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Ecological Indicators 95 (2018) 673–686
Contents lists available at ScienceDirect
Ecological Indicators
journal homepage: www.elsevier.com/locate/ecolind
Structural optimization for industrial sectors to achieve the targets of energy
intensity mitigation in the urban cluster of the Pearl River Delta
T
⁎
Wencong Yuea, Yanpeng Caib, Zhifeng Yanga,b, , Qiangqiang Ronga, Zhi Dangc
a
Research Center for Eco-Environmental Engineering, Dongguan University of Technology, Dongguan 523808, China
State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
c
School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong 510006, China
b
A R T I C LE I N FO
A B S T R A C T
Keywords:
Risk analysis
Energy-intensity mitigation
Industrial sectors
Copula function
Urban cluster of the Pearl River Delta
An integrated approach was developed through incorporating a copula-based violation risk analysis into a
programming model for optimizing industrial structures of urban clusters in uncertain conditions. Also, this
approach can be used to support decision making about promoting advanced manufacturing sectors (AMSs) and
mitigating industrial energy intensity. The advantages and the improvements of this approach lie in (a) reflecting
the relationships between energy consumption and economic benefits in industrial sectors, and (b) incorporating
the violation risk of the targets in energy-intensity mitigation into an optimizing model. A case study was
conducted to illustrate the application of this approach in the Pearl River Delta of China, a highly urbanized area
that includes the cities of Guangzhou, Shenzhen, Zhuhai, Dongguan, Huizhou, Zhaoqing, Foshan, and Jiangmen.
The results indicated that under the desired industrial structures, violation risk of energy-intensity mitigation in
the urban cluster of the Pearl River Delta would be indistinctive in the single-city perspective. Also, except
Dongguan, Jiangmen and Zhaoqing, the cities of the urban cluster would achieve the goal for developing AMSs,
based on the Industrial Plans for the 13th Five Year.
1. Introduction
The management of energy resources is an integral component of
regional economic development and environmental protection (Zhou
et al., 2015). The generation, consumption, and conservation of energy
are central to economic activities which are measured as the growth of
gross domestic product (GDP) (Bian et al., 2016; Sreekanth, 2016).
Energy consumption is a major contributor to global climate change
(Liu et al., 2016; Zhao et al., 2018). Approximately 70% of China’s total
energy consumption derives from the industrial sectors (Liu et al.,
2015a; Wang et al., 2013). The International Energy Agency predicted
that the energy used by industrial sectors would continue to increase,
and the energy use would approximately double by 2050, assuming
present trends continue (Edelenbosch et al., 2017). The Chinese government has thus proposed that the energy intensity (i.e., energy consumption per value added) in 2020 should be 15% less than its level in
2015 (Li and Lin, 2016; Liu et al., 2015b). As well as reducing the
energy intensity, it is worth noting that energy consumption and economic benefits may face correlated variations in the process of industrial development (Zhou et al., 2015). Industrial agglomeration
among urban cluster would also lead to complexities for mitigating
⁎
energy intensity (Pan et al., 2015). Energy intensity reduction would
become a tough constraint in the background of urban or urban-cluster
development (Xu et al., 2017). Therefore, novel methods are required
to consider how the energy intensity would be reduced over the longterm industrial development.
At the global level, industrial activities were responsible for over a
third of the total energy demand (Fais et al., 2016). Industrial activities
in China have had an enormous impact on energy consumption (Mi
et al., 2015; Xu et al., 2014). China’s industrial energy consumption has
been the focus of various studies in recent years (Li and Shi, 2014). For
example, Lu et al. (2015) used the decomposition technique and the
decoupling method to determine the main influences on the energy
consumption of industries. Yang et al. (2017) validated the inverted Ushaped relationship between the GDP per capita and the economy-related GHG emissions per capita. The mining and manufacturing sectors
have a great effect on the energy intensity at both the national and
provincial scales (Cai et al., 2016). Against the backdrop of the current
energy mitigation goal in China, most industrial sectors need to reduce
their energy intensity (Wu et al., 2016). In this context, the reduction of
energy intensity poses a great challenge to China, especially for energyintensive industrial sectors (Feng and Wang, 2017).
Corresponding author at: Research Center for Eco-Environmental Engineering, Dongguan University of Technology, Dongguan 523808, China.
E-mail address: zfyang@bnu.edu.cn (Z. Yang).
https://doi.org/10.1016/j.ecolind.2018.08.009
Received 6 May 2018; Received in revised form 1 August 2018; Accepted 4 August 2018
1470-160X/ © 2018 Elsevier Ltd. All rights reserved.
Ecological Indicators 95 (2018) 673–686
W. Yue et al.
Industrial structural optimization for energy-intensity mitigation
Data
collection
Conditions in the base year
| Energy consumption |
| Employees' number |
| Economic benefits |
Objectives in the planning year
| Energy supply |
| Development objectives |
| Economic benefits |
Uncertainty analysis
Energy consumption
Copula functions
Economic benefits
Demand for employees
Methodology
Joint probability distributions
Violation risk analysis
Monte Carlo simulation
Violation risk in energy-intensity mitigation
Industrial structural optimization
Case study
The urban cluster of the Pearl River Delta
Uncertainty analysis
Industrial structural
optimization
Violation risk
analysis
Fig. 1. Framework of industrial structural optimization in the background of energy-intensity mitigation.
Table 1
Equations of Copula function.
Family*
Parameter
Cθ (x , y )
Gaussian copula
θ ∈ [−1, 1]
ϕθ (Φ−1 (x ), Φ−1 (y ))
ϕ (Φ−1 (x )) ϕ (Φ−1 (y ))
Gumbel-Hougaard
θ ∈ [1, +∞]
exp{−[(−ln x )θ + (−ln y )θ]1/ θ }
Clayton
θ ∈ [−1, ∞] {0}
max[(x −θ + y−θ −1)−1/ θ , 0]
Frank
θ ∈ [−∞, +∞] {0}
Student t
⎡1⋯θd1 ⎤
1, i = j
θ = ⎢ ⋮ ⋱ ⎥ θij = ⎧
θ , i≠j
⎨
⎢θ1d⋯1⎥
⎩ ij
⎦
⎣
− ln ⎡1 +
θ
⎣
(e−θx − 1)(e−θy − 1) ⎤
e−θ − 1
−1 (x )
1
1
tk
∫−∞
−1 (y )
tk
∫−∞
⎦
2π 1 − θ2
⎡1 +
⎣
−(k + 2)/2
s2 − 2θst + t 2
⎤
dsdt
k (1 − θ2)
⎦
* References: Nelsen (2006) and Zhang et al. (2016).
developed an multi-objective optimization model based on the inputoutput method to obtain the adaptive industrial structures, considering
the energy-intensity mitigation and economic benefits. In addition to
industrial structural transformation, fuel prices and the number of
employees can also influence the energy intensity of industrial sectors
(Kander et al., 2017).
To realize the targets for energy-intensity mitigation in the future,
the links between the environmental and economic performance of
industrial sectors need to be considered (Cheng et al., 2018; Segura
et al., 2018). Such relationships directly affected the mitigation of energy intensity in industrial sectors. For example, some researchers revealed the relationship between the economic growth and environmental protection in the framework of Environmental Kuznets Curves
(Wang et al., 2016a,b). Previously, approaches for correlation analysis
(e.g., fuzzy rough set model, threshold analysis, and gray relational
analysis) were introduced to indicate the relationship among energy
consumption, CO2 emissions and mitigation options (Chen et al., 2018;
The energy intensity was influenced by many factors in industrial
sectors (Ang, 1999). Over recent decades, some researchers have studied the effect of energy intensity in industrial sectors. Li and Lin
(2015) used a meta-frontier network to measure the energy intensity
performance of 30 provinces in China. Wang and Wei (2014) applied
data envelopment analysis (DEA) to evaluate the regional energy efficiencies and the energy saving potentials of the industrial sectors in
major cities. Wu et al. (2016) suggested that a DEA approach could be
used to allocate the total national energy intensity reduction targets to
China’s provincial industrial sectors, for the purpose of sustainable
development. Concurrently, structural effects in industries were revealed to be the dominant factors in reducing energy intensity of China
(Liu et al., 2015a). Structural transformation in industrial sectors can
help mitigate energy intensity (Liu and Xiao, 2018; Shi and Li, 2018).
To achieve mitigation targets in energy intensity, optimizing models
can provide the desired strategies for industrial structural transformation (IST) (Dong et al., 2014a,b). For example, Mi et al. (2015)
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Fig. 2. The main cities in the urban cluster of the Pearl River Delta of China.
Table 2
Advanced manufacturing sectors in the urban cluster of the Pearl River Delta of
China.
Industrial sectors
Table 3
Dependence of correlated variables in industrial energy-intensity analysis.
Co-related parameters
1*
2
3
4
5
6
7
8
Spearman’s ρ
Manufacturing
sectors
Spearman’s ρ
0.7582
0.7198
0.9696
0.8486
0.6662
0.3398
Total
Energy
Petroleum refining, coking, and nuclear fuel
processing
Manufacture of raw chemical materials and
chemical products
Manufacture of chemical fibers
Nonmetal mineral products
Smelting and pressing of ferrous metals
Smelting and pressing of nonferrous metals
Manufacture of general-purpose machinery
Manufacture of special-purpose machinery
Manufacture of automobile
Manufacture of railway, ship, aeronautics
and other transport equipment
Manufacture of electrical machinery and
equipment
Manufacture of communication equipment,
computers and other electronic
equipment
Mining sectors
Cities
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
Employees’ number
(EN)
Energy intensity
Economic
benefit
Economic
benefit
EN intensity
copula theory was commonly applied in economic and environmental
management (Min and Czado, 2010). For instance, Zhang and Lam
(2016) revealed nonlinear relationship in reliability engineering by the
copula theory. Samitas and Tsakalos (2013) employed copula functions
to explore the correlated dynamics between the Greek and European
markets during the debt crisis. Perez-Rodriguez et al. (2015) analyzed
the joint distributions of tourism industry and national economy by
copulas. Multiple copula functions can be used to analyze the joint
behavior of random processes during extreme events (Emmanouilides
and Fousekis, 2015; Miao et al., 2016). Selecting a suitable copula was
quite important for modeling dependence correctly among marginal
distributions (Montaseri et al., 2018). For example, Carta and Steel
(2012) select different copula functions to model the dependence by the
effectiveness of the their outputs.
Meanwhile, the randomness of energy consumption and economic
benefits may also lead to an unexpected violation event (i.e., exceeding
the target of energy-intensity mitigation). Violation risk analysis should
be applied in the IST system for determining how industrial sectors
adapt to uncertain and risky conditions. There were three main steps of
violation risk analysis for energy-intensity mitigation (i.e., identifying
the possible adverse events, estimating the likelihood that they might
occur, and predicting their consequences) (Tan et al., 2016). Capturing
extreme risk dependence, copula theory was also effective in modelling
multivariate dependencies in risk management (e.g., energy, wheat and
electricity market) (Ignatieva and Truck, 2016; Liu et al., 2017; Lourme
and Maurer, 2017; Qiu and Rude, 2016). For example, Yue et al. (2018)
* Note: Code numbers of cities in the above table are listed in Table S2 of the
Supporting material.
Xiao et al., 2016; Xu et al., 2016; Zi et al., 2016). Also, there might be
some variations and uncertainties in the future because of economic
and environmental disturbances (Cai et al., 2009a). Such complexities
may lead to some challenges to traditional methods, to predict variations of energy consumption and economic benefits in industrial sectors.
The random parameters in the IST system (e.g., energy consumption
and economic benefits) may present different probability distributions
(Yu et al., 2018). Considering their dependent relationship, joint
probability analyses of the random parameters can provide complementary distribution characteristics of energy intensity for decisionmaking in the IST system (Montaseri et al., 2018). In order to indicate
the joint distribution of energy consumption and economic benefits,
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Table 4
Parameter estimation of Copula parameters and Spearman’s rho in the case study.
Mining sectors#
Manufacturing sectors
C(x, y)
x
y
Energy intensity
GDP
EN
GDP
Energy intensity
EN
Energy
GDP
EN
GDP
Energy intensity
EN intensity
Gaussian copula
Spearman’s rho
Parameter (θ)
Spearman’s rho
Parameter (α)
Spearman’s rho
Parameter (α)
Spearman’s rho
Parameter (α)
Spearman’s rho
Parameter (ρ)
Parameter (k)
0.829
0.842*
0.999
83.872
0.999
27.600
0.998
27.600
−0.995
−0.998
1.507
0.048
0.050
0.999
78.690
0.999
21.519
0.997
21.519
−0.999
−1.000
1.834
0.022
0.023
1.000
274.288
1.000
50.357
0.999
50.357
−1.000
−1.000
2.643
0.661
0.679
0.777
2.882
0.752
6.768
0.647
1.883
0.733
0.768
2.816
0.827
0.840
0.853
4.139
0.858
9.915
0.816
2.702
0.840
0.871
2.431
0.291
0.304
0.489
1.035
0.355
2.274
0.248
1.202
0.356
0.385
2.850
Clayton copula
Frank copula
Gumbel copula
Student t copula
Note: # Mining sectors are composed by 7 sectors (i.e., No. 32–38 of Table S1 in the Supporting material). Manufacturing sectors are composed by 31 sectors (i.e., No.
1–37 Table S1). *The bold and italic numbers indicate the best-fit copula functions.
economic benefits, and number of employees, copula functions are
incorporated into the framework. Copula functions should be selected according to the original dependence features identified in
the data analysis section, i.e., with the indicator of Spearman’s ρ. In
other words, the uncertain and correlated features in energy consumption, economic benefits, and the number of employees are
grouped into distributions of random variables by copula functions.
(c) Violation risk analysis
In this study, violation risk analysis is focused on the probability
that the target of energy-intensity mitigation would be exceeded.
The violation risk should be applied to the control scenario for the
IST, based on the development goals of the industrial sectors. If
violation risk is prominent, the industrial structures need be
transformed. The violation risk can be evaluated by simulating the
random variables during uncertainty analysis based on Monte Carlo
sampling (MCS).
(d) Industrial structural transformation
Advanced manufacturing sectors (AMSs) have greatly improved
related manufacturing technologies, producing processes and economic benefits (King and Ramamurthy, 1992; Tao et al., 2017). The
sectors have occupied the central positions in many provinces of
China (Sun et al., 2018). With ever-increasing market competition,
the sectors have been top priorities in industrial transformation and
economic development (Chen, 2017). For example, the value-added
proportion of the sectors should be no less than 65% in 2020 according to the 13th Five-Year Plan in Guangdong Province. It is also
necessary to adjust the structures of the AMSs (Zhai et al., 2018).
Thus, the AMS proportion is chosen to be the objective function in
this study. To reflect the feedback from industrial structures, the
uncertain features and violation risk need to be incorporated into a
optimizing models. Targets for energy-intensity mitigation and
economic development are incorporated into the optimizing model
to provide decision support for industrial activities. In detail, the
violation risk of energy-intensity mitigation is the main constraint.
The desired industrial structures can thus directly support the decision-making in industrial adaptation strategies for mitigating energy intensity.
developed an copula-based violation risk analysis approach for GHG
mitigation strategies. Yang et al. (2018) assessed the drought risk based
on the drought duration and severity by copula functions. Thus, the
copula theory could help to deal with failure probabilities or violation
risk, focusing on the consequences of dependent and random variables
in industrial processes (Salvadori et al., 2016).
Though many efforts have been taken upon industrial energy-intensity mitigation, few studies have a) analyzed varied and dependent
characteristics of energy consumption and economic benefits in industrial sectors, b) evaluated the violation risk of energy-intensity mitigation because of uncertainties in industrial activities, or c) identified
the strategies for optimizing industrial structures in consideration of
energy-intensity mitigation. Therefore, the objective of this study is to
develop a integrated approach for transforming industrial structures
against a background of energy-intensity mitigation by incorporating
copula-based violation risk analysis into a programming model.
Considering the complexities of industrial agglomeration in the urban
cluster, the developed method will then be demonstrated into the urban
cluster in the Pearl River Delta of China.
2. Methodology
2.1. Framework of industrial structural optimization
The energy intensity highly depends on industrial development
level. Because energy consumption, economic benefits, and the number
of employees influence each other, the joint probability of correlated
parameters could influence the probability of achieving the energy-intensity mitigation goals. Therefore, hybrid methods of uncertainty
analysis and violation risk analysis are incorporated into a programming model. The method will i) facilitate the comprehensive evaluation
of the co-relationships among complicated parameters such as energy
consumption and economic benefits, and ii) strengthen decision making
in IST based on energy-intensity mitigation in China. Framework of
industrial structural optimization is summarized as follows (Fig. 1).
(a) Data collection and analysis
The industrial sectors in China are mainly composed of mining and
manufacturing sectors. Also, the mining and manufacturing sectors
include many sub-sectors. In order to reflect varying features of
industrial sectors, the annual data on energy consumption, economic benefits, and employees’ number in each sub-sector should
be collected. The indicator of Spearman’s ρ is used to measure the
dependence strength of the correlated data in the IST.
(b) Uncertainty analysis
To indicate the relationships between the energy consumption,
2.2. Uncertainty analysis based on copula functions
A copula is a function that combines two or more univariate marginal probability distributions to build a joint probability distribution,
which incorporates the interdependence among these univariate distributions (de Oliveira et al., 2017). As features of economic benefits,
the demand for employees and energy consumptions will induce a dependence structure to change over time. Copula functions can be
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Fig. 3. Joint probability distributions of energy consumption, employees’ number (EN) and economic benefits in mining sectors by multiple copula functions.
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Fig. 4. Joint probability distributions of energy consumption, employees' number (EN) and economic benefits in manufacturing sectors by multiple copula functions.
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C (FX (x ), FY (y )) ] (Eq. (1)):
Table 5
Plans for industrial development in the urban cluster of the Pearl River Delta.
Cities
Growth speed
of GDP
The AMS
proportion
Energy-consumption
decrease per value added
Guangdong
Province
Guangzhou
Shenzhen
Zhuhai
Huizhou
Dongguan
Jiangmen
Zhaoqing
Foshan
1.51%
65%
17%
1.71%
1.57%
1.49%
1.48%
–
1.47%
1.55%
1.44%
70%
75%
65%
63%
52%
51%
37%
40%
fX , Y (x , y ) = C (FX (x ), FY (y )) fX (x ) fY (y )
(1)
where fX, Y(x, y) is joint probability density function (PDF) of the
two random variables (i.e., x and y), FX(x) and FY(y) are cumulative
distribution functions (CDFs) of X and Y, and fX(x) and fY(y) are the
PDFs of x and y. In this study, Gaussian copula, Archimedean copulas
(i.e., Gumbel, Clayton, Frank, Oakes copulas), and Student t copula are
introduced for capturing upper and lower tail dependence of economic
benefits, demands for employees and energy consumption (Table 1).
In order to select the best-fitted copula function, original dependence features of economic benefits, demands for employees and energy consumptions in IST are analyzed based on statistic data.
Specifically, the indicator of Spearman’s ρ is introduced to choose the
best-fitted copula functions, whose θ is close to ρ (Eq. (2)).
Note: The base year is 2015. The planning year is 2020.
applied to capture the joint probabilistic characteristics between the
correlated variables in the IST (Vergni et al., 2015). In terms of two
continuous random correlated variables, the joint probability of the two
variables can be uniquely determined.
Let X and Y be the variables, with cumulative distribution functions
(CDFs) FX (X < x ) and FY (Y < y ) respectively (Vergni et al., 2015).
According to Sklar (1959), a unique function C (i.e., copula function)
can be determined for joint probability of the two variables [i.e.,
ρ=
12
n (n + 1)(n−1)
n
∑ Ri Si−
i=1
3(n + 1)
n−1
(2)
where n is the sample size, Ri is the rank of xi among x1, x2 , ⋯, x n , and Si
is the rank of yi among y1 , y2 , ⋯, yn .
Fig. 5. Violation risks of energy-intensity mitigation in the urban cluster of the Pearl River Delta upon the control scenario.
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Fig. 6. Solutions for industrial transformation. Note: Code numbers of cities and industrial sectors in the above figure are listed in Tables S1 and S2 of the Supporting
material.
where N is the total number of MCS conducted, and I is an indicator for
failure.
2.3. Violation risk analysis
Violation events are characterized by deviations from regulations,
rules or procedures (English and Branaghan, 2012). Affected by the
multiple uncertain conditions, the violation events in the future are
imprecisely known (Campi and Garatti, 2008). The energy intensity
mitigation of industrial sectors in 2020 [i.e., g2020(x)] is also affected by
randomness of energy consumption and economic benefits in industrial
activities. The violation risk in energy intensity mitigation is defined as
the probability (Pf) of the event occurrence, which the energy intensity
exceeds the maximum allowance limit of future, described by Eq. (3)
(Alzbutas et al., 2014; Wang and Li, 2018):
Pf = P [g2020 (x ) > (1−l) g2015] =
∫g
2020 (x ) > (1 − l ) g2015
f (x ) dx
2.4. Industrial structural optimization based on energy-intensity mitigation
Let’s imagine a situation in which a manager is responsible for optimizing industrial structures in the background of energy-intensity
mitigation. The manager can formulate the problem by maximizing the
AMS proportion. A programming model is effective for industrial adjustment in consideration of violation risk of energy-intensity mitigation. To indicate the synergistic effects among cities in urban clusters,
the methods of industrial structural optimization can be described by
Eqs. (5) and (6) in double perspectives (i.e., urban cluster and single
city):
(3)
n
x , x , …, x1m
⎡ 11 12
⎤
x , x , …, x2m ⎥
where x = ⎢ 21 22
is a n × m dimensional random vector, g(x)
⋱
⎢
⎥
…
x
,
x
,
,
x
nm ⎦
⎣ n1 n2
is the energy intensity function, g2020 (x ) > (1−l) g2015 indicates the exceeding the target of energy-intensity mitigation, and f(x) is the joint
probability density function (PDF) of x.
The violation risk can be analyzed by simulating the dependent and
random variables of industrial sectors by MCS, as shown in Eq. (4)
(Jahani et al., 2014):
Pf =
1
N
max F =
∑ ∑ GDPij Xij
(5a)
s.t.
N
1, if g2020 > (1−l) g2015
I=⎧
⎨
⎩ 0, if g2020 < (1−l) g2015
j = 1 i′= 1
n m
j=1 i=1
∑I
j=1
m′
∑ ∑ GDPi′j Xi′j
PF ⩽ p
(5b)
Xi′j ⩾ 0, ∀ i′, j
(5c)
Xij ⩾ 0, ∀ i, j
(5d)
where F is the value-added proportion of AMS in the urban-cluster
perspective (%), GDPi′j is the economic benefit of the i′th AMS in the jth
city of the base year (i.e., 2015), GDPij is the economic benefit of the ith
industrial sector in the jth city of the base year, Xi′j is the transformation
ratio of the i′th AMS in the jth city of the planning year (i.e., 2020), Xij is
(4a)
(4b)
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Fig. 7. Maximal and minimal solutions for industrial structural transformation. Note: Code numbers of cities and industrial sectors in the above figure are listed in
Tables S1 and S2 of the Supporting material.
x ij ⩾ 0, ∀ i
the transformation ratio of the ith industrial sector in the jth city of the
planning year, PF is the violation risk of energy intensity mitigation in
the urban cluster, and p is risk aversion levels of decision makers.
where fj is the value-added proportion of AMS in the single-city perspective (%), gdpi′j is the economic benefit of the i′th AMS in the jth city
of the base year, gdpij is the economic benefit of the ith industrial sector
in the jth city of the base year, xi′j is the transformation ratio of the i′th
AMS in the jth city of the planning year, xij is the transformation ratio of
the ith industrial sector in the jth city of the planning year, and Pfj is the
violation risk of energy intensity mitigation in the jth city.
m′
∑ gdpi′j x i′j
max f j =
i′= 1
m
∑ gdpij x ij
i=1
(6d)
,∀j
(6a)
s.t.
Pfj ⩽ p
(6b)
x i′j ⩾ 0, ∀ i′
(6c)
3. Case study
The urban cluster of the Pearl River Delta is located in Guangdong
Province of China, which comprises nine cities (i.e., Guangzhou,
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Fig. 8. Violation risk of energy-intensity mitigation in the urban cluster of the Pearl River Delta in 2020 based on optimal industrial structures.
in Table S1) and 7 mining sectors (No. 32–38 in Table S1). As outlined
in the Industrial Plans for the 13th Five Year (from 2015 to 2020),
Guangzhou, Shenzhen, Zhuhai, Dongguan, Huizhou, Zhaoqing, Foshan,
and Jiangmen will focus on developing AMSs, which cover more than
10 sub-sectors (Table 2).
As energy consumption, employees’ number (EN) and economic
benefits are correlated variables, joint probability of the variables
should be considered. Joint distribution features of energy consumption, EN and economic benefits of the 8 cities are obtained from statistical data for more than 15 years. Source of these data are attached in
the file of ‘datasource.xlsx’of Supporting material. Spearman’s ρ is used
to investigate the dependence of the correlated variables (Table 3).
Shenzhen, Zhuhai, Dongguan, Huizhou, Zhaoqing, Foshan, Zhongshan,
and Jiangmen) (Fig. 2). The region contributes most to the economic
development in China. For example, the GDP of the Pearl River urban
cluster accounted for more than 9% of the total GDP of Mainland China
in 2015. However, rapid industrialization greatly depend on the huge
energy consumption. The on-going development of industry in the Pearl
River Delta area is now challenged by resource shortages. The area will
be under tremendous pressure to achieve the energy-intensity mitigation target in the 13th Five-Year Plan (2016–2020).
Zhongshan City is not considered in this study, due to the data
availability on the energy consumption in the industrial sectors. Thirty
eight categories of industrial sub-sectors are analyzed in this case study.
The code numbers of the 38 industrial sectors are listed in Table S1 of
the Supporting Material. There are 31 manufacturing sectors (No. 1–31
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W. Yue et al.
Fig. 9. Probability distributions of employees’ number in the urban cluster of the Pearl River Delta in 2020 based on optimal solutions.
4. Results and discussion
copula functions are selected as suitable copulas for correlation analysis
in the mining sectors. Student t, Clayton and Frank copula functions are
selected as suitable copulas for correlation analysis in the manufacturing sectors.
4.1. Parameter estimation
In this study, Archimedean copulas (i.e., Gumbel, Frank, and
Clayton copulas), Gaussian copula and Student t copula are introduced
for capturing upper and lower tail dependence of the correlated parameters in mining and manufacturing sectors (Table 4). As shown in
Table 4 and Fig. 3, joint probability distributions obtained by Gaussian
and Gumbel copula functions are similar to the dependence of the
correlated variables in the mining sectors. As shown in Table 4 and
Fig. 4, joint probability distributions obtained by Student t, Clayton and
Frank copula functions are similar to the dependence of the correlated
variables in the manufacturing sectors. Thus, Gaussian and Gumbel
4.2. Uncertainty analysis
According to the 13th Five Year Agenda of Guangdong Province,
plans for industrial development in 2020 are formulated by the 8 cities
(Table 5). Assuming that each city could realize their original economic
plan within the industrial structures of 2015, the probability distributions of energy-intensity in a control scenario could be obtained
[Fig. 5(1)–(8)]. Violation risk of energy-intensity mitigation is analyzed
by the copula functions, whose parameters are obtained by Table 4.
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Table 6
Demands for employees in the urban cluster of the Pearl River Delta.
Unit: Million persons
Cities
Foshan
Dongguan
Huizhou
Zhuhai
Jiangmen
Zhaoqing
Guangzhou
Shenzhen
Total
Scales
Single
Urban
Single
Urban
Single
Urban
Single
Urban
Single
Urban
Single
Urban
Single
Urban
Single
Urban
Single
Urban
Employees’ number in 2015
city
cluster
city
cluster
city
cluster
city
cluster
city
cluster
city
cluster
city
cluster
city
cluster
city
cluster
Demands for employees
1.65
2.51
0.83
0.43
0.47
0.34
1.38
2.46
10.07
S1
S2
S3
[3.50, 4.67]
[3.44, 4.28]
[2.87, 3.49]
[3.47, 4.91]
[1.50, 1.90]
[1.90, 2.86]
[0.76, 1.09]
[0.95, 1.33]
[0.79, 0.94]
[0.89, 1.10]
[0.70, 0.85]
[0.58, 0.68]
[2.68, 3.32]
[2.83, 3.47]
[6.26, 9.36]
[6.22, 9.30]
[8.99, 15.54]
[10.20, 17.86]
[4.04, 5.29]
[2.51, 2.98]
[6.29, 8.55]
[3.47, 4.91]
[2.14, 3.16]
[1.90, 2.86]
[0.61, 0.77]
[0.76, 1.11]
[0.85, 1.00]
[0.70, 0.84]
[0.67, 0.79]
[0.54, 0.64]
[3.44, 4.18]
[2.83, 3.47]
[7.32, 10.20]
[6.22, 9.30]
[15.29, 23.87]
[8.86, 16.04]
[4.04, 5.29]
[2.51, 2.98]
[6.29, 8.55]
[3.47, 4.91]
[2.14, 3.16]
[2.04, 3.05]
[0.61, 0.77]
[0.84, 1.22]
[0.85, 1.00]
[0.59, 0.73]
[0.67, 0.79]
[0.54, 0.64]
[3.44, 4.18]
[2.83, 3.47]
[7.32, 10.20]
[6.22, 9.30]
[15.29, 23.87]
[8.98, 16.22]
Fig. 10. Proportion of advanced manufacturing sectors in the urban cluster of the Pearl River Delta. Note: Code numbers of cities in the above figure are listed in
Table S2 of Supporting material.
with the other cities; and b) violation risk of energy-intensity mitigation
in the urban cluster of the Pearl River Delta would reach 98.17%. These
results indicate that, in the current industrial structures, the target of
energy-intensity mitigation would not be achieved. Therefore, the industrial structures need to be further optimized.
Violation risk of energy-intensity mitigation in the urban cluster of the
Pearl River Delta would be prominent in the control scenario
[Fig. 5(9)]. The details of violation risks in the 8 cities and the urban
cluster are described as follows: a) violation risk of energy-intensity
mitigation in Jiangmen city would be the most prominent compared
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Ecological Indicators 95 (2018) 673–686
W. Yue et al.
Shenzhen (Table 6). Also, the total demand of employees in the
urban-cluster perspective would be less than the demand in the
single-city perspective under the S2 and S3.
(4) Proportion of advanced manufacturing sectors
Overall, the AMS proportion in the urban cluster would reach 72 to
83% with the desired industrial structures (Fig. 10 and Table S9 in
the Supporting material). Except for Dongguan, Jiangmen and
Zhaoqing, the cities of the urban cluster would achieve the goal for
developing AMSs, based on the Industrial Plans for the 13th Five
Year. The AMS proportion of Foshan and Zhaoqing would be more
in the urban-cluster perspective than in the single-city perspective.
4.3. Industrial structural optimization
The industrial structural optimization is based on the following two
assumptions: (1) energy efficiency in 2020 would be the same with the
one in 2015, and (2) economic benefits of industrial sectors in 2020 would
not exceed three times what it was 5 years ago. The optimal structures of
the industrial sectors are determined by Eqs. (5) and (6). The solutions are
obtained by Latin hypercube sampling (LHS). The LHS method is a stratified random procedure that can efficiently sample variables from their
multivariate distributions. The LHS algorithm includes the following four
steps: (i) sampling matrices of the industrial structures by LHS, (ii) examining the violation risk and other constraints in the optimization model,
(iii) checking whether the matrix fulfill the objective, and (iv) obtaining
optimal solutions for IST. Three scenarios have been established, according to multiple risk aversion levels in decision makers (i.e., p = 0.1,
0.5, and 0.9 in Eqs. (5) and (6)) (Cai et al., 2009b; Dai et al., 2014). In
detail, scenario 1 (S1) represents the pessimistic decision-making in risk
aversion (i.e., p = 0.1). Scenario 2 (S2) considers resilient decision-making
in risk aversion (i.e., p = 0.5). Scenario 3 (S3) denotes the optimistic decision-making in risk aversion (i.e., p = 0.9). The strategies for industrial
structural transformation are analyzed from the following four aspects:
industrial structures, energy-intensity mitigation, employees’ number and
AMS proportion.
5. Conclusions
In this research, a hybrid approach was proposed for industrial
structure optimization in the context of energy-intensity mitigation,
through incorporating copula-based violation risk analysis into a programming model. This approach represented an improvement over
conventional methods for optimizing industrial structures and analyzing the violation risk, as it reflected the uncertainties of industrial
development and tackled the joint probability of correlated variables by
copula functions. The developed method was then demonstrated in the
urban cluster of the Pearl River Delta. Three scenarios were established,
according to multiple risk aversion levels in decision makers. To indicate the synergistic effects among cities in urban clusters, the
methods of industrial structural optimization also emphasized strategies in urban-cluster and single-city perspectives. The results indicated
that under the desired industrial structures, violation risk of energyintensity mitigation in the urban cluster of the Pearl River Delta would
be indistinctive in the single-city perspective. Also, manufacture of
communication equipment, computers and other electronic equipment
(No. 27) would be encouraged in the single-city perspective.
Manufacture of special-purpose machinery (No. 23) and automobile
(No. 24) would be encouraged in the urban-cluster perspective. In terms
of employees’ number, the urban cluster would need more employees in
2020 than in 2015, to support the optimal industrial structures. The
results suggested that the maximum demands for employees would be
4.86 to 7.74 million in Shenzhen. The proportion of advanced manufacturing sectors (AMS) in the urban cluster would reach 72 to 83%
with the desired industrial structures. Except for Dongguan, Jiangmen
and Zhaoqing, the cities of the urban cluster would achieve the AMS
goals based on the Industrial Plans for the 13th Five Year. Therefore,
industrial structural optimization could be a crucial step for industrial
development in China, in order to fulfil the targets of energy-intensity
mitigation and AMS promotion in 2020.
(1) Industrial structures
The optimal solutions for industrial structural transformation are
presented in Fig. 6 and Tables S3–S8 of the Supporting material.
Manufacture of communication equipment, computers and other
electronic equipment (No. 27) would be encouraged in the singlecity perspective. Manufacture of special-purpose machinery (No.
23) and automobile (No. 24) would be encouraged in the urbancluster perspective. Concurrently, manufacture of textile wearing
apparel, and clothing (No. 6) would be discouraged in the singlecity perspective. Manufacture of raw chemical materials and chemical products (No. 14) would be discouraged in the urban-cluster
perspective.
The largest degrees (i.e., maximal and minimal) in the solutions for
industrial structural transformation are presented in Fig. 7. Manufacture of communication equipment, computers and other electronic equipment (No. 27) would be promoted by the greatest extent in Huizhou for the single-city perspective. Petroleum refining,
coking, and nuclear fuel processing (No. 13) would be cut down by
the greatest extent in Foshan for the single-city perspective. Also, in
terms of industrial structural transformation in the urban-cluster
perspective, manufacture of wine, beverage and tea (No. 3) would
be promoted by the greatest extent in Huizhou, and timber processing, bamboo, cane, palm fiber & straw products (No. 8) would
be cut down by the greatest extent in Foshan.
(2) Energy-intensity mitigation
In the desired industrial structures, violation risk of energy-intensity mitigation in the urban cluster of the Pearl River Delta
would be indistinctive in the single-city perspective (Fig. 8). The
violation risk of energy-intensity mitigation in the urban-cluster
perspective is described as follows: a) Shenzhen, Zhaoqing and the
urban cluster would reach the energy-intensity mitigation goals
under the S1; and b) Shenzhen and Huizhou would achieve the goal
under the S2 and S3. Thus, based on the optimal industrial structures, not all the cities in the urban cluster of the Pearl River Delta
would achieve the goal of energy-intensity mitigation in the urbancluster perspective.
(3) Employees’ number
The probability distribution of the employees’ number (EN) reflects
the conflict between the supply and demand of employees (Fig. 9).
To support the optimal industrial structures, the 8 cities would need
more employees in 2020 than in 2015. The results suggest that the
maximum demands for employees would be 4.86 to 7.74 million in
Acknowledgements
This work was supported by the National Key Research Program of
China (Grant No. 2016YFC0502800), the Fund for Innovative Research
Group of the National Natural Science Foundation of China (Grant No.
51421065), the National Natural Science Foundation of China (No.
71673027), the Funds for International Cooperation and Exchanges of
the National Natural Science Foundation of China (No. 51661125010),
the Natural Science Foundation for Distinguished Young Scholars of
Guangdong Province (No. 2017A030306032), GDUPS (2017), and the
Scientific Research Foundation for High-level Talents and Innovation
Team in Dongguan University of Technology (No. KCYKYQD2016001).
The authors much appreciate the editor and the anonymous reviewers
for their constructive comments and suggestions which are extremely
helpful for improving the paper.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in the
online version, at https://doi.org/10.1016/j.ecolind.2018.08.009.
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W. Yue et al.
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