Impacts of industrial structures reconstructing on carbon emission and energy consumption: A case of Beijing

Journal Pre-proof Impacts of industrial structures reconstructing on carbon emission and energy consumption: A case of Beijing Bing Zhu, Haiyan Shan PII:

S0959-6526(19)33786-2

DOI:

https://doi.org/10.1016/j.jclepro.2019.118916

Reference:

JCLP 118916

To appear in:

Journal of Cleaner Production

Received Date: 29 May 2019 Revised Date:

12 September 2019

Accepted Date: 16 October 2019

Please cite this article as: Zhu B, Shan H, Impacts of industrial structures reconstructing on carbon emission and energy consumption: A case of Beijing, Journal of Cleaner Production (2019), doi: https:// doi.org/10.1016/j.jclepro.2019.118916. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Impacts of industrial structures reconstructing on carbon emission and energy consumption: a case of Beijing Bing Zhu*, Haiyan Shan School of Economics and Management, Anhui Normal University, Wuhu,Anhui,241002,PR China [email protected], *Corresponding author

School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044 [email protected]

Energy consumption(Unit: ten thousand tons)

GRAPHICAL ABSTRACT

5785.2325 5785.232 5785.2315 5785.231 5785.2305 5785.23 5785.2295 5785.229 6861.6215 6861.621 6861.6205

3.3841 3.3841

6861.62 6861.6195

3.3841 3.3841 3.3841

6861.619 6861.6185

3.3841 3.3841

Carbon dioxide emissions(Unit:ten thousand tons)

Fig. 6.

4

x 10

3.3841 6861.618

GDP（ Unit： billion yuan）

Pareto front in Beijing region in 2020

The optimal Pareto solution for carbon emissions, energy consumptions and GDP targets after industrial structure reconstructuring

Amount of words?8613

Impacts of industrial structures reconstructing on carbon emission and energy consumption: a case of Beijing Abstract: Carbon emissions and energy consumption have serious impacts on humans and ecosystems. This paper investigates the effects of industrial reconstructuring on energy conservation and emission reduction. A multi-objective optimization model was established. The model classifies industrial sectors into four groups according to their carbon emission levels and contributions to economic growth, then non dominated sorting genetic algorithm is applied to solve the model and the Pareto frontier is obtained. The best solution is selected from the Pareto frontier by using a super data envelopment analysis model which measures the degree of coordination between economy and environment. The model is used to analyze the effects of industrial reconstructuring in Beijing during 2018–2020. The results show that industrial reconstructuring enabled economic growth to reach the government's planned rate while the carbon intensity and energy intensity surpassed the goal of the 13th Five-Year Plan. This case can provide decision-making basis for the sustainable development of ecology and economy in other regions. Keywords: Energy saving and emissions reduction; Industrial structure; Pareto frontier; super data envelopment analysis model; Multi-objective programming model

1. Introduction As a result of industrialization and urbanization, a large amount of energy is consumed and accompanied by lots of carbon emissions, which lead to serious environmental problems (Chen et al., 2017). The increasing global greenhouse gas emissions will lead to the greenhouse effect, which causes the rising of sea levels and increased extreme weather conditions. These changes threaten people’s daily lives and the natural environment. According to the statement of the US Energy Information Administration (EIA, 2009), China produced more than 6 billion tons of gas emissions in 2007. Since then China has become the largest emitter of carbon emissions in the world (Wu et al., 2018). This trend can be seen from the Fig.1. With the occurrence of global warming and frequent climate disasters, Global warming is becoming a common important problem to human beings. As the largest emitter of emissions, China has been committed to protecting the global environment through various means in recent years. At the end of 2009, China has made a positive commitment to controlling greenhouse gas emissions and has set the goal that carbon emissions per-unit GDP will fall by 40% to 45% compared with 2005 (Li et al., 2018). From 1978 to 2017, the economy in China is developing rapidly, but energy and carbon emissions have also increased, especially in the manufacturing process. Now China is recognized as a manufacturing power in the world. According to United Nations merchandise trade statistics, in 2013, China’s manufacturing exports reached US$220.9 billion, accounting for 11.7% of total global trade. However, accompanying China’s manufacturing boom, much energy is consumed and much carbon gas is emitted. How to achieve effective carbon emissions reduction while maintain sustainable industrial development is a problem for the government to explore economic sustainable development.

x 10

6

3 Spain USA Total CO2 emissions(thousand tons)

2.5

UK Italy Japan

2

India Germany Brazil

1.5

China Canada

1

0.5

1900

1920

1940

1960

1980

2000

2014

Year

Fig. 1. Carbon emissions trend of the major countries in the world Achieving the emissions reduction target can be divided into three main groups of government interventions: energy efficiency improvement, structure optimization, and energy mix upgrades (Nan et al., 2015). The adjustment of structures by integrated some research tools is the focus of this paper. The aim of structural optimization is to change the proportions among industrial sectors so as to mitigate total emissions and energy consumption (Li et al., 2017). In particular, accelerating the upgrading of industrial structure is a more efficient method to save energy and reduce emissions (Yu et al., 2016). Many previous researches have examined the industrial reconstructuring impact on economic growth and environmental pollution (Cristobal, 2010; Tian et al., 2014; Li et al., 2015). Some studies have grouped industries but have not analyzed the characteristics of each group, so the analysis of the effects of industrial structure optimization is not sufficient. It is necessary to study the proportion of the sectors and their interdependence and mutual restraint not only at the level of the industrial sector but also at the level of groups. And a few studies apply the multi-objective optimization decision model to explore the effect of industrial reconstructuring on carbon emissions. Some studies convert multiple targets into a single target by weighting the multi-objective function (Fu et al., 2017) or transform multiple targets into constraints. Other studies solve the Pareto frontier, and then select a solution based on a certain preference. These studies have a strong subjectivity in determining the weights, which lead to deviations in the selection of final schemes. This study intends to bridge the gap by analyzing how to optimize industrial structure affecting carbon emissions and energy consumption in China. For this paper, we supplement previous studies in the following aspects: (1) classifying industrial sectors into four groups according to their carbon emissions levels and contributions to economic growth, in order to find the relationship of economic, carbon emissions and energy consumption between these sectors in the whole industry. (2) Evaluating the selection of the final plan by using a super DEA model. The model measures the degree of coordination between economy and environment. The remainder of the article is organized as follows: Section 2 gives the literature review, Section 3 presents a detailed discussion of our model’s construction and methodology, Section 4 provides a case study of Beijing and includes an analysis of industrial reconstructuring impacts on emissions and energy consumption, and the final section gives the results and discusses some policy implications.

2. Literature review Industrial structure has a significant impact on energy consumption and carbon emissions (Wei et al., 2008; Zhao et al., 2010; Mi et al., 2015; Busch et al., 2018). For example, Proops et al．(1993) proved that changes in industrial

structure can result in the concentration variation of carbon dioxide in the atmosphere, and the changes will reduce carbon dioxide emissions in the next 20 years. Matthew et al. (2008) also confirmed that the secondary industry in China was the most important part of energy consumption and carbon emissions. However, Andrew (2007) analyzed multinational panel data and found that the development of the primary industry in a country had a significant positive correlation with the emissions of carbon dioxide. Also, the agricultural output level and the use of agricultural machinery were found to increase the emissions of carbon dioxide. Zhang et al. (2014) adopted the ARDL model to analyze this mechanism that how industrial structure and other factors affected the reduction of carbon emissions, then proposed that increasing the tertiary industry’s proportion of the total GDP can reduce carbon emissions. Other studies have decomposed changes of carbon intensity and energy intensity at the level of industrial sectors (Kumar, 2006; Liao, 2007; Zhu et al., 2014; Tamayao, 2014;Mi, 2015). Wu et al．(2018) found the adjustment of the industrial structure has a great importance in emissions reduction and the effect changes in response to shifts in different regions. Li et al. (2014) investigated the conditions under which industrial restructuring promoted reduction of energy intensity and observed that the impacts on energy intensity were different when industrial structure was greater or smaller than a certain ratio. So China must reduce the proportion of secondary industry, while using technological advances to reduce energy intensity continuously. Chen et al. (2017) applied an IO model to study carbon emissions at industrial sectors’ level in China in 2012 and found that China was faced with the urgent task of adjusting its coal energy structure. The method of linkage analysis is widely used and accepted because it can describe the relations between sectors and identify essential sectors. Many methods of linkages have been proposed. For example, Dietzenbacher (1992) used the eigenvector method to measure the interindustry linkages from the data of the Netherlands for the entire post-war period of 1948–1984. Cai et al. (2005) examined the backward and forward linkages between the Hawaii Fisheries Department and other economic sectors. Meanwhile linkage analysis has been extended to energy and environmental fields. For example, Sánchez-Chóliz et al. (2003) used linkage analysis and developed an input–output programming model that combined two types of constraints: environmental constraints on greenhouse gas emissions goals and economic constraints. The result of this model shows how to realize greenhouse gas emissions goals. Mi et al. (2015) used this approach to evaluate industrial reconstructuring impacts on energy consumption and emissions. Chang (2015) also used linkage analysis to analyze key sectors in carbon emissions. These studies that combine linkage analysis provide insight into the use of resources and the effects of carbon emissions associated with sectoral inputs and outputs. Industrial structure optimization is a particular issue that often has multiple goals. The planner must meet these goals by setting the proportion of each industry sector. Over the past few decades, many optimization approaches have been extended for adjustment of the industrial structure (Mi et al., 2015). These optimization methods would aid decisionmakers to formulate reasonable effective industrial restructuring management policies. Some important studies concerning the optimization methods in the early stage are summarized in Table 1. These techniques fall into three categories. The first category is the linear mathematical programming model. Oliveira and Antunes (2002) established an energy–economy planning model by this means, which provides the theoretical basis of the relationship between energy and economy for decision making. Carla Oliveira (2011) developed a linear mathematical programming model that provides a forward-looking analysis of industrial structure and energy system changes to evaluate the environmental effects and provide basis for policymaking. San Cristobal (2012) proposed a linear programming method to achieve emissions goals and affect the compositions of production activities. Yu et al. (2019) proposed a new multi-objective model of energy, environment, and economy to explore energy saving and emission reduction through industrial structure adjustment during the 13th Five-Year Plan. However, these researches do not provide how much the production of industrial sectors needs to adjust over the next few years.

The second category of models is nonlinear mathematical models. Kravanja ea al. (2010) developed a nonlinear programming model and used an example to demonstrate that this modern optimization technique could fundamentally improve economic competitiveness. Huang et al. (2018) also used the method to study the optimization of the layout of coal chemical industry in China with the restriction on carbon emissions. However, many parameters in the model and the relationship between them may not consider uncertainty. The third category is inexact optimization methods, including fuzzy programming models, stochastic models, and hybrid fuzzy-stochastic models. Loulou et al. (1999) developed a minimax regret formula to estimate key outcomes of minimax regret and minimum expectation plans for greenhouse gas reductions in Canada. Dong et al. (2013) proposed a fuzzy radial interval linear programming model to solve the energy management problem with uncertainties. Zhou (2013) used an inexact fuzzy multi-objective programming approach to optimize industrial structure in an uncertain condition. Table 1: Majors studies of exploring the optimization methods Authors (year)

Objects

Method

Oliveira et al. (2002)

Energy system

I–O method + MOLP

Carla Oliveira (2011)

Energy consumption (1999–2010)

MOLP

Yu (2018)

Pollutions (2012–2020)

I–O method + MOLP

Ning (2015)

Carbon emissions (2007)

I–O method + MOLP

Cristóbal (2012)

Carbon emissions (2005)

I–O method + MOLP

De Faria et al. (2009)

Water consumption

Nonlinear programming

Wei and Shen (2004)

Population, resource, environment (1949– 1999)

Nonlinear programming

Richard and Amit (1999)

Energy–environment systems (2008–2012)

Linear programming

Borges et al. (2003)

Energy system

I–O method + fuzzy MOLP

Chen et al. (2010)

Carbon emissions (1990–2006)

TISP

Dong et al. (2013)

Energy system and environmental emissions

FRILP

Zhou et al. (2013)

Watershed system (1980–2008)

IFMOP

These methods do not reflect the efficiency of the ecology–economy accurately. This paper proposes an energy, environment, and economy model using the multi-objective approach to optimize industrial structure in order to save energy and reduce emissions while maintain considerable GDP development during the 13th FYP period. The NSGA-II genetic algorithm is used to solve the model and the Pareto frontier will be obtained. A super-efficiency DEA model was then applied to select the final solution, i.e., the output structure of the 29 sectors can be gained from the Pareto-optimal front on the basis of industrial eco-efficiency.

3. Research methods and model construction 3.1 Industrial grouping Leontief (1936) proposed the input–output method, which was often applied to study the interdependence of inputs and outputs among various parts of an economic system. The method is expressed below:

X = AX + Y

(1)

Here X is a vector of the total output, Y is a vector of the final use, and representing the relationships among sectors in which

A

is a direct input coefficient matrix

 X  A = { Aij } =  ij   X j 

(2)

When solving the total output, the formula is as follows:

X = ( I − A) −1Y = LY Here

I

(3)

is the identity matrix.

L

( I − A) −1 .

represents the well-known Leontief inverse matrix

Linkage measures can be normalized according to the influence of each sector on other sectors in terms of the contribution to GDP. Let BL be the normalized measure. The higher the value of BL , the larger the impact a sector has on other sectors. The index can be defined as:

BL j =

∑ 1 n

n

l

i =1 ij n

∑ (∑ n

j =1

(4)

l ) i =1 ij

n

Where

∑l i =1

ij

is the accumulation of all elements of the jth column of the matrix.

1 n

∑ (∑ n

n

j =1

i =1 ij

l )

represents

the average of the sums the column in the matrix. For another index regarding carbon dioxide emissions, vector in industry

j and

it represents the volume of

co2

cj

is defined as the direct carbon emissions intensity

emissions per unit added value. Let BC j be the measure that can

explain the proportion of co2 emissions from individual sectors to total emissions from all sectors. The index can be defined as:

BC j = If

cj 1 n

∑

(5)

n

c j =1 j

BL j > 1 , then the per-unit output growth of sector j would produce a above-average growth in the final output.

Similarly, if

BL j < 1 , the per-unit output growth of sector j would produce a below-average growth in the final output.

So, a sector is considered to be an essential department if

BL j > 1 and non-essential if BL j < 1 . Because these essential

sectors have high-quality connections in the economic structure, a little change of these sectors will has a big impact on the economic output. It reveals the economic link between a sector and the overall economic output. For the same reason, the index BC j can also be explained in this way. BC j > 1 demonstrates that per-unit added value growth of sector

j would cause carbon emissions growing at rates far above the industry average. So, the sector

could be considered to be an essential department if

BC j > 1 but non-essential if BC j < 1 . Intuitively speaking, a little

change of these essential emission departments will have a big impact on the total emissions. In order to resolve the contradiction among economic growth, carbon reduction and energy consumption in a system, industry grouping is the basis for the path of industrial structural adjustment. It can better help decision makers solve problems by limiting the development of high emission and low economic growth industries, and encouraging low emission and high economic growth industries. So we divide a two-digit industry into four groups by economic linkages and carbon emissions across industries: Free development group (A):

BL j < 1 and BC j < 1 ;

Key development group (B):

BL j > 1 and BC j < 1 ;

Restricted development (C):

BL j < 1 and BC j > 1 ;

Optimized development group (D):

BL j > 1 and BC j > 1 .

3.2 Multi-objective programming model and solution Employing the principles of comprehensiveness, accuracy, simplicity, applicability, and controllability, this study establishes a multi-objective programming model according to a low-carbon economic development mode. 3.2.1 Setting the objective function According to the developmental goals of the regional economy during the 13th FYP period, a set of objective functions was derived from the total economic output, energy consumption, and carbon emissions. (1) One of the main goals is high GDP growth. So, the sum of final demand of the expressed by

t th

year over the sectors is

f1 :

n n   Max f1,t = ∑ ( X j )t 1 − ∑ ( aij )t  j =1  i =1 

(6)

X j represents output of the sector j , aij denotes the input–output technical coefficient, t indicates this

Where

th

year is the t year, and n represents the number of sectors. (2) The minimization of the amount of carbon emissions: n n   Min f 2,t = ∑ c j ( X j )t 1 − ∑ (aij )t  j =1  i =1  Where c j represents the carbon intensity in sector j .

(7)

(3) The minimization of total energy consumption, which is regarded as the third objective of the

  Min f3,t = ∑ e j ( X j )t 1 − ∑ (aij )t  j =1  i =1  n

ej

Where

t th

year:

n

denotes the energy intensity in sector

(8)

j

.

3.2.2 Define the constraints The constraints are divided into two categories. One category includes the constraints of the overall economic growth, energy consumption, and emissions in this region, whereas the other category is the same constraint but in each industrial group. 3.2.2.1 The constraints throughout the region (1) Economic growth constraints N N N     (9) ) 1 − ( a ) ≥ (1 + α ) ( X ) 1 − (aij )t −1  ∑ ∑ ∑ j t ij t  j t −1  j =1  i =1  j =1  i =1  Where α represents the lower limit of the annual growth rate of GDP during the13th Five-Year Plan period and N

∑( X

t−

1 is the year before the t

th

year.

(2) Total carbon emissions constraints N

N

N

N

j =1

i =1

j =1

i =1

∑ c j × ( X j )t (1 − ∑ (aij )t ) ≤ ∑ ( X j )t (1 − ∑ (aij )t ) ⋅ (1 − γ )tl ⋅ CSt 0 Where FYP,

γ

CSt 0

(10)

denotes the lower limit of the annual rate of decline of carbon emissions in the remaining years of the 13th

is the amount of carbon emissions per unit GDP in the base year 2017 across the whole industry, and

denotes the number of years from the base year to the target year. (3) Energy supply constraints

tl

N

N

j =1

i =1

∑ e j ×( X j )t (1 − ∑ (aij )t ) ≤ (1 + β )tl Et 0 Where

β

(11)

represents the upper limit of the annual growth rate of energy consumption during the remaining years of

the 13th FYP, and

Et 0

denotes the base year's energy consumption.

(4) Dynamic input–output balance constraints

X t − At X t = Y = Y1t + Y2t + Y3t Where

Y1t

(12)

is the column vector of the final consumption expenditure, including household consumption and

government consumption,

Y2t

is the column vector of total capital formation, and

Y3t

is the column vector of the net

outflow of goods and services. Since the input–output constraint is composed by multiple equality constraint equations which are hard to satisfy. So, these strict constraints are transformed into the goal programming model below:

Min f 4,t = eT ( Dt− + Dt+ )

X t − At X t = Y1t + Y2 t + Y3t + Dt− − Dt+ +

Where Dt in year

t

and

e

(13)

= ( dt+1 , dt+2 , L , d t+ N )T , Dt− = ( dt−1 , dt−2 ,L , dt− N )T , Dt+ ≥ 0, Dt− ≥ 0 are the deviation variables

N dimensions.

represents a unit vector of

3.2.2.2 The constraints in each group The second is the constraints of the economic output, energy, and carbon emissions of each industrial group. In Group A, economic, energy, and carbon emissions are not constrained. Group B requires the economic growth rate exceed the required rate

α

every year. Group C requires that the growth rate of GDP is less than zero. Group D requires

that the growth rate of GDP continues to increase. For both Groups C and D, the declining rate of carbon emissions per unit GDP cannot be lower than the limit rate γ , and the growth rate of energy consumption cannot be more than the limit rate

β

in the subsequent years.

Table 2: Economic growth demands and resource environment constraints of different industry groups Group Free Key development Restricted Optimized development group (B) development group development group Constraints group (A) (C) (D) GDP growth rate Not limited >α <0 >0 Declining rate of carbon intensity Growth rate of energy consumption

Not limited

Not limited

>γ

>γ

Not limited

Not limited

<β

<β

(5) Economic growth constraints in each group: N

N

∑X

j ,t

(1 − ∑ (aij )t ) ≥ (1 + α )∑ X j ,t −1 (1 − ∑ (aij )t −1 )

∑X

j ,t

(1 − ∑ (aij )t ) ≤ ∑ X j ,t −1 (1 − ∑ (aij )t −1 )

∑X

j ,t

(1 − ∑ (aij )t ) ≥ ∑ X j ,t −1 (1 − ∑ (aij )t −1 )

j∈B

j∈C

j∈D

i =1

j∈B

N

i =1

N

j∈C

N

i =1

(14)

i =1

(15)

i =1 N

j∈D

i =1

(16)

(6) Total carbon emissions constraints in each group: N

N

∑c X

j ,t

(1 − ∑ (aij ) t ) ≤ ∑ X j ,t (1 − ∑ (aij )t ) ⋅ (1 − γ )tl ⋅ CSC ,t 0

∑c X

j ,t

(1 − ∑ (aij ) t ) ≤ ∑ X j ,t (1 − ∑ (aij )t ) ⋅ (1 − γ )tl ⋅ CS D ,t 0

j∈C

j∈D

where

j

j

i =1

j∈C

N

(17)

i =1 N

i =1

j∈D

(18)

i =1

CSC ,t 0 and CS D,t 0 are the amount of carbon emissions per unit GDP of Groups C and D respectively in the

base year. (8) Energy supply constraints in each group: N

∑ e j X j ,t (1 − ∑ (aij )t ) ≤ ∑ (1 + β )tl EC ,t 0 j∈C

i =1

(19)

j∈C

N

∑ e j X j ,t (1 − ∑ (aij )t ) ≤ ∑ (1 + β )tl ED,t 0 j∈D

where

i =1

Ec,0 , ED,0

(20)

j∈D

is the base year's energy consumption of Groups C and D.

(9) Industrial production capacity constraints For the purpose of the sustainability and diversity of the industrial ecosystem, changes must be considered for sectoral production when an industrial reconstruction is being made:

(1 − Φ ) X i ,t −1 ≤ X i ≤ (1 + Φ ) X i ,t −1 Where

(21)

Φ is the i − th sector's production fluctuation. So (1 − Φ) X i ,t −1 is the lower limit while (1 + Φ ) X i ,t −1 is

the upper limit of the output for the i − th industry sector. (10) Non-negative constraints The variables of the paper are a non-zero production variable taking into account actual situation in reality, i.e.,

Xt ≥ 0 . 3.3 Solution 3.3.1 NSGA-II Many studies have converted multiple targets in a multi-objective optimization problem into one goal and used mathematical programming to solve the problem. This approach is usually using a utility function to convert multiple goals into a single goal, then the problem is solved by a single-objective optimization approach. However, under normal circumstances, the units between different targets are different and the order of magnitude is also very different. So it is difficult to set the weight distribution utility function of the goal before optimization. There are too many subjective random variables in the process of assigning weight coefficients. Usually, the methods include the evaluation function method, hierarchical solution method, and interactive planning method. The general multi-objective optimization solution requires many calculations and recalculations to obtain an optimal solution, especially for more complicated problems. Even then, the optimal solution may not be obtained and may be limited by many problems in practice. The application usually converts multiple targets directly into single targets to solve, but this approach is not true multi-objective optimization. NSGA-II introduces the fast non-inferiority ranking of computational complexity O ( m N 2 ) on the basis of overcoming the shortcomings of NSGA, such as low computational efficiency, lack of elite retention strategies, and the need to reserve parameters. Deb et al. (2002) introduced computational complexity O ( m N 2 ) . The new diversity of

protection methods presented by NSGA-II has the following advantages. First, a new hierarchical-based fast non-winning sorting algorithm is developed to reduce the complexity of computation from O ( m N 3 ) to O ( m N 2 ) , where m refers to the amount of objective functions and N refers to the amount of individuals in the population. Second, the concept of crowded distance is proposed. The crowded distance comparison operator is used to replace the suitable value sharing method that must calculate complex shared parameters. The third is to introduce the optimal mechanism for expanding the sampling space and generate the individual after breeding. The offspring compete with their parents to produce the next generation of populations, thereby helping to maintain good individuals, rapidly increasing the overall level of the population, and operating efficiently. In this paper, the NSGA-II genetic algorithm is used to solve the proposed model. The fitness function is composed of four different objective functions, which are uniformly transformed into minimum forms as fitness functions. The first target is the inverse of the accumulated value of GDP, the second target is the cumulative value of energy consumption, the third target is the cumulative value of carbon emissions, and the last target is the minimum balance of input–output balance. MATLAB was used to implement the NSGA-II algorithm to get the Pareto frontier. 3.3.2 Super-efficiency model The result of the solutions generated by the non-dominated sorting genetic algorithm form a Pareto front that satisfies the three objectives under constraints. In order to choose a representative solution, industrial eco-efficiency was analyzed. Here, carbon dioxide emissions and energy consumption were used as inputs and GDP was used as an output to measure industrial ecological development efficiency. The ecological development efficiency reflects the degree of coordination between economy and environment. A mature industrial ecosystem can have the most economic benefits with the lowest resource consumption and environmental costs. Therefore, on the basis of the super-efficiency model, the representative solution is selected as the optimal adjustment path for industrial restructuring. Because the traditional DEA model cannot effectively distinguish the points on the frontier’s surface, scholars have improved and perfected the traditional

DEA model. The super-efficiency evaluation method (Zhuang, 1993) is one of

the typical representative models. Supposing that there are

n

DMUS with

p

inputs and

q

outputs, the efficiency for

any given DMU k , could be solved through the super-efficiency model:

min θ n

s.t .

∑λ x j =1 j≠k

j ij

n

∑λ j =1 j≠k

j

≤ θ xik , j = 1, L , n.

yrj ≥ yrk

(22)

λ ≥ 0, i = 1, 2, L , p; r = 1, L , q; j = 1, 2, L , n ( j ≠ k ) Where

λj

represents unknown input and output weights for DMU k , and

θ

represents the efficiency of

DMU k . We can use this model to select a point on the frontier’s surface. Industrial restructuring based on this point can maximize industrial eco-efficiency. According to the solutions of this model, the modeling process can be demonstrated below.

Step 1: analyze the economic system and group all sectors. Step 2: built the linear programming model. Step 3: obtain the model’s parameters from a few predictive modeling in economy and environment. Step 4: use the NSGA-II algorithm to get the Pareto frontier. Step 5: select the representative solution based on the super DEA. Step 6: analyze the results and make a decision. The framework is demonstrated below in Fig. 2. Industrial optimization model

Grouping the sectors

Model’s objective

Decision variables

Economic development

Forecast model

Model’s constraints

Energy consumption objective

CO2 constraint objective

Input–output matrix forecast

Right side of the equation

Model’s parameters

Model solver

Solutions of Pareto Frontier

The best solution based on super DEA model

Analysis of results

Decision-making

Fig. 2. The framework for industrial structure optimization.

4. Empirical analysis

4.1 Data Beijing is a capital city of China. At the end of 2017, the resident population was 21.107 million with a GDP of 2,800.04 billion yuan and per capita GDP of 129, 000 yuan. As seen from the Fig. 3 and Fig. 4, the shares of carbon emission and energy in Beijing are only 0.71% and 1.57% respectively in 2016. However, Guangdong Province, which has the highest regional GDP, is only twice as high as Beijing. So the energy intensity and carbon intensity of Beijing are lower than other regions. Beijing is a demonstration area for energy conservation and emission reduction.

Fig.3. The province's share of carbon emissions in China in 2016

Fig.4. The province's share of energy consumption in China in 2016 Beijing also faces the requirements of energy saving and emission reduction during the 13th FYP. To meet the goals of the Plan, it needs the acceleration of the transformation of the economic development, promotion and improvement of industrial restructuring, and a scientific and rational industrial ecosystem. The industrial structure reconstructuring of the Beijing may provide a reference to decision-making regarding the sustainable development of the region’s economy. Beijing can also be an example to other provinces for the achievement of a high level of eco-environmental and economical coordinated development. According to the availability of Beijing’s data, the base year of Beijing was set to 2017. The output value of each sector and the energy intensity and the carbon intensity were sourced and calculated from the Beijing Statistical Yearbook. For the purpose of observing only the industrial restructuring impact on the economy, environment and energy, the energy intensity and the carbon intensity coefficients were assumed to be unchanged in the subsequent years. Some exogenous parameters during the planning period (2018–2020) had to be calculated according to the historical data, whereas other data had to be predicted according to the historical data by use of conventional predictive approaches. For example, the ARIMA model is used to predict final output, and the RAS method (Toh, 1998) is used to predict Inputoutput coefficient. The details are demonstrated in Table 3. Table 3: the setting of exogenous parameters in the model Parameters

At

Data sources and estimation methods The values for 2018–2020 were calculated by the RAS method according to the Beijing Input– Output Table 2012.

Y1,t

This value was predicted by the ARIMA model according to the historical data of 2008–2017. The expression of the model is as follows:

Y1,t = 1.952503Y1,t −1 − 0.9744491Y1,t −2 + ε t − 114.5927ε t −1

This value was also predicted by the ARIMA model according to the historical data of 2008–

Y2,t

2017. The model is expressed below:

Y2,t = 1.792771Y2,t −1 − 0.8528193Y2,t −2 + ε t + 393.9229ε t −1 This value was also predicted by the ARIMA model according to the historical data of 2008–

Y3,t

2017. The model is expressed below:

Y3,t = 1.450986Y3,t −1 − 0.8590418Y3,t −2 + ε t − 0.9999858ε t −1

CSt 0

CSt 0 is the volume of co2

emissions per unit value added in 2017. This value was calculated

from the prediction of the output of the Beijing region.

Et 0

Et

is the energy consumption in 2017. This value was calculated from the prediction of the

output of the Beijing region.

α

This value was obtained from the13th Five-Year Plan of Beijing.

β γ

The value for 2018–2020 was calculated based on the available data. The value for 2018–2020 was calculated based on the available data.

4.2 Analysis of results 4.2.1 The results of industrial groupings of Beijing First, we have grouped the industries of the Beijing region. Due to the data availability, the Input-output table of 2012 in Beijing was used for this research. The industrial departments come from the Beijing Statistical Yearbook and covered a total of 42 sectors. To comply with the relevant statistical data, some sectors have merged together, and a total of 29 industries sectors were produced. The sectors after reorganization were divided into four categories according to the two measures of the industry groupings as introduced earlier. The final groupings are shown in the following figure. 6 21

22 5 26

4

BCj 10

3

2

19 11 4

1 1 28 0 0

27 0.2

0.4

25 0.6

29 20 9 23 0.8

3 8

7

6 5 18 17

14 13 1

12 24

15 1.2

2

16 1.4

1.6

1.8

2

BLj

Fig. 5. Scatter diagram of the indices of carbon emissions and economic linkages The coal mining sector, chemical product sector, petroleum processing sector, coking sectors, nuclear fuel processing sectors, and wholesale and retail trading sector belong to the list of prohibitions and restrictions on industries in Beijing, so they were placed into the restricted development group. The final division of these sectors into four groups is shown in Table 4.

Table 4: Industrial groupings in Beijing City Free development group A

1. Agriculture, forestry, animal husbandry, and fishery products and services; 3. Food and tobacco; 5. Textiles, clothing, footwear, leather and down products; 6. Processed wood products and furniture; 17. Instrumentation; 18. Other manufactured products; 20. Metal products; 23. Water production and supply; 27. Finance; 28. Real estate; 29. Other services.

Key development group B

7. Paper and paper products, printing and reproduction; 12. Metal products; 13. Common and special equipment; 14. Transportation equipment; 15. Electrical machinery and equipment; 16. Communications equipment, computers and other electronic equipment; 24. Construction.

Restricted

2.Coal mining; 8. Petroleum processing, coking, and nuclear fuel processing; 9.

development group C

Chemical products; 19. Waste scrap; 25. Wholesale and retail trading; 26. Transport and warehousing.

Optimized

4. Textiles; 10. Non-metallic mineral products; 11. Metal smelting and rolled processed

development group D

products; 21. Heat production and supply; 22. Gas production and supply.

4.2.2 Results of Pareto optimization of the industrial structures After several iterations, the optimal Pareto solution set for different schemes in 2018-2020 was obtained. The optimal Pareto solution for 2020 is shown in Fig. 6. The population converges to a small area and the individuals are evenly distributed in the solution set. Each point corresponds to a Pareto optimal solution and individuals are also well

Energy consumption(Unit: ten thousand tons)

preserved. As can be seen, the optimization was successful.

5785.2325 5785.232 5785.2315 5785.231 5785.2305 5785.23 5785.2295 5785.229 6861.6215 6861.621 6861.6205

3.3841 3.3841

6861.62 6861.6195

3.3841 3.3841 3.3841

6861.619 6861.6185

3.3841

4

x 10

3.3841 6861.618

3.3841

Carbon dioxide emissions(Unit:ten thousand tons)

GDP （ Unit： billion yuan）

Fig. 6. Pareto front in Beijing region in 2020 4.2.3 Results of optimization of the industrial structures based on super DEA The industrial ecological development efficiency reflects the degree of the coordinated development of economy and ecological environment. The industrial ecosystem obtains many economic benefits with the lowest resource consumption and environmental costs. Therefore, we used this index to choose the optimal industrial structural adjustment path. According to the results of the super DEA, the best plan was selected according to the maximum ecological development efficiency in the in the next few years. The GDP, energy consumption, and carbon emissions of the optimal plan in each year are demonstrated in Table 5.

Table 5： ： The best options of indicator values for each year 2018

2019

2020

29836.05

31775.40

33840.80

(ten thousand tons)

5549.77

5666.24

5785.23

Carbon emissions (ten thousand tons)

6930.46

6949.19

6861.62

GDP (one hundred million yuan) Energy consumption

4.2.4 Analysis and discussion (1) The reduction of energy intensity and carbon intensity Through the industrial restructuring, the energy intensity and carbon intensity would be greatly decreased in Beijing. As shown in Table 6, energy consumption will be 5,785.23 million tons and the carbon emissions of industry will be 6,861.62 million tons in 2020. Based on the results, the carbon emission intensity will be 0.2028 (million tons/billion yuan) in 2020, which is 48.35% less than in 2015 (0.3926 million tons/billion yuan), and energy intensity can achieve 0.171 million tons/billion yuan in 2020, which is 40.9% less than in 2015 (0.2893 million tons/billion yuan). So the reduction in carbon intensity and energy intensity is not in conflict with the growth of GDP. This is consistent with other literatures. For example, Mi et al. (2015) finds energy intensity reaches the minimum when GDP reaches the maximum. For easy to compare, energy consumption and carbon emissions will be 6,565.99 and 8,743.67 million tons, respectively, in 2020, which were calculated within the initial industrial structure in 2017. If this industrial structure is not adjusted, the intensities would far exceed the goal of the 13th Five-Year Plan. Therefore, the adjustment of industry structure can save energy consumption by 11.9%, and reduce carbon emissions by 21.52%. Table 6: Growth rates of added value and the energy and carbon intensities from 2018 to 2020 Indicators 2018 2019 2020 Growth rate of GDP 6.5% 6.5% 6.5% GDP (billion yuan) 29836.05 31775.40 33840.80 Energy intensity (million tons/billion yuan) 0.186 0.1783 0.171 Carbon emission intensity (million tons/billion yuan) 0.2323 0.2187 0.2028 (2) Changes in economic growth The growth rate of GDP maintains consistently at 6.5% after the structure adjustment from 2018 to 2020. The growth rate of GDP from 2018 to 2020 is much smaller than the growth rate of 2017 but has reached the government's planned rate of 6.5%. This level of GDP growth is consistent with the current growth rate under the new normal economy in China. But it is somewhat different from other literatures. For example, Mi (et al., 2015) find the average annual growth rate of GDP is 8.29% from 2010 to 2020 in Beijing when achieving energy saving and emission reduction targets through adjusting industrial structure. The reason is that the study period is different, especially China entry into the new normal economy since 2014. And the other reason is the strict restrictions of industry grouping in the model. It reduced the space for feasible solutions and had further restrictions on different groups of sectors. So by structural adjustment, the growth rate of GDP in Beijing has met the requirements of the 13th FYP. The results show that Beijing can achieve triangular "win-win" goals and meet not only the requirements for economic growth but also energy conservation and emission reduction by structure reconstruction. (3) Trends of the structures, energy consumption, and carbon emissions of three main industries As can be seen from the Table 7, the annual added value growth rate of three major industries has increased by 21.13%, 6.13%, and 6.5%, respectively, from 2018 to 2020. The proportion of secondary industry in GDP declines slightly, whereas the proportion of primary industry increases and the proportion of tertiary industry has been maintained

at more than 80%. In some literatures, the tertiary industry in GDP often has an increased growth and proportion trend (Zhang et al., 2018; Mi et al., 2015). The reason is that the length of the duration of these studies is inconsistent, which may lead to different results; and another reason is that carbon intensity and GDP growth rate of sectors in the tertiary industry are quite different, such as S25 (Wholesale and retail trading) and S26 (Transport and warehousing) are in restricted development Group C. The goal of structural optimization is to develop low carbon and low energy consumption industries. Although the primary industry has the largest growth rate, its contribution to GDP is far less than that of the secondary and tertiary industries due to its small size. The tertiary industry has maintained such a large proportion and has been playing a significant role in economic growth. The result indicates that structural adjustments of the three types of industries have made positive contributions to reducing emissions and energy consumption. Although the annual growth rates of emissions and energy consumption are positive in the secondary industry, the growth rate is less than the growth of GDP. The tertiary industry not only contributes to GDP but also reduces emissions and energy consumption. As for the primary industry, although the growth rate is large, it has less impact on emissions and energy consumption due to its lower proportion of GDP. Table 7: Annual growth rate of three major industries from 2018 to 2020 Three main industries Annual growth rate Annual growth rate of GDP Annual growth rate of carbon emissions Annual growth rate of energy consumption

Primary industry

Secondary industry

Tertiary industry

21.13%

6.13%

6.50%

21.13%

1.27%

-4.84%

21.13%

5.19%

-0.08%

(4) The different effects of industrial groups The optimization of industrial structure should consider not only the total amount of three targets but also the synergistic effects and interactions among different groups. In the Table 8, for the free development Group A , the annual average growth rate of GDP exceeds 8%, the annual average growth rate of carbon emission is 7.18%, the annual average growth rate of energy consumption is 7.1%. While the key development Group B, the annual average growth rate of GDP has reaches 6.6%, the annual average growth rate of carbon emissions is 5.27%, and the annual average growth rate of energy consumption is 7.1%. The annual average growth rate of Group C has not changed, but its energy consumption and emission growth rate has been greatly reduced. The optimized development Group D has a growth rate of 1.27% , and it has only a little change in the carbon emissions and energy consumption growth rate which are 0.24% and 0.65% respectively. Table 8: Annual growth rate of each group from 2018 to 2020 Group Indicators Annual growth rate of GDP Annual growth rate of carbon emissions Annual growth rate of energy consumption

Free development Group A

Key development Group B

Restricted development Group C

Optimized development group D

8.16%

6.6%

0.00%

1.27%

7.18%

5.27%

-8.24%

0.24%

7.1%

10.11%

-5.31%

1.93%

As can be seen from the data, the growth rate of GDP of the group A and group C is greater than their growth of energy consumption and carbon emissions. So the carbon intensity and energy consumption intensity naturally decrease. But the changes in GDP share of Group A and Group C are opposite. This is consistent with the conclusions of other literature (Chang., 2015). For group B and group D, their growth rate of GDP is greater than growth rate of carbon emissions, but less than growth rate of energy consumption. This makes the carbon intensity decrease, while the energy intensity increases. The free development Group A has the largest GDP growth, but only accounts for less than 10% of the total GDP. Its contribution to GDP is far less than that of the key development Group B. These results indicate that the free development Group A and key development Group B should be set to a medium-to-high growth rate and the optimized development Group D should be set to a low rate of development, while the restricted development Group C should focus on adjusting its internal industrial structure. Therefore, in the process of optimizing the industrial structure, we need to consider the intricate relationship between these groups in economic growth, energy consumption and carbon emissions in the economy as a whole. (5) The different contributions of the industrial sectors to GDP For the goals of reducing emissions and energy consumption while maintaining economic growth, the changes of the structural adjustment on the sectors are demonstrated in Fig. 7 and Fig. 8. It can be seen that added value of sectors do not all increase. In 2020, nearly 17 sectors show significant changes positively in value added compared to those in 2017. However, it can be seen that the added value of S1 (agriculture, forestry, animal husbandry, and fishery products and services), S27 (finance); S28 (real estate), S5(textiles, clothing, footwear, leather and down products) increased significantly, whereas the added value of S22 (gas production and supply), S19 (waste scrap), S26 (transport and warehousing), S2 (coal mining) are declining. Same to other literatures’ findings (Chen et al. , 2017; Yu et al. ,2019; Chang., 2015), the sectors which can be observed to be growing faster are often low carbon emission intensive and low energy consumption intensive. They are mainly in the free development Group A and key development Group B. The sectors which show an annual negative growth rate are often high carbon emission intensive and high energy intensive. They are mainly in the restricted development Group C and optimized development Group D. So, by optimizing the structures of the industrial sectors through expanding Group A and Group B, optimizing Group D, and limiting the restricted development group C, the goals of reducing carbon emissions and energy consumption are achieved while economic growth is ensured.

The added value(billion yuan)

10

10

10

10

10

10

5

2018 sectors' added value 2019 sectors' added value 2020 sectors' added value

4

3

2

1

0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Sectors

Fig. 7. Comparisons of added values of adjusted structures in Beijing from 2018 to 2020

25%

The annual growth rate of added value

20% 15% 10% 5% 0 -5% -10% -15% -20% -25%

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Sectors

Fig. 8. Comparisons of the annual added value growth rates in Beijing from 2018 to 2020

5. Conclusion China is not only the largest developing country in the world, but also the country with the largest total carbon dioxide emissions. Considering that the contradiction between environmental protection and economic development is increasingly prominent, this paper aims to provide a solution by adjusting the region’s industrial structure to save energy and reduce emissions with considerable GDP growth. The proposed multi-objective programming model classifies industrial sectors into four groups according to their carbon emissions levels and contributions to economic growth. Discussions of the types of division help us understand the relationship of sectors within the industry. Then the Pareto frontier is obtained by using non dominated sorting genetic algorithm, and the best solution is selected through a super data envelopment analysis model. The combination of these two methods not only avoids the artificial subjective weights which often appear in other literatures, but also better reflects the harmonious development of the economy and the environment. Therefore, the paper proposed the approaches which are the combination of the analysis of grouping industry and multi-objective programming model in modeling, and the combination of non dominated sorting genetic algorithm and a super data envelopment analysis in solving the model could be a complement to other methods. To demonstrate the effect of this model, the application in Beijing from 2018–2020 during the 13th FYP has been discussed. From the analysis, a few conclusions can be gained. (1) Structure reconstruction has significant effects to save energy and reduce emissions. When the growth rate of GDP maintains at 6.5% during 2018–2020 in Beijing, the energy consumption will decrease by 11.89% (78.10 million tons) and carbon dioxide emissions will decrease by 21.52% (18.82 million tons) through industrial restructuring. Therefore, industrial structural optimization and adjustment will seriously reduce the energy intensity and carbon intensity. (2) Through reasonable industrial restructuring, energy intensity and carbon intensity may be reduced without negative association with economic growth. Although Beijing’s economic growth rate has been slower than in previous years, it has achieved a harmonious relationship between economy and ecological environment. (3) The upgrading of the industrial structure cannot simply look at the development of the tertiary industry. After the adjustment of Beijing's industrial structure, the tertiary industry does not grow rapidly, but remains near 80%. The industry sectors in the tertiary industry have differentiated. Some sectors have grown faster, while some sectors have slower growth. So no matter what industry this sector belongs to, it needs to develop from labor-intensive, capitalintensive industries to technology-intensive and knowledge-intensive industries.

(4) An effectual way to reduce emissions is to increase the proportion of these sectors in the Group A and Group B. The economic growth of these two groups is faster than the growth of carbon emissions, so it can also bring about a reduction in carbon intensity. And the relationships between economy development, energy consumption and carbon emissions are complex. Optimizing the output composition of the Group C and Group D can also reduce carbon intensity, but these groups have little contribution to the added value growth. If increasing the proportion of these sectors in the Group B and Group D, there will be growth in the added value growth, and in carbon intensity reduction, but energy intensity will increase. Therefore, it needs to balance these complex relationships. However, some limitations of this study and further research directions need to be considered. First, some of the parameters in the model are simplified. For example, carbon intensity and energy intensity remain unchanged during the study period, which is somewhat inconsistent with the reality. Therefore, future research can consider the trend of changes in carbon intensity and energy intensity during the study period. Second, The model in this paper consider the industry grouping which result in stricter constraints. It makes the space of feasible solutions smaller, and the model may have no feasible solution. When the model is used for other provinces and cities, the parameters of the added value and energy consumption growth rates, and the declining rate of carbon intensity in each group need to be adjusted according to the specific conditions of the region. How to scientifically set these parameters is worth further research. Third, This study only considers the impact of industrial restructuring on energy conservation and emission reduction, but it is not enough to solve the environment problem from this single approach. So further research need to consider other approaches, such as cleaner technology innovation, the implementation of clean energy price subsidies and tax reduction. How to incorporate these approaches into the model to study environmental problems is worth considering in future research.

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Highlights ► Classifying industrial sectors into four groups according to their carbon emission levels and contributions to economic growth. ► A multi-objective optimization model was established and a multi-objective genetic algorithm, known as NSGA-II was applied to solve the model. ► Evaluating the selection of the final plan by using a super DEA model that reflected the coordination of the economy and environment. ► Analysis of GDP growth, carbon emissions and energy consumption of each group.