Submitted:
13 November 2024
Posted:
14 November 2024
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Abstract
As an important economic belt in western China, the Chengdu–Chongqing region boasts rich sports tourism resources and immense development potential. Based on coupling coordination theory, a sports tourism evaluation index system was constructed. The coupling coordination model was used to calculate the coupling coordination degree of sports tourism in the Chengdu–Chongqing region and to determine the coordination type. Additionally, the obstacle degree model was employed to identify the obstructive factors affecting the coordinated development of sports tourism in the region. The results showed that the sports tourism evaluation index system included four secondary indicators and 15 tertiary indicators. The coupling coordination degree of sports tourism in the Chengdu–Chongqing region continuously increased from 2015 to 2019, with a decline observed in 2020. Financial allocations to sports departments were identified as the main obstructive factor affecting the coordinated development of sports tourism in the region. Accordingly, future development recommendations are proposed.
Keywords:
1. Introduction
2. Theoretical Background
3. Research Questions
4. Methods
4.1. Entropy Method
4.2. Coupling Coordination Degree Model
4.3. Obstacle Degree Model
4.4. Data Source
5. Results
5.1. Construction of the Sports Tourism Evaluation Index System
| Secondary Indicators | Tertiary Indicators | Unit | Symbol | Nature |
|---|---|---|---|---|
| Overall Scale | Total Scale of the Sports Industry | 100 million yuan | X1 | + |
| Sports Lottery Sales | 10,000 yuan | X2 | + | |
| Total Tourism Revenue | 100 million yuan | X3 | + | |
| Domestic Tourism Revenue | 100 million yuan | X4 | + | |
| Tourism Foreign Exchange Revenue | 10,000 US dollars | X5 | + | |
| Market Entities | Number of Domestic Tourist Visits | 10,000 people | X6 | + |
| Number of Inbound Overseas Tourists | people | X7 | + | |
| Passenger Transport Volume | 10,000 people | X8 | + | |
| Development Foundation | Number of Employees in the Sports and Entertainment Industry | 10,000 people | X9 | + |
| Gross Regional Product | 100 million yuan | X10 | + | |
| Per Capita Disposable Income | yuan | X11 | + | |
| Number of Enrolled University Students | people | X12 | + | |
| Government Support | Sports Department Revenue | 10,000 yuan | X13 | + |
| Financial Allocations to Sports Departments | 10,000 yuan | X14 | + | |
| Government Expenditure on Sports | 10,000 yuan | X15 | + |
| Symbol | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 |
|---|---|---|---|---|---|---|
| X1 | 392.24 | 451.35 | 558 | 632.16 | 800 | 805.02 |
| X2 | 454416 | 476132 | 471638 | 848504 | 893138 | 737592 |
| X3 | 2040 | 2502 | 3033 | 3713 | 4664 | 3005 |
| X4 | 1987 | 2426 | 2946 | 3617 | 4551 | 3002 |
| X5 | 87264 | 124193 | 130656 | 144662 | 162327 | 4426 |
| X6 | 18904 | 19757 | 20704 | 23977 | 27642 | 20395 |
| X7 | 2305427 | 2723102 | 3013364 | 3406112 | 3814178 | 233091 |
| X8 | 55581.8 | 79359.9 | 99359.9 | 138356.9 | 164563.3 | 137414.7 |
| X9 | 6.17 | 8.61 | 8.83 | 7.85 | 9.19 | 9.31 |
| X10 | 10662.3 | 11874.1 | 13931.4 | 15698.9 | 17012.7 | 17716.7 |
| X11 | 33476 | 35902 | 38917.5 | 42127.85 | 45878.14 | 48593 |
| X12 | 755767 | 791593 | 817432 | 840297 | 879335 | 927111 |
| X13 | 30579.85 | 28218.21 | 31749.57 | 34870.1 | 52088.27 | 72009.11 |
| X14 | 21813.1 | 23821.45 | 20881.25 | 22522.63 | 38408.44 | 60681.13 |
| X15 | 17507.11 | 17432.96 | 19262.79 | 21515.23 | 38565.54 | 58820.02 |
| Symbol | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 |
|---|---|---|---|---|---|---|
| X1 | 262.78 | 309.15 | 362.59 | 423.99 | 504.72 | 541.33 |
| X2 | 305770 | 350921 | 474659 | 596670 | 560006 | 407674 |
| X3 | 2251.31 | 2645.21 | 3308.04 | 4344.15 | 5739.07 | 3301.12 |
| X4 | 2149.15 | 2533.21 | 3176.55 | 4199.24 | 5564.61 | 3088.65 |
| X5 | 146857 | 168682 | 194759 | 218989 | 252483 | 10792 |
| X6 | 38885.1 | 44769.55 | 53871.86 | 59335.69 | 65296.69 | 53138.18 |
| X7 | 2825339 | 3165843 | 3583545 | 3880233 | 4113439 | 146342 |
| X8 | 64164 | 63402 | 63298 | 63634 | 63558 | 39797 |
| X9 | 6.96 | 7.61 | 8.31 | 9.12 | 9.75 | 9.89 |
| X10 | 16040.54 | 18023.4 | 20066.29 | 21588.8 | 23605.77 | 25002.79 |
| X11 | 27239 | 29610 | 32193 | 34889 | 37939 | 40006 |
| X12 | 767114 | 784631 | 805208 | 827945 | 907426 | 998650 |
| X13 | 49378.32 | 50612.61 | 45269.07 | 48505.76 | 57541.81 | 50543.39 |
| X14 | 34564.82 | 36714.4 | 32364.86 | 36576.29 | 37993.54 | 39194.28 |
| X15 | 25295.56 | 26529.77 | 25969.22 | 24665.67 | 25866.27 | 32366.39 |
5.2. Determination of Comprehensive Scores Using the Entropy Method
5.3. Measurement and Classification of Coupling Coordination Degree of Sports Tourism in the Chengdu–Chongqing Region
5.4. Obstacle Factors for the Coordinated Development of Sports Tourism in the Chengdu–Chongqing Region
6. Discussion
6.1. Foundation for the Coordinated Development of Sports Tourism in the Chengdu–Chongqing Region
6.2. Recommendations for the Future Development of Sports Tourism in the Chengdu–Chongqing Region
6.2.1. Further Urban Integration
6.2.2. Further Expand Open Cooperation
6.2.3. Further Promote Industrial Agglomeration
6.2.4. Further Strengthen Policy Support
6.2.5. Further Improve the Transportation Network
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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| Coupling Coordination Interval | Grade | Coupling Coordination Type | Coupling Coordination Interval | Grade | Coupling Coordination Type |
|---|---|---|---|---|---|
| [0.0, 0.1) | 1 | Severe Imbalance | [0.5, 0.6) | 6 | Barely Coordinated |
| [0.1, 0.2) | 2 | High Imbalance | [0.6, 0.7) | 7 | Primary Coordination |
| [0.2, 0.3) | 3 | Moderate Imbalance | [0.7, 0.8) | 8 | Intermediate Coordination |
| [0.3, 0.4) | 4 | Mild Imbalance | [0.8, 0.9) | 9 | Good Coordination |
| [0.4, 0.5) | 5 | On the Brink of Imbalance | [0.9, 1.0] | 10 | High-Quality Coordination |
| Years | T | C | D | Grade | Type |
|---|---|---|---|---|---|
| 2015 | 0.0894 | 0.8640 | 0.2779 | 3 | Moderate Imbalance |
| 2016 | 0.2195 | 0.9594 | 0.4589 | 5 | On the Brink of Imbalance |
| 2017 | 0.3089 | 0.9830 | 0.5511 | 6 | Barely Coordinated |
| 2018 | 0.5082 | 0.9957 | 0.7114 | 8 | Intermediate Coordination |
| 2019 | 0.7933 | 0.9999 | 0.8906 | 9 | Good Coordination |
| 2020 | 0.7082 | 0.9973 | 0.8404 | 9 | Good Coordination |
| Symbol | Chengdu-19 | Chengdu-20 | Chongqing-19 | Chongqing-20 | Weights |
|---|---|---|---|---|---|
| X1 | 0.9833 | 1.0000 | 0.0000 | 0.1219 | 0.0607 |
| X2 | 1.0000 | 0.6796 | 0.3138 | 0.0000 | 0.0453 |
| X3 | 0.6068 | 0.0000 | 1.0000 | 0.1083 | 0.0628 |
| X4 | 0.6045 | 0.0000 | 1.0000 | 0.0338 | 0.0755 |
| X5 | 0.6366 | 0.0000 | 1.0000 | 0.0257 | 0.0769 |
| X6 | 0.1614 | 0.0000 | 1.0000 | 0.7292 | 0.0559 |
| X7 | 0.9246 | 0.0219 | 1.0000 | 0.0000 | 0.0759 |
| X8 | 1.0000 | 0.7824 | 0.1904 | 0.0000 | 0.0533 |
| X9 | 0.0000 | 0.1714 | 0.8000 | 1.0000 | 0.0549 |
| X10 | 0.0000 | 0.0881 | 0.8252 | 1.0000 | 0.0643 |
| X11 | 0.7452 | 1.0000 | 0.0000 | 0.1940 | 0.0530 |
| X12 | 0.0000 | 0.4004 | 0.2354 | 1.0000 | 0.0547 |
| X13 | 0.0720 | 1.0000 | 0.3260 | 0.0000 | 0.0776 |
| X14 | 0.0183 | 1.0000 | 0.0000 | 0.0529 | 0.1308 |
| X15 | 0.3854 | 1.0000 | 0.0000 | 0.1972 | 0.0584 |
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