Journal of Resources and Ecology ›› 2022, Vol. 13 ›› Issue (6): 1037-1047.DOI: 10.5814/j.issn.1674-764x.2022.06.009
• Tourism Resource and Ecotourism • Previous Articles Next Articles
LIU Jia1,2,*(
), LI Jing1, AN Keke1
Received:2021-10-15
Accepted:2022-05-30
Online:2022-11-30
Published:2022-10-12
Contact:
LIU Jia
Supported by: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.
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URL: http://www.jorae.cn/EN/10.5814/j.issn.1674-764x.2022.06.009
Fig. 1 Location of the study area
| 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 |
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 |
| 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 |
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 |
| Variables | TIA | ECO | DEN | EFF | TEC | ERS |
|---|---|---|---|---|---|---|
| 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 |
Table 3 Matrix of correlations
| Variables | TIA | ECO | DEN | EFF | TEC | ERS |
|---|---|---|---|---|---|---|
| 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 |
Fig. 2 The temporal evolution of the TIA and TECC (a) and the five subsystems (b)
Fig. 3 The spatial evolution of the TIA (a-d) and TECC (e-h)
| 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 |
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 |
| 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 |
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 |
| 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 |
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 |
| [1] |
Adam I, Mensah E A. 2014. Perceived spatial agglomeration effects and hotel location choice. Anatolia, 25(1): 49-60.
DOI URL |
| [2] |
Adrianto L, Kurniawan F, Romadhon A, et al. 2021. Assessing social-ecological system carrying capacity for urban small island tourism: The case of Tidung Islands, Jakarta Capital Province, Indonesia. Ocean & Coastal Management, 212: 105844. DOI: 10.1016/j.ocecoaman.2021.105844.
DOI |
| [3] |
Atkins J P, Burdon D, Elliott M, et al. 2011. Management of the marine environment: Integrating ecosystem services and societal benefits with the DPSIR framework in a systems approach. Marine Pollution Bulletin, 62(2): 215-226.
DOI PMID |
| [4] |
Billings S B, Johnson E B. 2012. The location quotient as an estimator of industrial concentration. Regional Science and Urban Economics, 42(4): 642-647.
DOI URL |
| [5] |
Bushra W, Faisal K, Brian V. 2009. Linkage-based frameworks for sustainability assessment: Making a case for driving Force-Pressure-State- Exposure-Effect-Action (DPSEEA) frameworks. Sustainability, 1(3): 441-463.
DOI URL |
| [6] | Duan P L, Liu S G, Yin P, et al. 2018. Spatial-temporal coupling coordination relationship between development strength and resource environmental bearing capacity of coastal cities in China. Economic Geography, 38(5): 60-67. (in Chinese) |
| [7] |
Ellison G, Glaeser E L. 1999. The geographic concentration of industry: Does natural advantage explain agglomeration. American Economic Review, 89(2): 311-316.
DOI URL |
| [8] | Fan X F, Kang X Q. 2013. Agglomeration level measurement of manufacturing in Shaanxi Province and its influencing factors empirical analysis. Economic Geography, 33(9): 115-119, 160. (in Chinese) |
| [9] |
Franke A, Blenckner T, Duarte C M, et al. 2020. Operationalizing ocean health: Toward integrated research on ocean health and recovery to achieve ocean sustainability. One Earth, 2(6): 557-565.
DOI URL |
| [10] |
Gabe T M, Abel J R. 2012. Specialized knowledge and the geographic concentration of occupations. Journal of Economic Geography, 12(2): 435-453.
DOI URL |
| [11] | Gai M, Nie C, Ke L N. 2018. Carrying capacity and coordinated development of ERE system in the coastal area of Bohai Sea. Economic Geography, 38(7): 163-172. (in Chinese) |
| [12] | Gao P, Xi J C. 2017. Tourism industry agglomeration and mechanisms for rural settlements of Gouge Village in Yesanpo, Hebei Province. Resources Science, 39(8): 1535-1544. (in Chinese) |
| [13] |
Graymore M L, Sipe N G, Rickson R E. 2010. Sustaining human carrying capacity: A tool for regional sustainability assessment. Ecological Economics, 69(3): 459-468.
DOI URL |
| [14] | Guo X Y, Ming Q Z, Ding Z S. 2021. Does highway traffic have spatial spillover effect on urban tourism economy? Take the Yangtze River Economic Belt as an example. Journal of Sichuan Normal University, 48(2): 80-91. (in Chinese) |
| [15] |
Hansen B E. 1999. Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics, 93(2): 345-368.
DOI URL |
| [16] |
Hanson G H. 2001. Scale economies and the geographic concentration of industry. Journal of Economic Geography, 1(3): 255-276.
DOI URL |
| [17] | He X L, Zhang Z Y. 2015. Exploration on the distribution of economic activity: Technology spillover, environmental pollution and trade liberalization. Scientia Geographica Sinica, 35(2): 161-167. (in Chinese) |
| [18] |
Huang J B, Liu Q, Cai X C, et al. 2018. The effect of technological factors on China's carbon intensity: New evidence from a panel threshold model. Energy Policy, 115: 32-42.
DOI URL |
| [19] | Jackson J, Muephy P. 2011. Tourism destination as clusters: Analytical experience from the new world. Tourism and Hospitably Research, 4(1): 36-52. |
| [20] | Jia J C, Kong W, Ren L. 2019. Research on coordinated development of tourism economy and ecological environment in the northwest of Hebei Province under the background of coordinated development of Beijing-Tianjin-Hebei. Chinese Journal of Agricultural Resources and Regional Planning, 40(2): 167-173. (in Chinese) |
| [21] |
Kim Y R, Williams A M, Park S, et al. 2021. Spatial spillovers of agglomeration economies and productivity in the tourism industry: The case of the UK. Tourism Management, 82: 104201. DOI: 10.1016/j.tourman.2020.104201.
DOI URL |
| [22] | Liu J, Li Y Y, Wang J. 2017a. An analysis of dynamic relation between tourism environmental carrying capacity and urbanization level in China's coastal regions. Commercial Research, 4: 178-185. (in Chinese) |
| [23] | Liu J, Liu N, Zhang Y M, et al. 2019. Evaluation of the non-use value of beach tourism resources: A case study of Qingdao coastal scenic area, China. Ocean & Coastal Management, 168: 63-71. |
| [24] | Liu J, Wang J. 2016. Research on associated action between tourism industrial agglomeration development and carrying capacity ascension in China's coastal regions. Commercial Research, 10: 145-156. (in Chinese) |
| [25] |
Liu S X, Zhu Y M, Du K Q. 2017b. The impact of industrial agglomeration on industrial pollutant emission: Evidence from China under new normal. Clean Technologies and Environmental Policy, 19(9): 2327-2334.
DOI URL |
| [26] | Liu W. 2010. Research on environmental carrying capacity of island tourism. China Population, Resources and Environment, 20(S2): 75-79. (in Chinese) |
| [27] |
Ma X B, Sun B, Hou G L, et al. 2021. Evaluation and spatial effects of tourism ecological security in the Yangtze River Delta. Ecological Indicators, 131: 108190. DOI: 10.1016/j.ecolind.2021.108190.
DOI URL |
| [28] | Mao J M. 2006. Comparative analysis of tourism and manufacturing industry agglomeration. Inquiry into Economic Issues, 6: 125-128. (in Chinese) |
| [29] |
Marinella S, Gentile F, Davies C, et al. 2017. The DPSIR framework in support of green infrastructure planning: A case study in southern Italy. Land Use Policy, 61: 242-250.
DOI URL |
| [30] | Miller M M, Gibson L J, Wright N G. 1991. Location quotient: A basic tool for economic development analysis. Economic Development Review, 9(2): 65-68. |
| [31] |
Nakajima E S, Ortega E. 2016. Carrying capacity using emergy and a new calculation of the ecological footprint. Ecological Indicators, 60: 1200-1207.
DOI URL |
| [32] |
Papageorgiou M. 2016. Coastal and marine tourism: A challenging factor in marine spatial planning. Ocean and Coast Management, 129: 44-48.
DOI URL |
| [33] | Pedro M, Garcia V, Dioni E, et al. 2020. Understanding pioneering orientation in tourism clusters: Market dynamism and social capital. Tourism Management, 76(2): 1-15. |
| [34] |
Porter M E. 1998. Clusters and new economics of competition. Harvard Business Review, 76(6): 77-90.
PMID |
| [35] |
Ruan W Q, Li Y Q, Zhang S N, et al. 2019. Evaluation and drive mechanism of tourism ecological security based on the DPSIR-DEA model. Tourism Management, 75: 609-625.
DOI URL |
| [36] | Shi Y Q. 2016. Study on the basic mechanism and application of rural tourism industry agglomeration. Agricultural Economy, 11: 30-32. (in Chinese) |
| [37] |
Sousa R C, Pereira L C C, Costa R M D, et al. 2017. Management of estuarine beaches on the Amazon Coast though the application of recreational carrying capacity indices. Tourism Management, 59: 216-225.
DOI URL |
| [38] |
Sun S K, Wang Y B, Liu J, et al. 2016. Sustainability assessment of regional water resources under the DPSIR framework. Journal of Hydrology, 532: 140-148.
DOI URL |
| [39] | Tang C C, Zha J P, Zhang J K, et al. 2021a. Dual-carbon goal of China's tourism industry under high-quality development: Evaluation & prediction, major challenges and realization path. Journal of Chinese Ecotourism, 11(4): 471-497. (in Chinese) |
| [40] | Tang Z P, Wu W, Liu W C, et al. 2021b. Effects of high-speed rail on the spatial agglomeration of producer services: A case study of the Yangtze River Delta urban agglomeration. Progress in Geography, 40(5): 746-758. (in Chinese) |
| [41] |
Urtasun A, Gutiérrez I. 2006. Tourism agglomeration and its impact on social welfare: An empirical approach to the Spanish case. Tourism Management, 27(5): 901-912.
DOI URL |
| [42] | Wang J, Hu Y. 2020. Spatial correlation and spatial spillover effects: A study on the impact of industrial ecological innovation on resource and environmental carrying capacity. The Theory and Practice of Finance and Economics, 41(1): 117-124. (in Chinese) |
| [43] |
Wang J, Huang X J, Gong Z Q, et al. 2020a. Dynamic assessment of tourism carrying capacity and its impacts on tourism economic growth in urban tourism destinations in China. Journal of Destination Marketing & Management, 15: 100383. DOI: 10.1016/j.jdmm.2019.100383.
DOI |
| [44] | Wang S P, Qiao H F, Feng J, et al. 2020b. The spatio-temporal evolution of tourism eco-efficiency in the Yellow River Basin and its interactive response with tourism economy development level. Economic Geography, 40(5): 81-89. (in Chinese) |
| [45] | Wang Z F, Zhao S S. 2021. Research on spatial consistency between tourism resource environmental carrying capacity and land space function of urban agglomeration in the middle reaches of the Yangtze River. Resources and Environment in the Yangtze Basin, 30(5): 1027-1039. (in Chinese) |
| [46] |
Wang Z, Liu Q, Xu J, et al. 2020c. Evolution characteristics of the spatial network structure of tourism efficiency in China: A province-level analysis. Journal of Destination Marketing and Management, 18: 100509. DOI: 10.1016/j.jdmm.2020.100509.
DOI URL |
| [47] | Wei M. 2019. A study on the mechanism for the impetus of business clustering of China's tourism industry. Frontiers, 11: 89-101. (in Chinese) |
| [48] |
Wu Z, Chen R, Meadows M E, et al. 2021. Application of the ocean health index to assess ecosystem health for the coastal areas of Shanghai, China. Ecological Indicators, 126: 107650. DOI: 10.1016/j.ecolind.2021.107650.
DOI URL |
| [49] |
Xiao Q L, Tian C, Wang Y J, et al. 2020. Measurement and comparison of urban haze governance level and efficiency based on the DPSIR model: A case study of 31 cities in north China. Journal of Resources and Ecology, 11(6): 549-561.
DOI |
| [50] | Yang H. 2018. Threshold effect analysis of ecological environment and tourism industry development. Statistics & Decision, 34(21): 146-149. (in Chinese) |
| [51] |
Yang W, Cai F, Liu J H, et al. 2021. Beach economy of a coastal tourist city in China: A case study of Xiamen. Ocean & Coastal Management, 211: 105798. DOI: 10.1016/j.ocecoaman.2021.105798.
DOI |
| [52] | Yang X P, Wang G M, Li J, et al. 2019. Optimization of tourism environment carrying capacity in coastal cities: A case study of Qinhuangdao City. Geography and Geo-Information Science, 35(4): 134-140. (in Chinese) |
| [53] |
Zacarias D A, Williams A T, Newton A. 2011. Recreation carrying capacity estimations to support beach management at Praia de Faro, Portugal. Applied Geography, 31(3): 1075-1081.
DOI URL |
| [54] | Zhang B S, Wang T L, Wang X H. 2021. The relationship between government investment in science and technology, R & D personnel scale and output of basic research in universities-Threshold regression analysis based on provincial panel data. Forum on Science and Technology in China, 4: 55-63, 74. (in Chinese) |
| [55] | Zhang G H, Liu J, Wang L, et al. 2008. Study on synthesis evaluation of tourism environmental carrying capacity of Shandong Peninsula urban agglomeration. Progress in Geography, 27(2): 74-79. (in Chinese) |
| [56] |
Zhang H, Gu C L, Gu L W, et al. 2011. The evaluation of tourism destination competitiveness by TOPSIS & information entropy-A case in the Yangtze River Delta of China. Tourism Management, 32(2): 443-451.
DOI URL |
| [57] | Zhao J Q. 2011. Study on the bias of the Herfindahl-index system when being used in measuring the centralization property of markets. The Journal of Quantitative & Technical Economics, 28(12): 132-145. (in Chinese) |
| [58] |
Zhao R D, Fang C L, Liu H M, et al. 2021. Evaluating urban ecosystem resilience using the DPSIR framework and the ENA model: A case study of 35 cities in China. Sustainable Cities and Society, 72(4): 102997. DOI: 10.1016/j.scs.2021.102997.
DOI URL |
| [59] |
Zhou B, Wen Z H, Yang Y. 2021. Agglomerating or dispersing? Spatial effects of high-speed trains on regional tourism economies. Tourism Management, 87: 104392. DOI: /10.1016/j.tourman.2021.104392.
DOI URL |
| [60] | Zhou J W, Jiang Z Y, Zhao Y. 2019. The impact of tourism industry agglomeration on environmental pollution from the perspective of ecological civilization: Taking the western region as an example. Ecological Economy, 35(4): 132-139. (in Chinese) |
| [61] |
Zhu M F, Wu Q L, Zhang H M, et al. 2020. The threshold effect of rationalization of industrial structure on air quality in Shanxi Province. Journal of Resources and Ecology, 11(2): 206-212.
DOI |
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