How does the implementation of the Policy of Electricity Substitution influence green economic growth in China?

Energy Policy 131 (2019) 251–261

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How does the implementation of the Policy of Electricity Substitution influence green economic growth in China?

T

Wanlu Wua, Yuanyuan Chengb, Xiqiao Lina, Xin Yaob,∗ a b

Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning, 530023, China China Center for Energy Economics Research, School of Economics, Xiamen University, Xiamen, 361005, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Green economic growth Electricity substitution policy Input-output (IO)

To promote the level of electrification, reduce environmental pollution, and achieve the goal of clean energy usage and green economic growth in China, the National Development and Reform Commission and other eight departments jointly issued and implemented the Policy of Electricity Substitution in 2016. Electricity substitution aims to replace traditional fossil fuels such as coal and fuel oil with clean electric energy in different fields, e.g., industry, transportation, and construction, and deal with the pollutants at power generation. However, we still need to find out the exact impact of the Policy of Electricity Substitution on energy saving and emission reduction. In this paper, we estimate the economically feasible increment of electricity demand according to different classifications in electricity substitution technology. Furthermore, based on the Input-Output model, we discuss the relative influence of the Policy of lectricity Substitution. The main conclusions are: First, the Policy of Electricity Substitution alone is not adequate to reduce the dependence on fossil energy and environmental pollution under China's current power generation structure, we should also increase the use of renewable energy. Second, the power sector plays a leading role in stimulating economic output, and the increasing electricity consumption due to the Policy of Electricity Substitution can boost the green development of China's economy.

1. Introduction Due to poor storage of oil and gas in energy resource endowments, coal has always been dominant in China's energy consumption structure (Lin and Zhu, 2017; Zhang et al., 2018). However, excessive use of fired coal exacerbates the environmental problems in China (Chai et al., 2019). And the greenhouse gas emissions and smoke have become severe and urgent environmental pollution problems for China (Chang et al., 2019). Facing the dual pressure concerning environmental issues nationally and internationally, electricity substitution is a popular solution to change the energy-using pattern now. With the rapid development of urbanization in China, the demand for electricity by economic development is also growing at a high speed (Lin and Liu, 2016; Zhang et al., 2017). Also, electric energy has become the most preferable way so far because of its clearness and convenience (He et al., 2018; Fang et al., 2019). In 1980, the residential electricity consumption per capita was only 11 kWh in China, however, it reached 611 kWh in 2016.1 Although China's electrification level has greatly improved over the past few decades, coal-fired and oil-fired

production equipment with high energy consumption and pollution are still in use in many social fields. In addition, rural areas prefer to use low-efficiency fossil fuels such as charcoal and coal compared to urbanized areas (Wu et al., 2019a, b). Electricity substitution emphasizes the preferential consumption of electric energy through technological innovation. To reduce the consumption of fossil energy, electricity substitution technology encourages the use of various types of power products, electric power facilities and so on (Liang et al., 2015). For example, the traditional coal burning boiler, coal-burner, residential heating, and cooking now have electricity mode to reduce the consumption of coal (Wang and Huang, 2014). Electricity substitution can effectively promote energy efficiency since the use of electrical equipment has higher energy conversion efficiency than the direct consumption of traditional fossil energy. However, fossil fuel dominated electricity production in China and the proportion of thermal power still reached 71% in 2017.2 As a result, the electricity consumption in China could still lead to environmental pollution, because coal is the primary fuel for electricity production (Lin et al., 2016; Zhang et al., 2019). If there is no significant change in

∗

Corresponding author. E-mail address: [email protected] (X. Yao). 1 China Energy Statistical Yearbook 2017. 2 Data source: China Electricity Council. https://doi.org/10.1016/j.enpol.2019.04.043 Received 24 December 2018; Received in revised form 25 March 2019; Accepted 28 April 2019 Available online 15 May 2019 0301-4215/ © 2019 Published by Elsevier Ltd.

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2006 in seven countries of South America, Yoo & Kwak (2010) found that there is a one-way causal relationship between electricity consumption and real GDP in Argentina, Brazil, Chile, Colombia and Ecuador in short-term, but the causal relationship is two-way in Venezuela and there is no causal relationship in Peru. Cowan et al. (2014) explored the different impact mechanisms between electricity consumption and economic growth in the BRICS countries. Taking into consideration heterogeneity and cross-sectional dependence, Osman et al. (2016) studied the relationship between electricity consumption and economic growth in the GCC countries using the PMGE. By analyzing the annual data of 174 countries from 1980 to 2012, Atems and Hotaling (2018) found that regardless of the power is clean or not, it can promote economic growth, so the country can promote renewable energy to pursue environmental protection goals under the premise of ensuring economic development. In addition, many scholars are interested in the relationship between energy consumption and industry (Yang et al., 2018; Rahman and Kashem, 2017; Huo et al., 2018; Zhao and Yin, 2011; Ouyang et al., 2018). According to industrial data from 2001 to 2014 in China, Meng et al. (2018) analyzed the relationship between the output value of industry and its consumption of fossil energy, and predicted that the energy intensity of industry would decline. With the IO model, Mi et al. (2015) explored the impact of Beijing's industrial structure transformation on energy consumption and carbon emissions. Based on the binary choice model and the DID model, Lin and Du (2017) found that the construction of urban rail transit can significantly reduce the energy consumption of the automotive industry. Duran et al. (2015) analyzed the impact of different driving factors on energy consumption in Chilean industrial sector through IDA, and proved that energy efficiency policies would be important for the industrial sector. Based on translog production functions, Pablo-Romero et al. (2019) studied the effect of energy consumption in different industries of the EU. However, there are currently few relevant works of literature on electricity consumption and industry. Al-Bajjali and Shamayleh (2018) studied the impact of many different factors such as GDP, electricity price, population, urbanization, industrial structure, and water consumption on electricity consumption, and found that industrial structure is significantly related to electricity consumption in Jordan. In addition, Mordue (2017) analyzed the impact of electricity costs on the competitiveness of the automotive industries in the US and Canada through financial analysis and interview research methods. Based on the optimized grey prediction model, Ding et al. (2018) predicted industrial electricity consumption in China. Wang et al. (2010) studied the different influencing factors of power consumption growth in China's industrial sector and found that activity effect, shift effect and structural change had positive effects on electricity consumption, but the technological effect had negative impacts. In this paper, we will analyze the actual impact of electricity demand growth on the industrial structure under the implementation of the Policy of Electricity Substitution, which enriches the researches in the associated academic field. The IO table was first proposed by W. Leontief in 1936, and has been used by many scholars as a research method for the related issues of energy, environment and economics, such as energy efficiency (Freire-González and Vivanco, 2017; Wu et al., 2017; Jiang et al., 2015; Guevara and Domingos, 2017), carbon emissions and carbon reduction (Yuan and Zhao, 2016; Su et al., 2017; Li et al., 2015; Long et al., 2019; Guo et al., 2018), air pollution (Wang et al., 2018b; Song et al., 2018, 2019; Alcántara et al., 2017), embodied carbon in trade (Deng and Xu, 2017; Liu et al., 2015; Wang et al., 2019), and embodied energy in trade (Wu and Chen, 2017; Rocco and Colombo, 2016; Zhang et al., 2015; Tang et al., 2019). Based on the IO model, we will study the impact of the Policy of Electricity Substitution on the industrial output and structure in China, and further obtain some policy implications.

China's coal-based energy structure, the carbon reduction effect due to replacing fossil energy with electricity may not be significant enough (Yu et al., 2018). Hence, it is necessary to study the actual effect of the Policy of Electricity Substitution under the current power production structure in China. The main contribution of this study is threefold. Firstly, we estimate the potential increment of electricity consumption under the implementation of the Policy of Electricity Substitution based on different products and service fields of the whole society, thus we can qualify the policy effect. In fact, there are many classifications in electricity substitution technology, including the construction, industry, transportation, agriculture, and many other fields (Cheng et al., 2016). In this paper, we will calculate the latent capacity of electricity substitution in China according to the classifications referred by Cheng et al. (2016). Secondly, based on China's current power generation structure, we estimate the actual effect of energy saving and emission reduction brought by the Policy of Electricity Substitution, and further propose some policy recommendations for this policy to better accelerate China's green development. Finally, we verify the positive impact of electricity consumption on GDP, thus demonstrating that the implementation of the Policy of Electricity Substitution can promote sustainable economic development in China. This paper comprehensively evaluates the role of the Policy of Electricity Substitution, which is a critical prerequisite for the green economic growth in China. Since the latest IO table published by Chinese Input-Output Association is the 2012 IO table, this paper mainly uses China's 2012 IO table (42 sectors) for analysis. We first calculate the amount of increasing electricity demand based on the different technical classifications of electricity substitution. Then, we use 2012 China's competitive IO table to analyze the productive and environmental effects of electricity substitution. Furthermore, we calculate the influence coefficients and response coefficients of different sectors and compare the power sector with other sectors. Finally, we construct 2012 China's noncompetitive IO table to discuss the influence of electricity substitution on the industrial output and structure. The remainder of the paper is organized as follows. Section 2 is the literature review. Section 3 illustrates the methodology of this paper. In Section 4 we discuss the data and the results. Section 5 is conclusions and policy recommendations. 2. Literature review The causality between electricity consumption and economic growth is often inconsistent in different countries or regions. Mozumder and Marathe (2007) found that Bangladesh's per capita GDP has a oneway causal relationship with per capita electricity consumption, but the contrary is not established. This conclusion shows that the implementation of energy conservation and emission reduction would not hold back the economic development in Bangladesh. However, in the research of Shahbaz et al. (2017), the growth of electricity consumption in Portugal can promote economic development, and the energy-saving and emission reduction policy will inhibit Portugal's economic growth. Some scholars have also proved the impact of electricity consumption on the GDP. Also, Tang (2008) found that in Malaysia electricity contributes to its economy because Malaysia is very dependent on energy. Likewise, Iyke (2015) studied dynamic causal linkages between electricity consumption and economic growth in Nigeria and found that electricity consumption has an impact on economic growth. There are also some other different conclusions here. Through the Toda and Yamamoto Granger causality test, Bah and Azam (2017) proved that there didn't exist any causality between electricity consumption and economic growth in South Africa. Additionally, Liu et al. (2018) studied the causal relationship between the industrial output value and electricity consumption of different industries in Beijing, and found that there exist inconsistencies among various industries. Some scholars have also included multiple countries in the analytical framework at the same time. Using electricity and economic growth data from 1975 to 252

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3. Model

3.3. Impact on industrial output and structure

3.1. Equilibrium output model

The total output value of a sector is often positively correlated with its added value, and the sum of all the different sectors' added value is the gross domestic product. In 2016, the GDP of China was 74,412.72 billion yuan, of which the primary industry was 6367.07 billion yuan, the secondary industry was 29,623.6 billion yuan, and the tertiary industry was 38,422.05 billion yuan.3 The tertiary industry in China accounted for more than half of the total value of GDP. In the IO table, the second quadrant shows the final demand (consumption, investment, and exports) of various sectors, and the third quadrant displays the added value of various sectors. In this regard, the IO table is an effective tool to combinative analyze the final demand and the added value of different sectors. When the final demand of the power sector increases, we can use IO table to calculate the changes in the value-added of various sectors, and then analyze the impact of power demand increment on the industrial structure of the economy.

Production relationship among different economic sectors is often interrelated. Since the production process of one sector often needs other sectors' products as intermediate inputs, therefore the demand growth of one sector will also lead to an output increase of other sectors. Each line in the IO table reflects the connection between the production of various sectors, that is, the total output of one sector can be split into intermediate inputs for other sectors and the final product consumption, which is shown in Eq. (1): n

∑ Xij + Yi = Xi ,

i = 1,2, ..., n (1)

j=1

This row-balance equation in the IO table measures the interrelationship among different economic sectors, and more generally, in matrix form:

3.3.1. Non-competitive IO table construction The IO tables published by Chinese Input-Output Association are all competitive IO tables, that is, the intermediate inputs and final demand are not differentiated between domestic products and imported products. This kind of competitive IO table cannot be used to analyze the impact of final demand on added value, because the pulling effect of domestic products will be expanded, and the GDP will increase in line with the final demand of each sector if the influence of imported products is not excluded (Shen, 2009). Only the non-competitive IO table can effectively analyze the impact of the final demand change on GDP. The non-competitive IO table is shown in Table 1. It is obvious that compared with the competitive IO table, the noncompetitive IO table distinguishes the imported products and the domestic products in both the intermediate and the final use. From Table 1 we can know that there are two row-balance equations now in the non-competitive IO table, which are:

(2)

AX + Y = X

Where, A is direct consumption coefficient matrix, X is total output matrix, and Y is final demand matrix. After Eq. (2) is deformed, a common equilibrium output model can be obtained:

ΔX = (I − A)−1ΔY

(3)

Where, (I − A)−1 is the Leontief inverse matrix. Eq. (3) shows that when the demand fluctuations of various sectors in the economy are known, the Leontief inverse matrix can be used to obtain the output fluctuations of all sectors. Therefore, we can calculate the output growth of different sectors such as coal and oil by using the increasing electricity demand. 3.2. Influence coefficient and response coefficient

n

There are often backward linkages and forward linkages among various sectors in the economic society. The backward linkage is the relationship between a sector and the sectors that provide intermediate inputs to it. The backward linkage is often expressed by the influence coefficient, which is:

Domestic products:

∑ xijd + Yid = Xi ,

i = 1,2, ..., n (6)

j=1

n

Imported products:

∑ xijim + Yiim = IMi,

i = 1,2, ..., n

j=1

(7)

42

αi =

∑i = 1 lij 1 42

42

42

∑i = 1 ∑ j = 1 lij

When the imported products are separated from total products, a direct consumption coefficient matrix Ad can be obtained which is only for domestic products, and each element of this matrix is:

(4)

Where, αi is the influence coefficient, lij is the element in the Leontief inverse matrix. When αi < 1, the pulling power of the i-th sector is lower than the social average. Conversely, αi > 1 means that the pulling power of the i-th sector is higher than the social average. The greater the influence coefficient of a sector, the more output of other sectors can be stimulated by this sector, thus the greater influence of this sector will have on economic and social development. Forward linkage measures the relationship between a sector and the sectors that use its products as an intermediate input. The forward linkage is often expressed by the response coefficient, which is:

aijd =

Ad X + Y d = X X = (I − Ad )−1Y d EXiim

42

42

∑i = 1 ∑ j = 1 nij

(8)

(9)

is the amount of imports in export products. According to Where, Shen (2009), the vast majority of export products are domestic products or domestic processing products. Only a small number of products are directly exported after import. The data of this part are represented as two ways in international trade which are Customs Warehousing Trade and Logistics Goods by Customs Special Control Area in the China Customs Statistical Yearbook. In 2012, the total amount of these two parts was 866.6 billion yuan.4 According to the fixed ratio of the exports we can get EXid and EXiim for each sector. What's more, intermediate use, final consumption expenditure, and total investment can also be split

∑ j = 1 nij 1 42

Xj

Therefore, the row-balance equation of domestic products can be expressed in matrix form:

42

βi =

x ijd

(5)

Where, βi is the response coefficient, nij is the element of matrix N = (I − M )−1, and M is the direct distribution coefficient matrix of xij which element is mij = X . When βi < 1, the driver power of the i-th i sector is lower than the social average. Conversely, βi > 1 means that the driver power of the i-th sector is higher than the social average. The greater the response coefficient of a sector, the more output of this sector will be supplied to other sectors as intermediate inputs, so the impetus for economic and social development will also be greater.

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Table 1 Non-competitive IO table.

Intermediate Use

Final Use

Final consumption expenditure

1,2,…,n

Domestic products

1 2 … n

Imported products

1 2 … n

Intermediate input

Imported Products Total Investment

Total Output

Export Products

Added value

Total input

according to this fixed ratio:

x ijd x ijim

=

Cid Ciim

=

Iid Iiim

=

Xi − IMi −

EXid EXiim

value of different industries and the changes in industrial structure. In the IO table of 42 sectors, S01 belongs to the primary industry, S02-S28 belong to the secondary industry, and the remaining sectors belong to the tertiary industry (see Table 2). Therefore, if we know about the fluctuation of electricity demand due to the Policy of Electricity Substitution, it is possible to calculate the output change of various industries.

(10)

Hence, we can get the 2012 non-competitive IO table of China. 3.3.2. Change of different industries' added value Refer to Shen (2011), the ratio of the added value to the total output value in a sector is fixed, which means that sectors have a fixed rate of added value. Suppose that the added value of i-th sector is Vi , the total output is Xi , then the fixed rate of added value can be expressed:

V Ri = i Xi

4. Analysis of results 4.1. Data description The policy document (Guidance on Promoting Electricity Substitution5) which published in 2016 did not explicitly indicate the specific electricity consumption increment. Instead, policymaker plans to achieve a consumption reduction of coal and crude oil in the terminal energy-usage by about 130 Mtce from 2016 to 2020. Therefore, this paper first estimates the economically feasible increment in electricity demand during the 13th Five-Year period through different technical classifications of electricity substitution. The specific illustration of calculation methods is in Appendix A and the final result is shown in Table 3. To begin with, we outline the main electricity substitution

(11)

Suppose that V = (V1, V2, ..., V42) and R = (R1, R2, ..., R 42) represent the row vectors of 42 sectors' added value and added value rate respectively. And X = (X1 , X2 , ..., X 42 )T is the column vector of 42 sectors' output. Then we have:

GDP = V1 + V2 + ...+V42 = R1⋅X1 + R2⋅X2 + ...+R 42⋅X 42 Substituting X = (I −

GDP = R⋅X = R (I −

Ad )−1 Y d

Ad )−1Y d

(12)

into Eq. (12), we have: (13)

As can be seen from Eq. (13), we can use the change of final demand to calculate the change in GDP. By decomposing GDP into the added value of various industries, we can get the increment in the output

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Table 2 Sectoral classification of IO table in China (42 sectors). Industry

Code

Sector

Primary industry

S01

Secondary industry

S02 S03 S04 S05 S06 S07 S08

Products and services of agriculture, forestry, animal husbandry and fishery Mining of coal Extraction of petroleum and natural gas Mining and processing of metal ores Mining and processing of non-metal ores Food and tobacco Textile Products of textile, clothing, shoes, hats, leather and feather Wood processed goods and furniture Paper printing and products of cultural education and sports Processing of petroleum, coking and nuclear fuel Products of chemistry Products of non-metallic mineral Products of metal smelting and calendaring Products of metal General purpose machinery Special purpose machinery Transportation equipment Electrical machinery and equipment Communication apparatus, computers, and other electronic devices Measuring instruments and machinery Other manufacture Waste Repair of metal products, machinery and equipment Production and supply of electric power and heat power Production and supply of gas Production and supply of water Construction Wholesale and retail Transport, Storage and Post Accommodation and catering Information transfer, software and information technology services Finance Real estate Services of leasing and business Scientific research and technical services Management of water, environment and public facilities Residents' services, repairs and other services Education Health and social work Culture, sports and entertainment Public administration, social security and social organization

S09 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25

Tertiary industry

S26 S27 S28 S29 S30 S31 S32 S33 S34 S35 S36 S37 S38 S39 S40 S41 S42

Table 3 Prediction of electricity substitution capacity in China from 2016 to 2020. Field

Technology

Electricity increment (100 million kWh)

Industry

Electric boiler Electric kiln Captive power plant Electric vehicle Airport bridge loading equipment Port shore power Electric bicycle Rail transit Agricultural electric irrigation and drainage Others Electric storage air conditioning Heat storage electric boiler Distributed electric heating Heat pump Electromagnetic kitchen Electric water heater

1224.79 1313.86 710.00 172.13 19.50 19.08 138.75 352.64 7.99

Transportation

Agriculture

Construction

Commerce Total

108.75 97.84 610.07 762.59 262.54 108.24 48.31 5957.08

consumption in China will increase 595.708 billion kWh under the implementation of the Policy of Electricity Substitution from 2016 to 2020. In 2016, the total amount of electricity consumption in China was 5919.8 billion kWh.6 Therefore, the electricity demand will increase by 10.06% with 2016 as the base period due to the implementation of the Policy of Electricity Substitution. 4.2. Impact on output and environment 4.2.1. Output change Table 4 shows the output growth of various sectors calculated by the equilibrium output model when the demand for the electricity sector increases by 10.06%. According to Table 4, when the electricity demand increased by 10.06%, the maximum increment of the final output is in sector S25 (Production and supply of electric power and heat power), which is 15.57%. The final output of sector S02 (Mining of coal) increased by 3.53%, and the final output of sector S11 (Processing of petroleum, coking and nuclear fuel) increased by 1.04%. In China, the total energy consumption was 4360 Mtce in 2016.7 Among them, coal accounts for 62%, which is about 2703.2 Mtce, and crude oil accounts for 18.3%, which is about 797.88 Mtce. According to the growth rate in Table 4, we can draw the conclusion that the increase of electricity consumption demand will lead to the output increment of coal, which is about 95, 422, 960 tce, and the output increment of crude oil, which is about 8,297,950 tce. As we mentioned in Section 4.1, the replacement of coal is about 95, 730, 620 tce, and the replacement of crude oil is about 24, 151, 110 tce due to the implementation of the Policy of Electricity Substitution. Combining these two calculation results, we can draw that the actual production reductions of coal and oil are 307,660 tce and 15, 853, 160 tce respectively. The actual reduction of coal is only about 2% of crude oil. However, if just from the perspective of the amount of substitution, the replacement of coal due to electricity substitution is far greater than the replacement of crude oil. In 2017, the proportion of China's power generation structure was 70.99% for thermal power, 18.59% for hydropower, 4.73% for wind power, 3.87% for nuclear power, and 1.82% for solar power. As shown in Fig. 1, although the amount of China's clean power generation has increased year by year, thermal power generation is still the most essential source for the current power generation structure in China. The implementation of the Policy of Electricity Substitution can greatly

technologies in different fields of social production and service, including industry, transportation, agriculture, construction, and commerce. Then we calculate the increment in power demand under these electricity substitution technologies respectively. Where, on the one hand, the electricity substitution technologies in the fields of transportation and Agricultural electric irrigation and drainage aim to replace crude oil with electricity. On the other hand, the electricity substitution technologies in the fields of industry, construction, Electric water heater, and others plan to substitute coal with electricity. Besides, the electricity substitution technology in the field of Electromagnetic kitchen seeks to displace both crude oil and coal with electricity. Suppose that the thermal efficiencies of electricity, coal and crude oil are 0.9, 0.6 and 0.35 respectively, according to the equivalent calorie value and the growing amount of electricity in Table 3, we can calculate that the replacement of coal is about 95, 730, 620 tce, and the substitution of crude oil is about 24, 151, 110 tce. The total amount of replacement is about 120 Mtce, which is similar to the planning objective in the Policy of Electricity Substitution, so our results in Table 3 are reliable. According to the predicted result in Table 3, the electricity

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of Electricity Substitution.

Table 4 The output change of 42 sectors. Code

Output change

Code

Output change

Code

Output change

S01 S02 S03 S04 S05 S06 S07 S08 S09 S10 S11 S12 S13 S14

0.27% 3.53% 0.77% 0.24% 0.12% 0.30% 0.10% 0.06% 0.13% 0.23% 1.04% 0.94% 0.19% 1.22%

S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28

0.28% 0.36% 0.18% 0.15% 1.05% 0.52% 0.48% 0.02% 0.07% 0.05% 15.57% 0.04% 0.03% 0.11%

S29 S30 S31 S32 S33 S34 S35 S36 S37 S38 S39 S40 S41 S42

0.53% 0.68% 0.16% 0.13% 1.32% 0.14% 0.45% 0.21% 0.07% 0.11% 0.01% 0.01% 0.06% 0.01%

4.2.2. Environmental effect Through reducing the use of traditional fossil energy, the Policy of Electricity Substitutioncan help to achieve the goal of pollutants and greenhouse gas emissions abatement, and environmental protection. IPCC provides a series of greenhouse gas emission factors for various types of fossil energy. The emission factors for carbon dioxide, methane and nitrous oxide of coal and crude oil are shown in Table 5. From Section 4.2.1, we know that the actual reduction of coal and crude oil due to the implementation of the Policy of Electricity Substitution is 307,660 tce and 15, 853, 160 tce respectively from 2016 to 2020. Therefore, with the IPCC emission factors, we can calculate that the implementation of this policy contributes to reducing 34.92 million tons of carbon dioxide, 1429 tons of methane and 292 tons of nitrous oxide.

Fig. 1. Power generation structure in China.

4.3. Influence coefficient and response coefficient

Table 5 Greenhouse gas emission factor. carbon dioxide

methane

nitrous oxide

coal

3.96 × 10−4Kg / Kcal

4.19 × 10−9Kg / Kcal

6.28 × 10−9Kg / Kcal

crude oil

3.07 × 10−4Kg / Kcal

1.28 × 10−8Kg / Kcal

2.51 × 10−9Kg / Kcal

Table 6 shows the specific influence coefficients and response Table 6 Influence coefficient and response coefficient of 42 sectors.

stimulate the demand for electricity in the whole society and reduce the demand for fossil energy in the terminal energy-using. However, since the power generation structure of China heavily depends on fossil energy, it will also promote the consumption of fossil energy indirectly when electricity consumption increases. The primary purpose of implementing the Policy of Electricity Substitution is to reduce environmental pollution by substituting traditional fossil energy such as coal and crude oil with electricity in the terminal energy consumption. However, due to the low cost and strong peak shaving ability of thermal power, it will still account for a large proportion of power generation. Therefore, the significant abatement of coal and crude oil will fail because the increment of electricity demand will stimulate their consumption. In this regard, it is unavoidable to consider such impacts comprehensively to estimate the actual effect of the Policy of Electricity Substitution on energy consumption and the environment. Thence, when the Policy of Electricity Substitution is implemented, we should pay attention to gradually increase the proportion of clean power and reduce pollution and carbon emissions during power generation to maximize the beneficial effects of the Policy 256

Code

Influence coefficient

Response coefficient

Code

Influence coefficient

Response coefficient

S01 S02 S03 S04 S05 S06 S07 S08 S09 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21

0.7248 0.8366 0.7441 0.9727 0.9364 0.9850 1.1821 1.1923 1.1591 1.1557 1.0023 1.2194 1.1237 1.2069 1.2529 1.2683 1.2596 1.2851 1.3275 1.3748 1.2653

0.8997 1.7196 3.1654 2.4955 1.6795 0.7400 0.9704 0.5032 0.7800 0.9851 1.3852 1.2700 0.8320 1.2211 0.9247 0.8477 0.6229 0.6369 0.7948 0.9991 1.3070

S22 S23 S24 S25 S26 S27 S28 S29 S30 S31 S32 S33 S34 S35 S36 S37 S38 S39 S40 S41 S42

1.1975 0.5605 1.2681 1.0766 0.9785 0.8742 1.1532 0.6116 0.9489 0.8665 0.8836 0.6883 0.5498 1.0325 1.0109 0.9051 0.8304 0.5829 0.9723 0.8132 0.7208

0.8701 2.1105 1.3946 1.4369 0.7918 0.7952 0.3336 0.7912 0.9930 0.7334 0.6018 1.0425 0.5247 1.1007 0.8248 0.5488 0.6960 0.3375 0.3107 0.6572 0.3258

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Fig. 2. Influence coefficient and response coefficient of 42 sectors.

coefficients of 42 sectors. We can find that sector S03 (Extraction of petroleum and natural gas), sector S04 (Mining and processing of metal ores), sector S23 (Waste), sector S02 (Mining of coal) and sector S05 (Mining and processing of non-metal ores) have the highest response coefficients. Among them, two are from energy sectors, and it shows that energy is the pillar industry for China's economy and largely used as intermediate input for other sectors. The response coefficient of sector S25 (Production and supply of electric power and heat power) is 1.4369, which ranks sixth and is also at the leading position in the whole. Therefore, as essential energy for social production and residential life, the power sector plays a pivotal role in promoting economic output. In addition, the influence coefficient of sector S25 is 1.0766, which ranks the 18th in all sectors. Whether it is the response coefficient or the influence coefficient, the power sector performs better than the average value. Then we use an X–Y plot to represent the distribution of coefficients in different sectors. Where the horizontal axis represents the influence coefficient, the vertical axis represents the response coefficient, and the origin is set to be the social average (1, 1). Fig. 2 shows that just a few sectors' influence coefficients and response coefficients are both higher than the social average, and the electricity sector (S25) is one of them. In most sectors, only one coefficient performs better than the social average. And the influence coefficients and response coefficients of some sectors are both lower than the social average. Therefore, the power sector is in an important position in China's economy and society.

industries. And this result is consistent with Shen (2011). From the perspective of economics, when the final demand of the sector increases, it will inevitably lead to the total output increment of this sector. Besides, it will also lead to a significant increase in the added value of the industry where this sector belongs. However, since the increment in the final demand of this sector only stimulates the output of other sectors indirectly, the added value increment of other sectors is not very large. Also, from Fig. 3 we can find that the total impact of the power sector (S25) on the GDP is relatively high in all sectors of the economy and society. Besides, each additional unit of electricity demand will stimulate the output value increment of the primary industry by 0.0111 units, the secondary industry by 0.6789 units, and the tertiary industry by 0.1691 units. The final demand for the power sector in China's 2012 IO table was 293.00277 billion yuan. Therefore, if the electricity demand increases by 10.06%, the output value of the primary industry will rise by 327.18 million yuan, the secondary industry will grow by 20,011.31 million yuan, and the tertiary industry will increase by 4984.4 million yuan. From this perspective, implementing the Policy of Electricity Substitution can not only realize the transformation of China's terminal energy consumption structure, reduce dependence on fossil energy and the environmental pollution, but also promote the economic growth of different sectors in China. Therefore, electricity substitution can better promote green development in China by realizing the goals of energy conservation and emission reduction and economic development.

4.4. Changes in industrial structure

5. Conclusions and policy recommendations

Through China's 2012 non-competitive IO table we constructed, we can calculate the effect of each sector's final demand increment on the industrial output values. The specific results are shown in Table 7. From Table 7, the fluctuation in GDP due to one unit of demand increment in each sector is not consistent. Furthermore, all variations are less than 1. This is because, when deducting the influence of imported products, the actual pulling power of domestic products to domestic output value is less than its nominal value, and the driving force of domestic products for the GDP will be less than 1. As can be seen from Fig. 3, the tertiary industry has the greatest driving force for GDP compared with the primary industry and the secondary industry. In addition, it is evident that the driving force of each sector in its belonged industry is far greater than in the other two

Through the IO model, this paper comprehensively evaluates the whole impacts of the Policy of Electricity Substitution on energy consumption, economic development, and environment during the 13th Five-Year Plan period in China. The main conclusions of this paper are as follows. Firstly, through different electricity substitution technology classifications, we estimate that the economically feasible amount of increasing electricity demand due to the implementation of the Policy of Electricity Substitution is 595.708 billion kWh. Besides, we find that the replacement of coal is about 95, 730, 620 tce and the replacement of crude oil is about 24, 151, 110 tce. Secondly, the increase in electricity demand has also driven the use of coal and crude oil. The Policy of Electricity Substitution will lead to the output of coal increase by 257

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Table 7 Impact of each sector's final demand increment on various industries. Code

Primary industry

Secondary industry

Tertiary industry

Total

Code

Primary industry

Secondary industry

Tertiary industry

Total

S01 S02 S03 S04 S05 S06 S07 S08 S09 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21

0.7185 0.0135 0.0086 0.0147 0.0161 0.3220 0.2174 0.1333 0.1386 0.0731 0.0088 0.0666 0.0210 0.0120 0.0182 0.0172 0.0185 0.0191 0.0201 0.0151 0.0167

0.1271 0.7401 0.8051 0.6538 0.6899 0.4190 0.4957 0.5432 0.5616 0.5838 0.5127 0.5480 0.6434 0.5708 0.5906 0.5610 0.5629 0.5561 0.5452 0.4235 0.5007

0.0837 0.1487 0.0905 0.1542 0.1627 0.1587 0.1656 0.2105 0.1739 0.1840 0.0865 0.1851 0.1837 0.1462 0.1796 0.1898 0.1952 0.2115 0.1924 0.1869 0.1845

0.9293 0.9023 0.9042 0.8228 0.8687 0.8996 0.8787 0.8870 0.8741 0.8408 0.6080 0.7997 0.8480 0.7290 0.7884 0.7680 0.7766 0.7866 0.7578 0.6255 0.7019

S22 S23 S24 S25 S26 S27 S28 S29 S30 S31 S32 S33 S34 S35 S36 S37 S38 S39 S40 S41 S42

0.0859 0.0081 0.0172 0.0111 0.0081 0.0202 0.0251 0.0088 0.0263 0.1753 0.0156 0.0165 0.0075 0.0344 0.0287 0.0681 0.0260 0.0199 0.0367 0.0458 0.0244

0.5474 0.8740 0.5714 0.6789 0.5305 0.6939 0.6195 0.0625 0.2010 0.1764 0.1344 0.0816 0.0518 0.2241 0.1912 0.1871 0.1584 0.0714 0.2392 0.1573 0.1158

0.1980 0.0592 0.1954 0.1691 0.1218 0.2026 0.1968 0.8822 0.6248 0.5729 0.7193 0.8460 0.9113 0.5908 0.6192 0.6408 0.7114 0.8646 0.5936 0.7186 0.7930

0.8313 0.9413 0.7840 0.8591 0.6604 0.9168 0.8414 0.9536 0.8521 0.9246 0.8693 0.9441 0.9706 0.8494 0.8391 0.8960 0.8959 0.9559 0.8694 0.9217 0.9331

following policy recommendations.

approximately 95, 422, 960 tce, and the output of crude oil increase by about 8,297,950 tce. This result shows that even though the direct replacement of coal and crude oil is large enough, the actual reduction will only help to reduce 34.92 million tons of carbon dioxide, 1429 tons of methane and 292 tons of nitrous oxide. Thirdly, by calculating the influence coefficients and the response coefficients of all the 42 sectors, we find that the electricity sector performs better than the social average level, which means that electricity plays a massive role in the entire production process of society as an important type of energy. Fourthly, with the construction of China's 2012 non-competitive IO table, we find that for each additional unit of electricity demand, the output value of the primary industry will grow by 0.0111 unit, the output value of the secondary industry will increase by 0.6789 unit, and the output value of the tertiary industry will rise by 0.1691 unit. The influence of the power sector on industries' output value is relatively high among these 42 sectors. Based on the results of this study, we mainly summarize the

(1) Promote the reform and transformation of the thermal power industry The thermal power has dominated the power generation structure in China so far and the thermal power plants have always been the large CO2 emitters (Li et al., 2018). In the short term, the situation that thermal power is the primary source of electricity production in China will not change much. Therefore, we should effectively control the pollution at the power generation of thermal power plants, vigorously promote the application of clean technologies such as desulfurization, deamination, dehydrogenation and dust removal, achieve low emission and low pollution at the power generation, and effectively promote the development of the thermal power industry to be clean and efficient. In addition, to better optimize the power generation structure and reduce the proportion of thermal power, the government should introduce

Fig. 3. The driving force of each sector. 258

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(3) Subsidizing electrification equipment

relevant policies to strictly control the development scale and the installed capacity of thermal power, and effectively solve the problem of overcapacity of coal power. Hence, we can truly reduce the consumption of fossil energy and maximize the environmental benefits of implementing the Policy of Electricity Substitution.

Nevertheless, compared with traditional production equipment, the purchase cost of electrification equipment is relatively high. As a result, many enterprise owners are reluctant to replace the existing old production equipment. Hence there are some difficulties in the implementation of the Policy of Electricity Substitution. Therefore, the government should propose some preferential policies to reduce the cost of purchasing electrical equipment, and encourage business owners to replace coal-fired equipment and oil-fired equipment. To better promote the implementation of the Policy of Electricity Substitution, we should accelerate the construction of electrified society. Electricity is a type of clean energy which can reduce pollution compared with other kinds of fossil energy. Furthermore, electricity plays a significant role in economic development (Ren and Dong, 2018). In the past few decades of China's economic development, the demand for electricity consumption has become higher and higher (He and Reiner, 2016). Hence, the development of electricity substitution will not only promote China's demand for electricity, but also play a leading role in environmental protection and stimulate the green growth of China's economy.

(2) Encourage the development of renewable energy power generation National energy security is very crucial and important, but China's external energy dependence degree is so high due to the resource endowment and huge population (Ji and Zhang, 2019). Developing renewable energy can help China to achieve the goal of sustainable development and reduce external energy dependence and environmental pollution (Lin and Li, 2015; Wang et al., 2018a). However, the utilization efficiency of renewable energy power in China is still relatively low at present, and the problem of abandoning hydropower, wind power, and solar power is severe (Peng and Tao, 2018). The development of renewable energy power generation requires a focus on cost reduction and consumption increment. Therefore, the government should optimize the power investment structure, encourage preferential power generation of renewable energy through policy support, and increase the installed capacity of renewable energy power generation. Additionally, the government should focus on subsidizing the R&D and investment in the renewable energy industry, reducing R&D costs and promoting the development of renewable energy technologies. In addition, the problem of renewable power consumption determines the importance of energy storage technology. By encouraging the development of energy storage technology, it can effectively reduce the instability of renewable energy generation, thereby increasing the utilization rate of renewable energy.

Acknowledgments This work is supported by the National Natural Science Foundation of China (71874149), Fujian Social Science Planning Fund Program (FJ2018B073), and Open Fund of Operation and Control of Renewable Energy & Storage Systems (NYB51201801579).

Appendix A Table A.1 Instruction of calculation Alternative Technology

Computational Formula

Electromagnetic kitchen

qek = ∑i = 1,2 ⋯ n κi1 − κi2) × niek × piek × tiek

Caption Refer to Jiao and He (2017), i represents EK's user type. κi1 is the expected EK's future market popularity rate of the i-th type. κi2 is the popularity rate now. niek is the total number of kitchens of ith type. piek is the average total power of i-th type. tiek is the annual utilization hours of i-th type. The

Electric vehicle

qev = ∑i = 1,2 ⋯ n αi ev × Ni ev × ci ev

types of EK's users can be divided into accommodation and catering industry, enterprises and institutions, schools, and resident users. i represents cars' type, which can be divided into large, medium, small and mini vehicles. αiev is the expected proportion of actual alternatives in the theoretical alternatives of i-th type. Niev is the number of theoretical alternatives of i-th type. ciev is the annual average power consumption of i-th electric vehicles.

Replacing APU with airport bridge loading equipment

qabl = N abl × w abl

Port shore power

q psp = ∑i = 1,2 ⋯ n Ni psp × wi psp × hi psp

N abl is the replaceable amount of abl. w abl is the annual average electricity consumption of one single abl. i represents ports' type. Nipsp is the planned number of port shore power of i-th type. wipsp is the annual average power of i-th type. hipsp is the annual utilization hours of i-th type.

Electric bicycle

qeb = α eb × N eb × w eb

Rail transit

qrt = αrt × lrt × mrt

Agricultural electric irrigation and drainage

qeid = α eid × N eid × w eid × heid

of substitutes for motorcycles. w eb is the annual average power consumption of electric bicycles. αrt is the expected proportion of actual alternatives in the theoretical alternatives. lrt is the number of new and renovated rail transit mileage during the 13th Five-Year Plan period. mrt is the annual average electricity consumption per kilometer of electrified railway or urban rail transit.

α eid is the expected proportion of actual alternatives in the theoretical alternatives. N eid is the number of fuel drainers, w eid is the power of drainers. heid is the annual utilization hours of drainers.

Electric boiler & Electric kiln

qbk = α bk

Captive power plant

qcpp = α cpp × C cpp × hcpp

Electric water heater

α eb is the expected proportion of actual alternatives in the theoretical alternatives. N eb is the number

qewh =

Qbk × τ bk ηbk e

α ewh × Qewh × w ewh e ewh

α bk is the expected proportion of actual alternatives in the theoretical alternatives. Q bk is the energy consumption of coal-fired boilers except for electricity. τ bk is the operating efficiency. ηbk is the operating efficiency of Electric boiler & Electric kiln. e is a conversion factor between coal and electricity. α cpp is the expected proportion of actual alternatives in the theoretical alternatives. C cpp is the installed capacity. hcpp is the annual average power generation hours for CPP.

α ewh is the expected proportion of actual alternatives in the theoretical alternatives. Q ewh is the number of gas water heaters. w ewh is the annual gas consumption of gas water heaters. e ewh is a conversion factor between natural gas and electricity.

(continued on next page)

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Table A.1 (continued) Alternative Technology

Computational Formula

Electric storage air conditioning qA = & Heat pump

αA × sA × pA × t A θA

Heat storage electric boiler & qB = α B × s B × pB × t B Distributed electric heating

Caption

α A is the expected proportion of actual alternatives in the theoretical alternatives. s A is the area. p A is the power per unit area. t A is the annual utilization hours. θ A is the COP value.

α B is the expected proportion of actual alternatives in the theoretical alternatives. s B is the area. p B is the power per unit area. t B is the annual utilization hours.

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