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A load-carrier perspective examination on the change of ecological environment carrying capacity during urbanization process in China
Journal Pre-proof A load-carrier perspective examination on the change of ecological environment carrying capacity during urbanization process in China
Mengcheng Zhu, Liyin Shen, Vivian W.Y. Tam, Zhi Liu, Tianheng Shu, Wenzhu Luo PII:
S0048-9697(20)30353-3
DOI:
https://doi.org/10.1016/j.scitotenv.2020.136843
Reference:
STOTEN 136843
To appear in:
Science of the Total Environment
Received date:
15 November 2019
Revised date:
16 January 2020
Accepted date:
20 January 2020
Please cite this article as: M. Zhu, L. Shen, V.W.Y. Tam, et al., A load-carrier perspective examination on the change of ecological environment carrying capacity during urbanization process in China, Science of the Total Environment (2020), https://doi.org/ 10.1016/j.scitotenv.2020.136843
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© 2020 Published by Elsevier.
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A load-carrier perspective examination on the change of ecological environment carrying capacity during urbanization process in China Mengcheng Zhua,b, Liyin Shena,b*, Vivian W. Y. Tamc, Zhi Liua,b, Tianheng Shua,b, Wenzhu Luoa,b a
School of Management Science and Real Estate, Chongqing University, Chongqing,
PRChina. b
International Research Center for Sustainable Built Environment, Chongqing
School of Computing, Engineering and Mathematics, Western Sydney University,
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c
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University, Chongqing, PRChina.
NSW 2751, Australia
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E-mail address:
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*Liyin Shen, corresponding author, [email protected] Mengcheng Zhu, [email protected].
Zhi Liu, [email protected]
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Vivian W. Y. Tam, [email protected]
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Tianheng Shu, [email protected]
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Wenzhu Luo, [email protected]
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A load-carrier perspective examination on the change of ecological environment carrying capacity during urbanization process in China Abstract Urbanization has prompted a dramatic social and economic development during the past decades in China. As a long-term national strategy, urbanization can only be
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implemented effectively with sufficient and sustainable ecological environment
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resources. By appreciating that the ecological environment carrying capacity (EECC) is a yardstick for guiding the practice of sustainable urban development, it is therefore
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pressing to examine the change of EECC adequately, so that the sustainable
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urbanization can be addressed appropriately. This paper develops a new method from
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load-carrier perspective to explore the change of EECC performance in the rapid urbanizing China. The EECC performance on water, land, atmosphere and overall
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perspectives were measured for 30 provinces in China based on the established
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method. The results show that most provinces in China are experiencing an improving EECC performance during the urbanization process, particularly with an obvious progress in land dimension. In referring to the spatial difference of overall EECC performance, the gap between 30 provinces has been narrowing during surveyed years. However, few provinces including Chongqing, Shandong and Jiangxi have undergone a degradation in overall EECC performance. The EECC performance in atmosphere dimension is still considered as a challenge faced by most provinces, evidenced by high level of PM2.5 concentration. These research findings provide valuable references not only for Chinese governments to formulate effective policy instruments and strategy measures for improving ecological environmental carrying status, but also for researchers to further study in the ecological environment carrying capacity in 2
Journal Pre-proof the context of other countries. Keywords: ecological environment carrying capacity (EECC); urbanization; indicator system; carrier; load
1. Introduction China has experienced unprecedented speed and scale of urbanization process in the past several decades (Zhang et al., 2019; Shen et al., 2019). According to National
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Bureau of Statistics of China (2018), the urbanization rate in China has been risen
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from 17.9% in 1978 to 58.5% in 2017. This national strategy will continue in the
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coming future in China. According to the 13th Five-Year-Plan, the Chinese government committed to further promote urbanization and reach the urbanization
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rate of 60% in 2020 (The State Council of China, 2016b). As a major developing
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country, China has profound influence to global development through implementing its urbanization mission. It is widely appreciated that China has become the second
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largest economy entity (Wu et al., 2019c). According to the World Bank (2017), the
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total Gross Domestic Product (GDP) in China has increased at an annual rate of 9% during1978-2017, and reached to US $ 10.161 trillion (constant 2010 US $ prices) in 2017.
However, as a major national development strategy in China, urbanization can only be sustainable if the resources needed are provided sustainably (Akotia and Sackey, 2018; Bamgbade et al., 2018). Land, water and atmosphere are essential ecological resources for enabling the implementation of urbanization. In the context of China, the ecological environment has been changing over the last several decades in line with its dramatic urbanization process. For example, according to the annual report on Environment Development of China (2013), Chinese cities are suffering from severe environmental problems such as air pollution, water scarcity and water 3
Journal Pre-proof pollution, heavy metal pollution, during the dramatic urbanization process (Liu, 2013). Wang et al. (2018b) pointed out that air pollution accompanied with rapid urbanization is seriously threatening the public health in China. Other city problems such as housing shortage (Lee, 1988) and transportation congestion (Wu et al., 2019a) have also emerged in the process of Chinese urbanization. On the other hand, also in China, it has been found that certain ecological benefits have been obtained in some areas during the urbanization process. As opined by He et al. (2017), the coupling
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degree between urbanization and eco-environment in Shanghai has significantly
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increased during the urbanization process of 1980-2013. It is thus not explicit how the
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ecological environment changes in the urbanizing China.
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Ecological environment carrying capacity (EECC) is a yardstick for guiding urban development towards sustainability (Wei et al., 2015). It is considered that the
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ecological environment can be overloaded if the urbanization strategy is not adjusted
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to match the EECC appropriately, and the change of ecological environment will affect the implementation of urbanization strategy in turn. Under such circumstance,
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both improving the ecological environment and implementing the sustainable urbanization process are essential for Chinese governments, which can only be achieved by exploring the change of EECC during urbanizing China. This argument is also echoed by other studies (Liu et al., 2018; Wang et al., 2018a). Without such understanding, the mission of sustainable urbanization in China might be sabotaged due to the overloaded ecological environment. In fact, it has been reported that urbanization has been hindered in some Chinese cities because of the degrading ecological environment. For example, Wu and Tan (2012) pointed out that water scarcity has become a common constraint to urban development in Eastern coastal provinces in China.
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Journal Pre-proof Previous studies have contributed greatly to the literature development in the disciplines of ecological environment and urbanization. Specifically, existing studies related to ecological environment can be summarized into three aspects, namely, exploring the relationship between urbanization and ecological environment, assessing the ecological environment status, as well as investigating the performance of EECC. For instance, In order to explore the relationship between atmospheric environment and urbanization, Ali et al. (2019) explored the impact of urbanization
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on carbon emissions in Pakistan during the period of 1972-2014. The research results
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showed the increase of carbon emissions can be induced by implementing
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urbanization both in long and short run. From the perspective of single city, Liu et al.
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(2018) studied the relationship between urbanization and atmosphere environment in Jinan city of China from 1996 to 2004 by applying the theory of Environmental
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Kuznets Curves. Their study indicated that the introduction of advanced technology,
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higher efficiency of resource consumption, and less emissions of environmental pollution should be emphasized in the policymaking process. Li et al. (2012)
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developed a coupling coordination degree model for evaluating the coordination degree between urbanization and environment from 2000 to 2008 in Lianyungang city in China. The results of this study indicated that the dynamic of coordination between these two systems shows a U-shaped curve. The study by He et al. (2017) also demonstrated that there is S-shaped relationship existing between urbanization and eco-environment in Shanghai from 1980 to 2013. From a perspective of urban agglomeration, Wang et al. (2019c) investigated the coordination between urbanization and the ecological system in Beijing-Tianjin-Hebei urban agglomeration, and found that the dominant factors influencing the coordination include social consumer goods, gross domestic product, and the disposable income of urban
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Journal Pre-proof residents. Cui et al. (2019) developed a comprehensive coordination development index for urbanization-resource-environment system to represent the sustainability of urbanization in Jing-Jin-Ji region, the results suggested that a zoning strategy can improve the coordination degree and promote sustainable urbanization. Many scholars have also examined ecological environment status from either single or multiple perspectives. For example, Xian et al. (2007) investigated the change of urban land use in Tampa Bay in West-Central Florida. Chen (2007) outlined
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the change of the cultivation land in China, and the causes of the change were also
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analyzed. The study results suggested that urbanization is a great threat to future
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agricultural production. Wang et al. (2008) examined the temporal variations of
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surface water quality in urban, suburban and rural areas between 1982to 2005 in Shanghai, and found that water quality in urban and suburban areas were improved by
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strengthening environmental policies and management. Zhou et al. (2018) measured
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the air quality of 31 main cities in China by using Air Quality Index, and the results demonstrated that the best air quality appeared in August and September while the
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worst occurred in December and January. Wu et al. (2019b) established an evaluation indicator system to investigate the ecological environment state for Kunming city in China, the results showed that the ecological level in this city increased from 2000 to 2015.
In referring to the study of ecological environment carrying capacity, an increasing number of studies have been conducted to evaluate EECC performance by employing the methods of ecological footprint, system dynamics model, and comprehensive index system. For example, based on ecological footprint theory, Peng et al. (2019) estimated the dynamic evolution of ecological carrying capacity in Jiangsu province in China from 2012-2017. Fang et al. (2017) introduced a system
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Journal Pre-proof dynamics model to measure urban ecological carrying capacity in Beijing during 1996-2012. Ma et al. (2017) proposed an index system for the evaluation of marine ecological carrying capacity in Dongtou Islands in China from 2009-2014. However, it seems that previous research work has not offered a clear definition for EECC, or failed in discussing the implication of ecological environment carrying capacity (Liu and Borthwick, 2011; Price, 1999). . Furthermore, limitations of the existing methods for assessing EECC have also been pointed out. For instance, the ecological footprint
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theory method is more applicable at global level than regional level, because this
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method evaluating EECC based on the land area needed to sustain human lives (Fiala,
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2008; Wei et al., 2015). The system dynamics model can analyze the interrelationship
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within a complex system for a long-term research, but it is subjective and difficult to select parameter index (Zhang et al., 2019). Zhang et al. (2019) commented that the
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index system approach cannot help examine whether the pressure of human social
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activities on ecological environment has exceeded the ecological environment carrying capacity, although this method can help reveal the level of EECC. In fact,
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carrying capacity is originally a physical concept to describe the carrier’s ability for supporting the load it carries (Zhang et al., 2019; Shen et al., 2020). Without considering the interrelationship between carrying objects and carriers, it is difficult to judge whether the examined carrying capacity is overloaded. Thus, both the carrier and the load it carries should be incorporated when evaluating the carrying capacity for a specific object, so that the interrelationship between load and carrier can be reflected. Based on the above discussion, this study aims to develop a novel measurement model which embodies the relationship between carriers and loads, and examine the change of EECC in the Chinese urbanization process. Empirical study will be
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Journal Pre-proof conducted by using the data collected from 30 provinces in China. The differences and similarities of the EECC change between the provinces will be discussed. The rest of this paper is structured as follows: Section 2 presents the methodologies of this research. The establishment of evaluation index for EECC is showed in Section 3. Research data and results are displayed in Section 4 and Section 5 respectively. Finally, discussions and conclusions are provided in Section 6 and Section7
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respectively.
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2. Research methodology
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2.1 Research Roadmap
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In order to conduct this study, the research roadmap is designed as follows.
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Firstly, the measurement for EECC will be defined through literature review and theoretical analysis. Secondly, research data will be collected from multiple resources,
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including Statistical Yearbooks, National Bureau of Statistics of China and research institutions. Thirdly, the in-depth analysis on the change of EECC will be conducted
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from both temporal and spatial perspectives. The research roadmap can be graphically highlighted in Figure1.
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Journal Pre-proof Figure 1 Research roadmap
2.2 Measurement for ecological environment carrying capacity Great efforts have been paid to develop various methods for assessing the value of carrying capacity in the previous research. In studying the mechanics of materials, the carrying capacity of an object refers to the maximum loads that the object could bear without causing any physical damage (Feng et al., 2018). In line with this, some
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researchers attempt to find a threshold as the limit of ecological environment carrying
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capacity. For example, ecological footprint method has been widely adopted by researchers for evaluating the limit of ecological carrying capacity (Peng et al., 2019;
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Świąder et al., 2018). In their study, ecological environment carrying capacity refers
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to the sum of all productive lands in an area that can provide the resources for
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consumption by local residents. Fang et al. (2017) introduced system dynamics approach for examining ecological environment carrying capacity to judge whether
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the capacity is sufficient to support social and economic activities. However, different
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from physical object, ecological environment system is not static (Costanza, 1996). In other words, the carrying capacity of ecological environment is dynamic and changeable.
As a sub-system of nature, ecological environment system consists of numerous natural elements such as water, land and atmosphere, which provide available resources and ecological service to support human survival and assimilate waste and pollution generated by human activities (Liu and Borthwick, 2011). Nature system is an open, dynamic, adaptive, resilient and complex system, characterized by variety of social, economic and environmental factors (Chelleri, 2012). These social and economic factors can not only exert various loads to ecological environment system, but also strengthen ecological environment carriers through environmental 9
Journal Pre-proof governance such as trees planting and water source protection, and reduce ecological environment loads by developing pollution treatment facilities. In addition, the improvement of environment management and technological progress is also beneficial for reducing the ecological environment loads. Ideally, the ecological environment system itself will keep its function in a relatively dynamic equilibrium state. However, this equilibrium may be affected and even broken by the surge of ecological environment loads or the degradation of ecological environment carriers,
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as the existing carriers may not be able to offer sufficiently supporting capacity (Shen
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et al, 2020). Consequently, the state of ecological environment may arrive at a
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crossing threshold, thereafter moving into an uncertain transitional process (Walker
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and Meyers, 2004). Nevertheless, it should be noted that the threshold of the ecological environment carrying capacity would not occur in reality since the damage
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experiment for human-nature system cannot be conducted. In other words, it is hardly
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possible to find the actual value of potential threshold because the destroy experiment is not affordable in human-nature system. Therefore, the measurement for EECC in
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this study is not considered as a threshold, but a relationship between ecological environment loads and ecological environment carriers, which can be written as follows:
ρ (carrying capacity index) =
𝐿𝑜𝑎𝑑 𝐶𝑎𝑟𝑟𝑖𝑒𝑟
(1)
where load refers to various objects related to human activities; carrier is natural or man-made support to the load. ρ denotes for the carrying capacity index of the concerned carrier. The lower value of ρ represents the better performance of carrying status. The novelty of this method lies in that an innovation evaluation framework of 10
Journal Pre-proof EECC is proposed by considering carriers and loads collectively, and also the interaction between corresponding loads and carriers. The specific indicators for ecological environment carriers and loads will be discussed in the next section.
3. Establishment of evaluation index system for EECC 3.1 Identifying indicators for ecological environment carriers and loads
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Various indicator systems have been introduced for evaluating the performance of ecological environment. For example, He et al. (2017) examined the
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eco-environment level in Shanghai by using a group of indicators under the
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dimensions of ecological environment level, ecological environment endowment,
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ecological environment pressure and ecological environment response. Wang et al.
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(2019c) evaluated the ecological environment system of Beijing-Tianjin-Hebei region by using the indicators across ecological environment pressure, ecological
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environment state and ecological environment response sub-systems. Zhang et al. (2018) established an indicator system for evaluating the performance of ecological
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environment, in which indicators from water, land, atmosphere, energy and solid waste dimensions were applied. In referring to these existing work, it can be seen that the major indicators for investigating ecological environment performance are under the dimensions of water, land and atmosphere. The typical research work involving ecological environment indicators is listed in Table 1. Accordingly, the three dimensions of water, land and atmosphere are adopted in this study for evaluating EECC performance. In referring to model (1), the indicators in each dimension are divided into load-indicators and carrier-indicators in the process of indicator identification.
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Journal Pre-proof Table 1 Summary of typical indicators for ecological environment evaluation in previous studies Literature Liu et al. (2018)
Indicators employed in literature Water dimension
Land dimension
Atmosphere dimension
Discharge of industrial wastewater
Disposed industrial solid waste
Emission of industrial SO2
Total water supply
Land use per capita Cultivated area per capita Green area per capita
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Greening ratio within constructed areas
He et al. (2017)
Total volume of water per capita
Percentage of vegetation with green areas
Discharge of industrial wastewater
Green areas per capita
Urban domestic sewage treatment rate
Cultivate land area per capita
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Discharge of industrial dust per capita
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Comprehensive utilization rate of industrial solid waste
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Hazard-free treatment rate of domestic garbage Li et al. (2012)
Wang et al. (2019c)
Per capita water consumption
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Per capita water resources
Per capita industrial wastewater discharge
Sewage treatment plant centralized treatment rate
Forest cover rate
Per capita carbon emissions
Green land coverage in built-up areas
Per capita industrial sulfur dioxide emissions
Ecological land area per 10,000 people
PM2.5 concentration
Per capita industrial solid waste discharge Harmless treatment rate of domestic garbage Comprehensive utilization rate of industrial solid waste
Tang (2015)
Annual average ambient air concentrations of SO2 Annual average ambient air concentrations of PM10 Annual average ambient air concentrations of NO2
Per capita water volume
Forest coverage rate
Ratio of industrial wastewater
Nature reserve coverage rate 12
Journal Pre-proof meeting discharge standards Per capita public green areas Per capita cultivated area
Fan et al. (2019)
Per capita water resources
Green ratio for built-up area
Domestic swage treated ratio
Per capita public green area
Fine rate of atmospheric environment quality
Percentage of harmless disposal of domestic garbage
Ding et al. (2014)
Per capita local water resources
Forest coverage rate
Zhang et al. (2018)
Water quality compliance rate of drinking water source
Comprehensive utilization rate of industrial solid waste
River proportion worse than Grade V
Green coverage rate of built area
Per capita water resources
Per capita park green space
Desalination of seawater
Output rate of unit construction land
Sewage treatment rate
Harmless disposal rate of municipal solid waste
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Rate of ecological water consumption Rate of reclaimed water to total water supply
Wang et al. (2018a)
Water coverage rate
e
r P
n r u
Total emission of industrial waste gas SO2 emission intensity of per unit GDP
Forest coverage
Industrial waste air emissions
Green coverage area
Industrial sulfur dioxide emissions
Public recreational green space
Industrial smoke emissions
Urban sewage treatment rate
Proportion of the nature reserve area
Industrial nitrogen oxides emissions
Recycling rate of industrial water
Comprehensive utilization rate of general industrial solid waste
Discharged of wastewater per unit of GDP
Percentage of vegetation with green areas
Sewage treatment plants per 10,000 people
Garbage disposal plants per 10,000 people
Sewage discharged
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Industrial wastewater discharged
Cui et al. (2019)
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Days of air quality equal to or above Grade 2 in the whole year
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Discharge of volume of SO2 per unit of GDP
Journal Pre-proof EE indicators from water dimension perspective By comprehensively reviewing the studies listed in Table 1, typical indicators for presenting water carriers are identified as natural water resources (including surface water and underground water), recycled water production, desalination of seawater, transferred water across region, the facility capacity of urban sewage treatment, the capacity of water self-purification, and the facility capacity of industrial wastewater treatment. Among these carriers, natural water resources, recycled water production,
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desalination of seawater, and transferred water across region are the providers for total
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water consumption in supporting all types of human activities. They have also been
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adopted officially in counting water supply and water resources in China (National
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Bureau of Statistics of China, 2007-2016a). Urban sewage and industrial wastewater treatment are man-made facilities for water purification, while water self-purification
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is regard as an ecological repair in water ecosystem (Ostroumov, 2005).
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Furthermore, typical indicators for presenting water loads are identified as total water consumption, domestic sewage discharge and industrial wastewater discharge.
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For the load of total water consumption, it is induced by various human activities including agricultural, industrial, living and ecological activities. The other two water loads are triggered by wastewater discharging. Domestic and industrial wastewater is considered as a type of water load as it needs to be purified in order to prevent the sabotage of water ecological environment. EE indicators from land dimension perspective According to the references in Table 1, typical indicators for presenting land carriers include total forest coverage, urban green areas, the facility capacity for household garbage disposal, the capacity of natural degradation for household garbage, and the facility capacity of industrial solid waste disposal. Forestry area and
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Journal Pre-proof green land can offer the ecological functions for the concerned region. They provide the services to maintain soil, water, and biodiversity, and mitigate the effects of global warming (Hao et al., 2019). Those man-made facilities are functional to dispose the household garbage and industrial solid waste, thus the harm to land ecological environment can be reduced. Therefore, the capacity of these facilities can significantly support the land ecological environment. Furthermore, the natural degradation of household garbage describes the ability of microorganisms that can
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degrade garbage in land ecosystem.
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The ecological environment loads in land dimension are identified including total
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land areas, urban areas, the volume of household garbage and industrial solid waste.
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Total land areas and urban areas need the ecological services provided by forests and green land. In other words, they can be regarded as a type of load to forestry areas and
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green areas. On the other hand, the volume of household garbage and industrial solid
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waste present another type of load influencing the land ecological environment, which will be processed by man-made facilities and natural degradation.
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EE indicators from atmosphere dimension perspective According to previous studies, PM2.5 is one of the major air pollutants contributing to the severe atmospheric environment problems. It can levitate for a long time and travel in a long distance, which will threaten the human health, human activities and ecology system (Han et al., 2017). The maximum concentration of PM2.5 without causing adverse effects on human health is restricted above the Grade 2 level in the Chinese Ambient Air Quality Standards (GB3095-2012). Thus, the ambient air concentration of PM2.5 is employed as the load indicator, and the PM2.5 standard of Grade 2 is used as the carrier indicator for assessing atmospheric environment.
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Journal Pre-proof Based on the above discussions, the load and carrier indicators of ecological environment under the dimensions of water, land and atmosphere can be listed in Table 2. Table 2 Indicators system for ecological environment carrying capacity (EECC) Carrier indicators
Load indicators
Natural water resources Recycled water production
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Total water consumption
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Desalination of seawater
Water
The facility capacity of urban sewage treatment
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Transferred water across region
Domestic sewage discharge
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The capacity of water self purification
Atmosphere
Industrial wastewater discharge
𝐿
Total forestry coverage
𝐿
Total land area for human activities
𝐿
Urban green areas
𝐿
Urban area
𝐿
The facilities capacity for household garbage disposal 𝐿
The volume of household garbage
𝐿
The capacity of natural degradation for household garbage
𝐿
The facility capacity of industrial solid waste disposal
𝐿
Industrial solid waste
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Land
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The facility capacity of industrial wastewater treatment
PM2.5 standard of Grade 2 in the whole year
Annual average ambient air concentration of PM2.5
It can be seen from Table 2 that, there are several couples of indicators for corresponding loads and carriers under each dimension. By referring to model (1), the primary EECC indexes under water, land and atmosphere dimensions can be obtained. 16
Journal Pre-proof In water dimension, water load ; water load
will be supported by water carriers
corresponds to the water carriers
corresponds to the water carrier
and
,
,
,
; water load
. Therefore, water ecological carrying capacity
can be presented by using the following three primary EECC indexes: 𝐿
=𝐶
𝐶
=
𝐶
(2)
𝐶
𝐿 𝐶
(3)
𝐶 𝐿
; load 𝐿
and
corresponds to the land carrier 𝐿
; load
𝐿
𝐿
can be supported by land
; load
𝐿
corresponds to the land carrier
will be carried by 𝐿
. The primary
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land carriers
𝐿
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𝐿
𝐿
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In referring to land dimension in Table 2, land load carrier
(4)
of
=𝐶
indexes for measuring land ecological carrying capacity can be calculated as follows: =𝐶
𝐿
=
𝐿
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𝐿
𝐿
=𝐶
(5)
𝐿
(6)
𝐶 𝐿 𝐶 𝐿
𝐿
=𝐶
(7) (8)
From Table 2, it can be seen that there is only one index for measuring atmosphere ecological carrying capacity. =
𝐿 𝐶
(9)
Accordingly, the dimensional indexes for measuring EECC in water, land and atmosphere dimensions can be obtained by integrating their primary indexes respectively: ρ
= ∑3𝑖=1(
ρ𝐿 = ∑4𝑖=1( 17
𝑖
𝐿𝑖
× ω 𝑖)
(10)
× ω𝐿𝑖 )
(11)
Journal Pre-proof ρ = ∑1𝑖=1(
where ω 𝑖 , ω𝐿𝑖 , ω
𝑖
𝑖
× ω 𝑖)
(12)
are weighting values.
Thus, the overall EECC performance can be calculated by the following formula: ρ𝑂 = ∑(
×𝜔 +
𝐿
× 𝜔𝐿 +
×𝜔 )
(13)
where 𝜔 , 𝜔𝐿 and 𝜔 are weighting values. All the weighting values in (10), (11), (12), (13) will be discussed in the next section.
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3.2 Defining the weighting values for EECC indexes
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For evaluating the performance of EECC in this study, entropy method is used to
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determine the weighting between EECC indexes. Entropy method was originated
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from thermodynamics and it was introduced into the information management
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discipline by Shannon (1948) for expression of information or uncertainty. This method has widely been used in engineering, economy, finance and other disciplines
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(Zou, Yun & Sun, 2006), and its application has also been extended to the study of urban ecosystems (Larsen & Gujer, 1997; Antrop, 1998; Peixoto et al, 1991). Previous
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studies have further opined that this method can be used effectively for determining weightings between indicators where evaluation work is conducted based on a group of indicators (Shemshadi et al., 2011; Wang et al., 2015; Sun et al., 2017, Shen et al., 2015). The principle of this method is that the greater difference between sample values of an indicator shows, additional information about the indicator can be obtained, greater significance for the indicator in comprehensive evaluation, thus a higher weight value will be assigned to this indicator (Wang et al., 2018a). Therefore, in order to capture significant information between EECC indexes, entropy method is adopted in this study for integrating primary EECC indexes and dimensional EECC indexes. The process of applying entropy method is following.
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Journal Pre-proof (1) Construction of the initial decision matrix Assuming that there are m evaluation indexes and n evaluation objects (provinces in this study), the value of index j in object i is 𝑎𝑖𝑗 , the decision matrix of all objects (provinces) can be presented as: 𝑎11 A = (𝑎𝑖𝑗 )𝑚×𝑛 = [ ⋮ 𝑎𝑚1
⋯ 𝑎1𝑛 ⋱ ⋮ ] … 𝑎𝑚𝑛
(14)
(2) Standardization of decision matrix
𝑎𝑖𝑗 − min𝑗 (𝑎𝑖𝑗 )(
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𝑟𝑖𝑗 =
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The value 𝑎𝑖𝑗 in (14) needs to be standardized by using the following formula:
max𝑗(𝑎𝑖𝑗 ) − min𝑗 (𝑎𝑖𝑗 )
(15)
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Then, the standardized decision matrix can be obtained as:
re
⋯ 𝑟1𝑛 ⋱ ⋮ ] … 𝑟𝑚𝑛
(16)
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R = (𝑟𝑖𝑗 )𝑚×𝑛
𝑟11 = [ ⋮ 𝑟𝑚1
(3) Establishment of weighting values for indexes
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Firstly, 𝑓𝑖𝑗 , the proportion of 𝑟𝑖𝑗 needs to be calculated as follows: 𝑟𝑖𝑗 ∑𝑛 𝑖= 𝑟𝑖𝑗
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𝑓𝑖𝑗 =
(17)
Then, 𝐸𝑖 , the entropy value for each index can be determined: 𝐸𝑖 = −1/ln 𝑛 × ∑𝑛𝑖=1(𝑓𝑖𝑗 × ln 𝑓𝑖𝑗 )
(18)
when 𝑟𝑖𝑗 = 0, 𝐸𝑖 = 0
Accordingly, the weight of each index can be obtained by following formula: 𝑤𝑖 =
1−𝐸𝑖 𝑚−∑𝑚 𝑗= 𝐸𝑖
(19)
4. Research data The original data of EECC indicators listed in Table 2 are collected from multiple resources during the period of 2007-2016 for assessing the EECC performance in 30 provinces of China. In fact, this period has been evidenced as the 19
Journal Pre-proof highest urbanization process in China. The 30 provinces include Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Hainan, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. The data resource of indicators in water and land dimensions are from National Bureau of Statistics of China(2007-2016a), China Urban Construction
Statistical
Yearbook
(Ministry
of
Housing
and
Urban-Rural
of
Development of the People's Republic of China, 2007-2016) and China City
ro
Statistical Yearbook (National Bureau of Statistics of China, 2007-2016b). The annual
-p
mean values of surface PM2.5 concentrations with 0.1°×0.1° latitude/longitude
re
resolution in each province during the study period are collected from Atmospheric Composition Analysis Group of Dalhousie University (2007-2016). Based on
lP
Ambient Air Quality Standards (GB3095-2012), PM2.5 Standard of Grade 2 in the
na
whole year is 35 µg/m3, therefore data at this stage, indicators of
= 35µg/m3. However, due to the inaccessible ,
,
,
，
,
𝐿
,
, and
𝐿
𝐿
were
Jo ur
removed in the demonstration of this study. All the data collected are considered effective and valid for further analysis, as they are published by official documents. Due to the large volume of the data, the sample sheet of original indicator data for the year 2016 is provided in Table 3.
Table 3 Sample sheet of original indicator data for the year 2016 𝐿 4
Province
(108 m3)
3
⁄
𝐿
𝐿
⁄
𝐿
4
(10 m
(10
per
m3 per
day)
day)
(108
(108
ton)
ton)
𝐿
(ton (%)
(%)
per day)
𝐿
(104
(μg/
ton)
m³)
Beijing
35.1
513.5
630.9
38.8
15.79
35.8
48.4
24341
872.6
47
Tianjin
18.9
35.3
291.5
27.2
7.35
9.9
37.2
10800
269.0
67
Hebei
208.3
196.5
607.0
182.6
22.10
23.4
40.8
23140
725.2
49
Shanxi
134.1
147.0
257.9
75.5
11.34
18.0
40.5
13456
469.4
37
20
Journal Pre-proof Inner
426.5
109.9
245.5
190.3
8.32
21.0
39.9
11969
345.3
21
Liaoning
331.6
115.6
831.4
135.4
17.25
38.2
36.4
25603
933.1
33
Jilin
488.8
15.0
318.7
132.5
8.04
40.4
35.0
15095
534.1
28
Heilongjiang
843.7
28.0
758.2
352.6
10.87
43.2
35.4
16306
541.9
20
Shanghai
61.0
0.0
806.9
104.8
18.42
10.7
38.6
23530
629.4
45
Jiangsu
741.7
334.1
1742.9
577.4
43.46
15.8
42.9
55403
1562.3
55
Zhejiang
1323.3
64.5
1001.8
181.1
30.09
59.1
41.0
48250
1433.5
28
Anhui
1245.2
52.0
507.7
290.7
19.15
27.5
41.7
18087
540.0
48
Fujian
2109.0
11.0
382.8
189.1
16.82
66.0
43.3
19431
657.0
18
Jiangxi
2221.1
2.8
259.9
245.4
16.63
60.0
43.6
10505
399.5
32
Shandong
220.3
388.8
1069.9
214
34.70
16.7
42.3
42484
1466.3
63
Henan
337.3
142.6
679.7
227.6
33.40
21.5
24757
915.4
58
Hubei
1498.0
49.1
687.7
282
22.57
38.4
37.6
25136
880.1
43
of
Hunan
2196.6
136.9
613.1
330.4
25.12
47.8
40.6
23013
681.6
37
Guangdong
2458.6
68.3
2039.1
435
82.89
51.3
42.4
71217
2391.0
25
Guangxi
2178.6
0.2
718.0
290.6
14.52
56.5
37.6
12651
411.2
27
Hainan
489.9
19.1
90.9
45
4.36
55.4
40.3
6133
188.7
12
Chongqing
604.9
19.3
289.7
77.5
17.62
38.4
40.8
11753
494.1
37
Sichuan
2340.9
65.9
609.5
267.3
32.16
35.2
39.9
24500
886.7
19
Guizhou
1066.1
5.1
183.9
100.3
9.07
37.1
36.8
9290
294.0
24
Yunnan
2088.9
13.4
250.9
150.2
14.93
50.0
37.8
11079
432.1
13
Shaanxi
271.5
90.5
348.0
90.8
13.66
41.4
40.1
18075
532.8
34
Gansu
168.4
20.3
131.7
118.4
2.36
11.3
31.5
7840
257.2
38
Qinghai
612.7
6.8
51.8
26.4
2.73
5.6
31.1
2253
82.0
35
Ningxia
9.6
31.2
91.0
64.9
2.49
11.9
40.4
4460
112.2
34
Xinjiang
1093.4
79.7
255.3
565.4
8.79
4.2
38.5
9643
378.7
51
re
lP
na
Jo ur
5. Results
-p
39.3
ro
Mongolia
In order to conduct the calculation for EECC indexes according to Equations (10)-(13), the primary EECC indexes
𝑖
,
𝐿𝑖 ,
𝑖
should be calculated first. By
applying the data collected in Section 4 to Equations (2)-(9), the results of 𝐿
,
𝐿
,
𝐿
,
,
,
can be obtained, as presented in Appendix 1-5 respectively.
Then, based on the Entropy weighting method described in Section 3.2, the weighting values 𝜔 𝑖 , 𝜔𝐿𝑖 , 𝜔 index values
𝑖
,
𝐿𝑖 ,
𝑖
𝑖
can be obtained by applying the calculated primary
to Equations (14)-(19) respectively. Accordingly, the
21
Journal Pre-proof dimensional EECC indexes ρ , ρ𝐿 , ρ , can be calculated according to Equations (10)-(13), as shown in Table 5-7. Furthermore, the weighting values of 𝜔 , 𝜔𝐿 , 𝜔 can be calculated by applying the dimensional index values ρ , ρ𝐿 , ρ , to Equation (14)-(19). The overall EECC index ρ𝑂 can be calculated based on Equation (13), as shown in Table 8. The results of all the weighting values are presented in Table 4. Table 4 Weighting values between dimensional and primary indexes 𝜔 𝜔
ω𝐿
0.31
𝜔𝐿 𝜔𝐿 𝜔𝐿
ω
0.34
𝜔
0.46 0.54 0.34 0.31 0.35 1.00
lP
re
0.35
ω
Weights
of
Primary Index
ro
Weights
-p
Dimensional Index
(ρ )
na
Table 5 Evaluation results of EECC index in water dimension across surveyed 30 provinces 2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
1.09
0.77
1.01
1.12
1.07
0.90
1.04
0.96
0.96
0.70
1.19
0.83
0.95
1.33
0.96
0.68
1.07
1.29
1.23
0.99
1.14
0.95
1.05
1.06
1.00
0.89
1.06
1.36
1.17
0.93
0.81
0.84
0.86
0.86
0.85
0.98
1.01
0.97
0.98
0.90
0.69
0.71
0.70
0.66
0.77
0.77
0.69
0.72
0.68
0.70
Liaoning
0.65
0.63
0.79
0.48
0.55
0.45
0.42
0.76
0.67
0.49
Jilin
0.63
0.78
0.70
0.58
0.66
0.58
0.52
0.65
0.57
0.50
Heilongjiang
0.54
0.68
0.52
0.55
0.72
0.67
0.35
0.41
0.43
0.40
Shanghai
2.09
1.93
1.81
2.04
3.12
1.94
2.36
1.37
1.08
1.13
Jiangsu
0.81
0.93
0.89
0.93
0.84
1.01
1.25
1.02
0.81
0.72
Zhejiang
0.49
0.49
0.49
0.55
0.65
0.59
0.57
0.55
0.54
0.51
Anhui
0.48
0.52
0.51
0.46
0.74
0.73
0.71
0.65
0.62
0.67
Fujian
0.49
0.44
0.59
0.59
0.74
0.63
0.58
0.62
0.72
0.69
Jiangxi
0.73
0.72
0.62
0.62
0.92
0.92
0.98
0.95
0.85
1.00
Shandong
0.62
0.65
0.74
0.73
0.71
0.82
0.86
1.15
1.08
0.90
Henan
0.81
0.90
0.97
0.82
1.03
1.16
1.29
1.11
1.04
1.03
Hubei
0.57
0.60
0.68
0.61
0.71
0.71
0.71
0.68
0.66
0.57
Tianjin Hebei Shanxi Inner Mongolia
Jo ur
Province
22
Journal Pre-proof 0.62
0.68
0.57
0.55
0.62
0.61
0.73
0.70
0.68
0.68
Guangdong
0.76
0.68
0.71
0.55
0.66
0.67
0.67
0.70
0.67
0.68
Guangxi
0.41
0.35
0.27
0.27
0.36
0.34
0.38
0.39
0.39
0.36
Hainan
1.13
1.08
0.73
0.72
0.60
0.65
0.54
0.58
0.62
0.75
Chongqing
0.60
0.70
0.73
0.73
0.73
0.71
0.72
0.69
0.70
0.96
Sichuan
0.81
0.74
0.70
0.69
0.80
0.82
0.85
0.78
0.76
0.83
Guizhou
1.28
1.12
0.90
0.62
0.76
0.85
0.61
0.86
0.90
0.77
Yunnan
0.58
0.64
0.60
0.43
0.69
0.76
0.77
0.78
0.81
0.91
Shaanxi
0.82
0.66
0.62
0.55
0.63
0.69
0.69
0.70
0.70
0.73
Gansu
0.58
0.59
0.69
0.75
0.66
0.62
0.61
0.70
0.78
0.59
Qinghai
1.41
1.10
1.16
1.03
1.00
0.62
0.61
0.66
0.55
0.80
Ningxia
3.42
3.92
4.14
3.67
3.84
3.20
3.14
3.49
3.54
3.18
Xinjiang
0.69
0.77
0.79
0.76
0.68
0.74
0.70
0.81
0.72
0.75
Mean value
0.90
0.88
0.88
0.84
0.94
0.86
0.88
0.90
0.86
0.83
Variance
0.34
0.40
0.44
0.38
0.49
0.26
0.31
0.29
0.29
0.23
ro
of
Hunan
-p
Table 6 Evaluation results of EECC index in land dimension across surveyed 30 provinces 2012
2013
2014
2015
2016
1.98
1.98
1.89
1.89
1.89
1.92
4.30
4.28
4.28
4.32
4.28
4.27
2.54
2.56
2.56
2.61
2.48
2.42
2.47
2.95
2.99
2.98
2.95
2.98
2.88
2.90
2.85
2.85
2.80
2.72
2.61
2.63
2.61
2.37
2.15
2.14
2.09
2.10
2.06
2.03
2.11
2.60
2.51
2.32
2.34
2.33
2.18
2.08
2.10
( ρ𝐿 ) 2007
2008
2009
2010
Beijing
2.95
2.89
2.04
1.97
Tianjin
4.99
5.09
4.49
4.40
Hebei
3.44
3.14
2.71
Shanxi
4.10
3.59
2.94
Mongolia
3.68
3.46
2.94
Liaoning
2.56
2.49
Jilin
2.95
2.94
lP
Jo ur
Heilongjiang
na
Inner
2011
re
Province
3.05
2.84
2.61
2.39
2.36
2.25
2.15
2.16
2.05
2.02
10.89
11.11
4.43
4.43
4.62
4.35
4.11
4.06
4.06
4.02
5.29
5.22
3.04
3.03
3.02
3.04
3.05
3.01
3.02
3.02
1.85
1.79
1.72
1.71
1.71
1.67
1.64
1.63
1.65
1.66
2.88
2.80
2.56
2.49
2.36
2.35
2.30
2.26
2.24
2.24
Fujian
1.92
1.78
1.70
1.63
1.58
1.58
1.60
1.60
1.58
1.59
Jiangxi
1.94
1.83
1.75
1.70
1.61
1.61
1.64
1.61
1.63
1.67
Shandong
3.57
3.52
2.96
2.95
2.96
2.95
2.95
2.91
3.01
2.99
Henan
3.34
3.29
2.73
2.71
2.71
2.72
2.68
2.68
2.70
2.67
Hubei
3.56
2.92
2.26
2.25
2.21
2.10
2.03
2.02
2.08
2.06
Hunan
2.30
2.28
2.12
2.00
2.03
1.91
1.91
1.81
1.81
1.79
Guangdong
2.13
2.05
1.97
1.98
1.89
1.91
1.78
1.77
1.76
1.74
Guangxi
2.43
2.18
1.89
1.83
1.77
1.78
1.79
1.83
1.89
1.78
Hainan
1.97
1.95
1.88
1.90
1.78
1.66
1.69
1.75
1.82
1.71
Chongqing
2.83
2.69
2.05
2.04
2.08
2.00
2.04
2.09
2.11
2.05
Sichuan
2.50
2.35
2.19
2.16
2.20
2.16
2.15
2.15
2.12
2.09
Guizhou
2.80
3.10
2.49
2.37
2.28
2.25
2.16
2.32
2.14
2.08
Shanghai Jiangsu Zhejiang Anhui
23
Journal Pre-proof Yunnan
2.19
2.16
2.02
1.88
1.99
1.80
1.91
1.87
1.94
1.91
Shaanxi
2.44
2.23
2.01
2.00
2.00
1.97
1.93
1.93
1.89
1.89
Gansu
7.36
6.69
4.84
4.79
4.77
4.68
4.62
4.38
4.36
4.13
Qinghai
9.75
8.70
7.10
7.53
7.09
6.86
7.07
6.93
7.05
6.94
Ningxia
6.84
6.95
4.03
3.79
3.95
3.99
3.84
3.87
3.81
3.68
Xinjiang
12.64
12.19
8.78
8.71
8.71
8.72
8.65
8.66
8.61
8.57
Mean value
4.03
3.87
2.97
2.92
2.89
2.84
2.81
2.79
2.78
2.75
Variance
7.34
6.90
2.46
2.60
2.52
2.47
2.48
2.42
2.44
2.37
Table 7 Evaluation results of EECC index in atmosphere dimension across surveyed 30 provinces (ρ ) 2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
1.49
1.47
1.52
1.32
1.48
1.28
1.57
1.49
1.45
1.35
Tianjin
2.11
2.13
2.23
2.00
2.03
1.72
2.35
2.20
1.99
1.92
Hebei
1.60
1.47
1.55
1.42
1.45
1.36
1.67
1.54
1.48
1.41
Shanxi
1.40
1.16
1.21
1.16
1.18
1.10
1.31
1.12
1.14
1.07
Mongolia
0.61
0.61
0.62
0.64
Liaoning
0.96
1.05
1.09
1.02
Jilin
0.69
0.78
0.85
0.79
Heilongjiang
0.40
0.47
0.49
0.48
Shanghai
1.59
1.56
1.59
1.39
Jiangsu
1.76
1.68
1.70
Zhejiang
1.16
1.18
1.03
Anhui
1.72
1.66
1.56
Fujian
0.74
0.68
Jiangxi
1.23
1.18
ro 0.55
0.65
0.64
0.69
0.59
0.92
0.78
1.02
1.15
1.23
0.95
0.68
0.60
0.82
0.99
1.07
0.81
0.40
0.39
0.52
0.73
0.69
0.56
1.35
1.17
1.49
1.46
1.53
1.28
1.77
1.67
1.41
1.75
1.81
1.76
1.56
1.03
0.97
0.97
1.05
1.09
0.97
0.79
1.62
1.49
1.36
1.58
1.73
1.53
1.37
0.63
0.60
0.57
0.57
0.58
0.62
0.57
0.51
1.12
1.08
1.00
1.02
1.03
1.14
0.98
0.92
na
lP
re
0.57
Jo ur
Shandong
-p
Inner
of
Province
2.21
1.96
1.86
2.06
1.84
1.77
2.06
2.00
1.97
1.80
2.26
1.85
1.84
2.00
1.90
1.76
2.13
1.90
1.88
1.67
1.62
1.52
1.50
1.60
1.51
1.34
1.50
1.55
1.44
1.22
1.59
1.54
1.47
1.39
1.32
1.36
1.30
1.43
1.22
1.07
1.02
1.03
1.01
0.89
0.85
0.84
0.84
0.93
0.83
0.71
Guangxi
1.14
1.15
1.11
1.01
1.02
1.07
1.03
1.04
0.93
0.78
Hainan
0.44
0.50
0.39
0.35
0.44
0.30
0.43
0.38
0.42
0.35
Chongqing
1.50
1.36
1.36
1.46
1.29
1.30
1.30
1.18
1.13
1.05
Henan Hubei Hunan Guangdong
Sichuan
0.65
0.67
0.64
0.73
0.66
0.64
0.65
0.59
0.54
0.53
Guizhou
1.01
1.00
1.01
0.95
0.95
0.97
0.87
0.86
0.81
0.69
Yunnan
0.41
0.43
0.42
0.42
0.46
0.39
0.47
0.37
0.37
0.36
Shaanxi
1.33
1.06
1.13
1.15
1.13
1.06
1.24
1.06
1.03
0.96
Gansu
1.33
1.30
1.30
1.28
1.20
1.15
1.45
1.44
1.24
1.08
Qinghai
1.17
1.19
1.14
1.25
1.09
1.04
1.18
1.49
1.00
0.99
Ningxia
1.22
1.24
1.26
1.31
1.07
1.03
1.33
1.17
1.05
0.98
Xinjiang
1.53
1.53
1.45
1.66
1.46
1.46
1.62
1.84
1.56
1.45
Mean value
1.26
1.21
1.20
1.19
1.13
1.06
1.23
1.23
1.15
1.03
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0.25
0.19
0.20
0.21
0.19
0.16
0.24
0.22
0.19
0.17
Table 8 Evaluation results of overall EECC index across surveyed 30 provinces (ρ𝑂) 2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
2.13
1.77
1.48
1.22
1.30
1.15
1.09
1.41
1.54
1.20
Tianjin
2.21
2.27
2.45
2.56
2.20
2.06
2.02
2.15
1.90
2.21
Hebei
1.80
1.44
1.66
1.64
1.55
1.64
1.72
1.76
1.89
1.65
Shanxi
1.78
1.74
1.45
1.35
1.78
1.88
1.67
1.86
1.49
1.64
2.18
1.89
1.71
1.47
1.55
1.29
1.61
1.29
1.41
1.20
Liaoning
1.49
1.40
1.44
1.37
1.37
1.55
1.31
1.31
1.07
1.20
Jilin
1.31
1.73
1.64
1.40
1.53
1.17
1.32
1.58
1.49
1.36
Heilongjiang
1.50
1.52
1.17
1.41
1.22
1.28
0.94
1.18
1.24
1.18
Shanghai
4.65
4.66
2.78
2.62
2.93
2.23
2.46
2.03
1.81
2.20
Jiangsu
2.29
2.49
1.47
1.65
1.88
1.93
1.90
1.80
1.57
1.58
Zhejiang
1.22
0.95
1.06
0.87
1.15
1.18
1.10
1.14
1.21
1.15
Anhui
1.56
1.46
1.20
1.29
1.27
1.15
1.45
1.54
1.27
1.49
Fujian
1.14
1.43
1.43
1.27
1.23
1.02
1.05
0.83
1.25
0.99
Jiangxi
1.00
1.22
1.20
1.18
1.27
1.40
1.36
1.53
1.30
1.28
Shandong
1.65
1.56
1.32
1.64
1.75
1.54
1.74
1.50
1.66
1.88
Henan
1.85
1.79
1.48
1.48
1.35
1.69
1.51
1.55
1.36
1.58
Hubei
1.69
1.49
1.45
1.36
1.53
1.37
1.26
1.05
1.15
1.05
Hunan
1.34
1.44
1.20
1.29
1.05
1.16
1.46
1.42
1.26
1.26
Guangdong
1.30
0.98
1.31
1.03
1.15
0.96
1.15
1.19
1.14
1.14
Guangxi
1.45
1.62
1.27
1.12
0.90
1.03
0.98
0.89
1.24
1.29
Hainan
1.55
1.18
1.19
1.56
1.50
1.26
1.17
1.04
1.14
0.94
Chongqing
1.32
1.38
1.05
1.32
1.41
1.28
1.35
1.46
1.42
1.73
Sichuan
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Mongolia
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Inner
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Province
1.45
1.21
1.33
1.26
1.22
1.47
1.59
1.32
1.53
1.15
2.01
2.01
1.63
1.45
1.30
1.36
1.02
1.43
1.18
1.21
1.25
1.39
1.36
1.14
1.50
1.32
1.55
1.37
1.28
1.15
1.42
1.16
1.38
1.43
1.18
1.39
1.04
1.18
1.51
1.50
2.93
2.59
2.08
1.90
2.12
1.87
1.94
1.74
1.99
1.93
Qinghai
3.90
3.64
3.09
3.38
3.05
2.73
2.63
2.72
2.68
2.64
Ningxia
3.84
3.78
3.14
2.61
2.86
2.96
2.85
2.82
2.76
2.47
Xinjiang
4.32
4.45
3.21
3.23
3.09
3.32
3.33
3.33
3.29
3.45
Mean value
1.99
1.92
1.65
1.62
1.64
1.59
1.59
1.58
1.57
1.56
Variance
0.91
0.92
0.38
0.38
0.36
0.32
0.33
0.31
0.26
0.31
Guizhou Yunnan Shaanxi Gansu
5.1 The change of EECC in water dimension (𝛒𝑾) As shown in Figure 2, for demonstrating the change of EECC in water 25
Journal Pre-proof dimension (ρ ) graphically, a scatter box plot between 30 provinces is produced by using the data in Table 5. This Figure highlights that ρ
varies dramatically
between the provinces each year. For example, in the year of 2007, the maximum value of ρ
was undergone by Ningxia at 3.42, while the minimum value was
enjoyed by Guangxi at 0.41, and the best three provinces of EECC performance in water dimension were Guangxi, Anhui and Zhejiang, while Ningxia, Qinghai and
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Guizhou were the worst three.
Figure 2 EECC performance in water dimension
It should be noted that the mean value of the index ρ
between 30 provinces
has been changing during the surveyed period. It has continuously dropped at the first four years from 0.90 in 2007 to 0.84 in 2010, and backed to 0.94 in 2011, then fluctuated in the last five years between 0.83 and 0.90. The maximum value of ρ has been changing between 3.14 in 2013 and 4.14 in 2009 offered by Ningxia, whilst the minimum value varies between 0.27 in 2009 and 0.41 in 2007 both achieved by Guangxi.
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5.2 The change of EECC in land dimension (𝛒𝑳 ) For presenting the change of EECC in land dimension (ρ𝐿 ), the data listed in Table 6 are employed for producing the Figure 3. It can be observed that the ρ𝐿 value appears significantly different between provinces annually. For instance, in the year 2007, the maximum value appeared in Xinjiang with the value ρ𝐿 of 12.64, while the minimum value emerged in Zhejiang with 1.85, and the mean value between the 30
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provinces is 4.03. The best three provinces of EECC performance in land dimension
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in the same year were Zhejiang, Fujian and Jiangxi, whilst Xinjiang, Shanghai and
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Qinghai were the worst three.
Figure 3 EECC performance in land dimension
On the other hand, the mean ρ𝐿 value changed during the surveyed period towards better performance, and the most significant change happened in the first three years. The mean value of 30 provinces decreased from 4.03 in 2007 to 2.75 in 2016. The maximum value of ρ𝐿 changes between 8.54 in 2016 to 12.60 in 2007, offered by Xinjiang, while the minimum value varies between 1.58 enjoyed by Fujian in 2011 and 1.85 experienced by Zhejiang in 2007. Furthermore, it is interesting to 27
Journal Pre-proof note that the difference in ρ𝐿 between different provinces has been narrowing during surveyed years as the variance value decreased from 2.71 in 2007 to 1.54 in 2016.
5.3 The change of EECC in atmosphere dimension (𝛒𝑨 ) The change of EECC in atmosphere dimension (ρ ) can be seen in Figure 4, generated from the data in Table 7. From Table 7 and Figure 4, the value of ρ varies
ρ
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among the provinces annually. For example, in the year 2007, the maximum value of is 2.26 offered by Henan, while the minimum is 0.40 from Heilongjiang, and the
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mean value between the 30 provinces is 1.26. The best three provinces of EECC
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performance in atmosphere dimension in the same year were Hainan, Heilongjiang
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and Inner Mongolia, while Henan, Shandong and Tianjin were the worst three.
Figure 4 EECC performance in atmosphere dimension
Figure 4 also shows that the atmospheric environment is characterized by an increasingly improving performance, evidenced by the decreasing value of ρ . The mean value of ρ decreased from 1.26 in 2007 to 1.06 in 2012, then increased to 1.23 in 2013, later it decreased again from 1.23 in 2013 to 1.03 in 2016. The maximum value of ρ changed between 1.77 offered by Shandong in 2012 to 2.26 offered by Henan in 2007. And the minimum value varies among 0.30 offered by 28
Journal Pre-proof Hainan in 2012 and 0.43 offered by Yunnan in 2008.
5.4 The change of the overall EECC Table 8 presents the values of ρ𝑂 from an overall perspective which integrates the three dimensional indexes ρ , ρ𝐿 and ρ . The data in table 8 can be used to produce a scatter box plot diagram, as show in Figure 5. From Table 8 and Figure 5, it
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can be seen that the value ρ𝑂 varies among provinces annually. For example, in the year 2007, the maximum value was 4.64 in Shanghai, while the minimum value was
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1.00 in Jiangxi, and the mean value among the 30 provinces was 1.99, the best three
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provinces of EECC performance in overall perspective were Jiangxi, Fujian and
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Zhejiang, while Shanghai, Xinjiang and Qinghai were the worst three.
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On the other hand, the ρ𝑂 shows a temporal variation during the year of 2007 to 2016. According to Figure 5, the mean value decreased significantly from 1.99 in
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2007 to 1.62 in 2010, then slightly increased to 1.64 in 2011, and continuously decreased to 1.56 in 2015, later increased to 1.67 in 2016. The maximum value of ρ𝑂
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in each year changes between 3.09 offered by Xinjiang in 2011 and 4.66 offered by Shanghai in 2008, while the minimum value varies between 0.83 enjoyed by Fujian in 2014 and 1.07 shared by Liaoning in 2015. In addition, the difference of ρ𝑂 between 30 provinces has been narrowing during the surveyed years as the variance value dropped from 0.94 in 2007 to 0.55 in 2016.
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Figure 5 Overall performance of EECC in the period of 2007-2016
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The changes of ρ𝑂 can be further demonstrated by observing the distribution of
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EECC performance in the beginning year (2007) and the ending year (2016).
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According to Table 8, the overall EECC performances among the 30 provinces for the beginning year and the ending year are presented as shown in Figure 6. In this Figure,
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the 30 surveyed provinces are classified into three groups, namely, Good, Average and
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Poor according to their EECC performance. The classification criterion is generated by ordering the 300 values in Table 8, the smallest 100 values are divided into Good group, followed by Average group, and the largest 100 value are in Poor group. In other words, the classification is based on the follows: Good (0<ρ𝑂 ≦1.3), Average (1.3<ρ𝑂 ≦1.6), Poor (ρ𝑂 >1.6). The figure shows that most provinces have made good progress from the year of 2007 to 2016. For example, Beijing has improved its performance from “Poor” grade in 2007 to “Good” grade in 2016, with the index ρ𝑂 decreased from 2.1 to 1.2. However, there are still three provinces Jilin, Shandong and Chongqing experienced an EECC degradation. Specifically, Jilin has fallen from “Good” grade to “Average” grade, Shandong has dropped from “Average” grade to “Poor”, and Chongqing showed a decline from “Good” to “Poor”. 30
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Figure 6 Overall EECC performance between 30 provinces in the year 2007 and 2016
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6. Discussions
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6.1 Improvements of EECC performance
According to the evaluation results listed in Table 6, 7 and 8, it is interesting to
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find that the mean value of index ρ𝐿 , ρ , ρ𝑂 showed a downward trend during the
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period of 2007 to 2016, which indicates that the EECC performance in land dimension, atmosphere dimension and overall perspective experienced a reasonable improvement in the urbanization process of China. As shown in Figure 6, 27 provinces out of the 30 surveyed provinces have improved their overall EECC performance during the surveyed period. This evaluation results appears not in consensus with the common appreciation that the urbanization contributes to the deterioration of ecological environment (Chen et al., 2010; Wu et al., 2019d; Yang et al., 2018). However, the results of this study respond to some viewpoints, such as Wang et al. (2019b), suggesting that the ecological environment performance has been gradually improving in China in recent years. It is considered that there is a synergetic effect existing between urbanization and EECC. More specifically, the provinces with 31
Journal Pre-proof better EECC performance can attract additional residents and consequently contribute to urbanization growth. On the other hand, urbanization development will prompt the development of technologies and economy, and strengthen the residents’ and governments’ awareness of protecting ecological environment. Therefore, the ecological resources will be utilized more efficiently, and urban infrastructure for supporting ecological environment will be constructed more accessibly. These improvements can benefit ecological environment system by reducing waste
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discharges and enhancing waste treatment facilities, which will consequently work on
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better EECC performance. This opinion is echoed by Zhang et al. (2019), implying
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that higher urbanization level is associated with better ecological environment
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performance.
In referring to the average EECC index values (ρ , ρ𝐿 , ρ ) in Table 5, 6 and 7,
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the most obvious improvement has been given to EECC in land dimension during the
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surveyed period. This research finding is consistent with the news report from National Aeronautics and Space Administration (2019) and the study by Chen Chi
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(2019), suggesting that the expansion of vegetation area on the earth from 2000 to 2017 is mainly contributed by China and India. The possible reason for the great improvement of land ecological environment is that the Chinese government has paid increasing attention to land protection. For example, the “The Forest Law” in 2009 (Standing Committee of the National People's Congress, 2009) and “The Soil and Water Conservation Law” in 2010 (Standing Committee of the National People's Congress, 2010) implemented by government has emphasized the importance of promoting land ecological environment. Furthermore, in Chinese 12th Five-Year-Plan for Construction of Urban Household garbage Disposal Facilities, a sustainable development goal of increasing garbage disposal rate above 90% before 2015 was
32
Journal Pre-proof proposed (The State Council of China, 2012). In Chinese 13th Five-Year Plan for Ecological Environmental Protection, the central government has committed to increase the urban green areas coverage rate to 38.9% in 2020 (The State Council of China, 2016a). At provincial level, the good performance of EECC in land dimension can be attributed to multiple aspects, including good social and economic circumstances, effective policy measures from the government, and the geographical features of a specific area. For example, Zhejiang is the best EECC performer in land
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dimension, as shown in Table 6. In line with the rapid social and economic
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development in this province, the local investment of urban infrastructure has
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increased from RMB 221.5 billion in 2007 to RMB 9365.48 billion in 2016 (Zhejiang
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Statistics Bureau, 2007-2016), which benefits to the improvement of environmental facilities, such as waste disposal facilities. According to National Bureau of Statistics
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of China (2007-2016a), the facilities capacity for household garbage disposal (
𝐿
) in
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Zhejiang increased from 22881 (t/day) in 2007 to 48250 (t/day) in 2016. Accordingly, the loads influencing the performance of land environment can be mitigated.
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Furthermore, the government of Zhejiang has implemented many policy measures in improving land ecological environment. For instance, the local government has promoted the tree planting progress by issuing “Color forest passageway construction” (Zhejiang Provincial Forestry Department, 2014) and “The precious color forest construction overall plan in Zhejiang province (2015-2020)” (State administration of forestry and grassland in China, 2016). The forest coverage rate in Zhejiang has increased by 4.7% from 2007 to 2016 (National Bureau of Statistics of China, 2007-2016a). Therefore, the land environment can be improved effectively by enhancing the land carriers. As the natural geography aspect, benefited from humid subtropical climate and fertile soil (You et al., 2018), the forest coverage rate in
33
Journal Pre-proof Zhejiang (59.1% in 2016) is much higher than the national average level (21.6% in 2016) (National Bureau of Statistics of China, 2007-2016a). The Chinese government has also made great efforts to the improvement of EECC performance in atmosphere and water dimensions. In order to improve air quality in China, the government initiated the “de-coal” appeal in 2014, which was considered to have achieved significant progress by the report of “Greenpeace Achievement in 2016” (Greenpeace, 2016). The policies of “Action Plan of Air
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Pollution Prevention and Control” issued in 2013 by The State Council of China
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(2013) and “The law on the Prevention and Control of Atmospheric Pollution”
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introduced in 2015 (Standing Committee of the National People's Congress, 2015)
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have also contributed to the reduction of air pollution and the development of clean energy (Yang et al., 2017). As a result, the atmospheric ecological carrying capacity
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presents an improving trend in China, with the index of EECC in atmosphere
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dimension (ρ ) decreased from 1.23 in 2013 to 1.03 in 2016 as shown in Table 7. This research finding can be also evidenced by the report of “Breakthroughs China’s Path
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to Clean Air 2013-2017” issued by Clean Air Asia (2018), which shows that the concentration of PM2.5 in China has dropped from 72
g/
3
in 2013 to 47
g/
3
in 2017. And the monitoring air pollutants data published by Ministry of ecology and environment of China (2019) shows a similar trend, suggesting the concentration of PM2.5 in China has decreased by 42% from2013 to 2018. In terms of EECC in water dimension, despite of the small progress across the country, many provinces have witnessed a good development in water recycling. Beijing is a typical example with significant improvement in recycling water during the study period. In order to promote the technology and facility utilization in recycling water, the policies of “Measures for the administration of drainage and recycled water in Beijing” (The
34
Journal Pre-proof people's government of Beijing, 2009), and “The three-year action plan on sewage treatment and recycled water utilization in Beijing (2016-2019)” (The people's government of Beijing, 2016) was issused by Beijing government. As a result, the productivity of recycled water (
) in Beijing increased from null in 2007 to 513.5
(104 m3 per day) in 2016, during which the index of EECC in water dimension (ρ ) decreased from 1.09 to 0.70 as shown in Table 5.
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It is also worthy to note that the difference in overall EECC performances between 30 provinces has been narrowing during surveyed years, as the variance
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value of EECC decreased from 0.74 in 2007 to 0.25 in 2016 as shown in Table 8.
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Whilst those provinces with better initial EECC performance have made some
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progress, those traditionally poor performers have made significant progress. For example, the forest areas in Xinjiang improved from 484.07 (108m2) in 2007 to
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698.25 (108m2) in 2016, while that in Beijing increased from 37.88 (108m2) to
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58.81(108m2) in the same period (National Bureau of Statistics of China, 2007-2016a). The possible reason behind this is that the Central Government has emphasized the
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balanced development between different provinces by introducing policies and programs, such as the implementation of “Grain for Green Project” (State Council of the People's Republic of China, 2007) which contributes to the improvement of ecological environment in Western region of China. The above discussions suggest that the government plays an essential role in improving EECC by raising environmental awareness and promoting technology development during the urbanization process. Therefore, Chinese government should continuously implement these effective policies in order to achieve better ecological environment carrying status. In addition, the Central Government should encourage the cooperation work through transferring technology and sharing experience between
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6.2 Severe challenges existing in EECC performance Despite certain ecological achievements obtained, challenges still exist in reaching desirable EECC performance, particularly in atmosphere dimension. According to the data presented in Table 7, there were still 14 provinces with the
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index value of ρ larger than 1 in 2016, indicating that the air quality in these provinces is worse than Ambient Air Quality Standards (GB3095-2012). This finding
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is echoed by the study of Wang et al. (2018b), indicating that the PM2.5 concentration
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is an alarming environmental challenge in China. Furthermore, whilst 27 provinces
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have made progress in overall EECC, there are three provinces having experienced a
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degradation in overall EECC, including Jilin, Chongqing and Shandong. The degradation in Jilin is mainly due to the decreasing atmosphere performance, which is
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attributed to the extensive industrial activities involving enormous coal consumption and air pollutants emissions (Jie et al., 2012). Chongqing is another one with the
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degradation of overall EECC performance, as shown in Figure 6. According to the Appendix 2, its EECC in water dimension (
) has increased from 1.02 in 2007 to
1.67 in 2016, indicating that the lack of capacity in dealing with soaring wastewater discharges resulted in the degradation of overall EECC performance in Chongqing city. For example, the domestic sewage discharge (
) in Chongqing increased 2.7
times from 6.5 (108 ton) in 2007 to 17.6 (108 ton) in 2016, while the capacity of disposal facilities for sewage treatment (
) only increased 1.6 times, from 175.7
(104 m3 per day) in 2007 to 289.7 (104 m3 per day) in 2016 (National Bureau of Statistics of China, 2007-2016a). Shandong province is ranked one of the worst performers in terms of EECC degradation, as shown in Figure 6. The EECC
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Journal Pre-proof degradation in this province is mainly resulted from the poor performance in water dimension. According to the study by Wu and Tan (2012), the natural water resources in Shandong have been excessively exploited due to the intensive water consumption in line with the rapid urban development. That may be the reason for the decrease of ) in Shandong from 387.11 (108m3) in 2007 to 220.30
total water resources (
(108m3) in 2016 (National Bureau of Statistics of China, 2007-2016a), during which the index
(EECC in water dimension) of Shandong increased from 0.62 to 0.90,
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as shown in Table 5.
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Based on the above discussions, it is considered that opportunities and challenges
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coexist in line with the change of EECC during the rapid urbanization process in
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China. In order to prevent ecological environment from being overloaded,
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governments should enhance ecological environment carriers on one hand, for example protecting natural water resources, strengthening regional forest coverage,
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and improving waste treatment facilities. On the other hand, actions should be taken to control ecological environment loads, such as to reduce garbage by encouraging
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recycle and reuse, to abate air pollution by developing clean energy, to curb the increase of water consumption by advocating water saving.
7. Conclusion
This study examined the change of ecological environment carrying capacity in the context of China under 30 provinces during the period of 2007-2016. The main findings are concluded as: (1) During the urbanization process, the EECC performances in water, land, atmosphere and overall perspectives of most provinces are changing towards good status, with a remarkable progress has been achieved in land dimension, while few provinces such as Chongqing, Shandong and Jiangxi have experienced a degradation. 37
Journal Pre-proof (2) The difference of overall EECC performances between 30 provinces has been narrowing during surveyed years, suggesting that the emphasis has been given by the Chinese Central Government to balance the ecological development between the provinces. (3) Severe challenge still exists in the EECC performance of atmosphere dimension in many provinces. This paper contributes to the development of the literature associated with
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exploring the change of ecological environment. By incorporating both carriers and
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loads, this model provides an innovative measurement for investigating ecological
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environment carrying capacity. Practically, the research findings provide valuable
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references for Chinese administration to understand the changes of the ecological environment carrying capacity during the urbanization process, based on which
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experiences and lessons can be shared, proper and targeted policy measures can also
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be designated to prevent ecological environmental from overloading. Limitations of this study are appreciated for further research. Firstly, although a
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comprehensive list of evaluating indicators has been identified for EECC assessment, those indicators without accessibility at this stage are excluded in the demonstration. Secondly, the research data applied are on annual basis, thus the analysis results on EECC can only reflect the change of annual state. If more changeable temporal data such as real-time data are available, the evaluation results on EECC would be more dynamic. This study inspires that further study can be developed to examine the impacts of urbanization to the change of EECC performance.
Appendix Appendix 1 Calculated results of primary EECC index Province
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
1.46
0.76
1.06
1.35
1.18
0.91
1.18
1.05
1.09
0.72
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2.00
1.20
1.47
2.21
1.42
0.68
1.53
1.95
1.85
1.35
Hebei
1.67
1.20
1.35
1.35
1.21
0.81
1.05
1.72
1.32
0.85
Shanxi
0.57
0.62
0.65
0.69
0.59
0.68
0.57
0.62
0.76
0.54
Inner Mongolia
0.61
0.43
0.48
0.34
0.44
0.36
0.19
0.34
0.34
0.44
Liaoning
0.54
0.53
0.82
0.24
0.48
0.26
0.30
0.94
0.77
0.40
Jilin
0.29
0.31
0.37
0.17
0.41
0.28
0.22
0.43
0.40
0.27
Heilongjiang
0.59
0.64
0.32
0.38
0.56
0.43
0.26
0.39
0.44
0.42
Shanghai
3.48
3.24
3.01
3.43
6.01
3.42
4.40
2.25
1.62
1.72
1.12
1.46
1.35
1.42
1.11
1.45
1.96
1.44
0.97
0.77
0.24
0.25
0.21
0.15
0.27
0.14
0.21
0.17
0.13
0.14
Anhui
0.33
0.38
0.40
0.32
0.49
0.42
0.50
0.35
0.32
0.23
Fujian
0.18
0.19
0.25
0.12
0.27
0.13
0.18
0.17
0.15
0.09
Jiangxi
0.21
0.17
0.21
0.11
0.25
0.11
0.19
0.16
0.12
0.11
Shandong
0.55
0.66
0.76
0.70
0.63
0.78
0.72
1.35
1.16
0.91
Henan
0.45
0.61
0.71
0.42
0.69
0.89
1.12
0.73
0.76
0.66
Hubei
0.25
0.26
0.34
0.23
0.39
0.37
0.37
0.31
0.30
0.19
Hunan
0.23
0.20
0.23
0.17
0.29
0.17
0.21
0.18
0.17
0.15
Guangdong
0.29
0.21
0.29
Guangxi
0.22
0.14
0.20
Hainan
0.16
0.11
0.09
Chongqing
0.12
0.14
0.19
Sichuan
0.09
0.08
Guizhou
0.09
Yunnan
0.23
0.32
0.22
0.20
0.26
0.23
0.18
0.17
0.22
0.15
0.15
0.15
0.12
0.13
0.09
0.09
0.12
0.09
0.12
0.23
0.09
0.19
0.17
0.17
0.18
0.13
0.17
0.13
0.10
0.09
0.10
0.09
0.10
0.09
0.12
0.11
0.09
0.11
0.11
0.15
0.10
0.12
0.08
0.08
0.09
0.07
0.07
0.10
0.08
0.10
0.09
0.09
0.09
0.08
0.07
Shaanxi
0.22
0.28
0.20
0.16
0.14
0.22
0.25
0.25
0.27
0.33
Gansu
0.53
0.63
0.58
0.57
0.51
0.46
0.45
0.61
0.72
0.70
Qinghai
0.05
0.05
0.03
0.04
0.04
0.03
0.04
0.03
0.05
0.04
lP
na
Jo ur
Ningxia
re
-p
ro
of
Jiangsu Zhejiang
Xinjiang
6.60
7.72
8.09
7.24
7.61
6.06
5.94
6.50
7.08
6.04
0.60
0.65
0.70
0.48
0.59
0.65
0.61
0.80
0.62
0.52
Appendix 2 Calculated results of primary EECC index Province
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
0.77
0.78
0.96
0.93
0.98
0.90
0.92
0.88
0.85
0.69
Tianjin
0.50
0.53
0.52
0.59
0.56
0.68
0.69
0.73
0.71
0.69
Hebei
0.69
0.73
0.79
0.81
0.83
0.96
1.07
1.05
1.05
1.00
Shanxi
1.02
1.02
1.04
1.00
1.07
1.24
1.37
1.26
1.16
1.21
Inner Mongolia
0.77
0.95
0.89
0.93
1.05
1.13
1.12
1.05
0.97
0.93
Liaoning
0.75
0.71
0.76
0.69
0.61
0.62
0.52
0.60
0.58
0.57
Jilin
0.93
1.17
0.99
0.92
0.88
0.83
0.78
0.83
0.72
0.69
Heilongjiang
0.49
0.72
0.69
0.70
0.87
0.88
0.43
0.43
0.42
0.39
Shanghai
0.91
0.81
0.79
0.85
0.67
0.68
0.62
0.62
0.62
0.63
Jiangsu
0.55
0.48
0.49
0.50
0.61
0.63
0.64
0.67
0.68
0.68
Zhejiang
0.71
0.69
0.73
0.90
0.98
0.97
0.87
0.88
0.89
0.82
39
0.61
0.63
0.60
0.59
0.96
1.00
0.89
0.90
0.89
1.03
Fujian
0.75
0.65
0.88
0.99
1.15
1.05
0.92
1.00
1.20
1.20
Jiangxi
1.18
1.19
0.98
1.07
1.49
1.62
1.65
1.62
1.47
1.75
Shandong
0.67
0.64
0.73
0.76
0.78
0.85
0.98
0.98
1.02
0.89
Henan
1.12
1.14
1.18
1.17
1.32
1.38
1.44
1.44
1.28
1.35
Hubei
0.84
0.88
0.97
0.94
0.98
1.01
1.00
0.99
0.97
0.90
Hunan
0.95
1.09
0.87
0.87
0.90
0.99
1.18
1.13
1.11
1.12
Guangdong
1.16
1.08
1.06
0.81
0.95
1.05
1.08
1.07
1.04
1.11
Guangxi
0.57
0.54
0.33
0.36
0.47
0.51
0.58
0.60
0.62
0.55
Hainan
1.95
1.90
1.27
1.25
1.03
1.10
0.93
0.98
0.95
1.31
Chongqing
1.02
1.18
1.20
1.19
1.22
1.17
1.18
1.18
1.14
1.67
Sichuan
1.42
1.30
1.21
1.19
1.40
1.45
1.50
1.37
1.31
1.45
Guizhou
2.29
2.00
1.57
1.05
1.28
1.49
1.02
1.52
1.59
1.35
Yunnan
1.01
1.13
1.03
0.74
1.19
1.33
1.35
1.37
1.42
1.63
Shaanxi
1.34
0.98
0.98
0.89
1.04
1.09
1.07
1.07
1.06
1.08
Gansu
0.62
0.56
0.78
0.91
0.79
0.75
0.74
0.79
0.83
0.49
Qinghai
2.56
1.99
2.12
1.88
1.82
1.12
1.09
1.18
0.99
1.44
Ningxia
0.71
0.68
0.77
Xinjiang
0.77
0.88
0.87
ro
of
Anhui
-p
Journal Pre-proof
0.64
0.77
0.75
0.93
0.52
0.75
1.01
0.76
0.82
0.78
0.82
0.81
0.94
re
0.64
lP
Appendix 3 Calculated results of primary EECC index
𝐿
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
4.69
4.69
2.79
2.79
2.79
2.79
2.79
2.79
2.79
2.79
Tianjin
12.35
12.35
10.10
10.10
10.10
10.10
10.10
10.10
10.10
10.10
Hebei
5.65
5.65
4.27
4.27
4.27
4.27
4.27
4.27
4.27
4.27
Shanxi
7.52
7.52
na
Province
5.56
5.56
5.56
5.56
5.56
5.56
5.56
5.65
5.65
4.76
4.76
4.76
4.76
4.76
4.76
4.76
4.76
Liaoning
3.03
3.03
2.62
2.62
2.62
2.62
2.62
2.62
2.62
2.62
Jilin
2.62
2.62
2.48
2.48
2.48
2.48
2.48
2.48
2.48
2.48
Heilongjiang
2.53
2.53
2.31
2.31
2.31
2.31
2.31
2.31
2.31
2.31
Shanghai
31.25
31.25
9.35
9.35
9.35
9.35
9.35
9.35
9.35
9.35
Jiangsu
13.33
13.33
6.33
6.33
6.33
6.33
6.33
6.33
6.33
6.33
Zhejiang
1.84
1.84
1.69
1.69
1.69
1.69
1.69
1.69
1.69
1.69
Anhui
4.17
4.17
3.64
3.64
3.64
3.64
3.64
3.64
3.64
3.64
Fujian
1.59
1.59
1.52
1.52
1.52
1.52
1.52
1.52
1.52
1.52
Jo ur
5.56
Inner Mongolia
Jiangxi
1.79
1.79
1.67
1.67
1.67
1.67
1.67
1.67
1.67
1.67
Shandong
7.46
7.46
5.99
5.99
5.99
5.99
5.99
5.99
5.99
5.99
Henan
6.17
6.17
4.65
4.65
4.65
4.65
4.65
4.65
4.65
4.65
Hubei
3.73
3.73
2.60
2.60
2.60
2.60
2.60
2.60
2.60
2.60
Hunan
2.46
2.46
2.09
2.09
2.09
2.09
2.09
2.09
2.09
2.09
Guangdong
2.15
2.15
1.95
1.95
1.95
1.95
1.95
1.95
1.95
1.95
Guangxi
2.42
2.42
1.77
1.77
1.77
1.77
1.77
1.77
1.77
1.77
Hainan
2.04
2.04
1.81
1.81
1.81
1.81
1.81
1.81
1.81
1.81
40
Journal Pre-proof Chongqing
4.48
4.48
2.60
2.60
2.60
2.60
2.60
2.60
2.60
2.60
Sichuan
3.30
3.30
2.84
2.84
2.84
2.84
2.84
2.84
2.84
2.84
Guizhou
4.20
4.20
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
Yunnan
2.45
2.45
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
Shaanxi
3.07
3.07
2.42
2.42
2.42
2.42
2.42
2.42
2.42
2.42
Gansu
14.93
14.93
8.85
8.85
8.85
8.85
8.85
8.85
8.85
8.85
Qinghai
22.73
22.73
17.86
17.86
17.86
17.86
17.86
17.86
17.86
17.86
Ningxia
16.39
16.39
8.40
8.40
8.40
8.40
8.40
8.40
8.40
8.40
Xinjiang
34.48
34.48
23.81
23.81
23.81
23.81
23.81
23.81
23.81
23.81
Appendix 4 Calculated results of primary EECC index
𝐿
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
2.76
2.69
2.10
2.15
2.19
2.16
2.12
2.04
2.07
2.07
Tianjin
2.86
3.13
3.30
3.12
2.90
2.87
2.87
2.87
2.75
2.69
Hebei
2.74
2.58
2.50
2.34
2.38
2.44
2.43
2.39
2.43
2.45
Shanxi
3.07
2.84
2.74
2.63
2.61
Inner Mongolia
3.56
3.27
3.09
2.50
2.49
2.49
2.47
2.99
2.93
2.76
2.76
2.51
2.55
2.51
Liaoning
2.67
2.62
2.61
2.54
2.51
2.49
2.49
2.49
2.48
2.75
Jilin
3.19
3.17
3.05
2.93
2.92
2.95
3.18
2.79
2.77
2.86
Heilongjiang
3.47
3.18
2.98
2.87
2.75
2.78
2.78
2.78
2.79
2.82
Shanghai
2.66
2.65
2.63
2.62
2.62
2.61
2.60
2.60
2.60
2.59
Jiangsu
2.34
2.35
2.38
2.38
2.38
2.37
2.36
2.35
2.34
2.33
Zhejiang
2.77
2.65
2.62
2.61
2.60
2.51
2.48
2.45
2.46
2.44
Anhui
2.77
2.78
2.69
2.67
2.53
2.58
2.51
2.43
2.43
2.40
Fujian
2.70
2.57
2.52
2.44
2.42
2.38
2.34
2.34
2.33
2.31
Jiangxi
2.56
2.39
na
lP
re
2.59
-p
ro
of
Province
2.15
2.14
2.17
2.22
2.24
2.27
2.29
2.59
2.51
2.43
2.41
2.41
2.38
2.35
2.34
2.36
2.36
Henan
2.92
2.82
2.75
2.73
2.73
2.71
2.66
2.61
2.65
2.54
2.66
2.66
2.65
2.65
2.60
2.57
2.62
2.64
2.67
2.66
2.81
2.79
2.73
2.73
2.72
2.70
2.66
2.59
2.52
2.46
Guangdong
2.60
2.48
2.45
2.42
2.43
2.43
2.41
2.42
2.42
2.36
Guangxi
3.12
3.06
2.97
2.86
2.67
2.67
2.65
2.54
2.66
2.66
Hainan
2.47
2.38
2.39
2.35
2.39
2.43
2.38
2.42
2.65
2.48
Chongqing
3.14
2.79
2.60
2.46
2.49
2.33
2.40
2.46
2.48
2.45
Sichuan
2.92
2.83
2.75
2.64
2.62
2.58
2.60
2.67
2.58
2.51
Guizhou
3.28
3.36
3.65
3.38
3.10
3.05
2.90
2.94
2.79
2.72
Yunnan
3.23
3.13
2.75
2.68
2.58
2.54
2.65
2.62
2.68
2.65
Shaanxi
2.65
2.58
2.58
2.61
2.58
2.48
2.49
2.47
2.46
2.49
Gansu
4.15
3.86
3.66
3.69
3.58
3.33
3.12
3.25
3.31
3.17
Qinghai
3.64
3.55
3.45
3.40
3.22
3.08
3.21
3.16
3.36
3.22
Ningxia
2.99
2.65
2.58
2.58
2.67
2.60
2.60
2.63
2.64
2.48
Xinjiang
3.13
3.13
2.75
2.75
2.73
2.79
2.75
2.72
2.67
2.60
Hubei Hunan
Jo ur
2.25
Shandong
41
Journal Pre-proof Appendix 5 Calculated results of primary EECC index
𝐿
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
1.59
1.48
1.31
1.04
1.03
1.06
0.84
0.94
0.91
0.98
Tianjin
0.59
0.63
0.68
0.63
0.55
0.54
0.52
0.63
0.65
0.68
Hebei
2.19
1.45
1.52
1.19
1.22
1.16
1.30
0.98
0.76
0.86
Shanxi
2.09
0.83
0.79
0.94
1.08
1.08
1.07
1.16
0.88
0.96
Inner Mongolia
2.05
1.68
1.15
1.00
1.06
1.07
0.85
0.79
0.80
0.79
Liaoning
2.02
1.87
1.91
1.33
1.32
1.20
1.24
1.11
1.04
1.00
Jilin
2.98
2.98
2.26
2.11
1.56
1.59
1.31
1.27
1.01
0.97
Heilongjiang
3.08
2.77
2.49
1.95
1.99
1.65
1.35
1.38
1.05
0.91
Shanghai
1.13
1.78
1.88
1.90
2.46
1.67
0.98
0.81
0.82
0.73
Jiangsu
1.13
0.91
0.76
0.74
0.73
0.77
0.81
0.73
0.76
0.77
Zhejiang
0.92
0.87
0.81
0.79
0.80
0.78
0.72
0.73
0.76
0.81
of
Province
1.83
1.59
1.47
1.27
1.03
0.97
0.88
0.84
0.78
0.82
1.40
1.15
1.03
0.90
0.77
0.82
0.92
0.90
0.87
0.93
Jiangxi
1.44
1.28
1.30
1.28
1.02
0.98
1.02
0.91
0.93
1.04
Shandong
1.09
1.03
0.80
0.77
0.80
0.81
0.86
0.75
1.00
0.95
Henan
1.23
1.18
0.97
0.93
0.95
1.00
0.95
0.98
1.01
1.01
Hubei
4.33
2.48
1.55
1.52
1.46
1.15
0.91
0.84
1.00
0.96
Hunan
1.62
1.58
1.51
1.17
1.27
0.93
0.97
0.76
0.82
0.81
Guangdong
1.62
1.53
1.49
1.56
1.27
1.36
0.97
0.93
0.91
0.92
Guangxi
1.74
1.07
0.88
0.82
0.85
0.88
0.93
1.15
1.19
0.89
Hainan
1.37
1.43
1.41
1.52
1.12
0.72
0.88
1.02
0.96
0.84
Chongqing
1.01
0.98
0.98
1.09
1.19
1.13
1.17
1.26
1.29
1.15
Sichuan
1.33
1.00
1.04
1.06
1.21
1.11
1.05
0.99
1.00
0.99
Guizhou
1.06
1.84
1.11
1.03
1.07
1.03
0.92
1.35
0.98
0.87
Yunnan
0.87
0.90
1.27
0.94
1.37
0.84
1.07
0.96
1.12
1.07
Gansu Qinghai Ningxia
Xinjiang
-p
re
lP
na
Jo ur
Shaanxi
ro
Anhui Fujian
1.66
1.11
1.06
0.99
1.01
1.06
0.91
0.94
0.81
0.81
3.89
2.23
2.45
2.27
2.34
2.33
2.37
1.55
1.42
0.90
4.43
1.45
1.23
2.54
1.42
0.91
1.38
1.01
1.17
1.00
2.27
2.94
1.61
0.90
1.30
1.47
1.04
1.09
0.91
0.69
2.88
1.54
1.52
1.32
1.35
1.32
1.16
1.20
1.13
1.08
Acknowledgments This research work was supported by the National Planning Office of Philosophy and Social Science Foundation of China(Grant No.”17ZDA062”), and Chongqing Federation of Social Science (Nos. “2019CDJSK03PT06”).
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Journal Pre-proof Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
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Journal Pre-proof Graphical abstract
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Heilongjiang
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Jilin Inner Mongolia BeijingLiaoning Ningxia Gansu ShanxiHebei Shandong Qinghai ShaanxiHenan Jiangsu Chongqing Anhui Hubei Sichuan Zhejiang HunanJiangxi Guizhou Taiwan Yunnan GuangxiGuangdong Hainan
Heilongjiang
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Hong Kong Macau
Hainan Macau
(b) EECC performance in 2016
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(a) EECC performance in 2007
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Highlights: ·A novel EECC evaluation method was developed from load-carrier perspective. · EECC performance in China is changing towards good during urbanization process.
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·The difference of overall EECC performances between 30 provinces has been narrowing.
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· Severe challenge still exists in atmosphere dimension of EECC
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performance in China.
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Mengcheng Zhu, Liyin Shen, Vivian W.Y. Tam, Zhi Liu, Tianheng Shu, Wenzhu Luo PII:
S0048-9697(20)30353-3
DOI:
https://doi.org/10.1016/j.scitotenv.2020.136843
Reference:
STOTEN 136843
To appear in:
Science of the Total Environment
Received date:
15 November 2019
Revised date:
16 January 2020
Accepted date:
20 January 2020
Please cite this article as: M. Zhu, L. Shen, V.W.Y. Tam, et al., A load-carrier perspective examination on the change of ecological environment carrying capacity during urbanization process in China, Science of the Total Environment (2020), https://doi.org/ 10.1016/j.scitotenv.2020.136843
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© 2020 Published by Elsevier.
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A load-carrier perspective examination on the change of ecological environment carrying capacity during urbanization process in China Mengcheng Zhua,b, Liyin Shena,b*, Vivian W. Y. Tamc, Zhi Liua,b, Tianheng Shua,b, Wenzhu Luoa,b a
School of Management Science and Real Estate, Chongqing University, Chongqing,
PRChina. b
International Research Center for Sustainable Built Environment, Chongqing
School of Computing, Engineering and Mathematics, Western Sydney University,
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University, Chongqing, PRChina.
NSW 2751, Australia
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E-mail address:
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*Liyin Shen, corresponding author, [email protected] Mengcheng Zhu, [email protected].
Zhi Liu, [email protected]
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Vivian W. Y. Tam, [email protected]
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Tianheng Shu, [email protected]
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Wenzhu Luo, [email protected]
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A load-carrier perspective examination on the change of ecological environment carrying capacity during urbanization process in China Abstract Urbanization has prompted a dramatic social and economic development during the past decades in China. As a long-term national strategy, urbanization can only be
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implemented effectively with sufficient and sustainable ecological environment
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resources. By appreciating that the ecological environment carrying capacity (EECC) is a yardstick for guiding the practice of sustainable urban development, it is therefore
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pressing to examine the change of EECC adequately, so that the sustainable
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urbanization can be addressed appropriately. This paper develops a new method from
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load-carrier perspective to explore the change of EECC performance in the rapid urbanizing China. The EECC performance on water, land, atmosphere and overall
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perspectives were measured for 30 provinces in China based on the established
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method. The results show that most provinces in China are experiencing an improving EECC performance during the urbanization process, particularly with an obvious progress in land dimension. In referring to the spatial difference of overall EECC performance, the gap between 30 provinces has been narrowing during surveyed years. However, few provinces including Chongqing, Shandong and Jiangxi have undergone a degradation in overall EECC performance. The EECC performance in atmosphere dimension is still considered as a challenge faced by most provinces, evidenced by high level of PM2.5 concentration. These research findings provide valuable references not only for Chinese governments to formulate effective policy instruments and strategy measures for improving ecological environmental carrying status, but also for researchers to further study in the ecological environment carrying capacity in 2
Journal Pre-proof the context of other countries. Keywords: ecological environment carrying capacity (EECC); urbanization; indicator system; carrier; load
1. Introduction China has experienced unprecedented speed and scale of urbanization process in the past several decades (Zhang et al., 2019; Shen et al., 2019). According to National
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Bureau of Statistics of China (2018), the urbanization rate in China has been risen
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from 17.9% in 1978 to 58.5% in 2017. This national strategy will continue in the
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coming future in China. According to the 13th Five-Year-Plan, the Chinese government committed to further promote urbanization and reach the urbanization
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rate of 60% in 2020 (The State Council of China, 2016b). As a major developing
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country, China has profound influence to global development through implementing its urbanization mission. It is widely appreciated that China has become the second
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largest economy entity (Wu et al., 2019c). According to the World Bank (2017), the
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total Gross Domestic Product (GDP) in China has increased at an annual rate of 9% during1978-2017, and reached to US $ 10.161 trillion (constant 2010 US $ prices) in 2017.
However, as a major national development strategy in China, urbanization can only be sustainable if the resources needed are provided sustainably (Akotia and Sackey, 2018; Bamgbade et al., 2018). Land, water and atmosphere are essential ecological resources for enabling the implementation of urbanization. In the context of China, the ecological environment has been changing over the last several decades in line with its dramatic urbanization process. For example, according to the annual report on Environment Development of China (2013), Chinese cities are suffering from severe environmental problems such as air pollution, water scarcity and water 3
Journal Pre-proof pollution, heavy metal pollution, during the dramatic urbanization process (Liu, 2013). Wang et al. (2018b) pointed out that air pollution accompanied with rapid urbanization is seriously threatening the public health in China. Other city problems such as housing shortage (Lee, 1988) and transportation congestion (Wu et al., 2019a) have also emerged in the process of Chinese urbanization. On the other hand, also in China, it has been found that certain ecological benefits have been obtained in some areas during the urbanization process. As opined by He et al. (2017), the coupling
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degree between urbanization and eco-environment in Shanghai has significantly
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increased during the urbanization process of 1980-2013. It is thus not explicit how the
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ecological environment changes in the urbanizing China.
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Ecological environment carrying capacity (EECC) is a yardstick for guiding urban development towards sustainability (Wei et al., 2015). It is considered that the
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ecological environment can be overloaded if the urbanization strategy is not adjusted
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to match the EECC appropriately, and the change of ecological environment will affect the implementation of urbanization strategy in turn. Under such circumstance,
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both improving the ecological environment and implementing the sustainable urbanization process are essential for Chinese governments, which can only be achieved by exploring the change of EECC during urbanizing China. This argument is also echoed by other studies (Liu et al., 2018; Wang et al., 2018a). Without such understanding, the mission of sustainable urbanization in China might be sabotaged due to the overloaded ecological environment. In fact, it has been reported that urbanization has been hindered in some Chinese cities because of the degrading ecological environment. For example, Wu and Tan (2012) pointed out that water scarcity has become a common constraint to urban development in Eastern coastal provinces in China.
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Journal Pre-proof Previous studies have contributed greatly to the literature development in the disciplines of ecological environment and urbanization. Specifically, existing studies related to ecological environment can be summarized into three aspects, namely, exploring the relationship between urbanization and ecological environment, assessing the ecological environment status, as well as investigating the performance of EECC. For instance, In order to explore the relationship between atmospheric environment and urbanization, Ali et al. (2019) explored the impact of urbanization
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on carbon emissions in Pakistan during the period of 1972-2014. The research results
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showed the increase of carbon emissions can be induced by implementing
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urbanization both in long and short run. From the perspective of single city, Liu et al.
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(2018) studied the relationship between urbanization and atmosphere environment in Jinan city of China from 1996 to 2004 by applying the theory of Environmental
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Kuznets Curves. Their study indicated that the introduction of advanced technology,
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higher efficiency of resource consumption, and less emissions of environmental pollution should be emphasized in the policymaking process. Li et al. (2012)
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developed a coupling coordination degree model for evaluating the coordination degree between urbanization and environment from 2000 to 2008 in Lianyungang city in China. The results of this study indicated that the dynamic of coordination between these two systems shows a U-shaped curve. The study by He et al. (2017) also demonstrated that there is S-shaped relationship existing between urbanization and eco-environment in Shanghai from 1980 to 2013. From a perspective of urban agglomeration, Wang et al. (2019c) investigated the coordination between urbanization and the ecological system in Beijing-Tianjin-Hebei urban agglomeration, and found that the dominant factors influencing the coordination include social consumer goods, gross domestic product, and the disposable income of urban
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Journal Pre-proof residents. Cui et al. (2019) developed a comprehensive coordination development index for urbanization-resource-environment system to represent the sustainability of urbanization in Jing-Jin-Ji region, the results suggested that a zoning strategy can improve the coordination degree and promote sustainable urbanization. Many scholars have also examined ecological environment status from either single or multiple perspectives. For example, Xian et al. (2007) investigated the change of urban land use in Tampa Bay in West-Central Florida. Chen (2007) outlined
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the change of the cultivation land in China, and the causes of the change were also
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analyzed. The study results suggested that urbanization is a great threat to future
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agricultural production. Wang et al. (2008) examined the temporal variations of
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surface water quality in urban, suburban and rural areas between 1982to 2005 in Shanghai, and found that water quality in urban and suburban areas were improved by
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strengthening environmental policies and management. Zhou et al. (2018) measured
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the air quality of 31 main cities in China by using Air Quality Index, and the results demonstrated that the best air quality appeared in August and September while the
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worst occurred in December and January. Wu et al. (2019b) established an evaluation indicator system to investigate the ecological environment state for Kunming city in China, the results showed that the ecological level in this city increased from 2000 to 2015.
In referring to the study of ecological environment carrying capacity, an increasing number of studies have been conducted to evaluate EECC performance by employing the methods of ecological footprint, system dynamics model, and comprehensive index system. For example, based on ecological footprint theory, Peng et al. (2019) estimated the dynamic evolution of ecological carrying capacity in Jiangsu province in China from 2012-2017. Fang et al. (2017) introduced a system
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Journal Pre-proof dynamics model to measure urban ecological carrying capacity in Beijing during 1996-2012. Ma et al. (2017) proposed an index system for the evaluation of marine ecological carrying capacity in Dongtou Islands in China from 2009-2014. However, it seems that previous research work has not offered a clear definition for EECC, or failed in discussing the implication of ecological environment carrying capacity (Liu and Borthwick, 2011; Price, 1999). . Furthermore, limitations of the existing methods for assessing EECC have also been pointed out. For instance, the ecological footprint
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theory method is more applicable at global level than regional level, because this
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method evaluating EECC based on the land area needed to sustain human lives (Fiala,
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2008; Wei et al., 2015). The system dynamics model can analyze the interrelationship
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within a complex system for a long-term research, but it is subjective and difficult to select parameter index (Zhang et al., 2019). Zhang et al. (2019) commented that the
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index system approach cannot help examine whether the pressure of human social
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activities on ecological environment has exceeded the ecological environment carrying capacity, although this method can help reveal the level of EECC. In fact,
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carrying capacity is originally a physical concept to describe the carrier’s ability for supporting the load it carries (Zhang et al., 2019; Shen et al., 2020). Without considering the interrelationship between carrying objects and carriers, it is difficult to judge whether the examined carrying capacity is overloaded. Thus, both the carrier and the load it carries should be incorporated when evaluating the carrying capacity for a specific object, so that the interrelationship between load and carrier can be reflected. Based on the above discussion, this study aims to develop a novel measurement model which embodies the relationship between carriers and loads, and examine the change of EECC in the Chinese urbanization process. Empirical study will be
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Journal Pre-proof conducted by using the data collected from 30 provinces in China. The differences and similarities of the EECC change between the provinces will be discussed. The rest of this paper is structured as follows: Section 2 presents the methodologies of this research. The establishment of evaluation index for EECC is showed in Section 3. Research data and results are displayed in Section 4 and Section 5 respectively. Finally, discussions and conclusions are provided in Section 6 and Section7
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respectively.
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2. Research methodology
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2.1 Research Roadmap
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In order to conduct this study, the research roadmap is designed as follows.
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Firstly, the measurement for EECC will be defined through literature review and theoretical analysis. Secondly, research data will be collected from multiple resources,
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including Statistical Yearbooks, National Bureau of Statistics of China and research institutions. Thirdly, the in-depth analysis on the change of EECC will be conducted
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from both temporal and spatial perspectives. The research roadmap can be graphically highlighted in Figure1.
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Journal Pre-proof Figure 1 Research roadmap
2.2 Measurement for ecological environment carrying capacity Great efforts have been paid to develop various methods for assessing the value of carrying capacity in the previous research. In studying the mechanics of materials, the carrying capacity of an object refers to the maximum loads that the object could bear without causing any physical damage (Feng et al., 2018). In line with this, some
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researchers attempt to find a threshold as the limit of ecological environment carrying
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capacity. For example, ecological footprint method has been widely adopted by researchers for evaluating the limit of ecological carrying capacity (Peng et al., 2019;
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Świąder et al., 2018). In their study, ecological environment carrying capacity refers
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to the sum of all productive lands in an area that can provide the resources for
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consumption by local residents. Fang et al. (2017) introduced system dynamics approach for examining ecological environment carrying capacity to judge whether
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the capacity is sufficient to support social and economic activities. However, different
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from physical object, ecological environment system is not static (Costanza, 1996). In other words, the carrying capacity of ecological environment is dynamic and changeable.
As a sub-system of nature, ecological environment system consists of numerous natural elements such as water, land and atmosphere, which provide available resources and ecological service to support human survival and assimilate waste and pollution generated by human activities (Liu and Borthwick, 2011). Nature system is an open, dynamic, adaptive, resilient and complex system, characterized by variety of social, economic and environmental factors (Chelleri, 2012). These social and economic factors can not only exert various loads to ecological environment system, but also strengthen ecological environment carriers through environmental 9
Journal Pre-proof governance such as trees planting and water source protection, and reduce ecological environment loads by developing pollution treatment facilities. In addition, the improvement of environment management and technological progress is also beneficial for reducing the ecological environment loads. Ideally, the ecological environment system itself will keep its function in a relatively dynamic equilibrium state. However, this equilibrium may be affected and even broken by the surge of ecological environment loads or the degradation of ecological environment carriers,
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as the existing carriers may not be able to offer sufficiently supporting capacity (Shen
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et al, 2020). Consequently, the state of ecological environment may arrive at a
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crossing threshold, thereafter moving into an uncertain transitional process (Walker
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and Meyers, 2004). Nevertheless, it should be noted that the threshold of the ecological environment carrying capacity would not occur in reality since the damage
lP
experiment for human-nature system cannot be conducted. In other words, it is hardly
na
possible to find the actual value of potential threshold because the destroy experiment is not affordable in human-nature system. Therefore, the measurement for EECC in
Jo ur
this study is not considered as a threshold, but a relationship between ecological environment loads and ecological environment carriers, which can be written as follows:
ρ (carrying capacity index) =
𝐿𝑜𝑎𝑑 𝐶𝑎𝑟𝑟𝑖𝑒𝑟
(1)
where load refers to various objects related to human activities; carrier is natural or man-made support to the load. ρ denotes for the carrying capacity index of the concerned carrier. The lower value of ρ represents the better performance of carrying status. The novelty of this method lies in that an innovation evaluation framework of 10
Journal Pre-proof EECC is proposed by considering carriers and loads collectively, and also the interaction between corresponding loads and carriers. The specific indicators for ecological environment carriers and loads will be discussed in the next section.
3. Establishment of evaluation index system for EECC 3.1 Identifying indicators for ecological environment carriers and loads
of
Various indicator systems have been introduced for evaluating the performance of ecological environment. For example, He et al. (2017) examined the
ro
eco-environment level in Shanghai by using a group of indicators under the
-p
dimensions of ecological environment level, ecological environment endowment,
re
ecological environment pressure and ecological environment response. Wang et al.
lP
(2019c) evaluated the ecological environment system of Beijing-Tianjin-Hebei region by using the indicators across ecological environment pressure, ecological
na
environment state and ecological environment response sub-systems. Zhang et al. (2018) established an indicator system for evaluating the performance of ecological
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environment, in which indicators from water, land, atmosphere, energy and solid waste dimensions were applied. In referring to these existing work, it can be seen that the major indicators for investigating ecological environment performance are under the dimensions of water, land and atmosphere. The typical research work involving ecological environment indicators is listed in Table 1. Accordingly, the three dimensions of water, land and atmosphere are adopted in this study for evaluating EECC performance. In referring to model (1), the indicators in each dimension are divided into load-indicators and carrier-indicators in the process of indicator identification.
11
Journal Pre-proof Table 1 Summary of typical indicators for ecological environment evaluation in previous studies Literature Liu et al. (2018)
Indicators employed in literature Water dimension
Land dimension
Atmosphere dimension
Discharge of industrial wastewater
Disposed industrial solid waste
Emission of industrial SO2
Total water supply
Land use per capita Cultivated area per capita Green area per capita
f o
Greening ratio within constructed areas
He et al. (2017)
Total volume of water per capita
Percentage of vegetation with green areas
Discharge of industrial wastewater
Green areas per capita
Urban domestic sewage treatment rate
Cultivate land area per capita
o r p
e
Discharge of industrial dust per capita
r P
Comprehensive utilization rate of industrial solid waste
l a
Hazard-free treatment rate of domestic garbage Li et al. (2012)
Wang et al. (2019c)
Per capita water consumption
o J
n r u
Per capita water resources
Per capita industrial wastewater discharge
Sewage treatment plant centralized treatment rate
Forest cover rate
Per capita carbon emissions
Green land coverage in built-up areas
Per capita industrial sulfur dioxide emissions
Ecological land area per 10,000 people
PM2.5 concentration
Per capita industrial solid waste discharge Harmless treatment rate of domestic garbage Comprehensive utilization rate of industrial solid waste
Tang (2015)
Annual average ambient air concentrations of SO2 Annual average ambient air concentrations of PM10 Annual average ambient air concentrations of NO2
Per capita water volume
Forest coverage rate
Ratio of industrial wastewater
Nature reserve coverage rate 12
Journal Pre-proof meeting discharge standards Per capita public green areas Per capita cultivated area
Fan et al. (2019)
Per capita water resources
Green ratio for built-up area
Domestic swage treated ratio
Per capita public green area
Fine rate of atmospheric environment quality
Percentage of harmless disposal of domestic garbage
Ding et al. (2014)
Per capita local water resources
Forest coverage rate
Zhang et al. (2018)
Water quality compliance rate of drinking water source
Comprehensive utilization rate of industrial solid waste
River proportion worse than Grade V
Green coverage rate of built area
Per capita water resources
Per capita park green space
Desalination of seawater
Output rate of unit construction land
Sewage treatment rate
Harmless disposal rate of municipal solid waste
l a
Rate of ecological water consumption Rate of reclaimed water to total water supply
Wang et al. (2018a)
Water coverage rate
e
r P
n r u
Total emission of industrial waste gas SO2 emission intensity of per unit GDP
Forest coverage
Industrial waste air emissions
Green coverage area
Industrial sulfur dioxide emissions
Public recreational green space
Industrial smoke emissions
Urban sewage treatment rate
Proportion of the nature reserve area
Industrial nitrogen oxides emissions
Recycling rate of industrial water
Comprehensive utilization rate of general industrial solid waste
Discharged of wastewater per unit of GDP
Percentage of vegetation with green areas
Sewage treatment plants per 10,000 people
Garbage disposal plants per 10,000 people
Sewage discharged
o J
Industrial wastewater discharged
Cui et al. (2019)
o r p
f o
Days of air quality equal to or above Grade 2 in the whole year
13
Discharge of volume of SO2 per unit of GDP
Journal Pre-proof EE indicators from water dimension perspective By comprehensively reviewing the studies listed in Table 1, typical indicators for presenting water carriers are identified as natural water resources (including surface water and underground water), recycled water production, desalination of seawater, transferred water across region, the facility capacity of urban sewage treatment, the capacity of water self-purification, and the facility capacity of industrial wastewater treatment. Among these carriers, natural water resources, recycled water production,
of
desalination of seawater, and transferred water across region are the providers for total
ro
water consumption in supporting all types of human activities. They have also been
-p
adopted officially in counting water supply and water resources in China (National
re
Bureau of Statistics of China, 2007-2016a). Urban sewage and industrial wastewater treatment are man-made facilities for water purification, while water self-purification
lP
is regard as an ecological repair in water ecosystem (Ostroumov, 2005).
na
Furthermore, typical indicators for presenting water loads are identified as total water consumption, domestic sewage discharge and industrial wastewater discharge.
Jo ur
For the load of total water consumption, it is induced by various human activities including agricultural, industrial, living and ecological activities. The other two water loads are triggered by wastewater discharging. Domestic and industrial wastewater is considered as a type of water load as it needs to be purified in order to prevent the sabotage of water ecological environment. EE indicators from land dimension perspective According to the references in Table 1, typical indicators for presenting land carriers include total forest coverage, urban green areas, the facility capacity for household garbage disposal, the capacity of natural degradation for household garbage, and the facility capacity of industrial solid waste disposal. Forestry area and
14
Journal Pre-proof green land can offer the ecological functions for the concerned region. They provide the services to maintain soil, water, and biodiversity, and mitigate the effects of global warming (Hao et al., 2019). Those man-made facilities are functional to dispose the household garbage and industrial solid waste, thus the harm to land ecological environment can be reduced. Therefore, the capacity of these facilities can significantly support the land ecological environment. Furthermore, the natural degradation of household garbage describes the ability of microorganisms that can
of
degrade garbage in land ecosystem.
ro
The ecological environment loads in land dimension are identified including total
-p
land areas, urban areas, the volume of household garbage and industrial solid waste.
re
Total land areas and urban areas need the ecological services provided by forests and green land. In other words, they can be regarded as a type of load to forestry areas and
lP
green areas. On the other hand, the volume of household garbage and industrial solid
na
waste present another type of load influencing the land ecological environment, which will be processed by man-made facilities and natural degradation.
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EE indicators from atmosphere dimension perspective According to previous studies, PM2.5 is one of the major air pollutants contributing to the severe atmospheric environment problems. It can levitate for a long time and travel in a long distance, which will threaten the human health, human activities and ecology system (Han et al., 2017). The maximum concentration of PM2.5 without causing adverse effects on human health is restricted above the Grade 2 level in the Chinese Ambient Air Quality Standards (GB3095-2012). Thus, the ambient air concentration of PM2.5 is employed as the load indicator, and the PM2.5 standard of Grade 2 is used as the carrier indicator for assessing atmospheric environment.
15
Journal Pre-proof Based on the above discussions, the load and carrier indicators of ecological environment under the dimensions of water, land and atmosphere can be listed in Table 2. Table 2 Indicators system for ecological environment carrying capacity (EECC) Carrier indicators
Load indicators
Natural water resources Recycled water production
of
Total water consumption
ro
Desalination of seawater
Water
The facility capacity of urban sewage treatment
-p
Transferred water across region
Domestic sewage discharge
re
The capacity of water self purification
Atmosphere
Industrial wastewater discharge
𝐿
Total forestry coverage
𝐿
Total land area for human activities
𝐿
Urban green areas
𝐿
Urban area
𝐿
The facilities capacity for household garbage disposal 𝐿
The volume of household garbage
𝐿
The capacity of natural degradation for household garbage
𝐿
The facility capacity of industrial solid waste disposal
𝐿
Industrial solid waste
Jo ur
Land
na
lP
The facility capacity of industrial wastewater treatment
PM2.5 standard of Grade 2 in the whole year
Annual average ambient air concentration of PM2.5
It can be seen from Table 2 that, there are several couples of indicators for corresponding loads and carriers under each dimension. By referring to model (1), the primary EECC indexes under water, land and atmosphere dimensions can be obtained. 16
Journal Pre-proof In water dimension, water load ; water load
will be supported by water carriers
corresponds to the water carriers
corresponds to the water carrier
and
,
,
,
; water load
. Therefore, water ecological carrying capacity
can be presented by using the following three primary EECC indexes: 𝐿
=𝐶
𝐶
=
𝐶
(2)
𝐶
𝐿 𝐶
(3)
𝐶 𝐿
; load 𝐿
and
corresponds to the land carrier 𝐿
; load
𝐿
𝐿
can be supported by land
; load
𝐿
corresponds to the land carrier
will be carried by 𝐿
. The primary
re
land carriers
𝐿
-p
𝐿
𝐿
ro
In referring to land dimension in Table 2, land load carrier
(4)
of
=𝐶
indexes for measuring land ecological carrying capacity can be calculated as follows: =𝐶
𝐿
=
𝐿
Jo ur
na
lP
𝐿
𝐿
=𝐶
(5)
𝐿
(6)
𝐶 𝐿 𝐶 𝐿
𝐿
=𝐶
(7) (8)
From Table 2, it can be seen that there is only one index for measuring atmosphere ecological carrying capacity. =
𝐿 𝐶
(9)
Accordingly, the dimensional indexes for measuring EECC in water, land and atmosphere dimensions can be obtained by integrating their primary indexes respectively: ρ
= ∑3𝑖=1(
ρ𝐿 = ∑4𝑖=1( 17
𝑖
𝐿𝑖
× ω 𝑖)
(10)
× ω𝐿𝑖 )
(11)
Journal Pre-proof ρ = ∑1𝑖=1(
where ω 𝑖 , ω𝐿𝑖 , ω
𝑖
𝑖
× ω 𝑖)
(12)
are weighting values.
Thus, the overall EECC performance can be calculated by the following formula: ρ𝑂 = ∑(
×𝜔 +
𝐿
× 𝜔𝐿 +
×𝜔 )
(13)
where 𝜔 , 𝜔𝐿 and 𝜔 are weighting values. All the weighting values in (10), (11), (12), (13) will be discussed in the next section.
of
3.2 Defining the weighting values for EECC indexes
ro
For evaluating the performance of EECC in this study, entropy method is used to
-p
determine the weighting between EECC indexes. Entropy method was originated
re
from thermodynamics and it was introduced into the information management
lP
discipline by Shannon (1948) for expression of information or uncertainty. This method has widely been used in engineering, economy, finance and other disciplines
na
(Zou, Yun & Sun, 2006), and its application has also been extended to the study of urban ecosystems (Larsen & Gujer, 1997; Antrop, 1998; Peixoto et al, 1991). Previous
Jo ur
studies have further opined that this method can be used effectively for determining weightings between indicators where evaluation work is conducted based on a group of indicators (Shemshadi et al., 2011; Wang et al., 2015; Sun et al., 2017, Shen et al., 2015). The principle of this method is that the greater difference between sample values of an indicator shows, additional information about the indicator can be obtained, greater significance for the indicator in comprehensive evaluation, thus a higher weight value will be assigned to this indicator (Wang et al., 2018a). Therefore, in order to capture significant information between EECC indexes, entropy method is adopted in this study for integrating primary EECC indexes and dimensional EECC indexes. The process of applying entropy method is following.
18
Journal Pre-proof (1) Construction of the initial decision matrix Assuming that there are m evaluation indexes and n evaluation objects (provinces in this study), the value of index j in object i is 𝑎𝑖𝑗 , the decision matrix of all objects (provinces) can be presented as: 𝑎11 A = (𝑎𝑖𝑗 )𝑚×𝑛 = [ ⋮ 𝑎𝑚1
⋯ 𝑎1𝑛 ⋱ ⋮ ] … 𝑎𝑚𝑛
(14)
(2) Standardization of decision matrix
𝑎𝑖𝑗 − min𝑗 (𝑎𝑖𝑗 )(
ro
𝑟𝑖𝑗 =
of
The value 𝑎𝑖𝑗 in (14) needs to be standardized by using the following formula:
max𝑗(𝑎𝑖𝑗 ) − min𝑗 (𝑎𝑖𝑗 )
(15)
-p
Then, the standardized decision matrix can be obtained as:
re
⋯ 𝑟1𝑛 ⋱ ⋮ ] … 𝑟𝑚𝑛
(16)
lP
R = (𝑟𝑖𝑗 )𝑚×𝑛
𝑟11 = [ ⋮ 𝑟𝑚1
(3) Establishment of weighting values for indexes
na
Firstly, 𝑓𝑖𝑗 , the proportion of 𝑟𝑖𝑗 needs to be calculated as follows: 𝑟𝑖𝑗 ∑𝑛 𝑖= 𝑟𝑖𝑗
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𝑓𝑖𝑗 =
(17)
Then, 𝐸𝑖 , the entropy value for each index can be determined: 𝐸𝑖 = −1/ln 𝑛 × ∑𝑛𝑖=1(𝑓𝑖𝑗 × ln 𝑓𝑖𝑗 )
(18)
when 𝑟𝑖𝑗 = 0, 𝐸𝑖 = 0
Accordingly, the weight of each index can be obtained by following formula: 𝑤𝑖 =
1−𝐸𝑖 𝑚−∑𝑚 𝑗= 𝐸𝑖
(19)
4. Research data The original data of EECC indicators listed in Table 2 are collected from multiple resources during the period of 2007-2016 for assessing the EECC performance in 30 provinces of China. In fact, this period has been evidenced as the 19
Journal Pre-proof highest urbanization process in China. The 30 provinces include Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Hainan, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. The data resource of indicators in water and land dimensions are from National Bureau of Statistics of China(2007-2016a), China Urban Construction
Statistical
Yearbook
(Ministry
of
Housing
and
Urban-Rural
of
Development of the People's Republic of China, 2007-2016) and China City
ro
Statistical Yearbook (National Bureau of Statistics of China, 2007-2016b). The annual
-p
mean values of surface PM2.5 concentrations with 0.1°×0.1° latitude/longitude
re
resolution in each province during the study period are collected from Atmospheric Composition Analysis Group of Dalhousie University (2007-2016). Based on
lP
Ambient Air Quality Standards (GB3095-2012), PM2.5 Standard of Grade 2 in the
na
whole year is 35 µg/m3, therefore data at this stage, indicators of
= 35µg/m3. However, due to the inaccessible ,
,
,
，
,
𝐿
,
, and
𝐿
𝐿
were
Jo ur
removed in the demonstration of this study. All the data collected are considered effective and valid for further analysis, as they are published by official documents. Due to the large volume of the data, the sample sheet of original indicator data for the year 2016 is provided in Table 3.
Table 3 Sample sheet of original indicator data for the year 2016 𝐿 4
Province
(108 m3)
3
⁄
𝐿
𝐿
⁄
𝐿
4
(10 m
(10
per
m3 per
day)
day)
(108
(108
ton)
ton)
𝐿
(ton (%)
(%)
per day)
𝐿
(104
(μg/
ton)
m³)
Beijing
35.1
513.5
630.9
38.8
15.79
35.8
48.4
24341
872.6
47
Tianjin
18.9
35.3
291.5
27.2
7.35
9.9
37.2
10800
269.0
67
Hebei
208.3
196.5
607.0
182.6
22.10
23.4
40.8
23140
725.2
49
Shanxi
134.1
147.0
257.9
75.5
11.34
18.0
40.5
13456
469.4
37
20
Journal Pre-proof Inner
426.5
109.9
245.5
190.3
8.32
21.0
39.9
11969
345.3
21
Liaoning
331.6
115.6
831.4
135.4
17.25
38.2
36.4
25603
933.1
33
Jilin
488.8
15.0
318.7
132.5
8.04
40.4
35.0
15095
534.1
28
Heilongjiang
843.7
28.0
758.2
352.6
10.87
43.2
35.4
16306
541.9
20
Shanghai
61.0
0.0
806.9
104.8
18.42
10.7
38.6
23530
629.4
45
Jiangsu
741.7
334.1
1742.9
577.4
43.46
15.8
42.9
55403
1562.3
55
Zhejiang
1323.3
64.5
1001.8
181.1
30.09
59.1
41.0
48250
1433.5
28
Anhui
1245.2
52.0
507.7
290.7
19.15
27.5
41.7
18087
540.0
48
Fujian
2109.0
11.0
382.8
189.1
16.82
66.0
43.3
19431
657.0
18
Jiangxi
2221.1
2.8
259.9
245.4
16.63
60.0
43.6
10505
399.5
32
Shandong
220.3
388.8
1069.9
214
34.70
16.7
42.3
42484
1466.3
63
Henan
337.3
142.6
679.7
227.6
33.40
21.5
24757
915.4
58
Hubei
1498.0
49.1
687.7
282
22.57
38.4
37.6
25136
880.1
43
of
Hunan
2196.6
136.9
613.1
330.4
25.12
47.8
40.6
23013
681.6
37
Guangdong
2458.6
68.3
2039.1
435
82.89
51.3
42.4
71217
2391.0
25
Guangxi
2178.6
0.2
718.0
290.6
14.52
56.5
37.6
12651
411.2
27
Hainan
489.9
19.1
90.9
45
4.36
55.4
40.3
6133
188.7
12
Chongqing
604.9
19.3
289.7
77.5
17.62
38.4
40.8
11753
494.1
37
Sichuan
2340.9
65.9
609.5
267.3
32.16
35.2
39.9
24500
886.7
19
Guizhou
1066.1
5.1
183.9
100.3
9.07
37.1
36.8
9290
294.0
24
Yunnan
2088.9
13.4
250.9
150.2
14.93
50.0
37.8
11079
432.1
13
Shaanxi
271.5
90.5
348.0
90.8
13.66
41.4
40.1
18075
532.8
34
Gansu
168.4
20.3
131.7
118.4
2.36
11.3
31.5
7840
257.2
38
Qinghai
612.7
6.8
51.8
26.4
2.73
5.6
31.1
2253
82.0
35
Ningxia
9.6
31.2
91.0
64.9
2.49
11.9
40.4
4460
112.2
34
Xinjiang
1093.4
79.7
255.3
565.4
8.79
4.2
38.5
9643
378.7
51
re
lP
na
Jo ur
5. Results
-p
39.3
ro
Mongolia
In order to conduct the calculation for EECC indexes according to Equations (10)-(13), the primary EECC indexes
𝑖
,
𝐿𝑖 ,
𝑖
should be calculated first. By
applying the data collected in Section 4 to Equations (2)-(9), the results of 𝐿
,
𝐿
,
𝐿
,
,
,
can be obtained, as presented in Appendix 1-5 respectively.
Then, based on the Entropy weighting method described in Section 3.2, the weighting values 𝜔 𝑖 , 𝜔𝐿𝑖 , 𝜔 index values
𝑖
,
𝐿𝑖 ,
𝑖
𝑖
can be obtained by applying the calculated primary
to Equations (14)-(19) respectively. Accordingly, the
21
Journal Pre-proof dimensional EECC indexes ρ , ρ𝐿 , ρ , can be calculated according to Equations (10)-(13), as shown in Table 5-7. Furthermore, the weighting values of 𝜔 , 𝜔𝐿 , 𝜔 can be calculated by applying the dimensional index values ρ , ρ𝐿 , ρ , to Equation (14)-(19). The overall EECC index ρ𝑂 can be calculated based on Equation (13), as shown in Table 8. The results of all the weighting values are presented in Table 4. Table 4 Weighting values between dimensional and primary indexes 𝜔 𝜔
ω𝐿
0.31
𝜔𝐿 𝜔𝐿 𝜔𝐿
ω
0.34
𝜔
0.46 0.54 0.34 0.31 0.35 1.00
lP
re
0.35
ω
Weights
of
Primary Index
ro
Weights
-p
Dimensional Index
(ρ )
na
Table 5 Evaluation results of EECC index in water dimension across surveyed 30 provinces 2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
1.09
0.77
1.01
1.12
1.07
0.90
1.04
0.96
0.96
0.70
1.19
0.83
0.95
1.33
0.96
0.68
1.07
1.29
1.23
0.99
1.14
0.95
1.05
1.06
1.00
0.89
1.06
1.36
1.17
0.93
0.81
0.84
0.86
0.86
0.85
0.98
1.01
0.97
0.98
0.90
0.69
0.71
0.70
0.66
0.77
0.77
0.69
0.72
0.68
0.70
Liaoning
0.65
0.63
0.79
0.48
0.55
0.45
0.42
0.76
0.67
0.49
Jilin
0.63
0.78
0.70
0.58
0.66
0.58
0.52
0.65
0.57
0.50
Heilongjiang
0.54
0.68
0.52
0.55
0.72
0.67
0.35
0.41
0.43
0.40
Shanghai
2.09
1.93
1.81
2.04
3.12
1.94
2.36
1.37
1.08
1.13
Jiangsu
0.81
0.93
0.89
0.93
0.84
1.01
1.25
1.02
0.81
0.72
Zhejiang
0.49
0.49
0.49
0.55
0.65
0.59
0.57
0.55
0.54
0.51
Anhui
0.48
0.52
0.51
0.46
0.74
0.73
0.71
0.65
0.62
0.67
Fujian
0.49
0.44
0.59
0.59
0.74
0.63
0.58
0.62
0.72
0.69
Jiangxi
0.73
0.72
0.62
0.62
0.92
0.92
0.98
0.95
0.85
1.00
Shandong
0.62
0.65
0.74
0.73
0.71
0.82
0.86
1.15
1.08
0.90
Henan
0.81
0.90
0.97
0.82
1.03
1.16
1.29
1.11
1.04
1.03
Hubei
0.57
0.60
0.68
0.61
0.71
0.71
0.71
0.68
0.66
0.57
Tianjin Hebei Shanxi Inner Mongolia
Jo ur
Province
22
Journal Pre-proof 0.62
0.68
0.57
0.55
0.62
0.61
0.73
0.70
0.68
0.68
Guangdong
0.76
0.68
0.71
0.55
0.66
0.67
0.67
0.70
0.67
0.68
Guangxi
0.41
0.35
0.27
0.27
0.36
0.34
0.38
0.39
0.39
0.36
Hainan
1.13
1.08
0.73
0.72
0.60
0.65
0.54
0.58
0.62
0.75
Chongqing
0.60
0.70
0.73
0.73
0.73
0.71
0.72
0.69
0.70
0.96
Sichuan
0.81
0.74
0.70
0.69
0.80
0.82
0.85
0.78
0.76
0.83
Guizhou
1.28
1.12
0.90
0.62
0.76
0.85
0.61
0.86
0.90
0.77
Yunnan
0.58
0.64
0.60
0.43
0.69
0.76
0.77
0.78
0.81
0.91
Shaanxi
0.82
0.66
0.62
0.55
0.63
0.69
0.69
0.70
0.70
0.73
Gansu
0.58
0.59
0.69
0.75
0.66
0.62
0.61
0.70
0.78
0.59
Qinghai
1.41
1.10
1.16
1.03
1.00
0.62
0.61
0.66
0.55
0.80
Ningxia
3.42
3.92
4.14
3.67
3.84
3.20
3.14
3.49
3.54
3.18
Xinjiang
0.69
0.77
0.79
0.76
0.68
0.74
0.70
0.81
0.72
0.75
Mean value
0.90
0.88
0.88
0.84
0.94
0.86
0.88
0.90
0.86
0.83
Variance
0.34
0.40
0.44
0.38
0.49
0.26
0.31
0.29
0.29
0.23
ro
of
Hunan
-p
Table 6 Evaluation results of EECC index in land dimension across surveyed 30 provinces 2012
2013
2014
2015
2016
1.98
1.98
1.89
1.89
1.89
1.92
4.30
4.28
4.28
4.32
4.28
4.27
2.54
2.56
2.56
2.61
2.48
2.42
2.47
2.95
2.99
2.98
2.95
2.98
2.88
2.90
2.85
2.85
2.80
2.72
2.61
2.63
2.61
2.37
2.15
2.14
2.09
2.10
2.06
2.03
2.11
2.60
2.51
2.32
2.34
2.33
2.18
2.08
2.10
( ρ𝐿 ) 2007
2008
2009
2010
Beijing
2.95
2.89
2.04
1.97
Tianjin
4.99
5.09
4.49
4.40
Hebei
3.44
3.14
2.71
Shanxi
4.10
3.59
2.94
Mongolia
3.68
3.46
2.94
Liaoning
2.56
2.49
Jilin
2.95
2.94
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Heilongjiang
na
Inner
2011
re
Province
3.05
2.84
2.61
2.39
2.36
2.25
2.15
2.16
2.05
2.02
10.89
11.11
4.43
4.43
4.62
4.35
4.11
4.06
4.06
4.02
5.29
5.22
3.04
3.03
3.02
3.04
3.05
3.01
3.02
3.02
1.85
1.79
1.72
1.71
1.71
1.67
1.64
1.63
1.65
1.66
2.88
2.80
2.56
2.49
2.36
2.35
2.30
2.26
2.24
2.24
Fujian
1.92
1.78
1.70
1.63
1.58
1.58
1.60
1.60
1.58
1.59
Jiangxi
1.94
1.83
1.75
1.70
1.61
1.61
1.64
1.61
1.63
1.67
Shandong
3.57
3.52
2.96
2.95
2.96
2.95
2.95
2.91
3.01
2.99
Henan
3.34
3.29
2.73
2.71
2.71
2.72
2.68
2.68
2.70
2.67
Hubei
3.56
2.92
2.26
2.25
2.21
2.10
2.03
2.02
2.08
2.06
Hunan
2.30
2.28
2.12
2.00
2.03
1.91
1.91
1.81
1.81
1.79
Guangdong
2.13
2.05
1.97
1.98
1.89
1.91
1.78
1.77
1.76
1.74
Guangxi
2.43
2.18
1.89
1.83
1.77
1.78
1.79
1.83
1.89
1.78
Hainan
1.97
1.95
1.88
1.90
1.78
1.66
1.69
1.75
1.82
1.71
Chongqing
2.83
2.69
2.05
2.04
2.08
2.00
2.04
2.09
2.11
2.05
Sichuan
2.50
2.35
2.19
2.16
2.20
2.16
2.15
2.15
2.12
2.09
Guizhou
2.80
3.10
2.49
2.37
2.28
2.25
2.16
2.32
2.14
2.08
Shanghai Jiangsu Zhejiang Anhui
23
Journal Pre-proof Yunnan
2.19
2.16
2.02
1.88
1.99
1.80
1.91
1.87
1.94
1.91
Shaanxi
2.44
2.23
2.01
2.00
2.00
1.97
1.93
1.93
1.89
1.89
Gansu
7.36
6.69
4.84
4.79
4.77
4.68
4.62
4.38
4.36
4.13
Qinghai
9.75
8.70
7.10
7.53
7.09
6.86
7.07
6.93
7.05
6.94
Ningxia
6.84
6.95
4.03
3.79
3.95
3.99
3.84
3.87
3.81
3.68
Xinjiang
12.64
12.19
8.78
8.71
8.71
8.72
8.65
8.66
8.61
8.57
Mean value
4.03
3.87
2.97
2.92
2.89
2.84
2.81
2.79
2.78
2.75
Variance
7.34
6.90
2.46
2.60
2.52
2.47
2.48
2.42
2.44
2.37
Table 7 Evaluation results of EECC index in atmosphere dimension across surveyed 30 provinces (ρ ) 2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
1.49
1.47
1.52
1.32
1.48
1.28
1.57
1.49
1.45
1.35
Tianjin
2.11
2.13
2.23
2.00
2.03
1.72
2.35
2.20
1.99
1.92
Hebei
1.60
1.47
1.55
1.42
1.45
1.36
1.67
1.54
1.48
1.41
Shanxi
1.40
1.16
1.21
1.16
1.18
1.10
1.31
1.12
1.14
1.07
Mongolia
0.61
0.61
0.62
0.64
Liaoning
0.96
1.05
1.09
1.02
Jilin
0.69
0.78
0.85
0.79
Heilongjiang
0.40
0.47
0.49
0.48
Shanghai
1.59
1.56
1.59
1.39
Jiangsu
1.76
1.68
1.70
Zhejiang
1.16
1.18
1.03
Anhui
1.72
1.66
1.56
Fujian
0.74
0.68
Jiangxi
1.23
1.18
ro 0.55
0.65
0.64
0.69
0.59
0.92
0.78
1.02
1.15
1.23
0.95
0.68
0.60
0.82
0.99
1.07
0.81
0.40
0.39
0.52
0.73
0.69
0.56
1.35
1.17
1.49
1.46
1.53
1.28
1.77
1.67
1.41
1.75
1.81
1.76
1.56
1.03
0.97
0.97
1.05
1.09
0.97
0.79
1.62
1.49
1.36
1.58
1.73
1.53
1.37
0.63
0.60
0.57
0.57
0.58
0.62
0.57
0.51
1.12
1.08
1.00
1.02
1.03
1.14
0.98
0.92
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0.57
Jo ur
Shandong
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Inner
of
Province
2.21
1.96
1.86
2.06
1.84
1.77
2.06
2.00
1.97
1.80
2.26
1.85
1.84
2.00
1.90
1.76
2.13
1.90
1.88
1.67
1.62
1.52
1.50
1.60
1.51
1.34
1.50
1.55
1.44
1.22
1.59
1.54
1.47
1.39
1.32
1.36
1.30
1.43
1.22
1.07
1.02
1.03
1.01
0.89
0.85
0.84
0.84
0.93
0.83
0.71
Guangxi
1.14
1.15
1.11
1.01
1.02
1.07
1.03
1.04
0.93
0.78
Hainan
0.44
0.50
0.39
0.35
0.44
0.30
0.43
0.38
0.42
0.35
Chongqing
1.50
1.36
1.36
1.46
1.29
1.30
1.30
1.18
1.13
1.05
Henan Hubei Hunan Guangdong
Sichuan
0.65
0.67
0.64
0.73
0.66
0.64
0.65
0.59
0.54
0.53
Guizhou
1.01
1.00
1.01
0.95
0.95
0.97
0.87
0.86
0.81
0.69
Yunnan
0.41
0.43
0.42
0.42
0.46
0.39
0.47
0.37
0.37
0.36
Shaanxi
1.33
1.06
1.13
1.15
1.13
1.06
1.24
1.06
1.03
0.96
Gansu
1.33
1.30
1.30
1.28
1.20
1.15
1.45
1.44
1.24
1.08
Qinghai
1.17
1.19
1.14
1.25
1.09
1.04
1.18
1.49
1.00
0.99
Ningxia
1.22
1.24
1.26
1.31
1.07
1.03
1.33
1.17
1.05
0.98
Xinjiang
1.53
1.53
1.45
1.66
1.46
1.46
1.62
1.84
1.56
1.45
Mean value
1.26
1.21
1.20
1.19
1.13
1.06
1.23
1.23
1.15
1.03
24
Journal Pre-proof Variance
0.25
0.19
0.20
0.21
0.19
0.16
0.24
0.22
0.19
0.17
Table 8 Evaluation results of overall EECC index across surveyed 30 provinces (ρ𝑂) 2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
2.13
1.77
1.48
1.22
1.30
1.15
1.09
1.41
1.54
1.20
Tianjin
2.21
2.27
2.45
2.56
2.20
2.06
2.02
2.15
1.90
2.21
Hebei
1.80
1.44
1.66
1.64
1.55
1.64
1.72
1.76
1.89
1.65
Shanxi
1.78
1.74
1.45
1.35
1.78
1.88
1.67
1.86
1.49
1.64
2.18
1.89
1.71
1.47
1.55
1.29
1.61
1.29
1.41
1.20
Liaoning
1.49
1.40
1.44
1.37
1.37
1.55
1.31
1.31
1.07
1.20
Jilin
1.31
1.73
1.64
1.40
1.53
1.17
1.32
1.58
1.49
1.36
Heilongjiang
1.50
1.52
1.17
1.41
1.22
1.28
0.94
1.18
1.24
1.18
Shanghai
4.65
4.66
2.78
2.62
2.93
2.23
2.46
2.03
1.81
2.20
Jiangsu
2.29
2.49
1.47
1.65
1.88
1.93
1.90
1.80
1.57
1.58
Zhejiang
1.22
0.95
1.06
0.87
1.15
1.18
1.10
1.14
1.21
1.15
Anhui
1.56
1.46
1.20
1.29
1.27
1.15
1.45
1.54
1.27
1.49
Fujian
1.14
1.43
1.43
1.27
1.23
1.02
1.05
0.83
1.25
0.99
Jiangxi
1.00
1.22
1.20
1.18
1.27
1.40
1.36
1.53
1.30
1.28
Shandong
1.65
1.56
1.32
1.64
1.75
1.54
1.74
1.50
1.66
1.88
Henan
1.85
1.79
1.48
1.48
1.35
1.69
1.51
1.55
1.36
1.58
Hubei
1.69
1.49
1.45
1.36
1.53
1.37
1.26
1.05
1.15
1.05
Hunan
1.34
1.44
1.20
1.29
1.05
1.16
1.46
1.42
1.26
1.26
Guangdong
1.30
0.98
1.31
1.03
1.15
0.96
1.15
1.19
1.14
1.14
Guangxi
1.45
1.62
1.27
1.12
0.90
1.03
0.98
0.89
1.24
1.29
Hainan
1.55
1.18
1.19
1.56
1.50
1.26
1.17
1.04
1.14
0.94
Chongqing
1.32
1.38
1.05
1.32
1.41
1.28
1.35
1.46
1.42
1.73
Sichuan
ro
-p
re
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na
Mongolia
Jo ur
Inner
of
Province
1.45
1.21
1.33
1.26
1.22
1.47
1.59
1.32
1.53
1.15
2.01
2.01
1.63
1.45
1.30
1.36
1.02
1.43
1.18
1.21
1.25
1.39
1.36
1.14
1.50
1.32
1.55
1.37
1.28
1.15
1.42
1.16
1.38
1.43
1.18
1.39
1.04
1.18
1.51
1.50
2.93
2.59
2.08
1.90
2.12
1.87
1.94
1.74
1.99
1.93
Qinghai
3.90
3.64
3.09
3.38
3.05
2.73
2.63
2.72
2.68
2.64
Ningxia
3.84
3.78
3.14
2.61
2.86
2.96
2.85
2.82
2.76
2.47
Xinjiang
4.32
4.45
3.21
3.23
3.09
3.32
3.33
3.33
3.29
3.45
Mean value
1.99
1.92
1.65
1.62
1.64
1.59
1.59
1.58
1.57
1.56
Variance
0.91
0.92
0.38
0.38
0.36
0.32
0.33
0.31
0.26
0.31
Guizhou Yunnan Shaanxi Gansu
5.1 The change of EECC in water dimension (𝛒𝑾) As shown in Figure 2, for demonstrating the change of EECC in water 25
Journal Pre-proof dimension (ρ ) graphically, a scatter box plot between 30 provinces is produced by using the data in Table 5. This Figure highlights that ρ
varies dramatically
between the provinces each year. For example, in the year of 2007, the maximum value of ρ
was undergone by Ningxia at 3.42, while the minimum value was
enjoyed by Guangxi at 0.41, and the best three provinces of EECC performance in water dimension were Guangxi, Anhui and Zhejiang, while Ningxia, Qinghai and
Jo ur
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ro
of
Guizhou were the worst three.
Figure 2 EECC performance in water dimension
It should be noted that the mean value of the index ρ
between 30 provinces
has been changing during the surveyed period. It has continuously dropped at the first four years from 0.90 in 2007 to 0.84 in 2010, and backed to 0.94 in 2011, then fluctuated in the last five years between 0.83 and 0.90. The maximum value of ρ has been changing between 3.14 in 2013 and 4.14 in 2009 offered by Ningxia, whilst the minimum value varies between 0.27 in 2009 and 0.41 in 2007 both achieved by Guangxi.
26
Journal Pre-proof
5.2 The change of EECC in land dimension (𝛒𝑳 ) For presenting the change of EECC in land dimension (ρ𝐿 ), the data listed in Table 6 are employed for producing the Figure 3. It can be observed that the ρ𝐿 value appears significantly different between provinces annually. For instance, in the year 2007, the maximum value appeared in Xinjiang with the value ρ𝐿 of 12.64, while the minimum value emerged in Zhejiang with 1.85, and the mean value between the 30
of
provinces is 4.03. The best three provinces of EECC performance in land dimension
ro
in the same year were Zhejiang, Fujian and Jiangxi, whilst Xinjiang, Shanghai and
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Qinghai were the worst three.
Figure 3 EECC performance in land dimension
On the other hand, the mean ρ𝐿 value changed during the surveyed period towards better performance, and the most significant change happened in the first three years. The mean value of 30 provinces decreased from 4.03 in 2007 to 2.75 in 2016. The maximum value of ρ𝐿 changes between 8.54 in 2016 to 12.60 in 2007, offered by Xinjiang, while the minimum value varies between 1.58 enjoyed by Fujian in 2011 and 1.85 experienced by Zhejiang in 2007. Furthermore, it is interesting to 27
Journal Pre-proof note that the difference in ρ𝐿 between different provinces has been narrowing during surveyed years as the variance value decreased from 2.71 in 2007 to 1.54 in 2016.
5.3 The change of EECC in atmosphere dimension (𝛒𝑨 ) The change of EECC in atmosphere dimension (ρ ) can be seen in Figure 4, generated from the data in Table 7. From Table 7 and Figure 4, the value of ρ varies
ρ
of
among the provinces annually. For example, in the year 2007, the maximum value of is 2.26 offered by Henan, while the minimum is 0.40 from Heilongjiang, and the
ro
mean value between the 30 provinces is 1.26. The best three provinces of EECC
-p
performance in atmosphere dimension in the same year were Hainan, Heilongjiang
Jo ur
na
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re
and Inner Mongolia, while Henan, Shandong and Tianjin were the worst three.
Figure 4 EECC performance in atmosphere dimension
Figure 4 also shows that the atmospheric environment is characterized by an increasingly improving performance, evidenced by the decreasing value of ρ . The mean value of ρ decreased from 1.26 in 2007 to 1.06 in 2012, then increased to 1.23 in 2013, later it decreased again from 1.23 in 2013 to 1.03 in 2016. The maximum value of ρ changed between 1.77 offered by Shandong in 2012 to 2.26 offered by Henan in 2007. And the minimum value varies among 0.30 offered by 28
Journal Pre-proof Hainan in 2012 and 0.43 offered by Yunnan in 2008.
5.4 The change of the overall EECC Table 8 presents the values of ρ𝑂 from an overall perspective which integrates the three dimensional indexes ρ , ρ𝐿 and ρ . The data in table 8 can be used to produce a scatter box plot diagram, as show in Figure 5. From Table 8 and Figure 5, it
of
can be seen that the value ρ𝑂 varies among provinces annually. For example, in the year 2007, the maximum value was 4.64 in Shanghai, while the minimum value was
ro
1.00 in Jiangxi, and the mean value among the 30 provinces was 1.99, the best three
-p
provinces of EECC performance in overall perspective were Jiangxi, Fujian and
re
Zhejiang, while Shanghai, Xinjiang and Qinghai were the worst three.
lP
On the other hand, the ρ𝑂 shows a temporal variation during the year of 2007 to 2016. According to Figure 5, the mean value decreased significantly from 1.99 in
na
2007 to 1.62 in 2010, then slightly increased to 1.64 in 2011, and continuously decreased to 1.56 in 2015, later increased to 1.67 in 2016. The maximum value of ρ𝑂
Jo ur
in each year changes between 3.09 offered by Xinjiang in 2011 and 4.66 offered by Shanghai in 2008, while the minimum value varies between 0.83 enjoyed by Fujian in 2014 and 1.07 shared by Liaoning in 2015. In addition, the difference of ρ𝑂 between 30 provinces has been narrowing during the surveyed years as the variance value dropped from 0.94 in 2007 to 0.55 in 2016.
29
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Figure 5 Overall performance of EECC in the period of 2007-2016
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The changes of ρ𝑂 can be further demonstrated by observing the distribution of
re
EECC performance in the beginning year (2007) and the ending year (2016).
lP
According to Table 8, the overall EECC performances among the 30 provinces for the beginning year and the ending year are presented as shown in Figure 6. In this Figure,
na
the 30 surveyed provinces are classified into three groups, namely, Good, Average and
Jo ur
Poor according to their EECC performance. The classification criterion is generated by ordering the 300 values in Table 8, the smallest 100 values are divided into Good group, followed by Average group, and the largest 100 value are in Poor group. In other words, the classification is based on the follows: Good (0<ρ𝑂 ≦1.3), Average (1.3<ρ𝑂 ≦1.6), Poor (ρ𝑂 >1.6). The figure shows that most provinces have made good progress from the year of 2007 to 2016. For example, Beijing has improved its performance from “Poor” grade in 2007 to “Good” grade in 2016, with the index ρ𝑂 decreased from 2.1 to 1.2. However, there are still three provinces Jilin, Shandong and Chongqing experienced an EECC degradation. Specifically, Jilin has fallen from “Good” grade to “Average” grade, Shandong has dropped from “Average” grade to “Poor”, and Chongqing showed a decline from “Good” to “Poor”. 30
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Figure 6 Overall EECC performance between 30 provinces in the year 2007 and 2016
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6. Discussions
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6.1 Improvements of EECC performance
According to the evaluation results listed in Table 6, 7 and 8, it is interesting to
na
find that the mean value of index ρ𝐿 , ρ , ρ𝑂 showed a downward trend during the
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period of 2007 to 2016, which indicates that the EECC performance in land dimension, atmosphere dimension and overall perspective experienced a reasonable improvement in the urbanization process of China. As shown in Figure 6, 27 provinces out of the 30 surveyed provinces have improved their overall EECC performance during the surveyed period. This evaluation results appears not in consensus with the common appreciation that the urbanization contributes to the deterioration of ecological environment (Chen et al., 2010; Wu et al., 2019d; Yang et al., 2018). However, the results of this study respond to some viewpoints, such as Wang et al. (2019b), suggesting that the ecological environment performance has been gradually improving in China in recent years. It is considered that there is a synergetic effect existing between urbanization and EECC. More specifically, the provinces with 31
Journal Pre-proof better EECC performance can attract additional residents and consequently contribute to urbanization growth. On the other hand, urbanization development will prompt the development of technologies and economy, and strengthen the residents’ and governments’ awareness of protecting ecological environment. Therefore, the ecological resources will be utilized more efficiently, and urban infrastructure for supporting ecological environment will be constructed more accessibly. These improvements can benefit ecological environment system by reducing waste
of
discharges and enhancing waste treatment facilities, which will consequently work on
ro
better EECC performance. This opinion is echoed by Zhang et al. (2019), implying
-p
that higher urbanization level is associated with better ecological environment
re
performance.
In referring to the average EECC index values (ρ , ρ𝐿 , ρ ) in Table 5, 6 and 7,
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the most obvious improvement has been given to EECC in land dimension during the
na
surveyed period. This research finding is consistent with the news report from National Aeronautics and Space Administration (2019) and the study by Chen Chi
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(2019), suggesting that the expansion of vegetation area on the earth from 2000 to 2017 is mainly contributed by China and India. The possible reason for the great improvement of land ecological environment is that the Chinese government has paid increasing attention to land protection. For example, the “The Forest Law” in 2009 (Standing Committee of the National People's Congress, 2009) and “The Soil and Water Conservation Law” in 2010 (Standing Committee of the National People's Congress, 2010) implemented by government has emphasized the importance of promoting land ecological environment. Furthermore, in Chinese 12th Five-Year-Plan for Construction of Urban Household garbage Disposal Facilities, a sustainable development goal of increasing garbage disposal rate above 90% before 2015 was
32
Journal Pre-proof proposed (The State Council of China, 2012). In Chinese 13th Five-Year Plan for Ecological Environmental Protection, the central government has committed to increase the urban green areas coverage rate to 38.9% in 2020 (The State Council of China, 2016a). At provincial level, the good performance of EECC in land dimension can be attributed to multiple aspects, including good social and economic circumstances, effective policy measures from the government, and the geographical features of a specific area. For example, Zhejiang is the best EECC performer in land
of
dimension, as shown in Table 6. In line with the rapid social and economic
ro
development in this province, the local investment of urban infrastructure has
-p
increased from RMB 221.5 billion in 2007 to RMB 9365.48 billion in 2016 (Zhejiang
re
Statistics Bureau, 2007-2016), which benefits to the improvement of environmental facilities, such as waste disposal facilities. According to National Bureau of Statistics
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of China (2007-2016a), the facilities capacity for household garbage disposal (
𝐿
) in
na
Zhejiang increased from 22881 (t/day) in 2007 to 48250 (t/day) in 2016. Accordingly, the loads influencing the performance of land environment can be mitigated.
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Furthermore, the government of Zhejiang has implemented many policy measures in improving land ecological environment. For instance, the local government has promoted the tree planting progress by issuing “Color forest passageway construction” (Zhejiang Provincial Forestry Department, 2014) and “The precious color forest construction overall plan in Zhejiang province (2015-2020)” (State administration of forestry and grassland in China, 2016). The forest coverage rate in Zhejiang has increased by 4.7% from 2007 to 2016 (National Bureau of Statistics of China, 2007-2016a). Therefore, the land environment can be improved effectively by enhancing the land carriers. As the natural geography aspect, benefited from humid subtropical climate and fertile soil (You et al., 2018), the forest coverage rate in
33
Journal Pre-proof Zhejiang (59.1% in 2016) is much higher than the national average level (21.6% in 2016) (National Bureau of Statistics of China, 2007-2016a). The Chinese government has also made great efforts to the improvement of EECC performance in atmosphere and water dimensions. In order to improve air quality in China, the government initiated the “de-coal” appeal in 2014, which was considered to have achieved significant progress by the report of “Greenpeace Achievement in 2016” (Greenpeace, 2016). The policies of “Action Plan of Air
of
Pollution Prevention and Control” issued in 2013 by The State Council of China
ro
(2013) and “The law on the Prevention and Control of Atmospheric Pollution”
-p
introduced in 2015 (Standing Committee of the National People's Congress, 2015)
re
have also contributed to the reduction of air pollution and the development of clean energy (Yang et al., 2017). As a result, the atmospheric ecological carrying capacity
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presents an improving trend in China, with the index of EECC in atmosphere
na
dimension (ρ ) decreased from 1.23 in 2013 to 1.03 in 2016 as shown in Table 7. This research finding can be also evidenced by the report of “Breakthroughs China’s Path
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to Clean Air 2013-2017” issued by Clean Air Asia (2018), which shows that the concentration of PM2.5 in China has dropped from 72
g/
3
in 2013 to 47
g/
3
in 2017. And the monitoring air pollutants data published by Ministry of ecology and environment of China (2019) shows a similar trend, suggesting the concentration of PM2.5 in China has decreased by 42% from2013 to 2018. In terms of EECC in water dimension, despite of the small progress across the country, many provinces have witnessed a good development in water recycling. Beijing is a typical example with significant improvement in recycling water during the study period. In order to promote the technology and facility utilization in recycling water, the policies of “Measures for the administration of drainage and recycled water in Beijing” (The
34
Journal Pre-proof people's government of Beijing, 2009), and “The three-year action plan on sewage treatment and recycled water utilization in Beijing (2016-2019)” (The people's government of Beijing, 2016) was issused by Beijing government. As a result, the productivity of recycled water (
) in Beijing increased from null in 2007 to 513.5
(104 m3 per day) in 2016, during which the index of EECC in water dimension (ρ ) decreased from 1.09 to 0.70 as shown in Table 5.
of
It is also worthy to note that the difference in overall EECC performances between 30 provinces has been narrowing during surveyed years, as the variance
ro
value of EECC decreased from 0.74 in 2007 to 0.25 in 2016 as shown in Table 8.
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Whilst those provinces with better initial EECC performance have made some
re
progress, those traditionally poor performers have made significant progress. For example, the forest areas in Xinjiang improved from 484.07 (108m2) in 2007 to
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698.25 (108m2) in 2016, while that in Beijing increased from 37.88 (108m2) to
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58.81(108m2) in the same period (National Bureau of Statistics of China, 2007-2016a). The possible reason behind this is that the Central Government has emphasized the
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balanced development between different provinces by introducing policies and programs, such as the implementation of “Grain for Green Project” (State Council of the People's Republic of China, 2007) which contributes to the improvement of ecological environment in Western region of China. The above discussions suggest that the government plays an essential role in improving EECC by raising environmental awareness and promoting technology development during the urbanization process. Therefore, Chinese government should continuously implement these effective policies in order to achieve better ecological environment carrying status. In addition, the Central Government should encourage the cooperation work through transferring technology and sharing experience between
35
Journal Pre-proof the provinces to ensure a sustainable ecological environment nationwide.
6.2 Severe challenges existing in EECC performance Despite certain ecological achievements obtained, challenges still exist in reaching desirable EECC performance, particularly in atmosphere dimension. According to the data presented in Table 7, there were still 14 provinces with the
of
index value of ρ larger than 1 in 2016, indicating that the air quality in these provinces is worse than Ambient Air Quality Standards (GB3095-2012). This finding
ro
is echoed by the study of Wang et al. (2018b), indicating that the PM2.5 concentration
-p
is an alarming environmental challenge in China. Furthermore, whilst 27 provinces
re
have made progress in overall EECC, there are three provinces having experienced a
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degradation in overall EECC, including Jilin, Chongqing and Shandong. The degradation in Jilin is mainly due to the decreasing atmosphere performance, which is
na
attributed to the extensive industrial activities involving enormous coal consumption and air pollutants emissions (Jie et al., 2012). Chongqing is another one with the
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degradation of overall EECC performance, as shown in Figure 6. According to the Appendix 2, its EECC in water dimension (
) has increased from 1.02 in 2007 to
1.67 in 2016, indicating that the lack of capacity in dealing with soaring wastewater discharges resulted in the degradation of overall EECC performance in Chongqing city. For example, the domestic sewage discharge (
) in Chongqing increased 2.7
times from 6.5 (108 ton) in 2007 to 17.6 (108 ton) in 2016, while the capacity of disposal facilities for sewage treatment (
) only increased 1.6 times, from 175.7
(104 m3 per day) in 2007 to 289.7 (104 m3 per day) in 2016 (National Bureau of Statistics of China, 2007-2016a). Shandong province is ranked one of the worst performers in terms of EECC degradation, as shown in Figure 6. The EECC
36
Journal Pre-proof degradation in this province is mainly resulted from the poor performance in water dimension. According to the study by Wu and Tan (2012), the natural water resources in Shandong have been excessively exploited due to the intensive water consumption in line with the rapid urban development. That may be the reason for the decrease of ) in Shandong from 387.11 (108m3) in 2007 to 220.30
total water resources (
(108m3) in 2016 (National Bureau of Statistics of China, 2007-2016a), during which the index
(EECC in water dimension) of Shandong increased from 0.62 to 0.90,
of
as shown in Table 5.
ro
Based on the above discussions, it is considered that opportunities and challenges
-p
coexist in line with the change of EECC during the rapid urbanization process in
re
China. In order to prevent ecological environment from being overloaded,
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governments should enhance ecological environment carriers on one hand, for example protecting natural water resources, strengthening regional forest coverage,
na
and improving waste treatment facilities. On the other hand, actions should be taken to control ecological environment loads, such as to reduce garbage by encouraging
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recycle and reuse, to abate air pollution by developing clean energy, to curb the increase of water consumption by advocating water saving.
7. Conclusion
This study examined the change of ecological environment carrying capacity in the context of China under 30 provinces during the period of 2007-2016. The main findings are concluded as: (1) During the urbanization process, the EECC performances in water, land, atmosphere and overall perspectives of most provinces are changing towards good status, with a remarkable progress has been achieved in land dimension, while few provinces such as Chongqing, Shandong and Jiangxi have experienced a degradation. 37
Journal Pre-proof (2) The difference of overall EECC performances between 30 provinces has been narrowing during surveyed years, suggesting that the emphasis has been given by the Chinese Central Government to balance the ecological development between the provinces. (3) Severe challenge still exists in the EECC performance of atmosphere dimension in many provinces. This paper contributes to the development of the literature associated with
of
exploring the change of ecological environment. By incorporating both carriers and
ro
loads, this model provides an innovative measurement for investigating ecological
-p
environment carrying capacity. Practically, the research findings provide valuable
re
references for Chinese administration to understand the changes of the ecological environment carrying capacity during the urbanization process, based on which
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experiences and lessons can be shared, proper and targeted policy measures can also
na
be designated to prevent ecological environmental from overloading. Limitations of this study are appreciated for further research. Firstly, although a
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comprehensive list of evaluating indicators has been identified for EECC assessment, those indicators without accessibility at this stage are excluded in the demonstration. Secondly, the research data applied are on annual basis, thus the analysis results on EECC can only reflect the change of annual state. If more changeable temporal data such as real-time data are available, the evaluation results on EECC would be more dynamic. This study inspires that further study can be developed to examine the impacts of urbanization to the change of EECC performance.
Appendix Appendix 1 Calculated results of primary EECC index Province
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
1.46
0.76
1.06
1.35
1.18
0.91
1.18
1.05
1.09
0.72
38
Journal Pre-proof Tianjin
2.00
1.20
1.47
2.21
1.42
0.68
1.53
1.95
1.85
1.35
Hebei
1.67
1.20
1.35
1.35
1.21
0.81
1.05
1.72
1.32
0.85
Shanxi
0.57
0.62
0.65
0.69
0.59
0.68
0.57
0.62
0.76
0.54
Inner Mongolia
0.61
0.43
0.48
0.34
0.44
0.36
0.19
0.34
0.34
0.44
Liaoning
0.54
0.53
0.82
0.24
0.48
0.26
0.30
0.94
0.77
0.40
Jilin
0.29
0.31
0.37
0.17
0.41
0.28
0.22
0.43
0.40
0.27
Heilongjiang
0.59
0.64
0.32
0.38
0.56
0.43
0.26
0.39
0.44
0.42
Shanghai
3.48
3.24
3.01
3.43
6.01
3.42
4.40
2.25
1.62
1.72
1.12
1.46
1.35
1.42
1.11
1.45
1.96
1.44
0.97
0.77
0.24
0.25
0.21
0.15
0.27
0.14
0.21
0.17
0.13
0.14
Anhui
0.33
0.38
0.40
0.32
0.49
0.42
0.50
0.35
0.32
0.23
Fujian
0.18
0.19
0.25
0.12
0.27
0.13
0.18
0.17
0.15
0.09
Jiangxi
0.21
0.17
0.21
0.11
0.25
0.11
0.19
0.16
0.12
0.11
Shandong
0.55
0.66
0.76
0.70
0.63
0.78
0.72
1.35
1.16
0.91
Henan
0.45
0.61
0.71
0.42
0.69
0.89
1.12
0.73
0.76
0.66
Hubei
0.25
0.26
0.34
0.23
0.39
0.37
0.37
0.31
0.30
0.19
Hunan
0.23
0.20
0.23
0.17
0.29
0.17
0.21
0.18
0.17
0.15
Guangdong
0.29
0.21
0.29
Guangxi
0.22
0.14
0.20
Hainan
0.16
0.11
0.09
Chongqing
0.12
0.14
0.19
Sichuan
0.09
0.08
Guizhou
0.09
Yunnan
0.23
0.32
0.22
0.20
0.26
0.23
0.18
0.17
0.22
0.15
0.15
0.15
0.12
0.13
0.09
0.09
0.12
0.09
0.12
0.23
0.09
0.19
0.17
0.17
0.18
0.13
0.17
0.13
0.10
0.09
0.10
0.09
0.10
0.09
0.12
0.11
0.09
0.11
0.11
0.15
0.10
0.12
0.08
0.08
0.09
0.07
0.07
0.10
0.08
0.10
0.09
0.09
0.09
0.08
0.07
Shaanxi
0.22
0.28
0.20
0.16
0.14
0.22
0.25
0.25
0.27
0.33
Gansu
0.53
0.63
0.58
0.57
0.51
0.46
0.45
0.61
0.72
0.70
Qinghai
0.05
0.05
0.03
0.04
0.04
0.03
0.04
0.03
0.05
0.04
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na
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Ningxia
re
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ro
of
Jiangsu Zhejiang
Xinjiang
6.60
7.72
8.09
7.24
7.61
6.06
5.94
6.50
7.08
6.04
0.60
0.65
0.70
0.48
0.59
0.65
0.61
0.80
0.62
0.52
Appendix 2 Calculated results of primary EECC index Province
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
0.77
0.78
0.96
0.93
0.98
0.90
0.92
0.88
0.85
0.69
Tianjin
0.50
0.53
0.52
0.59
0.56
0.68
0.69
0.73
0.71
0.69
Hebei
0.69
0.73
0.79
0.81
0.83
0.96
1.07
1.05
1.05
1.00
Shanxi
1.02
1.02
1.04
1.00
1.07
1.24
1.37
1.26
1.16
1.21
Inner Mongolia
0.77
0.95
0.89
0.93
1.05
1.13
1.12
1.05
0.97
0.93
Liaoning
0.75
0.71
0.76
0.69
0.61
0.62
0.52
0.60
0.58
0.57
Jilin
0.93
1.17
0.99
0.92
0.88
0.83
0.78
0.83
0.72
0.69
Heilongjiang
0.49
0.72
0.69
0.70
0.87
0.88
0.43
0.43
0.42
0.39
Shanghai
0.91
0.81
0.79
0.85
0.67
0.68
0.62
0.62
0.62
0.63
Jiangsu
0.55
0.48
0.49
0.50
0.61
0.63
0.64
0.67
0.68
0.68
Zhejiang
0.71
0.69
0.73
0.90
0.98
0.97
0.87
0.88
0.89
0.82
39
0.61
0.63
0.60
0.59
0.96
1.00
0.89
0.90
0.89
1.03
Fujian
0.75
0.65
0.88
0.99
1.15
1.05
0.92
1.00
1.20
1.20
Jiangxi
1.18
1.19
0.98
1.07
1.49
1.62
1.65
1.62
1.47
1.75
Shandong
0.67
0.64
0.73
0.76
0.78
0.85
0.98
0.98
1.02
0.89
Henan
1.12
1.14
1.18
1.17
1.32
1.38
1.44
1.44
1.28
1.35
Hubei
0.84
0.88
0.97
0.94
0.98
1.01
1.00
0.99
0.97
0.90
Hunan
0.95
1.09
0.87
0.87
0.90
0.99
1.18
1.13
1.11
1.12
Guangdong
1.16
1.08
1.06
0.81
0.95
1.05
1.08
1.07
1.04
1.11
Guangxi
0.57
0.54
0.33
0.36
0.47
0.51
0.58
0.60
0.62
0.55
Hainan
1.95
1.90
1.27
1.25
1.03
1.10
0.93
0.98
0.95
1.31
Chongqing
1.02
1.18
1.20
1.19
1.22
1.17
1.18
1.18
1.14
1.67
Sichuan
1.42
1.30
1.21
1.19
1.40
1.45
1.50
1.37
1.31
1.45
Guizhou
2.29
2.00
1.57
1.05
1.28
1.49
1.02
1.52
1.59
1.35
Yunnan
1.01
1.13
1.03
0.74
1.19
1.33
1.35
1.37
1.42
1.63
Shaanxi
1.34
0.98
0.98
0.89
1.04
1.09
1.07
1.07
1.06
1.08
Gansu
0.62
0.56
0.78
0.91
0.79
0.75
0.74
0.79
0.83
0.49
Qinghai
2.56
1.99
2.12
1.88
1.82
1.12
1.09
1.18
0.99
1.44
Ningxia
0.71
0.68
0.77
Xinjiang
0.77
0.88
0.87
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of
Anhui
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Journal Pre-proof
0.64
0.77
0.75
0.93
0.52
0.75
1.01
0.76
0.82
0.78
0.82
0.81
0.94
re
0.64
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Appendix 3 Calculated results of primary EECC index
𝐿
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
4.69
4.69
2.79
2.79
2.79
2.79
2.79
2.79
2.79
2.79
Tianjin
12.35
12.35
10.10
10.10
10.10
10.10
10.10
10.10
10.10
10.10
Hebei
5.65
5.65
4.27
4.27
4.27
4.27
4.27
4.27
4.27
4.27
Shanxi
7.52
7.52
na
Province
5.56
5.56
5.56
5.56
5.56
5.56
5.56
5.65
5.65
4.76
4.76
4.76
4.76
4.76
4.76
4.76
4.76
Liaoning
3.03
3.03
2.62
2.62
2.62
2.62
2.62
2.62
2.62
2.62
Jilin
2.62
2.62
2.48
2.48
2.48
2.48
2.48
2.48
2.48
2.48
Heilongjiang
2.53
2.53
2.31
2.31
2.31
2.31
2.31
2.31
2.31
2.31
Shanghai
31.25
31.25
9.35
9.35
9.35
9.35
9.35
9.35
9.35
9.35
Jiangsu
13.33
13.33
6.33
6.33
6.33
6.33
6.33
6.33
6.33
6.33
Zhejiang
1.84
1.84
1.69
1.69
1.69
1.69
1.69
1.69
1.69
1.69
Anhui
4.17
4.17
3.64
3.64
3.64
3.64
3.64
3.64
3.64
3.64
Fujian
1.59
1.59
1.52
1.52
1.52
1.52
1.52
1.52
1.52
1.52
Jo ur
5.56
Inner Mongolia
Jiangxi
1.79
1.79
1.67
1.67
1.67
1.67
1.67
1.67
1.67
1.67
Shandong
7.46
7.46
5.99
5.99
5.99
5.99
5.99
5.99
5.99
5.99
Henan
6.17
6.17
4.65
4.65
4.65
4.65
4.65
4.65
4.65
4.65
Hubei
3.73
3.73
2.60
2.60
2.60
2.60
2.60
2.60
2.60
2.60
Hunan
2.46
2.46
2.09
2.09
2.09
2.09
2.09
2.09
2.09
2.09
Guangdong
2.15
2.15
1.95
1.95
1.95
1.95
1.95
1.95
1.95
1.95
Guangxi
2.42
2.42
1.77
1.77
1.77
1.77
1.77
1.77
1.77
1.77
Hainan
2.04
2.04
1.81
1.81
1.81
1.81
1.81
1.81
1.81
1.81
40
Journal Pre-proof Chongqing
4.48
4.48
2.60
2.60
2.60
2.60
2.60
2.60
2.60
2.60
Sichuan
3.30
3.30
2.84
2.84
2.84
2.84
2.84
2.84
2.84
2.84
Guizhou
4.20
4.20
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
Yunnan
2.45
2.45
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
Shaanxi
3.07
3.07
2.42
2.42
2.42
2.42
2.42
2.42
2.42
2.42
Gansu
14.93
14.93
8.85
8.85
8.85
8.85
8.85
8.85
8.85
8.85
Qinghai
22.73
22.73
17.86
17.86
17.86
17.86
17.86
17.86
17.86
17.86
Ningxia
16.39
16.39
8.40
8.40
8.40
8.40
8.40
8.40
8.40
8.40
Xinjiang
34.48
34.48
23.81
23.81
23.81
23.81
23.81
23.81
23.81
23.81
Appendix 4 Calculated results of primary EECC index
𝐿
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
2.76
2.69
2.10
2.15
2.19
2.16
2.12
2.04
2.07
2.07
Tianjin
2.86
3.13
3.30
3.12
2.90
2.87
2.87
2.87
2.75
2.69
Hebei
2.74
2.58
2.50
2.34
2.38
2.44
2.43
2.39
2.43
2.45
Shanxi
3.07
2.84
2.74
2.63
2.61
Inner Mongolia
3.56
3.27
3.09
2.50
2.49
2.49
2.47
2.99
2.93
2.76
2.76
2.51
2.55
2.51
Liaoning
2.67
2.62
2.61
2.54
2.51
2.49
2.49
2.49
2.48
2.75
Jilin
3.19
3.17
3.05
2.93
2.92
2.95
3.18
2.79
2.77
2.86
Heilongjiang
3.47
3.18
2.98
2.87
2.75
2.78
2.78
2.78
2.79
2.82
Shanghai
2.66
2.65
2.63
2.62
2.62
2.61
2.60
2.60
2.60
2.59
Jiangsu
2.34
2.35
2.38
2.38
2.38
2.37
2.36
2.35
2.34
2.33
Zhejiang
2.77
2.65
2.62
2.61
2.60
2.51
2.48
2.45
2.46
2.44
Anhui
2.77
2.78
2.69
2.67
2.53
2.58
2.51
2.43
2.43
2.40
Fujian
2.70
2.57
2.52
2.44
2.42
2.38
2.34
2.34
2.33
2.31
Jiangxi
2.56
2.39
na
lP
re
2.59
-p
ro
of
Province
2.15
2.14
2.17
2.22
2.24
2.27
2.29
2.59
2.51
2.43
2.41
2.41
2.38
2.35
2.34
2.36
2.36
Henan
2.92
2.82
2.75
2.73
2.73
2.71
2.66
2.61
2.65
2.54
2.66
2.66
2.65
2.65
2.60
2.57
2.62
2.64
2.67
2.66
2.81
2.79
2.73
2.73
2.72
2.70
2.66
2.59
2.52
2.46
Guangdong
2.60
2.48
2.45
2.42
2.43
2.43
2.41
2.42
2.42
2.36
Guangxi
3.12
3.06
2.97
2.86
2.67
2.67
2.65
2.54
2.66
2.66
Hainan
2.47
2.38
2.39
2.35
2.39
2.43
2.38
2.42
2.65
2.48
Chongqing
3.14
2.79
2.60
2.46
2.49
2.33
2.40
2.46
2.48
2.45
Sichuan
2.92
2.83
2.75
2.64
2.62
2.58
2.60
2.67
2.58
2.51
Guizhou
3.28
3.36
3.65
3.38
3.10
3.05
2.90
2.94
2.79
2.72
Yunnan
3.23
3.13
2.75
2.68
2.58
2.54
2.65
2.62
2.68
2.65
Shaanxi
2.65
2.58
2.58
2.61
2.58
2.48
2.49
2.47
2.46
2.49
Gansu
4.15
3.86
3.66
3.69
3.58
3.33
3.12
3.25
3.31
3.17
Qinghai
3.64
3.55
3.45
3.40
3.22
3.08
3.21
3.16
3.36
3.22
Ningxia
2.99
2.65
2.58
2.58
2.67
2.60
2.60
2.63
2.64
2.48
Xinjiang
3.13
3.13
2.75
2.75
2.73
2.79
2.75
2.72
2.67
2.60
Hubei Hunan
Jo ur
2.25
Shandong
41
Journal Pre-proof Appendix 5 Calculated results of primary EECC index
𝐿
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Beijing
1.59
1.48
1.31
1.04
1.03
1.06
0.84
0.94
0.91
0.98
Tianjin
0.59
0.63
0.68
0.63
0.55
0.54
0.52
0.63
0.65
0.68
Hebei
2.19
1.45
1.52
1.19
1.22
1.16
1.30
0.98
0.76
0.86
Shanxi
2.09
0.83
0.79
0.94
1.08
1.08
1.07
1.16
0.88
0.96
Inner Mongolia
2.05
1.68
1.15
1.00
1.06
1.07
0.85
0.79
0.80
0.79
Liaoning
2.02
1.87
1.91
1.33
1.32
1.20
1.24
1.11
1.04
1.00
Jilin
2.98
2.98
2.26
2.11
1.56
1.59
1.31
1.27
1.01
0.97
Heilongjiang
3.08
2.77
2.49
1.95
1.99
1.65
1.35
1.38
1.05
0.91
Shanghai
1.13
1.78
1.88
1.90
2.46
1.67
0.98
0.81
0.82
0.73
Jiangsu
1.13
0.91
0.76
0.74
0.73
0.77
0.81
0.73
0.76
0.77
Zhejiang
0.92
0.87
0.81
0.79
0.80
0.78
0.72
0.73
0.76
0.81
of
Province
1.83
1.59
1.47
1.27
1.03
0.97
0.88
0.84
0.78
0.82
1.40
1.15
1.03
0.90
0.77
0.82
0.92
0.90
0.87
0.93
Jiangxi
1.44
1.28
1.30
1.28
1.02
0.98
1.02
0.91
0.93
1.04
Shandong
1.09
1.03
0.80
0.77
0.80
0.81
0.86
0.75
1.00
0.95
Henan
1.23
1.18
0.97
0.93
0.95
1.00
0.95
0.98
1.01
1.01
Hubei
4.33
2.48
1.55
1.52
1.46
1.15
0.91
0.84
1.00
0.96
Hunan
1.62
1.58
1.51
1.17
1.27
0.93
0.97
0.76
0.82
0.81
Guangdong
1.62
1.53
1.49
1.56
1.27
1.36
0.97
0.93
0.91
0.92
Guangxi
1.74
1.07
0.88
0.82
0.85
0.88
0.93
1.15
1.19
0.89
Hainan
1.37
1.43
1.41
1.52
1.12
0.72
0.88
1.02
0.96
0.84
Chongqing
1.01
0.98
0.98
1.09
1.19
1.13
1.17
1.26
1.29
1.15
Sichuan
1.33
1.00
1.04
1.06
1.21
1.11
1.05
0.99
1.00
0.99
Guizhou
1.06
1.84
1.11
1.03
1.07
1.03
0.92
1.35
0.98
0.87
Yunnan
0.87
0.90
1.27
0.94
1.37
0.84
1.07
0.96
1.12
1.07
Gansu Qinghai Ningxia
Xinjiang
-p
re
lP
na
Jo ur
Shaanxi
ro
Anhui Fujian
1.66
1.11
1.06
0.99
1.01
1.06
0.91
0.94
0.81
0.81
3.89
2.23
2.45
2.27
2.34
2.33
2.37
1.55
1.42
0.90
4.43
1.45
1.23
2.54
1.42
0.91
1.38
1.01
1.17
1.00
2.27
2.94
1.61
0.90
1.30
1.47
1.04
1.09
0.91
0.69
2.88
1.54
1.52
1.32
1.35
1.32
1.16
1.20
1.13
1.08
Acknowledgments This research work was supported by the National Planning Office of Philosophy and Social Science Foundation of China(Grant No.”17ZDA062”), and Chongqing Federation of Social Science (Nos. “2019CDJSK03PT06”).
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Journal Pre-proof Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
49
Journal Pre-proof Graphical abstract
±
±
Heilongjiang
Xinjiang
Tibet
Jilin Inner Mongolia BeijingLiaoning Ningxia Gansu ShanxiHebei Shandong Qinghai ShaanxiHenan Jiangsu Chongqing Anhui Hubei Sichuan Zhejiang HunanJiangxi Guizhou Taiwan Yunnan GuangxiGuangdong Hainan
Heilongjiang
Xinjiang
Tibet
Jilin Inner Mongolia BeijingLiaoning Ningxia Gansu ShanxiHebei Shandong Qinghai ShaanxiHenan Jiangsu Chongqing HubeiAnhui Sichuan Zhejiang Jiangxi GuizhouHunan Taiwan Yunnan GuangxiGuangdong Hong Kong
Hong Kong Macau
Hainan Macau
(b) EECC performance in 2016
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(a) EECC performance in 2007
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Journal Pre-proof
Highlights: ·A novel EECC evaluation method was developed from load-carrier perspective. · EECC performance in China is changing towards good during urbanization process.
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·The difference of overall EECC performances between 30 provinces has been narrowing.
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· Severe challenge still exists in atmosphere dimension of EECC
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performance in China.
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