**1. Introduction**

Tourism has solidified its role as the strategic-pillar industry in China [1], accounting for 6.69% of the total gross domestic product (GDP) in 2019. For a long time, tourism has been regarded as an industry with low-carbon and environmental protections [2]. Therefore, adequate attention is not paid to the pollution brought by tourism industry in China. With the expansion and improvement of the tourism economy, the negative environment impacts of the tourism industry have gradually been exposed [3]. The Fourteenth Five Year Plan, issued by the central government in March 2021, emphasized the high-quality development is the main direction of the tourism industry in the next five years. Tourism eco-efficiency is defined as creating more economic value in tourism products and service, while eliminating negative environmental effects and reducing resource consumption [4–6]. The improvement of tourism eco-efficiency cannot only be in accordance with the goal of carbon emission peaking and carbon neutrality (Two Carbon) but also promote the high-quality development of tourism industry [7].

With the exposure of the negative impacts of tourism development on ecological environment, quite a few scholars have gradually begun to concentrate how to achieve low-carbon development in tourist destinations. Moreover, the Sustainable Tourism Development Action Strategy (STDAS) put forward the concept of sustainable tourism, and pointed out that both ecological environment and economic benefit should be taken into

**Citation:** Liu, Q.; Song, J.; Dai, T.; Xu, J.; Li, J.; Wang, E. Spatial Network Structure of China's Provincial-Scale Tourism Eco-Efficiency: A Social Network Analysis. *Energies* **2022**, *15*, 1324. https://doi.org/10.3390/ en15041324

Academic Editors: Roberto Alonso González Lezcano, Francesco Nocera and Rosa Giuseppina Caponetto

Received: 18 January 2022 Accepted: 8 February 2022 Published: 11 February 2022

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consideration [7]. Based on the core principles of sustainable tourism, Gössling, Peeters, Ceron, Dubois, Patterson and Richardson [6], calculating the carbon emissions from the tourism industry, put forward the concept of tourism eco-efficiency. Since then, it has triggered a huge wave in academic tourism circle. Extant assessment methods are divided into two types, namely, the single indicator method, and the model method [4]. With regard to the single indicator method, tourism eco-efficiency can be expressed by the ratio of tourism economic benefit to the environmental effect [8]. During the application of the single indicator method, the tourism receipt and the carbon dioxide emission from tourism industry were defined as the tourism input and the environmental effects, respectively, by Perch-Nielsen, et al. [9] Additionally, some scholars regarded tourism-related carbon footprint as the environmental effect of tourism [10–12]. However, the superior rational is not obtained by the single indicator method; meanwhile, this method is suitable for a single object or item. Thus, the single indicator method may not provide more targeted implications for tourist destination management (TDM) [2]. In terms of the model method, constructing the index system of input-output is a prerequisite for evaluating tourism eco-efficiency; the choice of evaluation models can reduce error, and make the results more scientific [4]. The index system of input–output mainly contains capital, labor, energy consumption, tourism receipt and the environmental effects [4,13]. Moreover, the non-convex metafrontier DEA-based model, Super-DEA model, and Super-EBM model are used to take into consideration the undesired output, such as carbon emissions from the tourism industry, tourism solid waste discharge, and tourism wastewater discharge [2,13,14]. In addition, some scholars have also investigated the factors driving the evolution of tourism eco-efficiency. The main factors include the level of tourism economy [7], technological innovation [15], industrial structure of tourism [16] and environmental regulation [17].

With the improvement of market mechanism and regional integration, the production factors such as talent, technology, and capital of the low-carbon tourism development flow among various areas, resulting in tourism eco-efficiency in different areas, has grown more connected [18]. Due to the implementation of eco-environmental policies, there is more communication and cooperation regarding tourism sustainable development. Therefore, technologies relevant to low-carbon tourism and the experience of managers are exchanged among various regions, resulting in the formation of a spatial network structure of tourism eco-efficiency [2]. What's more, exploring the spatial network structure of tourism eco-efficiency can provide reference for the cooperation and communication of sustainable tourism development. Some scholars have explored the spatiotemporal evolution characteristics regarding tourism eco-efficiency by adopting exploratory spatial data analysis (ESDA) based on the attribute data [15–17,19,20]. However, scholars failed to devote to enough attentions to the spatial network structure of tourism eco-efficiency based on the relational data. In particular, the roles that various provinces play in the spatial network structure of tourism eco-efficiency have seldom been studied.

In order to fill this gap, taking 30 provinces in China as the case studies, this study explored the evolution characteristics regarding spatial network structure of tourism ecoefficiency. To our best knowledge, this study is among the first to explore the spatial network structure of provincial-scale tourism eco-efficiency. Our research makes three contributions to the extant literature on tourism eco-efficiency. First, this study casts new light on our understanding of the spatial connection and spatial spillover of tourism eco-efficiency from the relational data rather than attribute data. Secondly, the previous literature mainly concentrated on the evaluation of tourism eco-efficiency, whereas this study enriched and broadened research topics such as spatial characteristics. Thirdly, our research sought to advance original methodological and empirical contributions. To be more specific, this study established a comprehensive research framework regarding the spatial network structure of tourism eco-efficiency. Although the example is limited to China, this research framework is, to a certain extent, universalizable.

The rest of this study is structured as follows. Section 2 introduced the main research method, the index system and the data source. The empirical results were presented in Section 3, in which this study evaluated the tourism eco-efficiency and explored the evolution characteristics regarding the spatial network structure of tourism eco-efficiency. The discussion and conclusions were presented in Section 4, in which this study discussed the empirical results, summarized the literature contributions, and provided recommendations for tourist destination management.

#### **2. Materials and Methodology**

*2.1. Method*

#### 2.1.1. Super-SBM

Data envelopment analysis (DEA) is a model that evaluates multiple decision units with similar inputs and outputs [21]. However, there are two main shortcomings in the traditional DEA morel. First, the slack variables may affect the accuracy of the evaluation results. Second, there solely exists expected output, and the undesirable output is not fully taken into account. In order to make up the for above-mentioned deficiencies, Tone [22] proposed the slack-based model (SBM) based on the undesirable output, which not only takes into consideration the issue of slack, but also includes undesirable output. There is no doubt that the modifications of the model can make the evaluation more accurate. Nevertheless, when the SBM is employed to assess the efficiency of several decision-making units (DMUs), it is easy to generate the phenomenon wherein the efficiency values of DMUs all reach the optimal frontier production surface [23]. In other words, the efficiency values of multiple DMUs are 1, which makes the comparison of DMUs' efficiency impossible. Considering this issue, Tone [24] put forward the Super-SBM model based on the traditional SBM. The formula of Super-SBM is as follows.

$$\begin{cases} \displaystyle Min = \frac{\frac{1}{m} \sum\_{i=1}^{m} (\mathsf{T}\_{f\_{\overline{u}\_{i}}})}{\frac{1}{r1+r2} \left( \sum\_{i=1}^{m} \overline{\varphi\_{i}} \lambda\_{j}^{d} + \sum\_{q=1}^{L} \overline{\varphi\_{j}} \lambda\_{jq}^{d} \right)} \\ \displaystyle \overline{\pi} \geq \sum\_{j=1, \neq k}^{n} \underline{\omega}\_{ij} \lambda\_{j}; \overline{\mathcal{Y}^{d}} \leq \sum\_{j=1, \neq k}^{n} \underline{y}\_{ij}^{d} \lambda\_{j}; \overline{\mathcal{Y}^{d}} \geq \sum\_{j=1, \neq k}^{n} \underline{y}\_{qj}^{d} \lambda\_{j} \\ \displaystyle \overline{\pi} \geq \underline{\omega}\_{k}; \overline{\mathcal{Y}^{d}} \leq y\_{k}^{d}; \overline{\mathcal{Y}^{d}} \geq y\_{k}^{n}; \lambda\_{j} \geq 0, i = 1, 2, \dots, m \\ j = 1, 2, \dots, n; \ j = 1, 2, \dots, n; \ q = 1, 2, \dots, r\_{2} \end{cases} \tag{1}$$

where, *ρ* is the tourism eco-efficiency; *x*, *yd* and *yu* are input, expected output and undesirable output, respectively. *m*, *r*1, and *r*<sup>2</sup> are the quantities of inputs, expected outputs and undesirable outputs, respectively. A value of tourism eco-efficiency greater than or equal to 1 indicates that the tourism eco-efficiency is in an effective state; otherwise, it is in an invalid state.

#### 2.1.2. Modified Gravity Model

A province is a point in the spatial network structure of tourism eco-efficiency, and the spatial connection of tourism eco-efficiency among provinces is the line [25]. At present, the vecto autoregressive (VAR) model, and the modified gravity model are universally used to establish the spatial correlation matrix of tourism eco-efficiency. Given that the sensibility regarding the choice of lag order may reduce the accuracy of examining the network structure characteristics [26], the modified gravity model has been universally applied to construct the spatial correlation matrix. More importantly, this model can take consideration into the "quality" and "distance", and reflect the evolution characteristics regarding the spatial network structure [27]. Based on the above-mentioned advantages, this study applied the modified gravity model. The formula is as follows.

$$F\_{i\dot{j}} = K\_{i\dot{j}} \frac{E\_i \cdot E\_j}{D\_{i\dot{j}}^2}, K\_{i\dot{j}} = \frac{E\_i}{E\_i + E\_j} \tag{2}$$

where *Fij* denotes the gravity between province *i* and province *j*. *Ei* and *Ej* represent the tourism eco-efficiency in province *i* and province *j*, respectively. *Dij* represents the distance between province *i* and province *j*, which is represented by the shortest straightline distance among two provincial capitals [25]. *Kij* is the correction coefficient. Generally speaking, provinces with high tourism eco-efficiency possess a stronger radiation effect on provinces than that of provinces with low tourism eco-efficiency, through radiation of tourism low-carbon technology and spillovers of tourism low-carbon information [2]. In this study, *Kij* is calculated by the proportion of tourism eco-efficiency of province *i* in the sum of tourism eco-efficiency of province *i* and province *j*.

#### 2.1.3. Social Network Analysis

Social network analysis (SNA), an interdisciplinary research method, aims to describe the relationship among members in a network and the influence of different relationship patterns on the characteristics regarding network structure based on graph theory and algebra [1,28]. Due to the advantages of intuitive graphics and accurate characterization, the application of SNA has been gradually expanded from sociology to economics, management, psychology, geography and other disciplines [27,29,30]. This study mainly adopted the SNA to explore the overall and individual characteristics regarding spatial network structure of tourism eco-efficiency in China and to reveal the actual or potential relationship between provinces. The relevant formulas can be seen in Table 1.


**Table 1.** Formulas of indicators regarding social network analysis.

(1) The overall network structure characteristics. The overall network structure characteristics are mainly reflected by six indexes, namely, network density, network connectedness, network hierarchy, network efficiency, clustering coefficient and average path length. Network density and network connectedness mainly reflect the density degree of spatial network structure. The higher the network density is, the more connectedness there is, and the closer the spatial network structure is. The network hierarchy mainly assess the asymmetric accessibility of network individual. The higher the network hierarchy, the

more rigid the network structure, and more provinces play the role of edge in the spatial network structure. Network efficiency mainly reflects the connection efficiency among nodes in the spatial network structure. If the network efficiency is lower, there will be more spillover channels among various provinces, and the more stable the network structure is. The clustering coefficient and the average path length are used to mirror the characteristics of "small-world" regarding the spatial network structure [31], among which, the clustering coefficient mainly represents the cohesion of the spatial network structure. The higher the clustering coefficient is, the more frequent the network connections are. The average path length can indicate the distance between nodes.

(2) The individual network structure characteristics. The individual network structure characteristics are mainly reflected by three indexes, namely degree centrality, betweenness centrality and closeness centrality. Specifically, the point centrality reflects the central position of the node in the network structure. The greater power in the network structure, and the more prominent the central position of the node in the network structure. Betweenness centrality mirrors the degree to which nodes control the connections among other nodes. The higher the betweenness centrality, the greater the priority and control of the node. Closeness centrality reflects the ability a node to be controlled by other nodes. The greater closeness centrality, the more direct spatial associations among nodes, and the easier it is for the node to play the role of the center.

(3) Network cohesive sub-groups analysis. Cohesive sub-groups can explain the substructure within a group, which is a broad concept of sub-group [28]. Nodes in a subgroup possess relatively strong, relatively close and relatively direct relationships, whose fundamental purpose is to reveal the actual or potential relationship between nodes [32].

### *2.2. Index System Selection*

This study regards capital, labor and energy as the tourism input indicators. Land is one of the most basis production factors in economic activities, but it is not a decisive factor affecting the intensive or extensive management of tourism industry in the process of tourism economic development [33]. More seriously, there is a dearth of the dataset on the number of tourism land-use [1]. Therefore, the land is not seen as the basis input in this study. Capital input has a significant influence on the development pathway to a certain extent, thus, playing an indispensable role in the low-carbon tourism development. In this study, the total of fixed asset investment regarding tourist attractions, travel agencies and star-hotels is used to represent the level of tourism capital investment [28]. Additionally, tourism industry is regarded as a labor-intensive industry with significant employment attributes. In this study, the number of tourism employees is used to represent the labor input of tourism [34]. Tourism energy input is of great importance in evaluating tourism eco-efficiency; this study selects tourism energy consumption to represent tourism energy input [13].

With respect to the expected output index, the total tourism revenue is the direct embodiment of economic benefits from tourism. Moreover, the total number of tourists can better reflect the spillover effect of the tourism industry [35]. In terms of undesired output, this study adopts carbon emissions from the tourism industry to reflect the negative impact of tourism-related economic development on the ecological environment [7,13].

#### *2.3. Data Source*

In this study, 30 provinces (excluding Tibet, Hong Kong, Macao and Taiwan) were taken as the case studies. The data on inbound tourism revenue, inbound tourist arrival, and fixed asset investment in the tourism industry were mainly received from the China Statistical Yearbook (2001~2018) and China Tourism Statistical Yearbook (2001~2018). Domestic tourism revenue and domestic tourist arrival were mainly taken from the statistical yearbooks of 11 provinces during the period of 2001~2018. Quite a few data were supplemented and improved by the statistical bulletins of national economic and social development of each province. With regard to carbon emissions and energy consumption, this study adopted the "bottom-up" method, including decomposition and summation based on determining the key areas, overall tourism energy consumption and carbon emissions [36,37]. The specific calculation process was played in Appendix A. The data involved in the calculation of energy consumption and carbon emissions from the tourism industry were mainly received from the China Transport Statistical Yearbook (2001~2018) and the China Energy Statistical Yearbook (2001~2018). A part of the data is collected from the Tourism Sample Survey Data (2001~2018) and the Statistical Bulletin of National Economic and Social Development. Additionally, in order to avoid the interference of price factors on the empirical results, the data of income nature is adjusted, with year 2000 as the baseline period.

#### **3. Results**

#### *3.1. Measurement of Tourism Eco-Efficiency*

China's tourism eco-efficiency, shown in Table 2, witnessed a fluctuating growth trend during the study period, which increased from 0.441 in 2000 to 0.525 in 2017, with an average annual growth rate of 1.1%. This indicated that tourism-related economic development in China still relied on resources and harmed the environment; thus, there is tremendous room for progress in low-carbon tourism development. According to the partition criterion, formulated by the *National Bureau of Statistics* in 2011, 30 provinces were divided into four areas, namely Eastern area, Central area, Western area, and Northeastern area. From the perspective of the sub-area, the tourism eco-efficiency in the Eastern, Central, Western and Northeastern areas all showed a fluctuating growth trend, with the largest growth rate (32.70%), and the smallest growth rate (14.31%), respectively (Figure 1). The order of the spatial heterogeneity distribution pattern regarding the mean of tourism ecoefficiency by area was Eastern (0.740), Northeastern (0.440), Central (0.429) and Western (0.217) during the period of 2000~2017. At the provincial level, except for Beijing, Tianjin, Liaoning, Henan, Hunan, Chongqing, Sichuan, Qinghai and Ningxia, tourism eco-efficiency of the remaining 21 provinces experienced varying degrees of increase during the study period; the largest increase was in Jilin Province (197.18%); the largest decrease was in Qinghai Province (43.33%), indicating that, due to the differences in the input of the material elements of the tourism economy, there was great spatial heterogeneity in the tourism eco-efficiency among these provincial areas in China.

**Figure 1.** The evolution trend of tourism eco-efficiency in China and four sub-regions from 2000 to 2017. Eastern area includes Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. Central area consists of Shanxi, Jiangxi, Anhui, Henan, Hubei, Hunan. Western area includes Inner Monglia, Guangxi, Chongqing, Sichuang, Guizhou, Yunan, Shannxi, Gansu, Qinghai, Ningxia, and Xinjiang. Northeastern area includes Liaoning, Jilin, and Heilongjiang.


**Table 2.** Tourism eco-efficiency of 30 provinces from 2000 to 2017.

Notes: Due to the length limitation, the results of 2000, 2003, 2006, 2009, 2012, 2015, and 2017 are only represented in Table 2.
