in this paper could also be applied to other emerging and developing countries that are tending to establish low carbon transition economy. Acknowledgment The paper is supported by Report Series from Ministry of Education of China (No.10JBG013), and China National Social Science Fund. (No.17AZD013) Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.eneco.2019.06.015.

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