Ecological Environmental Quality in China: Spatial and Temporal Characteristics, Regional Differences, and Internal Transmission Mechanisms

Lv, Jiehua; Zhou, Wen

doi:10.3390/su15043716

Open AccessArticle

Ecological Environmental Quality in China: Spatial and Temporal Characteristics, Regional Differences, and Internal Transmission Mechanisms

by

Jiehua Lv

^* and

Wen Zhou

College of Economics and Management, Northeast Forestry University, Harbin 150040, China

^*

Author to whom correspondence should be addressed.

Sustainability 2023, 15(4), 3716; https://0-doi-org.brum.beds.ac.uk/10.3390/su15043716

Submission received: 27 December 2022 / Revised: 14 February 2023 / Accepted: 15 February 2023 / Published: 17 February 2023

(This article belongs to the Special Issue Towards the Sustainable Development Goals: Monitoring, Assessment and Management of Eco-Environmental Space)

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Abstract

:

In recent years, ecological environmental problems such as the greenhouse effect, soil erosion, climate change, and biodiversity reduction have become more and more salient, and ecological environmental quality has gradually become a research hotspot. This paper constructs an index system for evaluating ecological environment quality based on the pressure–state–response (PSR) model, which contains three elemental layers, natural resources, ecological environment, and government inputs, measures the ecological environment quality index by using the “vertical and horizontal layer by layer” scatter degree method, and discusses the spatial and temporal evolution trends of ecological environment quality in each province and six regions in China during 2005–2020. This paper further measures the regional ecological environment quality differences by using the Thiel index and analyzes the transmission mechanism within the pressure–state–response model by using the mediation models. The results show that the ecological environment quality of all Chinese provinces and six regions has improved significantly during the period under study, the response system and state system scores have improved significantly, the unbalanced development of ecological environment quality within north China has improved the most, and there are significant direct and mediation effects among the subsystems within the ecological environment quality with high system transmission efficiency. Therefore, the government should improve the quality of the ecological environment by seeking cross-provincial linkage development, improving the level of pollution control, and formulating relevant standards and laws and regulations.

Keywords:

ecological environment quality; the “vertical and horizontal layer by layer” scatter degree method; regional differences; mediation effects

1. Introduction

Ecological environment quality reflects the suitability of an area’s ecological condition for human survival and economic development, which is the basic condition for the survival and development of human society and is closely related to the level of social and economic development. Since the industrialization era, urbanization and population growth have brought a series of impacts to the earth’s ecology, and the ensuing ecological and environmental problems have posed a threat to human survival and development and are related to the sustainable development of human society and economy. They have thus attracted great attention from developed and developing countries around the world.

At the legal and national levels, the Ministry of Ecology and Environment of the People’s Republic of China issued the Notice on the Issuance of the 14th Five-Year Plan for Environmental Health in 2022, which calls for strengthening environmental health risk monitoring and assessment as a way to improve ecological and environmental quality. The Security Council of the Russian Federation stated that environmental security is an important part of Russia’s national security. The U.S. Department of State considers concerns about environmental threats to be an ongoing part of national security strategy. In the last 50 years, environmental protection has become an indispensable part of most European initiatives and the basis of economic sustainable development [1]. Therefore, there is a broad international consensus to improve the quality of the ecological environment and to reduce the threats posed by ecological security.

Scientific evaluation of regional ecological environment status provides the main basis for improving the ecological environment and promoting the sustainable development of humans and nature, which can improve ecological security, and also helps us understand the relationship between ecological environment quality and sustainable development. So, what is the development trend of ecological environment quality in China? What is the current status? What are the differences among regions? What are the transmission mechanisms within the system? These are all questions that need to be answered urgently.

Based on the pressure–state–response model, this paper measures the eco-environmental quality index of each province and six regions in China. On this basis, the spatial and temporal evolution trends of China’s eco-environmental quality index are comprehensively analyzed; the regional differences of China’s eco-environmental quality are measured using the Thayer index; the transmission mechanism within the system is comprehensively analyzed using the mediating effect model, which provides theoretical reference and empirical basis for the development of China’s eco-environmental quality. The rest of the paper is structured as follows. The second part is a literature review, the third part is a theoretical analysis and hypothesis of the construction of the eco-environmental quality index system based on the PSR model and its internal transmission mechanism. The fourth part is an introduction to the materials and methods used in the empirical evidence, the fifth part is a comprehensive analysis of the spatial and temporal evolution trends, regional differences, and internal transmission mechanism of eco-environmental quality in China, and the sixth part is a conclusion and discussion.

2. Literature Review

In recent years, domestic and foreign scholars have mainly conducted statistical research on ecological environment quality in three areas.

Firstly, they have used remote sensing and GIS technologies to monitor and evaluate the regional ecological environment quality. Jing et al. used remote sensing technology to extract relevant indicators reflecting the ecological environment quality in arid zones and constructed the remote sensing ecological index (RSEI) to evaluate the ecological environment quality of Xinjiang Aibi Lake Wetland National Nature Reserve and explore its spatial differentiation characteristics [2]; Zhang et al. constructed a remote sensing ecological index model based on Landsat remote sensing images to monitor and evaluate the ecological environment quality of the study area dynamically over 15 years; the drivers of ecological environment changes were discussed [3]. Liu et al. used the InVEST model to study the ecological environment quality changes in the Yangtze River Delta region based on LULC data [4].

Secondly, the regional ecological and environmental quality was evaluated by constructing an ecological and environmental evaluation index system. Lv constructed an ecological and environmental quality index system based on the PSR model and used principal component analysis to measure the ecological and environmental quality levels at the comprehensive level and regional level in China [5]. Li et al. constructed an evaluation index system of coordinated ecological and environmental development with population development, economic development, social development, and spatial development as first-level indicators and used the entropy weight method and coupled coordination degree model to analyze the spatial and temporal divergence characteristics and coupling relationship of 16 cities in Anhui Province from 2010 to 2020 [6]. Fan constructed an ecological environment evaluation system by selecting indicators from two levels, natural environmental factors and economic development, and used gray correlation theory to evaluate the ecological environment of cities along the Yangtze River Economic Belt [7]. Zhang, Jinliang et al. analyzed the ecological and environmental evolution characteristics of the Yellow River Basin from 1980 to 2019 by constructing Environment Development Index (EDI) through entropy weight analysis [8]. A related study by Han Jun [9] and Xiong and Li [10] also used the method of constructing an ecological environment index system to evaluate the ecological environment quality in China. Ghosh and Maiti constructed a new ecological sensitivity index (ESI) from multiple aspects and used F-AHP and AHP methods to determine the weights of priority criteria and sub-criteria for each factor [11]. Khadijeh Rahimi Balkanlou used the DPSIR framework to study the ecological environment of the Lake Urmia basin in Iran, focusing on the spatial and temporal evolution trends [12]. The US EPA measured the environmental quality index of all counties in the US by constructing the EQI index [13].

Thirdly, the multiple influencing factors of regional ecological environment and the coordinated development of ecosystem and other related systems were studied. Guo et al. found that land preparation not only improves the ecological quality of the preparation area, but also improves the surrounding environment [14]. Yang et al. analyzed the key factors and the degree of coordination between ecological environment and social economy by using the gray correlation degree model and coupling degree model based on analyzing the dynamic characteristics of the per capita ecological footprint in Meixian County [15]. Syed Abdul Rehman Khan PhD investigated how renewable energy consumption interacted with international trade and environmental quality in the Nordic countries, and the results showed that renewable energy consumption improved environmental quality [16]. Festus Victor Bekun used canonical cointegration regression (CCR), fully modified least squares (FMOLS), and dynamic least squares (DOLS) to explore the impact of renewable and non-renewable energy sources, economic growth, and energy sector investments on CO₂ emissions in India [17].

In addition, studies on the ecological environment evaluation index system involve various fields, including natural ecological environment [2,7,18], forest ecological environment [19], marine ecological environment [20], geological ecological environment [21], rural ecological environment [22], etc.

Through the literature review, we found that there have been many studies on the comprehensive evaluation of ecological environment quality domestically and abroad, but there are still directions that can be explored in depth. Firstly, most of the existing studies are based on time series data or cross-sectional data to monitor and assess the ecological environmental quality of local areas in China, but there are few studies on the comprehensive analysis of ecological environmental quality of Chinese provinces in a long time series. Secondly, most of the existing studies focus on the analysis of ecological environmental quality on neither a national scale, nor inter-provincial or urban clusters, but a few scholars have studied the development of ecological environmental quality in China from a regional perspective. The research that integrates multiple spatial scales to study the ecological environmental quality in China needs to be enriched. Thirdly, the common methods used for the comprehensive evaluation of ecological environmental quality are principal component analysis [5], the fuzzy neural network method [23], analytic hierarchy process analysis [24], the entropy method [25], etc., and there are a few studies that introduce the “vertical and horizontal layer by layer” scatter degree method into the comprehensive evaluation of ecological environmental quality. Last but not least, most of the existing studies use PSR models for comprehensive evaluation of ecological environment quality, but there are few articles that explore the internal mechanisms of PSR models in depth.

In view of this, this paper integrates various data sources, constructs a comprehensive index of ecological environment quality based on the pressure–state–response (PSR) model, and uses the “vertical and horizontal layer by layer” scatter degree method to comprehensively evaluate the ecological environment quality of each province and region from 2005 to 2020. On the one hand, we compare and analyze the spatial and temporal evolution trends and regional distribution differences of China’s ecological environment quality from 2005 to 2020. On the other hand, the dynamic evolution characteristics of the three subsystems of pressure, state, and response are analyzed. We further make an in-depth analysis of the internal transmission mechanism of the three subsystems of pressure, state, and response. The research results can provide some references for the management policies related to regional ecological environmental protection, ecological resource development, and sustainable economic development in China.

3. Research Hypotheses and Construction of Index System

3.1. Theoretical Analysis and Research Hypotheses

The “Pressure-State-Response” model has been widely used in the fields of ecological and environmental quality evaluation [5,10], ecological civilization construction [25], ecological security evaluation [19], ecological sustainable development, etc. It reflects the internal mechanism of interaction between human activities and the ecological environment: when the natural environment faces certain pressure due to human activities, its state level will change and government departments will respond to the new environmental state to cope with the change. The pressure–state–response is not a simple linear relationship, but an interaction between subsystems and subsystems. Therefore, an accurate grasp of the transmission mechanism within the eco-environmental quality system is conducive to promoting the improvement of eco-environmental quality and thus achieving sustainable economic and social development. Specifically, the mechanisms of action within the pressure–state–response system can be divided into two major categories.

The first category is the action and counteraction between any two subsystems within the system, such as: the interaction between pressure and state, the interaction between pressure and response, and the interaction between state and response, as in Figure 1a. Taking the interaction between pressure and state as an example, human economic production activities will generate environmental pressure on the ecological environmental quality state system, which in turn affects the ecological environmental quality state system’s internal forest resources, water resources, air quality, etc. On the other hand, the state of ecological environment quality provides necessary material support for human production and life. When the state system keeps deteriorating, it will in turn restrict human economic and social activities, which in turn affects the economic and social development of a region.

The second category is the direct effect and intermediary effect between the three subsystems within the system. For example, the change of the pressure system can act directly on the response system or indirectly change the response system by acting on the state system when the state system plays the intermediary effect, as in Figure 1b. It should be noted that the mechanism of the influence of the pressure system on the response system is only listed here separately; in addition to the one demonstrated in Figure 1b, there are five such possibilities within the pressure–state–response system.

Based on this, the following hypotheses can be formulated. Section 4.3 below will test these hypotheses based on the results of the measurement of pressure, state, and response subsystem indices within the ecosystem quality system.

Hypothesis 1 (H1):

Changes in the pressure system have a direct positive effect on the response system.

Hypothesis 2 (H2):

Changes in the pressure system have a positive effect on the state system.

Hypothesis 3 (H3):

Changes in the state system will promote changes in the response system.

Hypothesis 4 (H4):

The pressure system can act on the state system and thus have a positive effect on the response system.

3.2. Construction of the Indicator System

This paper combines the PSR model with the principles of scientificity, systematization, data availability, and typical representativeness, and constructs an index system for evaluating ecological environment quality by combining the indicators related to China’s ecological environment status bulletin issued by the Ministry of Ecology and Environment of the People’s Republic of China, The Sustainable Development Goals Report (SDGs) issued by the World Health Organization (WHO) in 2021, and comparing them with related research results [10,26] and selecting indicators from three aspects: pressure, state, and response.

3.2.1. Selection of Pressure Indicators

The pressure system refers to the stress on the ecological environment caused by human activities, such as food production and economic development, in order to meet the needs of survival. Energy consumption per unit of GDP, electricity consumption per unit of GDP, and water consumption per unit of GDP are selected to examine the pressure on water and energy in the process of industrial economic growth; the use of chemical fertilizers, pesticides, and plastic films per acre in agricultural activities are selected to measure the environmental pressure on rural soil and water bodies caused by agricultural production; the ownership of private automobiles per unit of GDP is selected to measure the ecological and environmental pressure caused by technological progress and urban construction.

3.2.2. Selection of State Indicators

The evaluation of the status system is based on the status of natural resources and the status of the urban environment. The forest coverage rate, per capita water resources, and per capita food production are selected to evaluate the status of water, forest, and food resources in the natural environment; the green coverage rate of built-up areas, per capita park green space, and the proportion of days with air quality better than Grade 2 in key cities are selected to measure the status of air and green space in cities.

3.2.3. Selection of Response Indicators

The response system refers to the response measures taken by the government, organizations, or individuals to change the state of the ecological environment when it is under pressure, including both financial investment and governance construction. In terms of capital investment, the proportion of social fixed asset investment in water conservancy, environment and public facility management, completed investment in industrial pollution treatment, and forestry investment in social fixed asset investment are selected to reflect the positive response of government departments to ecological damage and environmental pollution; in terms of governance, urban pollution treatment and natural ecological construction are focused on, and the rate of harmless treatment of domestic waste, urban sewage treatment rate, the comprehensive utilization rate of general industrial solid waste, the rate of forest pest and rodent control, and the proportion of the total afforestation area to the total area of the district were selected as five indicators to examine the ecological environment governance capacity.

To construct an index system for evaluating the quality of China’s ecological environment based on the indicators selected from the three subsystems of pressure, state, and response, the index system and index attributes are shown in Table 1. The evaluation objects of this paper are different provinces, cities, and regions in China. In order to facilitate quantitative analysis, the selected indicators are all relative indicators to avoid the differences caused by the size of population and land area.

4. Materials and Methods

4.1. Data Source

In this paper, 30 provinces in China (due to some missing data, the study area does not include Tibet Autonomous Region) are selected as the study area, and the research data are mainly obtained from China Statistical Yearbook 2005–2020, China Environmental Statistical Yearbook, China Energy Statistical Yearbook, China Statistical Yearbook of each province in China, China Investment Field Statistical Yearbook 2019 and 2020, and the website of the National Bureau of Statistics, and the mean interpolation method is used for individual missing data to assign a full value.

4.2. The “Vertical and Horizontal Layer by Layer” Scatter Degree Method

The “vertical and horizontal layer by layer” scatter degree method is a comprehensive evaluation method that can reflect the dynamic three-dimensional time series characteristics of the research object. Its basic idea is to maximize the overall differences between the evaluated objects and to assign an objective weight to the longitudinal and horizontal data. Compared with other evaluation methods, the “vertical and horizontal layer by layer” scatter degree method solves the problem that static measurement methods cannot determine the weight coefficients of panel data; it can add temporal elements to the characteristics of the data itself to determine the weight coefficients and integrate the index data dynamically in time and space, which can reflect the differences of the evaluated objects both “horizontally” and “vertically” at different moments, thus comprehensively reflecting the intrinsic characteristics of the stereoscopic data. Scholar Guo has demonstrated the evaluation principles and methods of the “vertical and horizontal layer by layer” scatter degree method in detail [27]. For the dynamic evaluation problem of multi-level systems, scholars Dong and Guo provide the “vertical and horizontal layer by layer” scatter degree method to determine the comprehensive evaluation value of each subsystem at different levels [28].

The index system and research data used in this paper have the following two major characteristics:

(1) The research data have “three-dimensional” characteristics. This study compares the ecological quality status of each Chinese province

S_{i}

(i = 1, 2…n) in a certain year

t_{k}

(k = 1, 2…K) compares the ecological environmental quality status of a province in a different year

t_{k}

. The indicator data involve K years, n evaluated objects, and m indicators.

(2) The index system has layers. This paper is divided into four layers based on the PSR model: target layer, criterion layer, element layer, and indicator layer. The target layer and criterion layer evaluation values need to be calculated to measure the ecological environment quality index and three subsystem evaluation values of pressure, state, and response.

Based on the above characteristics, this paper introduces the “vertical and horizontal layer by layer” scatter degree method, so that the evaluation process can not only reflect the basic characteristics of the time series three-dimensional data, but also process and integrate the index data from the bottom to the top layer by layer, fully exploit the data information, and realize the scientific and effective evaluation of the measured samples. The research methodology for the “vertical and horizontal layer by layer” scatter degree method can be found in Appendix A.

4.3. The Thiel Index

The Thiel index is commonly used to measure the overall differences between regions. Its advantage is that the overall variation can be decomposed into two parts: intra-regional variation and inter-regional variation, thus revealing the direction of change and the magnitude of change of intra-group variation and inter-group variation; the contribution of intra-regional variation and inter-regional variation in the overall variation can therefore be further assessed. The calculation formula is as follows.

T = \frac{1}{n} \sum_{i = 1}^{n} \frac{y_{i}}{\bar{y}} l n \frac{y_{i}}{\bar{y}}

(1)

where n is the number of study samples,

y_{i}

refers to the comprehensive evaluation value of ecological environment quality of 30 provinces and cities, and

\bar{y}

is the mean value of the comprehensive evaluation value of ecological environment quality of 30 provinces.

In this paper, using 30 provinces and cities as the basic spatial unit, we decompose the overall differences in China’s ecological environmental quality into six inter-regional differences

T_{b}

and six intra-regional differences

T_{w}

and calculate their contributions to the total differences

D_{b}

and D_w. The calculation formulae can be found in Appendix A.

4.4. Mediation Effects Model

The mediating effect model is designed to test whether the variable M plays a mediating role in the effect of the independent variable X on the dependent variable Y, such that the independent variable X has not only a direct effect on the dependent variable Y, but also an indirect effect on Y through M, which is called the mediating variable. If all variables have been standardized, the following equation can be used to describe the relationship between the variables, where coefficient

c

is the total effect of the independent variable

X

on the dependent variable

Y

, coefficient

a

is the effect of the independent variable

X

on the mediating variable

M

, coefficient

b

is the effect of the mediating variable

M

on the dependent variable

Y

, conditional on controlling for the independent variable

X

; coefficient

c^{'}

is the direct effect of the independent variable

X

on the dependent variable

Y

, conditional on controlling for the mediating variable

M

; and

e_{1}

~

e_{3}

are the regression residuals.

Y = c X + e_{1}

(2)

M = a X + e_{2}

(3)

Y = c^{'} X + b M + e_{3}

(4)

The most popular method to test for mediating effects is the causal steps approach proposed by Baron and Kenny [29], but many scholars believe that the stepwise method is flawed [30,31,32,33]. In this paper, we use the mediation effect analysis process proposed by Zhonglin Wen and Baojuan Ye, and the specific steps can be found in Appendix A.

5. Results and Interpretation

5.1. Spatial and Temporal Evolutionary Trends of Ecological Environmental Quality Based on PSR Model

5.1.1. Results of Ecological Environmental Quality Index Measurement Based on PSR Model

The indices of each level were measured according to Equations (A1)–(A6) in Appendix A. For visual comparison, each index was expanded by 100 times to obtain the comprehensive index of ecological environment quality and the scores of pressure, state, and response subsystems for each province and city in China from 2005 to 2020. Furthermore, in order to study the spatial and temporal trends and differences in ecological environmental quality among regions, China’s administrative regions are divided into six major regions, and the average value of the scores of each province in the region is used as the regional score, and the average value of each province and city in the country is used as the national score to calculate the comprehensive ecological environmental quality scores and subsystem scores of pressure, state, and response for China and the six major regions. Due to the limitation of space, only the evaluation results of key years are listed, and the specific values are shown in Appendix B. The results of the comprehensive evaluation are plotted in the form of a map, as detailed in Figure 2.

5.1.2. Spatial and Temporal Evolution Trends of Ecological Quality in China

Analysis of changes in China’s comprehensive index of ecological environment quality

(1) China’s eco-environmental quality index shows a fluctuating upward trend

According to the comprehensive measurement results, China’s ecological environment quality score from 2005 to 2020 shows a fluctuating upward trend, rising from 58.05 in 2005 to 71.73 in 2020, and the overall ecological environment quality is steadily improving. The provinces with more obvious improvement in score include Chongqing, Guizhou, Jiangxi, Gansu, Shanxi, and Shaanxi. Specifically, Chongqing City scored 58.95 in 2005, ranking 15th in the country, and 82.52 in 2020, ranking 1st in the country; Guizhou Province scored 54.82 in 2005, ranking 24th in the country, and 77.33 in 2020, ranking 3rd in the country. The reason for this is that after the promulgation and implementation of the 12th and 13th Five-Year Plans, Chongqing and Guizhou have achieved good results in optimizing their industrial structures and promoting green and low-carbon development. During the study period, the urban sewage treatment rate, domestic waste treatment rate, and other response indicators jumped from about 30% to about 95%, per capita water sources, forests, and other natural resources were at a high level, so the ecological and environmental status system and response system scores improved significantly, and the ecological and environmental quality level has improved significantly.

(2) The changes in ecological environment quality vary in each Five-Year Plan, with the greatest increase in the 11th Five-Year Plan

In order to show the trend of ecological environment quality changes in each province and city in China during the period under investigation, this paper divides 2005–2020 into three stages according to the “five-year plan”, and selects 2005, 2010, 2015, and 2020 as the four “five-year plan” end years. This paper divides the period 2005–2020 into three phases according to the “five-year plan” and selects the four “five-year plan” years 2005, 2010, 2015, and 2020 as the time points to draw the graphs of ecological environmental quality changes, as detailed in Figure 3.

From 2005 to 2010 (during the 11th Five-Year Plan period), except for Hainan Province, where the composite index dropped by 2.97, all other provinces and cities have significantly improved their ecological environment quality. However, the ecological environment quality index rose by different rates, with Chongqing City having the largest increase of 18.98, followed by Ningxia Hui Autonomous Region, Shanxi Province, Shaanxi Province, and Jiangxi Province, with increases ranging from 13.77 to 15.86. The main reason for the increase in ecological quality in this period is that the national 11th Five-Year Plan focuses on clarifying the responsibilities and tasks of the people’s governments and environmental protection departments at all levels and insists on economic development under the premise of ecological construction. The ecological response system score in this period has improved significantly. As a result of the government’s efforts to strengthen the ecological environment, the ecological environment status has improved, and the percentage of greenery coverage in built-up areas, the area of parkland per capita, and the number of days with air quality at or better than level 2 in key cities have increased significantly, resulting in a higher score for the ecological environment quality system.

From 2010 to 2015 (during the 12th Five-Year Plan period), the ecological quality of the country’s provinces and municipalities diverged. The number of provinces with a declining ecological environment quality index is 9, which is significantly higher than that of the 11th Five-Year Plan. At the same time, the increase in the ecological environment quality index was significantly reduced, with the largest increase in Guizhou Province being only 9.11. The average change in all provinces was 1.42, which was significantly lower than that of the eleventh Five-Year Plan. The main reason for the significant decrease in the ecological environment quality score of Hainan Province is the sharp decrease in water and food resources due to the “land reclamation project”, as well as the huge pressure on the natural environment caused by the rise of real estate and tourism in the urbanization process and the significant decrease in the ecological environment pressure system and state system score. The ecological environment quality of Guizhou Province has improved because the rise of Internet industry has optimized the economic structure in the economically underdeveloped areas of Guizhou Province and reduced the number of heavily polluting enterprises, so the ecological environment quality has improved.

From 2015 to 2020 (during the 13th Five-Year Plan period), all Chinese provinces and cities show an improvement in ecological environment quality, with Qinghai, Gansu, and Xinjiang showing a significantly larger increase in ecological environmental index than other provinces, with increases ranging from 8.09 to 13.34, while most other provinces showed increases ranging from 2 to 7, with an average increase of 4.36. The main reason for the increase in scores during this period is the significant increase in the scores of the pressure and state systems and the effectiveness of the government’s efforts to strengthen pollution control and environmental protection during 2005–2020. The effectiveness of the government’s pollution control and environmental protection work between 2005 and 2020 is evident, indicating that the government’s ecological control and response measures can significantly improve the quality of the ecological environment. Due to the lagging effect of policies and the time required for ecosystem recovery, the effect of the previous treatment and investment on the improvement of the ecological environment can be realized during this period.

2.: Analysis of the changes of pressure, state, and response subsystem indices of ecological environment quality in China

(1) Pressure system scores tend to be stable, while response and state subsystem scores increase significantly

According to the evaluation results, the trend of China’s ecological environment quality index and pressure, state, and response subsystem indices from 2005 to 2020 are plotted. As can be seen in Figure 4, the score of China’s ecological environment pressure system is always at a high position during 2005–2020. China’s ecological environment is under greater pressure and less improvement, while the score of China’s ecological environment quality and the scores of the state and response subsystems show a significant growth trend, and the ecological environment state improves significantly. This is due to the government actively responding when faced with greater ecological pressure by planning long-term development strategies, improving the economic structure, and increasing investment in environmental protection, resulting in significant improvements in forest resources, water resources, and air conditions, and an improvement in the ecological environment. The significant decrease in the overall ecological quality score and state system score in 2013 stems from the fact that the 2011–2015 period (the “12th Five-Year Plan” period) is an important period for China to build a moderately prosperous society and accelerate the transformation of economic structure, and provinces and cities continue to impose loads on the environment while pursuing economic goals, which greatly affects the ecological environment status and thus the level of ecological environment quality.

(2) Stress system index tends to be stable and there is more room for improvement

At the pressure level, from 2005 to 2020, the national pressure system score shows a U-shaped change and is currently in the rising phase on the right side of the U-shape, with more room for improvement. The average level of 30 provinces and cities in 2020 decreases by 1.27% compared with 2005, indicating that nationwide, the pressure on the ecosystem in 2020 is greater than that in 2005, and economic activities have a greater impact on the ecosystem. In terms of specific changes by province, the provinces with the most improved scores in 2020 compared to 2005 are Shanghai and Tianjin, where the pressure on the environment caused by economic activities has been alleviated and the level of stress on the ecosystem has decreased significantly. On the contrary, the provinces and cities with the most decreasing scores are Beijing and Hainan.

(3) The state subsystem scores are on the rise, and the state of the ecological environment has been greatly improved

From the status level, the national ecological environment status scores show a gradual upward trend between 2005 and 2020, and the average scores of 30 provinces and cities in 2020 are 40.60% higher than those in 2005, so the ecological environment status has been greatly improved. A total of 16 provinces have scored higher than the national average in 2020, and the highest score in Fujian Province (78.75) is 1.99 times higher than the lowest score in Tianjin City (39.59). In 2005, there were 13 provinces with scores higher than the national average, while the highest score in Fujian Province (63.94) was 2.45 times higher than the lowest score in Gansu Province (26.13). Most provinces have improved their eco-environmental status system and the score gap between provinces is narrowing.

(4) Response subsystem scores show a rapid upward trend, and the government’s level of ecological environment governance has increased significantly

At the response level, the government’s level of ecological environment governance has increased significantly between 2005 and 2020. The ecological environment quality response system scores show a rapid upward trend, with the average level of 30 provinces and cities in 2020 increasing by 67.02% compared to 2005, indicating that the government has gradually increased its ecological environment governance efforts during the study period and the ecological environment quality response level has been greatly improved. A total of 18 provinces scored above the average in 2020 for their ecological response systems, with Chongqing’s highest score (85.43) being 1.54 times higher than the lowest score (55.63) in Guangxi. Most of the provinces in China have response levels above the mean, but inter-provincial differences still exist.

5.2. Regional Differences in Ecological Quality

5.2.1. Results of Regional Differences in Ecological Quality

From the above analysis, we can see that there are differences in the development trend of ecological environmental quality between provinces and regions in China. In this paper, the Thiel index is used to measure the difference characteristics of the development of ecological environmental quality from 2005 to 2020 to further clarify and grasp trend of the difference characteristics and evolution of ecological environmental quality between provinces and regions. The results are shown in Table 2.

5.2.2. Regional Differences in Ecological Quality in China

(1) Regional differences in ecological environment quality in China show a decreasing trend year by year and the differences in ecological environment have been improved significantly after 2013

Table 2 gives the results of the calculation of the Thiel index of China’s ecological environment quality for each year, which shows that the Thiel index of China’s ecological environment quality showed a fluctuating downward trend during the period under investigation, indicating that the differences in ecological environment quality among Chinese provinces were reduced. Among them, the Thiel index fluctuated between 0.004 and 0.005 during 2005–2012, and there were differences in ecological environment quality among provinces, but the changes were not significant; from 2013 to 2020, the Thiel index decreased continuously from 0.0050 to 0.0026, and the differences in ecological environment quality among provinces decreased rapidly. Comparing the decline in the Thiel index between 2005–2012 and 2014–2020, it is found that the former is about four times the latter, which indicates that the inter-provincial differences in ecological and environmental quality in China have been greatly improved after 2013, and the unbalanced development of ecological and environmental quality between provinces and regions has been alleviated.

(2) The contribution rate of the differences in ecological environment quality among the six major regions to the overall differences in China’s ecological environment exceeds 60%

Table 2 shows the contribution rate of inter-regional and intra-regional differences to the overall differences, which shows that the average contribution rate of inter-regional differences is as high as 63.13%, indicating that inter-regional differences are the main component of the overall differences in China’s ecological and environmental quality, and the problem of unbalanced development of ecological and environmental quality among regions is prominent. In contrast, the average contribution rate of the difference in ecological and environmental quality within the six regions is 36.85%, and the level of ecological and environmental quality within the six regions is relatively close. The problem of imbalance is not obvious. To a certain extent, this indicates that the ecological environments of neighboring provinces affect each other. If one province implements industrial transformation and environmental regulation, pollution will be transferred to neighboring provinces in the vicinity, which will have negative impact on the ecological environment quality of neighboring provinces. Therefore, the ecological environment quality levels of geographically neighboring provinces are similar, and the ecological environment quality differences within the regions are closer.

5.2.3. Regional Differences in Ecological Quality

(1) The inter-regional ecological environment differences among the six regions are outstanding, but the inter-regional differences in ecological environment quality show an obvious decreasing trend year by year

As can be seen from the changes in the inter-regional Thiel index during the examination period in Table 2, the inter-regional Thiel index showed a fluctuating downward trend from 2005 to 2020, indicating that the inter-regional differences in ecological environment quality in China are gradually decreasing, with the Thiel index fluctuating down from 0.0029 in 2005 to 0.0015 in 2020, a decrease of 47.2%. During the examination period, the inter-regional average Thiel index of the six regions is 0.0026, which is much higher than the intra-regional Thiel index of the six regions of 0.0015, as the former is 1.7 times higher than the latter, indicating that the inter-regional differences in ecological environment quality among the six regions of China are more prominent.

(2) The intra-regional differences in ecological environment quality of the six regions are gradually narrowing but severely differentiated, with large intra-regional differences in the northwest and small intra-regional differences in the northeast

From the measurement value of the intra-regional Thiel index, the difference in ecological quality of the six regions within the region is narrowing, but the difference in the regional ecological environment quality is seriously divided. Compared with 2005, the degree of difference in ecological environment quality within the region in 2020 has expanded in the central and southwestern regions, while other regions show a decreasing trend, with the largest change in the intra-regional Thiel index in northern China, with a decrease of 0.00586, indicating that the uneven development of ecological environment quality within the region of northern China has improved the most. During the examination period, the intra-regional Thiel index in northwest China fluctuated the most and was always higher than other regions during 2007–2016, indicating that the degree of difference in ecological environmental quality among provinces within northwest China was always greater than the degree of difference within other regions during this period, and the problem of unbalanced ecological environmental quality among regions was more prominent; the variability among provinces within northeast China and east China was smaller and the development of eco-environmental quality is more balanced.

5.3. Internal Transmission Mechanisms of the Ecosystem Quality System

5.3.1. Measurement Results of Mediation Effect

In this paper, we use panel data of 30 Chinese provinces from 2005 to 2020 to study the internal transmission mechanisms of each subsystem of the pressure–state–response model using the mediating effects model, and the results are shown in Table 3.

5.3.2. Analysis of the Internal Transmission Mechanism of the Ecosystem Quality System

Table 3 shows that among the six transmission mechanisms formed by pressure, state, and response, there are significant direct and mediating effects between any two, indicating that there is a significant role of relationship between pressure, state, and response. Subsystem a can act on subsystem b through direct effects, but also affects b through the mediating effects of subsystem c, which acts as an intermediary between subsystems a and b.

Taking the conduction model of the pressure system to the response system as an example, the pressure system can achieve the purpose of influencing the response system through the intermediary effect of the state system. As shown in Table 3, the direct effect c’ = 0.326 (p < 0.10), the mediating effect of pressure through state on response ab = 0.059 (p < 0.10), the mediating effect accounted for 22% of the total effect, and both the direct and mediating effects were significant. From the calculated results, the total effect of the pressure system on the response system, c, is 0.268, which passes the significance test at the 10% level and verifies hypothesis 1. It shows that the pressure system has a direct and positive effect on the response system, and when the ecological environment is under greater pressure, the government or individuals will take measures. On the one hand, the government will enact laws or increase the investment in environmental protection when the ecological environment is under greater pressure. On the other hand, when the ecological environment is under pressure, individuals or public welfare organizations will take the initiative to limit their own behavior or call on others to join the environmental protection action as well as reduce the pressure on the ecological environment, and these measures will be reflected in the response system. At the same time, the effect of the pressure system on the state system a is 0.173, which does not pass the significance test, and the effect of the state system on the response system b is 0.341, which passes the significance test at the 1% level. The state system plays a mediating effect in the action of the pressure system on the response system, and the pressure system can change the response system by changing the state system. In addition, when the state system was added as a mediating variable in the model, the coefficient of the pressure system would increase from 0.268 to 0.326, indicating that the state system strengthened the influence of the pressure system on the response system, and the change of the ecological environment pressure system strengthened the change of the response system by affecting the state system. The results show that the state system plays a part as a mediating effect in the transmission mechanism of the pressure system to the response system, and the pressure brought by human living and production activities can not only act directly on the response system, but also be transmitted to the response system through the state system, and the addition of the state system strengthens the effect of the pressure system to the response system.

Similarly, the other five different transmission paths can be analyzed in this way, and the direct and mediated effects between the three subsystems of pressure, state, and response in each transmission path, as well as the proportion of the mediated effect to the total effect, can be obtained.

In general, the direct and mediated effects among the three subsystems of pressure, state, and response are significant, which indicates that the transmission of information between pressure–state–response within the Chinese ecological environment quality system is smooth and there is no information transmission barrier; the transmission efficiency within the system is high, which helps to improve the ecological environment quality in China.

6. Discussion and Conclusions

6.1. Conclusions

Based on the panel data of 30 Chinese provinces from 2005 to 2020, this paper constructs an ecological environmental quality index to empirically study the spatial and temporal evolution trends and regional differences of ecological environmental quality in each province and region, as well as draws conclusions from the following three aspects.

Firstly, the ecological environment quality of all Chinese provinces improved significantly from 2005 to 2020, and the ecological environment optimization was effective, among which the ecological environment quality of most provinces and cities increased significantly from 2005 to 2010 (the 11th Five-Year Plan period), mainly because the government departments increased the ecological environment protection and treatment during this period. The response subsystem score increased significantly. Northwest China has the largest increase in ecological quality, but it is always at a lower level than the rest of the country.

Secondly, from the three subsystems of pressure, state, and response, the scores of the pressure system are relatively stable during 2005–2020, while the scores of the response system and state system increase significantly, indicating that the environmental management work of each province is effective and should continue to strengthen the ecological environment construction efforts and improve the ecological environment quality. Furthermore, the significant increase in the scores of the ecological pressure system and state system during the 13th Five-Year Plan period shows that the government’s pollution control policies and environmental protection measures, such as financial investment, have a certain lagging effect, and the effectiveness of control will be reflected in the change of ecological state after a period.

Thirdly, based on Thiel index analysis, it can be seen that the regional differences in ecological environmental quality among provinces in China are gradually narrowing, and the imbalance in the development of ecological environmental quality has slowed down more after 2013; there is obvious spatial heterogeneity in the differences in ecological environmental quality among the six regions, and the imbalance in the development of ecological environmental quality within the region of north China has improved the most. The differences in ecological environmental quality mainly come from inter-regional ecological environmental quality, and the trend of inter-regional differences is decreasing.

Fourth, the analysis results based on the mediating effect model show that there are direct and mediating effects between the three subsystems of pressure, state, and response, and there are mutual direct effects between any two subsystems, which can also change another subsystem through the mediating effect of one subsystem. There is no barrier to information flow within the ecosystem quality system.

6.2. Policy Implications

Firstly, the development of China’s eco-environmental quality is a complex synthesis, and a “one-size-fits-all” development response cannot be implemented. Provinces should be encouraged to seek linkage development while improving their own eco-environmental quality, adjusting their industrial structure to regional characteristics, changing production methods, and strengthening inter-regional division of labor and cooperation. For example, Shanxi and Shaanxi have mature coal and natural gas industries [34], and their ecological and environmental quality is relatively poor due to their industrial characteristics, but it is impossible to shut down such enterprises within a short period of time, so we need to increase investment in environmental protection, improve the rate of treatment of the three wastes of the relevant enterprises, and promote sustainable ecological and environmental development.

Secondly, we must rely on technological advances to promote the development of clean energy progress, reduce the production of pollutants at the source, promote the use of new degradable agricultural film to reduce the production of traditional agricultural plastic film. We must prevent coal-based energy consumption from causing excessive pressure on the ecological environment; at the same time, we must improve environmental pollution control technology, establish a sound environmental pollution monitoring system, and strengthen the management of high pollution emissions of enterprises and institutions to improve their pollution control level.

Thirdly, we should increase ecological and environmental investment and governance, innovate ecological and environmental governance mechanisms, formulate relevant standards and laws and regulations, actively cultivate a variety of subjects to participate in ecological and environmental governance and protection, and promote the institutionalization and normalization of ecological and environmental governance in order to effectively improve the state of the ecological environment and reduce ecological and environmental pressure. An example would be the creation of a multi-provincial joint ecological protection alliance, joint universities, government, and enterprises holding academic forums and conferences on the theme of ecological and environmental quality.

6.3. Limitations and Prospects

In this study, the comprehensive index of ecological environment quality in China has been analyzed and discussed in detail, but the spatial and temporal evolution trends of the pressure system, state system, and response system within the PSR model, as well as the impact of the changes of each subsystem on the comprehensive index, are lacking in this part, and it is suggested that scholars can generate in-depth discussions from this perspective. In addition, how to protect the ecological environment while guaranteeing high-quality socio-economic development is worthy of further in-depth study.

Author Contributions

Conceptualization, J.L. and W.Z.; methodology, W.Z. and J.L.; software, W.Z.; validation, J.L. and W.Z.; formal analysis, J.L.; resources, J.L.; data curation, W.Z.; writing—original draft preparation, J.L. and W.Z.; writing—review and editing, J.L. and W.Z.; visualization, W.Z.; supervision, J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Social Science Fund of China, grant number 21BGL166.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are mainly from “China Environmental Statistical Yearbook”, “China Statistical Yearbook” and provincial and municipal statistical yearbooks, etc.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

To improve the readability of the article, some mathematical expressions are placed in the appendix for the reference of the research readers.

Appendix A.1. The “Vertical and Horizontal Layer by Layer” Scatter Degree Method

(1) Data pre-processing

Since the statistical data of each index in the ecological environment quality evaluation index system has its own properties and scale, it cannot be calculated and compared directly without processing. Therefore, the original data should be standardized before the comprehensive evaluation, and the processing formula is as follows.

Positive indicators : y_{i j} (t_{k}) = \frac{x_{i j} (t_{k}) - x_{j}^{m i n}}{x_{j}^{m a x} - x_{j}^{m i n}}

(A1)

Negative indicators : y_{i j} (t_{k}) = \frac{x_{j}^{m a x} - x_{i j} (t_{k})}{x_{j}^{m a x} - x_{j}^{m i n}}

(A2)

where

x_{i j} (t_{k})

is the observed value of the indicator j of the evaluation object i at year

t_{k}

;

y_{i j} (t_{k})

is the standardized value of the indicator j of the evaluation object i at year

t_{k}

.

x_{j}^{m i n}

denotes the minimum value of the indicator j on the time series, and

x_{j}^{m a x}

denotes the maximum value of the indicator j on the time series. After the above processing, the three-dimensional time series dataset

y_{i j} (t_{k})

, and the dimensionless matrix

A_{k}

(k = 1, 2…K) are obtained.

A_{k} = [\begin{matrix} y_{11 t k} & \dots & y_{1 n t k} \\ ⋮ & ⋱ & ⋮ \\ y_{m 1 t k} & \dots & y_{m n t k} \end{matrix}]

(2) Determining evaluation indices of subsystems

Firstly, the original dataset is collected and constructed in chronological order, and for the data of year

t_{k}

, it is assumed that the index f of the subsystem p constitutes the standard dataset

{y_{i j}^{(p)} (t_{k})}

, which is expressed as matrix

B_{k}

, and its transpose matrix is written as

B_{k}^{T}

.

B_{k} = [\begin{matrix} y_{11^{(p)} t k} & \dots & y_{1 n^{(p)} t k} \\ ⋮ & ⋱ & ⋮ \\ y_{f 1^{(p)} t k} & \dots & y_{f n^{(p)} t k} \end{matrix}]

B_{k}^{T} = [\begin{matrix} y_{11^{(p)} t k} & \dots & y_{f 1^{(p)} t k} \\ ⋮ & ⋱ & ⋮ \\ y_{1 n^{(p)} t k} & \dots & y_{f n^{(p)} t k} \end{matrix}]

The evaluation index of the subsystem p can be calculated by the integrated evaluation function as follows.

g_{i} {(t_{k})}^{(p)} = \sum_{j = 1}^{f} ω_{j} y_{i j}^{(p)} (t_{k})

(A3)

where

ω_{j}

is the weight of each indicator in the subsystem p. The “vertical and horizontal layer by layer” scatter degree method determines the indicator weights based on the maximum possible reflection of the differences between the evaluated objects, which are determined by the total sum of squared deviations of

g_{i} (t_{k})

. The total sum of squared deviations of

g_{i} (t_{k})

is

σ^{2} = \sum_{k = 1}^{K} \sum_{i = 1}^{n} {(g_{i} (t_{k}) - \bar{g})}^{2}

.

\bar{g} = \frac{1}{K} \sum_{k = 1}^{K} (\frac{1}{n} \sum_{i = 1}^{n} \sum_{j = 1}^{f} ω_{j} y_{i j} (t_{k})) = 0

(A4)

σ^{2} = \sum_{k = 1}^{K} \sum_{i = 1}^{n} {(g_{i} (t_{k}))}^{2} = \sum_{k = 1}^{K} [ω^{τ} H ω] = ω^{τ} \sum_{k = 1}^{K} H_{k} ω = ω^{τ} H ω

(A5)

where

ω = {(ω_{1}, ω_{2} \dots, ω_{j})}^{τ}

, and

H_{k} = B_{k}^{T} * B_{k}

(k = 1, 2…K),

H = \sum_{k = 1}^{K} H_{k}

. To avoid arbitrarily large values of

σ^{2}

, we need to restrict

ω^{τ} ω

=1. To find the maximum value of

σ^{2}

, we just need to take the standard eigenvector corresponding to the maximum eigenvalue

λ_{m a x} (H)

of the matrix

H

, which is denoted as

ω_{j}

.

The evaluation values of other subsystems can be derived according to the above steps. If there are multiple layers of the index system, the evaluation index of each layer can be derived separately according to the above steps.

(3) Determine the comprehensive evaluation index of ecological environment quality

G_{i} (t_{k})

G_{i} (t_{k}) = \sum_{j = 1}^{n_{p}} ω_{j} g_{i}^{(p)} (t_{k})

(A6)

Appendix A.2. Decomposition of Thiel Index

The following content is the method of Theil index decomposition.

T = T_{b} + T_{w}

(A7)

T_{b} = \sum_{k = 1}^{K} y_{k} l n \frac{y_{k}}{\bar{n_{k} / n}}

(A8)

T_{w} = \sum_{k = 1}^{K} y_{k} \cdot T_{k} = \sum_{k = 1}^{K} y_{k} (\sum_{i ϵ g_{k}} \frac{y_{i}}{y_{k}} l n \frac{y_{i} / y_{k}}{\bar{1 / n_{k}}})

(A9)

T_{k} = \sum_{i ϵ g_{k}} \frac{y_{i}}{y_{k}} l n \frac{y_{i} / y_{k}}{\bar{1 / n_{k}}}, i ϵ g_{k}

(A10)

D_{w} = \frac{T_{w}}{T}

(A11)

D_{b} = \frac{T_{b}}{T}

(A12)

where n is the number of study basic units, K is the number of clusters, and the number of study units in each group is

g_{k}

, (k = 1, 2…K);

y_{k}

is the comprehensive evaluation value of ecological environment quality in the group k;

n_{k}

is the number of individuals in the group k;

T_{k}

is the intra-group gap in the group k.

Appendix A.3. Methods to Test for Mediating Effect

The first step is to test whether the coefficient

c

is significant. If the coefficient c is significant, the theory is based on the mediating effect; otherwise, the theory is based on the masking effect. Regardless of whether the coefficient

c

is significant or not, a sequential test is required.

In the second step, the coefficients

a

and

b

are tested for significance. If both coefficients

a

and

b

are significant, the mediating effect of

M

is indicated and the test is carried out in the fourth step; if at least one of the coefficients

a

and

b

is not significant, the test is carried out in the third step by bootstrap test.

In the third step, use the bootstrap method to test

a b

. If it is significant, it indicates that

M

plays a mediating effect, and we move to the fourth step; if it is not significant, it indicates that there is no mediating effect, and we stop the test.

In the fourth step, test whether the coefficient

c^{'}

is significant. If significant, it indicates that there is a direct effect of

X

on

Y

, and we move to step 5 to calculate the proportion of the mediating effect to the total effect; if not significant, it indicates that there is no direct effect of

X

on

Y

, but only a full mediating effect.

In the fifth step, to calculate the proportion of mediating effect to the total effect, we need to compare the sign of

a b

and

c^{'}

. If they are the same, the proportion of mediating effect to the total effect is

a b / c

, and if they are different, then it is

| a b / c^{'} |

.

Appendix B

Table A1. Ecological environment quality scores of Chinese provinces and regions.

Region	2005				2010				2015				2020
Region	Total	P	S	R	Total	P	S	R	Total	P	S	R	Total	P	S	R
National	58.05	79.86	43.51	43.29	65.96	78.40	55.06	60.04	67.38	77.52	52.85	67.44	71.73	78.85	61.18	72.31
North China	58.29	79.26	38.95	48.66	66.20	77.57	51.31	65.02	66.32	76.61	49.08	68.49	69.02	76.08	54.38	73.08
Beijing	66.30	79.34	46.73	66.99	69.91	78.24	59.19	69.10	67.99	73.10	58.65	70.06	69.61	66.60	68.04	75.41
Tianjin	64.83	80.85	40.19	65.95	67.48	78.16	38.14	79.36	67.72	81.00	35.56	78.65	70.54	85.90	39.59	77.83
Hebei	57.37	78.42	42.23	44.10	67.61	75.07	59.16	65.94	64.75	74.13	46.18	69.19	69.43	76.33	51.15	76.79
Shanxi	47.57	77.75	29.98	24.74	62.31	75.67	46.77	59.14	62.36	74.16	44.91	62.79	66.82	73.57	49.70	73.37
Inner Mongolia	55.36	79.91	35.62	41.53	63.70	80.73	53.29	51.57	68.79	80.68	60.11	61.74	68.69	78.01	63.42	62.01
Northeast China	58.00	82.68	48.54	35.34	63.72	80.95	58.12	47.28	67.37	81.56	52.14	62.90	72.47	82.02	62.94	69.11
Liaoning	59.29	79.85	50.66	41.14	65.00	76.50	57.99	56.98	66.37	77.75	49.18	67.10	70.06	78.33	59.98	68.77
Jilin	56.03	82.58	50.82	27.41	61.01	80.02	57.54	40.49	66.26	79.72	53.17	60.91	72.75	80.07	64.28	71.34
Heilongjiang	58.69	85.61	44.15	37.48	65.15	86.33	58.82	44.35	69.47	87.22	54.08	60.68	74.59	87.65	64.57	67.24
East China	62.41	81.51	48.39	50.66	70.22	80.45	59.64	66.85	71.29	79.91	55.55	74.31	74.85	81.82	63.02	76.68
Shanghai	57.98	76.76	33.56	55.24	67.63	81.62	44.38	70.03	69.16	83.74	38.57	76.95	72.66	85.40	45.14	80.20
Jiangsu	66.45	83.38	47.22	61.87	70.61	83.60	53.76	69.03	71.50	84.67	49.05	74.39	74.05	85.64	57.15	74.27
Zhejiang	67.89	81.88	55.23	61.53	71.88	77.89	62.62	72.77	71.02	74.70	60.11	76.22	76.41	79.17	70.09	78.95
Anhui	57.83	85.60	40.45	37.95	68.47	84.87	52.49	61.89	72.84	84.43	52.22	76.27	74.86	83.69	61.34	75.82
Fujian	66.35	79.41	63.94	52.53	68.75	73.70	71.07	61.19	72.97	69.25	74.03	77.45	76.09	73.39	78.75	77.98
Jiangxi	60.19	84.93	57.66	31.64	73.96	84.72	76.36	59.05	73.51	84.63	72.51	61.01	79.61	86.26	77.32	73.90
Shandong	60.15	78.59	40.69	53.83	70.25	76.78	56.80	73.98	68.05	77.93	42.37	77.86	70.29	79.22	51.35	75.63
Central and South China	60.23	81.89	49.98	42.09	66.84	78.98	60.11	57.81	67.74	78.43	56.15	64.64	72.12	80.10	65.50	68.30
Henan	60.15	84.77	41.34	45.50	68.53	83.00	48.16	68.10	65.96	82.59	34.81	71.87	71.82	82.82	51.48	75.78
Hubei	59.79	81.03	42.76	47.90	66.21	80.39	52.12	60.80	67.86	83.56	45.11	67.84	75.04	85.22	62.41	73.53
Hunan	57.95	82.72	45.11	38.03	68.54	84.05	58.71	57.89	73.72	85.31	55.58	75.09	75.53	86.43	64.22	72.02
Guangdong	58.72	83.01	56.95	30.05	68.52	79.64	68.79	54.89	71.61	78.07	69.52	65.88	76.64	81.47	73.64	73.76
Guangxi	58.97	81.38	53.61	35.75	66.39	79.93	61.83	53.77	67.70	79.11	65.08	56.14	68.65	77.09	72.23	55.63
Hainan	65.81	78.44	60.09	55.31	62.85	66.88	71.04	51.41	59.57	61.97	66.77	51.01	65.05	67.58	69.04	59.08
Southwest China	59.43	83.07	41.81	45.00	71.07	82.82	56.58	69.16	73.33	83.66	59.45	72.68	77.17	84.57	68.05	76.24
Chongqing	58.95	88.48	31.55	45.33	77.93	87.82	60.53	80.87	78.13	89.15	63.70	77.12	82.52	89.61	69.40	85.43
Sichuan	61.45	84.75	43.21	48.00	68.66	84.53	55.07	60.72	68.35	85.51	48.66	63.95	73.34	86.81	61.11	67.34
Guizhou	54.82	77.70	42.63	36.74	66.07	79.36	47.80	65.35	75.18	81.38	60.96	80.08	77.33	82.00	71.27	77.27
Yunnan	62.48	81.34	49.83	49.94	71.64	79.57	62.92	69.68	71.64	78.61	64.47	69.58	75.48	79.86	70.43	74.93
Northwest China	48.01	71.48	31.83	32.47	55.93	70.61	43.32	48.57	57.75	66.64	44.00	58.67	64.83	69.48	53.63	69.00
Shanxi	53.52	86.37	40.83	23.24	67.91	83.62	55.99	58.78	71.01	80.68	55.29	72.74	72.41	81.29	55.75	75.94
Gansu	46.54	75.66	26.13	27.42	50.21	73.92	26.96	40.37	54.47	69.49	36.63	51.04	65.85	74.24	50.74	68.62
Qinghai	49.07	72.47	31.30	34.95	51.62	70.01	38.71	39.77	52.01	67.96	38.62	43.62	65.35	72.39	51.71	68.55
Ningxia	42.09	57.46	29.59	33.61	57.95	59.43	55.80	58.45	58.11	56.56	50.44	67.08	59.26	56.73	60.69	61.80
Xinjiang	48.83	65.43	31.27	43.14	51.97	66.09	39.12	45.49	53.17	58.51	39.02	58.87	61.26	62.74	49.28	70.09

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Figure 1. Internal conduction mechanism of the pressure–state–response model. (a) The action and reaction between any two subsystems within the system; (b) direct and mediating effects between the three subsystems within the system.

Figure 2. Distribution of ecological environment quality by provinces in China. Figure 2 can be divided into two panels, with the four panels on the left showing the distribution of scores in 2005 and the panel on the right showing the distribution of scores in 2020. (a) Comprehensive ecological environment score of each province in 2005; (b) pressure system score of each province in 2005; (c) state system score of each province in 2005; (d) response system score of each province in 2005; (e) comprehensive ecological environment score of each province in 2020; (f) pressure system score of each province in 2020; (g) state system score of each province in 2020; (h) response system scores for each province in 2020.

Figure 3. The magnitude of changes in ecological quality in each province by stage.

Figure 4. Trends of comprehensive index and subsystem index of ecological environment quality from 2005 to 2020.

Table 1. Ecological environment quality evaluation index system.

Objective Layer	Criterion Layer	Element Layer	Indicator Layer	Indicators Properties
Ecological environmental quality evaluation index system	Pressure	Resource pressure	Energy consumption per unit GDP	Negative Indicators
			Electricity consumption per unit GDP
			Water consumption per unit GDP
		Environmental pressure	Amount of plastic film used for agriculture per mu of land
			Fertilizer application per mu
			Amount of pesticide use per mu of land
			Private car ownership per unit GDP
	State	State of Resources	Forest coverage rate	Positive Indicators
			Water resources per capita
			Grain production per capita
		Environmental state	Greening coverage rate of built-up areas
			Park green space per capita
			Air quality in key cities reached and better than two days of the proportion of the year
	Response	Input Response	Water, environment, and public facilities management industry investment accounting for the proportion of the total social fixed asset investment	Positive Indicators
			Industrial pollution control completed investment accounting for the proportion of social fixed asset investment
			Forestry investment accounting for the proportion of social fixed asset investment
		Governance Response	Harmless treatment rate of domestic waste
			Urban sewage treatment rate
			Comprehensive utilization rate of general industrial solid waste
			Forest pest and rodent control rate
			Total afforestation area accounting for the proportion of the total area of the jurisdiction

Table 2. Results of Thiel index measurement.

Index	Thiel Index	Intra-Regional Differences							Inter-Regional Differences
Index	Thiel Index	North China	Northeast China	East China	Central and South China	Southwest China	Northwest China	Contribution Rate	Contribution Rate
2005	0.0048	0.0060	0.0007	0.0008	0.0005	0.0006	0.0036	40.50	59.50
2006	0.0048	0.0060	0.0007	0.0008	0.0005	0.0006	0.0036	40.50	59.50
2007	0.0052	0.0029	0.0003	0.0006	0.0001	0.0020	0.0058	34.50	65.50
2008	0.0042	0.0020	0.0003	0.0005	0.0003	0.0027	0.0044	37.05	62.71
2009	0.0051	0.0017	0.0006	0.0004	0.0003	0.0025	0.0057	32.87	67.13
2010	0.0046	0.0009	0.0005	0.0004	0.0005	0.0019	0.0066	34.93	65.07
2011	0.0048	0.0013	0.0001	0.0004	0.0013	0.0025	0.0072	40.46	59.54
2012	0.0042	0.0008	0.0003	0.0003	0.0003	0.0013	0.0061	31.84	68.40
2013	0.005	0.0015	0.0002	0.0004	0.0008	0.0019	0.0075	37.00	63.00
2014	0.0044	0.0012	0.0004	0.0002	0.0013	0.0008	0.0064	36.32	63.68
2015	0.0045	0.0007	0.0002	0.0004	0.0022	0.0013	0.0069	40.53	59.47
2016	0.0034	0.0007	0.0004	0.0003	0.0016	0.0006	0.0035	34.03	65.67
2017	0.0033	0.0009	0.0002	0.0005	0.0016	0.0012	0.0027	36.00	64.00
2018	0.003	0.0004	0.0001	0.0008	0.0016	0.0011	0.0018	33.44	66.56
2019	0.0022	0.0005	0.0002	0.0006	0.0014	0.0007	0.0016	38.57	61.43
2020	0.0026	0.0002	0.0003	0.0007	0.0017	0.0010	0.0024	41.09	58.91
2021	0.0041	0.0017	0.0004	0.0005	0.0010	0.0014	0.0047	36.85	63.13

Table 3. Results of the mediating effect test.

Subsystems	Mediation	Direct Effect	Mediation Effect	Percentage of Mediation Effect
Pressure ➝ Response	State	0.326 *	0.059 *	22%
Pressure ➝ State	Response	0.224 *	0.0512 **	30%
State ➝ Pressure	Response	0.0531 *	0.0142 **	37%
State ➝ Response	Pressure	0.341 ***	0.0126 *	4%
Response ➝ Pressure	State	0.0434 *	0.0098 **	3%
Response ➝ State	Pressure	0.191 ***	0.0075 *	4%

Note: *, **, *** significant at 0.1, 0.05, 0.01 level.

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Lv, J.; Zhou, W. Ecological Environmental Quality in China: Spatial and Temporal Characteristics, Regional Differences, and Internal Transmission Mechanisms. Sustainability 2023, 15, 3716. https://0-doi-org.brum.beds.ac.uk/10.3390/su15043716

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Lv J, Zhou W. Ecological Environmental Quality in China: Spatial and Temporal Characteristics, Regional Differences, and Internal Transmission Mechanisms. Sustainability. 2023; 15(4):3716. https://0-doi-org.brum.beds.ac.uk/10.3390/su15043716

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Lv, Jiehua, and Wen Zhou. 2023. "Ecological Environmental Quality in China: Spatial and Temporal Characteristics, Regional Differences, and Internal Transmission Mechanisms" Sustainability 15, no. 4: 3716. https://0-doi-org.brum.beds.ac.uk/10.3390/su15043716

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Ecological Environmental Quality in China: Spatial and Temporal Characteristics, Regional Differences, and Internal Transmission Mechanisms

Abstract

1. Introduction

2. Literature Review

3. Research Hypotheses and Construction of Index System

3.1. Theoretical Analysis and Research Hypotheses

3.2. Construction of the Indicator System

3.2.1. Selection of Pressure Indicators

3.2.2. Selection of State Indicators

3.2.3. Selection of Response Indicators

4. Materials and Methods

4.1. Data Source

4.2. The “Vertical and Horizontal Layer by Layer” Scatter Degree Method

4.3. The Thiel Index

4.4. Mediation Effects Model

5. Results and Interpretation

5.1. Spatial and Temporal Evolutionary Trends of Ecological Environmental Quality Based on PSR Model

5.1.1. Results of Ecological Environmental Quality Index Measurement Based on PSR Model

5.1.2. Spatial and Temporal Evolution Trends of Ecological Quality in China

5.2. Regional Differences in Ecological Quality

5.2.1. Results of Regional Differences in Ecological Quality

5.2.2. Regional Differences in Ecological Quality in China

5.2.3. Regional Differences in Ecological Quality

5.3. Internal Transmission Mechanisms of the Ecosystem Quality System

5.3.1. Measurement Results of Mediation Effect

5.3.2. Analysis of the Internal Transmission Mechanism of the Ecosystem Quality System

6. Discussion and Conclusions

6.1. Conclusions

6.2. Policy Implications

6.3. Limitations and Prospects

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

Appendix A

Appendix A.1. The “Vertical and Horizontal Layer by Layer” Scatter Degree Method

Appendix A.2. Decomposition of Thiel Index

Appendix A.3. Methods to Test for Mediating Effect

Appendix B

References

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