sustainability

Article

Analysis of the Potential Impacts on China’sIndustrial Structure in Energy Consumption

Yushen Tian *, Siqin Xiong ID and Xiaoming Ma *

School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China;xiongsiqin@pku.edu.cn* Correspondence: tianyushen1211@pku.edu.cn (Y.T.); xmma@pku.edu.cn (X.M.);

Tel.: +86-755-2603-2413 (X.M.)

Received: 13 November 2017; Accepted: 5 December 2017; Published: 8 December 2017

Abstract: Industrial structure is one of the main factors that determine energy consumption. Based onChina’s energy consumption in 2015 and the goals in 13th Five-Year Plan for Economic and SocialDevelopment of the People’s Republic of China (The 13th Five-Year Plan), this paper establishedan input–output fuzzy multi-objective optimization model to estimate the potential impacts ofChina’s industrial structure on energy consumption in 2015. Results showed that adjustmentsto industrial structure could save energy by 19% (1129.17 million ton standard coal equivalent(Mtce)). Second, China’s equipment manufacturing industry has a large potential to save energy.Third, the development of several high energy intensive and high carbon intensive sectors needsto be strictly controlled, including Sector 25 (electricity, heat production, and supply industry),Sector 11 (manufacture of paper and stationery, printing), and Sector 14 (non-metallic mineralproducts industry). Fourth, the territory industry in China has a great potential for energy saving,while its internal structure still needs to be upgraded. Finally, we provide policy suggestions thatmay be adopted to reduce energy consumption by adjusting China’s industrial structure.

Keywords: energy consumption; fuzzy optimizing model; input–output model; China’s industrial structure

1. Introduction

As the country with the largest amount of greenhouse gas emissions, China is facing enormouspressure to reduce the consumption of fossil energy. In 2014, China proposed decreasing energyintensity in 2020 to 15% of the 2015 level. Therefore, it is very important to study the issue ofenergy conservation.

Industrial structure is an important factor affecting energy consumption. Many researchers havestudied the impact of industrial structure change in energy consumption. Fernández González [1] usedthe Logarithmic Mean Divisia Index method to analyze the factors behind the change in aggregateenergy consumption in 27 European member states. These results showed that Mediterraneancountries, especially former communist states, increased their energy consumption, most of themfavored by structural change. Hammond and Norman [2] employed the activity refactorization (AR)approach to track efficiency improvements through the intensity effect, and to separate the effect onenergy demand from such improvements from changes in activity and structure in the industrialsector. Results also showed that changes in the industrial sector could reduce the annual growth ratesin energy demand. Llop [3] used the demand-driven input–output model and proposed a simplemethod to decompose the changes in energy gross output into different determinants. The resultsshowed a positive contribution of technology to increasing energy output, while the contribution ofthe final demand for energy was negative. Likewise, many scholars are concerned about such impactsin China. Wang et al. [4] used energy decomposition analysis to estimate the total industrial energy

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consumption and energy-related carbon emissions in Tianjin from 2003–2012. The results showedthat energy-saving efforts and the optimization of the industrial structure increased energy efficiency.Xiao et al. [5] used the Computable General Equilibrium model to analyze the background and overallsituations of energy consumption and CO2 emissions in China. The results showed that the decline ofa secondary industry could cause an emission reduction effect, but was at the expense of the grossdomestic product whereas the development of a tertiary industry could boost the economy and helpsave energy.

Thus, adjusting the industrial structure is of great significance when realizing energy saving.Industrial structure adjustment is an optimization problem where the proportions of various

types of industry are adjusted to satisfy one or more goals [6]. In this paper, the goal was to achieveenergy saving and economic development.

Many researchers have studied structural upgrading. Based on data from the European Union,Janger et al. [7] developed a unique conceptual framework for measuring innovative outcomesthat distinguished structural change and structural upgrading as two key dimensions in bothmanufacturing and services. They found that the results for the modified indicator differedsubstantially for a number of countries, with potentially wide-ranging consequences for innovationand industrial policies. Sadhukhan and Smith [8] presented a novel methodology for the flexible designof industrial systems based on their detailed differential value analysis. Yun et al. [9] evaluated themicro- and macro-economic effects with the hybrid mixed complementary approach they designed totake account of these unique features of the Korean electricity industry. Results showed that the micro-and macro-economic indices were negatively impacted, especially in cases where the share of nuclearpower was lower than that of the basis case. For saving energy in China, many scholars have studiedthe optimizing of China’s industrial structure. Mao et al. [10] calculated the 2007 industrial influencecoefficients (IIC) and the industrial carbon emission coefficients (ICEC) of all industries in China.Scenario analyses showed that the industrial structure adjustment based on the calculations of IIC andICEC was better than the one based on the Chinese Industrial Restructuring Catalog. Using provincialpanel data from the period 1995–2009 to analyze the relationship between the industrial structuraltransformation and carbon dioxide emissions in China, Zhou et al. [11] found that the first-order lag ofindustrial structural adjustment effectively reduced emissions. Wu and Xu [12] provided a systemdynamics and fuzzy multi-objective programming integrated approach for the prediction of energyconsumption at a regional level in China, which revealed that the energy consumption increaseddramatically with rapid economic growth.

However, most of the research did not take into account the relationships between the industrysectors. From a policy perspective, information on sectoral relations and dependence helps us to betterunderstand the structure of an economy and how it changes over time, which in turn is importantin formulating industrial policies [13]. Obviously, it is necessary to consider inter-departmentalrelationships when optimizing the industrial structure to achieve energy conservation.

The input–output model (I–O model) [14] has been employed in this task and the main purposeof the I–O model is to establish an I–O table and a system of linear equations. An I–O table showsthe interactions between the economic sectors and therefore their interdependence. Li et al. [15]developed an integrated evaluation model based on Input–Output Analysis and Social NetworkAnalysis to quantify the evolutionary trends of industrial structure, demonstrate the inter-relationshipbetween different sectors, and investigate the industrial structure-related carbon emissions. Resultsshowed that industrial structure was gradually improved in China and various connections wereestablished between different sectors. Borghi and Alexandre [16] used the I–O model to addressthe Brazilian productive structure and the major economic policies undertaken in the face of theinternational economic crisis. Results showed that industrial sectors had stronger linkages in termsof production and employment maintenance in the economy, but have been losing ground in theproductive structure. Mi et al. [6] used an optimization model based on the I–O model to assessthe potential impacts of industrial structure on energy consumption in Beijing. Results showed that

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industrial structure adjustment could save energy by 39.42% in Beijing in 2020 and it was possible todecrease energy intensity (energy consumption per unit GDP) through reasonable industrial structureadjustment without negatively affecting economic growth.

Most importantly, in industrial structure optimization models, many system parameters and theirinterrelationships may appear uncertain. Moreover, when the model consists of more than one policyobjective, the optimum solution usually does not exist. The uncertainty can be further multiplied bythe complexity of the system components and their relevance to economic implications if they violatethe goal of policies. Therefore, with the above uncertainties and complexities, more advanced methodsneed to be adopted to effectively manage the industrial system. The multi-objective fuzzy optimizationmodel has the advantages of tackling the above problems [17], and has been widely used in helpingplan energy systems under uncertainty. Rong and Lahdelma [18] proposed a fuzzy chance constrainedlinear programming model for optimizing the scrap charge in European steel production. Zhou andChen [17] developed an inexact fuzzy multi-objective programming model and applied it to a real caseof planning the industrial structure of the South Four Lake watershed in Shandong Province, China.However, there is no current research focusing on the whole industrial structure adjustment of Chinabased on energy consumption, which is a research gap that needs to be filled.

Against this background, according to the goals set by the Chinese government [19], this paperused a fuzzy multi-objective optimization model based on the input–output model to assess thepotential impacts of industrial structure on energy consumption in China in 2020. The rest of the paperis organized as follows: Section 2 introduces the methodology and presents the data; Section 3 showsthe results; while Section 4 provides some concluding remarks.

2. Materials and Methods

2.1. Methods

The adjusted industrial structure of China in 2020 (the target year) was obtained by a fuzzyoptimizing model based on an input–output model. The initial industrial structure in 2015 (the initialyear) was chosen as the baseline. The potential impacts of industrial structure were assessed bycomparisons between the adjusted industrial structure and initial structure.

First, a multi-objective model for industrial structure optimization was established as follows:

Objective 1: Economic objectiveObjective 2: Energy consumption objective

Subject to:

Constraint 1: Economic constraintsConstraint 2: Energy consumption constraintsConstraint 3: Input–output model constraintsConstraint 4: Structure adjustment constraints

Maximizing the whole industrial system’s economic benefit was chosen as the economic objective,and minimizing the total energy consumption was chosen as the energy consumption objective.The restriction of the total energy consumption set by policy makers in China was chosen as theconstraints of the model. Thus, by using the added value of each sector as a decision variable,a mathematical multi-objective model for industrial structure optimization was formulated.

2.1.1. Objectives of the Optimization Model

Objective 1: Maximization of Gross Domestic Product

Max G = ∑ni=1 vi1, (1)

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Objective 2: Minimization of energy consumption

Min E = ∑ni=1 eitvit, (2)

where G is the GDP; vi is the added value of sector i; n is the number of sectors; E is the total energyconsumption; and ei is the sectoral energy intensity (energy consumption per unit added value) ofsector i. The subscripts 0 and t represent the initial year and the target year, respectively. eit is predictedby the goal of the energy intensity set in The 12th Five-Year Plan [19].

2.1.2. Constraints of the Optimization Model

The establishment of the optimization model requires reasonable constraints. Based on The 13thFive-Year Plan and China’s economic historical data, the constraints were developed as follows:

Constraint 1: Economic constraints

G = ∑ni=1 vi, (3)

G = ∑ni=1 vi, (4)

Constraint 2: Energy consumption constraints

E = ∑ni=1 eivi, (5)

Et ≤ (1 + µ)mE0, (6)

where m is the number of years between the initial year and the target year; and α is the lowest averageannual growth rate of GDP. µ is the lowest annual growth rate of energy consumption and is anexogenous parameter.

Constraint 3: Input–output model constraints:

(I − A0)(I − AC0)−1Vt ≥ Yt, (7)

The I–O model is used to establish an I–O table and a system of linear equations. In an I–O model,the total output of each sector is:

X − AX = Y, (8)

where (suppose there are n sectors in the economy) X is the total output vector with n dimensionswhose element xij is the output of sector j; Y is the final demand vector with n dimensions whoseelement yi is the final demand of sector j; A is the direct requirement matrix with n × n dimensionswhose element denotes the direct requirement of sector i for per unit output of sector j; and aij isobtained through

aij = xij/xj (i, j = 1, 2, . . . , n), (9)

where xij is the monetary value from sector i to sector j.Y is obtained though

Yt = (1 + π)mY0, (10)

where π is the annual growth rate of final demand.The added value of each sector is:

(I − AC)X = V, (11)

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V is the added value vector in the target year with n dimensions whose element vj is the addedvalue of sector j; and I is the n × n dimension identity matrix. AC is obtained through

AC =

∑ni=1 ai1 · · · 0

.... . .

...0 · · · ∑n

i=1 ain

, (12)

According to Equations (8)–(12), to satisfy the final demand in 2020, we have the input–outputmodel constraints (Equation (7)).

Constraint 4: Structure adjustment constraints

(1 + ω1)V0 ≤ Vt ≤ (1 + ω2)V0, (13)

Considering that the industrial structure cannot be adjusted freely within a period of time,the lower and upper limits of the proportions of sectoral added value are constrained in the modelaccording to historical data. ω1 and ω2 are the exogenous parameters.

2.1.3. Objectives and Constraints of the Fuzzy Optimization Model

Obviously, the above-described model can effectively address issues of industrial structure withenvironmental constraints.

However, in many real-world problems, uncertainties expressed as fuzzy sets may exist in manysystem components, and their interactions affect the related decision processes, which have placedindustrial structure management problems beyond the above method. Therefore, fuzzy linear modelwas introduced into the above framework [17]. In this paper, we used a fuzzy multi-objective model forindustrial structure optimization. The above model (Equations (1)–(7) and (13)) can be transformed [20]as follows:

Max λ, (14)

Subject to:n

∑i=1

vit ≥ G− + λ(G+ − G−), (15)

∑ni=1 eitvit ≤ E+ − λ

(E+ − E−), (16)

∑ni=1 vit ≥ (1 + α)t ∑n

i=1 vi0, (17)

∑ni=1 eitvit ≤ (1 + µ)m ∑n

i=1 ei0vi0, (18)

(I − A0)(I − AC0)−1Vt ≥ Yt, (19)

(1 + ω1)V0 ≤ Vt ≤ (1 + ω2)V0, (20)

0 ≤ λ ≤ 1, (21)

The λ represents the possibility of satisfying the objectives and constraints under the givensystem conditions; and superscripts ‘−’ and ‘+’ represent the lower and upper bounds of the intervalparameters, respectively. By solving the above model, solutions for optimized industrial added valuescan be obtained.

2.2. Materials

2.2.1. Energy Consumption Data

As shown in Table S1, the energy consumption data were derived from the China Energy statisticalyearbook 2016 [21].

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2.2.2. Noncompetitive Import-Oriented Input–Output Table

In this study, the direct requirement matrix of China’s input–output table in 2015 (Table S2) werepredicted based on the previous table from 2012 [22] with the Bi-proportional Scaling Method [23],and were revised in the following ways:

First, the competitive input–output tables were adjusted to a non-competitive import-orientedinput–output table. The tables created by the National Bureau of Statistics of the People’s Republicof China are competitive input–output tables whose intermediate use and end-use products containboth domestic and imported products. Imported products are produced in countries outside China,as is the case for their energy consumption. In this study, we were only concerned about the energyconsumption inside China. Thus, this study assumed that each economic sector and final demandcategory used imports in the same proportions; therefore, imported goods from the direct requirementstable and from components of the GDP of each sector from the input–output table of each year wereremoved [24].

Second, the national economic subsectors were combined. To make the classification ofinput–output tables consistent with the energy consumption data according to the Classification andCode Standard of National Economy Industry [25], the national economic subsectors were combinedinto 34 sectors as shown in Table 1. For convenience, we used the numbers in Table 1 to refer tothe sectors.

Table 1. Classification and codes of national economic sectors.

No. Sector No. Sector

1 Agriculture, forestry, animal husbandry and fishery 18 Transportation equipment manufacturing

2 Coal mining and dressing 19 Electric equipment andmachinery manufacturing

3 Extraction of petroleum and natural gas 20 Electronic, computers andtelecommunication equipment

4 Mining of minerals 21 Instrument and meter and cultural andofficial goods manufacturing

5 Mining of nonmetal minerals 22 Other manufacturing

6 Exploitation of ancillary services, and othermining products 23 Scrap waste

7 Manufacture of food and tobacco products 24 Metal products, machinery andequipment repair services

8 Textile industry 25 Electricity, heat production andsupply industry

9 Manufacture of apparel, leather and related products 26 Gas production and supply

10 Processing of wood and manufacture of furniture 27 Water production and supply

11 Manufacture of paper and stationery; printing 28 Construction

12 Processing and cooking of oil; processing ofnuclear fuel 29 Wholesale and retail trades

13 Chemical industry 30 Transportation, warehousing andpostal industry

14 Non-metallic mineral products industry 31 Hotels and catering services

15 Metal smelting and rolling processing industry 32 Finance and insurance

16 Manufacture of metal products 33 Real estate trade

17 General and special equipment manufacturing 34 Other services

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2.2.3. Exogenous Parameters in the Model

When setting the exogenous parameters, we referenced the Chinese government plans includingthe work plan for energy conservation during the 13th Five-Year Plan period. Table 2 shows thesettings of all the exogenous parameters in the model.

Table 2. The settings of exogenous parameters in the model.

Parameter Setting

α 0.065µ 0.03

ω1 −0.5ω2 5π 0.55m 5

3. Results

Based on the optimization model, the adjusted industrial structure of China in 2020 was obtained.In addition, the initial industrial structure in 2015 was chosen as the baseline as usual (BAU). In theBAU scenario, it was supposed that the annual growth rate of GDP was 6.5%, which was the lowestgoal set by the Chinese government in The 13th Five-Year Plan. Figure 1 shows the total amountof GDP and energy consumption by 2020 in China from the optimization model results and BAU,separately. Figure 2 shows the difference in the added value and energy consumption in 2020 in Chinafor each sector between the adjusted industrial structure and BAU, separately.

Figure 1. The total amount of GDP and energy consumption of baseline as usual (BAU) and adjustedindustrial structure. The blue bar shows the BAU data. The orange bar shows the data after optimizationin 2020.

In our results showed that λ = 0.9, which meant that it was very possible to adjust industrialstructure to the results.

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Figure 2. Comparisons of adjusted industrial structure and initial structure in 2020 on a sectoral level.The blue bar shows the BAU data, the orange bar shows the data of the adjusted industrial structure in2020, and the gray bar shows the energy intensity in 2015.

3.1. Effects of Industrial Structure Optimization

Figure 1 shows that the energy consumption declined through industrial structure adjustment.Compared to the BAU, the energy consumption of the adjusted industrial structure was smaller.In the adjusted industrial structure, the annual growth rate of GDP was 7%, which was greaterthan 6.5% in BAU. Energy consumption was 4932.76 million ton standard coal equivalent (Mtce).Using the initial industrial structure, China will consume 6061.93 Mtce; however, industrial structureadjustment can save energy by 19% (1129.17 Mtce). This means that structure adjustment can saveenergy without negatively affecting economic growth. Such results are consistent with the results ofa study in Beijing [6]. Although the energy consumption optimization result was different to the studyby Xiong et al. [26], it should be noted that the prediction in [26] as based on data from 1995–2012.Moreover, according to the data in the 2016 China Energy Statistical Yearbook [21], the predicted dataof such a study has a three-year-delay, which makes the results of this paper reasonable.

3.2. Analysis of Optimizing Results in Sectoral Level

Figure 2 shows the sectoral added value of BAU in 2015 and the adjusted industrial structurein 2020. The sectoral energy intensity in 2015 is also shown in Figure 2. It can be seen that the addedvalue of Sectors 17–20, 32 and 33 increased significantly in 2020.

Sectors 17–20 developed rapidly as these four sectors are classified as equipment manufacturingindustries, which are the priority of The 13th Five-Year Plan. It can be seen that all four sectorshad a low energy intensity. In 2020, after adjusting the industry structure, Sector 20 (electronic,

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computers and telecommunication equipment) was the fastest growing sector in the manufacturingindustry. The growth amount of its added value accounted for 13% in total increased added valueand only caused 1.20% (59.25 Mtce) of total energy consumption. Sectors 17 (general and specialequipment manufacturing), 18 (transportation equipment manufacturing), and 19 (electric equipmentand machinery manufacturing) also accounted for 2.19%, 3.52%, and 2.78%, respectively, of the totaladded value, but only accounted for 0.72%, 1.24% and 0.93% of total energy consumption, respectively.Thus, Sectors 17–20 have high potential impact on energy conservation.

Sectors 17–20 are sub-sectors of the machinery industry [27]. Machinery production can playa crucial role in economic development and energy consumption in the process of industrializationand urbanization. The results of Sectors 17–20 were consistent with the results presented byLin and Liu [27], whose study showed that there was a substitution relationship between energyand capital as well as labor in China’s machinery industry. This is a clear indication that allocatingmore capital or labor to China’s machinery industry instead of energy will be vital in the energyconsumption mitigation effort.

The increase of added value in Sector 32 (finance and insurance) accounted for 20% of the totalincrease added value. Sector 33 (real estate trade) had the largest increase of added value with7528.11 billion yuan, which accounted for 28% of the total increase added value. Both of these sectorshave a low energy intensity and are classified as tertiary industries. These results were consistentwith the study of Lin and Zhang [28]. However, there is room for energy efficiency improvement inthe Chinese service sector. Improving the proportion of modern service industries with low energyconsumption such as information transmissions, computer services, information services, and thesoftware industry can increase the overall energy efficiency of the service industry.

On the other hand, sectors which are high energy intensive are slow to develop. The energyintensity of Sector 25 (electricity, heat production, and supply industry) was the largest of all the sectors.The annual GDP growth rate of Sector 25 was only 1.13%, and was consistent with Zhang et al. [29].From 2015 to 2016, the growth rate of added value in China’s electricity sector has decreased, while theGDP of the whole country has not. Considering that reliance on coal in China’s energy consumptionwill not be eliminated in the short-term, such changes are helpful for energy saving. Meanwhile,the average energy intensity of the tertiary industry is less than that of the secondary industry inChina, and changes in the electricity sector also show that tertiary industries may play a guiding andsupporting role in the structure of industries. Sectors 11 (manufacture of paper and stationery; printing)and 14 (non-metallic mineral products industry) remained low, which indicated that these two sectorshave a few potentials in energy conservation. Such results were consistent with Huang et al. [30],and Zhang and Chen [31], whose studies showed that the main driving force in the energy consumptionof these two sectors were their scale of production. Such findings indicated that policy-makers shoulddecrease high energy consumption productions in these two sectors and eliminate excess capacity assoon as possible.

3.3. Analysis of Adjusted Structure of Tertiary Industry

Figure 3 shows that the proportion of tertiary industry changed the most. It increased from 48.82%in 2015 to 66.88% in 2020. More specifically, although the energy intensity of the tertiary industryremained low, the optimization results showed that the internal structure of the tertiary industry alsoneeded to be changed. According to the sectoral classification standards, Sectors 27–34 are termed astertiary industries. When comparing Figure 3a with Figure 3b, it can be seen that the proportion ofadded value of Sector 32 rose from 7% to 11% and that of Sector 33 increased from 5% to 10%. On theother hand, the proportion of added value of Sectors 28–30 dropped from 19% to 13%, while that ofSector 34 decreased from 17% to 11%.

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Figure 3. (a) The added value proportion of each sector in the BAU; (b) The added value proportion ofeach sector in adjusted industrial structure.

After optimization, an increase in the proportion of Sector 32 will be of great benefit to Chineseurban residents [32], which will also have a significant positive effect on the assessment of ruralcitizens towards government performance [33]. On the other hand, the proportion of Department 33increased, which was consistent with its current trend shown in [34]. This means that the governmentcan continue to develop the real estate industry to achieve a win-win economic goals and energysaving targets. More relevant discussions regarding Sectors 32 and 33 have already been addressed inSection 3.2.

At the same time, the optimization results showed that the added value of Sectors 28–30 and 34did not change much, while their proportion of added value was reduced among the all industrialsectors. The results of such optimization may be due to the fact that the output of these sectors is verylarge (21% and 19%, respectively in 2015), causing them to consume much energy. Such facts indicatethat though some sectors have low energy intensity, the government should not let it grow freely dueto its potential impacts on energy consumption [35].

4. Discussion

A fuzzy multi-objective optimization model was developed based on the input–output model toassess the potential impacts of industrial structure on energy consumption in China. According to theresults, several conclusions can be drawn:

First, industrial structure adjustment has great potential in energy conservation. When the averageannual growth rate of GDP is 7% from 2015–2020, industrial structure adjustment can save energy by19% (1129.17 Mtce). Such GDP growth rate is consistent with the growth rate of GDP required duringthe period of the “new normal economy” in China. This fact indicates that while attention shouldbe paid to the research and development of energy saving technologies or other factors affectingenergy consumption, the Chinese government should also focus on upgrading the industrial structuretowards a low energy-consumption structure for its great potential in energy conservation and the lowsacrifice of growth rate in GDP.

Second, developing an equipment manufacturing industry can save energy and may grant thestable and fast development as the economy. To be specific, increasing the proportion of Sectors 17(general and special equipment manufacturing), 18 (transportation equipment manufacturing), and 19(electric equipment and machinery manufacturing) is an effective way to save energy. Currently,developing these sectors are the priority of the Chinese government [36], which means that energy

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consumption may be further reduced in the future and China’s current policy may also shift theindustrial structure toward low energy consumption.

Third, the development of several high energy intensive sectors should be strictly controlledsuch as Sectors 25 (electricity, heat production and supply industry), 11 (manufacture of paper andstationery, printing), and 14 (non-metallic mineral products industry). The rising of the productionscale of such sectors has caused a great growth in their energy consumption.

Finally, the structure of tertiary industry also needs to change. While the priority of developmentsectors has been Sectors 32 and 33, the development of Sectors 28–30 has to slow down. Althoughthe energy consumption of the tertiary industry is very low, the Chinese government cannot let itgrow due to the large output in some sectors. The GDP of the tertiary industry has become the largestproportion in China, thus the adjustment of its internal structure needs the attention of policymakersfor the sake of energy consumption.

Some limitations in this paper may affect the credibility of the result:First, the parameter of energy intensity in 2020 was set based on policy goals; whether this goal

can be achieved may have an impact on the results in this paper.Second, there was a big difference between the provinces in China regarding energy consumption.

This article conducted a simulation without focusing on these differences, so the optimization may tosome degree, be too macroscopic.

Finally, as the Chinese government currently has not provided an input–output table for 2015,this article conducted an estimation of it based on the input–output table for 2012. Therefore,the difference between the estimated result and the actual data may have also affected the finalresults of this article. The potential impact of energy saving in China’s industrial structure by region isa topic of future studies.

Supplementary Materials: The following are available online at www.mdpi.com/2071-1050/9/12/2284/s1,Table S1: Energy consumption in 2015. Table S2: direct requirement matrix of noncompetitive oriented-importChina’s input–output table in 2015.

Author Contributions: All authors contributed extensively to the work presented in this paper. Yushen Tianconceived the method and analyzed the results; Siqin Xiong collected the data; Yushen Tian wrote the paper andXiaoming Ma revised the paper.

Conflicts of Interest: The authors declare no conflict of interest.

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