*Article* **Does High-Speed Railway Influence Convergence of Urban-Rural Income Gap in China?**

#### **Weidong Li 1 , Xuefang Wang 1 and Olli-Pekka Hilmola 2,\***

Received: 27 March 2020; Accepted: 18 May 2020; Published: 21 May 2020

**Abstract:** Transportation is an important factor affecting the balance of regional economic pattern. The construction of high-speed railway enhances the mobility of population, capital, technology and information resources between urban and rural areas. Will it further affect the income gap between urban and rural areas? Based on the nonlinear time-varying factor model, this paper analyzes the convergence of urban-rural income gap with the angle of high-speed railway. After rejecting the assumption of overall convergence in the traditional four economic regions, three convergence clubs of urban-rural income gap were found. For these ordered logit regression model is used to explore the initial factors that may affect the formation of "convergence club". Empirical results show that the construction of High-speed railway has effectively narrows the urban-rural income gap in China, but it is not the cause of the formation of the three convergence clubs. The convergence effect of High-speed railway on the urban-rural income gap in China is still relatively weak.

**Keywords:** high-speed railway; income gap; club convergence; nonlinear time-varying factor model

#### **1. Introduction**

Since the reform and opening up, China's economy has maintained rapid growth. The income level of residents in urban and rural areas also has increased significantly. In recent years, China's urban-rural income gap has shown a long-term gradual decline, but in absolute terms, it is still substantial. According to the National Bureau of Statistics, the disposable income ratio of urban and rural residents in China during 2017 was 2.71:1 [1]. Although it has declined compared with previous years, it is still one of the important factors that constraints China's economic development. In 2014, China's economic development has entered a new normal era. Under the background of the new economic development in the future, China's economic development will pay more attention to structural adjustment, regional development and income distribution. Under the grim situation of China's urban-rural income gap, it is debated how to find a solution. Approaching this gap is an important issue of concern to academics and government.

Due to the complexity of economic development and the variability of social conditions, there are many factors affecting the income gap between urban and rural areas. In the past decade, China's high-speed railway has developed rapidly. Chen et al. (2018) used high-speed rail parking frequency data in 274 cities in China from 2007 to 2014 to examine the impact of high-speed rail development on the income gap between urban and rural residents. The research results show that the development of high-speed rail is generally conducive to the narrowing of the income gap between urban and rural residents in China. Moreover, population flow and capital flow are important mechanisms for

the development of high-speed rail to narrow the income gap between urban and rural residents in China [2].

Does high-speed railway affect the income gap between urban and rural areas to form a convergence club? The research goal of this paper is to evaluate the impact of the opening of high-speed railway on urban-rural income gap based on club convergence and deeply analyze the mechanism of high-speed railway's economic convergence effect on urban-rural income gap. The high-speed railway is both capital-intensive and labor-intensive large-scale transportation infrastructure. Its investment in construction and operation can have a profound impact on social and economic activities. The purpose of this paper is to find the convergence characteristics of urban-rural income gap under high-speed rail network situation. The contributions of the paper are as follows. At first, the paper firstly discusses the impact of the opening of high-speed railway on club convergence of urban-rural income gap in China with the nonlinear time-varying factor model. Secondly, three convergence clubs of urban-rural income gap in China are found. Thirdly, it is found that the construction of High-speed railway has effectively narrows the urban-rural income gap in China, but it is not the cause of the formation of the three convergence clubs. The convergence effect of High-speed railway on the urban-rural income gap in China is still relatively weak. It will enrich the impact of traffic on regional balance development. At the same time, it provides reference for alleviating the income gap between urban and rural areas in China. It also provides a policy basis for the implementation of regional balanced development strategy.

The rest of this research is organized as follows: In Section 2 we provide literature review follows, and Section 3 describes heterogeneity of urban-rural income gap within China's cities. The convergence club identification is being introduced in Section 4. The causes of convergence club are discussed in Section 5. Finally, conclusions are drawn in Section 6.

#### **2. Literature Review**

High-speed rail is a type of rail transport that operates significantly faster than traditional rail traffic. New lines in excess of 250 km/hr and existing lines in excess of 200 km/hr are considered to be high-speed rail [3]. High-speed railway has brought great impact on many aspects of social & economic life. Many scholars have conducted numerous research works on the influence of HSR. Zhang, et al., and Chai, et al. focus on the impact of HSR on aviation [4–6]. Li, et al., and Vickerman, et al. Discuss on the impact of HSR on economic growth [7–16]. Besides, some scholars pay attention to the social impact of HSR [17,18]. Chen et al., and Jiang, et al. take explorative analysis on the impact of HSR on regional income disparities and found that HSR can accelerate regional economic convergence and reduce the regional income disparities [19,20].

With regard to the urban-rural income gap, Chinese scholars have carried out many constructive studies on this theme. The researches mainly focus on the status quo of urban-rural income gap, analysis of its causes, measurement and convergence issues In the related research on the convergence of urban-rural income gap, Wang et al. (2016) used the panel data of 71 counties in Wuling Mountain District from 2000 to 2012 to empirically examine the urbanization process, financial development and financial expenditure. Other factors promote the convergence of urban-rural income gap [21]. Deng (2016) used Hansen's panel threshold model to construct a nonlinear panel threshold model using China's 1997-2014 provincial panel data to study the impact of urbanization and industrialization on urban-rural income gap [22]. Song and Wang (2018) used data in 262 prefecture-level cities from 2000 to 2016 to explore the dynamic convergence of urban-rural income gap from the perspective of urban scale and analyze the influences of urban-rural income gap between population migration and household registration [23]. Wu (2019) used panel data from 1999 to 2017 in 30 provinces in China to construct a convergence model between government investment and urban-rural income gap. Research shows that the convergence of government investment structure promotes the convergence of urban and rural income gaps. Therefore, preventing government investment from being too fast can effectively reduce the urban-rural income gap [24]. Xu and Zhang (2019) used panel data from 30 provinces other than Tibet in China from 2000 to 2016 to analyze the dynamic evolution of China's

urbanization and urban-rural income gap. The spatial and temporal effects of new urbanization on China's urban-rural income gap are also discussed. The study clarifies whether the transformation of urbanization can promote the convergence of the urban-rural income gap in China [25]. Besides China, some scholars conduct research on club convergence about income in other regions. Bartkowska, M., & Riedl, A. (2012) used two-step method and ordered logit model to analyze convergence clubs in per capita incomes of European regions. It is found European regions form six separate groups converging to their own steady state paths. The level of initial conditions such as human capital and per capita income plays a crucial role in determining the formation of convergence clubs among European regions [26]. Canova, F. (2004) developed a predictive density approach to identify convergence clubs in Organization for Economic Co-operation and Development (OECD) countries [27]. Galor, O. (2007) used Unified Growth Theory to explain the emergence of convergence clubs [28]. and temporal effects of new urbanization on China's urban-rural income gap are also discussed. The study clarifies whether the transformation of urbanization can promote the convergence of the urbanrural income gap in China [25]. Besides China, some scholars conduct research on club convergence about income in other regions. Bartkowska, M., & Riedl, A. (2012) used two-step method and ordered logit model to analyze convergence clubs in per capita incomes of European regions. It is found European regions form six separate groups converging to their own steady state paths. The level of initial conditions such as human capital and per capita income plays a crucial role in determining the formation of convergence clubs among European regions [26]. Canova, F. (2004) developed a predictive density approach to identify convergence clubs in Organization for Economic Cooperation and Development (OECD) countries [27]. Galor, O. (2007) used Unified Growth Theory to explain the emergence of convergence clubs [28]. In the study of the convergence of urban-rural income gap, most scholars focus on the impact of

In the study of the convergence of urban-rural income gap, most scholars focus on the impact of urbanization, population migration, regional economic growth, human capital and other factors on the convergence of urban-rural income gap [29–38]. However, the impact of high-speed rail on the convergence of urban-rural income gap has not yet been discussed. The research will shed some light on the understanding of how HSR influence the convergence of urban-rural income gap. The research results can provide reference for investment construction of high-speed rail and narrowing the income gap between urban and rural areas in China. urbanization, population migration, regional economic growth, human capital and other factors on the convergence of urban-rural income gap [29–38]. However, the impact of high-speed rail on the convergence of urban-rural income gap has not yet been discussed. The research will shed some light on the understanding of how HSR influence the convergence of urban-rural income gap. The research results can provide reference for investment construction of high-speed rail and narrowing the income gap between urban and rural areas in China.

#### **3. Heterogeneity of Urban-Rural Income Gap in China's Cities 3. Heterogeneity of Urban-Rural Income Gap in China's Cities**

The factors affecting the income gap between urban and rural areas arise from many aspects such as nature, economy, society and culture [39–41]. Consumption expenditure ratio of urban and rural residents, urbanization level, population migration, regional economic growth and human capital are all important factors affecting the income gap between urban and rural areas. Based on the feasibility of data collection, we first calculate the mean and coefficient of variation of the urban-rural income gap of 274 prefecture-level cities in China (see Figure 1). The ratio of the per capita disposable income of urban residents to the per capita disposable income of rural residents is used as a measure of the income gap between urban and rural areas. The selected research period is from 2005 to 2017. The data are collected from the China Regional Statistical Yearbook and China's City Statistical Yearbook. The factors affecting the income gap between urban and rural areas arise from many aspects such as nature, economy, society and culture [39–41]. Consumption expenditure ratio of urban and rural residents, urbanization level, population migration, regional economic growth and human capital are all important factors affecting the income gap between urban and rural areas. Based on the feasibility of data collection, we first calculate the mean and coefficient of variation of the urbanrural income gap of 274 prefecture-level cities in China (see Figure 1). The ratio of the per capita disposable income of urban residents to the per capita disposable income of rural residents is used as a measure of the income gap between urban and rural areas. The selected research period is from 2005 to 2017. The data are collected from the China Regional Statistical Yearbook and China's City Statistical Yearbook.

**Figure 1.** Mean and coefficient of variation of urban-rural income gap in 274 administrative regions in China. **Figure 1.** Mean and coefficient of variation of urban-rural income gap in 274 administrative regions in China.

From Figure 1 it can be identified the average income gap between urban and rural areas that the urban-rural income gap in China continued to grow before 2007. However, it has showed clear downward trend thereafter. In terms of the difference in urban-rural income gap, the coefficient of variation is an indicator reflecting σ convergence. The larger the value, the higher the dispersion of urban-rural income gap among cities. The trend of coefficient of variation of urban-rural income gap in prefecture-level cities in China has experienced a process of increasing first and then decreasing. The coefficient of variation reached its highest value in 2007. It shows that the difference in urban-rural income gap between cities shows a process of σ convergence.

#### **4. Convergence Club Identification**

The basic concept of convergence came first from Solow's neoclassical growth model (Solow, 1956), starting with Baumol (1986) [42,43]. It has been used in different economic processes involving groups of regions or units. Generally there are three types of convergence: β-convergence, σ convergence and club convergence. β-convergence implies that the units analyzed converge to one another in the long run. σ convergence implies convergence that is conditional on the units having similar characteristics. Club convergence means that a set of economies with similar conditions and structural characteristics will tend to converge to the same steady state [44]. Phillips and Sul (2007) proposed the concept of relative convergence, which considers the transition path of each country together with its growth performance to find convergence [44].This concept can be easily extended to the field of convergence in income gap.

#### *4.1. Club Convergence Test Method Based on Nonlinear Time-Varying Factor Model*

This paper first explains the logt test method based on nonlinear time-varying factor model [45]. Then the paper gives the steps to identify the convergence club according to the logt test method. The identification steps of the convergence club was obtained by simplifying on the basis of Phillips and Sul (2007) [44].

#### 4.1.1. Logt Test

At first a simple factor model is built as follows:

$$\mathcal{X}\_{\rm it} = \delta\_{\rm i} \mu\_{\rm t} + \varepsilon\_{\rm it} \tag{1}$$

The model is designed to capture the evolution of individual Xit with respect to µt under the influence of two heterogeneity factor system terms δi and error term εit. Among them, δi measures the heterogeneity distance between the public factorµt and the system part Xit, µt represents the common behavior of Xit aggregation, which reflects the influence of individual behavior plus common factors. It should be pointed out that although the system termδi expresses the heterogeneity of individuals, this heterogeneity does not change with time. Therefore, when considering the heterogeneity of individuals with time-varying characteristics, the following improvements are needed:

$$\mathcal{X}\_{\rm it} = \delta\_{\rm it} \mu\_{\rm t} \tag{2}$$

Among them, δit expresses the change of individual heterogeneity with time, including the random component εit. Therefore, equation (2) contains the time-varying characteristics of individual heterogeneity and the common trend term of the system, which is the nonlinear time-varying factor model.

To model the time-varying parameter δit, we define a relative transfer coefficient:

$$\mathbf{h\_{it}} = \frac{\mathbf{x\_{it}}}{\mathbf{N}^{-1} \sum\_{i=1}^{N} \mathbf{x\_{it}}} = \frac{\boldsymbol{\delta\_{it}}}{\mathbf{N}^{-1} \sum\_{i=1}^{N} \boldsymbol{\delta\_{it}}} \tag{3}$$

$$\mathbf{H}\_{\rm t}^{2} = \mathbf{N}^{-1} \sum\_{i=1}^{N} \left(\mathbf{h}\_{\rm it} - \mathbf{1}\right)^{2} \tag{4}$$

Specifically, the hit relative transfer coefficient, Ht 2 is its variance. Since the common growth path, that is, the common factor portion is eliminated, when the convergence occurs, hit→1, Ht 2→0.

Further, in order to verify the null hypothesis of convergence, it is also necessary to construct a semi-parametric model:

$$
\delta\_{\rm it} = \delta\_{\rm i} + \delta\_{\rm i} \zeta\_{\rm it}/\mathcal{L}(\mathbf{t}) \mathbf{t}^{-\infty} \tag{5}
$$

where δi is fixed and does not change with time t; σi is a heterogeneous scale parameter; ξit is subject to iid(0,1); L(t) is a slowly changing function and when t→∞, L(t)→∞; α is the decay rate. The semi-parametric model shows that as long as α≥ 0 is satisfied, δit→δi , that is, the convergence is established.

Based on the above derivation process, the null hypothesis of the test convergence can be written as follows:

$$\mathbf{H}\_0 \mathbf{:} \delta\_\mathbf{i} = \delta \text{ and } \mathbf{a} \ge \mathbf{0} \tag{6}$$

$$\mathbf{H}\_{\rm A} \colon \delta\_{\mathbf{i}} \neq \boldsymbol{\delta} \text{ or } \boldsymbol{\alpha} < 0 \tag{7}$$

In order to verify the original hypothesis of convergence, regression is performed using the following formula:

$$
\log\left(\frac{\text{H}\_1}{\text{H}\_t}\right) - 2\log\text{L}(\text{t}) = \text{a} + \text{blog}\text{t} + \mu\_\text{t} \tag{8}
$$

where L(t) = log(t + 1); t = [γT], [γT] + 1,...,T; γ is the parameter determining the starting time t, according to Monte Carlo simulation experiment of Phillips and Sul. As a result, γ = 0.3, a = 0.5b, and then heteroscedasticity and autocorrelation (HAC) one-sided T test. If tb ≥ −1.65, the null hypothesis cannot be rejected. Otherwise the null hypothesis is rejected. Since the above method utilizes a cross-sectional variance ratio to perform a linear regression of the logt time series, this convergent regression test is called a logt test.

#### 4.1.2. Convergence Club Identification Step Based on Logt Test

When the null hypothesis of the convergence of all cities is rejected, it can be further identified according to the principle of logt test whether there is a convergence club. The identification method is an endogenous convergence club identification method. It is based entirely on urban-rural income disparity data for each city and is not based on any external criteria such as geographic zoning. The specific identification steps are as follows:

Step 1: Sort the individual segments. The basis is:

$$\left(\text{T}-\text{[Ta]}\right)^{-1}\sum\_{\text{t=[Ta]}+1}^{\text{T}}\chi\_{\text{it}\prime}\mathbf{a} = 1-\text{f}\tag{9}$$

The observations are sorted in descending order, and the time span f of the observations is selected. The value of f is chosen as 1/2.

Step 2: Identify the initial core group. Select the first two cities in the results of step 1 and do the logt test. If the original hypothesis is rejected, the first city is removed, the second and third cities are grouped into new groups, and the logt test is performed. Repeat above processes until you find two cities that cannot reject the null hypothesis. These two cities are the initial core groups and jump to step 3. If you traverse all two cities, you can't find the initial core group, indicating that there are no converging clubs and all cities are not aligned with each other.

Step 3: Identify the new core group members. According to the ranks arranged in the first step, the cities that have not been inspected together with the initial core group members are added to the

initial core group one by one for logt test. In order to cluster members of similar nature as much as possible, the standard c of the selected club is set. c will be significantly higher than the threshold of the convergence club - 1.65, generally recommended c = 0. Here c is chosen as 0. According to this cycle, after traversing all cities, all members of the core group are identified, and one convergence club is obtained. Phillips recommends numerical simulations, selecting a 50% significance level for a sample of 20 or 50; a 40% significance level for a sample of 100; and a 20% significant level for a sample of 200. The significant level chosen in this paper is 20%.

Step 4: Do a logt test on all cities that do not belong to the Convergence Club in Step 3. If you cannot reject the null hypothesis, then these cities are considered as a convergence club. If you reject the null hypothesis, repeat steps 1 through 3. Circulate this until you can identify all existing convergence clubs.

Step 5: Convergence clubs formed according to the clustering process are carried out with the members entering the core group criteria. Therefore, at a significant level of 5%, there may still be convergence between different clubs, which need to be combined to merge the clubs with tk > −1.65 into one group. The specific integration methods are as follows:

The first is the column value. In the club group, you get M Convergence Clubs: Club1, ..., ClubM. A logt test is performed for every two adjacent clubs. Let m = 1, 2, ..., M−1, and obtain the tm value of the (M−1) group. The second is the combination: starting from the first group, if tm > −1.65 is satisfied and tm > tm+1, the two groups are merged and the first step is repeated; if not, the remaining group remains as a single group.

#### *4.2. Identification of the Convergence Club*

#### 4.2.1. Judgment of Overall Convergence

Before identifying the Convergence Club, the overall urban-to-urban income gap convergence was judged by performing logt tests on 274 prefecture-level cities in China. Using the methods and steps in Section 4.1, 274 cities were fitted to obtain the following estimation results.

$$\begin{aligned} \log\left(\frac{\text{H}\_{\text{I}}}{\text{H}\_{\text{I}}}\right) - 2\text{logt} &= 0.4934 - 0.3163 \text{logt} \\ \quad &(5.34) \ (-3.20) \end{aligned} \tag{10}$$

It can be seen that b = −0.3163, tb = −3.2, far less than the convergence threshold of −1.65 at the significant level of 5%. Therefore, there is no overall convergence in the urban-rural income gap in China. It is necessary to explore whether there is a region where economic growth converges to the club. According to the division standard of the National Bureau of Statistics for economic regions in 2011, the convergence test of the clubs in the four major economic zones of northeast, eastern, central and western regions was obtained with the regression results shown in Table 1. It can be found that there is a convergence of the urban-rural income gap in the northeastern region and the western region, while the eastern and central regions do not have the convergence of the urban-rural income gap. This shows that although the division of the traditional economic zone reflects the facts of urban and rural areas to some extent. The spatial correlations of the development of income gap can be identified, but it cannot accurately reflect the internal relationship and similarity of regional income gap between urban and rural areas. Only some economic regions can meet the criteria for judging the convergence of urban-rural income gap. The study of Xu and Zhang analyzed the dynamic evolution of urbanization and urban-rural income gap in China, as well as the spatiotemporal effects and differences of new urbanization on urban-rural income gap in China. It was found in this study that there is an obvious regional heterogeneity in the impact of urbanization transformation on urban-rural income gap. With the in-depth transformation of urbanization, the urban income gap in the eastern region is still significantly expanding, while the expansion trend in the central region is not obvious, and the expansion trend in the western region is restrained [25].The convergence of the income gap between urban and rural areas in the western region further verifies the conclusion of Xu and Zhang

that the widening trend of the income gap between urban and rural areas in the western region has been restrained.

**Table 1.** Convergence test of the four major economic zones.

4.2.2. Convergence Club Judgment

#### 4.2.3. Analysis of Empirical Results

Table 3 shows the distribution of convergence clubs in China's four major economic zones. In general, most cities in the western region are located in the first club. The cities in northeast region are mainly distributed in the First Club and the Second Club. The central and eastern regions are distributed in all three major clubs.

**Table 3.** Distribution of clubs in the four economic zones.

It could be observed the variation coefficient of the urban-rural income gap among different clubs. As shown in Figure 2 below, we can clearly see that the coefficient of variation of urban-rural income gap among different clubs fluctuates constantly. Overall, we have experienced the process of expanding and then shrinking. Since 2009, the three clubs have shown a process of continuous convergence. It shows that the income gap between urban and rural areas within the club is constantly narrowing.

**Figure 2.** Coefficient of variation of urban-rural income gap among clubs. **Figure 2.** Coefficient of variation of urban-rural income gap among clubs.

#### **5. Causes of Club Convergence 5. Causes of Club Convergence**

For studying further the factors affecting the urban and rural income gap club grouping, this paper will use the ordered logit model to conduct regression analysis on the club grouping and explore the effects of internal determinants of the urban-rural income gap convergence club For studying further the factors affecting the urban and rural income gap club grouping, this paper will use the ordered logit model to conduct regression analysis on the club grouping and explore the effects of internal determinants of the urban-rural income gap convergence club formation.

#### formation. *5.1. Variable Selection and Model Construction*

*5.1. Variable Selection and Model Construction* According to the needs of the empirical research and considering the availability of data, this paper selects: (1) The level of local economic development (devel), expressed by the per capita GDP of each city. The level of local economic development is an important influencing factor of social development, such as the urban–rural income gap. (2) The degree of local intervention (gbeha), measured by the ratio of local fiscal expenditure to local GDP. The degree of local intervention denotes the government management capacity, which is crucial to social development.(3)Foreign direct investment (fdi), measured by the proportion of actual use of foreign capital in GDP in the year of Chinese Yuan (CNY) conversion. The fdi reflects the openness of a city. It is the representative of the government performance. The fdi brings the introduction of foreign enterprises and causes the hiring of more employees. It determines the income levels of different residents. It is also an important factor of the urban–rural income gap. (4) High-speed rail effect (gt), measured by the number of high-speed railway stations opened. (5)The proportion of urban population to total population, measured by the level of urbanization(urb). Urbanization is an important factor that will influence the income gap between urban and rural residents.(6)The industrial structure, measured by the sum of the added value of the secondary industry and the added value of the tertiary industry as a percentage of GDP (ind). Industrial structure determines the income level of residents. It is an influencing factor for the income gap. The sample interval is from 2005 to 2016. The data mainly come from the Economy Prediction System (EPS) China database, the statistical yearbook of each city, the website of the China Railway Corporation, the news reports or announcements of the National Railway Administration, and the website of the China Railway Corporation 12306. All the data except devel and gtare relative indexes. To avoid the difference of units, the variable devel is calculated with 10,000 RMB.Here we focus on the High-speed railway effect on the urban-rural income gap. Based According to the needs of the empirical research and considering the availability of data, this paper selects: (1) The level of local economic development (devel), expressed by the per capita GDP of each city. The level of local economic development is an important influencing factor of social development, such as the urban–rural income gap. (2) The degree of local intervention (gbeha), measured by the ratio of local fiscal expenditure to local GDP. The degree of local intervention denotes the government management capacity, which is crucial to social development.(3)Foreign direct investment (fdi), measured by the proportion of actual use of foreign capital in GDP in the year of Chinese Yuan (CNY) conversion. The fdi reflects the openness of a city. It is the representative of the government performance. The fdi brings the introduction of foreign enterprises and causes the hiring of more employees. It determines the income levels of different residents. It is also an important factor of the urban–rural income gap. (4) High-speed rail effect (gt), measured by the number of high-speed railway stations opened. (5)The proportion of urban population to total population, measured by the level of urbanization(urb). Urbanization is an important factor that will influence the income gap between urban and rural residents.(6)The industrial structure, measured by the sum of the added value of the secondary industry and the added value of the tertiary industry as a percentage of GDP (ind). Industrial structure determines the income level of residents. It is an influencing factor for the income gap. The sample interval is from 2005 to 2016. The data mainly come from the Economy Prediction System (EPS) China database, the statistical yearbook of each city, the website of the China Railway Corporation, the news reports or announcements of the National Railway Administration, and the website of the China Railway Corporation 12306. All the data except devel and gtare relative indexes. To avoid the difference of units, the variable devel is calculated with 10,000 RMB.Here we focus on the High-speed railway effect on the urban-rural income gap. Based on the literature review and above analysis, the hypotheses are constructed as follows:

on the literature review and above analysis, the hypotheses are constructed as follows: **Hypothesis 1.** *High-speed railway e*ff*ect of urban-rural income gap is significant.*

• Hypothesis 1: High-speed railway effect of urban-rural income gap is significant.

#### **Hypothesis 2.** *High-speed railway e*ff*ect of convergence of urban-rural income gap is significant.*

According to the regression results estimated by the panel fixed effect model, as shown in Table 4, we have determined the influencing factors of the urban-rural income gap. It can be seen that the regression coefficient is negative and significant at 1% level. The results shows High-speed railway effect (gt) have significant negative effects on the urban-rural income gap. It shows the hypothesis 1 is correct. High-speed railway effect of urban-rural income gap is significant. Besides, foreign direct investment (fdi), local economic development level (devel), and urbanization rate (urb) also have significant negative effects on the urban-rural income gap. In other words, the construction of High-speed railway, foreign direct investment, urbanization, and the improvement of regional development levels are conducive to narrowing the urban-rural income gap in China. The degree of local intervention (gbeha) and industrial structure changes (ind) have significant positive effects on the urban-rural income gap. It can be found that the intervention of local governments is an important factor in expanding the urban-rural income gap in China. Moreover, the industrial structure upgrade has widened the urban-rural income gap.

\*\*\* p < 0.01, \*\* p < 0.05, \* p < 0.1.

We use the indicators that significantly affect the urban-rural income gap in the above regression analysis as the independent variables of the ordered logit model. Then the ranked logit regression method is used to test the factors that affect the three major convergence clubs. The club categories (club1 ~ club3) as explained variables are ordered discrete variables. In this paper, ordered logit model is used to explain the mechanism of club classification. Through the model analysis of various factors on the probability of the formation of convergence club. In this paper, the club's category value is 1, 2, 3, the model is set as follows:

$$\mathbf{Y}^\* = \mathbf{x}'\mathfrak{B} \; + \; \varepsilon \tag{11}$$

Among them, y\* is the latent variable corresponding to the explained variable "club category (y)". x' is the set of explained variables, including devel, gbeha, fci, gt, urb and ind. β is the parameter to be estimated. ε is the random disturbance term. Its selection rules meet the following conditions:

$$y = \begin{cases} 1 & y^\* \ge a\_0 \\ 2 & a\_0 \le y^\* \le a\_1 \\ 3 & y^\* \ge a\_1 \end{cases} \tag{12}$$

Among them, α 0 < α 1 < α 2 is the parameter to be estimated, also known as threshold or cut. y is the rating score, which is 1, 2,3. y represents the category of the club. For example, y = 1 represents club1. If β > 0, with the increase of x 0 , the probability of y \* in the higher category will increase, which means that the category of the club will increase. Based on this, the probability effect of each independent variable on the observation value y of the dependent variable can be calculated.

#### *5.2. Empirical Results and Discussion*

Table 5 shows the orderd logit model parameter estimation results with y\* as the dependent variable. It can be seen that fdi, indand gebha have a significant effect on the latent variable y\*. The estimated β of fdi is 11.6, which is significantly positively correlated at the 1% level. The estimated value of β for gebha is −3.957, which is significantly negatively correlated at the 1% level. The estimated value of β for ind is −1.275, which is significantly negatively correlated at the 5% level.The independent variables gt, devel, and urb have no significant effect on the latent variable y\*. In other words, although the construction of High-speed railway, the improvement of regional development level and urbanization can effectively reduce the urban-rural income gap between cities, it is not an important factor in forming the three convergence clubs. It shows hypothesis 2 is not correct. High-speed railway effect of convergence of urban-rural income gap is not significant. This indicates that the current impact of China's High-speed railway construction on the urban-rural income gap is relatively weak compared to the impact of local government intervention and foreign direct investment.

**Table 5.** Estimation results of the ordered logit model.

\*\*\* p < 0.01, \*\* p < 0.05, \* p < 0.1.

It should be pointed out that since the ordered logit regression uses the maximum likelihood method to estimate the regression coefficient, the size of the above regression coefficient does not have the meaning of the marginal effect, and the direction of the influence of the variable on the interpreted variable can only be explained according to its symbol analysis. Further exploration of the marginal effects of each influencing factor on the probability of each club is shown in Table 6.

According to the estimation results in Table 6, it can be seen that with the increase of foreign investment, the possibility of the city belonging to the club1 will decrease, and the possibility of belonging to the club2 and club3 will increase. As the level of local government intervention increases and the industrial structure upgrades, the possibility of the city belonging to the club1 will increase, and the possibility of belonging to the club2 and club3 will increase. In other words, foreign investment will help cities converge to clubs with lower average urban-rural income gaps. Local government intervention and industrial structure upgrade will promote cities to converge to clubs with high average urban-rural income gap.

**Table 6.** Average marginal effect results of the ordered logit model.

#### **6. Conclusions**

Since the reform and opening up, China's economy has shifted from high-speed development to a new normal of medium-to-high-speed growth in the latter period [46]. Whether China can cross the current middle-income trap after the economic slowdown and achieve regional coordinated development is still a puzzle. Exploring the convergence of urban-rural income gaps can provide a basis for the formulation of national and regional industrial policies.

This paper examines the urban-rural income gap of prefecture-level cities from 2005 to 2016 through the nonlinear time-varying factor model for automatic identification and grouping, then tests the conditions and internal mechanisms that determine the convergence of urban-rural income gaps. This study found the following results: In the traditional four economic regions of China, the convergence of urban-rural income gap exists in the northeast and the west, while the convergence of urban-rural income gap does not exist in the east and the middle. Although the division of traditional economic zones reflects the spatial correlation of the development of urban-rural income gap to some extent, and it cannot accurately reflect the internal relationship and similarity of the change of regional urban-rural income gap. Only some economic zones meet the criteria of convergence of urban-rural income gap. However, cities are divided into three major convergence clubs by logt test. The eastern and central regions are scattered in three clubs. The western region and the northeast region are concentrated in the largest club.

Secondly, the construction of High-speed railway has effectively narrows the urban-rural income gap in China, but it is not the cause of the formation of the three convergence clubs. The convergence effect of High-speed railway on the urban-rural income gap in China is still relatively weak. Related research shows that the impact of High-speed railway on urban economic growth is relatively lagging behind. Moreover, in some cities, the opening time of the High-speed railway is relatively late, and the role has not yet appeared. Therefore, the sample data still has certain limitations.

Thirdly, foreign direct investment promotes cities to converge to high-class clubs with lower average urban-rural income gap. Local government intervention and industrial structure upgrades will promote cities to converge to low-class clubs with higher average urban-rural income gap.

Based on the above research conclusions, we propose the following policy recommendations. In the construction of High-speed railway, it is necessary to carry out overall planning considering the changes of the industrial structure and the equalization of welfare. Providing farmers with supporting policies and other opportunities to enable the High-speed railway to bring better urban back-feeding effects and narrow the urban-rural income gap. The government needs to reverse the urban bias policy in terms of fiscal expenditure and investment. To achieve coordinate development in urban and rural

areas, China's government should provide more infrastructure construction and financial investment in education, healthcare, social security or other social undertakings in rural areas.

**Author Contributions:** Conceptualization, W.L.; Methodology, W.L. and X.W.; Software, X.W.; Validation, W.L., O.-P.H. and X.W.; Formal Analysis, W.L., X.W. and O.-P.H.; Investigation, W.L., O.-P.H. and X.W.; Resources, W.L., O.-P.H.; Data Curation, W.L. and X.W.; Writing-Original Draft Preparation, W.L.,X.W.; Writing-review & Editing, W.L., O.-P.H. and X.W.; Visualization, X.W.; Supervision, W.L., O.-P.H..; Funding acquisition, W.L.; Project Administration, W.L.. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the China National Key Research and Development Plan, grant number 2018YFB1201400.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


*Review* **Photocatalytic Degradation of Organic and Inorganic Pollutants to Harmless End Products: Assessment of Practical Application Potential for Water and Air Cleaning**

**Monica Pavel , Crina Anastasescu, Razvan-Nicolae State, Anca Vasile , Florica Papa and Ioan Balint \***

"Ilie Murgulescu" Institute of Physical Chemistry of the Romanian Academy, 202 Spl. Independentei, 060021 Bucharest, Romania

**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** It is well-documented that large-scale pollution generated by human activity has a dramatic impact on ecosystems. In this context, removing harmful chemicals via photocatalysis has tremendous potential as a depollution method, utilizing freely available solar light and catalytic materials with low or negligible ecotoxicity. The main drawbacks, which aren't often addressed in the available literature, are the formation of harmful intermediate products, low reaction rates, limited catalyst stability, and difficult catalyst recovery. In most cases, published works assess the efficiency of tested photocatalysts from pollutant degradation studies, whereas identifying and quantifying by-products is not often conducted. This review summarizes the recent advances reported for the photocatalytic removal of some organic (e.g., alcohols, carboxylic acids, volatile organic compounds, phenol) and inorganic (e.g., NO3 −) contaminants. The efficiency of various UV- and visible-light active photocatalysts and the reaction degradation pathways were explained, emphasizing the main factors contributing to their mineralization. The reaction mechanisms, the identification and quantification of degradation intermediates, and the implication of reactive active species (ROS) were discussed and analyzed for each category of model target pollutant. Additionally, the actual challenges and future photocatalytic approaches to improve environmental remediation were presented.

**Keywords:** photocatalysis; air cleaning; wastewater treatment; reactive oxygen species; degradation pathways


**7. Summary and conclusions**

Since the initial description of the Fontan operation in the early 1970s by Fontan, Kruetzer, and their associates, several modifications have been introduced. These include avoiding classic Glenn anastomosis; not using a prosthetic valve in the IVC; RA-PA anastomosis, direct or through a non-valved conduit; RA-PA anastomosis through a valved conduit; RA-RV anastomosis, direct or non-valved anastomosis; RA-RV anastomosis through a valved conduit; bidirectional Glenn procedure (cavopulmonary anastomosis); lateral tunnel; total cavopulmonary connection; extra-cardiac conduit, staged Fontan; fenestrated Fontan; and closure of Fontan fenestration. Currently staged, total cavopulmonary connection with extra-cardiac conduit and fenestration has become the most commonly used multistage surgery in accomplishing the Fontan.

The indications for Fontan are patients who have one functioning ventricle, and these include tricuspid atresia, double-inlet left ventricle, HLHS, mitral atresia with normal aortic root, unbalanced AVSDs, pulmonary atresia with intact ventricular

**147**

**Author details**

Stage IIIA.

**Conflict of interest**

P. Syamasundar Rao

Hermann Hospital, Houston, TX, USA

provided the original work is properly cited.

University of Texas at Houston, McGovern Medical School and Children's Memorial

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

septum with markedly hypoplastic right ventricle, and other complex heart defects with one functioning ventricle. Recently there has been a trend for biventricular

Both the immediate and follow-up results have remarkably improved, both in terms of mortality and morbidity, following the introduction of staged total cavopulmonary connection with extra-cardiac conduit and fenestration with subsequent catheter closure of Fontan fenestration. Complications do occur during follow-up,

Stage I consists of performing palliative procedures on the basis of pathophysiology of the defect complex at presentation, usually in the neonatal period. Stage II involves performing a bidirectional Glenn procedure (diversion of the superior vena caval blood flow into both lungs) usually at about the age of 6 months. During stage IIIA diversion of the IVC blood flow into the lungs, usually by an extra-cardiac conduit plus a fenestration, usually at about the age of 2 years. Stage IIIB consists of transcatheter closure of the fenestration 6–12 months after

\*Address all correspondence to<EMAIL_ADDRESS>
*Fontan Operation: A Comprehensive Review DOI: http://dx.doi.org/10.5772/intechopen.92591*

repair, particularly for patients with unbalanced AVSDs.

and they should be addressed as and when they are detected.

The author declares no conflict of interest.

### *Fontan Operation: A Comprehensive Review DOI: http://dx.doi.org/10.5772/intechopen.92591*

*Advances in Complex Valvular Disease*

the wide use of staged TCPC.

**Figure 16.**

in accomplishing the Fontan.

**7. Summary and conclusions**

Following a short trial of any of the above treatment modes, largely on the basis of the cardiologist's preference, a more definitive treatment methods such as lessening the conduit pressure by creating a fenestration between the conduit and the atrium [99–101], converting atriopulmonary type of Fontan to TCPC [87, 102, 103], instituting sequential atrioventricular pacing [104, 105], and performing cardiac transplantation [106–108] should all be considered. Again, it is essential to emphasize that timely treatment should be instituted as soon as PLE is identified [85]. Fortunately, the need for use of these methods has progressively diminished since

*(A) Selected cine frame from a right subclavian artery (RSA) cineangiogram showing branches (white arrows) of the thyrocervical (TC) trunk which supplied a number of small vessels, giving a good degree of levophase. (B) Complete occlusion occurred following the implantation of a Gianturco coil (C) (Reproduced from [95]).*

Since the initial description of the Fontan operation in the early 1970s by Fontan, Kruetzer, and their associates, several modifications have been introduced. These include avoiding classic Glenn anastomosis; not using a prosthetic valve in the IVC; RA-PA anastomosis, direct or through a non-valved conduit; RA-PA anastomosis through a valved conduit; RA-RV anastomosis, direct or non-valved anastomosis; RA-RV anastomosis through a valved conduit; bidirectional Glenn procedure (cavopulmonary anastomosis); lateral tunnel; total cavopulmonary connection; extra-cardiac conduit, staged Fontan; fenestrated Fontan; and closure of Fontan fenestration. Currently staged, total cavopulmonary connection with extra-cardiac conduit and fenestration has become the most commonly used multistage surgery

The indications for Fontan are patients who have one functioning ventricle, and these include tricuspid atresia, double-inlet left ventricle, HLHS, mitral atresia with normal aortic root, unbalanced AVSDs, pulmonary atresia with intact ventricular

**146**

septum with markedly hypoplastic right ventricle, and other complex heart defects with one functioning ventricle. Recently there has been a trend for biventricular repair, particularly for patients with unbalanced AVSDs.

Stage I consists of performing palliative procedures on the basis of pathophysiology of the defect complex at presentation, usually in the neonatal period. Stage II involves performing a bidirectional Glenn procedure (diversion of the superior vena caval blood flow into both lungs) usually at about the age of 6 months. During stage IIIA diversion of the IVC blood flow into the lungs, usually by an extra-cardiac conduit plus a fenestration, usually at about the age of 2 years. Stage IIIB consists of transcatheter closure of the fenestration 6–12 months after Stage IIIA.

Both the immediate and follow-up results have remarkably improved, both in terms of mortality and morbidity, following the introduction of staged total cavopulmonary connection with extra-cardiac conduit and fenestration with subsequent catheter closure of Fontan fenestration. Complications do occur during follow-up, and they should be addressed as and when they are detected.


*Article* **Factors Predicting Post-Traumatic Positive and Negative Psychological Changes Experienced by Nurses during a Pandemic COVID-19: A Cross-Sectional Study**

**Grzegorz Józef Nowicki 1,\*, Barbara Slusarska ´ 1, Bozena Zboina ˙ 2, Aneta J ˛edrzejewska 1 and Marzena Kotus 3**

**Abstract:** It is common knowledge that COVID-19 affects physiopathological changes in all systems of the human body. On the other hand, events related to the COVID-19 pandemic also have a significant impact on the social and mental sphere of human functioning. The aim of this study is to determine the relationship between selected sociodemographic variables and selected subjective cognitive resources, and the positive and negative perception of the consequences of the COVID-19 pandemic in a group of nurses working in Poland. The computer-assisted web interviewing method was conducted between 1 and 15 May 2020. Participants were requested to complete the following questionnaires: The Changes in Outlook Questionnaire (CIOQ), The Impact Event Scale-Revised (IES-R), The Multidimensional Scale of Perceived Social Support (MSPSS), The Safety Experience Questionnaire (SEQ), and The Meaning in Life Questionnaire (MLQ). Three-hundred and twenty fivenurses working all over Poland participated in the study. Their mean age was 39.18 ± 11.16 years. A higher average level was noted among the surveyed nurses in the Positive Change subscale (18.56 ± 4.04). In a multivariate model, taking into account both sociodemographic and cognitive variables, the level of perceived traumatic stress, the level of social support, a sense of security, reflection on safety and a sense of meaning and meaning in life were independent predictors of a positive perception of the consequences of the COVID-19 pandemic. Those variables explained as much as 37% of the dependent variable, and the nature of the relationship was positive. While we are still a long way from understanding the full range of the long-term impact of the COVID-19 pandemic on mental health and psychosocial well-being, it is possible that in this challenging context there are many individual resources available to perceive the effects of the current pandemic positively. Therefore, they should be strengthened through the development and implementation of intervention programs to improve the mental state of nurses.

**Keywords:** COVID-19; pandemic; nurses; changes in outlook; post-traumatic growth; influencing factors

#### **1. Introduction**

According to the World Health Organization (WHO) [1], the Corona Virus Disease 2019 (COVID-19) pandemic of March 2020 [2] is the most significant global public health threat, placing enormous pressure on healthcare systems. Among the various occupational groups, health care workers (HCW) are most vulnerable to adverse effects related to the pandemic, especially in the field of mental health [3]. Moreover, HCWs represent systemically important professions, which means that their work is necessary, and the demand for it increases in a pandemic.

**Citation:** Nowicki, G.J.; Slusarska, B.; ´ Zboina, B.; J ˛edrzejewska, A.; Kotus, M. Factors Predicting Post-Traumatic Positive and Negative Psychological Changes Experienced by Nurses during a Pandemic COVID-19: A Cross-Sectional Study. *Int. J. Environ. Res. Public Health* **2022**, *19*, 7073. https://doi.org/10.3390/ijerph 19127073

Academic Editors: Richard Gray and Sonia Udod

Received: 22 April 2022 Accepted: 2 June 2022 Published: 9 June 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

129

COVID-19 influences physiopathological changes in all human body systems, especially the immune and respiratory systems. On the other hand, events related to the pandemic also significantly impact the social and mental sphere of human functioning [4,5]. Research shows that HCWs are at high risk of developing mental stress and other adverse mental health symptoms due to exposure to COVID-19 in healthcare settings [6,7]. The results of the available literature reviews also emphasise that fear of infection and transmission of the virus to family members, colleagues and friends is the dominant concern among HCW, limiting their social interactions and the most important risk factor for their physical functioning, as well as well-being and mental health [8]. In addition, there are well-founded concerns about the mental health, mental adjustment, and recovery of HCW caring for COVID-19 patients during and after the pandemic.

Previous studies conducted during epidemics such as the Severe Acute Respiratory Syndrome (SARS) of the 2003 Middle East Respiratory Syndrome (MERS) epidemic of 2013–2016 and Ebola of 2014–2016 indicate adverse psychological effects connected to those epidemics, such as anxiety, depression among HCWs, burnout and post-traumatic stress disorder (PTSD), and their symptoms persist from one to three years [9–11]. On the other hand, a meta-analysis concerning the psychological impact of the COVID-19 pandemic on HCWs showed that the total incidence of depression was 21.7%, anxiety was 22.1%, and PTSD was 21.5% [12]. Another meta-analysis showed that among HCWs, the highest incidence of PTSD was recorded among nurses, followed by physicians and other medical professionals (physiotherapists, medical caregivers) [13]. Furthermore, a metanalysis by Vizheh et al. [14] showed that the mental burden affected 12% to 67% of nurses during the COVID-19 pandemic. In contrast, the meta-analysis by Slusarska et al. [ ´ 15] showed that during the COVID-19 pandemic, the prevalence of depression among nurses was 22%, while anxiety disorders were experienced by 29% of them.

Even though a traumatic event can cause post-traumatic symptoms (PTS), it can also become a catalyst for positive change. This phenomenon is called post-traumatic growth (PTG). According to the concept of Calhoun and Tedeschi [16], PTG defines a positive psychological change that follows very stressful and demanding life situations [17]. This means that people have the ability to grow despite experiencing trauma. HCWs have great potential to develop PTG due to their personal and professional characteristics. Olson et al. [18] and Huecker et al. [19] emphasise the great importance of the study of PTG and its predictors among HCW during the COVID-19 pandemic. Despite the high interest of researchers in the negative mental health consequences of the COVID-19 pandemic, research has begun documenting the positive psychological effects of the pandemic. For example, moderately increased levels of PTG have been found in frontline nurses and have been associated with social support [20]. The narrative review showed that HCW working on the frontline had a higher level of PTG compared to HCW away from the frontline [21]. In contrast, studies by Vazquez et al. [22] conducted amongst the general adult population in Spain showed that PTG was associated with primal beliefs about a good world, openness to the future and identification with humanity.

Gathered and analysed research indicates that nurses are the group most at risk of developing PTSD during the COVID-19 pandemic. Therefore, examining the positive and negative perceptions of the COVID-19 pandemic and the predictive factors in this professional group seems to be quite significant. The main mechanisms responsible for the negative consequences of the COVID-19 pandemic may be primarily related to the level of anxiety and depression. Negative feelings can worsen the mental and physical well-being of HCW, undermining their confidence and sense of security and even meaning in life. Personal resources usually play an essential role in risk perception during a pandemic, with its negative consequences, as well as in shaping the well-being and positive consequences of COVID-19 stress in HCW. Personal resources are generally regarded as qualities that are valued by a person and are able to improve their effective functioning in terms of control and impact on the environment [23]. In creative adaptation, subjective personal resources can be used to assist individuals in the effective reinterpretation of challenging

and stressful life experiences and ineffective adaptation to professional tasks. The risk of developing COVID-19 increases the occupational requirements of HCWs, leading to a decline in well-being with negative consequences. At the same time, perceptions of social support, security and meaning in life can mitigate the adverse effects and develop positive growth associated with the pandemic.

Studies have shown that meaning in life was positively associated with well-being among American HCW [24], but negatively associated with depression in Turkish HCW [25]. In studies in the Israeli adult population conducted during the COVID-19 pandemic, perceived support from a loved one negatively correlated with the severity of depression, anxiety, obsessive-compulsive disorder (OCD) and PTSD [26]. In contrast, a qualitative study on the protection of HCW against exposure to SARS-CoV-2 during patient care during a pandemic found that the sense of security in terms of confidence in personal protective equipment (PPE) and infection prevention and control (IPC) strategies was associated with a lower level of emotional exhaustion [27]. Given that the COVID-19 pandemic is a prolonged stressful situation, especially for HCWs, the availability of social support, a sense of security, and finding meaning and meaning in life would enable workers to cope with stress and promote well-being and positive functioning.

Therefore, considering the need to understand these phenomena, the aim of this study was to determine the relationship between selected sociodemographic variables and selected subjective cognitive resources, and the positive and negative perception of the consequences of the COVID-19 pandemic in the group of nurses working in Poland. The second goal of the research was to determine which sociodemographic variables and cognitive factors explain the variability of positive and negative perceptions of the consequences of the COVID-19 pandemic. We are particularly interested in factors with a buffer effect for the positive aspects of nurses' perceptions of the aftermath of a pandemic, such as perceived social support, experiencing safety, and a sense of meaning and meaning in life.

#### **2. Materials and Methods**

#### *2.1. Study Design and Participants*

The analysis was conducted using data collected as part of a cross-sectional study between 1 and 15 May 2020. The detailed study protocol in terms of study stages, participant inclusion and exclusion criteria and data collection methods is described elsewhere [28]. A summary of this analysis is provided below. The research material was collected among 325 nurses using the computer-assisted web interviewing (CAWI) method due to the restrictions related to the COVID-19 pandemic in terms of social isolation.The questionnaire was posted on the "Google Surveys" portal and the link to the questionnaire was promoted twice: on the first and the seventh day of the study on the ten most popular fan pages addressed to nurses on Facebook. Participants were allowed to complete the survey only once.The completion of online questionnaires is an established method in healthcare research [29].

After giving informed consent to participate in the study, in order to verify the respondents, the information that the study was granted to nurses and the question, "Are you a nurse?" appeared on the next page. The respondent could respond "Yes" or "No". In the case of marking the answer "No", the questionnaire was closed automatically, thanking them for their time. To be eligible to participate in the study, respondents had to meet certain inclusion and exclusion criteria. The inclusion criteria included: (1) a nurse working during the COVID-19 epidemic, defined as the period from 20 March 2020; (2) professional activity before the coronavirus epidemic, meaning during January and February 2020; and (3) informed consent to participate in the study by responding "Yes". The exclusion criteria were: (1) being on sick leave, maternity, parental or care leave prior to the announcement of the epidemic in Poland (January and February 2020); (2) being on sick leave, maternity, educational or care leave after the announcement of the epidemic in Poland; (3) withdrawal from work for health reasons; and (4) refusal to give informed consent to participate in the

study. There is no target recruitment size. As direct comparisons are not being drawn, a power calculation has not been performed.

#### *2.2. Study Questionnaire*

In order to achieve the objectives of the study, a questionnaire consisting of five standardized tools and an original tool were used. All respondents completed the same questionnaire. In the instructional manual concerning each listed tool, the respondents were asked to rate certain factors, taking into account the current epidemiological situation. A detailed description of the questionnaires used is presented in another publication [28], while a short description of the scales is provided below. All the scales used in the study were characterised by optimal internal consistency, presented in our earlier publication in Supplementary Materials [28]. The standardised research scales used in the study include:

The questionnaire was supplemented with a personal information form in order to collect information on several basic sociodemographic data: gender, age, marital status, place of residence, the respondent's cohabitants, whether they have children, education, completed postgraduate education, seniority as a nurse, position held, whether the respondent took care of a suspected or diagnosed patient with SARS-CoV-2 at work, and whether the respondent participated in training on the use of personal protective equipment, and the functioning of the medical facility where they worked during the COVID-19 pandemic.

#### *2.3. Ethical Considerations*

Ethical approval was issued by the Bioethics Committee at the Medical University of Lublin (decision number: KE-0254/73/2020). The research was conducted in accordance with the ethical principles contained in Recommendations from the Association of Internet Researchers [39]. Participation in the study was voluntary and anonymous. All study participants gave their informed consent to participate in the study electronically. The informed consent form preceding the questionnaire contained an explanation of the purpose, subject of the research, the approximate duration of the study and the method of answering the questionnaire. After reading the information about the survey, the respondent was asked to express their willingness to participate in the survey by clicking "Yes" or withdrawing from the survey by closing the page in the web browser containing the survey or selecting the "No" option. Only those who chose "Yes" were transferred to the questionnaire page. The respondent could resign from the survey at any time by closing the website with the questionnaire. We have described a detailed method of obtaining informed consent elsewhere [28].

#### *2.4. Statistical Analysis*

Continuous variables were presented as means (M) with standard deviation (SD). The Shapiro-Wilk test was used to assess conformity with a normal distribution. Categorical variables were reported as absolute numbers and percentages. Differences between groups were assessed by *t*-test or analysis of variance (ANOVA). Pearson correlation was used to investigate the relationships between numerical variables. Simple and multiple linear regression models were performed to assess the significant predictors of CIOQ—Positive Change or Negative Change. The variables with *p*-value < 0.1 were included in the multiple regression model. Three sets of models were constructed: Model A was performed for each independent variable separately (univariable analysis); Model B, included sociodemographic or cognitive factors, which were significant in the simple model; Model Cincluded all significant variables in simple models. The coefficient of determination (R2) was provided to describe the adequateness of fit for the performed models. Moreover, in the case of IES-R, MSPSS and MLQ scales, a strong correlation between subscales was observed. One of the subscales—Total Score (which was the strongest related to CIOQ)—was used for each scale to avoid collinearity in multivariable models. Additionally, the analysis restricted to women was performed.Statistical analyses were conducted using IBM Corp. (released in 2017) and IBM SPSS Statistics for Windows, Version 25.0. (IBM Corp, Armonk, NY, USA). Statistical A *p*-value < 0.05 was considered significant for two-tailed tests.

#### **3. Results**

#### *3.1. Characteristics of Participants*

Three hundred twenty-five nurses working all over Poland participated in the study, most of whom were women (96.7%, *n* = 311). The mean age was 39.18 ± 11.16 years. Most of the respondents lived in a city (66.46%, *n* = 216). 57.75% (*n* = 188) of the respondents were married, while the rest were single, widowed, or divorced. 67.7% (*n* = 220) of the respondents lived with their family, 20.3% with a partner, 3.4% (*n* = 11) with a roommate, and 8.6% (*n* = 28) lived alone. 65.2% (*n* = 212) had children living in their house. 43.7% (*n* = 142) had a master's or higher education (more than a master's). Postgraduate education finished by the surveyed nurses: 44.6% (*n* = 145) completed the specialization training, and 42.2% (*n* = 137) completed a qualification course in the field of nursing. 76% (*n* = 247) of respondents were nurses employed in the ward, 13.6% (*n* = 44) were a two-ward nurse, 4.9% (*n* = 13.6%) were a head nurse, and 5.5% (*n* = 18) were a primary care nurse. 46.5% (*n* = 151) cared for a patient with COVID-19, and 67.4% (*n* = 219) received training concerning the use of personal protective equipment and the functioning of the medical facility in which they work during the COVID-19 pandemic.

#### *3.2. Distribution of the Analysed Features According to Scales CIOQ, IES-R, MSPSS, SEQ and MLQ*

Table 1 presents the results of the respondents on the scales used in the study. Among the surveyed nurses, a higher average level was noticed in the subscale of perception of positive consequences of the COVID-19 pandemic (18.56 ± 4.04).

**Table 1.** Distribution of the analysed features in scales.

M: mean; SD: standard deviation; CIOQ: Changes in Outlook Questionnaire; IES-R: Impact Event Scale-Revised; MSPSS: Multidimensional Scale of Perceived Social Support; SEQ: Safety Experience Questionnaire; MLQ: Meaning in Life Questionnaire.

*3.3. Relationship between Selected Sociodemographic Variables and the Assessment of Positive and Negative Consequences of the COVID-19 Pandemic*

Table 2 presents the relationships between sociodemographic variables and positive and negative consequences related to the traumatic event, which is the COVID-19 pandemic. Age was negatively related to the Positive Change subscale (r = −0.15, *p* = 0.007), but no significant relationship was observed between age and the Negative Change subscale (r = 0.04, *p* = 0.48). Respondents living in cities (18.55 ± 4.06) and being widowed or divorced (17.91 ± 3.08) obtained a significantly lower mean value of the Positive Change subscale compared to respondents from rural areas (19.49 ± 3.94) and those who were married (19.41 ± 3.87) or living alone (18.19 ± 4.45). The other analysed variables did not significantly differentiate the mean scores on the Positive Change subscale in the study group.

**Table 2.** Associations between selected sociodemographic variables on the assessment of positive and negative consequences of the COVID-19 pandemic.

#### **Table 2.** *Cont.*

M: mean; SD: standard deviation; CIOQ: Changes in Outlook Questionnaire.

In the case of the Negative Change subscale, education, completed postgraduate education, marital status and having children, significantly differentiated mean values were obtained. Higher mean values of this subscale occurred among respondents with a master's degree or higher education, respondents who completed aqualification course in postgraduate education, and those living alone and without children.

#### *3.4. Relationship between Positive and Negative Consequences Related to the Traumatic Event Which Is the COVID-19 Pandemic and Selected Cognitive Factors*

Table 3 shows the relationship between the CIOQ subscales and selected cognitive variables. Significant and positive relationships were observed between the CIOQ Positive Change subscale and the level of perceived traumatic stress, and reflection on safety, with the strongest relationship between the sense of meaning and meaning in life (r = 0.403, *p* < 0.001) and the sense of social support (r = 0.401, *p* < 0.001). Conversely, there was no significant correlation between the CIOQ Positive Change subscale and the sense of security.

In the case of the second CIOQ Negative Change subscale, it was observed that the level of social support, the sense of security and the sense of meaning and meaning in life were negatively correlated with this subscale, while the level of perceived traumatic stress was positively related. The strongest correlation occurred in the case of experienced traumatic stress (r = 0.481, *p* < 0.001). There was no significant correlation between the CIOQ Negative Change subscale and reflection on safety.

**Table 3.** The relationship between the CIOQ subscales and selected cognitive variables.

CIOQ: Changes in Outlook Questionnaire; IES-R: Impact Event Scale-Revised; MSPSS: Multidimensional Scale of Perceived Social Support; SEQ: Safety Experience Questionnaire; MLQ: Meaning in Life Questionnaire; r: correlation coefficient.

#### *3.5. Features Related to the Positive and Negative Perspective of the COVID-19 Pandemic—Multivariable Analysis*

Tables 4 and 5 present univariable models (Models A) and multivariable models (Models B) presenting the results of analysis of features significantly related in one-dimensional models for sociodemographic features and cognitive factors, (Model C) taking into account both sociodemographic features and cognitive determinants. In the case of the CIOQ Positive Change subscale, among the analysed sociodemographic features, only marital status was a significant predictor, with age explaining only 3% of the variability of this variable. The cognitive determinants which significantly related to the CIOQ Positive Change subscale included the level of perceived traumatic stress, the level of social support, a sense of security, reflection on safety, and a sense of meaning and meaning in life (Model B), explaining in total 36% of the variability of the dependent variable. In the full multivariable model (Model C), none of the analysed sociodemographic features turned out to be an independent predictor of the CIOQ Positive Change subscale value, and all of the analysed cognitive features explained as much as 37% of the dependent variable, and the nature of the relationship was positive.

In the case of the CIOQ Negative Change subscale, in the multivariable model, among the analysed sociodemographic features, marital status, education and completion of a qualification course in postgraduate education were significantly related to this variable and explained 8% of its variability (Model B). In Model B (among cognitive traits), the level of perceived traumatic stress, the level of social support and the sense of meaning and meaning in life were significantly related to the CIOQ Negative Change subscale, explaining in total 30% of its variability. In the full multivariable model (Model C), variables such as education, completion of a postgraduate qualification course, level of perceived traumatic stress, level of social support, and sense of meaning and meaning in life turned out to be independent predictors of the CIOQ Negative Change subscale (Model C). Together, these features accounted for 38% of the variability of the dependent variable. However, the relationship of such traits as the sense of social support, the sense of security and the sense of meaning and meaning in life in relation to the CIOQ Negative Change variable was negative.

SE: standard error.

SE: standard error.

The results of the analysis restricted to female participants are presented in Supplementary Materials, Tables S1 and S2. Features significant in the analyses conducted on the entire sample (both including women and men) maintained statistical significance as well as the direction and strength of dependence among women.

#### **4. Discussion**

The COVID-19 pandemic has severe, multi-faceted consequences for people's psychosocial and mental health [40], especially HCW. Therefore, a better understanding of the underlying protective factors and risks of both negative and positive psychological effects of a pandemic [41] is warranted. Given the need to investigate both the protective factors and risk factors associated with the negative and positive psychological consequences of the current global COVID-19 pandemic, the presented research aimed to identify the relationship between selected socio-demographic variables and cognitive factors, and positive and negative perceptions of the COVID-19 pandemic in the aforementioned group of nurses. Overall, the results of our study indicate that the sociodemographic variables significantly differentiating the surveyed group of nurses in terms of the perception of positive and negative consequences of the COVID-19 pandemic were age, place of residence, education, postgraduate education, marital status, and having children. However, when considering the analysed cognitive variables, it transpired that the majority was associated with a positive and negative perception of the consequences of the COVID-19 pandemic. Although the strongest positive perception of the consequences of the COVID-19 pandemic was associated with the sense of meaning and meaning in life and the perception of social support, the level of perceived traumatic stress was most strongly associated with the negative perception of the consequences of the COVID-19 pandemic. In multivariate models, the analysed sociodemographic and cognitive variables explained 37% of the variable of positive perception of the consequences of the COVID-19 pandemic and 38% of the variable of the negative perception of the consequences of the COVID-19 pandemic, of which, in the analysis of the positive perception of the consequences of the COVID-19 pandemic, none of the analysed sociodemographic features proved to be an independent predictor.

Moreover, the positive perception of the consequences of the COVID-19 pandemic has decreased. Additionally, it was observed that nurses living in rural areas and those who were married were characterised by a higher positive perception of the consequences of the COVID-19 pandemic, whereas a significantly lower level of perception concerning the negative consequences of the COVID-19 pandemic was observed in respondents characterised as having lower education, nurses who completed a qualifying nursing course, and those who were married and those who had children. Cui et al. [42] conducted a study among 167 frontline nurses during the COVID-19 pandemic in China, in Henan and Hubei provinces. In this research, PTG was measured using the Post-traumatic Growth Inventory (PTGI). Their results indicated that, as in our study, married nurses had higher PTG levels.

Conversely, the results of the cited studies indicate that, unlike the authors' research, senior nurses and nurses with a higher level of education had a higher PTG score, while having children did not differentiate the group. Peng et al. [20] conducted a study amongst 116 frontline nurses during the COVID-19 pandemic, where PTG was significantly higher in nurses with children. At the same time, such variables as age, marital status, and education did not differentiate the study group. There are several mechanisms explaining the influence of some sociodemographic variables on the level of PTG. One of the sociodemographic variables which can increase PTG is having children. It relates to the duties and role of the mother, making the woman bolder and stronger in the face of new difficulties and challenges. Psychological research on frontline nurses has shown that identifying with the role of "mother" influences the level of PTG [43]. Updegraff and Taylor [44] proposed an explanation of the impact of being married on a higher level of PTG by linking it with positive mental development after trauma through a support system provided by another close person. However, our research only observed that the respondents remaining in a relationship had a significantly lower level of negative perception of the COVID-19 pandemic. Another variable is the age of the respondent, which in the case of nurses is associated with longer work experience, therefore some studies indicate that senior nurses had a significantly higher PTG [42] This may be related to the fact that nurses with morework experience show higher levels of critical thinking [45], but the results of our research have not confirmed this. A similar mechanism may apply to the level of education and PTG.

The second group of analysed variables that may influence the positive and negative perceptions of the COVID-19 pandemic were cognitive variables that can be involved in creative adaptation to help individuals effectively reinterpret difficult and stressful situations. In our research, the level of traumatic stress was weakly correlated and positively correlated with the positive perception of the consequences of the COVID-19 pandemic, and moderately correlated and positively correlated with the negative perception of the consequences of the COVID-19 pandemic. It should be emphasised that in both analysed situations, the correlations are positive, although the higher strength of the correlation of traumatic stress associated with COVID-19 was associated with a negative perception of the consequences of this pandemic. This discovery was partially in line with our expectations, because the PTG theory was proposed as a possible positive psychological consequence of the encountered traumatic events [46,47], and the individual's perception of a traumatic event becomes a necessary condition for development [48,49]. On the basis of the obtained research results, it can be hypothesized that too high an intensity of traumatic stress causes a negative perception of the consequences of a pandemic. Chen et al. [50] obtained similar results amongst nurses, while Park et al. [51] confirmed this relationship among Amazon MTurk employees. Thus, PTG is associated with the symptoms of post-traumatic stress disorder and can be treated as a coping mechanism in the face of persistent suffering from trauma [52].

Tedeschi and Calhoun [53] stressed the great importance of social support as a direct predictor of PTG. People who experienced a traumatic event with a high level of social support more often received emotional or material support from family members, friends, or various social groups [54]. Conversely, one of the ways to combat the spread of the SARS-CoV-2 virus was the introduction of social isolation rules, limiting social support through face-to-face contact. This was especially truein the case of nurses, who were more exposed to the virus and became more isolated than the rest of society due to the nature of their work. The authors' results showed that the sense of social support was positively correlated with a positive and negatively correlated with a negative perception of the COVID-19 pandemic. Obtained results were confirmed in previous studies among nurses during the COVID-19 pandemic [55–57]. One possible explanation is that high perceived social support can provide a sense of having a safe environment, emphasize feelings of belonging, serve as a buffer against stress, provide new meaning, and generate more positive perceptions, which endorse growth [58].

Safety is one of the most important categories which allows a description of the context of human life and the way people function [59]. In the context of experiencing security, the emotional aspect is feeling, while the rational, cognitive aspect is a reflection on security [60]. During the COVID-19 pandemic, especially at its beginning, nurses faced numerous problems that disturbed their sense of security and influenced their reflection on safety. These problems include, among others, a shortage of personal protective equipment, nursing staff shortages, fear for the health of oneself and relatives, etc. The authors' research revealed the sense of security and reflection on safety as positively correlated with a positive perception of the COVID-19 pandemic, while the feeling of safety negatively correlated with a negative perception of the COVID-19 pandemic. Other research has confirmed that fear of infection and awareness of the risks are associated with PTG, but the relationship between the availability of personal protective equipment and PTG has not been confirmed [42,61]. Unfortunately, there are no studies assessing the impact of the sense of security on PTG among health care workers on the level of PTG. Therefore, further research is required to assess this aspect considering many intermediary variables, such as the availability of

personal protective equipment, the workplace, the level of knowledge about the virus and many others.

According to the literature, the confrontation of an individual with stressful life events accompanied by various losses poses a challenge to the desire to perceive the world as meaningful and predictable, and thus may contribute to a search for meaning [62]. Earlier studies have shown a positive relationship between the presence of meaning in life and PTG [63,64]. Our research confirmed this fact, which showed a positive relationship between the search for meaning in life and a positive and negative relationship between the negative perception of the consequences of the COVID-19 pandemic. Unfortunately, there are few studies assessing the impact of meaning in life on PTG during the COVID-19 pandemic. Interesting observations about the sense of meaning and importance of life and other elements of psychological functioning during the COVID-19 pandemic were made by Baños et al. [65]. They assessed, inter alia, the meaning in life in various periods of the COVID-19 pandemic and found that its level was stable over time in the assessed periods of the lockdown. On the other hand, Trzebi ´nski et al. [66] showed that the level of meaning and meaning in life positively correlates with lower anxiety and lower stressrelated to COVID-19. The explanatory mechanism may be that the search for meaning and meaning in life allows the individual to positively reassess traumatic events, strengthen the psychological resources needed to rediscover themselves, restore the individual to a basic, complex world, and be oriented towards future goals [67].

#### *4.1. Implications for Nursing Practice and Education*

The practical implications of our research indicate the importance of social support, a sense of security, reflection on safety and a sense of the meaning and meaning of life as protective factors in creating post-traumatic positive psychological changes in the face of this and future pandemics. Accordingly, we recommend that healthcare leaders provide support from their supervisors and develop safe practice procedures. Highly engaged and participatory leadership facilitates dealing with group problems, sharing and processing ideas, and empathetic team leaders can provide an understanding of nurses*'* needs and awareness of the thoughts and feelings of the nursing staff [68]. Another aspect of the practical implications in creating post-traumatic positive psychological changes should include conducting psychological interventions in nurses working in environments with high stress related to contact with an infected patient. Both psychologists, as well as direct close associates, can provide psychological first aid [69].

As for the educational aspects, they apply not only to the nurses themselves, working in direct contact with the infected patient but also to healthcare leaders in understanding the problem and organizational support in solving new problems that arise in the face of a pandemic. Our research indicates the critical role of cognitive variables in mitigating the adverse psychological effects of a pandemic. Therefore, it is vital to develop cognitive resources during both undergraduate and postgraduate education to be easier for nurses, with the participation of nursing leaders, to cope with the adverse psychological consequences of a pandemic. Nurses who can cope well with the adverse psychological effects of a pandemic will contribute to helping individuals in society cope with stress when an epidemiological emergency occurs. This research can contribute to the development of the nursing practice by identifying some significant cognitive resources contributing to the protection of the mental health of oneself, one*'*s family and the community, which can be developed during training on how to deal with a pandemic.

#### *4.2. The Strengths and Limitations*

The strengths and weaknesses of this study deserve consideration. Firstly, to our knowledge, it is one of the few studies which assesses, inter alia, the impact of the sense of security, reflection on safety and the meaning and meaning of life on the positive and negative perception of the consequences of the COVID-19 pandemic carried out with a group of nurses. Secondly, in our research we used standardised questionnaires which had good reliability coefficients and were adapted to the assessment for the COVID-19 pandemic [28]. Thirdly, the nurses we studied worked in Poland, where they had not participated in combating the COVID-19 pandemic for many decades.

Our research has several limitations. First, the study's cross-sectional design is a constraint from which we can infer correlation, not construct causation. Our sample of nurses is not a nationwide representative sample. The average age of the respondents was lower than the average age of nurses in Poland. In our sample, the mean age was 39.18 ± 11.16, while the mean age of nurses in Poland, according to the statistics of the Supreme Chamber of Nurses and Midwives, in 2020, was 53.16 years [70]. The age difference between the group we studied and nurses in general, is particularly visible in the overrepresentation of the youngest age group of 21–30 years.

Nevertheless, the research results may be helpful in modeling research hypotheses and recommendations in the implementation of psychological care during a pandemic, especially among the younger age group of nurses. It is worth emphasizing that the study was conducted at the beginning of the COVID-19 pandemic in conditions of epidemic restrictions. The only chance to obtain data was to conduct CAWI studies, which was associated with more frequent participation in the study by younger people. Secondly, we adopted an online dissemination strategy due to the limitations of social contacts. Therefore, it was not possible to collect data on people who refused to participate in the study, and no percentage of refusals was recorded. In addition, when recruiting participants in the study, we relied on access to social networking sites, which is why the surveyed population does not include participants who do not have access to social networking sites and nurses who do not use these sites. Third, the study was conducted at the beginning of the COVID-19 pandemic, and as some authors suggest, PTG measured shortly after contact with a traumatic event may be an initial coping strategy [22]. Nevertheless, the results of our study can provide a valuable reference point for the discussion of PTG studies conducted in later or post-pandemic periods. Fourth, the research subject was Polish nurses, whose cultural context may cause their responses to differ from those of nurses working in other countries. Fifthly, filling in the questionnaire on their own could cause the respondents to underestimate or exaggerate the severity of certain symptoms to minimize or exacerbate their problems.

#### **5. Conclusions**

The COVID-19 pandemic quickly altered the working conditions for nurses in Poland, increasing the level of traumatic stress related to the pandemic. The authors' observations revealed the prevalence of positive post-traumatic psychological changes among nurses. It has been observed that younger nurses living in rural areas and are married more often perceive the consequences of a pandemic positively. Whereas respondents with a higher education, with a qualification in the field of nursing, single people and those without children perceive the negative effects of the COVID-19 pandemic significantly more often. In a multivariate analysis, lower levels of perceived traumatic stress and higher levels of social support, a sense of security, reflection on safety, and a sense of meaning and meaning in life are the main sources of a 37% buffering explaining the variability of the prospect of positive consequences of the COVID-19 pandemic among nurses. Factors predicting post-traumatic negative psychological changes experienced by nurses during the COVID-19 pandemic in a multidimensional model reveal 38% of variability of negative consequences, and, among them, apart from sociodemographic features, a high level of perceived traumatic stress and low levels of social support, sense of security and sense and importance are important.

Our research clarifies the insufficient knowledge concerning the predictors of posttraumatic positive and negative psychological changes experienced by nurses during the COVID-19 pandemic. Moreover, we were able to identify the importance of the level of perceived traumatic stress, social support, sense of security, reflection on safety, and the

sense of meaning and importance of life as protective factors in the mechanisms of creating post-traumatic positive psychological changes.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/ijerph19127073/s1, Table S1: Relationship between the positive outlook of the COVID-19 pandemic and selected sociodemographic and cognitive variables in woman; Table S2. Relationship between the negative perspective of the COVID-19 pandemic and selected sociodemographic and cognitive variables in woman.

**Author Contributions:** Conceptualized the design and the data analysis, conducted the experimental procedure, interpreted the data and drafted, finalized the manuscript, contributed to the design of the analysis, conducted the statistical analysis and contributed to the draft manuscript, G.J.N. and B.S.; ´ interpreted the data and contributed to the draft manuscript, B.Z. and A.J.; contributed to the draft manuscript and revised the final version, M.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding. The article processing charge was funded by the Medical University of Lublin.

**Institutional Review Board Statement:** The study was conducted in accordance with the HelsinkiDeclaration (updated in 2013). The study was approved by the Bioethics Committee of the MedicalUniversity of Lublin (decision number: KE-0254/73/2020).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The datasets used and/or analyzed during the current study areavailable from the corresponding author on reasonable request.

**Acknowledgments:** The authors would like to express our gratitude to Maciej Polak, DHSC for his assistance inpreparing statistical analyses.

**Conflicts of Interest:** The authors declare that they have no conflicts of interest.

#### **References**


**Rethinking the Man-Land Relations in China**

Editors

**Li Ma Yingnan Zhang Muye Gan Zhengying Shan**

Basel • Beijing • Wuhan • Barcelona • Belgrade • Novi Sad • Cluj • Manchester

*Editors* Li Ma School of Public Policy and Administration Chongqing University Chongqing China Zhengying Shan Department of Geography University of Wisconsin–Madison Madison United States

Yingnan Zhang School of Public Affairs Zhejiang University Hangzhou China

Muye Gan The Rural Development Academy Zhejiang University Hangzhou China

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Land* (ISSN 2073-445X) (available at: www.mdpi.com/journal/land/special issues/man land).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

Lastname, A.A.; Lastname, B.B. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-8669-4 (Hbk) ISBN 978-3-0365-8668-7 (PDF) doi.org/10.3390/books978-3-0365-8668-7**

© 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) license.


*2.1. Materials*

Three commercial aluminum alloys AA5083-O, AA5754-H14 and AA7020-T6 were chosen for producing dissimilar friction stir butt welds. The alloys were purchased in the form of rolled plates of 10 mm thick. The butt welds were designed to be 200 mm total width which composed of two plates; each plate was 100 mm wide and 200 mm long. The nominal chemical compositions of the parent materials are listed in Table 1. Moreover, the tensile strength, temper condition and hardness of the parent materials are summarized in Table 2.

**Table 1.** Nominal chemical composition of aluminum alloys AA5083, AA5754 and AA7020.

**Table 2.** Mechanical properties of the aluminum alloys AA5083, AA5754 and AA7020.