Tourism Resource and Ecotourism

The Effects of Tourism Industry Agglomeration on Tourism Environmental Carrying Capacity: Evidence from a Panel Threshold Model

  • LIU Jia , 1, 2, * ,
  • LI Jing 1 ,
  • AN Keke 1
  • 1. School of Management, Ocean University of China, Qingdao, Shandong 266100, China
  • 2. Institute of Marine Development, Ocean University of China, Qingdao, Shandong 266100, China
* LIU Jia, E-mail:

Received date: 2021-10-15

  Accepted date: 2022-05-30

  Online published: 2022-10-12

Supported by

The National Social Science Fund Project of China(21BGL021)

The National Social Science Fund Project of China(19BGL138)

The Macro Decision-making Projects on Culture and Tourism of China Tourism Academy(2021HGJCG04)

The Natural Science Planning Project in Shandong Province(ZR202102200015)


By utilizing the panel data of 26 cities in the Yangtze River Delta urban agglomeration of China from 2000 to 2018, this study constructs a panel threshold model to examine the nonlinear relationship between Tourism Environmental Carrying Capacity (TECC) and Tourism Industry Agglomeration (TIA). TECC is evaluated based on the Driver-Pressure-State-Impact-Response (DPSIR) model, and TIA is estimated by the location quotient index. The analysis reveals that TIA and TECC both show growth trends and significant regional differences among the 26 cities, but the latter fluctuates at certain stages. Moreover, TIA has a significant double threshold effect on TECC, which shows that the positive impact of TIA is enhanced initially but then weakens afterwards. Theoretically, this study contributes to enriching the current literature on TECC from the perspective of TIA. Practically, it could help local governments effectively arrange agglomerations to promote the sustainable development of the tourism industry in China.

Cite this article

LIU Jia , LI Jing , AN Keke . The Effects of Tourism Industry Agglomeration on Tourism Environmental Carrying Capacity: Evidence from a Panel Threshold Model[J]. Journal of Resources and Ecology, 2022 , 13(6) : 1037 -1047 . DOI: 10.5814/j.issn.1674-764x.2022.06.009

1 Introduction

Coastal tourism has progressively become an important pillar industry in the development of China's marine economy under the guidance of the Maritime Power Strategy and the construction of the Maritime Silk Road, leading to large-scale agglomeration development (Liu et al., 2019; Yang et al., 2021). However, the tourism industry in coastal areas has caused more controversy regarding the compatibility between tourists' activities and environmental protection (Papageorgiou, 2016), as the over-development of tourism has caused negative impacts on the regional culture, environment, and resources (Graymore et al., 2010; Wang et al., 2020a). As a special industrial spatial organization mode appearing in the process of national economic development, Tourism Industry Agglomeration (TIA) has gradually become an important intermediary for regional economic development and environmental governance (He and Zhang, 2015; Kim et al., 2021). TIA can produce scale effect, cost effect, and competitive advantages, which could play a vital role in promoting tourism environmental carrying capacity (TECC) in coastal areas (Liu and Wang, 2016). However, the spatial concentrations of tourism enterprises and tourists may also aggravate the degree of environmental pollution, including overfishing and resource extraction (Wu et al., 2021), and the TECC in coastal areas will be reduced to a certain extent (Franke et al., 2020). Therefore, some light should be shed on the impact that TIA has had on TECC at different stages and the improvement path should be explored from the perspective of TIA for the continuous transformation and upgrading of the tourism industry and sustainable development of the coastal areas.
Previous academic studies have already explored the agglomeration in the tourism industry (Urtasun and Gutiérrez, 2006; Adam and Mensah, 2014). According to Porter's (1998) definition, TIA can be defined as “geographic concentrations of interconnected tourism enterprises, firms in related industries and associated institutions in related fields that cooperate but also compete”. Studies concerning TIA have mainly considered it from the perspectives of causes, measurements, and externalities of TIA. Firstly, previous studies have shown that the factors affecting the agglomeration of the tourism industry mainly include the internal thrust and the external pull (Gao and Xi, 2017; Wei, 2019). While the internal thrust mainly includes tourism resources, geographic conditions, and economy conditions (Ellison and Glaeser, 1999; Shi, 2016; Guo et al., 2021), the external pull mainly includes government policies, market demand, etc. (Jackson and Muephy, 2011; Pedro et al., 2020). Secondly, the measurement methods of TIA have been developed by referring to the methods of other industrial agglomerations, which mainly include location quotient, Herfinda-index system, spatial Gini-coefficient, and so on (Zhao et al., 2011; Gabe and Abel, 2012; Fan and Kang, 2013). Thirdly, previous studies have clearly demonstrated the significant role of TIA in improving the economic benefits of enterprises or regions. For example, the agglomeration of tourism firms could improve their productivity through labor pooling and knowledge spillovers (Hanson, 2001). However, the previous studies paid little attention to the environmental externalities of TIA on TECC, especially in the coastal areas. Because the increasingly large number of tourists in coastal areas has resulted in scenic spot crowding, biodiversity reduction and water pollution, improving the awareness of environmental externalities of the TIA is of significance for the tourism industry (Yang et al., 2019). Accordingly, this study focuses on the environmental externalities of the TIA in coastal areas of China.
The carrying capacity is an essential consideration for achieving sustainable tourism development (Sousa et al., 2017; Adrianto et al., 2021), which has assumed an increasingly prominent position. Scholars explain TECC mainly from ecological, economic, and social perspectives (Zacarias et al., 2011; Wang et al., 2020a). The assessment methods of TECC include evaluation model (Wang and Zhao, 2021), tourist ecological footprint (Nakajima and Ortega, 2016), system dynamic model (Wang et al., 2020a), and so on. To improve the accuracy of the evaluation method, this study constructs the Driver-Pressure-State-Impact-Response (DPSIR) framework to evaluate TECC, which could compensate for the problem of incomplete index selection in previous studies (Ruan et al., 2019). The tourism environmental system is a typical compound system, so when a single index is used to evaluate TECC, the result cannot truly reflect the details of the tourism environment. The DPSIR framework is a commonly used analytical framework for studying compound systems, which has applications in assessing ecosystem health, evaluating environmental security, etc. (Zhao et al., 2021). In this framework, the driver, pressure, state, impact, and response constitute a complete causal chain (Xiao et al., 2020), which can comprehensively reflect the change in the level of TECC from a system perspective.
As the ecological and environmental problems caused by TIA have gradually attracted attention, many scholars tend to study the characteristics of correlations between TIA and TECC. However, there is no generally accepted consensus. Some scholars confirm that TIA is an important way to control environmental pollution (Mao, 2006; Jia et al., 2019); while others believe that TIA aggravates environmental pollution to a certain extent (Liu et al., 2017b). Most scholars point out that the relationship between TIA and TECC is not clear. Under the restrictions of a variety of factors, it can be manifested as a “U” type, inverted “V” type, or “N” type (Yang, 2018; Zhou et al., 2019). Besides, linear regression models are often applied in studies on the relationship between tourism industry development and environmental protection, however the problem of structural mutation in independent variables has been neglected (Zhu et al., 2020). The effect of TIA on TECC can be affected by the different levels of TIA, which is a deficiency of previous studies.
The major purpose of this study is to estimate and examine the relationship between TIA and TECC under a multivariate framework quantitatively. This paper utilizes the panel data of 26 cities in the Yangtze River Delta urban agglomeration of China from 2000 to 2018, establishes an evaluation index system of TECC based on the DPSIR model, identifies the level of TIA using the location quotient model, and then applies the threshold model for analyzing the relationship between the TIA and TECC in the Yangtze River Delta urban agglomeration. Specifically, this study is focused on the following three questions: 1) Does the TIA have an impact on TECC? 2) Does the TIA have a threshold effect on TECC? 3) How can the environmental quality be improved from the perspective of the TIA?

2 Study area

In this study, the Yangtze River Delta urban agglomeration of China, composed of 26 cities in three provinces and one municipality, is selected as the research area (Fig. 1). The Yangtze River Delta urban agglomeration is a critical convergence zone of “the Silk Road Economic Belt and the 21st-Century Maritime Silk Road” and “the Yangtze River Economic Belt”. The gross domestic product (GDP) of the Yangtze River Delta urban agglomeration reached 19.73 trillion yuan in 2020, accounting for one-fifth of China's total economic output with less than 3% of the country's land area. The Yangtze River Delta urban agglomeration is also a typical region with the prominent contradiction between humans and environment (Ma et al., 2021). The Joint Ecological Environment Protection Plan in the Yangtze River Delta Region issued in 2020 aims to promote the continuous improvement of regional environmental quality and transform the Yangtze River Delta ecological green integrated development demonstration zone into an important window for China to display its achievements in ecological civilization construction. Consequently, taking the Yangtze River Delta urban agglomeration as the study area is of great reference value for guiding other areas in properly coordinating the relationship between tourism development and environment protection.
Fig. 1 Location of the study area

3 Methods and data

3.1 Panel threshold model

Conventional regression typically examines the nonlinear effects of TIA on TECC by introducing an interactive term. However, it ignores the potential structural breaks that may occur over a relatively long period (Huang et al., 2018; Zhang et al., 2021). The panel threshold model developed by Hansen (1999) can address the problems of the conventional regression analysis and allow for possible changes in the effects that each independent variable has on the dependent variable at different time intervals. To investigate the impact of TIA on TECC, this paper employs a panel threshold model:
$TEC{{C}_{it}}={{\mu }_{i}}+{{{\beta }'}_{1}}{{x}_{it}}I(TI{{A}_{it}}\le \gamma )+{{{\beta }'}_{2}}{{x}_{it}}I(TI{{A}_{it}}>\gamma )+{{\varepsilon }_{it}}$
where TECCit and xit denote the dependent variable and independent variables, respectively; i stands for the cross- section and t stands for the time dimensions; μi stands for the individual effects; ${{{\beta }'}_{1}}$ and ${{{\beta }'}_{2}}$ are the coefficients between the dependent variables and independent variables; I(•) is the indicator function, which equals 1 when a single-threshold effect exists or 0 when a linear effect exists; TIAit is the threshold variable; γ is the optimal threshold value; and the error term εit is assumed to be independent and identically distributed with zero mean and a finite variance σ2.
The second threshold could also be tested:
$\begin{align} & TEC{{C}_{it}}={{\mu }_{i}}+{{{{\beta }'}}_{1}}{{x}_{it}}I(TI{{A}_{it}}\le {{\gamma }_{1}})+ \\ & \ \ \ \ \ \ \ \ \ \ \ \ \ {{{{\beta }'}}_{2}}{{x}_{it}}I({{\gamma }_{1}}<TI{{A}_{it}}\le {{\gamma }_{2}})+{{{{\beta }'}}_{3}}{{x}_{it}}I(TI{{A}_{it}}>{{\gamma }_{2}})+{{\varepsilon }_{it}} \\ \end{align}$
where ${{{\beta }'}_{1}}$, ${{{\beta }'}_{2}}$, and ${{{\beta }'}_{3}}$ stand for the vectors of coefficients of the independent variables on the three sides of the threshold; and the meanings of μi, I(•), γ, TIAit, and εit are same as before.

3.2 Variables and data

3.2.1 Independent variable—TIA

The core independent variable and the threshold variable, TIA, represents the degree of geographic concentrations of the tourism industry in a spatial unit. This paper uses the method of location quotient to measure the TIA index. The location quotient eliminates the impact of regional scale, so it can more truly reflect the agglomeration state of geographical elements (Tang et al., 2021b) and has been extensively used to reflect industry agglomeration (Miller et al., 1991; Billings and Johnson, 2012). In this paper, the TIA is defined as follows:
where TIAit is the level of the tourism industry agglomeration of city i in year t; rit stands for the tourism revenue of city i in year t; Rit is the GDP of city i in year t; $\sum{{{r}_{it}}}$ denotes the sum of tourism revenue in Yangtze River Delta urban agglomeration in year t; and $\sum{{{R}_{it}}}$ denotes the sum of GDP in Yangtze River Delta urban agglomeration year t. A value of TIAit>1 suggests that the tourism in city i has a higher- than-average spatial density level of tourism economies in year t (Zhou et al., 2021), while TIAit<1 suggests the opposite.

3.2.2 Dependent variable—TECC

The dependent variable, TECC, represents the ability of the destination to manage the increasing tourists' activities without any degradation in the destination. To estimate the TECC index, this paper establishes an evaluation index system based on the DPSIR model proposed in 1993 by the European Environmental Agency (Bushra et al., 2009). The DPSIR model describes a general chain that triggers environmental problems between origin and outcome (Sun et al., 2016), and is an effective approach for exploring the relationships between environmental systems and socioeconomic systems. In the DPSIR framework, social, economic, and population development act as drivers that exert pressure on the environmental system, leading to state changes and a range of impacts that may require responses; while the responses can simultaneously generate feedback to the drivers, reduce the pressures, improve the states, and reduce negative impacts, thus creating a feedback loop of Driver- Pressure-State-Impact-Response (Atkins et al., 2011; Marinella et al., 2017; Zhao et al., 2021). Combined with the population, economy, tourism resources, and environmental conditions, and based on the analysis of previous research results (Zhang et al., 2008; Liu, 2010; Gai et al., 2018) and fully considering the availability and representativeness of the data, this paper establishes an evaluation index system of TECC based on the DPSIR model, which consists of 5 subsystems and 30 indicators (Table 1).
Table 1 Evaluation index system of TECC
Target Subsystems Indicators Indicator interpretations
TECC Driver (D) GDP (D1) Represents the driving force of economic aggregate on TECC
Per capita disposable income of households (D2) Represents the driving force of economic development level on TECC
Ratio of the number of tourists to the number of residents (D3) Represents the driving force of tourist crowding on TECC
Turnover of passenger traffic (D4) Represents the driving force of tourist turnover on TECC
Number of tourists (D5) Represents the driving force of tourist stay on TECC
Pressure (P) Energy consumption per unit of GDP (P1) Represents the pressure of energy consumption on TECC
Electricity consumption per unit of GDP (P2) Represents the pressure of electricity consumption on TECC
Water consumption per unit of GDP (P3) Represents the pressure of water consumption on TECC
State (S) Number of A-grade tourist attractions (S1) Represents the number of tourism resources
Number of tourist attractions above AAAA-grade (S2) Represents the quality of tourism resources
Number of air quality standard days (S3) Represents the quality of air
Impact (I) Total tourism revenue (I1) Represents the impact of TECC on tourism economy
Tourism earnings as percentage of GDP (I2) Represents the impact of TECC on industrial structure
Tertiary industry product as percentage of GDP (I3) Represents the impact of TECC on economic structures
Number of tourism professionals (I4) Represents the impact of TECC on regional employment
Per capita consumption expenditure of households (I5) Represents the impact of TECC on residents' consumption expenditure
Response (R) Number of cultural and art institutions (R1) Represents the response of infrastructure to TECC
Ratio of waste water centralized treated of sewage work (R2) Represents the response of water quality optimization to TECC
Ratio of consumption on wastes treated (R3) Represents the response of waste utilization to TECC
Green covered area as percentage of built-up area (R4) Represents the response of air quality optimization to TECC
Investment in anti-pollution projects as percentage of GDP (R5) Represents the response of government governance to TECC
Actual utilization of foreign capital as percentage of GDP (R6) Represents the response of regional openness to TECC
Intramural expenditure on R&D as percentage of GDP (R7) Represents the response of scientific research investment to TECC
Number of taxis (R8) Represents the response of infrastructure to TECC
Number of hospitals (R9) Represents the response of public services to TECC
Per capita years of school attainment (R10) Represents the response of tourism residents to TECC
Number of students enrolled in regular institutions of higher education (R11) Represents the response of practitioner quality to TECC
Specifically, the driver reflects the effects of the society and the economy on TECC, which includes indicators such as GDP, per capita disposable income of households, and turnover of passenger traffic. The pressure reflects the factors that lead to changes in the tourism environmental system and the impact on TECC, which is caused by the influence of the driver. The pressure includes indicators such as energy consumption per unit of GDP and volume of industrial sulphur dioxide emission. The state refers to the state of the tourism environmental system under the pressure of the driver, which includes indicators such as number of A-grade tourist attractions and number of air quality standard days. The impact refers to changes in the system of the tourism environment caused by the driver and pressure, which includes indicators such as total tourism revenue, number of tourism professionals, and per capita consumption expenditure of households. The response refers to the different measures taken to ensure the higher efficiency of the local tourism environmental resources system in the process of tourism resource development and utilization, which includes indicators such as ratio of consumption to wastes treated and number of taxis.
The entropy weight method is an objective and effective method to determine the weight of each indicator (Xiao et al., 2020). In this paper, the evaluation initial matrix {xijt}m×n is established, where m is the number of cities in the Yangtze River Delta urban agglomeration, with a value of 26, and n is the number of indicators of the evaluation index system of TECC, with a value of 30. To transform the different units among various indices into common measurable units (Zhang et al., 2011), the data for each index are standardized. Considering that the normalized data might be zero, all values are offset to the right by 0.01.
Positive indicator:
${{X}_{ijt}}=({{x}_{ijt}}-{{x}_{jt\min }})/({{x}_{jt\max }}-{{x}_{jt\min }})+0.01$
Negative indicator:
${{X}_{ijt}}=({{x}_{jt\max }}-{{x}_{ijt}})/({{x}_{jt\max }}-{{x}_{jt\min }})+0.01$
In the formula, Xijt is the dimensionless value converted by indicators j; xijt is the original value corresponding to the indicators j of the cities i in year t; xjt max is the maximum value of indicators j and xjt min is the minimum value of indicators j.
The proportion of the index value (Pijt) of city i under index j in year t is calculated as:
The information entropy of index j in year t (ejt) is calculated as:
${{e}_{jt}}=-\frac{1}{\ln m}\sum\limits_{i=1}^{m}{{{P}_{ijt}}}\ln {{P}_{ijt}}$
The weight of index j in year t (wjt) is calculated as:
The comprehensive score (TECCit) of each city is then calculated as:

3.2.3 Variable descriptions and data sources

In accordance with previous studies, setting up a set of control variables could alleviate the potentially omitted variable bias. Accordingly, based on reference to relevant research (Liu et al., 2017a; Duan et al., 2018; Wang and Hu, 2020), control variables are incorporated into the empirical model (Table 2). Among them, the variable tourism ecological efficiency is calculated by the Super-efficiency SBM model, which consists of input, expected output, and unexpected output dimensions (Wang et al., 2020b).
Table 2 Definitions of variables
Variables Attributes Measurements
Tourism environmental carrying capacity (TECC) Dependent variable Entropy weight method
Tourism industry agglomeration (TIA) Independent variable Location quotient
Economic development level (ECO) Control variable Per capita disposable income
Tourist density (DEN) Control variable Ratio of the number of tourists to the number of residents
Tourism ecological efficiency (EFF) Control variable Super-efficiency SBM model
Technological progress level (TEC) Control variable Science and technology expenditure/local government financial expenditure
Environmental regulation strength (ERS) Control variable Investment in anti-pollution projects as percentage of GDP
This study employs a panel data set covering the 26 cities in the Yangtze River Delta urban agglomeration between 2000 and 2018. Relevant data are mainly from the statistical yearbooks and database platforms of various provinces and cities, including “China Regional Economic Statistical Yearbooks”, “China Tourism Statistical Yearbooks”, and “China City Statistical Yearbooks”. The missing data are pre-processed by using the linear interpolation method (Wang et al., 2020c). The correlation matrix of the independent variables is presented in Table 3. The results demonstrate that the correlations between the variables are limited, indicating that there is no multicollinearity problem.
Table 3 Matrix of correlations
TIA 1.0000
ECO 0.2431 1.0000
DEN 0.0029 0.0140 1.0000
EFF 0.5018 0.6258 -0.0095 1.0000
TEC 0.0288 0.6843 0.0225 0.3638 1.0000
ERS 0.1151 0.0737 0.0024 0.2215 0.0099 1.0000

4 Empirical results analysis

4.1 Evolution of TIA and TECC

According to the calculations (Fig. 2a), within the study period, the overall location quotient index shows a significant growth trend, increasing from 0.70 in 2000 to 1.50 in 2018, which indicates that the agglomeration effect of the
tourism industry in the Yangtze River Delta urban agglomeration was strengthened gradually. Overall, the TECC index during the sample period shows limited fluctuation, but with an upward trend similar to the location quotient index. Specifically, the value improves from 0.14 in 2000 to 0.23 in 2018, with the average of 0.20. Selecting 2000, 2006, 2012, and 2018 as the observation years to further demonstrate the status of each subsystem of the TECC, the results are shown in Fig. 2b. From the perspective of each DPSIR subsystem, the state subsystem shows a significantly increasing trend during the study period, while the driver, response, and impact subsystems each have fluctuating trends. The pressure subsystem accounts for a large proportion of the influence on the overall TECC index, which indicates that the pressure on the TECC is the key factor causing the fluctuation and even the decline of the TECC.
Fig. 2 The temporal evolution of the TIA and TECC (a) and the five subsystems (b)
ArcGIS 10.2 was used to elaborate the spatial evolution graph of TIA and TECC. Figure 3(a)-(d) demonstrate the spatial distributions of the level of TIA in 2000, 2006, 2012, and 2018, respectively. All sample regions are divided into three groups according to their values for the location quotient of the tourism industry (Zhou et al., 2021). Figure 3(a) shows that in 2000, the level of TIA in most cities is generally low, while Fig. 3(b) and (c) reveal that from 2000 to 2012, the spatial concentrations of tourism economies in the central and southern regions have significant increasing trends. By 2018, the spatial distribution pattern demonstrates the greatest agglomeration in the western region, followed in order by the central, southern, and northeastern regions. Besides, Fig. 3(e)-(h) demonstrate the spatial distributions of the level of TECC in 2000, 2006, 2012, and 2018, respectively. Similarly, the TECC index is divided into three groups according to the numerical characteristics. Figure 3(e) shows that in 2000, the indexes of TECC in the northern and western areas of the Yangtze River Delta urban agglomeration are generally low, while the index in the southeastern region with a high TIA is high in the same period. Figure 3(f)-(h) show the fluctuations of the TECC index, revealing the instability of the tourism environmental system. Cities with a high TECC in this period are mainly distributed in the central and eastern regions of the study area, such as Shanghai, Suzhou, and Nanjing. It is inappropriate to analyze the specific relationship between TIA and TECC by just relying on the information provided in this figure, and additional analyses are required.
Fig. 3 The spatial evolution of the TIA (a-d) and TECC (e-h)

4.2 Results of threshold effect test

In order to examine the role of TIA in influencing TECC, this study proposes a panel threshold model. In order to verify the rationality and significance of the threshold model and determine the number of the thresholds accurately, it is critical to test the panel threshold model (Zhu et al., 2020). The results are shown in Table 4. The test for a single threshold of the F-statistic is significant at the 5% level, with a value of 19.832. The double threshold of the F-statistic is statistically significant at the 1% level, with a value of 57.151, while the triple threshold tests are not significant (data not shown). The test results indicate that there are two thresholds in the relationship between the TIA and TECC, which implies that the TECC is quite sensitive to the changes in the TIA. In other words, the TECC experiences structural breaks when the TIA is at different intervals. Therefore, the double threshold model should be constructed separately. Moreover, the results show that the two threshold value estimates are 0.991 and 1.626, respectively.
Table 4 Test of the threshold effect
Type of threshold test 95% confidence interval F-statistic P-value Threshold estimate Critical value
1% 5% 10%
Single threshold model [1.194, 1.217] 19.832** 0.022 1.217 21.426 17.807 15.074
Double threshold model [0.974, 2.460]; [0.931, 1.064] 57.151*** 0.000 0.991; 1.626 -12.300 -18.261 -21.545

Note: ** and *** indicate the tests are significant at levels of 5% and 1%, respectively. The triple threshold test is not significant, so the results are not listed here.

According to the two threshold values of TIA, the statistical features of the 26 cities of the Yangtze River Delta urban agglomeration from 2000 to 2018 are shown in Table 5. For the first threshold value (0.991), Shanghai, Hangzhou, Zhoushan, Nanjing, Ningbo cities cross it firstly in 2000. By 2018, the number of cities crossing the first threshold value has reached 16, with the threshold crossing rate rising from 19.23% to 69.54%. For the second threshold value (1.626), only a few of the 26 cities in the Yangtze River Delta urban agglomeration cross the second threshold value. Shanghai takes the lead in 2000. This is because Shanghai has clear advantages in the development of tourism resources and the construction of tourism infrastructure. It is also one of the cradles of modern tourism in China where the tourism economy is developed, and the source market is sufficient. Its modern urban style tourism resources have attracted a large number of tourists, and the tourism industry has formed an agglomeration advantage. By 2018, Chizhou, Zhoushan, Huzhou, Anqing, and Jinhua, also crossed the second threshold value, and the threshold crossing rate increased from 3.85% to 19.23%. It can be seen that tourism industry has gradually developed in most cities of the Yangtze River Delta urban agglomeration, the agglomeration level of tourism industry is in the stage of improvement, and the advantages of industrial scale are gradually reflected.
Table 5 Numbers and proportions of the 26 sample cities in the two threshold value intervals
Year Across the first threshold 0.991 First threshold crossing rate (%) Across the second threshold 1.626 Second threshold crossing rate
2000 5 19.23 1 3.85
2001 4 15.39 0 0
2002 4 15.39 0 0
2003 5 19.23 1 3.85
2004 5 19.23 1 3.85
2005 7 26.92 1 3.85
2006 9 34.62 1 3.85
2007 10 38.46 2 7.69
2008 8 30.77 2 7.69
2009 10 38.46 2 7.69
2010 10 38.46 2 7.69
2011 9 34.62 2 7.69
2012 11 42.31 2 7.69
2013 15 57.69 2 7.69
2014 15 57.69 3 11.54
2015 15 57.69 4 15.39
2016 15 57.69 4 15.39
2017 14 53.85 5 19.23
2018 16 61.54 5 19.23

4.3 Results of threshold effect regression

The empirical results of the threshold effect regression are shown in Table 6. Overall, TIA has a significant positive impact on TECC, but the effect is not exactly the same when the TIA is at different intervals. Specifically, when TIA is below the first threshold (0.991), the effect of TIA on TECC is significantly positive, with a value of 0.028. When the TIA increases but remains below the second threshold, the positive effect on TECC increases in size and signifi cance, and the regression coefficient changes from 0.028 to 0.053. Once the TIA exceeds the second threshold (1.626), the impact of TIA on TECC decreases in size, but it remains a positive effect, and the regression coefficient changes from 0.053 to 0.023. The interesting empirical evidence presented in Table 6 shows that the effect of TIA on TECC will be influenced by the level of TIA. Since tourism is a complex natural-economic-social system, the scale effect, cost effect, and competitive advantages of TIA could enhance the level of TECC. The TIA in the urban agglomeration of China can help tourism enterprises to achieve the goals of sharing information, reducing market development costs, and improving service quality. For example, the sharing of clean technology will reduce the amount of waste produced by tourism enterprises. However, TIA in the urban agglomeration of China will also attract a large number of tourists, resulting in a waste of resources and environmental damage. Consequently, the positive effect of TIA will be reduced. Furthermore, the coefficients of the control variables are all estimated to be positive and significant, suggesting their significantly positive effects on the level of TECC.
Table 6 Test results of the double threshold of TIA
Variable Regression coefficient T-statistic P value
ECO 0.001* 1.80 0.072
DEN 0.001*** 6.73 0.000
ERS 1.373*** 4.84 0.000
TEC 0.466*** 3.75 0.000
EFF 0.024** 2.10 0.036
TIA≤0.991 0.028*** 2.58 0.010
0.991<TIA≤1.626 0.053*** 6.85 0.000
TIA>1.626 0.023*** 5.72 0.000

Note: *, **, and *** indicate the tests are significant at levels of 10%, 5%, and 1%, respectively.

5 Discussion

This study differs from the existing literature on this topic in several aspects. Although the previous studies have made numerous efforts to measure TECC from different research perspectives, they mainly adopted a single indicator or an established evaluation index system from economic, social, or ecological aspects, neglecting the comprehensiveness of the tourism industry as a compound and causal system. The evaluation system based on the DPSIR model that is constructed in this study describes the general chain that triggers environmental issues between the origin and the results (Sun et al., 2016), and it is also refined by the inclusion of desirable and undesirable output indexes. In addition, the panel threshold model could solve the problem of structural mutation in the mutual effect that had always been ignored in previous linear regression models (Zhu et al., 2020). This study also utilizes the panel threshold model to examine the nonlinear effect of TIA on TECC in the Yangtze River Delta urban agglomeration of China, which shows that the positive impact is enhanced initially and weakens afterwards.
In addition to the theoretical implications, this study also contributes to several managerial implications concerning how to enhance the sustainable carrying capacity and promote the high-quality development of the tourism industry. On the one hand, the appropriate degree of TIA is proven to be crucial as a means of strengthening the TECC. Positive measures should be taken to promote the transformation and upgrading of the tourism industry to give full play to the scale effect, cost effect, and competitive advantages of the TIA on TECC (Liu and Wang, 2016). Meanwhile, it is revealed that significant spatial differences occur in TIA and TECC, and the negative impact of excessive TIA may weaken the scale effect, cost effect, and competitive advantages of TIA on TECC. Regulatory measures suitable for local conditions should be used to avoid the unlimited expansion of the future tourism investment scale. On the other hand, the influencing factors that are identified can effectively guide practitioners to improve TECC. For instance, investments in environmental protection, ecological compensation, and technological innovation in the pursuit of tourism development should be highly valued. It is critical to pay full attention to the increasingly significant carbon-intensive characteristics of tourism and formulate promotional or regulatory measures conducive to improving TECC. Managers must also strive for the implementation of safety warnings for peak seasons and high-density tourist destinations. Additionally, the awareness of the carbon reductions of tourism consumption subjects, consumption sites, and service organizations should be promoted in the tourism process to improve the efficiency of tourism resource allocation and utilization (Tang et al., 2021a).
Despite the theoretical and practical contributions, this study also faces some limitations which will require further research. Firstly, the data set is limited to the Yangtze River Delta urban agglomeration, which may limit the generalizability of the results. It is recommended that future research collect data from other destinations to examine the broader applicability of these findings. Secondly, this study takes only TIA as the threshold variable to explore its threshold effect on TECC. There may be other threshold variables that drive nonlinear relationship changes. Future studies can introduce different threshold variables to examine the reasonable range of TIA when the threshold effects change. Finally, this study considers the TIA as a whole, neglecting the fact that TIA can be classified as specialized agglomeration and diversified agglomeration, and future studies can further investigate this issue in more detail.

6 Conclusions

By utilizing the panel data of 26 cities in the Yangtze River Delta urban agglomeration of China from 2000 to 2018, this paper establishes an evaluation index system of TECC based on the DPSIR model, and identifies the level of TIA using the location quotient index. The threshold model is then applied to analyze the dynamic relationship between the TIA and TECC in the Yangtze River Delta urban agglomeration. The results illustrate three key features of this system. 1) The degree of TIA in the Yangtze River Delta urban agglomeration of China is strengthening gradually, and the tourism industry there has formed a scale advantage. 2) The level of TECC in the Yangtze River Delta urban agglomeration of China is improving continually, and there is great potential for tourism industrial development. 3) The TIA has a double threshold promotion effect on TECC, which shows that these positive effects will strengthen at first and then weaken afterwards. The findings could provide policy guidance for facilitating the sustainable development of the tourism industry of urban agglomerations in China.
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