Assessing the Sustainable Development Level of the Tourism Eco-Security System in the Chengdu-Chongqing Urban Agglomeration: A Comprehensive Analysis of Dynamic Evolution Characteristics and Driving Factors
<p>Map of the Chengyu region.</p> "> Figure 2
<p>Development trend of the TESS (2011–2021).</p> "> Figure 3
<p>Analysis of differences in the regional TESS-SDL (2011–2021).</p> "> Figure 4
<p>Scatter plot of Local Moran’s I for the sustainable development level of the regional TESS. Full names of the cities: CD, Chengdu; DZ, Dazhou; DY, Deyang; GA, Guang’an; LS, Leshan; LZ, Luzhou; MS, Meishan; MY, Mianyang; NC, Nanchong; NJ, Neijiang; SN, Suining; YA, Ya’an; YB, Yibin; CQ, Chongqing; ZY, Ziyang; ZG, Zigong.</p> "> Figure 5
<p>Changes in the regression coefficients with the GTWR model (2011–2021).</p> ">
Abstract
:1. Introduction
2. Literature Review
2.1. Definition and Importance of Tourism Eco-Security
2.2. Evaluation Methods for Tourism Eco-Security
2.3. Driving Factors of Tourism Eco-Security
2.4. Review of Tourism Eco-Security Research
3. Materials and Methods
3.1. Construction of an Indicator System
3.2. Research Methods
3.2.1. Indicator Weight of the TESS
3.2.2. Evaluation of the Regional TESS-SDL
3.2.3. Evolution Analysis of the TESS-SDL
3.2.4. Spatiotemporal Geographically Weighted Regression of the TESS-SDL
3.3. Data Sources
4. Results
4.1. Development Trend of the TESS
4.2. Analysis of the Dynamics of and Differences in the Regional TESS-SDL
4.3. Spatial Autocorrelation Analysis
4.4. Spatial Differentiation Characteristics of the Regional TESS-SDL
4.4.1. Path Analysis of State Transfers Based on Traditional Markov Chains
- (1)
- The TESS-SDL, in the Chengyu region, needed to maintain the proportion of the original state is larger, indicating that the system is more stable, regardless of the kind of state transfer the TESS undergoes; the probability for falling on the diagonal is significantly greater than the probability for falling elsewhere;
- (2)
- In Table 4, the self-locking probabilities of state types I, II, III, IV, and V in the process of the state transfer of the sustainable development level of the regional TESS were 0.594, 0.470, 0.484, 0.559, and 0.797, respectively, indicating that the regional TESS was the most stable in state V, and once it reached this state, the system had the greatest possibility of staying in it. The stability of state I was also higher. In contrast, the self-locking probabilities of the intermediate states (II, III, and IV) were lower, implying that these states were less persistent, and the system was more prone to transfer in these states. Therefore, it can be hypothesized that the system’s dynamics changed more frequently in intermediate states and exhibited greater stability and irreversibility in extreme states;
- (3)
- Outside the diagonal of the transfer matrix, the transfer power of the TESS-SDL of the cities in the Chengyu region was insufficient, and the mean values of the state elevation (a low-level state transferring to a high-level state) and state regression (falling back from a high-level state to a low-level state) were 8.2% and 12.7%, respectively; the state elevation included continuous elevation and jumping elevation, and the state regression included continuous regression and jumping regression. The level of the state transfer of the sustainable development levels of the TESS in the Chengyu region was greater than the level of the jumping state changes, and continuous regression was more common than continuous elevation. The greater downward compatibility of the regional TESS than the upward compatibility suggested that the regional TESS was more likely to deteriorate and be difficult to recover in the face of unfavorable external conditions, a finding that underscores the importance of preventive and restorative measures.
4.4.2. Path Analysis of State Transfer Based on Spatial Markov Chains
4.5. Analysis of the Driving Factors of the TESS-SDL
4.5.1. Data Verification
4.5.2. Analysis of the GTWR Results
4.5.3. Recommendations Based on the GTWR Results
- (1)
- The rational application of information technology in the tourism industry should be strengthened to avoid overreliance on the short-term tourism boom brought about by network exposure; in addition, it is necessary to formulate long-term planning, focus on sustainable development, and avoid wasting resources. At the same time, intelligent tourism management systems should be promoted to optimize the allocation of tourism resources and improve management efficiency through the use of big data analysis and artificial intelligence technology;
- (2)
- While promoting technological innovation, it is necessary to consider its long-term impact, avoid negative effects caused by the expansion of technological applications and the lagging behind of environmental governance measures, and strengthen technological innovation and environmental impact assessment regulations. The application of green technology in tourism should be encouraged and supported to improve ecological protection and reduce the negative impact of technological innovation on the environment;
- (3)
- Green development policies should be further improved to reduce the negative impacts of high-cost inputs and restrictions on economic activities and promote the adaptation of enterprises and communities through policy incentives. At the same time, environmental protection education should be strengthened to enhance public awareness of environmental protection, encourage public participation in green tourism, and promote the effective implementation of green policies;
- (4)
- While raising the level of openness to the outside world, attention should be paid to environmental protection and resource management, tourism should be promoted by attracting international tourists and foreign investment, and tourism facilities and services should be upgraded. While promoting economic development, attention should be paid to the sustainable use of resources and environmental protection, and scientific and reasonable development plans should be formulated to avoid the overutilization of resources and environmental pollution problems;
- (5)
- Strict norms for the development of tourism should be formulated and enforced to avoid the negative impacts of irregular development, and tourism enterprises should be encouraged to adopt a sustainable development model to improve the quality and efficiency of the tourism industry. At the same time, long-term interests should be considered in terms of infrastructure construction to avoid unnecessary construction because of short-term tourism booms, promote the sustainable development of infrastructure, and improve resource utilization efficiency.
5. Discussion
5.1. Main Findings
5.2. Application of Innovative Methods
5.3. Limitations and Prospects
5.3.1. Limitations
5.3.2. Study Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
City | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | S.D. |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Chengdu | 0.562 | 0.577 | 0.567 | 0.592 | 0.601 | 0.642 | 0.49 | 0.616 | 0.642 | 0.579 | 0.646 | 0.046 |
Dazhou | 0.325 | 0.309 | 0.333 | 0.38 | 0.369 | 0.371 | 0.375 | 0.374 | 0.371 | 0.349 | 0.48 | 0.044 |
Deyang | 0.472 | 0.512 | 0.533 | 0.547 | 0.561 | 0.533 | 0.555 | 0.547 | 0.562 | 0.568 | 0.638 | 0.041 |
Guang’an | 0.45 | 0.499 | 0.54 | 0.571 | 0.559 | 0.597 | 0.58 | 0.606 | 0.537 | 0.496 | 0.583 | 0.049 |
Leshan | 0.522 | 0.561 | 0.578 | 0.604 | 0.597 | 0.627 | 0.595 | 0.64 | 0.673 | 0.686 | 0.71 | 0.056 |
Luzhou | 0.44 | 0.503 | 0.467 | 0.406 | 0.486 | 0.486 | 0.558 | 0.529 | 0.592 | 0.468 | 0.504 | 0.052 |
Meishan | 0.499 | 0.568 | 0.58 | 0.535 | 0.521 | 0.587 | 0.548 | 0.562 | 0.576 | 0.562 | 0.583 | 0.028 |
Mianyang | 0.424 | 0.464 | 0.523 | 0.53 | 0.514 | 0.516 | 0.499 | 0.479 | 0.53 | 0.536 | 0.572 | 0.040 |
Nanchong | 0.37 | 0.434 | 0.415 | 0.424 | 0.441 | 0.454 | 0.444 | 0.482 | 0.471 | 0.536 | 0.59 | 0.060 |
Neijiang | 0.385 | 0.459 | 0.477 | 0.478 | 0.45 | 0.503 | 0.484 | 0.473 | 0.483 | 0.505 | 0.578 | 0.046 |
Suining | 0.589 | 0.537 | 0.569 | 0.589 | 0.61 | 0.621 | 0.587 | 0.596 | 0.613 | 0.659 | 0.663 | 0.037 |
Ya’an | 0.51 | 0.647 | 0.614 | 0.625 | 0.637 | 0.64 | 0.598 | 0.606 | 0.686 | 0.654 | 0.713 | 0.052 |
Yibin | 0.475 | 0.445 | 0.468 | 0.486 | 0.512 | 0.485 | 0.5 | 0.548 | 0.534 | 0.52 | 0.58 | 0.039 |
Chongqing | 0.565 | 0.472 | 0.497 | 0.502 | 0.608 | 0.64 | 0.653 | 0.675 | 0.759 | 0.712 | 0.841 | 0.116 |
Ziyang | 0.418 | 0.503 | 0.484 | 0.487 | 0.502 | 0.463 | 0.367 | 0.455 | 0.513 | 0.541 | 0.609 | 0.063 |
Zigong | 0.614 | 0.618 | 0.62 | 0.628 | 0.64 | 0.633 | 0.567 | 0.58 | 0.561 | 0.638 | 0.712 | 0.042 |
Mean | 0.476 | 0.507 | 0.517 | 0.524 | 0.538 | 0.55 | 0.525 | 0.548 | 0.569 | 0.563 | 0.625 | 0.039 |
S.D. | 0.082 | 0.081 | 0.076 | 0.077 | 0.077 | 0.084 | 0.08 | 0.079 | 0.094 | 0.092 | 0.089 | 0.006 |
CV | 0.173 | 0.16 | 0.147 | 0.147 | 0.144 | 0.153 | 0.152 | 0.144 | 0.165 | 0.164 | 0.143 | 0.010 |
Parameter | Bandwidth | Sigma | Residual Squares | AICc | R2 | Adjusted R2 | Spatiotemporal Distance Ratio |
---|---|---|---|---|---|---|---|
Value | 0.1106 | 0.0333 | 0.3901 | −1182.15 | 0.8324 | 0.8290 | 2.1135 |
Information Technology | Technological Innovation | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
SUNE | Mean | Std. Dev. | Min. | Max. | SUNE | Mean | Std. Dev. | Min. | Max. | |
2011 | 3 | 0.061 | 0.050 | −0.036 | 0.122 | 11 | 0.000 | 0.102 | −0.089 | 0.300 |
2012 | 4 | 0.021 | 0.048 | −0.085 | 0.069 | 10 | −0.008 | 0.051 | −0.053 | 0.131 |
2013 | 7 | −0.013 | 0.050 | −0.114 | 0.047 | 1 | 0.049 | 0.057 | −0.009 | 0.170 |
2014 | 9 | −0.013 | 0.036 | −0.066 | 0.049 | 0 | 0.064 | 0.058 | 0.003 | 0.202 |
2015 | 8 | −0.006 | 0.021 | −0.046 | 0.020 | 4 | 0.049 | 0.058 | −0.011 | 0.194 |
2016 | 13 | −0.007 | 0.036 | −0.049 | 0.066 | 8 | 0.014 | 0.068 | −0.069 | 0.169 |
2017 | 12 | −0.021 | 0.039 | −0.072 | 0.061 | 12 | −0.043 | 0.087 | −0.182 | 0.126 |
2018 | 15 | −0.052 | 0.032 | −0.111 | 0.004 | 14 | −0.118 | 0.109 | −0.315 | 0.077 |
2019 | 16 | −0.099 | 0.027 | −0.145 | −0.051 | 16 | −0.189 | 0.114 | −0.398 | −0.008 |
2020 | 16 | −0.154 | 0.037 | −0.207 | −0.061 | 16 | −0.234 | 0.101 | −0.428 | −0.072 |
2021 | 16 | −0.204 | 0.054 | −0.271 | −0.063 | 16 | −0.249 | 0.103 | −0.430 | −0.088 |
Mean | −0.044 | Mean | −0.060 | |||||||
Open to the Outside World | Green Development Environment | |||||||||
SUNE | Mean | Std. Dev. | Min. | Max. | SUNE | Mean | Std. Dev. | Min. | Max. | |
2011 | 16 | −0.113 | 0.052 | −0.204 | −0.024 | 7 | 0.065 | 0.302 | −0.289 | 0.597 |
2012 | 14 | −0.042 | 0.039 | −0.118 | 0.033 | 9 | 0.076 | 0.277 | −0.455 | 0.390 |
2013 | 9 | 0.007 | 0.037 | −0.041 | 0.084 | 4 | 0.206 | 0.361 | −0.945 | 0.355 |
2014 | 2 | 0.034 | 0.042 | −0.022 | 0.123 | 5 | 0.216 | 0.292 | −0.805 | 0.303 |
2015 | 2 | 0.057 | 0.052 | −0.011 | 0.165 | 6 | 0.152 | 0.194 | −0.530 | 0.177 |
2016 | 0 | 0.091 | 0.068 | −0.002 | 0.224 | 7 | 0.102 | 0.122 | −0.311 | 0.115 |
2017 | 0 | 0.146 | 0.091 | 0.023 | 0.325 | 8 | 0.072 | 0.086 | −0.205 | 0.101 |
2018 | 0 | 0.226 | 0.116 | 0.075 | 0.454 | 9 | 0.041 | 0.077 | −0.186 | 0.105 |
2019 | 0 | 0.311 | 0.120 | 0.148 | 0.544 | 10 | 0.011 | 0.077 | −0.151 | 0.112 |
2020 | 0 | 0.379 | 0.102 | 0.244 | 0.589 | 11 | −0.007 | 0.069 | −0.100 | 0.126 |
2021 | 0 | 0.425 | 0.090 | 0.248 | 0.603 | 12 | −0.010 | 0.060 | −0.088 | 0.122 |
Mean | 0.138 | Mean | 0.084 | |||||||
Economic Development | Tourism Development | |||||||||
SUNE | Mean | Std. Dev. | Min. | Max. | SUNE | Mean | Std. Dev. | Min. | Max. | |
2011 | 0 | 0.314 | 0.152 | 0.116 | 0.626 | 2 | 0.164 | 0.121 | −0.164 | 0.251 |
2012 | 0 | 0.167 | 0.092 | 0.024 | 0.330 | 1 | 0.128 | 0.074 | −0.092 | 0.190 |
2013 | 3 | 0.061 | 0.059 | −0.049 | 0.148 | 1 | 0.060 | 0.039 | −0.051 | 0.124 |
2014 | 5 | 0.020 | 0.062 | −0.093 | 0.124 | 4 | 0.011 | 0.030 | −0.067 | 0.043 |
2015 | 7 | 0.004 | 0.075 | −0.117 | 0.149 | 6 | −0.015 | 0.038 | −0.092 | 0.023 |
2016 | 10 | −0.006 | 0.103 | −0.122 | 0.299 | 9 | −0.066 | 0.046 | −0.111 | 0.016 |
2017 | 11 | −0.013 | 0.118 | −0.115 | 0.359 | 8 | −0.075 | 0.046 | −0.111 | 0.020 |
2018 | 12 | −0.023 | 0.120 | −0.107 | 0.359 | 9 | −0.081 | 0.040 | −0.103 | 0.021 |
2019 | 13 | −0.041 | 0.115 | −0.123 | 0.325 | 11 | −0.091 | 0.033 | −0.096 | 0.013 |
2020 | 13 | −0.073 | 0.107 | −0.158 | 0.264 | 14 | −0.127 | 0.031 | −0.098 | 0.009 |
2021 | 14 | −0.114 | 0.101 | −0.210 | 0.187 | 14 | −0.139 | 0.037 | −0.112 | 0.021 |
Mean | 0.027 | Mean | −0.021 |
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Standardized Layer | Indicator Layer | Description of Indicator | Weight |
---|---|---|---|
Driver | Fixed-asset investment total | Reflecting the investment level and development trend of the economy | 0.021 |
GDP per capita | Reflecting the regional average economic performance or per capita standard of living | 0.037 | |
Population density | Reflecting the concentration of the population in a region | 0.011 | |
Natural population growth rate | Reflecting demographic changes in potential areas | 0.004 | |
Tourism revenue | Reflecting the level of tourism development and the economic contribution of the region | 0.042 | |
Passenger transport volume | Reflecting the number of people moving through the region | 0.032 | |
Tourism resource density | Reflecting the potential for tourism development or activity in that area | 0.012 | |
Pressure | Tourism spatial density | The ratio of the number of tourists to the urban space area in a year | 0.013 |
Tourism population density | The ratio of the number of tourists to the permanent urban population in a year | 0.020 | |
Tourism transport level | Refers to the number of road miles per 100 square kilometers | 0.048 | |
Annual electricity consumption | Social electricity consumption for the year | 0.030 | |
Annual gas supply total | Social natural gas consumption for the year | 0.018 | |
SO2 concentration | Reflecting the air quality for tourism | 0.017 | |
Urban domestic sewage discharge | Reflecting the consumption level of the urban population (including tourists) and environmental pressures from domestic sewage | 0.017 | |
Urban domestic waste removal capacity | Reflecting the consumption level of the urban population (including tourists) and environmental pressures from domestic waste | 0.018 | |
State | Industrial structure | Reflecting the share of tourism in the economy | 0.081 |
Green development of tourism | Reacting to the natural landscape of the tourist attraction and its quality as a percentage | 0.017 | |
Ecosystem services | Evaluating the total value of various land types using the market value approach | 0.032 | |
Proportion of days with good air | Reflecting the regional air quality | 0.030 | |
Drinking water compliance rate | Reacting to the compliance of water sources for the centralized drinking water supply | 0.034 | |
Proportion of protected areas | Reflecting the levels of nature conservation and scientific civilization in the region | 0.087 | |
Per capita urban green space | Reflecting the urban ecological environment, life quality of the residents, and management level | 0.034 | |
Influence | Urbanization rate | Reflecting the transformation of the local social structure | 0.040 |
Employees in the tertiary sector | Reflecting the structure of the economy and the importance of service activities | 0.014 | |
Retail sales of consumer goods | Reflecting consumer spending patterns and the overall economic activity | 0.014 | |
Product sales revenue | Revenue from purchasing units on sales of finished goods and self-made semi-finished products by industrial enterprises at the sale price | 0.025 | |
Per capita disposable income | Reflecting the standard of living and economic wellbeing of the population | 0.067 | |
Response | Environmental investment | Reflecting the strength of government investment in environmental protection | 0.065 |
Green coverage in built-up areas | Reflecting the level of urban greening | 0.037 | |
Disposal rate of domestic waste | Reflecting the level of technology and efforts of people in dealing with household waste | 0.054 | |
Sewage treatment rate | Reflecting the level of technology and efforts of people in treating domestic wastewater | 0.028 |
Composite Index | 0~0.2 | 0.2~0.4 | 0.4~0.6 | 0.6~0.8 | 0.8~1 |
---|---|---|---|---|---|
Level | I | II | III | IV | V |
Coupling Coordination Type | Incoordination | Less Coordination | Critical Coordination | General Coordination | Coordination |
Variable Type | Name | Indicator (Unit) | Mean | S.D. | Min. | Max. |
---|---|---|---|---|---|---|
Explained variable | SDL-TESS (Sdl) | Coupling coordination degree of TESS | 0.549 | 0.081 | 0.285 | 0.730 |
Explanatory variable | Information technology level (Tech) | Number of mobile phone users (per 100 persons) | 20.637 | 5.738 | 10.986 | 41.606 |
Technological innovation level (Inn) | Number of patents granted | 60.5402 | 209.785 | 0 | 1490 | |
Openness level (Ope) | Total exports and imports as a share of GDP (%) | 0.1313 | 0.1385 | 0.0095 | 0.5458 | |
Green policy level (Gre) | Environmental investment as a share of GDP (%) | 0.028 | 0.062 | 0.008 | 0.035 | |
Industrial structure level (Ind) | Value added in tertiary industry as a share of GDP (%) | 38.432 | 9.380 | 20.16 | 70.28 | |
Economic development level (Eco) | GDP per capita (Yuan) | 33,226.2 | 25,119.79 | 678.977 | 122,972 | |
Infrastructure quality (Inf) | Tourism transport level (km/100 km2) | 137.961 | 55.959 | 25 | 326.564 | |
Tourism development level (Tou) | Tourism revenue (108 Yuan) | 341.829 | 567.226 | 34 | 4650 |
t/t + 1 | n | I | II | III | IV | V |
---|---|---|---|---|---|---|
I | 69 | 0.594 | 0.333 | 0.072 | 0.000 | 0.000 |
II | 66 | 0.227 | 0.470 | 0.288 | 0.015 | 0.000 |
III | 64 | 0.016 | 0.141 | 0.484 | 0.344 | 0.016 |
IV | 68 | 0.000 | 0.044 | 0.191 | 0.559 | 0.206 |
V | 69 | 0.014 | 0.029 | 0.014 | 0.145 | 0.797 |
Neighborhood Type | t\t + 1 | n | I | II | III | IV | V |
---|---|---|---|---|---|---|---|
I | I | 37 | 0.730 | 0.189 | 0.081 | 0.000 | 0.000 |
II | 11 | 0.364 | 0.455 | 0.182 | 0.000 | 0.000 | |
III | 6 | 0.167 | 0.500 | 0.167 | 0.167 | 0.000 | |
IV | 1 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | |
V | 1 | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | |
II | I | 25 | 0.480 | 0.520 | 0.000 | 0.000 | 0.000 |
II | 33 | 0.242 | 0.545 | 0.212 | 0.000 | 0.000 | |
III | 12 | 0.000 | 0.167 | 0.583 | 0.167 | 0.083 | |
IV | 2 | 0.000 | 0.000 | 0.000 | 0.500 | 0.500 | |
V | 6 | 0.000 | 0.000 | 0.000 | 0.000 | 1.000 | |
III | I | 6 | 0.167 | 0.500 | 0.333 | 0.000 | 0.000 |
II | 14 | 0.214 | 0.357 | 0.357 | 0.071 | 0.000 | |
III | 11 | 0.000 | 0.091 | 0.545 | 0.364 | 0.000 | |
IV | 6 | 0.000 | 0.000 | 0.000 | 0.500 | 0.500 | |
V | 6 | 0.000 | 0.000 | 0.000 | 0.167 | 0.833 | |
IV | I | 1 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 |
II | 5 | 0.000 | 0.200 | 0.800 | 0.000 | 0.000 | |
III | 25 | 0.000 | 0.040 | 0.480 | 0.480 | 0.000 | |
IV | 34 | 0.000 | 0.000 | 0.176 | 0.618 | 0.206 | |
V | 36 | 0.028 | 0.000 | 0.000 | 0.056 | 0.917 | |
V | I | 0 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
II | 3 | 0.000 | 0.667 | 0.333 | 0.000 | 0.000 | |
III | 10 | 0.000 | 0.200 | 0.500 | 0.300 | 0.000 | |
IV | 25 | 0.000 | 0.120 | 0.280 | 0.520 | 0.080 | |
V | 20 | 0.000 | 0.100 | 0.050 | 0.300 | 0.550 |
Variable | Sdl | Tech | Inn | Open | Gre | Ind | Eco | Inf | Tou | VIF | Tolerance |
---|---|---|---|---|---|---|---|---|---|---|---|
Sdl | 1 | 1.877 | 0.533 | ||||||||
Tech | 0.582 ** | 1 | 2.762 | 0.362 | |||||||
Inn | 0.289 ** | 0.425 ** | 1 | 5.67 | 0.176 | ||||||
Open | 0.342 ** | 0.493 ** | 0.256 ** | 1 | 3.927 | 0.255 | |||||
Gre | 0.195 ** | 0.305 ** | 0.470 ** | 0.434 ** | 1 | 1.463 | 0.684 | ||||
Ind | 0.001 | 0.163 ** | 0.835 ** | 0.195 ** | 0.169 ** | 1 | 12.561 | 0.079 | |||
Eco | 0.626 ** | 0.715 ** | 0.573 ** | 0.514 ** | 0.368 ** | 0.012 | 1 | 3.364 | 0.297 | ||
Inf | 0.069 | 0.355 ** | 0.249 ** | 0.247 ** | 0.091 | 0.154 ** | 0.201 ** | 1 | 12.291 | 0.081 | |
Tou | 0.424 ** | 0.513 ** | 0.659 ** | 0.781 ** | 0.539 ** | 0.082 | 0.837 ** | 0.252 ** | 1 | 4.981 | 0.201 |
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© 2024 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/).
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Li, H.; Weng, G.; Wang, D. Assessing the Sustainable Development Level of the Tourism Eco-Security System in the Chengdu-Chongqing Urban Agglomeration: A Comprehensive Analysis of Dynamic Evolution Characteristics and Driving Factors. Sustainability 2024, 16, 6740. https://doi.org/10.3390/su16166740
Li H, Weng G, Wang D. Assessing the Sustainable Development Level of the Tourism Eco-Security System in the Chengdu-Chongqing Urban Agglomeration: A Comprehensive Analysis of Dynamic Evolution Characteristics and Driving Factors. Sustainability. 2024; 16(16):6740. https://doi.org/10.3390/su16166740
Chicago/Turabian StyleLi, Hongyan, Gangmin Weng, and Dapeng Wang. 2024. "Assessing the Sustainable Development Level of the Tourism Eco-Security System in the Chengdu-Chongqing Urban Agglomeration: A Comprehensive Analysis of Dynamic Evolution Characteristics and Driving Factors" Sustainability 16, no. 16: 6740. https://doi.org/10.3390/su16166740