Analysis of Tuojiang River Basin’s Green Development Level and Difference Based on PSR Model

. Since the the policy that, building a beautiful Sichuan and Tuojiang River green development economic belt has been launched, the Tuojiang River Basin has become one of the most important river basins in Sichuan. Building the Tuojiang River Basin as a pioneer area for green development is of vital signiﬁcance. This paper selected ﬁve cities as the research objects, which are Zigong, Luzhou, Deyang, Neijiang, and Ziyang. Firstly, the paper analyzed the factors a ﬀ ecting the green development of the Tuojiang River Basin, and then combined the PSR model with the “economy-society-resources-nature” indicators to construct a green development indicator system suitable for the Tuojiang River Basin. After that, the entropy weight-grey relational analysis and spatial autocorrelation analysis were used to analyze the green development evaluation system. The results show that average green development levels of Zigong, Luzhou, Deyang, Neijiang and Ziyang are 0.7263, 0.8498, 0.4357, 0.6890 and 0.6856 respectively. Through the spatial analysis, the Moran’s I is 0.104, indicating a signiﬁcant positive spatial correlation of green development in the Tuojiang River Basin. And the green development level of Ziyang belongs to the high-low agglomeration type. The rest of the areas are not signiﬁcantly aggregated, and ﬁnally suggestions were proposed for improvement.


Introduction
In recent decades, countries are increasingly focusing on environmental protection in pursuit of economic development. The problems of steady deterioration of the ecological environment, global warming, and unsustainable use of the natural resources are still serious. Green development has been proposed as a concept for pursuing the harmonious coexistence between human beings and the natural environment by constructing an ecologically civilized society. The concept of green development has been deeply rooted in the hearts of the whole society and become an important global development trend. Many countries and regions have widely incorporated green development into various fields [1][2][3] as an important initiative to promote economic restructing.
Research on green development in China and foreign countries began with the socialeconomic-natural composite ecosystem theory proposed by Chinese ecologist Shijun Ma in 1984 and the concept of "green economy" proposed by British environmental economist David Pearce in 1989 [4]. The research on green development mainly focuses on the concept, efficiency, industry, dynamic mechanism, policy system and evaluation of green development. Hsu et al. [5]measured the relationship between green innovation and the performance of financial development by using an econometric estimation. Gan et al. [6] calculated the green logistics efficiency of 11 cities in Jiangxi Province by three-stage DEA. Among the relevant studies on green development evaluation, Luo et al. [7] established a set of innovative index system and HFFS-MULTIMOORA method based group decision-making framework to comprehensively evaluate the green development level of five provinces and municipalities along the Yangtze River Economic Zone, China. Yang et al. [8] explored a new evaluation indicator system from the four dimensions of energy conservation, emission reduction, efficiency increase and harmony. Ren et al. [9] used the analytic hierarchy process-drivingpressure-state-response (AHP-DPSR) model to evaluate the ecosystem health of the Liaohe River Basin in Jilin Province, China. Foroozesh et al. [10] analyzed the sustainable urban development based on a hybrid decision-making approach(group fuzzy BWM, AHP, and TOPSIS-GIS). Barika et al. [11] employed statistical analysis such as ANOVA, Pearson's correlation, principal component analysis (PCA), and discriminant analysis (DA) to conduct a green assessment of coastal regions and freshwater lake basins. Chen et al. [12] used the super-efficient SBM model and Malmquist index to measure the green development efficiency of Chengdu-Chongqing economic circle from 2007 to 2019 and analyzed its spatial and temporal evolution characteristics and influencing factors.
At present, the indicator system evaluation method is widely used in green development evaluation, mainly including principal component method, pressure-state-response (PSR) evaluation method model, and driving force state response (DFSR) model and so on. Among them, PSR model proved to be effective in the field of ecology and environment. Zhou et al. [13] combined the logarithmic mean divisia index (LMDI) and the PSR framework to analyze the change of ecosystem service value (ESV). Ding et al. [14] innovatively introduced PSR model to analyze the internal coupling mechanism of water-energy-food-ecology system. Lai et al. [15] selected a total of 15 indicators to construct the ecological security evaluation system based on the PSR model to study the ecological status of Fuzhou. However, the selection of green evaluation indicators is mostly inclined to economic indicators ignoring some resource and nature statistical data. The factors affecting green development have not been comprehensively and systematically sorted out. Few studies have been able to integrate "economy-society-resources-nature" into evaluation systems.
Tuojiang River Basin, also known as Tuojiang Green Development Economic Belt, is the first economic belt to be built in Sichuan in 2022 based on the river basin. With an area of 21,800 square kilometers, Tuojiang Green Development Economic Belt is formed by the county-level administrative regions through which the main stream of the Tuojiang River and the first-level tributaries of the Tuojiang River pass. It mainly involves 28 counties in six cities (Chengdu, Deyang, Ziyang, Neijiang, Zigong, and Luzhou) in the Tuojiang River Basin, including more than a thousand large and medium-sized industrial plants. At the same time, it is also the largest cotton and sugar cane producing area in Sichuan. However, as one of the most important watersheds in Sichuan province, the Tuojiang River Basin suffers from serious water pollution, and the water quality monitoring shows the water environment is deteriorating. This reality is contrary to the policy of building a beautiful Sichuan, so it is urgent to promote the green development of the Tuojiang River Basin. Among the green development evaluation studies related to the Tuojiang River Basin, most studies have focused on water quality evaluation, but few studies have been conducted to evaluate the green development level of cities in the Tuojiang Green Development Economic Belt.
Based on above, this paper took five representative cities in the Tuojiang River Basin as the research objects, considered the special characteristics of green development in different regions, and innovatively combined the PSR model with "economy-society-resources-nature" to build a green development level evaluation system. The entropy weight-grey relational method and spatial autocorrelation analysis were used to study the green development level of cities and the differences among regions from 2016 to 2020. This paper provides the following main contributions:(1)We created a new green development evaluation index system based on the existing research indicators combined with policies, which facilitated a comprehensive evaluation and enriched the green development evaluation index system to some extent. (2)We selected the Tuojiang River Basin Economic established in 2022 Belt as the novel research object, which broadens the applicability objects of the entropy weight-grey relational method.(3)We explored the differences in the level of green development in various regions of the Tuojiang River Basin and conducted spatial correlation analysis, in order to provide theoretical support and decision-making basis for the Tuojiang Green Development Economic Belt.
This paper is structured as follows. Section 2 describes the methodology and model used in the study. Section 3 points out the specific steps for the construction of the green development evaluation index system. Section 4 details and analyzes the experimental results. Section 5 summarizes the paper and offers some proposals.

PSR Model
PSR (Pressure-State-Response) model is widely used in the fields of sustainable utilization, sustainable development and environmental evaluation. It includes three aspects of pressure, state and response. Pressure indicators are ecological stress indicators, like natural disasters or human activities that impose a burden on the ecological environment. State indicators are indicators that describe the ecological health state due to changes in the ecological state caused by stress indicators. Response indicators are indicators of human or natural countermeasures to improve ecological degradation and describe ecological sustainability [16]. PSR model can reflect the relationship between ecosystems and human activities, emphasizing the causal link between them. The green development of the Tuojiang River Basin is consistent with the PSR model, and the establishment of the PSR model fully considers the internal relations between human factors and natural factors, as well as the impact caused by human factors on the development of the Tuojiang River Basin. The PSR model framework is shown in figure. 1.

Entropy Weight-grey Relational Method
(1) Entropy weight method The entropy weight method is an objective method of determining the weights of indicators, which is based on the principle of using the dispersion of the established indicators to determine the weights of different indicators. The calculation steps are as follows [17]:

R=
The selected indicators are analyzed and classified into positive and negative correlation indicators according to the nature of the indicators. The higher the value of positively correlated indicators, the higher the level of green development. The lower the value of negatively correlated indicators, the higher the level of green development. The standardized formulas are shown in Eq. (1) and Eq. (2) below, respectively, where x i j refers to the raw data.
• Calculate the proportion of different indicators P i j .
• Calculate the entropy value of the j-th indicator.
Among them K = 1/ ln n(K > 0, 0 ≤ P i j ≤ 1), assuming that P i j = 0, P i j ln P i j = 0. • Determine the final weight .The coefficient of variation of the j-th indicator is firstly calculated using the formula ,and then the weights of each indicator are obtained.
(2) Grey relational analysis The grey relational analysis method is used to quantitatively describe and compare the development trend of a system, using the similarity degree of the set shape of the sequence curve to judge the importance of its relation [18]. This method has very low requirements for sample size of data, with no distribution assumptions. And it can perform quantitative analysis well.
Assuming that the indicator data is x i , and the index sequence is is the optimal value of the kth index, and the optimal sequence is x 0 = x 0 (1), x 0 (2), · · · , x 0 (n). (k = 1, 2, ..., n, i = 1, 2, · · · , m), m is the number of evaluation objects. n is the number of indicators). The calculation steps are as follows: • Dimensionless processing of data. The interval valorization operator is used according to the actual situation of the data selected in this paper.
• Calculate the correlation coefficient ξ i (k), and grey correlation degree γ i (k) is obtained according to w j .

Spatial Autocorrelation Analysis
Spatial correlation refers to the fact that the same indicator in different spatial locations will have certain spatial properties. When the indicators show spatial similarity, they are positively spatially correlated. When differences occur, they are negatively spatially correlated. The global spatial autocorrelation is often reflected by the Global Moran's I coefficient, which reflects the dispersion effect of the study area unit. The calculation formula is as follows: In Eq. (11), I is the Moran index. n is the number of cities.
x i x j is the green development level of the five cities. W is the spatial weight matrix, which represents the location proximity relationship of the five cities. W i j indicates the proximity of city i to city j. Local spatial autocorrelation can effectively determine the degree of aggregation or dispersion in a local area. This paper obtains a LISA clustering map based on the local Moran's I coefficient to explore the spatial agglomeration of the green development level of each region and its adjacent areas [19].

Selection and Determination of Evaluation Indicators
Green development should be based on the principle of protecting the environment and promoting economic development within the limits of resource carrying capacity, which can efficiently maintain a balance between environmental protection and economic development. Therefore, considering the current situation of green development in the Tuojiang River Basin, the green development indicators were sorted out from four aspects of "economysociety-resources-nature", guided by the reference to the literature on green development evaluation system and the Green Development Index System issued by the General Office of the CPC Central Committee and the General Office of the State Council. The literature with the theme of "green development" and keywords of "green development indicators" was searched through the advanced search of CNKI, and the text of the indicator system was retained. The number and frequency of different indicators in scholars' green development evaluation studies were obtained by running Visual Studio. Accordingly, some indicators with relatively low occurrence and frequency, such as precipitation, natural population growth rate, etc., can be reasonably excluded. This article innovatively combined the four major sectors of "economy-society-resourcesnature" with the PSR model. Judging from the internal causality of the PSR model, human activities affect the ecological environment of the Tuojiang River Basin, leading to certain changes in the ecological status of the Tuojiang River Basin. Due to changes in ecological conditions, humans or the environment respond passively or actively to alleviate or improve the ecological conditions in order to promote the green development of the Tuojiang River Basin. The PSR model not only fits the characteristics of the green development of the Tuojiang River, but also encourages managers to regulate the ecological environment of the Tuojiang River Basin. On the whole, it can continuously optimize the decision to improve the level of green development of the Tuojiang River Basin. The green development level evaluation index system is shown in table 1.

Data Source and Processing
The Tuojiang River Basin covers a large area, and it is difficult to collect data. Therefore, this paper focuses on five representative cities in the Tuojiang River Basin in Sichuan (Luzhou, Deyang, Ziyang, Zigong, and Neijiang) to evaluate the green development level. In order to ensure the validity of the research, relevant data of five cities from 2016 to 2020 were collected from the Sichuan Provincial Statistical Yearbook and regional bulletins, and the data were preprocessed according to the requirements of entropy weight method and grey relational method, so as to facilitate the subsequent analysis.

Evaluation of Green Development Level
We used Python to calculate the entropy weight of each indicator of green development level of five cities in the Tuojiang River Basin from 2016 to 2020, and the specific data are shown in Table 2. The weights of 17 indicators are within the range of 0.0161-0.1297, and the overall distribution is relatively even, which indicates that the indicators are selected reasonably. The weights of the pressure layer, state layer and response layer are 38.89%, 36.42% and 24.69% respectively, indicating that both the pressure layer and state layer play an important role in the green development of the Tuojiang River Basin.
According to the weights of green development evaluation indicators in the Tuojiang River Basin listed in table 2, we calculated the grey correlation degree of green development in five representative cities during 2016-2020, as shown in table 3. The greater the grey correlation degree, the closer the data series is to the optimal data series, and the higher the green development level of the city is. The green development level of each city has been relatively stable over the five years. Except for Deyang and Ziyang, the green development level of the other three cities has slightly increased. The average green development levels of the five cities are 0.7263, 0.8498, 0.4357, 0.6890 and 0.6856, respectively.

Analysis of Differences in Green Development Levels
According to the grey correlation of the five cities, it can be seen that the level of green development in the five cities varies greatly. The calculation results show that the green development levels from the highest to the lowest are Luzhou, Zigong, Neijiang, Ziyang and Deyang. Except for Luzhou whose green development score is greater than 0.8, the green development scores of Zigong, Neijiang and Ziyang are around 0.7, and the green development levels of Neijiang and Ziyang are very close. Deyang has the lowest green development level of 0.4357. Compared with the other four cities, Deyang has the highest GDP per capita and gross production value. In addition, it has a higher daily water consumption per capita, a lower forest coverage rate and a lower proportion of education spending in fiscal expenditure. Although all five cities are in the Tuojiang River Basin, the level of green development varies greatly. So the five cities can learn from each other's development experience, and jointly contribute to the sustainable development of the region. Using GIS software, we obtained the green development level location map of the five cities, as shown in figure. 2.
Through the PSR model, the weighted scores of the pressure layer, state layer and response layer of the five cities in the five-year period were obtained respectively. Except for Luzhou, which has a higher score in the state layer than the pressure layer and response layer, the other four cities have the highest to lowest scores in the pressure layer, state layer and response layer. Although Luzhou has a high level of green development, it has a low score   in the pressure layer. Both the significant gap between rich and poor, and an unbalanced and insufficient development prove serious ecological stress and high pressure on green development. Deyang city has a lower score in the state level, which indicates that its ecological pollution state is more serious, and it pursues urban economic development unilaterally while ignoring green environment construction and protection. The PSR stratified weighted score map of five cities is shown in figure. 3.

Spatial Autocorrelation Analysis of Green Development Level
In this paper, GeoDa software was applied to analyze the spatial global correlation and local correlation. Using the Euclidean distance as the weight, we calculated the Moran index of the green development level of the five cities in the Tuojiang River Basin. The Z value reaches within the threshold of rejecting the original hypothesis, and the P value meets the test of significance level of 0.05. The Moran index is 0.104, which indicates that there is a significant positive spatial correlation of green development in the Tuojiang River Basin, and the five cities have an aggregation trend.  The global spatial autocorrelation shows whether there is agglomeration in the region, while the local spatial autocorrelation explains its specific spatial location and the significance of the agglomeration [20]. Local correlation refers to the analysis of the correlation of variable values in a single city in the spatial domain. The local LISA diagram is shown below. It can be seen that among the five cities in the Tuojiang River Basin, the spatial correlation is insignificant for all four cities except Ziyang, which is surrounded by areas with a lower green development level and has a negative spatial correlation. From the perspective of the nature of the correlation, Ziyang City belongs to the type of high-low agglomeration and is a high-value heterogeneous center. It is in the development stage of gathering various elements of the surrounding cities, and relatively crowding the resources and development space of the surrounding cities, resulting in a significant difference between its green development level and that of surrounding cities. Results of spatial autocorrelation analysis of the Tuojiang River Basin are shown in figure. 4.

Conclusion and Suggestions
Considering the actual situation of green development in the Tuojiang River Basin, we selected five typical cities in the Tuojiang River Basin, and combined the PSR model with the four dimensions of "economy-society-resources-nature" to construct an evaluation index system for green development in the Tuojiang River Basin. Then the entropy weight-grey relational method was applied to evaluate the green development level of five cities in the Tuojiang River Basin, and then the difference analysis and spatial analysis were carried out. (3) There is a significant positive spatial correlation of green development in the Tuojiang River Basin, and the green development level of Ziyang city belongs to the high-low aggregation type. The rest of the areas are not significantly aggregated.
By analyzing the current situation of green development in five typical cities in the Tuojiang River Basin, this paper puts forward the following suggestions: (1) Green development should focus on relieving traditional economic pressure, and shouldn't seek for short-term rapid economic growth in a radical development mode while ignoring environmental cost. For example, while focusing on economic development, Deyang has high per capita water consumption and low forest coverage. While improving the ecological environment, it is necessary to pay attention to the detection of changes in the green state. Cities can respond and adjust in a timely manner according to the state changes. (2) To promote a steady increase in the level of green development, cities in the Tuojiang River Basin need to pay more attention to green development, improve the institutional mechanism, strengthen policy guidance, and optimize industrial structure. (3) It is necessary to strengthen the coordinated development of cities. Ziyang City can serve as a gathering center to play a gathering effect, leading cities in overcoming their own weaknesses by acquiring other's strong points. Cities with a higher level of green development need to maintain their advantages and use their development practical experience to drive the steady improvement of surrounding area.