Industrial structural upgrading and spatial optimization based on water environment carrying capacity

Accepted Manuscript Industrial Structural Upgrading and Spatial Optimization based on Water Environment Carrying Capacity

Xi-Yin Zhou, Kun Lei, Wei Meng, Soon-Thiam Khu PII:

S0959-6526(17)31703-1

DOI:

10.1016/j.jclepro.2017.07.246

Reference:

JCLP 10249

To appear in:

Journal of Cleaner Production

Received Date:

04 April 2017

Revised Date:

05 July 2017

Accepted Date:

31 July 2017

Please cite this article as: Xi-Yin Zhou, Kun Lei, Wei Meng, Soon-Thiam Khu, Industrial Structural Upgrading and Spatial Optimization based on Water Environment Carrying Capacity, Journal of Cleaner Production (2017), doi: 10.1016/j.jclepro.2017.07.246

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ACCEPTED MANUSCRIPT

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Industrial Structural Upgrading and Spatial Optimization

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based on Water Environment Carrying Capacity

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Xi-Yin Zhoua, Kun Leib, *, Wei Mengb, Soon-Thiam Khuc, **

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a

School of Environment, Tsinghua University, Beijing 100084, PR China

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b

Chinese Research Academy of Environmental Sciences, Beijing, 100012, PR China

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c

Civil Engineering, School of Engineering, Monash University, Sunway Campus, Malaysia

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Abstract: The industrial wastewater accompanying rapid industrialization has caused severe

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pollution problems, especially in China. Addressing industrial structure upgrading and spatial

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optimization based on water environment carrying capacity has become an urgent issue. This paper

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establishes an analytical framework that uses a combination of economic and water environment

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information for industrial structure upgrading and spatial optimization based on water environment

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carrying capacity. This framework promotes the practical application of water environment carrying

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capacity theory for socio-ecological sustainability. The input-output table, information entropy

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method and a simulation platform of water environment carrying capacity using a multi-agent

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system are integrated into the analytical framework. A spatial assessment of the water environment

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carrying capacity, industrial structure upgrading and spatial optimization is performed for

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Changzhou, China. With the measures implementation of industrial structure upgrading and spatial

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optimization, the economic scale of the electrical equipment and machinery industry, which is the

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most important industry in Changzhou City, would reach 126,814.68 billion yuan, nearly 7.3 times

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its current value. In addition, the total local industrial economy would reach 3,319.81 billion yuan,

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nearly 1.6 times its current scale. Due to the industrial concentration, the increased economic scale * Corresponding author. ** Corresponding author. E-mail address: [email protected] (X. Y. Zhou), [email protected] (K. Lei), [email protected] (S. T. Khu)

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would create additional benefits, including the whole study region reaching the water quality goal

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and the water quality in urban areas significantly improving. The measures of industrial structure

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upgrading and spatial optimization would help to achieve a mutually beneficial balance between

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environmental protection and economic development. The analytical framework establishes the

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internal link between industrial structure upgrading and spatial optimization based on the water

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environment carrying capacity. The links from water quality to industrial structure upgrading and

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spatial optimization are also established. These connections could support the fine-scale

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management of water environments and could help local governments to plan sustainable socio-

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ecologic development.

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Keywords: industrial structure upgrading, industrial spatial optimization, water environment

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carrying capacity, sustainability

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1 Introduction

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1.1 Overview of the water environmental carrying capacity (WECC)

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theory

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Currently, the contradiction between economic development and environmental protection is

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becoming increasingly serious (Grey and Sadoff, 2007). Excessive economic development has

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generated and discharged a large amount of pollutants into water bodies, causing severe water

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pollution problems. In certain instances, the deterioration of the local environment has constrained

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or affected local and regional economic development (Zhu et al., 2010). China is a rapidly

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developing country that has faced a serious industrial wastewater emission challenge. Industrial

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wastewater emissions are becoming the main cause of China’s water pollution (Geng et al., 2014).

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Water environmental carrying capacity (WECC) theory is a useful tool for supporting for

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sustainable socio-ecological development. Exploring the development of industrial structure

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upgrading and spatial optimization based on WECC is vital to achieving human- water

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sustainability. WECC can be defined as “the largest population and economic scale that the water

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environment can support in a specific region during a period of time without an adverse impact on

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the local water environment” (Yang et al., 2015). WECC can be regarded as a complex system

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related to the water environment, population, economy, technology, policy, space, and time. The

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WECC value changes with variations in any of the above factors.

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However, current WECC research remains predominantly theoretical and in the research stage.

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Due to the limitations of the methods, most studies focus on the total WECC status assessment of a

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research region without considering internal heterogeneities. For example, Gong and Jin (2009)

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used fuzzy comprehensive evaluation methods to evaluate the total WECC status of Lanzhou City.

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Pahlow et al (2015) analyzed the sustainability of the water usage in South Africa using water

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footprints. Wang et al (2014) applied the system dynamics method to the WECC assessment and

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policy simulation of Tieling City of China. There is still a gap between theoretical research and

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practical application in WECC theory. The only information that the current WECC research can

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provide is that about the maximum scale that the local water environment can support through the

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index calculations. However, how to reach varying scales through the structural adjustments or

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layout optimizations is unclear. As a useful tool for guiding local government managers to achieve

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socio-ecological sustainability, the potential power of WECC remains unknown. As a large,

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complex socio-ecological system, WECC cannot be researched based on only the field of natural

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science. To better guide socio-economic development, economic theory should be considered in

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WECC research.

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1.2 Literature review

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Over the past several decades, some analytical methods have been developed to achieve

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industrial structure upgrading or spatial optimization independently. However, research on the

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structural upgrades and layout optimization of industry is isolated in the current study of economic

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and environmental sustainability. An integrated analytical framework that considers both industrial

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structure upgrading and layout optimization has not yet been researched. In fact, industrial structure

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adjustments could directly influence the optimal spatial layout, and both the industrial structures

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and layout impact the water quality. Thus, to serve as a guide for effective industry policies for city

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managers all over the world, especially those in developing countries, the results should not only

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show how to optimize industrial structures but also demonstrate where to arrange such industries.

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Most scholars adopted linear or nonlinear mathematical models in researching industrial

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structure upgrading. Murillo-Alvarado et al (2015) used a multi-objective optimization approach to

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optimize the supply chain of biofuels to maximize their economic value and environmental

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performance. Gu et al (2013) developed an inexact fuzzy stochastic programming method to

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optimize industrial structures to achieve sustainability in a resource-based city. Zhou et al (2013)

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applied an inexact fuzzy multi-objective programming model to upgrade the industrial structures of

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a watershed with consideration of uncertainty. Li et al (2016) established an integrated model

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combining stochastic programming, interval linear programming, and multiple objective

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programming to research industrial structure upgrading based on WECC.

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As usual, in the previous study of industrial structure upgrading, the objective function consists

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of economic benefits maximization, pollutant discharge minimization, and pollutant-reduction cost

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minimization. The constraint function consists of an economic development constraint, population

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constraint, and pollutant-discharge intensity constraint. The impact of the pillar industry on regional

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economic development is considered less. A close contact between different industry types exists

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in industrial chains and production trades. An industry with a relatively small economic scale may

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hold large economic influences over other industries. The direct link between industrial structures

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and water quality has not been established, and only the pollution reduction of the whole region is

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concerned in the current research; the exact influence of industrial structure adjustments on natural

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water environment qualities is unclear.

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Industrial layout research can be divided into two categories: (1) The first category is influence

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mechanism research, such as that of Guo et al. (2013), who employed a conditional logit model to

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analyze the effects of woody biomass policies on the location decisions of the woody bioenergy

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industry at the state level in the US. Kolympiris et al. (2015) revealed that proximity to certain

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known assets is a key factor affecting the location choices of academic entrepreneurs using a case

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study of the US biotechnology industry at the state level. Ellram et al. (2013) used multiple

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regression analysis to analyze the influencing factors of manufacturing location decisions, and found

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that supply chain-related factors become more and more important. The environment is a crucial

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factor that influences industry location selection for all types of industry, especially for pollutant-

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intensive industries (Treitl and Jammernegg, 2014). The pollution havens hypothesis is a specific

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theory that describes the influence of environmental regulations on industrial layouts (Millimet and

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Roy, 2015). Lin and Sun (2016) found that foreign direct investment firms were located in those

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provinces with less stringent environmental regulations in China. Candau and Dienesch (2017)

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confirmed that easy market access to high-income countries and corruption opportunities are the

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two main factors in relocations of polluting firms in multiple countries. (2) The second category of

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research is industrial spatial distributions determination based on influence mechanism research.

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Rikalovic et al. (2014) summarized a GIS-based multi-criteria approach for industrial site selection.

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Wang et al. (2016) used a multinomial logit model to predict future industry distributions and assess

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the environmental impacts of different scenarios. As sustainability is becoming a point of interest

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all over the world, economic (cost, market, growth, etc.), social (governance, education, etc.), and

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environmental factors (environment pollution amounts and intensities) are considered in the process

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of industrial location decisions (Chen et al., 2014).

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However, in the current research on industrial layout optimization, the industrial locations are

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often determined at the regional scale of the country, province or city, and the main environmental

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factor considered is the pollution discharge. Only pollution discharge is considered by the current

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regional scale research, but discharge cannot provide detailed information for industrial layout

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planning of a specific industrial location for government managers. Industrial location selection

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with consideration of the local water environment is often analyzed by comparing the amount of

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pollution and the water environment capacity of a relatively large study area. The specific water

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quality standard and status of a certain river are neglected. The natural flow of the river is simplified

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to a single factor value, potentially causing excessive pollution discharge and water quality

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degradation in some microscale units and leading to too much treatment cost invested in others,

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wasting potential water environment capacity. Rivers have certain flows, and water environment

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capacity has natural spatial heterogeneities. The same amount of pollution entering a river at

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different locations would induce considerably different environmental impacts due to the uneven

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distributions of water environmental capacity. For example, in a region with a large amount of water

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environment capacity, the location of a plant could be in a subregion with little water environment

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capacity. At a regional level, this placement would be considered good, but within the subregion,

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the placement is poor; additionally, large amounts of water environment capacity in other spatial

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units remain unused. To manage the water environment and effectively improve the water quality

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on a fine scale, location selection at a region scale is not adequate. Specific point location

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information is necessary for the fine-scale management of industrial layouts.

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According to the above literature review, the current research on industrial structure upgrading

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and spatial optimization has two main deficiencies: (1) the link between industrial structure

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upgrading and industrial layout optimization is unquantified. Industrial structures and industrial

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layouts play key roles in socio-ecological sustainability of water resources. The results of industrial

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structure upgrading would also impact the optimal industrial locations. Any measure of industrial

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structure adjustments would influence the local WECC status and change the optimal spatial

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patterns of industrial layouts. Both the optimal structure upgrades and spatial locations of the

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required industries should be made clear for government managers. The application of only one of

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these could reduce the final environmental performance. (2) Additionally, the link of industrial

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structure and pattern with water environment quality has not been established; therefore, the goal of

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water quality attainment and the full usage of a water environment cannot be met. Most of the

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methods used for industrial structure and layout research, such as the multi-objective method and

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multinomial logit model, are statistical analysis techniques with a combination of factors. The causal

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relationships and physical significances of these factors are neglected when studying the processes

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of human activity, pollutant production, pollutant discharge, and pollutant flow degradation. The

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normal way of presenting a consideration of environmental protection is to adopt a factor

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representing pollution discharge. The greater the pollution reduction amount, the better the

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environmental performance. However, pollution reduction across the whole region does not

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necessarily indicate a microscale water quality improvement. The exact influence of pollution

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discharges on local water environments has not been analyzed. Furthermore, the excessive

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requirements of pollutant reductions not only may cause exorbitant costs but also may waste water

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environment capacity in the subregions with good WECC statuses.

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To resolve the above deficiencies, this study establishes an integrated analytical framework

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with a combination of industrial structure upgrading and industrial spatial optimization. The main

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improvements include the following: (1) establishing a link between industrial structure upgrading

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and spatial optimization. The influence of industrial structure adjustments on industrial spatial

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optimization is revealed based on a WECC status assessment. This influence is considered in the

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process of industrial spatial optimization, providing a system for combining industrial structure

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upgrading and spatial optimization based on WECC. (2) Another improvement is directly

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connecting the industrial structure upgrading and spatial optimization to the water environment

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quality directly. This connection could guarantee water quality goals and the full use of water

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environment capacity as well as achieving the maximum sustainable development of regional

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industries based on WECC. This integrated analytical framework would allow for economically

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optimized water environment sustainability and provide fine-scale solutions for water environment

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management and industrial development. (3) Finally, WECC theory could use this analytical

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framework for practical applications, such as for guiding socio-ecological sustainability.

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This paper is organized as follows: Section 2 presents the methodology, including the

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establishment of the analytical framework, industrial structure upgrading theory and a simulation

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platform of WECC. Section 3 introduces the data sources, data processes and study area. Section 4

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illustrates the results of industrial structure upgrading and spatial optimization based on WECC and

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assesses the influences on WECC. The last section evaluates the potential of the analytical

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framework and its policy implications.

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2 Methods

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2.1 Analytical framework

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An analytical framework of industry upgrades and location optimization is established using

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five main processes: industrial structure upgrading, industrial park locations, WECC status

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assessments, pairs of industry and site and WECC calculations (Figure 1). Each step includes

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multiple processes. The detailed procedure for each step is described below.

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In the process of industrial structure upgrading, five main criteria are established to assess the

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sustainability of each industry. The related data are collected from different sources. For each of the

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criteria, the influence coefficients and response coefficients are obtained from a local input-output

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table. Then, the weight of each criteria is calculated using the information entropy method. Finally,

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the industry weight could be obtained using the criteria weight multiplied by the corresponding

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criteria value. Each industry is sorted according their industry weights and divided into three types:

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weak industry, normal industry and pillar industry.

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In the process of industrial spatial optimization, the normal criteria are established using

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previously published works literature (Rikalovic et al., 2014), including their factors and constraints.

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The factors consist of roads, water infrastructure and the availability of construction land. The

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constraint is the ecological red line, which is a method of spatially limiting development planning;

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no economic activities are allowed in the space inside the ecological red line. Local governments

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have put forward these boundaries. In this study, the ecological red line is taken into consideration

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as a constraint for the industrial spatial optimization. Potential sites are generated based on these

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criteria.

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As one of its improvements, this paper links industrial structure upgrading and spatial

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optimization using the WECC status assessment process. The WECC status assessment process is

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implemented based on the multi-agent systems (MAS) model. First, the current WECC status is

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assessed. Then, based on the results of the industrial classification, the WECC status without the

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weak and pillar industries is simulated and assessed. The spatial units with good WECC statuses are

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selected. Finally, the optimal sites are selected and sorted based on the simulation results and

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potential sites generated.

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Using pairs of pillar industries and optimal sites determined according to ordination, the pillar

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industries and their specific locations are determined. Based on WECC theory, the maximum

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industrial scales under the precondition of water quality attainment is calculated.

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The analytical framework presents a systemic route of combinations of industrial structure

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upgrading and spatial optimizations. Thus, the internal influence links between industrial structure

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upgrading and spatial optimization are revealed, and the specific water quality improvement goal

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can be guaranteed. The locations and scales of the pillar industries can guarantee water quality

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attainment.

216 Industrial structure upgrading

WECC status assessment

Industrial spatial optimization

IO-table Data collection

Index value

MAS model establishment

Normal criteria establishment and evaluation

Information enthropy

Industrial weight

WECC current status assessment

Potential sites generation

Ordination and classification of industry

WECC status assessment without weak and pillar industries

Three types: Weak industry, normal industry and pillar industry

Spatial units selection with good WECC status

Optimal sites selection and ordination

Pairs of pillar industries and optimal sites according to ordination

WECC Calculation

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Maximum industrial scale under the precondition of water quality attainment

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Fig. 1. Analytical framework of industrial structure upgrading and spatial optimization based on

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WECC

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2.2 Industrial structure upgrading

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As one type of point source, industries can exploit the maximum economic benefits only when

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considering the industrial concentrations. In addition, agriculture is difficult to optimize spatially,

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and production is mainly determined by the demands of society. Mass transfers of population are

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not realistic, nor are those of tertiary industries. Therefore, the ideas of upgrading and spatially

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optimizing industries based on WECC is proposed to search for a mutually beneficial relationship

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between environmental protection and economic development.

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The information entropy method is adopted to calculate the weights of different industrial types

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and can measure the degree of usefulness of an index for a special object. For example, when an

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indicator shows a significant difference among different industries, the entropy would be small, and

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the indicator would be recognized as important and be assigned a relatively high weight, making

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this an objective method for weight calculation. Five indices are chosen to weight the influences on

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industry: industry output values, influence coefficients, response coefficients, cleaner production

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levels, and the pollutant treatment ratio. These indicators were chosen using the principles of

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economic importance and sustainability. Thus, the comprehensive assessment of the indicators for

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a certain industry could indicate its levels of economic, industrial, and environmental benefits. The

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industries with higher weights have greater sustainable benefits. These types of industries should be

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developed and set as pillar industries, while those industries with lower weights require restricted

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development. First, all the index values of the twenty types of industries form a matrix: Y = (𝑋𝑖𝑗)𝑚 × 𝑛

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, 𝑚 = 20,𝑛 = 5 (table 1)

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Table 1. Index matrix Industry

Influence

Response

Cleaner

output value

coefficient

coefficient

level

production

Pollutant treatment ratio

Industry 1

𝑥11

𝑥12

𝑥13

𝑥14

𝑥15

Industry 2

𝑥21

𝑥22

𝑥23

𝑥24

𝑥25

Industry 3

𝑥31

𝑥32

𝑥33

𝑥34

𝑥35

⋮

⋮

⋮

⋮

⋮

⋮

Industry m

𝑥m1

𝑥m2

𝑥m3

𝑥m4

𝑥m5

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The matrix should be normalized before calculating the industry weight. The normalization

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values of the twenty types of industries and five indices form a matrix Y = (𝑦𝑖𝑗)𝑚 × 𝑛, 𝑚 = 20,𝑛 = 5.

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𝑦𝑖𝑗 ∈ [0,1].

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The ratio of index j of industry i to the total value of index j would be calculated using the formula below:

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𝑝𝑖𝑗 =

y𝑖𝑗

(1)

∑𝑚 y𝑖𝑗 𝑖=1

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where 𝑝𝑖𝑗 indicates the ratio of index j of industry i to the total value of index j.

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The entropy of index j would be calculated through the formula below:

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1

𝑚

ℎ𝑗 =‒ ln 𝑚∑𝑖 = 1𝑝𝑖𝑗ln 𝑝𝑖𝑗

(2) 1

where ℎ𝑗 indicates the entropy value of index j and ln 𝑚 is defined as the normalization factor. The weight of index j could be calculated using the formula below: 𝑤𝑗 =

1 ‒ ℎ𝑗 ∑𝑛 (1 ‒ ℎ𝑗) 𝑗=1

(3)

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where 𝑤𝑗 indicates the weight of index j.

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The weight of each industry in the study region would be calculated using the formula below:

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𝑤𝑖 = ∑𝑗 = 1𝑝𝑖𝑗𝑤𝑗

𝑛

(4)

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where 𝑤𝑖 indicates the weight of industry i.

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All the industries were sorted by the values of the industry weights in descending order and

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were divided into three classes according to their order. The industries in the first class, with high

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economic and environmental benefits, were recognized as pillar industries, while the industries in

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the middle class were recognized as the remaining developing industries. The industries with low

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economic and environmental benefits, i.e., those in the last class, were recognized as outdated

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industries that should be eliminated.

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The values of the influence and response coefficients were calculated using the input-output

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table, which was first introduced by Leontief (1941). A hybrid method, which is a combination of

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a non-survey-based RAS-algorithm and a partial-survey-based method, is employed to create the

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input-output table of the research region. Using error calculations between the input-output table

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and other real statistical data, such as the industrial added value structure and gross domestic

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value, the input-output table achieves a relatively high accuracy (Zhou et al., 2016). The influence

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coefficient refers to the influence level of a unit increment of the final product of a certain industry

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on all the other industrial demands. The influence coefficient represents the pulling ability of a

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particular industry on other industries. The response coefficient refers to the impact level of a unit

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increment of the final demand of all the industries on the demand of a certain industry. The

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response coefficient reflects the pushing ability of a certain industry on other industries.

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The formula for the influence coefficient is shown below:

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𝐼𝑗 = 1

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where 𝐼𝑗 indicates the influence coefficient, 𝑏𝑖𝑗 is an element of the Leontief inverse matrix

∑𝑛

𝑏 𝑖 = 1 𝑖𝑗

∑𝑛 ∑𝑛 𝑛 𝑗 = 1 𝑖 = 1𝑏𝑖𝑗

(5)

(𝑗 = 1, 2,…𝑛)

1

279

𝑛 𝑛 𝑛 (Waugh, 1950), ∑𝑖 = 1𝑏𝑖𝑗 indicates the influence of industry j, 𝑛∑𝑗 = 1∑𝑖 = 1𝑏𝑖𝑗 indicates the average

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influence of all the industries, and i and j indicate the row and column numbers in the Leontief

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matrix, respectively.

282

The formula for the response coefficient is shown below: ∑𝑛

𝑏 𝑗 = 1 𝑖𝑗

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𝑅𝑖 = 1

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where 𝑅𝑖 indicates the response coefficient, ∑𝑗 = 1𝑏𝑖𝑗 indicates the response of industry j, and

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∑𝑛

∑𝑛

𝑛 𝑖 = 1 𝑗 = 1𝑏𝑖𝑗

(6)

(𝑗 = 1, 2,…𝑛) 𝑛

1 𝑛 ∑ ∑𝑛 𝑛 𝑖 = 1 𝑗 = 1𝑏𝑖𝑗

indicates the average response of all the industries.

The formulas of the influence and response coefficients show that the coefficients are

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calculated based on the relationship between the status of one industry and the average statuses of

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all the industries. The impact of a single abnormal value in the element of the Leontief inverse

289

matrix on the final results of the influence and response coefficients would be reduced. The

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influence and response coefficients could exactly represent the status of the industrial importance

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of a certain industry in the whole economic system based on the accuracy of the data in the input-

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output table. The other three indicators, the output value ratio, cleaner production level and

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pollutant treatment ratio, were calculated based on data from the statistical yearbook and

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environment statistical bulletin of Changzhou. The industrial structure upgrading direction is

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determined based on the comprehensive results of these five indicators. Economic benefit,

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industrial importance and environmental benefit play corresponding roles in the weighting

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assignments of each industry. The error assessments using actual statistical data guarantee the

298

accuracy of the input-output table. The values of the input-output table provide basic data for the

299

influence and response coefficient calculations; the calculation formulas for the influence and

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response coefficients further improve the overall accuracy. As the influence and response

ACCEPTED MANUSCRIPT 301

coefficients are some of the indicators used in the industry weight determination, the final industry

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weights and pillar industry selections can be largely guaranteed due to the rigorous process.

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2.3 A simulation platform for WECC

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To assess the spatial WECC status, a simulation platform for WECC was established using

305

MAS and NetLogo software. MAS have a theoretical basis in a computer science paradigm called

306

object-oriented programming, which has become increasingly popular since the 1980s,

307

accompanying the advent of fast computers and rapid advances in computer science (An, 2012).

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This paradigm has been adopted to simulate very different kinds of complex systems, from the

309

simulation of socio-economic systems to the elaboration of scenarios for logistics optimization, with

310

applications from biological systems to urban planning (Bandini et al., 2009). Thus, this type of

311

programming has become a major bottom-up tool that has been extensively employed to represent

312

and explain complex socio-ecological systems (An et al., 2005). WECC is a typical socio-ecological

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system that focuses on the interaction between human and water environment.

314

The properties and interactions among all the elements in WECC were defined in the

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framework (table 2). These include modules of pollution discharge on land and modules of pollution

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flow in rivers.

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It includes point sources and non-point sources in the pollution discharge module, the formula for pollution discharge in a river is written as follows: DA = P × PPC × PDC

(7)

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where P indicates the production value or population amount, PPC indicates the pollution

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production coefficient, and PDC indicates the pollution discharge coefficient. For point sources,

322

including urban populations, industry, tertiary industries, and large-scaled poultry and livestock

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breeding farms, the production and discharge coefficients are derived from local pollution

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census data. The locations that discharge pollution into rivers depend on the locations of sewage

325

outlets. For non-point sources, such as rural populations, aquaculture and scattered livestock

326

and poultry breeding farms, the pollution production and discharge coefficients are calculated

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based on field investigations. The locations of pollution discharged into rivers also depend on

328

the local elevations.

329

In the pollution flow module, the formula for calculating pollution flow is as follows:

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𝑃𝑑 + 1 = 𝑃𝑑 × 𝐷

331

where 𝑃𝑑 + 1 indicates the amount of pollution at the location d+1 of a certain river and 𝑃𝑑

332

indicates the amount of pollution at the location of d of the same river; pollution would flow from

333

location d to location d+1 in a certain period of time. D indicates the degradation ratio for a pollutant

334

flow 1 unit distance and is calculated based on field investigations.

335

(8)

Table 2. Properties and interaction rules of each agent type Agents types

Properties

Rules

Module of pollutant discharge Urban

Population amount, pollutant-production

population

coefficient

Industrial

Production value, pollutant-production coefficient

Produce value, generated

enterprises

pollutants treatment ratio

pollutants

Generate pollutants

Point

Tertiary

Produce value, generated Production value, pollutant-production coefficient

sources

industry

pollutants

Large-scaled poultry and

Livestock and poultry production, pollutant-

Produce value, generated

livestock

production coefficient, pollutant treatment ratio

pollutants

Population size, pollutant-production coefficient

Generated pollutants,

breeding farms Rural population Crop output, fertilizer usage intensity, pollutantProduce value, fertilizer use, Farm

production coefficient, pollutant-discharge generated pollutants coefficient

Non-point sources

Aquaculture production, pollutant-production

produce value, generated

coefficient, pollutant-discharge coefficient

pollutants

Aquaculture Scattered Livestock and poultry production, pollutantlivestock and

produce value, generated production coefficient, pollutant-discharge

poultry

pollutants coefficient

breeding farms Pollutant treatment capacity, pollutant treatment Sewage treatment plants

Pollutant treatment ratio

Sewage outlets

Location

Discharged pollutant

ACCEPTED MANUSCRIPT Module of pollutant flow Pollutants

Sources, amount, flow direction, location

Pollutant flow

Flow quantity, flow velocity, pollutantRiver

Reduced pollutant amount degradation coefficient Diverse river, diverse

River confluence

Location pollutants Monitored water quality,

Monitored sections

Location, water quality standard water quality status judgment

Landscape

Land use type, ecological red line

World environment

336

Based on the proposed framework, the spatial WECC status of the whole study region can be

337

assessed. The spatial units with good or bad WECC statuses can be identified. Then, the spatial units

338

were sorted in descending order of WECC status to pair pillar industrial parks and optimal spatial

339

units. In a virtual laboratory, the effects of spatial adjustments and pattern optimizations can be

340

observed using the proposed framework, as can the WECC calculation. Once the process of

341

industrial agglomeration is established, the scale of the industry should be calculated to meet the

342

WECC goal of reaching the maximum economic scale. The scale is calculated using the simulation

343

framework to guarantee meeting water quality goals while maintaining enough of a margin to

344

prevent emergency environmental events.

345

Implementing the analytical framework can accomplish the goal of achieving industrial

346

structure upgrading and spatial optimization based on WECC using methods. The water quality can

347

be improved, and the economic benefits of a fully used water environment result in a mutually

348

beneficial and sustainable balance between environmental protection and economic development.

349

3 Data and study area

350

Changzhou City, located near Taihu Lake in China, is a prefecture-level city in southern

351

Jiangsu Province (Figure 2) and is selected as the study area for this paper. The city contains an

352

urban district and two county areas, named Liyang and Jintan, and is situated in the affluent Yangtze

353

Delta region of China. The areas of the urban district, Liyang, and Jintan are 1,871 km2, 1,536 km2

354

and 976 km2, respectively. Moreover, their total populations are 3.36 million, 0.76 million and 0.55

355

million, and their gross domestic products in 2010 were 3,021,60 million yuan, 559,200 million

ACCEPTED MANUSCRIPT 356

yuan, and 373,800 million yuan, respectively. The city has highly developed industry and has

357

especially advanced manufacturing, textile, and chemicals industries, among others. Additionally,

358

the city is a water-rich environment, containing abundant reservoirs in Liyang, lakes in Jintan and

359

the Changjiang River in the urban district. As a result, although Changzhou City has abundant water

360

resources, typical pollution-induced water shortages occur. The region has a striking contradiction

361

in its economy and environment; the high population and economic densities of Changzhou induce

362

widespread contamination of the local environment. This study includes some main rivers, such as

363

the Jinghang river, Danjin river, and Zhong river. The water qualities of the monitored sections of

364

the main rivers are assessed. The ecological red line is established by the local governments, such

365

that any economic activities are allowed inside the ecological red line to guarantee local ecological

366

safety and sustainability.

367

The information required to achieve industrial structure upgrading and spatial optimization

368

based on WECC is obtained (table 3). Given that we are focusing on a systematic evaluation of the

369

socio-economic and environmental status of the study region, a large amount of data from various

370

sources are required for this complicated and comprehensive study. To guarantee data quality and

371

accuracy, most of the data are collected from local official statistical data or data measured by the

372

research group. The socio-economic and industrial data are collected from the Statistical Yearbook

373

of Changzhou, and the industries are classified based on the National Economical Industry

374

Classification (GB/T4754-2002); twenty types of industries exist in the study area. To evaluate the

375

industries important to the economic system, the influence and response coefficients are calculated

376

based on the local input-output table. The emission data of various pollution sources, including

377

industrial sources, are obtained through pollution census data from a statistical yearbook that

378

contains the discharge amounts of each water pollutant of every pollution source. The pollutant-

379

discharge coefficient data are from data measured by the research group. Water quality and quantity

380

data are obtained from measurement data of the local water bureau and environmental protection

381

agency. The pollution degradation ratio data are also from data measured by the research group.

382

These data include measurements from 26 hydrological stations and 83 water quality monitoring

383

sections. The historical data of the flow quantities and velocities of each river are obtained through

384

these hydrological stations. The data describing the surface water quality in the research year are

ACCEPTED MANUSCRIPT 385

obtained by the water quality monitoring sections.

386

The types of pollutants that are discharged into water bodies, including in terms of chemical

387

oxygen demand (COD), biochemical oxygen demand (BOD), ammonia, phosphorus, heavy metals,

388

organic acid, and alkali, vary considerably with economic and human activities. There is no need to

389

analyze all the pollutants in the coupled model to study the WECC. As in Changzhou, according to

390

the environmental statistical bulletin and water quality monitoring data, the prevalent pollutant is

391

COD, which exceeds the water quality standard in all monitoring sections. Therefore, COD is

392

adopted as the pollutant index for the case study of Changzhou. Furthermore, the water hydrology

393

and water quality situation in January is considered due to the concurrent drought period.

394

Table 3. Data collection and processing Data

Reference

Processing

The Changzhou City Statistical Social-Population data Yearbook Classification according to National The Changzhou City Statistical Industrial data

Economical Industry Classification Yearbook (GB/T4754-2002) The Changzhou City pollution

Pollution sources and discharge data

Spatial visualization through ArcGIS census data and monitoring data

Water environment data

Monitoring data

Land use map, drainage map,

Remote sensing images, local

ecological red line

government planning

Spatial visualization through ArcGIS Attribute overlap and integration

395

ACCEPTED MANUSCRIPT

396 397

Fig. 2. Location of the study area

398

The NetLogo software is adopted as the MAS platform. The ArcGIS software and R software

399

are used for the processing of input data. The package RNetLogo is used for data exchange between

400

the R and NetLogo software. The data in the study region were mainly collected from the

401

Changzhou social and economic statistical yearbook, pollution census data statistical yearbook,

402

water resources bulletin, and the hydrological and water quality monitoring data.

403

404

4 Results and discussion

405

4.1 Accuracy assessment of the model

406

To confirm the accuracy of the model, the water quality monitoring data of the main water

407

quality monitoring sections in January were used to verify the validity and reliability of the

408

simulated result. The analysis results of the data uncertainties are shown in table 4. The results

409

indicated that the majority of errors were controlled at the 10% level, and the testing error was

410

within the allowable bounds (Oliva et al., 2003). The simulated results from the Jinghan River in

411

the urban district have higher accuracies than those of the rivers in rural districts, possibly because

412

the point sources in the urban area are easier to model accurately. The simulated results of the

413

upstream section of the river are more accurate than those downstream because the upstream

ACCEPTED MANUSCRIPT 414

pollutant source structures are relative simple. Therefore, the model can be used to model the real

415

environment.

416

Table 4. The error test results COD concentration (mg/L) River basins

Jinghang

Danjin

Zhong

Monitor Section Simulated

Observed

Error (%)

132

14.49

15

3.4

159

31.8

30.4

-4.62

175

24.18

22.5

-7.45

119

25.45

26

2.11

108

26.29

25

-5.17

101

28.89

27.4

-5.45

95

16.75

17

1.45

109

29.89

27.8

-7.53

120

30.28

28.3

-6.98

417 418

4.2 Pillar industry selection

419

According to the formula in section 2.2, the weighted values of each industry type can be

420

determined. The types of industries are shown in table 5. The electric equipment and machinery

421

industry holds the most valuable position given a comprehensive consideration of the economic

422

benefits, industrial importance and environment benefits. The twenty types of industries in the study

423

region were divided into three classes. The industries in the first class were selected to be the pillar

424

industries and include the electric equipment and machinery, electricity, heat production and supply,

425

and the metal smelting and rolling processing industries. These are the key industries that require

426

for industrial structure upgrading and spatial optimization. Specific industrial parks would be

427

constructed to achieve the industrial concentration necessary to generate a scalable economic effect

428

while controlling their pollutant discharges and using the spatial regions with good WECC status.

429

According to the local development planning, the electric equipment and machinery industry would

430

be the most important industry for future development and would be considered first during the

431

spatial optimization process. The industries in the second class would be developed as usual. The

432

heat production and supply industry in particular is regarded as a normal development industry

ACCEPTED MANUSCRIPT 433

considering the local limitations. The industries in the third class, such as the textile industry, would

434

be eliminated due to their low economic benefits and considerable environment harm.

435

Table 5. The weights and classes of the industries Class

436

Industry Type

Weight

Rank

Electric equipment and machinery

0.1971

1

Electricity, heat production and supply industry

0.1086

2

First

Metal smelting and rolling processing industry

0.1054

3

Class

Chemical industry

0.0868

4

General and special equipment manufacturing

0.0720

5

Nonmetal mineral products

0.0528

6

Scrap waste industry

0.0478

7

Fabricated metal products

0.0421

8

Transportation equipment manufacturing

0.0403

9

Second

Instrumentation and cultural office machinery manufacturing

0.0342

10

Class

Art products and other manufacturing

0.0321

11

Paper printing and educational and sports goods

0.0287

12

Communication equipment, computers and other electronic equipment

0.0263

13

Food production and tobacco processing

0.0239

14

Timber processing and furniture manufacturing

0.0220

15

Textile industry

0.0188

16

Third

Metals mining and dressing

0.0184

17

Class

Textile clothing, shoes, hats, leather, down and related products

0.0177

18

Oil processing and coking and nuclear fuel processing industry

0.0134

19

Nonmetal minerals mining and dressing

0.0117

20

4.3 Current spatial WECC assessment

437

The current study thoroughly compares the water quality monitoring data and water quality

438

standard. Among the 54 monitoring sections, the water qualities of 16 monitoring sections exceed

439

the water quality standard. Among these, 6 monitoring sections are located in the urban area of the

440

urban district and 2 monitoring sections are located in the urban area of Liyang County. These

441

locations are also within the industrial concentration district (shown in Figure 3). The industrial and

442

spatial adjustments would significantly improve the local WECCs. According to the water quality

443

goal in WECC theory, it can be concluded that the WECC of the upstream spatial regions of these

ACCEPTED MANUSCRIPT 444

monitoring sections that exceeds the water quality standard is in an overloaded status, while the

445

other regions are in a good status. WECC theory considers the maximum scale of economy and

446

population that the local water environment can support while maintaining the local water

447

environment. This paper’s goal is to bridge the gap between theoretical research and practical

448

applications in the current WECC research and to achieve the applications of industrial structure

449

upgrading and spatial optimization using WECC. Therefore, only the industry scale is considered

450

in this study. The current industry scale is 210 billion yuan.

451 452

Fig. 3. Spatial patterns of industries and monitored sections

453 454

4.4 Industry upgrades and spatial optimization

455

4.4.1 WECC assessment considering only the normal development

456

industries

457

To achieve industry upgrades and spatial optimization, the weak industries should first be

458

removed to reduce the pressure on the water environment and to improve the WECC potential,

459

which has almost no effect on the local economy due to their low influence and response

ACCEPTED MANUSCRIPT 460

coefficients. The pillar industries would be concentrated in the newly constructed industrial parks

461

for scalable economic development and centralized pollutant disposal. The development scales of

462

the pillar industries is calculated in the next step. To achieve the maximum potential economic

463

impact, in this section, the WECC effects of removing both the weak and pillar industries and

464

keeping only the normal development industries are discussed. Once the above measures were

465

implemented, the COD concentrations in the monitored sections of the downstream urban areas

466

show a significant decrease and leave a large amount of water environment capacity to support the

467

development of pillar industries (table 6). The COD concentration in the Urban District is reduced

468

by nearly 50% of its current value. This change was mainly caused by the high concentration of

469

industry in the urban district. The pillar industries were paired with their optimal sites after

470

considering the order of the industry weight and water environment capacity potential. Liyang

471

County was planned as a tourist town due to the existence of a famous spa, according to the local

472

development planning; thus, the industrial parks would not be set in Liyang County. The ecological

473

red line of Changzhou was taken into consideration in this study as well. No industrial parks would

474

be set inside the ecological red line. Only the monitored sections that were significantly influenced

475

by adding pillar industrial parks were observed and presented here.

476

Table 6. Water quality comparison of the monitored sections under different situations Current status River

Monitored

Basins

Sections

Keeping only the normal

Adding pillar

development industries

industrial parks

COD

COD

Reduction

COD

concentration(mg/L)

concentration(mg/L)

Ratio (%)

concentration(mg/L)

157

36.90

18.61

49.57

27

159

30.28

19.73

34.84

23.54

175

22.50

15.16

32.62

27

141

14.60

10.07

31.03

18

154

15.80

7.95

49.68

20.88

156

10.20

5.36

47.45

12.98

171

19.59

10.53

46.25

18

173

19.60

17.12

12.65

18

103

23.00

20.23

12.04

27

Jinghang

Desheng Zaogang

Wujingang Danjin

ACCEPTED MANUSCRIPT 115

24.00

19.20

20.00

26.39

477

478

4.4.2 Spatial optimization and scale calculations of industrial parks

479

The spatial patterns of the newly added industrial parks are shown in Figure 4. The parks were

480

almost all located in those monitoring sections with low pollutant concentrations upstream, in

481

suburban areas with convenient transport. The entire study region achieves the water quality goal.

482

The urban district shows a significant improvement in water quality. Through the calculation of the

483

simulated framework, the economic scale of each industrial park was determined. The pollutant-

484

production intensity value was adopted according to the local average cleaner production level. The

485

pollutant-centered treatment rate was adopted according to the local standard.

486

487 488

Fig. 4. Spatial pattern of industrial parks and monitor sections

489

Once the spatial location and type of each industrial park was determined, the maximum

490

economical scales of the industrial parks were calculated according to the local water environment

491

status. To enhance the protection from extreme events that cause acute water insecurity, the water

492

environment capacity would not be exploited, and an adequate margin of safety is left, which

ACCEPTED MANUSCRIPT 493

accounts for 10% of the water quality of the nearest monitoring sections (table 6). Compared with

494

the previous economic scale and the scale after industry upgraded and spatial optimization, both the

495

economic benefits and environmental protection are considerably improved. The water quality also

496

shows a significant improvement; in addition, the economic scale of each pillar industry shows an

497

ideal potential. The ideal economic scale of each pillar industry is at least 1.2 times their current

498

scales. The economic scale of the electric equipment and machinery industry, which is the most

499

important industry in Changzhou City, would reach 126,814.68 billion yuan, nearly 7.3 times its

500

current development scale. Moreover, the total industrial economy would reach 3,319.81 billion

501

yuan, nearly 1.6 times its current value (table 7).

502

Table.7 Ideal economic scales of each industrial park Industrial

COD production intensity

COD

Economy

(kg/ 10,000 yuan)

treatment ratio

(billion yuan)

5

0.85

48030.84

0.34

0.85

48649.44

0.15

0.85

126814.68

0.67

0.85

8550.48

0.1

0.85

59865.12

0.67

0.85

1828.08

5

0.85

7345.68

Type park ID 1

Chemical industry General and special

2 equipment manufacturing Electric equipment and 3 machinery Metal smelting and rolling 4 processing industry 5

Nonmetal mineral products Metal smelting and rolling

6 processing industry 7

Chemical industry

503

However, industrial structure upgrading and spatial optimization results are just one type of

504

the most optimizable situation. Various other possibilities exist as any parameter value changes.

505

This study provides one of the possible ways of combining industry upgrading theory and WECC

506

theory. WECC theory can provide information about the interactions between human activities and

507

water environments. This theory analyzes the water quality status under certain pressures of the

508

local socio-economic scales and structures. Through this simulated framework, the spatial pattern

509

and networks of WECC could also be revealed. Thus, WECC theory is a possible guide for industrial

510

spatial layouts according to the spatial patterns and networks of WECC. However, specific

ACCEPTED MANUSCRIPT 511

knowledge for guiding industrial structure upgrading is still lacking. The information entropy

512

methods considering the five indices, especially the influence and response coefficients, provide

513

information about pillar industry selection. The current gap in WECC research between theoretical

514

research and practical applications has been resolved, partly through the methods proposed in this

515

study.

516

517

5 Conclusions

518

5.1 Policy implications

519

In general, our research outcome reflects the significant potential of industrial development

520

through the adequate optimization of industrial structures and layouts based on WECC. If

521

implemented, the total industrial economy would reach 3,319.81 billion yuan, nearly 1.6 times its

522

current scale, and the whole region would attain the water quality goal. The urban district shows a

523

significant improvement of water quality. This study provides policy implications in the fields of

524

industrial structure adjustment, industrial layout optimization, the fine management of water

525

environments, and sustainable management.

526

At the city level, there are strong connections between industrial structure upgrading and

527

industrial spatial optimization. The results of industrial structure adjustment would be finally

528

presented in a spatial form and would also influence the industrial spatial optimization results. This

529

study suggests that policies related to industrial structure upgrading and spatial optimization should

530

be considered and implemented simultaneously to provide an integrated solution with both

531

industrial structure upgrading and spatial optimization. The results show that eliminating weak

532

industries would significantly improve WECC statuses and allow pillar industries to relocate to

533

more suitable areas for exploiting water environment capacity and supporting maximum industrial

534

scales. Thus, industrial spatial optimization should consider WECC status.

535

To focus on the fine-scale management of the water environment, considering only pollution

536

discharge from economic systems is inadequate. The direct links between industrial production and

537

water quality should be established. The water quality use and improvement are the final goals of

ACCEPTED MANUSCRIPT 538

the water environment management. Water environment capacity is spatially heterogeneous.

539

Changes in discharge locations could cause different environmental influences. This study, with a

540

combination of WECC theory, provided a useful tool to connect economic activities and water

541

quality. As usual, scholars and managers tend to adopt measures that reduce environmental impact

542

without considering the specific socio-ecological conditions, even when it would cost more. This

543

study proposed that based on the precondition of water quality attainment, humans could take

544

measures to exploit the water environment capacity to actively support economic development. In

545

the sub-regions with adequate water environment capacity, the economic activities could be

546

strengthened to achieve a mutually beneficial balance between environmental protection and

547

economic development.

548

5.2 Analytical framework prospects

549

The analytical framework, with its combination of industrial structure upgrading theory,

550

industrial spatial optimization theory and WECC theory, has succeed in its use for guiding local

551

industrial structure upgrading and spatial pattern optimization. This framework presents three main

552

improvements through the case study of Changzhou: (1) The framework improves the isolated

553

research fields of industrial structure upgrading and industrial layout optimization. The link between

554

them has been established based on a WECC status assessment. The influence of the industrial

555

structure adjustment has been considered in the process of industrial layout optimization through a

556

WECC status assessment. (2) The direct connections of water quality to industrial structure

557

upgrading and spatial optimizations were established. This link can be used to achieve the maximum

558

industrial scale under the precondition of water quality use. (3) This study also moves WECC

559

research from theory to practice. WECC theory is a basic bridge to establish the above links ; it

560

supports the fine-scale management of water environments and sustainable development. The

561

spatial units with good WECC statuses or bad WECC statuses could be identified through this

562

platform. The results of the WECC analysis provide the limitations of industrial scales and patterns.

563

Information entropy supports the pillar industry selection. The spatial locations and economic scales

564

of each industrial park could be determined and calculated in the simulated platform, strengthening

565

the economic aspect of WECC research, indicating the possibility of researching WECC from an

566

economic perspective. The results provide a significant guide for local government managers to

ACCEPTED MANUSCRIPT 567

plan the city and industry developments. The combination of economic and water environment

568

knowledge provides powerful support for socio-ecological sustainability research.

569

The analytical framework in this study could be applied to any other study region once the

570

characteristics of the study regions are correctly identified and the necessary input data are acquired.

571

This unified framework for guiding industrial structure upgrading and spatial optimization based on

572

WECC is suitable for any other region worldwide.

573

The analytical framework currently has a few deficiencies. The research on uncertainty should

574

be expanded in future research. There are a variety of types of optimized industrial structures and

575

patterns. The results of this study provide one kind of optimized solution. The circular economic

576

form among industries can be exploited in the industrial park layouts to increase resource utilization

577

rates and to reduce pollutant discharge. More diverse optimized forms could be researched with a

578

wide range of disciplines, including economy, industry, ecology, resources and environment.

579

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ACCEPTED MANUSCRIPT

Highlights 1. A framework of industrial structure upgrading and spatial optimization is established. 2. It reveals the linkage between industrial structure and industrial spatial pattern. 3. It connects industrial structure and pattern with water quality. 4. It guides maximum sustainable development of regional industry based on WECC. 5. It exploits water environment capacity actively while reducing pollutions emission.