2021

Master's Thesis
Department of Energy and Environmental Engineering,Interdisciplinary Graduate School of Engineering Sciences,
Kyushu University

Title
Environmental and health co-benefits from de-capacity in
Chinese industrial sectors

Name

GUO Jiawen

Supervisor (KU) Assoc. Professor Hooman Farzaneh
Supervisor (SJTU) Assoc. Professor Huijuan DongTable of Contents
Abstract

p.1

Chapter 1 Introduction and background ........................................................................... p.3
1.1 Research Background and Significance...................................................................... p.3
1.2 Literature Review ....................................................................................................... p.3
1.3 What Will Be Elucidated in This Research ................................................................ p.7
Chapter 2 Methods and data sources ................................................................................ p.9
2.1 Data envelopment analysis (DEA) model................................................................. p.10
2.2 Emissions estimation model ..................................................................................... p.13
2.2.1 GAINS-China model .......................................................................................... p.13
2.2.2 Scenario definition.............................................................................................. p.14
2.2.3 Estimation of the avoided emissions .................................................................. p.15
2.3 Health risk assessment model .................................................................................. p.17
2.3.1 Methodological approaches ................................................................................ p.17
2.3.2 Meta-analysis module ........................................................................................ p.18
Chapter 3

Results........................................................................................................... p.23

3.1 Characteristics of overcapacity of Chinese industry ................................................. p.23
3.1.1 Comparison between industrial sectors ............................................................. p.25
3.1.2 Comparison between provinces.......................................................................... p.27
3.2 CO2 and PM2.5 emissions under different scenarios ................................................. p.28
3.3 Health impact assessment........................................................................................ p.32
Chapter 4 Discussion ....................................................................................................... p.38
4.1 Comparison with existing studies............................................................................. p.38
4.2 Policy implications .................................................................................................... p.39
4.2 Research limitations ................................................................................................. p.42
Chapter 5 Conclusion ...................................................................................................... p.43
Bibliography ....................................................................................................................... p.44

ABSTRACTWith the rapid development of China's industrial economy, the problem of excess
capacity in the industrial sector has been increasingly severe, causing not only a waste ofnatural, economic and social resources, but also serious adverse impacts on the environment
and public health. In order to conduct more precise and effective policy to solve this problem,
the key industries and regions with the most obvious capacity problems should first be
identified, and quantifying the green and low-carbon co-benefits of over-capacity management
policies can further reflect the long-term benefits of capacity removal and structure adjustment.
In view of this, this study constructs a comprehensive research framework, including dataenvelopment analysis (DEA) model, GAINS-China model (Greenhouse gas - Air pollution
Interactions and Synergies model), meta-analysis and air pollution - health risk assessment(AP - HRA) module, measured the capacity utilization rate of 41 industrial sectors in China in
2018, and analyzed the current situation of excess capacity and the characteristics of industry
and geographical distribution in China. What’s more, based on scenario analysis of fully
effective implementation of overcapacity management policies from 2025 to 2050, and cobenefits of low-carbon management, air quality promotion and positive public health were
further estimated.
The results show that the overall average capacity utilization rate of China's industrial
sector is 64.13% in 2018, which is lower than the consensual value of 75%, indicating that the
problem of overcapacity in China's industrial sector is really serious and there is large potential
room for greater capacity removal. From the perspective of industrial sectors, capacity
utilization rates in the mining industry and electricity, heat, gas and water production and1

supply sectors are generally low, with capacity utilization rates of just between 53% and 65%;
while in the manufacturing sector, capacity utilization rates are generally higher in lightindustry (around 70%) and lower in heavy industry (between 50% and 60%). From theperspective of regions, the overall capacity utilization rate in East China and South-Central
China is significantly higher than other regions, where the capacity utilization rate of
economically developed coastal provinces is generally higher. Guangdong, Jiangsu, Fujian,
Shandong, Zhejiang, Shanghai and Beijing cover the top seven, whose overall industrial
capacity utilization rate is 96.7%, 85.6%, 79.2%, 77.9%, 77.5%, 77.4%, 73.3%; while Gansu,
Qinghai, Ningxia, Xinjiang and Tibet have the lowest capacity utilization rate, generally below
40%. Under the scenario of full implementation of the de-capacity policy in 2050, CO2
emissions from China's industrial sector could be reduced by about 1.05 billion tons,
accounting for 9.6% of total CO2 emissions in 2050. PM2.5 emissions could be reduced byabout 57.8 kilotons, accounting for 5.8% of total PM2.5 emissions in 2050. At the same time,PM2.5 emission reduction will further reduce air pollution-related deaths nationwide, which is
expected to be reduced by about 792,100 cases in 2050, accounting for 1.6% of the total
number of deaths in 2050. In order to implement overcapacity management policies moreaccurately and effectively, it is recommended that priority be given to targeting industries withlow capacity utilization rate, as well as regions with intensive heavy industry, high levels ofgreenhouse gas emissions, severe air pollution, and dense population for improved
optimization of industrial restructuring and resource allocation.

Key words: Over capacity, Capacity utilization rate, DEA method, GAINS-China, Healthimpact assessment

2Chapter 1

Introduction and Background

1.1 Research Background and Significance
China has experienced rapid industrialization and urbanization since the reform andopening up in the late 1970s. However, problems such as haphazard investment, inefficientscale expansion, lower resource efficiencies, and environmental pollution have emerged,
which impede China's macroeconomic development [1]. According to several critical
governmental reports, industrial sectors, which is the major part of China’s economy and also
the main generators of air pollutants and GHG (Greenhouse Gases) emissions, are facing
severe excess capacity problem. Thus, to resolve such an issue has become a key focus onpromoting industrial restructuring by the central government [2]. Under such circumstances,
it is critical to evaluate the impact of excess capacity so that appropriate policies can be raised
to those decision makers. In addition, the successful resolution of this issue can lead tosignificant environmental benefits (i.e. the reduction of GHG and air pollutant emissions)and social benefits (i.e. improved public health) [3]. This means that co-benefits can beobtained if the excess capacity of industrial sectors can be removed [4].

1.2 Literature Review
An increasing number of researchers have discussed the quantification methods ofexcess capacity. Generally, researchers introduce the capacity utilization rate indicator, as
the ratio of actual output value to the ideal capacity value, to measure the degree of excess3capacity [5-8]. The higher the capacity utilization rate, the smaller the degree of excess
capacity. Judging from international experience, some European and American studies set
the threshold value of capacity utilization rate between 79% and 82% [9-12], which indicatesthe overcapacity problem for the values lower than 79%. While the typical value for the caseof China is 75% [13,14], which is also the threshold standard set in this research. Most studies
focus on existing micro-data to speculate on the capacity utilization of industrial enterprises,
mainly using the methods including Function Method (FM) and Data Envelopment Analysis(DEA). Cassels [15] and Morrison [16] proposed the input level corresponding to the lowest
point of the short-term average total cost curve to realize the capacity conversion. The
Wharton index series published by the United States since the 1960s uses a continuous
method based on the ratio of capital and the cumulative net investment at the end of theprevious period to predict the conversion of production capacity. Gallofalo and Malhotra [17]used the Cost Function Method to measure the production capacity of manufacturing invarious states in the United States. Kirkley [18] used the Data Envelopment Analysis method(DEA) and Stochastic Production Frontier method (SPF) to measure the productivity andcapacity conversion of the US fisheries.
Compared with foreign countries, domestic research started relatively late in China. Han[19] first adopted the cost function method combined with the generalized matrix method(GMM) of the panel model to measure the capacity utilization of 28 heavy industries in China
and found that seven industries had serious overcapacity, most of which belong to heavy
industries. Huang and Lv [20] and the research team of the People's Bank of China Business
Management Department [21] found China's potential output value undergoing an obvious

4expansion trend, using traditional Cobb-Douglas (C-D) production function. Wang andZhang [22] developed the production function method to investigate the capacity utilization
rate of China's photovoltaic industry from 2005 to 2012, with the result showing that value
decreased to just around 60%. Dong et al. [23] applied the DEA method to measure thecapacity utilization rate of China's industrial sectors and found that the average value was
69.3%. Feng et al. [24] also used the DEA method to estimate the capacity utilization rate of
China’s coal industry in consideration of the impact of resource depletion, and in 2012 thesituation was the worst, with the value of 69.88%. Cheng [25] used the stochastic frontierfunction analysis method to measure the capacity utilization rate of China’s industrial sectors.
The result showed that the overall capacity utilization rate of the eastern region was lower
than that of the central and western regions.
With China's efforts to deal with the excess capacity problem, dual challenges of air
pollution and climate change, which are highly relevant to industrial development, have also
promoted the significance of studying the co-benefits of GHG and air pollutants. The concept
of co-benefit was first proposed by Herman Haken, referring to the additional benefits
obtained from implementing a policy, rather than the direct benefits expected at the initialstage of the policy design [26]. Later, the Intergovernmental Panel on climate change (IPCC)
made a clear definition of the co-benefits about GHG emission reduction, that is, thequantitative monetization results of additional benefits after a GHG mitigation policy isimplemented [27]. Therefore, co-benefits has gained increasing population in the integrated
management research of energy, environment and climate change, and many methodologies
of co-benefit evaluation such as greenhouse gas – air pollution interaction and synergies

5

(GAINS), integration of environmental strategy (IES) of EPA and energy-environmenteconomics (3E) model for quantification of co-benefits have been widely applied [28]. Many
researchers have also focused on domestic studies of co-benefits in the past decade, coveringmodel application, policy analysis and case discussion. Li et al. [29] studied a co-benefit
project for total emission reduction based on three industrial mitigation approaches and foundthat different technologies and measures would contribute to different co-benefits and thestructural adjustment could provide the most value. Wang and Chen [30] used LEAP model
to estimate energy consumption and air pollutants emission in Shanghai under both the basic
scenario and the low-carbon scenario, and the result revealed that the total emission of CO2,SO2 and PM would decrease by 20%, 72% and 78% respectively in 2020. He et al. proposed
an integrated model to predict energy consumption and GHG and air pollutants emission
under different scenarios and compared the monetized benefits of emission reduction andpublic health, which would reach 100 billion dollars in 2030 [31]. Mao analyzed a case studyof the thermal power industry using the 3E model and proposed a method to estimate the cobenefits of technical mitigation measures, which supported that the front-end control
measures and in-the-process control measures had better synergy [32].
In the case of the de-capacity scenario, the emission of GHG and air pollutants would
be reduced due to less use of fossil fuels and resources and indirect benefits from energy
efficiency and local employment, and then the public health associated with air pollution
would also gain a positive impact. Therefore, the consideration of co-benefits could provide
a complete profile of the impact assessment of de-capacity policies, including the aspects ofeconomy, energy, environment and society.

61.3 What Will Be Elucidated in This Research
In general, the function methods such as the cost function method and production
function method have certain advantages in data availability, but the disadvantages include
complicated calculations and possible errors. As a non-parametric method, the DEA method
can estimate the inefficiency that deviates from the production frontier. Considering the
backward production capacity of China's industry is widespread, the capacity utilization rate
calculated by the production capacity from the perspective of the technical case may be moresuitable [23].
However, most of the existing studies on capacity assessment of China focus mainly on
one specific industrial sector or one region, mainly because of data limitations [33-36].
Moreover, further research about co-benefits under the excess capacity adjustment policy israrely considered. Consequently, this study aims to fill such a gap. First, it quantifies thecapacity utilization rate of all 41 industrial sectors in all Chinese provinces. It then evaluates
both the expected environmental and the public health benefits from implementing the decapacity strategies by 2050 by integrating the GAINS-China model and air pollution healthrisk assessment method.
The rest of this article is organized as follows. Section 2 illustrates the method toevaluate the capacity utilization rate and assess co-benefits by combining the DEA model,
GAINS-China model, meta-analysis module and the health module. Then research results
are presented in Section 3, including analysis of over-capacity characteristics, estimatedemission of CO2 and PM2.5, and positive public health impact associated with mitigation of
air pollution. Section 4 discusses the promising policy decision-making auxiliary means with7

attention paid to considering the relationship between de-capacity effects and co-benefits.
Finally, the conclusion and future prospects are presented in Section 5.

8

Chapter 2

Methods and data sources

Figure 1 shows the main methodological approach used in this research. First, the dataenvelopment analysis (DEA) model was used to quantify the content of the over-capacity
problem to assess the potential improving room for the scenario of de-capacity
implementation. Second, the modified activity level derived from DEA analysis was used the
GAINS-China model to simulate the air quality index, especially the concentration of CO2
and PM2.5. These two parts composed the analysis of environmental effect, which is one
aspect of the co-benefit assessment. Third, the meta-analysis was conducted to measure thevalue of the China-specified relative risk. Fourth, the health impact assessment model wasused to forecast the total mortality cases attributable to PM2.5 pollution.

Figure 1. Research flow diagram

92.1 Data envelopment analysis (DEA) model
Data envelopment analysis (DEA) is a linear programming quantitative analysis method
based on multiple input and output indicators. In the case of excess capacity problem, it’sapplied to estimate the maximum production capacity of equipment when variable input is
not limited. The overall calculation flowchart of capacity utilization rate using the DEA
method is depicted in Figure S1 of supporting material.
Capacity utilization rate CU is the ratio of actual output 𝑦 to potential production
capacity 𝑌(𝐹):
𝐶𝑈 =

(1)

( )

According to the DEA principle, potential production capacity (i.e., the gross industrial
output value) refers to the maximum capacity of production equipment when the variable
input (i.e., intermediate economic input and labor resources) is unlimited. Therefore, 𝑌(𝐹)can be expressed as the function of an initial fixed input F (i.e. fixed capital), while in fact,
the actual output y is always limited by input variables.
To establish the optimization formula of 𝑌(𝐹), the input-output tables at the industrial
sector level in all the investigated provinces are established. For example, in terms of the
maximum production capacity of the coal industry in Shanghai (𝑀𝑎𝑥 𝑌 𝐹 ,with 𝑝 being

the coal industry and 𝑞 being Shanghai), the total actual output of all the industries inShanghai (∑𝑦,with 𝑖 as each industry, 𝑛 = 41 ) that is transferred from the total

current fixed input (∑𝐹 , ) would be reallocated to a certain situation by introducing a10

weight vector (𝜆 , ). The more expected output of coal industry (∑expected input of coal industry (∑𝜆 , 𝑦 , ) is, the less

𝜆 , 𝐹 , ) is. In other words, this is the optimal allocation

process of existing fixed resources, which aims to realize the most potential output.
Based on the above idea, the basic optimization formula of 𝑌(𝐹) for industry j in eachprovince is established as below [37]:𝑀𝑎𝑥 𝑌 𝐹 ,𝑠. 𝑡. ∑

=∑

(2)

𝜆, 𝑦,

𝜆, 𝑦, ≥𝑦 , , ∑𝜆 , 𝐹, ≤ 𝐹 , , ∑𝜆 , = 1, 𝜆 , ≥ 0(3)After calculating the capacity utilization rate of each industry in each province, (i) the
overall capacity utilization rate of the industry 𝑝 (𝐶𝑈 ), (ii) the overall capacity utilization
rate of the province 𝑞 (𝐶𝑈 ), and (iii) the overall capacity utilization rate of the wholecountry (𝐶𝑈) can be calculated as follows:𝐶𝑈 == ∑ 𝑦 , / ∑ 𝑌 , (𝐹)

(4)

𝐶𝑈 =

= ∑ 𝑦 , / ∑ 𝑌 , (𝐹)

(5)

𝐶𝑈 =

= ∑ ∑ 𝑦 , / ∑ ∑ 𝑌 , (𝐹)(6)This study includes the estimation of 41 industries in 31 provinces, cities, andautonomous regions (hereinafter referred to as "provinces") with 2018 as the base year, andfurthermore, the capacity utilization rate on both levels of province and industry is alsomeasured.
For some cases that the ready data of fixed capital is starved, it’s estimated using the11perpetual inventory method through the following formula [38,39]:
𝐾 =𝐾

(7)

(1 − 𝛿 ) + 𝐼 /𝑃

Among them, 𝐾

and 𝐾 represent fixed capital in period 𝑡 − 1 and period 𝑡 ,respectively; 𝛿 represents the depreciation rate in period 𝑡; 𝐼 represents the amount of
new investment in period 𝑡, and 𝑃 represents the price index of investment products. The
value of these parameters can be estimated as follows:- New investment amount each year: using the original price difference of fixed assets
in two consecutive years as a substitute;- Investment product price index: using the fixed asset investment price index of eachprovince and municipality as a substitute;- Base period capital stock: using the original price of fixed assets in 2015 Interpolation
with accumulated depreciation is used as the base period data;- Depreciation rate: The difference between the accumulated depreciation amount ofeach year and the previous year is regarded as the depreciation of fixed assets this year, andthe depreciation rate is compared with the original price of fixed assets of the previous year.
The database of the actual output of each industry in each province (𝑦 , ) was collectedfrom "2019 China Economic Census Yearbook" [40], the "2019 China Statistical Yearbook"[41] and the "2019 China Industrial Statistics Yearbook" [42], and the database of fixed inputof each industry in each province ( 𝐹 , ) was collected provincial and local statistical
yearbooks in 2019, and China's economic and social big data research platform, etc. The

12

overall calculation flowchart of capacity utilization rate using the DEA method is depicted in
Figure 2.

Figure 2. Calculation flowchart of capacity utilization rate using DEA method
2.2 Emissions estimation model
2.2.1 GAINS-China model
In order to calculate the environmental benefit, the GAINS-China model provides aconsistent framework for the analysis of emission reduction strategies that simultaneously

13deal with air pollution and greenhouse gases [43]. On the one hand, the GAINS model allows
exploring the key characteristics of each region, including future energy use, emission control
measures, measure costs, and air pollutant emissions. The GAINS-China model is anextended application of the GAINS model, which combines the specific characteristics ofChina. It has been widely used in combination with other models to forecast the co-benefitof air pollution and climate change mitigation in Chinese provinces and regions [44,45] andthe economic and health impact caused by ozone and PM2.5 pollution [46].

Figure 3. Overall methodological framework of the GAINS-China model
2.2.2 Scenario definition
Two scenarios were defined in the GAINS-China model as follows.
a) BaU (S1: business as usual): This scenario reflects current air pollution policies in
China, considering sectoral and provincial differences with respect to emission limit

14

values and the time of their introduction. The mitigation technologies are listed inTable 1.
b) DE (S2: de-capacity): In this scenario, de-capacity policies are set for each province
according to the result of capacity utilization rate from the DEA model, so that theenergy intensity of each province will be reduced gradually from 2025 to 2050. The
GAINS-China model calculates the potential reduction of CO2 and PM2.5.
Table 1. Mitigation technologies adapted in this study
Air pollutant

Name

Application sectorPM

1

CycloneIndustry, power plants

2Electrostatic precipitator

Industry, power plants, industrial process

3

High efficiency deduster

Industrial process

4Good practiceIndustrial process

1

Limestone injection

Industry, power plants

2

Wet flue gas desulfurization

Industry, power plants3

Low sulphur coal

Industry, power plant

4

Process emissions - stage 1 SO2 Industrial process

SO2

controlNOx

1

Combustion

modification

on Power plants

existing hard coal power plants
2.2.3 Estimation of the avoided emissions
The following formula is used to calculate the avoided emission rates in this study:

15

(8)

𝐸 , , = ∑ ∑ 𝐴 , , 𝑒𝑓 , , , 𝑥 , , , ,Where, 𝑖, 𝑘, 𝑚, 𝑝, 𝑡

represent province, activity type, abatement measure, pollutant(CO2, PM2.5), period (2025, 2030, 2035, 2040, 2045, 2050), respectively. 𝐸 , ,emissions of pollutant 𝑝 in province 𝑖 during period 𝑡. 𝐴 , ,type 𝑘 in province 𝑖 during period 𝑡. 𝑒𝑓 , , ,represents

represents activity level ofrepresents emission factor of pollutant 𝑝

for activity type 𝑘 in province 𝑖 after application of control measure 𝑚 during period 𝑡.
𝑥, , , ,represents the share of total activity type 𝑘 in province 𝑖 to which a control

measure 𝑚 for pollutant 𝑝 is applied during period 𝑡.
For scenario S1 (Business as Usual)(9)𝑥, , , , ≠0
For scenario S2 (De-capacity)
𝑓, , =

,

%

(10)

∗

(11)

𝐴 , , = 𝐴 , , 𝑓, ,Here 𝐶𝑈 , is the capacity utilization rate for activity type 𝑘 in province 𝑖 in 2018.
Assuming that the excess capacity problem would be solved in 2050, that is, the capacity
utilization rate should reach the threshold value of 75%, the expected activity level at thattime will decrease to,%times of the original level in 2018. If this optimization progressjust advances linearly, the factor in year 𝑡 should be the 𝑓 , , which can be calculated byusing Equation (11). Therefore, 𝐴 , , refers to the modified activity data that is input into
the GAINS-China model.

162.3 Health risk assessment model

2.3.1 Methodological approaches
The critical section of health impact assessment associated with air pollution is to use
concentration-response functions (CRF) to establish the mathematical model of adverse
health impact assessment. There are generally three steps to link the change of air pollutant
concentration ∆𝑪, and the change of incidence or mortality cases ∆𝒚:(i) identify the functional form of CRF;
(ii) complete the coefficient in CRF;
(iii) establish the relationship between ∆𝑪 and ∆𝒚.
To perform computations, relevant data is necessary, such as population data, air quality
indicators and health risk coefficient (health impact on changes of air pollutant concentration,β) derived from epidemiological studies [47].
In this study, the linear functional form of CRF, which is often used for simplification
based on biological evidence, is adopted to assess the expected deaths caused by PM2.5
pollution as [48]:(12)𝐲 =𝛂+𝛃×𝐂

where 𝛂 represents a combination of all the independent variables, 𝐂 refers to theconcentration of the certain air pollutant, and the coefficient β is typically derived based on
Equation (13) from the level of Relative risk (also called risk ratio, RR) [49]:

17

(13)

𝑹𝑹 = 𝐞𝐱𝐩(𝛃 × ∆𝐂)

where β refers to the excess incidence rate of total mortality per 1 µg/m3 increase ofpollutants.
Therefore, the expected total mortality (ETM) of daily deaths due to PM2.5 exposure
derived from an increase in concentration can be calculated as follows, assuming all theresidents are exposed to the mean concentration in the city:(13)∆𝐲𝒊 = 𝛃𝒊 × ∆𝐂𝒊
𝐄𝐓𝐌𝒊 = ∆𝐲𝒊 × 𝑷𝒊
(14)

Here 𝒊 refers to different provinces and 𝑷𝒊 represents the population number inprovince 𝒊. ΔC is the difference between the annual concentration of PM2.5 in each province
and the threshold level determined by the WHO report.
The above health impact assessment model is used to estimate the potential reductions
in number and percentage of death attributable to PM2.5 reduction under the scenario
assuming the de-capacity competition. The assessment is made for the whole mainland of
China. Consensus data of each province was obtained from the latest report of the Seventh
National Census [50].

2.3.2 Meta-analysis module
As discussed above, RR is a key parameter in the CRF model for health impact
assessment. However, the value in the GAINS-China model is fixed and mainly derived from18

American or European research [47]. Considering the difference in air pollution levels
between China and other developed countries, the more reliable RR values of mortality
attributable to the average concentration of PM2.5 for China are needed. To this aim, a metaanalysis is performed in this study, as a kind of systematic quantitative review, using astatistical method to collect and combine all relevant empirical evidence that meets the prespecified eligibility standard and criteria to drive a pooled estimate closest to China-specified
RR values. The procedure of collecting and sifting through relevant literature is shown in
Figure 4. The meta-analysis database was developed based on a systematic search and review
of related studies on the estimation of daily mortality effects attributable to PM2.5 exposurein China between 2003 to 2020, which is given in Table 2. The database includes studies
conducted in medium-to-large size cities with dense populations and severe air pollution
levels in the past decade, including Beijing, Shanghai, Shenzhen, Jiangsu, Xi'an, Hefei,
Chongqing, Shenyang, Dongguan, Foshan, Gansu, and the Pearl River Delta (PRD) cities.

19

Figure 4. Database development process in the meta-analysis used in this research
Table 2. Literature review on the change in the mortality with every increase of 10μg/m3 in
PM2.5 concentration
PublicatioFirst author

Year ofArea

n Year

95% Confidence
RR

study

interval (CI)

Scott A. Venners

2003

Chongqing

1995

1.00000.9928, 1.0068

Haixia Dai

2004

Shanghai

2002-2003

1.00851.0032, 1.0139

Haidong Kan

2007

Shanghai

2004-2005

1.00361.0011, 1.0061

Wei Huang

2009

Shanghai-

1.003

1.0006, 1.0054

Yanjun Ma

2011

Shenyang

2006-2008

1.00491.0019, 1.0079

Wei Huang

2011

Xi’an

2004-2008

1.002

1.0007, 1.0033

Renjie Chen

2011

Beijing2007-2008

1.00531.0037, 1.0069

Renjie Chen

2011

Shanghai

2004-2008

1.00471.0022, 1.0072

Renjie Chen

2011

Shenyang

2006-2008

1.00351.0017, 1.0053

Chunxue Yang

2012

Guangzhou

2007-2008

1.009

1.0055, 1.0126

Junji Cao

2012

Xi'an

2004-2008

1.00161.0007, 1.0024

Fuhai Geng

2013

Shanghai-

1.00571.0012, 1.0101

Pei Li2014

Beijing

2005-2009

1.0042

1.0030, 1.0055

20

Hyewon Lee

2015

11 Asian cities-

1.00381.0021, 1.0055

Yuxia Xiong

2016

Guangzhou

2013-2015

1.01691.0094, 1.0245

2013-2015

1.01761.0147, 1.0206

6 cities of the
Hualiang Lin

2016

Pearl River
Delta region

Hualiang Lin

2016

Dongguan

2013-2015

1.01761.0155, 1.0399

Hualiang Lin

2016

Foshan2013-2015

1.02371.0138, 1.0337

Fengying Zhang

2016

Shenzhen2013

1.00691.0055, 1.0083

Renjie Chen2017

2013-2015

1.00221.0015, 1.0028

2013-2015

1.00131.0004, 1.0022

272 Chinese
cities30 Chinese
Chen Chen

2018
counties

Qingqing Wang

2019

Jiangsu

2015-2017

1.00291.0018, 1.0041

Ruoqian Lei

2019

Hefei

2013-2017

1.00561.0033, 1.0080

Bowen Huang

2019

Shenzhen

2013-2017

1.01

1.0018, 1.0183

Haiping Luo

2020

Gansu

2015-2018

1.00381.0031, 1.0045

The statistical method used in the meta-analysis is shown as below [51]:
𝒘𝒊 =

𝟏

(15)

𝑺𝑬{𝐥𝐨𝐠 𝑹𝑹𝒊 }𝟐21

𝑹𝑹∗ =∑𝒘𝒊 𝑹𝑹𝒊

(16)

∑ 𝒘𝒊

Here 𝑺𝑬 refers to the standard error and 𝑹𝑹∗ is the pooled result from the metaanalysis.

22

Chapter 3

Results

3.1 Characteristics of overcapacity of Chinese industry
According to the DEA model, the capacity utilization rates of various industries of 31
Chinese provinces in 2018 are obtained, as depicted in figure 5. The industries with darker
colors (that is, higher capacity utilization) are mainly light industry and fine manufacturingindustry, such as the manufacture of metal products, textile, and instruments, which aremainly located in the economically developed provinces in coastal areas such as Guangdong,
Jiangsu, Fujian, Shandong, Zhejiang, etc. This indicates that the overcapacity situation ofheavy industries and less developed provinces/cities are usually more severe. In 2018,
China's industrial average capacity utilization rate was 64.13%, which is much lower thanthe consensus value of 75% for overcapacity. Thus, there is still a lot of room for overcapacity
adjustment in the future for China.

23

Figure 5. Capacity utilization rate by industry in 31 provinces across in China (2018)24

3.1.1 Comparison between industrial sectors

Table 3 lists the capacity utilization rates of various industrial sectors of China in 2018. themining industry (Main category as B) and the production and supply industry of electricity,heat, gas & water (Main category as D) generally have low capacity utilization rates between
53% to 65%. On the other hand, in the manufacturing industry (C), the capacity utilization rateof the light industry is generally high, and the capacity utilization rate of heavy industry isusually low. Manufacture of metal products, textile, instruments, electrical machinery andequipment, and chemical fiber rank in the top five, with values of 75.37%, 74.71%, 73.85%,
73.74%, and 73.41%, respectively. However, mining of non-ferrous metal ores, othermanufacturing industries such as repairment of metal products, machinery and equipment,
comprehensive utilization of waste resources, and mining of ferrous metal ores rank at thebottom, with capacity utilization rates generally being lower than 56%.

Table 3. Capacity utilization rates of various industrial sectors in China (Baseyear=2018)
Rank

Category

Name of industrial sectors

CU

1

C

Manufacture of metal products

75.37%

2C

Manufacture of textile

74.71%

3

C

Manufacture of instruments

73.85%

4

C

Manufacture of electrical machinery and equipment

73.74%

5C

Manufacture of chemical fiber

73.41%

6C

Manufacture of medicines

73.17%

7C

Manufacture of general equipment

72.96%

25

Manufacture of leather, fur, feather products and8C

72.15%
footwear

9

C

Processing of agricultural and sideline food71.87%

10

C

Manufacture of special equipment

71.77%

Manufacture of culture, education, arts, sports and11

C

70.04%
entertainment products

12

C

Manufacture of automotive

69.32%

13C

Manufacture of tobacco

69.14%

14C

Manufacture of textile and clothing

68.97%

15

C

Manufacture of paper and its products

68.16%

16

C

Manufacture of railway, shipbuilding, aerospace and67.86%
other transportation equipment
17

C

Manufacture of wine, drinks and refined tea

67.85%

18

C

Manufacture of foods

67.53%

19

C

Manufacture of rubber and plastic

67.50%

Manufacture of computer, communications and other20C

67.42%
electronic equipment
Processing of timbers and manufacture of wood,

21

C

66.05%
bamboo, rattan, palm and straw products

22

C

Manufacture of furniture

65.36%

23

B

Mining professional and auxiliary activities

64.97%

24

C

Printing and reproduction of recording media industry

64.64%

25

C

Manufacture and processing of ferrous metals

64.64%

26

B

Mining and washing of coal

64.13%

26

27

B

Extraction of petroleum and natural gas

62.31%

28

C

Manufacture of non-metallic mineral products

62.11%

29

B

Mining of other ores

61.49%

30

C

Processing of oil, coal and other fuel60.75%

Manufacture of chemical raw material and chemical
31

C

60.40%
products

32

B

Mining and processing of non-metallic ores

60.14%

33

D

Production and supply of electric and heat power

59.41%

34

D

Production and supply of gas

57.75%

35

D

Production and supply of water

56.76%

36

C

Manufacture and processing of non-ferrous metals

56.17%

37

B

Mining of non-ferrous metal ores

55.11%

38

C

Other manufacture industry

54.61%

39

C

Repairment of metal products, machinery and equipment

54.58%

40

C

Comprehensive utilization of waste resources

53.19%

41

B

Mining of ferrous metal ores

52.91%

Note: The industry classification is implemented in accordance with the "National
Economic Industry Classification GB/T 4754-2017".

3.1.2 Comparison between provinces
China's industrial capacity utilization rate has not only significant industry differences,
but also large regional differences. Figure 6 shows the overall industrial capacity utilization
rate of each province in 2018. On the whole, the overall capacity utilization rate of eastern
China is significantly higher than that of other regions, among which Guangdong Province,

27

Jiangsu Province, Fujian Province, Shandong Province, Zhejiang Province, Shanghai and
Beijing take the top seven, with values of 96.7%, 85.6%, 79.2%, 77.9%, 77.5% and 77.4%,
73.3%, respectively. However, Gansu, Qinghai, Ningxia, Xinjiang and Tibet have the lowest
capacity utilization rate, generally ranging from 20% to 40%.

(%)

Figure 6. Distribution of capacity utilization rates in China (Base year=2018)
3.2 CO2 and PM2.5 emissions under different scenarios
According to the consensus value for capacity utilization rate as 75% proposed bydomestic scholars in line with China's national conditions [14], the overcapacity adjustment
threshold was determined for 31 provinces and different industries to investigate the decapacity scenario (S2). There are two adjustment strategies for industries with varying extentsof excess capacity problems. For industries with a capacity utilization rate between 50% to75%, it is assumed that the value can be increased to 75% by 2050. For industries with a28

capacity utilization rate of less than 50%, it is more challenging to reduce capacity, so it isassumed that their capacity utilization rate can increase to 50% in 2050.
The GAINS-China model is used to evaluate the carbon emission reduction potential of
overcapacity adjustment, and the results of CO2 and PM2.5 emissions under business as usual
scenario (S1) and de-capacity scenario (S2) are shown in Figure 7. It can be observed that, theCO2 emissions under both scenarios remain relatively stable, around 8276 million tons and8003 million tons from 2025 to 2035, and then increase to 10931 million tons and 9881 million
tons till 2050, respectively. The PM2.5 emissions decrease from 2025 to 2035 and then show asimilar increasing trend with CO2 emissions. Considering the release potential from excess
capacity, the emission reduction of both CO2 and PM2.5 from industries show a growing trend
from 2025 to 2050, resulting in about 1.05 billion tons of CO2 emission reduction and 57.8
kilotons of PM2.5 emission reduction in 2050, respectively. This indicates that, by adjusting
the overcapacity of Chinese industry, 9.6% of CO2 emission reduction and 5.8% of PM2.5
emission reduction can be achieved in 2050.

Figure 7. CO2 and PM2.5 emissions under the business as usual (S1) and de-capacity (S2)scenarios

29Figure 8 further provides detailed information about the emission trend of CO2 and PM2.5
under both scenarios in each province from 2025 to 2050, and figure 9 represents the exact
reduction amount as well as the reduction rate in 2050. It turns out that the emission of CO2
remains increasing in major provinces with large bases ranging from 150 to 1,500 million tons,
while it remains stable in Inner Mongolia, Fujian, Heilongjiang, Jilin, Xinjiang and Ningxia,
and it tends to decrease in Anhui, Guangdong, Qinghai and Hainan. As it can be observed from
Figure 9, the provinces with high carbon emission industries such as Liaoning, Shanxi,
Shandong, Inner Mongolia, and Hebei have higher carbon emission reduction potentials, withvalues of 117.21, 97.27, 80.39, 75.18, and 65.97 million tons, respectively. Provinces withlower capacity utilization rates, such as Hainan, Qinghai, Ningxia, Xinjiang, and Inner
Mongolia, have low carbon emission bases; thus, the total amount of emissions reduction issmall, but the proportion is considerable, about 40.4%, 34.2%, 29.1%, 26.8% and 25.5%,
respectively.
1,500
1,000
500
0

Hebei

Shandong

Henan

Jiangsu

Liaoning

Shanxi

Hubei

Sichuan

Guangxi

Anhui

Inner Mongolia

Shanghai

800
400
0

400
200
0

30

300
150
0

Hunan

Guangdong

Jiangxi

Zhejiang

TianjinYunnanGuizhou

Chongqing

FujianHeilongjiang

Jilin

Xinjiang

Shannxi

Gansu

Beijing

Ningxia

250
125
0

200
100
0

150
75
0

50
25
0

Qinghai

HainanXizangFigure 8. CO2 and PM2.5 emission trend from 2025 to 2050

31

Figure 9. CO2 and PM2.5 emission reduction potential under the de-capacity scenario in
2050

3.3 Health impact assessment
The numbers of selected studies and combined estimates derived in the present metaanalysis are summarized in Table 4. According to the algorithm computed by the software(Review Manager), the pooled result for China-specified RR value is 1.0057 (95% CI: 0.43,0.71). This finding is consistent with those previous studies that focused on the daily mortality
attributable to PM2.5 pollution in China and proposed the RR value ranging from 1.0038 to
1.0090 [52,53]. The statistical data I2 is 95% (P<0.00001), which indicates the extent ofinconsistency among these studies’ results, and is interpreted as the proportion of total
variation due to heterogeneity rather than sampling error [51].
The RR value 1.0057 obtained in this study means that total mortality will increase by
0.57% with per 10 μg/m3 increase of PM2.5 concentrations. The RR value applied in the32

GAINS-China model is 1.06, demonstrating that total mortality will increase by 6% with per
10 μg/m3 increase of PM2.5 concentrations. By comparing the two RR values obtained fromthe GAINS-China model and the meta-analysis in this study, it is obvious that the adjusted
mortality rate of China is only 1/10 of the European countries. The differences in concentrationand source of air pollutants, population size and chemical composition of PM2.5 in various
regions may partially explain this heterogeneity. In addition, the non-linear shape of the deathPM2.5 concentration function may also be an important reason. According to Bernett et al [54],although total death increases with the increase of PM2.5 concentrations, the increase rate (RRvalue) decreases with the increase of PM2.5 concentrations. In other words, the extra risk (RR)
is high in regions with low PM2.5 concentration levels such as the developed countries, while
in regions with high exposures of PM2.5 such as China, the extra risk (RR) is relatively low.
The adjusted RR value for China becomes smaller in our study, which is in line with thisprinciple. Therefore, our study provides a more precise estimation of air pollution-related daily
mortality cases in China when using GAINS-China model.

Table 4. Meta-analysis result for PM2.5 combined with total mortality
Study

Log[RR]SE

WeightRR [CI]

Venners 2003

00.000374.8%

1.0000 [0.9993, 1.0007]

Chen 2017

0.00220.00044.8%

1.0022 [1.0014, 1.0030]

Luo 2020

0.00380.00044.8%

1.0038 [1.0030, 1.0046]

Chen 2018

0.00130.00054.7%

1.0013 [1.0003, 1.0023]

Cao 2012

0.00160.00054.7%

1.0016 [1.0006, 1.0026]

33

Wang 2019

0.00290.0006

4.7%

1.0029 [1.0017, 1.0041]

Li 20140.00420.00064.7%

1.0042 [1.0030, 1.0054]

Zhang 2016

0.00690.00074.6%

1.0069 [1.0055, 1.0083]

Huang 2011

0.002

0.00074.6%

1.0020 [1.0006, 1.0034]

Chen_a 201

0.00530.00084.6%

1.0053 [1.0037, 1.0069]

Chen_c 2011

0.00350.00094.5%

1.0035 [1.0017, 1.0053]

Lee 2015

0.00380.00094.5%

1.0038 [1.0020, 1.0056]

Lin_b 2016

0.01740.00114.3%

1.0176 [1.0154, 1.0197]

Lei 2019

0.00560.00124.3%

1.0056 [1.0033, 1.0080]

Huang 2009

0.003

0.00124.3%

1.0030 [1.0006, 1.0054]

Kan 2007

0.00360.001134.2%

1.0036 [1.0011, 1.0062]

Chen_b 2011

0.00470.00134.2%

1.0047 [1.0022, 1.0073]

Ma 20110.00490.00154.0%

1.0049 [1.0020, 1.0079]

Lin_a 2016

0.017400.00154.0%

1.0176 [1.0146, 1.0205]

Yang 2012

0.009

0.00183.7%

1.0090 [1.0055, 1.0126]

Geng 2013

0.00570.00233.2%

1.0057 [1.0012, 1.0103]

Dai 2004

0.00850.00272.8%

1.0085 [1.0032, 1.0139]

Xiong 2016

0.01680.00382.0%

1.0169 [1.0094, 1.0245]

Huuang 2019

0.01

0.00421.8%

1.0101 [1.0018 1.0184]

34Lin_c 2016

0.02340.005

Total (95% CI)

1.4%

1.0237 [1.0137, 1.0338]

100%

1.0057 [1.0043, 1.0071]

The above obtained China specific RR values through meta-analysis are further put into
the health model to evaluate the health benefit caused by mortality reduction due to PM2.5
reduction among the population with ages above 30 years (Figure 10). The health benefit fromovercapacity adjustment was conducted for all 31 provinces in China, based on the BAU
pollution level of PM2.5 (S1), the emission reduction potential derived from the release ofexcess capacity under the de-capacity scenario (S2), and the population density. The results
indicate that northern and central provinces with heavy industries and large population suchas Henan, Anhui, Sichuan, Shandong and Jiangsu have relatively more mortality reduction
cases than Hainan, Inner Mongolia, Ningxia, Qinghai, Tibet and Xinjiang. It is also revealed
that the provinces with lower levels of PM2.5 pollution and higher population density havehigher reduction rates. The prevented mortality cases in provinces such as Sichuan, Henan,
Shandong, Hunan and Chongqing are estimated at 116,700, 64,900, 53,000, 50,400, and49,100, respectively. The top 5 provinces in terms of the reduction rate are Inner Mongolia,
Ningxia, Qinghai, Xinjiang, Hainan and Tibet as 4.92%, 4.58%, 4.03%, 3.87%, 3.67% and3.59%. More precisely, the implementation of the de-capacity policy reveals significant
prevention of about 792,100 mortality cases in all provinces across the country, which is about
0.094% of the national targeted population.

35

5%

5

4%

4
3%
3
2%

2
1

1%

0

0%

Henan
Anhui
SichuanShandong
Jiangsu
Hunan
Hubei
Hebei
Guangdong
Jiangxi
Chongqing
Guangxi
Zhejiang
Guizhou
Beijing
Shanxi
Shaanxi
Liaoning
Shanghai
YunnanTianjinFujianJilin
Gansu
Heilongjiang
HainanInnerMongolia
Ningxia
QinghaiTibet
XinjiangMortality case (million person)

66%

Expected Total Mortaltiy (ETM)
Mortality reduction
Reduction rate

Reduction rate (%)

7

Figure 10. Provincial public health benefits from the de-capacity policy in 2050
This study further explores the relationship between de-capacity potential and expectedco-benefits. The multiple linear regression analysis is conducted in order to explore the linear
relationship existing between the capacity utilization rates (CU), PM2.5 emission reduction(ER), and mortality cases reduction (MR) as follows:𝐥𝐧(𝑴𝑹) = 𝟎. 𝟑𝟎𝟗𝟎 ∗ 𝐥𝐧(𝑪𝑼) + 𝟏. 𝟎𝟗𝟑𝟑 ∗ 𝒍𝒏(𝑬𝑹)(17)The coefficient of determination, R2 is more than 0.96, indicating reliable regression
results. Furthermore, the F-statistic reaches 338.46 with a significance F of 3.78852*10-16.
More relevant statistical indicators are shown in Table 5.
Table 5. Statistical indicators of the regression
R2

Standard Error

36

F-statistic

Regression
0.984840395

0.969910603

338.4601323

Statistics
Standard Error

T-statistic

p-value𝒍𝒏(𝑪𝑼)

0.104614409

2.953470026

0.00758557987

𝒍𝒏(𝑬𝑹)

0.10597475

10.31622969

0.00000000112

The above model can be a supplementary tool to measure the public health benefit fromthe contribution of de-capacity policies and regulations.

37

Chapter 4

Discussion

4.1 Comparison with existing studies
Several studies assessed the excess capacity of one or more industrial sectors in China.
We find that most of their results are consistent with our result. For instance, the capacity
utilization rate in our study is 64.13% for the whole country in 2018, with variations indifferent industries and provinces. The study from Yu and Shen [55] showed that the industrial
capacity utilization from 30 Chinese provinces reached 69.56% in 2015, which is similar to
our result. Another study analyzed the coal resource differences within regions and pointed
out that the capacity utilization rate of China's coal industry was 68% in 2016 [56], which isalso similar to our result.
With regards to co-benefits, this study evaluates CO2 emission reduction, PM2.5 emission
reduction and related health benefits from de-capacity, while other relevant studies evaluate
co-benefits from other policies such as technological control, administrative regulations andeconomic instruments. For example, carbon emission and PM2.5 emission can be reduced by
12-32% under traditional command and control measures [57,58], by 10-40% if renewableenergy development can be implemented [59], by 15-40% if carbon tax and carbon emissions
trade policies can be implemented [60-62], and by 6-14% if industrial and urban symbiosis
can be applied [63,64]. Our results present that carbon emission can be reduced by 9.6% fromde-capacity, which is neither too significant nor too small compared with existing studies.
With regards to the PM2.5 emission and the related public health co-benefit, it is about 5.8%
and 1.6% in our study, respectively. Comparing with other co-benefits studies, although theoverall co-benefits from de-capacity are not as significant as the application of PM2.5 control

38technologies (70% reduction) [53,65], it is still promising and reasonable since the applicationof such technologies is costly and time-consuming. De-capacity can optimize industrial
structure, improve the overall resource efficiency, and avoid long term environmentalpressures with relatively less financial investment.
4.2 Policy implications
Based upon our research findings and considering the Chinese realities, we propose thefollowing policy recommendations.
First, appropriate differentiated target value for de-capacity (i.e. capacity utilization rate)should be determined for different regions and industries when implementing de-capacity
policy. As discussed in this study, capacity utilization rate is the key indicator to measure theovercapacity level, and its implementation can lead to less use of fossil fuels, economic input
and labor resources, rather than the output value that is always set as the goal in many decapacity policies. Therefore, to make the de-capacity policy more effective, the capacity
utilization rates for different industries in different provinces should be appropriately set,
based on the industrial characteristics and the current situation that is reflected by thequantitative results shown in Figure 2. Furthermore, when implementing de-capacity policies,
it is encouraged that explicit criteria and related parameters for monitoring the de-capacity
process be also provided to facilitate the enterprise and local government for accurate
understanding, supervising and implementation of de capacity policy. The parameters include
the output values and input resources of different industries in different provinces, which areclosely related with the industrial structure adjustment.
Second, three regions/industries are identified for accurate and effective implementationof de-capacity policies. In terms of the overcapacity level, regions with lower levels of39

capacity utilization rates such as Gansu, Qinghai, Ningxia, Xinjiang, and Tibet, and industries
such as the Mining and the Production & Supply of Electricity, Heat, Gas & Water should beselected for optimizing their industrial structure and adjusting their industrial layouts. In termsof environmental benefits, regions with heavy industries and higher emissions such as
Liaoning, Shanxi, Shandong, Inner Mongolia, and Hebei should make more efforts to improvetheir capacity utilization rates. Meanwhile, these regions can reduce their air pollutant
emissions and greenhouse gas emissions through various control methods, especially
technological measures. In terms of public health benefit, regions with higher air pollution
emissions and dense population intensities, such as Sichuan, Henan, Hunan, Chongqing, andHubei, should improve their air quality by more serious enforcement and application of moreadvanced technologies.
Finally, the integrated research framework proposed in this study can be further applied
to other international countries although the application of this framework should be adjusted
by considering the local realities. Pursuing co-benefits should be recognized as an international
goal since it can facilitate the achievement of United Nations’ Sustainable Development Goals(SDGs).
Table 6. Priority of provinces and industries
In terms of overcapacity extent
Province

Capacity Utilization Rate

1

Gansu

40.8%

2

Qinghai

33.0%

3

Ningxia

29.0%

40

4

Xinjiang

28.1%

5

Tibet

21.3%

Industry sector
1

Capacity Utilization Rate

The mining industry

Mostly 52%-65%

The production and supply2

industry of electricity, heat,< 60%

gas & water
In terms of environmental benefit
Province

CO2 Emission reduction amount (Mt)1

Liaoning

117.2

2

Shanxi97.3

3

Shandong

80.4

4

Inner Mongolia

75.2

5

Hebei

66.0

In terms of public health benefit
Province

Mortality cases reduced (person)

1

Sichuan

116,7002

Henan64,9003

Hunan

50,4004Chongqing

49,100

41

5

Hubei

44,0004.3 Research limitations
There are two limitations that exist with the current evaluation of the de-capacity policy
implementation and its related co-benefits in this study:Firstly, the statistical data required for capacity utilization evaluation is incomplete at theprovincial level and the industrial sector level, so there may be some deviation. Those missing
data are calculated by other available data according to specific principles and functions.
Therefore, if the databases at the provincial level and the industrial sector level are more
sufficient and complete, the results should be more reasonable.
Secondly, the air pollutant mitigation technologies and their penetration rates are
determined according to the database embodied in the current GAINS-China model. It is
assumed that these mitigation technologies and penetration rates have not changed when
activity data (energy consumption data) are updated because of de-capacity implementation.
Therefore, it will be more perfect if the GAINS optimization model can be further developed
to optimize the current air mitigation technology penetration rate according to the activity data
adjustment to achieve a more accurate result.

42

Chapter 5

Conclusion

With China’s continuous overcapacity growth and increasing environmental emissions,
it is critical to make appropriate mitigation policies. In order to fill such a study gap, this paper
first estimated the capacity utilization rates by 41 industrial sectors of 31 provinces in China
and then assessed the CO2 and PM2.5 emission reduction and total mortality associated with
air pollution under the de-capacity implementation. Based on the results obtained from the
BaU scenario, China's industrial average capacity utilization rate in 2018 was estimated at
64.13%, with a relatively lower value among mining industries and the production and supply
industry of electricity, heat, gas and water. The de-capacity scenario revealed a significant
reduction potential of CO2 and PM2.5 emissions from industries by 1.05 billion tons (9.6%)
and 57.8 kilotons (5.8%) in 2050, respectively. Generally, the mitigation potential of PM2.5
pollution derived from the release of excess capacity can decrease the number of mortality
cases in all provinces across the country by 792,100 by 2050.
However, besides the less input of resources, the reduction of excess production capacity
can also be derived from the higher technical production level and the improved operation ofenterprises or industries. The higher technical production level would enhance theconversation rate from input to output, and the improved operation is essentially theredistribution of inter-industrial interests. In this context, the whole model in this study should
further consider more aspects, including: Insert the parameter reflecting the technical level in the optimization formula by the
DEA model to consider the positive impact of technology development.
 Establish the link between industries and analyze the redistribution of inter-industrial
resources and co-benefits.
43

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