The Impacts of Water Ecological Carrying Capacity on Green Total Factor Productivity: The Case of Chemical Industry in Jiangsu,China

Abstract

The chemical industry is not only a crucial sector of national economy, but also a significant consumer of water resources and a major initiator of water pollution. The sustainable development of this sector is intricately linked to the regional water ecological carrying capacity (WECC). Based on SBM-DEA and Global Moran’s I, the green total factor productivity (GTFP) and spatial correlation characteristics of the chemical industry in 13 cities within China’s chemical agglomeration region in Jiangsu Province were estimated from 2015 to 2019. By combining the WECC results, the Tobit model was employed to reveal the driving factors of WECC in optimizing GTFP. The results indicated that the regional WECC in southern Jiangsu was increasing compared with that in northern Jiangsu, which promoted the growth of GTFP. WECC has been a positive radiation-driven effect since 2017, and the optimization of the various subsystems of WECC has had a different impact on GTFP. For the sustainable development of Jiangsu’s chemical industry, effective water resource policies should be formulated by the government, while enterprises need to pursue sustained structural adjustments.

Keywords

chemical industry green total factor productivity water ecological carrying capacity driving factor Jiangsu Province of China

Introduction

The concept of the carrying capacity was first proposed by Odum Eugene (1970). The research focus on carrying capacity has gradually shifted from the value of regional population carrying capacity to the value of environmental carrying capacity. In the early 21st century, the water ecological carrying capacity (WECC) received extensive attention. The regional WECC is a further development of the concepts of water resources carrying capacity, water environment carrying capacity, aquatic environment carrying capacity and water security carrying capacity. There exists a close relationship between these four capacity concepts. The safe carrying capacity of water ecosystem is an effective guarantee for social and economic development. As one of the basic industries of national economy, the chemical industry is characterized by relatively large water consumption, large discharge of wastewater and high concentration of wastewater pollutants. Industrial development has a close two-way influence on regional WECC. How to effectively meet the market demand for chemical products within the safe range of regional WECC values has remained a practical problem restricting the development of the chemical industry.

From the perspective of the measurement of the regional WECC, Bu et al. (2020), Chen et al. (2021), and Rong et al. (2019) constructed WECC evaluation index systems according to the characteristics of their research objectives, and based on system dynamics, analytic hierarchy process, multi-criteria decision analysis, inexact fuzzy programing, mixed-integer programing and other methods, the WECC of Changzhou, the Yangtze River Economic Belt and the Xinfengjiang Reservoir Basin was calculated, and the relationship and interaction between the WECC and economy, population and agricultural structure were further studied. This paper holds that the WECC is the ability of the water ecosystem in a certain space to provide ecological services for human social activities on the basis of maintaining the long-term stability of its own structure and function and the sustainable operation of water ecological process at a certain development stage and technical level. The evaluation system of WECC was put forward on the basis of water resources carrying capacity, water environment carrying capacity, aquatic environment carrying capacity and water security carrying capacity, which is a comprehensive evaluation index combining water quantity constraint, water quality constraint, aquatic habitat and regulation and storage safety. In order to study the impact of WECC on the chemical industry, it is necessary to quantify the industrial productivity.

According to the existing literature, the measurement of the total factor productivity (TFP) mainly includes stochastic frontier analysis (SFA) and data envelopment analysis (DEA; Cui et al., 2019; Li et al., 2020; Song et al., 2020; Zhong et al., 2020). DEA does not need to set a specific production function, can simultaneously simulate the production process of a variety of inputs and outputs, and can decompose productivity, which is highly concerned by many scholars, and has achieved remarkable results in application (Kumar, 2006; Yu et al., 2008). However, traditional TFP measurement only considers the desirable output generated in the production process, and ignores the constraints of resources and environment on achieving current and future sustainable economic development. If the TFP is directly measured without considering environmental factors, this could yield biased results (Nanere et al., 2007) and distort the meaning of TFP (Hailu & Veeman, 2000). With the proposal of the Malmquist-Lenberger index based on the directional distance function by Chung et al. (1997), scholars have introduced environmental pollution into the measurement system of economic growth and have recalculated and evaluated the TFP under resource and environmental constraints. At present, researchers have reported large differences and conflicting results on the treatment of pollution output and the selection of an efficiency measurement model, which are the key factors influencing the reliability and accuracy of GTFP measurement results.

In 2015, all member states of the United Nations unanimously adopted the Sustainable Development Goals, which aim to protect the earth and improve the lives of all people. Therefore, in the future, the goal of chemical industry development in Jiangsu Province will shift from quantity oriented to green and high-efficiency oriented sustainable development path. The sustainable development of the chemical industry should fully consider the carrying capacity of the water environment and enhance the aquatic ecology. The low efficiency of water use and large amount of pollution in the traditional chemical industry would not be sustained. Based on the perspective of the WECC, continuously improving the contribution of the industrial GTFP to economic growth will gradually become an important basis to evaluate the sustainable development of the Jiangsu chemical industry. However, researchers have seldom explored transformation and upgrading pathways of the chemical industry, which is a high-water consumption and high-wastewater discharge industry, from the perspective of the WECC (Lin & Long, 2014a, 2014b). In the process of efficiency evaluation of the chemical industry, the unexpected output of water environmental pollution has also been less considered (Jahangoshai Rezaee et al., 2020; Lin & Wu, 2021; T. Zhang et al., 2021). Given the development requirements of ecological priority, the evaluation of the development quality of the chemical industry considering only economic benefits is bound to be insufficient and could even lead to unscientific upgrading and transformation directions of the chemical industry (Abbass, Qasim et al., 2022). In this context, the research on GTFP of the chemical industry based on the perspective of WECC needs to cover both economic benefits and hydroecological factors, and reflect the situation of water environmental pollution and the carrying trend of water resources.

As for the choice of research method, first of all, DEA model is a scientific method used for non-parametric efficiency analysis, allowing for the relative comparison of evaluated objects (Charnes et al., 1978). It enables the evaluation of multiple inputs and outputs. There are two common categories of DEA models: CCR model based on constant returns to scale and VRS model based on variable returns to scale. Both are radial angles. To address the limitations of common DEA models, Tone (2001) proposed the SBM model, which takes into account the undesired outputs to solve the issue of variable relaxation, and has found widespread use in the field of production efficiency evaluation (Shah et al., 2023; Zheng, 2021). The paper adopted the super efficiency SBM model, including undesired outputs under the assumption of VRS, to measure the GTFP of the Jiangsu’s chemical industry. Furthermore, the global Moran’s I was used to analyze the spatial agglomeration and nuclear radiation effect of GTFP. This can be effectively used to discuss spatial variation and gravity center shift (Tsui et al., 2022). At last, Traditional linear regression methods may lead to significant deviation in results when analyzing the chemical industry in Jiangsu Province, due to the limited sample of research objects. Additionally, spatial econometric methods may not be effective for achieving the estimation of sub-regional influencing factors, as the dependent variable are subject to certain constraints (Anceschi et al., 2023). For these reasons, this paper analyzed the impact of WECC on GTFP based on Tobit model.

Previous studies in the field of sustainable industrial development have mostly taken production factors as the key point, neglecting the importance of environmental factors in the process of optimizing industrial structure (Abbass, Song et al., 2022). In order to fill this gap, in this study, under the concept of sustainable development of Jiangsu’s chemical industry, the “undesired output” of water pollution and the water consumption of the chemical industry as “input” were incorporated into the calculation framework of GTFP of the chemical industry in Jiangsu Province, and the GTFP of the chemical industry in each city in Jiangsu Province from 2015 to 2019 was estimated, and then its spatial-temporal differentiation characteristics were analyzed under the action of multiple factors such as water resources, water environment, aquatic environment, water security and economic benefits. Tobit regression model was used to explore the driving effects of each subsystem of WECC on the change of GTFP in Jiangsu’s chemical industry. In order to coordinate the development of WECC and chemical industry, from the following two angles, (1) The optimization path of chemical industry structure in the region is suitable for the WECC; (2) Adjust the regional WECC through policy means to improve the GTFP of the chemical industry. The research content not only helps us to improve the macro cognition of the status quo and basic characteristics of the development of Jiangsu’s chemical industry, but also has extremely important theoretical value and practical significance for comprehensively grasping the characteristics and evolution pattern of the influence of WECC on the chemical industry, so as to formulate differentiated industrial development policies according to the regional WECC to promote the sustainable development of the chemical industry.

Material and Methods

Study Area

Jiangsu Province, China (30°45′–35°08′N, 116°21′–121°56′E), was selected as the research case. Jiangsu Province is located in the Yangtze River Economic Belt. There are 13 prefecture-level cities in Jiangsu Province, including eight cities along the Yangtze River in southern Jiangsu (Nanjing, Wuxi, Suzhou, Changzhou, Zhenjiang, Yangzhou, Taizhou, and Nantong) and five cities not located along the Yangtze River in northern Jiangsu (Huai’an, Xuzhou, Yancheng, Suqian, and Lianyungang). There are significant differences in economic scale between regions, with the southern region being significantly better than the northern region.

The chemical industry is an important economic pillar of the Yangtze River Economic Belt and a necessary link between industries. Nearly half of China’s chemical enterprises are clustered in the Yangtze River Economic Belt. Although the Yangtze River Economic Belt produces about 45% of China’s chemical products, the total discharge of wastewater accounts for more than 40% of the total amount in China, including the chemical oxygen demand, ammonia nitrogen, sulfur dioxide, and nitrogen oxides, the emission intensity is 1.5 to 2.0 times the national average. As a key chemical industry cluster of the Yangtze River Economic Belt, Jiangsu Province has contributed more than 40% to the chemical industry output value in the Yangtze River Basin, ranking first in the Yangtze River Economic Belt. By the end of 2020, there were 2,341 chemical enterprises in Jiangsu Province, of which approximately 70% were still occurred in southern Jiangsu, and approximately 68% of the chemical parks were concentrated in cities along the river. The excessive discharge of pollutants by the densely distributed chemical enterprises in the region could cause long-term and chronic potential risks to the whole Yangtze River water ecosystem.

With the rapid development of the Yangtze River Economic Belt and the continuous promotion of various policies for the overall protection of the Yangtze River, the chemical industry in Jiangsu Province has gradually developed from a production mode characterized by a high energy consumption, high pollution intensity and low efficiency to a sustainable direction through excess capacity reduction and scientific transformation and upgrading. Since February 2017, Jiangsu Province has announced the special policy of Four Batches (i.e., one batch of shutdown, one batch of transfer, one batch of upgrading and one batch of reorganization measures) of the chemical enterprises. As such, the number of chemical production enterprises in the province should not exceed 1,000 by 2022. All 34 enterprises within a 1 km radius on both sides of the Yangtze River and outside the chemical industry park will withdraw by the end of 2020.

Methodology, Indicators, and Datasets

Super-Efficient SBM-DEA

Because the traditional DEA model only considers the expected output, ignoring the relaxation of the input and output, there occurs a deviation in the measurement results. Moreover, when the traditional DEA model evaluates the efficiency of decision-making units, the results may indicate that multiple evaluation units occur at the forefront and are relatively effective. Therefore, these relatively effective units cannot be further evaluated, but the nonradial multiangle super-efficient SBM model can effectively avoid this problem and can facilitate the enhancement of the accuracy of calculation result analysis (Khan et al., 2021). In this study, the super-efficiency SBM model considering the unexpected output was selected as the GTFP evaluation model of the chemical industry in Jiangsu Province. The super-efficiency SBM model considering the unexpected output is expressed as follows:

ρ^{*} = m i n \frac{\frac{1}{m} \sum_{i = 1}^{m} (\frac{\bar{x}}{x_{i k}})}{\frac{1}{r_{1} + r_{2}} [\sum_{s = 1}^{r_{1}} \frac{{\bar{y}}^{d}}{y_{s k}^{d}} + \sum_{q = 1}^{r_{2}} \frac{{\bar{y}}^{u}}{y_{q k}^{u}}]} s . t . {\bar{x} \geq \sum_{j = 1, j \neq k}^{n} x_{i j} λ_{j}, \bar{y^{d}} \leq \sum_{j = 1, j \neq k}^{n} λ_{j} \bar{y^{d}} \geq \sum_{j = 1, j \neq k}^{n} y_{q j}^{d} λ_{j}, \bar{x} \geq x_{k} \bar{y^{d}} \leq y^{d}, \bar{y^{u}} \leq y_{k}^{u} λ_{j} \geq 0, i = 1, 2, \cdot \cdot \cdot, m, j = 1, 2, \cdot \cdot \cdot, n s = 1, 2, \cdot \cdot \cdot, r_{1}, q = 1, 2, \cdot \cdot \cdot, r_{2} \sum_{j = 1}^{n} λ_{j} = 1

(1)

where s denotes the relaxation variable of the input and output, $λ$ is the weight; x, y^g, and u^b denote the input, expected output and unexpected output, respectively; and $ρ^{*}$ is the GTFP value. If $ρ^{*} = 1$ , $s^{*} = 0$ , and $s^{g} = 0$ , the evaluation unit is valid. If $ρ^{*} < 1$ , this indicates that the evaluation unit is invalid. As such, the input and output should be improved.

Global Moran’s I

Based on the calculation results of the super-efficiency SBM model and existing research results (Shang et al., 2020; C. Wang et al., 2020; Xu et al., 2017), global Moran’s I was selected to evaluate the spatial correlation of the GTFP of the Jiangsu chemical industry. The calculation equation of global Moran’s I is:

Global Moran' s I = \frac{N \sum_{i = 1}^{N} \sum_{j = 1}^{N} ω_{ij} (x_{i} - \bar{x}) (x_{j} - \bar{x})}{\sum_{i = 1}^{N} {(x_{i} - \bar{x})}^{2} \sum_{i = 1}^{N} \sum_{j = 1}^{N} ω_{ij}}

(2)

where N is the total number of cities (13 cities in this paper), $X_{i}$ is the efficiency observation value of city i, $ω_{ij}$ is the spatial weight matrix of units i and j, $ω_{ij} = 1$ , when i and j are adjacent units, and $ω_{ij} = 0$ , when i and j are nonadjacent units. The value of global Moran’s I range from −1 to 1. If its value is greater than 0, this indicates that there exists spatial self-positive correlation, and a value close to 1 indicates that the agglomeration degree of spatial units with similar GTFP values is higher. If the value is less than 0, this indicates that adjacent units are decentralized. The closer the value is to −1, the larger the difference between the units will be. If the value is equal to 0, a random spatial distribution is applied.

Tobit Model

This study selected a typical sample selection model, that is, the restricted dependent variable Tobit model, for regression purposes. The Tobit model has been widely applied by scholars in the study of efficiency drivers and action mechanisms. This model differs from other discrete and continuous models, and its dependent variable is a restricted variable (Teng & Han, 2017; S. Wang et al., 2018). The Tobit model consists of two kinds of equations: one equation is the selection equation representing the constraints, and the other equation is the continuous variable selection equation satisfying the constraints.

{\begin{matrix} ρ_{i}^{*} = α_{0} + \sum_{j = 1}^{I} α_{0} x_{ij} + ε_{i} \\ ρ_{i} = ρ_{i}^{*}, 0 \leq ρ_{i}^{*} \leq 1 \\ ρ_{i} = 0, ρ_{i}^{*} < 0 \\ ρ_{i} = 1, ρ_{i}^{*} > 1 \end{matrix}

(3)

where $ρ_{i}^{*}$ is the latent variable, $ρ_{i}$ is the actual explained variable, $x_{ij}$ is the explanatory variable, $α_{0}$ is a constant, $α_{j}$ is the correlation coefficient vector, and $ε_{i}$ obeys $ε_{i} ~ N (0, N)$ .

Production Process, Indicators, and Datasets

Construction of the GTFP Evaluation Index for the Chemical Industry

The special policy of Four Batches proposed by Jiangsu Provincial People’s Government in 2017 has resulted in a systematic plan for the development of chemical enterprises along the Yangtze River in Jiangsu, and the structure of the Jiangsu’s chemical industry have notably changed. Therefore, the period from 2015 to 2019 was selected as the research period in this study. Combined with relevant research results, referring to relevant technical regulations of the environmental assessment and the characteristics of Jiangsu’s chemical industry, a GTFP evaluation index system (Figure 1) was constructed from the input-output perspective. The target layer of GTFP evaluation index of the chemical industry was divided into input and output levels. Based on water ecology perspective, the GTFP of the chemical industry in 13 cities in Jiangsu Province was studied. The basic data pertaining to each index originated from the Statistical Yearbook and Ecological Environment Bulletin of 13 cities in Jiangsu Province.

(1) Input index: The design of the input elements of the GTFP mainly includes three parts: labor, capital and resources. Referring to the existing literature (Liu et al., 2020; H. Wang et al., 2019; Y. Zhang et al., 2020), the annual average number of employees in the chemical industry was selected as a labor input element. Since this paper studied the GTFP of the chemical industry from the perspective of the WECC, the water intake and energy consumption of the chemical industry were selected to measure the input and consumption of resources, respectively, in regard to industrial growth. Considering that the structure of the Jiangsu’s chemical industry have been considerably adjusted from 2015 to 2019, certain enterprises occur at the transitional stage of production suspension or business subcontracting to enterprises outside the province, and the total fixed asset investment does not truly and dynamically reflect the real current conditions of chemical industry production. This paper selected the main business cost of the Jiangsu chemical industry in a given year to measure the efficiency.

(2) Output indicators: The output indicators are divided into the expected outputs based on the total output value of the Jiangsu chemical industry and the unexpected outputs covering the negative effects on the water system. Based on Wu (2020), the discharge of major water pollutants (SO₂, COD, ammonia nitrogen, and wastewater) in chemical industry production and the state of regional water quality (the ratio of river sections below class III water quality) were selected as undesirable outputs in the growth of the chemical industry.

Figure 1.

GTFP evaluation index system of the chemical industry from the perspective of the WECC.

Construction of the Driving Factor Evaluation Index Based on the WECC

The WECC is a quantitative description of the current conditions of the regional water ecology. To explore the upgrading pathway of chemical industry structures suitable for the regional WECC and the mechanism of WECC optimization on the GTFP of the chemical industry, this paper established an evaluation index system based on existing operating procedures and references to measure the status of the regional WECC. On this basis, a Tobit regression model was applied to study the impact of changes in water resources, water environment, aquatic environment and water security system on the GTFP of the chemical industry.

According to the Technical Guidelines for Water Ecological Carrying Capacity Assessment (Chinese Society of Environmental Sciences, 2014) issued by the Chinese Society of Environmental Sciences and the Technical Guidelines for Lake Ecological Security Investigation and Assessment (China Environmental Monitoring Station, 2014) prepared by the Ministry of Ecological Environment of China, the evaluation index system of the WECC (Table 1) was constructed. The basic data originate from the Statistical Yearbook, Ecological Environment Bulletin, Water Resources Bulletin and Fishery Ecological Environment Quality Bulletin of Jiangsu cities from 2016 to 2020.

Table 1.

Evaluation Index System of the WECC.

Target layer	Rule layer	Subrule layer	Index layer	Index interpretation
Water Ecological Carrying Capacity	Water resources	Water resource endowment index	Water resources per capita	Total water resources/Total population
		Water resource utilization index	Water resource utilization	Total water consumption/Average annual total water resources
		Water resource utilization index	Water consumption per 10,000 yuan GDP	Total water consumption/GDP
	Water environment	Water environment pollution acceptance index	Pollutant emission intensity	Industrial wastewater discharge/Gross industrial output value
			Regional fertilizer utilization	Chemical fertilizer (pure)/Agricultural acreage
			Livestock breeding	Total livestock and poultry breeding/Land area
			COD emission	COD emission/Land area
			Ammonia nitrogen emission	Regional NH₃-N emission/Land area
			Surface water quality	Higher than class III water quality section/Total number of sections
	Aquatic environment	Aquatic habitat index	Forestland and grassland coverage	Vegetation area/Land area
			Water area index	Water area/Land area
			Wetland area index	Wetland area/Land area
		Aquatic index	Benthic biodiversity	Shannon-Wiener index
	Water security	Regulation and storage safety index	Hydrological regulation function	WC = (100-RC)×F RC=surface runoff/rainfall F:land ecological function adjustment coefficient
	Water security	Water safety index	Water quality of the drinking water source	Number of qualified drinking water sources/Total number of drinking water sources

According to the literature (Xu et al., 2017), all indicators are scored and assigned weights. The index weight reflects the status and role of the different indicators in the evaluation system. The underlying rationality directly affects the robustness and correctness of the evaluation results. The entropy weight method is an objective weighting method that can avoid the deviation caused by subjective weight assignment to a certain extent and ensures more objective evaluation results (Xiao et al., 2021). Therefore, the entropy weight method was adopted to determine the index weight in this study. First, given m evaluation objects, each evaluation object is associated with n indices, and the judgment matrix of the evaluation indices is ${(x_{ij})}_{m \times n}$ . Second, due to the occurrence of positive and negative indicators and different data dimensions and orders of magnitude, the extreme value method should be applied to standardize the original index data in advance to obtain $x_{ij}^{'}$ . Third, the entropy of each evaluation index $e_{j}$ is determined as $e_{j} = - k \sum_{i = 1}^{m} z_{ij} \ln z_{ij}, 0 \leq e_{j} \leq 1$ , where $k = \frac{1}{lnn}$ , $z_{ij} = X_{ij}^{'} / \sum_{i = 1}^{m} X_{ij}^{'}$ , and $e_{j}$ denotes the entropy of the j-th index. If $z_{ij} = 0$ , then $z_{ij} \ln z_{ij} = 0$ . Finally, the entropy weight of each evaluation index $ω_{j}^{*}$ is calculated as ${(ω_{j}^{*})}_{1 \times n} = {((1 - e_{j}) / \sum_{j = 1}^{n} (1 - e_{j}))}_{1 \times n}$ , where $0 \leq ω_{j}^{*} \leq 1$ . Comprehensive evaluation grading: Based on the score and weight value of each index, a comprehensive evaluation of the index layer, subrule layer and rule layer is performed from bottom to top, as follows:

(1) According to the score assigned to each evaluation index, the corresponding subrule layer values are weighted and summed as $F_{ij} = \sum_{k = 1}^{n} (w_{ijk} \cdot p_{ijk}),$ where $F_{ij}$ denotes the score of the j-th subcriterion layer in the i-th criterion layer, $p_{ijk}$ denotes the score of the k-th index in the j-th subcriterion layer in the i-th criterion layer, $w_{ijk}$ denotes the weight of the k-th index in the j-th subcriterion layer in the i-th criterion layer, and N is the number of indicators contained in the subcriterion layer.

(2) The score of each criterion layer is calculated via weighted summation, that is, $Z_{i} = \sum_{j = 1}^{m} (w_{ij} \cdot F_{ij})$ , where $Z_{i}$ is the score of the i-th criterion layer, $w_{ij}$ is the weight of the j-th subcriterion layer in the i-th criterion layer, and m is the number of subcriterion layers contained in the i-th criterion layer.

(3) The regional WECC index (WECCI) is calculated as $WECCI = \sum_{i = 1}^{4} (w_{i} \cdot Z_{i}),$ where $w_{i}$ denotes the weight of the i-th rule layer.

(4) Grade evaluation: The status of the regional water ecological carrying capacity is divided into five grades: severe overload, overload, critical load, relatively safe load-carrying and safe load-carrying states. The corresponding grade standards are listed in Table 2.

Table 2.

Classification Standard of the WECCI.

WECC	[0,20]	(20,40]	(40,60]	(60,80]	(80,100]
Grade	Severe overload carrying	Overload carrying	Critical load carrying	Relatively safe load carrying	Safe load carrying

Results and Discussion

GTFP Calculation and Difference Analysis of the Jiangsu Chemical Industry

In this paper, the super-efficiency SBM model including the unexpected output was adopted to analyze the GTFP and change trend of the chemical industry in 13 cities of Jiangsu Province. The research results indicate that (as shown in Figure 2), the overall GTFP of Jiangsu’s chemical industry exhibited a steady upward trend from 2015 to 2019 and changed from a relatively low efficiency to green efficiency after 2016. This is the result of the Jiangsu provincial government adhering to the core concept of ecological priority and sustainable development, issuance of targeted emission and energy consumption limit standards for chemical enterprises and promotion of the continuous optimization of the chemical industry structure across the province (Y. Wang et al., 2020). The GTFP level of cities in southern Jiangsu increased notably, and the GTFP level of Suzhou, Wuxi and Yangzhou gradually increased after downward fluctuations in 2016, 2017, and 2018, respectively. In 2019, except for Yangzhou, the efficiency values of the other cities in southern Jiangsu were higher than 1, reaching the forefront of effective production. The output value of the chemical industry in the region continued to decrease proportionally to the GDP, but the GDP continued to increase, reflecting the optimization and adjustment of the overall industrial structure and the upgrading of the main business of the chemical industry in the region. In addition to Huai’an, in regard to the five non-riverside cities in northern Jiangsu, thereby adopting 2017 as the time node, the overall trend changed from an upward trend to a downward trend. Although the GTFP of the chemical industry in Huai’an improved year by year, the improvement speed decelerated since 2017. The urban chemical industry structure in northern Jiangsu is dominated by the basic chemical industry. Due to the special action of Four Batches, the constructed industrial structure was affected to varying degrees in terms of the business scale, production cost and migration pressure.

Figure 2.

Changes in the GTFP of the chemical industry in 13 cities of Jiangsu Province from 2015 to 2019.

As shown in Figure 3, based on the time-line, with 2017 as the time node, upon the implementation of the special action of Four Batches, the GTFP of the Jiangsu’s chemical industry fluctuated greatly. Nanjing and Wuxi exhibited a rapid upward trend, which was rapidly transformed and upgraded from a low-efficiency sector to a high-efficiency sector. Owing to the sound industrial chain and strong support of innovative talent resources in the region, choosing Nanjing as an example, a relatively complete closed-loop industrial chain was established around ethylene oxide, and a professional and targeted production scheduling system was deployed. The change trend of the GTFP in Suzhou, Changzhou, Nantong and Taizhou was basically consistent with the average change trend of the whole province. The local government issued a series of regulations regarding the production of the chemical industry, reduced the excess capacity, established major environmental risk source monitoring and environmental risk early warning mechanisms, and optimized the regional chemical industry structure at the expense of certain economic benefits. The Beijing-Hangzhou Grand Canal connects Yangzhou and Huai’an. These two cities are neighboring cities, and their economic structures are quite similar. Although the GTFP of the chemical industry in these two cities continued to improve, it remains below the relatively effective level. The GTFP of the chemical industry in Zhenjiang and Xuzhou has remained above 1 for a long time, indicating a relatively efficient chemical industry structure. Although no spatial intersection occurs between these two cities, the governments of both cities insist on promoting the expansion of the chemical industry into green high-end areas. The GTFP level of Yancheng, Suqian and Lianyungang decreased after 2017. These three cities belong to regions with high concentrations of basic chemical industries in Jiangsu Province. The corresponding chemical products achieve a high output, high water consumption and relatively high pollutant emissions, which are the most notably affected by the above policy.

Figure 3.

GTFP of the chemical industry in Jiangsu Province from 2015 to 2019.

From the perspective of the spatial distribution, the GTFP of the chemical industry in cities along the Yangtze River in southern Jiangsu is generally higher than that in non-riverside cities in northern. This result is consistent with the research conclusions of other scholars. The implementation of green industrial policy will effectively reduce pollution emissions in regions with more reasonable industrial structure, but has no significant effect on regions with unbalanced industrial structure (Zhu & Tan, 2022).The implementation of the special action of Four Batches also exerts a highly different impact on the chemical industry in southern and northern Jiangsu (as shown in Figure 4). With the upgrading and adjustment of the industrial structure, the GTFP has increased in areas along the Yangtze River in southern Jiangsu. After reducing the production capacity and the number of enterprises in non-riverside areas across northern, the original relatively stable scale effect and the logistics chain relying on water transportation have been seriously affected, the trend of the GTFP of the chemical industry has changed from a rising trend to a falling trend, and the regional chemical industry has entered a new stage of industrial, logistics and value chain reconstruction efforts. The differences in regional resource endowment and chemical industry structure are the main factors causing this phenomenon. The eight cities in southern Jiangsu exhibited good economic development. In 2019, the regional GDP accounted for approximately 77% of that of Jiangsu Province, which indicates preconditions for technological innovation and new product Research and Development initiatives. The chemical output considers fine chemicals and new chemical materials as the target market, while the non-riverside cities in northern Jiangsu mainly focus on pesticides, chemical fertilizers and basic chemicals are mainly manufactured, with relatively low economic benefits and higher pollutant emissions per unit output value.

Figure 4.

Variation in the GTFP of the chemical industry among the various Jiangsu regions.

Spatial Correlation Analysis

To determine whether there are spatial correlation and agglomeration characteristics of GTFP in Jiangsu’s chemical industry, this paper applied ArcGIS 10.2 software to calculate global Moran’s I of the GTFP level of Jiangsu’s chemical industry from 2015 to 2019. The Moran scatter chart can be divided into four quadrants. The first quadrant represents high-high (HH) agglomeration, the second quadrant represents low-high (LH) agglomeration, the third quadrant represents low-low (LL) agglomeration, and the fourth quadrant represents high-low (HL) agglomeration. The first and third quadrants indicate that there existed a positive spatial autocorrelation of the GTFP among Jiangsu cities. The second and fourth quadrants indicate that there existed a negative spatial correlation. According to the analysis results of global Moran’s I, the spatial correlation of the GTFP of the chemical industry in the 13 cities of Jiangsu Province from 2015 to 2017 was not significant. Therefore, this paper only considered the local Moran scatter diagram and its spatial distribution in 2018 and 2019, as shown in Figure 5.

Figure 5.

Scatter diagram of local Moran’s I of the GTFP of the chemical industry in the 13 cities of Jiangsu Province in 2018 (a) and 2019 (b).

The results reveal that the spatial agglomeration characteristics of the chemical industrial efficiency of the cities in Jiangsu Province are mainly divided into two stages. The first stage is the period from 2015 to 2017. The Moran’s I value at this stage were all negative, indicating that the chemical industry GTFP level of the cities in Jiangsu Province exhibited a negative spatial correlation with different attributes. In particular, the high-efficiency areas are clustered and surrounded by low-efficiency areas, but this correlation is not significant. The second stage is the period from 2018 to 2019. The Moran’s I value at this stage were positive, indicating that the chemical industry GTFP level of the cities in Jiangsu Province exhibited a positive spatial correlation with a certain agglomeration effect. Notably, high-efficiency areas are clustered and surrounded by high-efficiency areas, low-efficiency areas are surrounded by low-efficiency areas, and all Moran’s I pass the significance test at the 10% level. Under the effect of environmental regulation, low efficiency regions constantly improve their own industrial structure, actively accept the radiation and driving effect from high efficiency regions, and achieve sustainable development (Fan et al., 2022). At the early stage, the cities in Jiangsu Province made great efforts to develop their economy and introduced chemical enterprises, resulting in damage to the water ecological environment in the process of economic development, and the GTFP of the chemical industry attained a negative correlation. However, since the implementation of the special policy of Four Batches in 2017, through policy guidance, chemical enterprises associated with serious environmental pollution were shut down, upgraded, moved or transferred, and other measures to reduce the excess capacity of the Jiangsu chemical industry, aimed at integrating the original disordered and scattered chemical industry chain in Jiangsu, efficiently manifested the spillover effect and overcame the dilemma of the Matthew effect. In addition, the transformation from low-efficiency areas to high-efficiency areas was promoted.

In 2018 and 2019, the chemical industry GTFP level of most cities in Jiangsu mainly plotted in the first and third quadrants, and only a few cities plotted in the second and fourth quadrants, indicating that the chemical efficiency of these cities largely exhibited HH and LL agglomeration characteristics. The spatial agglomeration characteristics of the ecological efficiency were significant. The reason may be that the superior geographical location of adjacent cities, while strengthening economic cooperation, conveniently enhances exchange and cooperation, technology diffusion, personnel flow, etc., thereby improving the spatial correlation of the GTFP.

Calculation and Difference Analysis of the WECC in Jiangsu Province

In 2019, the overall WECC score of Jiangsu Province reached 43.81, which was above the critical load carrying level. There occurred a significant difference among the cities, and the score ranged from 38.87 to 60.32. The span of the carrying level was large, which varied between the overload and safe load-carrying states. The order of the WECC (in descending sequence) is Yancheng, Suzhou, Wuxi, Yangzhou, Nantong, Huaian, Changzhou, Xuzhou, Lianyungang, Zhenjiang, Taizhou, and Nanjing (as shown in Figure 6). In terms of space, in 2019, the WECC of 8 cities along the Yangtze River in southern Jiangsu Province was relatively higher. Suzhou and Wuxi scored higher than 50, which indicates a high level of the critical carrying grade. Although Nanjing and Taizhou occurred at the overload level, they were close to the critical carrying grade. The scores of the non-riverside cities in northern Jiangsu, except for Yancheng city, varied between 40 and 42 and extensively fluctuated about a low critical carrying level. Yancheng city attained a safe load-carrying level by virtue of its unique geographical environment advantages (approximately 75% of all wetlands and 56% of the coastline of Jiangsu Province are located within Yancheng city, with a relatively large water area). The Yangtze River, which runs through the southern cities of Jiangsu, brings abundant water resources and convenient transportation and shipping options to the cities along the river. Compared to the southern cities, the economic development of the northern cities in Jiangsu is relatively slow, resulting in differences in the economic structure and production technology level. The northern Jiangsu focuses on resource-consuming industries, with a high energy consumption, notable water consumption and high wastewater discharge per unit output value.

Figure 6.

WECC score of Jiangsu Province from 2015 to 2019.

As shown in Figure 7, according to the change trend, the overall WECC of Jiangsu Province exhibited a promising trend. After 2017, the overall WECC improved from the overload level to the critical carrying level. On the basis of the River Chief System, the Jiangsu provincial government refined the assessment methods aimed at river water pollution prevention and established the Section Chief position to ensure information transparency of the section water quality status and eliminate water pollution from the source (Tang et al., 2020). The WECC scores of most cities in the province was continuously improved. Xuzhou and Nanjing notably increased by 44.4% and 39%, respectively, over the past 5 years. Suqian and Lianyungang fluctuated greatly and significantly declined in 2017, but the overall trend was good. The water resources of Huai’an in 2019 were relatively small, accounting for only 33.81% of the total water resources in 2015. This is the main unfavorable factor that led to the regional WECC score being slightly lower than the 2015 level in 2019, but the score still remained within the critical carrying capacity range. The WECC scores of all remaining cities have ultimately increased and reached the critical carrying capacity level or above in 2019.

Figure 7.

Status and change trend of the WECC across Jiangsu Province in 2015 (a), 2017 (b), and 2019 (c).

We further analyzed the regional WECC according to the score and change trend of the WECC score of each subsystem:

(1) Water resource carrying capacity analysis: In the assessment of the water resources of Jiangsu Province in 2019, the overall assessment status was overloaded, with a score of 26.67, which has remained overloaded for many years. Although the water resource utilization efficiency score continued to improve, the regional water resource endowment cannot effectively meet the development needs. The scores of all regions in the province were concentrated, ranging from 19.23 to 33.2, and all cities were overloaded. The above results indicate that the small total amount of water resources, high population density and poor utilization efficiency of water resources in Jiangsu Province exert a great impact on the overall WECC of the region. As shown in Figure 8, the water resource consumption per 10,000 yuan GDP is the main index contributing to the score of the water resource subsystem, which decreased by 24.7% over 5 years with the development of high-tech industries and strategic new industries in Jiangsu Province. To ensure stable industrial development of water ecosystems, it is of great importance to rationally use water resource endowments for industrial transformation and optimization and realize sustainable development through technological innovation, thereby improving the effective allocation and utilization of resources across the region.

(2) Water environment carrying capacity analysis: The overall assessment status of the water environment was good, which was the safe load-carrying state. There were major differences among the regions across the province, and the scores of the 13 cities varied between 35.27 and 77.86. The score of northern Jiangsu was generally lower than that of southern Jiangsu, while Lianyungang and Suqian attained the lowest scores, which were overloaded, and Wuxi and Suzhou achieved the relatively safe load-carrying grade. Based on the change trend from 2015 to 2019, the water environment indicators of all cities in the province continuously improved, with an increase of 75.6% over 5 years, especially after 2017, which reflects the phased results of the special action of Four Batches to a certain extent. The contribution rate of the water environment carrying capacity to the total WECC score was 34%, but there was still much room for improvement in certain areas. Moreover, as an important index to evaluate the pollution degree and treatment of the chemical industry along the river, the water environment system is one of the important constraints to realize the scientific and sustainable development of the chemical industry along the river.

(3) Aquatic environment carrying capacity analysis: The score of the aquatic environment subsystem in Jiangsu varied between 34.19 and 82.17, and the overall score was 48.48. The carrying state was good, and the scores of the various indicators of the subsystem were balanced. There were large differences in the distribution of the state grades across the region. Xuzhou, which accounts for a relatively small water area in the region, performs the worst. Yancheng, which contains a long coastline and a large area of wetlands, attains the highest score. The aquatic environment carrying capacity of the cities along the mainstream of the Yangtze River gradually improved from the upstream area to the downstream area, and Suzhou reached the safe load-carrying state. This indicates that the status of aquatic habitat maintenance and aquatic organism conservation in the Jiangsu section of the Yangtze River is very poor, which is the key point and difficulty in the process of ecological compensation.

(4) Water safety carrying capacity analysis: The evaluation status of the water safety in Jiangsu was relatively good, which was the critical carrying level, and the scores of each region varied between 34.93 and 77.53. The water safety carrying capacity of the eight southern cities was relatively coordinated, indicating that the cities along the river perform relatively good from the perspective of the regulation and storage safety of drinking water. This reflects that Jiangsu Province has fully implemented relevant policies on the renovation of chemical enterprises along the river and has actively responded to the important guidelines of the Ministry of Water Resources and National Development and Reform Commission to vigorously promote the construction of rural drinking water safety projects and the comprehensive renovation of rural water systems. In northern Jiangsu, sponge city construction should be further improved because surface runoff accounts for a large proportion of the total precipitation.

Figure 8.

Composition of the WECC of Jiangsu Province in 2019.

Tobit Regression Analysis

There are major differences in resource endowment characteristics, chemical industry structure and chemical industry scale between the southern and northern cities of Jiangsu Province, and the impact of the special action of Four Batches in 2017 varies. Therefore, a limited dependent variable panel model, that is, the Tobit model, was constructed in Stata/SE v12.0 software. Based on the measurement results of the WECC, the driving factors of the GTFP of the chemical industry in southern and northern Jiangsu were analyzed (Table 3).

Table 3.

Tobit Regression Results of the GTFP Driving Factors of the Jiangsu Chemical Industry.

	Explanatory variable	Regression coefficient	Robust standard deviation	t Value	p>\|t\|
Riverside cities in southern Jiangsu	Water resources	0.049**	0.021	2.28	.029
	Water environment	0.019***	0.007	2.75	.009
	Aquatic environment	−0.022**	0.009	−2.43	.02
	Water security	0.017	0.013	1.32	.194
non-riverside cities in northern Jiangsu	Water resources	−0.045*	0.024	−1.92	.069
	Water environment	−0.002	0.008	−0.26	.801
	Aquatic environment	−0.022*	0.012	−1.76	.092
	Water security	0.016	0.012	1.36	.189

Note.*, **, and *** indicate significance levels of 10%, 5%, and 1%, respectively.

(1) The impact of the water resource system on the GTFP of the chemical industry in southern and northern Jiangsu was different. The improvement in water resource utilization efficiency imposes a positive effect on the efficiency of the chemical industry in southern Jiangsu, but this is completely opposite to what is observed in the northern cities. This disparity stems from the difference in the chemical industry structure between these regions (Y. Wang et al., 2022). By taking advantage of technological innovations and the economic scale, southern Jiangsu vigorously developed fine customized chemicals, functional special chemical new materials and energy-saving and environmental protection industries to improve the economic benefits by enhancing the environmental efficiency. Moreover, the development of water rights trading further resulted in certain requirements for the water efficiency of enterprises, and effective water-saving measures became an important way for enterprises to reduce their production costs. The chemical industry in northern Jiangsu focuses on pesticides, basic chemicals and hazardous chemicals as the main products, with a low profit margin per unit product and high pollutant discharge. While improving the utilization efficiency of water resources, the operating costs of enterprises increased and their efficiency was reduced. At the new development stage of China, sustainable development aspects constitute important objectives of the Jiangsu chemical industry. The special action of Four Batches eventually eliminated certain chemical enterprises in northern Jiangsu, released space, freed up limited environmental capacity and energy capacity. Guidance through policies and the implementation of rigid requirements such as product chains, industrial chains, investment intensity, and environmental protection standards can facilitate high-quality improvement of enterprise products, which could undoubtedly inject vitality into the sustainable development of the chemical industry in northern Jiangsu.

(2) Improvement in the water environment system played a positive role in enhancing the GTFP of the chemical industry in cities in southern Jiangsu. There are three main reasons. First, adjustment of the regional industrial structure transformed the focus of cities in southern Jiangsu from agriculture and aquaculture to industry, which reduced the use of chemical fertilizers, agricultural medicine and feed per unit area. Second, Second, the chemical industry in Jiangsu Province realized the scale and integration of regional chemical production (Chen et al., 2022). Third, the centralized collection, treatment and discharge of sewage effectively reduces the cost of pollutant capture and purification. Against the background of the strict environmental protection requirements and effective implementation means of relevant departments, a number of enterprises focusing on chemical sewage treatment have been established, which has promoted the improvement of the chemical industry chain in southern Jiangsu.

(3) The improvement of the aquatic environment resulted in an increase in the forestland coverage and wetland area, reduced the area of industrial land, and limited the development rate of the chemical industry. Industrial development is one of the important reasons for the reduction of vegetation coverage (Huang et al., 2023). The attitudes of the chemical industry such as pollution first and treatment later, still occurs locally, and play a role as sources of aquatic environment pollution. Relevant government departments should resolutely eliminate the backward chemical production capacity, scientifically and reasonably transfer and upgrade enterprises, and effectively implement the ecological compensation mechanism. Jiangsu’s enterprises should consider implementing chemical industry refinement, optimization, and ecological improvement measures and should constantly increase their product quality to meet the development requirements in the new era.

(4) Improvement in the water safety system exerted a positive impact on the chemical industry in southern and northern Jiangsu but did not pass the significance test. Water safety is not only the basic guarantee of national life but also the basic requirement of social development. Generally, the more stable the water safety system, the higher the living standards of people are, the greater the labor enthusiasm is and the higher the efficiency is. However, the empirical results were not significant, this result can be attributed to the optimization of wastewater treatment methods (Estahbanati et al., 2021). Wastewater is the main factor affecting the regional water safety system. In the traditional sewage treatment process, wastewater is regarded as a pollution source and discharged into the water after dilution and treatment, but from the perspective of sustainable development, wastewater is a recyclable resource rich in nitrogen, phosphorus and carbon, which can be converted into valuable products through chemical processes. In the stage of rapid technological development, there are huge differences in economy and technology among regions in Jiangsu Province, and the treatment methods of wastewater are also different. As a result, the impact of water security system on the GTFP of the chemical industry is not significant.

Conclusions

This paper, taking 13 cities in Jiangsu Province as a case, applied the super-efficiency DEA-SBM model to calculate the GTFP of the chemical industry. The global spatial autocorrelation method was adopted to study the spatial correlation characteristics of the efficiency. From the perspective of the WECC, the Tobit regression model was utilized to explore the internal driving factors. The results revealed that the GTFP of Jiangsu’s chemical industry has gradually increased from the relatively ineffective level to the effective production frontier, and the GDP of the chemical industry has gradually decreased after 2017, which reflects the gradual optimization of the Jiangsu chemical industry structure. From the perspective of regional differences, selected the implementation of the special policy of Four Batches in 2017 as the time node, the industry in the southern Jiangsu along the river has a significant upward trend, while in the northern, it has changed from an upward trend to a downward trend. From the perspective of global spatial autocorrelation, the GTFP of the chemical industry in Jiangsu Province changed from a negative spatial correlation to a positive spatial correlation since 2017, and the agglomeration characteristics gradually became significant, indicating that the original disordered and scattered chemical industry chain was gradually optimized under the guidance of policies, and the high-efficiency areas in southern Jiangsu manifested the spillover effect toward surrounding cities. In terms of the driving factors, the level of the WECC in Jiangsu Province was improved year by year. Due to differences in the regional industrial structure, the driving factors of the GTFP of the chemical industry also differed.

To realize sustainable development of the chemical industry in Jiangsu Province under the premise of stable WECC levels, the following suggestions are proposed:

Based on the goal of sustainable city and community, combined with the characteristics of regional water element structure, it is necessary to optimize the structure of chemical industry and improve water use efficiency. First, in the face of the low per capita water resources in Jiangsu Province, in order to reduce the per capita negative environmental impact of the chemical industry on cities, it is necessary to promote the standardization and legalization of water rights transactions among social organizations, increase the economic cost of ineffective use of water resources, and ensure the safe level of WECC. Second, In the view of the characteristics of large discharge, high concentration of pollutants and difficulty in treatment of chemical wastewater, chemical enterprises should continue to explore and develop new water treatment technologies to improve the recovery rate and secondary utilization efficiency of wastewater.

Based on the goal of sustainable industry and innovation, all departments should stimulate innovation potential from multiple perspectives, adhere to technological innovation to promote production efficiency, and achieve green development. From the perspective of scientific research institutes, there are a large number of research institutes in southern Jiangsu Province, and the advantages of scientific research resources of local universities can be converted into the driving force for technological innovation of chemical enterprises through the establishment of school-enterprise cooperation platforms and promote the common development of northern cities. From the government’s point of view, the adjustment of tax policy is used to encourage technological innovation and technology sharing of chemical enterprises, reduce the innovation cost of chemical enterprises in southern Jiangsu, and help chemical enterprises in northern Jiangsu cities smoothly through the painful period of transformation. From the perspective of the market, green finance should be implemented to reduce the financing cost of chemical wastewater treatment enterprises in the embryonic stage of development, accelerate their growth rate, and improve the chemical industry chain.

Based on the goal of sustainable economic growth, chemical enterprises above designated scale need to exert spillover effects, standardize the production process of the chemical industry, and reduce inefficient production methods. Through the effective guidance of the government, with the large state-owned chemical enterprises in the region as the core and the capital as the link, several small and medium-sized chemical enterprises with interests in products are linked together to form a multi-level enterprise group with parent-child relationship. At present, small and medium-sized chemical enterprises along the Yangtze River lack standardized management. Taking large state-owned enterprises as the parent company of enterprise groups, it can supervise the production and operation of subsidiaries, realize the integration of production and operation activities of parent and subsidiary companies, and avoid the occurrence of overcapacity and disorderly discharge of pollutants.

Based on the goal of sustainable clean water management, the government and enterprises should work together to improve and implement the verification criteria of ecological compensation system. According to the actual water consumption and water pollution characteristics of chemical enterprises, the compensation base should be set according to local conditions. The stability of WECC has a positive effect on improving the GTFP of the chemical industry. Stakeholders should reasonably share the cost of ecological compensation among themselves, fully mobilize the enthusiasm of all parties for water ecological protection. While keeping regional drinking water clean, enterprises are forced to increase GTFP to reduce ecological compensation costs.

Taking water as a key point, this study analyzed the mechanism of WECC on chemical industry development, and made recommendations based on multiple sustainable development goals. However, water is only a part of the structure of environmental factors, and there is also a correlation between the chemical industry and the atmosphere, soil and other environmental factors. Therefore, in future studies, it is necessary to further analyze the interaction mechanism between the overall environmental factor structure and industrial structure, and discuss the long-term optimization path of the chemical industry structure based on the comparative advantages of environmental factors and production factors in the region under the goals and constraints of the comprehensive sustainable development of environment, economy and society.

Footnotes

Acknowledgements

We deeply appreciate the editors and anonymous reviewers for their outstanding work.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is financially supported by the Social Science Foundation of Jiangsu Province of China (Grant No. 21GLA006), the National Natural Science Foundation of China (Grant No. 42071278, 42001250), the National Key R&D Program of China (Grant No. 2019YFC0409000), the Fundamental Research Funds for the Central Universities (Grant Nos. B200204018,B210207036). The authors are grateful to the anonymous reviewers and editors for their valuable comments and suggestions.

ORCID iD

Yan Gu

Data Availability Statement

All data included in this study are available upon request by contact with the corresponding author.

References

Abbass

Qasim

M. Z.

Song

Murshed

Mahmood

Younis

(2022). A review of the global climate change impacts, adaptation, and sustainable mitigation measures. Environmental Science and Pollution Research, 29(28), 42539–42559.

Abbass

Song

Mushtaq

Khan

(2022). Does technology innovation matter for environmental pollution? Testing the pollution halo/haven hypothesis for Asian countries. Environmental Science and Pollution Research, 29(59), 89753–89771.

Anceschi

Fasano

Durante

Zanella

(2023). Bayesian conjugacy in probit, tobit, multinomial probit and extensions: A review and new results. Journal of the American Statistical Association, 118(542), 1451–1469.

Wang

Zhang

Yang

(2020). Assessment and prediction of the water ecological carrying capacity in Changzhou city, China. Journal of Cleaner Production, 277(7), 123988.

Charnes

Cooper

W. W.

Rhodes

(1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

Chen

Yang

Xia

(2021). Multi-criteria decision making and fairness evaluation of water ecological carrying capacity for inter-regional green development. Environmental Science and Pollution Research, 28(6), 6470–6490.

Chen

Zhu

Cheng

(2022). Industrial agglomeration and haze pollution: Evidence from China. Science of the Total Environment, 845, 157392.

China Environmental Monitoring Station. (2014). Technical guidelines for river water environmental quality assessment (Trial). Author.

Chinese Society of Environmental Sciences. (2014). Technical guidelines for hydro-ecological carrying capacity evaluation. Author.

10.

Chung

Y. H.

Färe

Grosskopf

(1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51(3), 229–240.

11.

Cui

Wang

Zhao

(2019). Which factors stimulate industrial green total factor productivity growth rate in China? An industrial aspect. Greenhouse Gases Science and Technology, 9(3), 505–518.

12.

Estahbanati

M. R. K.

Kumar

Khajvand

Drogui

Tyagi

R. D.

(2021). Environmental impacts of recovery of resources from industrial wastewater. In Pandey

Tyagi

R. D.

Varjani

(Eds.), Biomass, biofuels, biochemicals (pp. 121–162). Elsevier.

13.

Fan

Yang

(2022). Impact of environmental regulation on green total factor productivity: A new perspective of green technological innovation. Environmental Science and Pollution Research, 29(35), 53785–53800.

14.

Hailu

Veeman

T. S.

(2000). Environmentally sensitive productivity analysis of the Canadian pulp and paper industry, 1959-1994: An input distance function approach. Journal of Environmental Economics and Management, 40(3), 251–274.

15.

Huang

Zhao

Liu

Liao

(2023). Integrating territorial pattern and socioeconomic development into ecosystem service value assessment. Environmental Impact Assessment Review, 100, 107088.

16.

Jahangoshai Rezaee

Yousefi

Eshkevari

Valipour

Saberi

(2020). Risk analysis of health, safety and environment in chemical industry integrating linguistic FMEA, fuzzy inference system and fuzzy DEA. Stochastic Environmental Research and Risk Assessment, 34(1), 201–218.

17.

Khan

S. U.

Cui

Khan

A. A.

Ali

M. A. S.

Khan

Xia

Liu

Zhao

(2021). Tracking sustainable development efficiency with human-environmental system relationship: An application of DPSIR and super efficiency SBM model. Science of the Total Environment, 783, 146959.

18.

Kumar

(2006). Environmentally sensitive productivity growth: A global analysis using Malmquist–Luenberger index. Ecological Economics, 56(2), 280–293.

19.

Lin

Long

(2014a). How to promote energy conservation in China’s chemical industry. Energy Policy, 73, 93–102.

20.

Lin

Long

(2014b). Promoting carbon emissions reduction in China's chemical process industry. Energy, 77, 822–830.

21.

Lin

(2021). The dilemma of paraxylene plants in China: Real trouble for the environment? Science of the Total Environment, 779, 146456.

22.

Liu

K. D.

Yang

G. L.

Yang

D. G.

(2020). Investigating industrial water-use efficiency in mainland China: An improved SBM-DEA model. Journal of Environmental Management, 270, 110859.

23.

Shi

Han

Deng

Liu

(2020). Measurement and spatial variation of green total factor productivity of the tourism industry in China. International Journal of Environmental Research and Public Health, 17(4), 1159.

24.

Nanere

Fraser

Quazi

D'Souza

(2007). Environmentally adjusted productivity measurement: An Australian case study. Journal of Environmental Management, 85(2), 350–362.

25.

Odum Eugene

(1970). Optimum population and environment: A Georgian microcosm. Current History, 58(346), 355–359.

26.

Rong

Cai

Yue

Dang

Yang

(2019). Identification of the optimal agricultural structure and population size in a reservoir watershed based on the water ecological carrying capacity under uncertainty. Journal of Cleaner Production, 234, 340–352.

27.

Shah

W. U. H.

Hao

Yan

Yasmeen

(2023). Energy efficiency evaluation, changing trends and determinants of energy productivity growth across South Asian countries: SBM-DEA and Malmquist approach. Environmental Science and Pollution Research, 30(8), 19890–19906.

28.

Shang

Liu

(2020). Total factor energy efficiency in regions of China: An empirical analysis on SBM-DEA model with undesired generation. Journal of King Saud University - Science, 32(3), 1925–1931.

29.

Song

Bian

Zhu

Nan

(2020). Impacts of dual decentralization on green total factor productivity: Evidence from China’s economic transition. Environmental Science and Pollution Research, 27(12), 14070–14084.

30.

Tang

Zhao

Jiao

(2020). Ecological security assessment of Chaohu Lake Basin of China in the context of River Chief System reform. Environmental Science and Pollution Research, 27(3), 2773–2785.

31.

Teng

Han

(2017). Evaluation of environmental performance based on SBM-Tobit two-stage model. International Journal of Wireless and Mobile Computing, 13(4), 330.

32.

Tone

(2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498–509.

33.

Tsui

Derumigny

Peck

van Timmeren

Wandl

(2022). Spatial clustering of waste reuse in a circular economy: A spatial autocorrelation analysis on locations of waste reuse in the Netherlands using global and local Moran’s I. Frontiers in Built Environment, 8, 954642.

34.

Wang

Wood

Wang

Geng

Long

(2020). CO2 emission in transportation sector across 51 countries along the belt and road from 2000 to 2014. Journal of Cleaner Production, 266, 122000.

35.

Wang

Liu

Wang

Bai

Fan

(2019). A study of industrial relative water use efficiency of Beijing: An application of data envelopment analysis. Water Policy, 21(2), 326–343.

36.

Wang

Qiu

Liu

Peng

(2018). Benchmarking Toronto wastewater treatment plants using DEA window and Tobit regression analysis with a dynamic efficiency perspective. Environmental Science and Pollution Research International, 25(32), 32649–32659.

37.

Wang

Duan

Wang

(2020). Spatial distribution and source analysis of heavy metals in soils influenced by industrial enterprise distribution: Case study in Jiangsu Province. Science of the Total Environment, 710, 134953.

38.

Wang

Duan

Wang

Zou

(2022). Spatial temporal patterns and driving factors of industrial pollution and structures in the Yangtze River Economic Belt. Chemosphere, 303, 134996.

39.

M. R.

(2020). Measurement of regional industrial ecological efficiency in China and an analysis of its influencing factors. Journal of World Economic Research, 9(1), 43.

40.

Xiao

Wang

Huang

(2021). Does economic development bring more livability? Evidence from Jiangsu Province, China. Journal of Cleaner Production, 293, 126187.

41.

Liu

Wang

Zhang

(2017). Scale effect on spatial patterns of ecosystem services and associations among them in semi-arid area: A case study in Ningxia Hui Autonomous Region, China. Science of the Total Environment, 598, 297–306.

42.

M. M.

Hsu

S. H.

Chang

C. C.

Lee

D. H.

(2008). Productivity growth of Taiwan's major domestic airports in the presence of aircraft noise. Transportation Research Part E: Logistics and Transportation Review, 44(3), 543–554.

43.

Zhang

Liu

Jiang

Cui

(2021). Risk assessment and ecotoxicological diagnosis of soil from a chemical industry park in Nanjing, China. Ecotoxicology, 30, 1303–1314.

44.

Zhang

Song

Zou

(2020). Transformation of pollution control and green development: Evidence from China's chemical industry. Journal of Environmental Management, 275, 111246.

45.

Zheng

(2021). Energy efficiency evaluation model based on DEA-SBM-Malmquist index. Energy Reports, 7, 397–409.

46.

Zhong

T. H.

Filipe

J. M.

(2020). Impact of financial development and its spatial spillover effect on green total factor productivity: Evidence from 30 provinces in China. Mathematical Problems in Engineering, 2020, 1–11.

47.

Zhu

Tan

(2022). Can green industrial policy promote green innovation in heavily polluting enterprises? Evidence from China. Economic Analysis and Policy, 74, 59–75.