Seawater environmental Kuznets curve: Evidence from seawater quality in China's coastal waters

Journal of Cleaner Production 219 (2019) 925e935

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Seawater environmental Kuznets curve: Evidence from seawater quality in China's coastal waters Zhibao Wang a, Chao Bu b, Hongmei Li a, Wendong Wei c, * a

College of Geography and Environment, Shandong Normal University, Jinan, 250358, PR China Guoyan Culture and Media Group & Development Research Think Tank of China (DRTT), Beijing, 100176, PR China c Business School, University of Shanghai for Science and Technology, Shanghai 200093, PR China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 28 November 2018 Received in revised form 25 January 2019 Accepted 2 February 2019 Available online 6 February 2019

This paper builds an environmental Kuznets curve (EKC) multiple logistic regression model to analyze how socio-economic development impacts mainland China's coastal waters environment. China's coastal provinces have significantly different seawater quality at different stages of the seawater's EKC. Jiangsu and Hebei are still at the early stage of the seawater's EKC because of the large quantity of pollutionintensive industry, whereas the coastal provinces at the late stage of the seawater's EKC have presented obviously improved seawater quality based on adjustments in the industrial structure since 2001. Urbanization has a severe impact on the seawater quality of Shanghai and Zhejiang at the middle stage of the seawater's EKC. Furthermore, this paper proposes an overall strategy and several “tailored” strategies to address coastal waters environment improvement for current poorly targeted policies at different stages of the seawater's EKC. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Environmental Kuznets curve Seawater environment Seawater quality Coastal waters China

1. Introduction With the advent of the “Ocean Era”, marine resource utilization and shipping development have become increasingly important for human socio-economic development. However, human activities frequently affect the relatively fragile ecological environment of
et al., 2012; Kirwan and Megonigal, 2013). coastal waters (SarA Marine environmental protection and sustainable development have received increasing global attention, especially coastal waters, which is closely associated with human activities. China's coastal waters is typical, representative and universal because of their broad latitudinal span, various geomorphological types and high population density of the area. However, the seawater quality of the area has experienced severe deterioration due to the overexploitation and heavy pollution that accompanies local socioeconomic development. In 2017, 68 red tides occurred in China's coastal waters, and these tides had a cumulative area of 3679 km2 (SOA, 2018). Thus, research on the relationship between coastal waters environment and human socio-economic development is highly necessary. Although research on coastal waters environment has been

* Corresponding author. E-mail address: [email protected] (W. Wei). https://doi.org/10.1016/j.jclepro.2019.02.012 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

performed for a long time, the content of this research has been relatively decentralized, and special circumstances within individual case areas are frequently analyzed, such as eutrophication in coastal marine waters (Ryther and Dunstan, 1971); coastal marine environment in Dabob Bay, Washington State (Cowie and Hedges, 1984); microbial water quality (Crowther et al., 2001) and compounds (Thomas et al., 2001) in waters of the UK; dissolved organic matter (DOM) in coastal environment (Blough and Vecchio, 2002); salinization of coastal aquifers in Çes¸me caused by overexploitation (Burak et al., 2004); red tides in the coastal waters of southwestern Florida (Hu et al., 2005); floating seaweed in Belgian coastal waters (Vandendriessche et al., 2006); trends in the nutrient export from rivers to China's coastal waters (Hong and Kroeze, 2010); studies along the western coast of Korea (Choi et al., 2012); the high densities and types of acid mine drainage (AMD) in the coastal waters of northern-central Chile (Thiel et al., 2013); the coastline of northwestern Australia (Moore et al., 2017); and possible mechanisms that affect lead uptake by mosses in coastal environment (Renaudin et al., 2018). At present, none of these studies has described coastal waters environment at a relatively uniform level. Thus, comparative analyses of the coastal waters environment in different areas and different stages could be done with the environmental Kuznets curve (EKC). The EKC describes the relationship between socio-economic

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development and the ecological environment using an inverted Ushaped curve (Kuznets, 1955), in which environmental quality initially deteriorates and then improves with socio-economic development (Grossman and Krueger, 1995). The EKC has been verified through several case studies (Focacci, 2003; Dinda, 2004; Yabuta and Nakamura, 2008; Arouri et al., 2012); however, in some countries (or regions), there are various curves, such as the inverted “U” (Vinayagamoorthi et al., 2015; Hao et al., 2016), the “N” € € (Torras and Boyce, 1998; Ozokcu and Ozlem, 2017), and the monotonous rise (Akbostancı et al., 2009). In related research, the concentration of a single pollutant or multiple pollutants (i.e., stock index) or waste discharge (i.e., flow index) has been selected as the environmental quality index (Dinda, 2004), especially in terms of an air environmental quality index (Xu, 2018). According to the heterogeneity of different pollutants (Stern, 2004), an extended EKC that is named after a specific pollutant concentration is constructed, such as the carbon emission EKC (Sahli and Rejeb, 2015; Solarin et al., 2017). When different pollutants are chosen as environmental metrics, different research results are obtained. For example, in terms of industrial wastes, the provincial-level discharge of SO2 emissions and industrial solid waste are represented by an inverted U-shaped curve (Wang et al., 2016), while the chemical oxygen demand of pollutants in industrial wastewater is represented by a U-shaped curve (Yu and Lu, 2018). Mechanistic analyses have shown that the economic scale, industrial structure and technological level have important influences on the shape of the EKC (Selden et al., 1999), and the scale effect is the leading factor of the monotonic rise of the EKC (Stern, 2004). However, in terms of structure, the EKC experiences a turning point in response to technology, and the relationship between environmental quality and socio-economic development changes from a contradiction to a win-win situation (Tsurumi and Managi, 2010). In addition, governmental policies (Xu, 2018), international trade (Zhang et al., 2018; Li et al., 2018; Li et al., 2017; Wang et al., 2017), and spatial interactions (Hao et al., 2016) have impacts on the relationship between socio-economic development and environmental quality. The environmental regulation of neighboring countries (or regions) can lead to deviations in the estimated EKC turning point (Stern, 2004; Apergis et al., 2017; Atasoy, 2017). In summary, previous research has primarily used constructed models to study the relationship between socio-economic development and environmental quality, which is described by the shape of the curve, and analyzes the EKC's evolution mechanism with parameter features (Dinda, 2004; Narayan and Narayan, 2010; Azam and Khan, 2016). Generally, the EKC shows the combined effect of multiple factors, such as the economic scale, industrial structure, technical level and governmental policies. However, because of the dimensional limitation of model parameter estimation, previous studies on the EKC mechanism have primarily been focused on individual or multiple dominant factors. Theoretically, environmental characteristics obviously differ among regions, stages, dominant factors and starting points. Currently, a universal international standard for seawater quality in coastal waters is not available and a relatively comprehensive evaluation system for seawater quality has not been developed. Moreover, a framework for the comparative analysis of multiple cases and phases of the seawater's EKC is lacking. Therefore, this paper builds an EKC multiple logistic regression model to analyze multiple processes, stages and types of the seawater environment in China's coastal waters, thereby filling the aforementioned theoretical gaps. Additionally, imbalances in regional development are considered (Sun et al., 2016), and this paper first uses 11 coastal provinces in mainland China as the basic research units to analyze the differences comparatively among the stages of the seawater's EKC to assess the model's

representativeness and universality. Furthermore, this paper analyzes the factors affecting seawater quality at the different stages of the seawater's EKC in China's coastal provinces by presenting several logistic regression models with principal component analysis (PCA) to select the dominant factors. This study not only promotes the theoretical study of the seawater's EKC but also provides theoretical guidance for the future improvement of seawater environment. Increasing attention has been paid to the protection of marine environment, especially coastal waters, because the Chinese government has placed an unprecedented emphasis on environmental protection. A series of policies have been formulated, such as the “Protection of pollution in coastal waters”, which was announced in March of 2017. Since then, various coastal provinces have formulated corresponding prevention and control programs. By revealing the evolution of seawater quality characteristics in mainland China's coastal provinces and the mechanism underlying their different dominant factors, this study is instructive for generating regional policies toward overall protection, which is meaningful for the green and sustainable development of seawater environment in China's coastal waters. Overall, the innovation of this study is primarily reflected on the division of the different stages of the seawater's EKC among China's coastal provinces and the analysis of the mechanism underlying their different dominant factors to suggest targeted policies. The reminder of this paper is organized as follows. Section 2 describes the data sources and methods, including the data about the evolution of seawater quality characteristics in China's coastal waters. Section 3 presents the detailed results. Section 4 compares the present results with those from previous studies and discusses the uncertainties. Section 5 provides the final conclusions. 2. Methods and data sources 2.1. Data sources In this paper, seawater quality represents the environment of the coastal waters. Considering the availability and representativeness of the index data, the seawater quality index mainly selects the proportion of various classes of seawater quality in coastal waters as measured by five classes divided based on national standards (GB3097-1997).1 This selection process was performed for the 11 coastal provinces of mainland China during 2001e2016, and data were obtained from the Bulletin of Environmental Quality of China's Coastal Waters (2001e2016). The main socio-economic indicators for the study period and area included the per capita GDP, proportion of secondary industry, proportion of tertiary industry, per capita secondary industry added value, per capita tertiary industry added value, urban population, resident urbanization rate, and environmental pollution treatment investment. Among these, the resident urbanization rate was the proportion of the total provincial population accounted for in the resident urban population, which were based on the corrected and revised statistical data used during 2001e2005, and were mainly compiled from the statistical yearbooks of individual provinces from 2006 to 2015. Finally, the data for 2016 were obtained from the National Economic Development and Statistics Bulletin in 2016 from each province. Other socio-economic data were collected from individual province statistical yearbooks from recent years and the National Economic Development and Statistics Bulletin in 2016. The original data were standardized by the logarithmic method, and then the database for this research could be constructed.

1

This is a comprehensive water quality separation standard in China.

Z. Wang et al. / Journal of Cleaner Production 219 (2019) 925e935

2.2. Characteristics description Based on the overall variation, the proportion of class II seawater in China's coastal waters showed a fluctuating trend during 2001e2016, especially in 2005, while there was an inconspicuous increasing trend for the proportion of class I seawater; moreover, the proportion of class IV seawater declined in overall volatility during 2005e2016 (Fig. 1). During 2001e2008, the proportion of inferior class IV seawater declined but then increased after 2009 before declining again in 2016 (Fig. 1). In general, the seawater quality was relatively stable and the deteriorating trend was mitigated to some extent in the coastal waters of China during 2001e2016. Regarding regional differences, the proportion of (inferior) class IV seawater was high in Tianjin, Hebei, Shanghai, Zhejiang, and Jiangsu, which have poor seawater quality, while the proportion of inferior class IV seawater decreased in Guangdong, Fujian, Liaoning and Shandong while the proportion of class II seawater increased, which indicates that the seawater quality has been improved. Compared with that in other provinces, the seawater quality in Hainan and Guangxi was good. In 2001 (Fig. 2a.), the seawater quality in China's coastal waters was poor. The proportion of (inferior) class IV seawater was very high in Guangdong, Zhejiang, Shanghai and Jiangsu while the proportion of class II seawater was dominant in Guangxi and Hainan; thus, the seawater quality was superior in Guangxi and Hainan to that in the other provinces. In 2006 (Fig. 2b.), the seawater quality in China's coastal waters was improved. The proportion of inferior class IV seawater decreased in Guangdong and Shandong, while the proportion of class I and II seawater increased. In Hebei, the proportion of class Ш seawater decreased and the proportion of class II and inferior IV seawater increased, which indicates poor seawater quality. In 2011 (Fig. 2c.), the seawater quality fluctuated in China's coastal waters. In Tianjin, Guangdong and Liaoning, the proportion of inferior class IV seawater increased while the proportion of class II seawater decreased, which indicates worsening seawater quality. In Shandong, the proportion of class I seawater decreased while the proportion of class II and Ш seawater increased. In 2016 (Fig. 2d.), the proportion of class II seawater increased in Liaoning and Proportion(%) 100

Inferior class

Class

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Guangdong, which indicates improved seawater quality in these provinces; the proportion of class I seawater increased in Hainan and Guangxi, which present good seawater quality and tends to be stable; and the proportion of inferior class IV seawater was still relatively high in Zhejiang and Shanghai, which present poor seawater quality.

2.3. EKC multiple logistic regression model In the present research, the empirical analysis of the EKC primarily involves an economic index and an environmental index to establish a regression model of the environment and economy to describe its evolution mechanism. The existing EKC metrological models primarily include the simplification of the first, quadratic and cubic functions and the structural formula by increasing the control variables (Dinda, 2004), and these models include the autoregressive distributed lag (ARDL) (Jebli and Youssef, 2015), the Fully Modified OLS (FMOLS) (Al-Mulali et al., 2015), and the Spatial Durbin Model (SDM) (Hao et al., 2016). Considering the impact of socio-economic development on the coastal waters environment, factors are complex and there are limitations on data acquisition. In addition, the primary factors are variable at the different stages of the seawater's EKC among China's coastal provinces. Therefore, this paper uses PCA, which could assist in understanding the influencing factors more comprehensively, to select several independent variables related to economic development, industrialization and urbanization, among which the third, the second and the first terms of per capita GDP are used as explanatory variables. Other factors affecting seawater quality in coastal waters are used as control variables, and the proportions of various classes of seawater quality in coastal waters are used as the explanatory variable. The new EKC multiple logistic regression model is constructed as follows:

lnYj ¼ b3 ðlnX1 Þ3 þ b2 ðlnX1 Þ2 þ b1 lnX1 þ

Class

Class

Class

60

40

20

2001

2006

li lnXi þ v þ ε (1)

80

0

X

2011

Fig. 1. Proportions of different seawater quality in China's coastal waters during 2001e2016.

2016

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Fig. 2. Seawater quality in China's coastal waters in 2001, 2006, 2011 and 2016.

Z. Wang et al. / Journal of Cleaner Production 219 (2019) 925e935

lnYj ¼ b2 ðlnX1 Þ2 þ b1 lnX1 þ lnYj ¼ b1 lnX1 þ

X

X

li lnXi þ v þ ε

li lnXi þ v þ ε

(2) (3)

where Yj represents the proportion of various classes of seawater quality in different coastal provinces, j ¼ {1, …,5}; Xi represents the factors that affect seawater quality in coastal waters, i ¼ {2, …,8}, and X1 is the per capita GDP as an explanatory variable. For the control variables, v is a constant term, ε is a random error term, and b1, b2, b3, and l are estimated coefficients of the corresponding independent variables. According to the above mathematical model, Formula (1) reflects the linear relationship between the independent and dependent variables. Formula (2) reflects that the curve shape is either a “U” or an inverted “U”. When b2 > 0, the curve shape is “U”. When b2 < 0, the curve shape is “U”. Formula (3) indicates the curve shape is an “N” or an inverted “N”. When b1 > 0 and 4b2-12b1b3 > 0, the curve shape is an “N”. When b1 < 0 and 4b2-12b1b3 > 0, the curve shape is an inverted “N”. Based on the above three-stage division method of the EKC, this paper presents three types of models. By comparing the R2, adjusted R2, estimation standard error, F, regression significance test and coefficient significance test, the optimal model is selected. By obtaining the model curve's turning point, the correlation between seawater quality and socioeconomic development can be further determined for China's coastal waters during the study period. 2.4. Characteristics of different seawater's EKC stages According to the existing research (Kuznets, 1955; Rasli et al., 2017), the EKC can be roughly divided into three stages. Generally, environmental quality first deteriorates and then improves with socio-economic development. The existing research on the EKC generally focuses on the division of the environmental stage into a single area or into the differences among different stages of the same curve in the same dimensional space, whereas the starting point and environmental basis of socio-economic development in each area are generally ignored. Therefore, these factors cannot be expressed at different stages of various curves in the same dimensional space. The presence of an EKC does not indicate that environmental problems will be solved automatically with socio-economic € € development (Ozokcu and Ozlem, 2017); rather, the EKC only represents the general law of environmental evolution. In some areas with low socio-economic development, if environmental protection is a priority, then the initial stage of socio-economic development will usher in an environmental turning point (Atasoy, 2017; Apergis et al., 2017). Due to the differences in socioeconomic development, different coastal provinces exert different degrees of influence on seawater quality and implement environmental protection policies in China's coastal waters and these conditions lead to different evolutionary paths of seawater quality. Therefore, the occurrence of the turning point in improving the seawater quality in these coastal provinces does not display a ladder-shaped distribution based on the level of the area's socioeconomic development. According to the above empirical analysis, the deteriorating trend of seawater quality in China's coastal waters has been restrained; however the seawater quality in coastal provinces does not fully adhere to the general law of the seawater's EKC stages of evolution. Rather, there are different local characteristics caused by urbanization and industrialization. The differences in socio-economic development cause large-stage differences in the seawater quality of coastal provinces and different

929

starting points in the evolution of seawater quality among coastal provinces. As a result, there are obvious theoretical deviations in the seawater's EKC in China's coastal waters. 3. Results 3.1. Dominant socio-economic factor analysis Many factors are involved when investigating the mechanism underlying the EKC, such as industry, agriculture, fishery, urban life, and even tourism, and they can be summarized based on two aspects, namely: production, which is more oriented toward industrial production, especially industrialization; and life, which is relatively complicated but allows for a quantification of urbanization (Liu and Lei, 2018). During the socio-economic development process, industrial production and urban life are two important factors that affect the seawater environment in China's coastal waters. Industrialization and urbanization are the two driving forces of socio-economic development and they can also be viewed as the leading factors in environmental evolution (Azam and Khan, 2016; Solarin et al., 2017; Setareh and Salih, 2018). In this paper, we analyze the evolution mechanism of seawater quality in China's coastal waters using both aspects. Since the beginning of the 21st century, the seawater quality in China's coastal waters has been poor, and this degraded state is directly related to the extensive economic development model based on traditional heavy chemical industry and a large number of scattered small township enterprises. These factors have been relevant since the implementation of the Reform and Opening Up policy, which solidified China's role as the world's factory (Chen et al., 2017). In terms of industrial structure, China's coastal provinces have been dominated by secondary and tertiary industry, especially traditional pollution-intensive industry during 2001e2016; furthermore, the proportion of new industry is relatively low. Industrialization not only promotes economic growth but also adversely affects seawater quality in China's coastal waters (Table 1). Rapid urbanization involves enormous energy-intensive production, such as steel and concrete (Wei et al., 2017a, 2017b; 2018a), and other industry such as construction (Li et al., 2015) and power industry (Wei et al., 2018b), and inevitably results in environmental problems related to the urbanization process (Chen and Lu, 2015). Urbanization has brought about a large change in the production and living style of residents (Ottelin et al., 2015), and the improvement of urban residents' living standards and continuous satisfaction of their consumption demands have increased waste discharge, which has had a terrible impact on seawater quality in China's coastal waters (Table 1). 3.2. Stage division of seawater's EKC According to the variation in the ratios of different classes of seawater quality, the characteristics of seawater environment correspond to the characteristics of the seawater's EKC in three stages, namely, the early stage of seawater quality deterioration in EKC, the middle stage of the seawater's EKC, in which the deteriorating trend is slowing down, and the late stage of seawater's EKC, in which the environmental conditions are improving. China's coastal provinces were divided into these three stages according to their seawater quality during 2001e2016 (Fig. 3). Jiangsu and Hebei were at the early stage of the seawater's EKC, i.e., the seawater quality in the coastal waters has not obviously improved or deteriorated (Fig. 4a.). Tianjin, Zhejiang and Shanghai were at the middle stage of the seawater's EKC (Fig. 4b.). During the study period, almost no class I seawater was observed in Shanghai, while the proportion of inferior class IV seawater was above 60.00%.

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Table 1 Correlation between socio-economic development and seawater quality in China's coastal waters. Correlation

Per capita GDP

Proportion of secondary industry

Proportion of tertiary industry

Per capita secondary industry added value

Per capita tertiary industry Urbanization Urban rate population added value

Class I Class II Class III Class IV Inferior Class IV

0.311** 0.180* e e 0.359**

0.527** 0.322** e 0.252** 0.266*

0.026 e e e 0.598**

0.418** 0.246** e e 0.276**

0.282** 0.123 e e 0.436**

0.419** e e e 0.635**

0.430** 0.470** 0.316** 0.305** 0.302**

Note: ** indicates 0.01 significant level; * indicates 0.01 significant level; "-" indicates that the correlation is not significant.

Fig. 3. Seawater's EKC stages' division in China's coastal waters. Note: socio-economic development (per capita GDP), seawater quality deterioration (proportion of (inferior) class IV seawater).

Shandong, Liaoning, Fujian, Guangdong, Guangxi and Hainan were at the late stage of the seawater's EKC (Fig. 4c.). The seawater quality of the coastal waters of Guangxi and Hainan was good, and almost no inferior class IV seawater was observed in Hainan (Fig. 4d.). 3.3. Empirical model results According to previous research (Liobikiene_ and Butkus, 2017), the optimal regression model was constructed by selecting the dominant factors and proportions of different classes of seawater quality as dependent variables based on the different changing stages of seawater quality in China's coastal waters during 2001e2016 (Table 2), and a detailed analysis of the evolution mechanism was performed. 3.3.1. Early stage of the seawater's EKC Hebei and Jiangsu were at the early stage of the seawater's EKC. The primary factors selected were the per capita GDP (X1), population urbanization rate (X2), proportion of secondary industry (X4), proportion of tertiary industry (X5), and proportion of environmental pollution control investment to GDP (X8). In the regression model, the coefficients of the cubic, quadratic and first-order terms for explaining the per capita GDP (X1) of variables all passed the significance test. From the estimation of the coefficients, there was an inverted “U” curve between the proportion of class I seawater and the per capita GDP (X1) in Hebei and Jiangsu. According to the extreme value theory, the curve reached a turning point in approximately 2006 or 2007 when the per capita GDP was 15,300

RMB/person and 34,000 RMB/person in Hebei and Jiangsu, respectively, and at this time, the proportion of class I seawater showed a decreasing trend and the seawater quality tended to deteriorate. In the regression model, the proportion of secondary industry (X4) and the proportion of environmental pollution control investment to GDP (X8) were selected as the control variables in Hebei. According to the corresponding coefficient estimate, if the proportion of secondary industry (X4) increased by 1.00%, the proportion of class I seawater in Hebei would be reduced by 5.06%, and the development of secondary industry would have a negative impact on seawater quality in coastal waters. According to the actual situation, Hebei is close to Beijing and Tianjin but primarily relies on a regional cooperative network of traditional extensive production. The proportion of iron and steel and other heavy industry is high in Hebei, which means there is high energy consumption per unit industrial added value and low-quality benefits. The population urbanization rate (X2), the proportion of tertiary industry (X5), and the proportion of environmental pollution control investment to GDP (X8) were used as control variables in Jiangsu. According to the corresponding coefficient estimates, if the proportion of tertiary industry (X5) increased by 1.00% in Jiangsu, the proportion of class I seawater would increase by 6.16% and the proportion of class III seawater would decrease by 8.20% in Jiangsu; moreover, the proportion of class I seawater would decrease by 0.17% if the population urbanization rate (X2) increased by 1.00%. Township enterprises develop rapidly in Jiangsu, while the rural in situ urbanization (RISU) results were remarkable. The scale of township enterprises was relatively small and too decentralized in Jiangsu, a wide range of pollution sources was observed, and production efficiency and environmental pollution control measures were generally inconsistent, although industrial pollution emissions have been reduced by adjusting and optimizing the industrial structure and improving the technological level. However, the discharge of urban living pollution increased, which further degrades the seawater quality in coastal waters. Investments in environmental pollution control measures significantly impacted and improved seawater quality in Hebei and Jiangsu. When the proportion of environmental pollution control investment to GDP (X8) increased by 1.00%, the proportion of class I seawater increased by 1.92% and 1.35% in Hebei and Jiangsu, respectively.

3.3.2. Middle stage of the seawater's EKC Shanghai, Zhejiang and Tianjin were at the middle stage of the seawater's EKC. The dominant factors included the GDP per capita (X1), population urbanization rate (X2), proportion of secondary industry (X4) and proportion of tertiary industry (X5). According to the estimated value of the coefficient of explanatory variables in the regression model, the relationship between the proportion of inferior class IV seawater and the per capita GDP (X1) was “U"shaped in Shanghai, although the coefficient did not pass the

Z. Wang et al. / Journal of Cleaner Production 219 (2019) 925e935

a

Proportion(%) 80

Class Class Class

in Jiangsu in Jiangsu in Hebei

Class Class Class

in Jiangsu in Hebei in Hebei

b

Proportion(%) 100

70

931

Class

in Tianjin

Inferior class

Class

in Tianjin

Class

Inferior class

in Zhejiang

in Tianjin

in Zhejiang

Inferior class

in Shanghai

90

60

80

50

70 60

40

50

30

40

20

30 20

10

10

0 2001

c Proportion(%) 80

2006

2011

Class in Liaoning Class in Shandong Inferior class in Guangdong Inferior class in Fujian

2016

Inferior class in Liaoning Inferior class in Shandong Class in Guangdong Class in Fujian

70

0 2001

d Proportion(%) 100

2006

2011

2016

Class

in Guangxi

Class

in Guangxi

Class

in Hainan

Class

in Hainan

Class

in Hainan

90 80

60

70

50

60 50

40

40 30

30 20

20

10

10 0

0 2001

2006

2011

2016

2001

2006

2011

2016

Fig. 4. Proportions of different seawater quality in China's coastal provinces during 2001e2016.

significance test. A positive linear correlation was observed between the proportion of class IV seawater and the per capita GDP (X1) in Zhejiang. The proportion of class IV and inferior class IV seawater showed an upward trend. An inverse “N” curve relationship was observed between the proportion of class II seawater and the per capita GDP (X1) in Tianjin. According to the extreme value theorem, the curve reached its turning points when the per capita GDP was 1800 RMB/person and 55,300 RMB/person, respectively. Moreover, the first turning point did not appear during 2001e2016, and the second turning point corresponds to 2008, i.e., after 2008, the proportion of class II seawater shows a downward trend in Zhejiang. In the regression model, the population urbanization rate (X2) and the proportion of tertiary industry (X5) were selected as the dominant factors in Shanghai and Zhejiang, while the proportion of secondary industry (X4) was selected in Tianjin. Urbanization and the development of tertiary industry had a negative effect on seawater quality in China's coastal waters. When the population urbanization rate growth was 1.00%, the proportion of class Ш and inferior class IV seawater increased by 27.73% and 15.89%, respectively, in Shanghai, and the proportion of inferior class IV seawater increased by 15.89% in Zhejiang. For each 1.00% increase in the tertiary industry, the proportion of inferior class IV seawater increased by 0.02% and 5.98%, respectively, in Shanghai and Zhejiang, and the proportion of class IV seawater decreased by 4.96% in Zhejiang. The development of the secondary industry in Tianjin had a negative effect on seawater quality in coastal waters. Furthermore, with a 1.00% growth in secondary industry in Tianjin, the proportion of class II seawater decreased by 9.12%. This outcome is related to the natural structural system of the region's coastal waters, because Tianjin is located in the semi-open Bohai Bay. Compared with the other three major sea areas, Bohai Bay

experiences a lower amount of exchange with the offshore seawater; thus, the pollutants are not easily dispersed. To some extent, these conditions aggravate the pollution impacts on seawater quality and amplify the effect of socio-economic development on seawater quality in the coastal waters. 3.3.3. Late stage of the seawater's EKC Shandong, Liaoning, Fujian, Guangdong, Guangxi and Hainan were located at the late stage of the seawater's EKC. The per capita GDP (X1), population urbanization rate (X2), proportion of secondary industry (X4) and proportion of tertiary industry (X5) were selected as the dominant factors. The seawater quality in coastal waters was obviously improved during 2001e2016. A negative linear relationship was observed between the proportion of class IV seawater and the per capita GDP (X1) in Shandong and Liaoning. According to the extreme value theorem, the turning point of the seawater's EKC appeared when the per capita GDP (X1) was 3900 RMB/person, i.e., the proportion of inferior class IV seawater showed a downward trend during 2001e2016. An inverse “N” curve relationship was observed between the proportion of inferior class IV seawater and the per capita GDP (X1). The corresponding years of the turning points were approximately 2007 and 2012, respectively. The relationship between the proportion of class I seawater and the per capita GDP (X1) was depicted by an “N” curve in Guangxi, and the corresponding years of the turning points were 2005 and 2011, respectively. A negative linear relationship was observed between the proportion of class II seawater and the per capita GDP (X1) and an “N” curve between the proportion of class I seawater and the per capita GDP (X1) in Hainan. According to the extreme value theorem, when the per capita GDP was 23,000 RMB/ person and 23,100 RMB/person, respectively, the curve reached a turning point, and the corresponding year was approximately 2010;

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Table 2 Regression results of different seawater quality in China's coastal provinces. R2

Province

Dominant factors Regression model

Hebei

X1 、X4 、X8

lnY1 ¼ 2:022ðlnX1 Þ þ 1:723 lnX1 5:218 lnX4 þ 1:098X8 þ 23:655

0.777

Jiangsu

X1 、X5 、X8

lnY1 ¼ 2:414ðlnX1 Þ2 þ 5:852 lnX1 þ6:005 lnX5 þ 1:352 lnX8  22:095 lnY3 ¼ 1:707 lnX1  8:602 lnX5 þ32:210

Zhejiang

X1 、X2 、X5

X1 、X2 、X5

0.020

Fitting curve b1 Inverted “U”

X1 、X4

Shandong

X1 、X5 、X8

Liaoning

X1 、X4

Fujian

X1 、X4

Guangdong X1 、X5 、X8

b3

l2

l4

l5

l8

-

-

5.218

-

1.908*

e e

0.168 e

e e

5.882 1.346** 8.602*** –

e

24.597* 3.621

e e

e 0.024

e e

1.723* e 10.153*** 6.805**

e 1.890**

11.664* e 14.822** e

5.117** 5.838

e e

6.006***

e

0.684*** e

9.613*** e

e

e

e

e

e

0.951

1.723

2.022

6.083

0.950 0.913 0.729 0.680

0.142 0.367

26.334 0.000 Inverted 14.811 0.001 Rising

lnðY3 ¼ 0:250 lnX1 Þ2 þ 0:248 lnX1 þ24:597 lnX2  109:369

0.696 0.544 0.724 0.623

0.319 0.098

4.572 7.203

lnY5 ¼ 0:280ðlnX1 Þ2  1:359 lnX1 þ3:621 lnX2 þ 0:024 lnX5  10:446 lnY4 ¼ 1:723 lnX1  11:664 lnX2 5:117 lnX5 þ 66:988

0.851 0.814 0.753 0.629

0.269 0.195

22.923 0.000 Rising 6.086 0.008 Rising

lnY2 ¼ 0:684ðlnX1 Þ3 þ 6:006 lnX1 9:613 lnX4 þ 35:272 lnY5 ¼ 0:888 lnX1  3:250 lnX5 5:598 lnX8 þ 15:124 lnY5 ¼ 1:568 lnX1  0:951 lnX4 þ7:192

0.802 0.742

0.375

13.471 0.001

0.968 0.949

0.170

50.580 0.000 Dropping

0.888***

e

0.903 0.878

0.322

37.126 0.000 Dropping

1.568***

e

lnY5 ¼ 0:724ðlnX1 Þ2 þ 1:366 lnX1 8:424 lnX4 þ 35:026

0.716 0.644

0.338

10.061 0.001

lnY2 ¼ 1:045ðlnX1 Þ3  4:921ðlnX1 Þ2 þ7:454 lnX1 þ 0:280 lnX5  0:148 lnX8  0:647

0.972 0.956 0.893 0.854

0.118 0.203

61.362 0.000 “N00 22.983 0.000 Inverted “N00

“U”

0.054 “U00 0.004 “U00

5.849*** 1.707***

0.248 1.359

**

2.392** d

0.250 0.280

2

lnY5 ¼ 1:890ðlnX1 Þ þ 6:805ðlnX1 Þ 10:153 lnX1 þ 14:822 lnX2 þ5:838 lnX5  72:123 Tianjin

b2 *

0.338

3

0.649

P

lnY5 ¼ 1:994ðlnX1 Þ3 þ 8:331ðlnX1 Þ2 10:696 lnX1  2:222 lnX5 þ 15:027

Inverted

N

Inverted “U”

0.005 “N00

Guangxi

X1 、X4 、X8

lnY1 ¼ 1:922ðlnX1 Þ3  1:883ðlnX1 Þ2 1:265 lnX1 þ 1:140 lnX4 þ1:383X8 þ 0:291

0.837 0.735

0.195

8.215

Hainan

X1 、X5

lnY1 ¼ 0:948ðlnX1 Þ3  2:373ðlnX1 Þ2 þ1:963 lnX1  0:725 lnX5 þ 6:569 lnY2 ¼ 0:916 lnX1 þ 2:564 lnX5 5:947

0.954 0.938 0.641 0.586

0.122 0.272

57.426 0.000 “N00 11.609 0.001 Dropping

1.366

0.724

*

e

e

7.454*** 4.921*** 1.045** e 10.696*** 8.331*** 1.994*** e

e

3.250*

5.598**

e

e

e

e

e e

0.280 2.222

0.148* e

8.424

*

1.265

1.883*** 1.922**

e

1.140

e

1.383**

1.963*** 0.916***

2.373*** 0.948*** e e

e e

e e

0.725 2.564*

e e

Note: X1 X2 X4 X5 , andX8 indicate per capita GDP, population urbanization rate, proportion of secondary industry, proportion of tertiary industry, proportion of environmental pollution control investment, respectively; b1 b2 , andb3 indicate the estimated coefficients of lnX1 ðlnX1 Þ2 ðlnX1 Þ3 , respectively; l2 l4 l5 , andl8 indicate the estimated coefficients of lnX2 lnX4 lnX5 lnX8 , respectively; *, **, and *** indicate the estimated coefficients at the 0.1,0.05,0.01 statistically significant levels, respectively; “-“indicates no significant correlation.

Z. Wang et al. / Journal of Cleaner Production 219 (2019) 925e935

Shanghai

2

Adjust R2 Standard Error F

Z. Wang et al. / Journal of Cleaner Production 219 (2019) 925e935

that was, during 2001e2016, the proportion of class I seawater showed an overall increasing trend. According to the estimated coefficients of the control variables in the regression model, adjusting and optimizing the industrial structure and improving the industrial internal economic benefit were the primary drivers of the observed improvement in seawater quality in coastal waters. In the regression model, the proportion of tertiary industry (X5) and the proportion of environmental pollution control investment to GDP (X8) were selected as the control variables in Shandong and Guangdong. For each 1% increase in the tertiary industry, the proportion of inferior class IV seawater in Shandong and Guangdong decreased by 3.18% and 2.19%, respectively, and the proportion of class II seawater in Guangdong increased by 0.28%. The service industry (primarily tourism and real estate) was developed in Hainan. During the development process, attention should be focused on the environmental impact on coastal waters. The proportion of tertiary industry (X5) was selected as the control variable in Hainan. The proportion of class I seawater decreased by 0.72%, and the proportion of class II seawater increased by 2.58% for each 1.00% increase in the proportion of the tertiary industry. The proportion of secondary industry (X4) was selected as the control variable in Liaoning, Fujian and Guangxi. In addition, the proportion of environmental pollution control investment to GDP (X8) was also selected as the control variable in Guangxi. For every 1.00% increase in the proportion of secondary industry, the proportion of inferior class IV seawater in Liaoning and Fujian decreased by 0.94% and 8.04%, respectively, and the proportion of class I seawater in Guangxi increased by 1.14%. The increased investment in environmental pollution control had played a positive role improving the seawater quality in coastal waters. For each 1.00% increase in the proportion of environmental pollution control investment in GDP, the proportion of inferior class IV seawater in Shandong decreased by 5.42%, while the proportion of class I seawater in Guangxi increased by 1.39%. 4. Discussion and suggestions According to the State Oceanic Administration of China, the sea area of inferior class IV seawater spanned more than 37,000 square kilometers of China's coastal waters in 2017.2 More than 80% of the environmental quality of adjacent seawater discharge ports did not meet the environmental protection requirements established for marine functional areas. Traditional industrial development produces a large amount of the “three wastes”, which seriously affect the ecological system of coastal waters. During 2001e2016, the direct discharge of industrial pollution showed an increasing trend, which had a large impact on the coastal waters environment. However, there are regional differences among China's coastal provinces. Because of inclined regional development policies and the associated economic transformation and industrial structure adjustments, the proportion of secondary industry in China's coastal provinces first increased and then decreased. As industrial structures were adjusted, optimized and upgraded, a reduction was observed in the proportion of traditional heavy industry in China's coastal provinces dominated by “New and Old Kinetic Energy Conversion”. Therefore, the proportion of inferior class IV seawater decreased and the proportion of class II seawater increased in Guangdong, Fujian, Liaoning and Shandong (Fig. 2). However, the proportion of (inferior) class IV seawater was still high in Tianjin, Hebei, Shanghai, Zhejiang, and Jiangsu (Fig. 2). An increase in the proportion of tertiary industry does not offset the negative

2 State Oceanic Administration, People's Republic of China. China Ocean Disaster Bulletin in 2017,2018-04-23.

933

environmental effects caused by the expanding economic scale and the developing secondary industry in China's coastal provinces. The development of tertiary industry in China's coastal provinces, such as tourism, the transportation, warehousing and postal services (Liu et al., 2018), which are directly related to urban residents' life, also had a negative impact on coastal waters environment. Compared with other provinces, the seawater in Hainan and Guangxi maintains its originally good quality (Fig. 2). Historically, industrial pollution has been regarded as the main cause of negative environmental impacts. For example, electric power, heat production, and supply sector was the main fuelrelated direct CO2 emissions contributor (Li et al., 2016a,b). Thus industrial pollution has been the primary focus of environmental control. With the rapid development of urbanization, the continuous expansion of the urban scale and the continuous increase in the urban population, urban living pollution emissions continue to increase and have an increasingly negative impact on the coastal seawater environment. In 2015, the total amount of ammonia and nitrogen discharged into China's coastal provinces was 280 thousand tons; of this amount, 26 thousand tons were from industrial wastewater and 165 thousand tons were from urban domestic sewage; thus, these two uses accounted for 68.21%. At the same time, with the rapid development of urbanization and the lack of effective control and guidance, the spread and outward expansion of urban areas and tensions over urban construction land occur, which was always accompanied with the direct and indirect energy consumption and carbon emissions during the building process (Bin and Parker, 2012). Thus, dramatic changes in ecological landscape patterns in China's coastal waters have occurred, and these changes were not conducive to maintaining the ecosystem balance. To expand construction land, the area of coastal wetland has been reduced by transforming the tidal flat and reclaiming the land; additionally, the coastline has been degraded by overexploitation, and the self-purification ability of China's coastal ecosystem has been reduced. Combined with the previous analysis results and existing policies and regulations related to China's coastal waters, this paper proposes further protection of the seawater environment based on the following two perspectives. (1) The overall strategy for seawater environmental protection in China's coastal waters requires the development of a framework. To achieve this level of environmental protection, it will be critical to integrate the ecological protection of China's coastal waters into the local government's assessment system of its achievements. It is also necessary to ensure that planning and other safeguard measures are in place and the delegation of responsibilities is clear. The government must strengthen pollution control measures, scientifically select and establish sewage disposal areas, and implement a method to comprehensively reduce the pollution sources that discharge into the sea. At the same time, the government also needs to strengthen urban residents' awareness of the importance of marine ecological protection, promote the retreat of coastal waters to the sea and establish a long-term mechanism to protect the natural shoreline of China's coastal waters. Finally, according to the specific conditions of each coastal province in China, the government should gradually transition the comprehensive management of rivers that flow into the sea and identify and rectify any unreasonable sources of pollution. In addition, as urbanization advances, the government must rationally guide the ecological transformation in terms of the coastal urban residents' lifestyles.

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Z. Wang et al. / Journal of Cleaner Production 219 (2019) 925e935

(2) The targeted strategy for seawater environmental protection in China's coastal waters should be centered around consistency. China's coastal provinces are at different stages of the seawater's EKC. Industrialization plays a leading role in the early and middle stages of the seawater's EKC, while urbanization occurs during the late stage of the EKC. For China's coastal provinces, specific seawater pollution control schedules should be developed to ensure improvements in seawater quality. The government should further reduce the proportion of traditional heavy industry in secondary industry and develop green industry in the provinces that are still at the early stage of the seawater's EKC. For the provinces at the middle stage of the seawater's EKC, the government should encourage the adjustment of secondary and tertiary industrial structures and control the impacts of urbanization on the coastal waters of these provinces. For the provinces at the late stage of the seawater's EKC, the local government should reasonably control the utilization of the sea, enhance gulf remediation measures, and integrate ecological restoration into the urban construction process. In short, the appropriate corresponding seawater protection measures could provide insights for decisions that involve weighing potential trade-off (Chen et al., 2018), and the overall goal should involve minimizing human impacts on the local seawater environment of each coastal province in China according to their respective conditions.

5. Conclusions 5.1. Main conclusions Based on an analysis of the temporal and spatial evolution characteristics of seawater quality in China's coastal waters, this paper divides the seawater's EKC into three stages. By selecting dominant factors to establish a logistic regression model, the mechanism analysis is further performed from a socio-economic perspective, and the conclusions are as follows: (1) Jiangsu and Hebei are at the early stage of the seawater's EKC, meaning that the coastal waters environment has not been improved but shown a worsening trend with socio-economic development. Traditional secondary industry development is the primary reason for the deterioration of seawater quality in Hebei's coastal waters. An increase in the proportion of tertiary industry would optimize the industrial structure and help improve the seawater quality in Jiangsu's coastal waters, while urbanization also has a negative impact. (2) Tianjin, Shanghai and Zhejiang are at the middle stage of the seawater's EKC, meaning that the coastal waters environment is the worst and urgently requires improvement. The development of secondary industry is still an important factor that affects seawater quality in Tianjin's coastal waters. However, urbanization has a serious impact in Shanghai and Zhejiang, and the development of tertiary industry further exacerbates the negative effects. Therefore, improving the seawater environment in coastal waters requires the further optimization of the internal structure of the secondary and tertiary industry to achieve green production, and the impact of urban life on seawater quality in coastal waters should also be considered. (3) Shandong, Liaoning, Fujian, Guangdong, Guangxi and Hainan are at the late stage of the seawater's EKC, and the coastal waters environment has been improved to some extent. Promoting adjustments in industrial structure, speeding up

the conversion of “Old and New Kinetic Energy”, and increasing investments related to environmental pollution control measures are all actions that could positively improve the seawater quality in the coastal waters of these provinces. Due to the differences in socio-economic development, the present situation and the evolution track of seawater quality in China's coastal provinces are depicted by the obvious variations in seawater quality. In short, improving the seawater quality of coastal waters requires a number of steps. In addition to focusing on industrialization and urbanization, we should also increase investments in environmental pollution control measures and further adopt corresponding measures for seawater pollution control and protection to promote improvements in the seawater environment of China's coastal waters. 5.2. Future studies The selection, monitoring and acquisition of index data are of great significance when studying the causal relationship between the seawater environment and socio-economic development in coastal waters. Due to the self-purification ability of coastal waters against pollutants, seawater pollution problems are increasing, and the recovery of seawater environment also requires a certain process to be successful. In this paper, the proportion of different classes of seawater quality in China's coastal provinces is used as the seawater environmental index; however, limitations were observed when assessing the stage characteristics of the seawater environment based on variations in the proportion of different classes of seawater quality in China's coastal provinces. In this paper, the time span and dimension of study must be further expanded. In addition to human factors such as socio-economic development, other factors (e.g., the natural aquatic structural system, geological landforms) have an impact on the seawater quality in coastal waters. In future research, natural and human factors should be considered synthetically, which can more effectively reflect the actual situation of the seawater environment in coastal waters and have more guiding significance in terms of realworld applications. In addition, the linkage effects of various factors on the seawater's EKC have temporal importance and are comparable only during the same period. Acknowledgments This research was supported by National Social Science Foundation of China (No. 16CJY022), Shanghai Sailing Program (No. 18YF1417500) and Philosophy and Social Science Project of Shanghai (No. 2018EGL003). References _ 2009. The relationship between income and Akbostancı, E., Türüt-As¸ık, S., Tunç, G.I., environment in Turkey: is there an environmental Kuznets curve? Energy Policy 37 (03), 861e867. Al-Mulali, U., Tang, C.F., Ozturk, I., 2015. Estimating the environment Kuznets curve hypothesis: evidence from Latin America and the caribbean countries. Renew. Sustain. Energy Rev. 50, 918e924. Apergis, N., Christou, C., Gupta, R., 2017. Are there environmental Kuznets curves for US state-level CO2 emissions? Renew. Sustain. Energy Rev. 69, 551e558. Arouri, M.E.H., Youssef, A.B., M'Henni, H., Rault, C., 2012. Energy consumption, economic growth and CO2 emissions in Middle East and North African countries. Energy Policy 45 (6), 342e349. Atasoy, B.S., 2017. Testing the environmental Kuznets curve hypothesis across the U.S.: evidence from panel mean group estimators. Renew. Sustain. Energy Rev. 77, 731e747. Azam, M., Khan, A.Q., 2016. Testing the Environmental Kuznets Curve hypothesis: a comparative empirical study for low, lower middle, upper middle and high income countries. Renew. Sustain. Energy Rev. 63, 556e567.

Z. Wang et al. / Journal of Cleaner Production 219 (2019) 925e935 Bin, G., Parker, P., 2012. Measuring buildings for sustainability: comparing the initial and retrofit ecological footprint of a century home-The REEP House. Appl. Energy 93, 24e32. Blough, N.V., Vecchio, R.D., 2002. Chromophoric DOM in the coastal environment. Biogeochemistry of Marine Dissolved Organic Matter 509e546. an, E., Gaziog lu, C., 2004. Impact of urbanization and tourism on Burak, S., Dog coastal environment. Ocean Coast Manag. 47 (9), 515e527. Chen, B., Lu, Y., 2015. Urban nexus: a new paradigm for urban studies. Ecol. Model. 318, 5e7. Chen, B., Li, J.S., Chen, G.Q., Wei, W.D., Chen, H.P., 2017. China's energy-related mercury emissions: characteristics, impact of trade and mitigation policies. J. Clean. Prod. 141, 1259e1266. Chen, B., Han, M.Y., Peng, K., Peng, K., Zhou, S.L., Shao, L., Wu, X.F., Wei, W.D., Liu, S.Y., Li, Z., Li, J.S., Chen, G.Q., 2018. Global land-water nexus: agricultural land and freshwater use embodied in worldwide supply chains. Sci. Total Environ. 613, 931e943. Choi, K.H., Kim, Y.O., Lee, J.B., Wang, S.Y., Lee, M.W., Lee, P.G., Ahn, D.S., Hong, L.S., Soh, H.Y., 2012. Thermal impacts of a coal power plant on the plankton in an open coastal water environment. J. Mar. Sci. Technol. 20 (2), 187e194. Cowie, G.L., Hedges, J.I., 1984. Carbohydrate sources in a coastal marine environment. Geochem. Cosmochim. Acta 48 (10), 2075e2087. Crowther, J., Kay, D., Wyer, M.D., 2001. Relationships between microbial water quality and environmental conditions in coastal recreational waters: the Fylde coast, UK. Water Research 35 (17), 4029e4038. Dinda, S., 2004. Environmental Kuznets curve hypothesis: a survey. Ecol. Econ. 49 (4), 431e455. Focacci, A., 2003. Empirical evidence in the analysis of the environmental and energy policies of a series of industrialised nations, during the period 19601997, using widely employed macroeconomic indicators. Energy Policy 31 (4), 333e352. Grossman, G.M., Krueger, A.B., 1995. Economic growth and the environment. Q. J. Econ. 110 (2), 353e377. Hao, Y., Liu, Y., Weng, J.H., Gao, Y.X., 2016. Does the Environmental Kuznets Curve for coal consumption in China exist? New evidence from spatial econometric analysis. Energy 114, 1214e1223. Hong, J.Q., Kroeze, C., 2010. Past and future trends in nutrients export by rivers to the coastal waters of China. Sci. Total Environ. 408 (9), 2075e2086. Hu, C., Muller-Karger, F.E., Taylor, C., Carder, K.L., Kelble, C., Johns, E., Heil, C.A., 2005. Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters. Remote Sens. Environ. 97 (3), 311e321. Jebli, M.B., Youssef, S.B., 2015. The environmental Kuznets curve, economic growth, renewable and non-renewable energy, and trade in Tunisia. Renew. Sustain. Energy Rev. 47, 173e185. lu, Salih, Katirciog lu, 2018. Testing the role of urban development Setareh, Katirciog in the conventional Environmental Kuznets Curve: evidence from Turkey. Appl. Econ. Lett. 25 (11), 741e746. Kirwan, M.L., Megonigal, J.P., 2013. Tidal wetland stability in the face of human impacts and sea-level rise. Nature 504 (7478), 53e60. Kuznets, S., 1955. Economic growth and income inequality. Am. Econ. Rev. 45 (1), 1e28. Li, J.S., Chen, G.Q., Hayat, T., Alsaedi, A., 2015. Mercury emissions by Beijing's fossil energy consumption: based on environmentally extended inputeoutput analysis. Renew. Sustain. Energy Rev. 41, 1167e1175. Li, J.S., Luo, R., Yang, Q., Yang, H.P., 2016. Inventory of CO2 emissions driven by energy consumption in Hubei Province: a time-series energy input-output analysis. Front. Earth Sci. 10 (4), 717e730. Li, T., Wang, Y., Zhao, D., 2016. Environmental Kuznets Curve in China: new evidence from dynamic panel analysis. Energy Policy 91 (2), 138e147. Li, J.S., Chen, B., Chen, G.Q., Wei, W.D., Wang, X.B., Ge, J.P., Dong, K.Q., Xia, H.H., Xia, X.H., 2017. Tracking mercury emission flows in the global supply chains: a multi-regional input-output analysis. J. Clean. Prod. 140, 1470e1492. Li, J.S., Shi, M.M., Cai, P., Alejandro, G.C.B., Song, X.H., Chen, B., Yang, Q., Chen, H., 2018. How external trade reshapes air pollutants emission profile of an urban economy: a case study of Macao. Ecol. Indicat. 94, 74e82. _ G., Butkus, M., 2017. Environmental Kuznets Curve of greenhouse gas Liobikiene, emissions including technological progress and substitution effects. Energy 135, 237e248. Liu, L., Lei, Y., 2018. An accurate ecological footprint analysis and prediction for Beijing based on SVM model. Ecol. Inf. 44, 33e42. Liu, L., Lei, Y., Ge, J., Yang, K., 2018. Sector screening and driving factor analysis of the Beijing's ecological footprint using a multi-model method. J. Clean. Prod. 191, 330e338. Moore, S.A., Brown, G., Kobryn, H., Strickland-Munro, J., 2017. Identifying conflict potential in a coastal and marine environment using participatory mapping. J. Environ. Manag. 197, 706e718. Narayan, P.K., Narayan, S., 2010. Carbon dioxide emissions and economic growth:

935

panel data evidence from developing countries. Energy Policy 38, 661e666. Ottelin, J., Heinonen, J., Junnila, S., 2015. New energy efficient housing has reduced carbon footprints in outer but not in inner urban areas. Environ. Sci. Technol. 49 (16), 9574e9583. € € € Ozokcu, S., Ozlem, Ozdemir, 2017. Economic growth, energy, and environmental Kuznets curve. Renew. Sustain. Energy Rev. 72, 639e647. Rasli, A.M., Qureshi, M.I., Isah-Chikaji, A., Zaman, K., Ahmad, M., 2017. New toxics, race to the bottom and revised environmental Kuznets curve: the case of local and global pollutants. Renew. Sustain. Energy Rev. 81, 3120e3130. Renaudin, M., Leblond, S., Meyer, C., Rose, C., Lequy, E., 2018. The coastal environment affects lead and sodium uptake by the moss Hypnum cupressiforme used as an air pollution biomonitor. Chemosphere 193, 506e513. Ryther, J.H., Dunstan, W.M., 1971. Nitrogen, phosphorus, and eutrophication in the coastal marine environment. Science 171 (3975), 1008e1013. Sahli, I., Rejeb, J.B., 2015. The environmental Kuznets curve and corruption in the mena region. Procedia-Social and Behavioral Sciences 195 (3), 1648e1657.
G., Vizzini, S., Mazzola, A., 2012. Variations in community structure of SarA, phytoplankton in relation to physicochemical properties of coastal waters, southeast coast of India. Indian Journal of Geo Marineences 41 (3), 352e360. Selden, T.M., Forrest, A.S., Lockhart, J.E., 1999. Analyzing the reductions in U.S. Air pollution emissions: 1970 to 1990. Land Econ. 75 (1), 1e21. Solarin, S.A., Al-Mulali, U., Ozturk, I., 2017. Validating the environmental Kuznets curve hypothesis in India and China: the role of hydroelectricity consumption. Renew. Sustain. Energy Rev. 80, 1578e1587. State Oceanic Administration (SOA), People’s Republic of China, 2018-04-23. China Ocean Disaster Bulletin in 2017. Stern, D.I., 2004. The rise and fall of the environmental Kuznets curve. World Dev. 32 (8), 1419e1439. Sun, X.D., Li, J.S., Qiao, H., Zhang, B., 2016. Energy implications of China's regional development: new insights from multi-regional input-output analysis. Appl. Energy 196, 118e131. squez, N., Thiel, M., Hinojosa, I.A., Miranda, L., Pantoja, J.F., Rivadeneira, M.M., Va 2013. Anthropogenic marine debris in the coastal environment: a multi-year comparison between coastal waters and local shores. Mar. Pollut. Bull. 71 (1e2), 307e316. Thomas, K.V., Fileman, T.W., Readman, J.W., Waldock, M.J., 2001. Antifouling paint booster biocides in the UK coastal environment and potential risks of biological effects. Mar. Pollut. Bull. 42 (8), 677e688. Torras, M., Boyce, J.K., 1998. Income, inequality, and pollution: a reassessment of the environmental Kuznets Curve. Ecol. Econ. 25 (2), 147e160. Tsurumi, T., Managi, S., 2010. Decomposition of the environmental Kuznets curve: scale, technique, and composition effects. Environ. Econ. Policy Stud. 11 (1), 19e36. Vinayagamoorthi, V., Selvam, M., Lingaraja, K., Miencha, I.O., Raja, M., 2015. Testing environmental Kuznets curve hypothesis at firm level in India. J. Sustain. Dev. 8 (8), 201e212. Wang, Y., Han, R., Kubota, J., 2016. Is there an Environmental Kuznets Curve for SO2 emissions? A semi-parametric panel data analysis for China. Renew. Sustain. Energy Rev. 54, 1182e1188. Wang, X., Wei, W., Ge, J., Wu, B., Bu, W., Li, J., Yao, M., Guan, Q., 2017. Embodied rare earths flow between industrial sectors in China: A complex network approach. Resour. Conserv. Recycl. 125, 363e374. Wei, W., Wang, X., Zhu, H., Li, J., Zhou, S., Zou, Z., Li, J., 2017a. Carbon emissions of urban power grid in Jing-Jin-Ji region: characteristics and influential factors. J. Clean. Prod. 168, 428e440. Wei, W., Wu, X., Wu, X., Xi, Q., Ji, X., Li, G., 2017b. Regional study on investment for transmission infrastructure in China based on the State Grid data. Front. Earth Sci. 11 (11), 162e183. Wei, W., Wu, X., Li, J., Jiang, X., Zhang, P., Zhou, S., Zhu, H., Liu, H., Chen, H., Guo, J., Chen, G., 2018a. Ultra-high voltage network induced energy cost and carbon emissions. J. Clean. Prod. 178, 276e292. Wei, W., Guo, Y., Gu, D., Li, J., 2018b. Low carbon transformation of production structure in China's power sector. Journal of Environmental Economics 3 (3), 6e18. Xu, T., 2018. Investigating environmental Kuznets curve in China-aggregation bias and policy implications. Energy Policy 114, 315e322. Yabuta, M., Nakamura, K., 2008. Empirical analysis of environmental Kuznets curve hypothesis in asian countries. Stud. Reg. Sci. 34 (1), 361e375. Yu, S., Lu, H., 2018. Relationship between urbanisation and pollutant emissions in transboundary river basins under the strategy of the Belt and Road Initiative. Chemosphere 203, 11e20. Zhang, P., Zhang, Y.Y., Ren, S.C., Chen, B., Luo, D., Shao, J.A., Zhang, S.H., Li, J.S., 2018. Trade reshapes the regional energy related mercury emissions: a case study on Hubei province based on a multi-scale input-output analysis. J. Clean. Prod. 185, 75e85.