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Research on Optimizing the Development of Sports and Leisure Industry Using Genetic Algorithm to Promote the Growth of Local Sports Economy

Yanhua Jiao

Yanhua Jiao

Handan UniversityHandan, China
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Jiao, Yanhua
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Bing Cao

Bing Cao

Hebei University of EngineeringHandan, China
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Cao, Bing
  
Mar 21, 2025
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Published Online: Mar 21, 2025
Received: Oct 07, 2024
Accepted: Feb 01, 2025
DOI: https://doi.org/10.2478/amns-2025-0564
Keywords
© 2025 Yanhua Jiao et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Flowchart of genetic algorithm
Flowchart of genetic algorithm

Figure 2.

Solving surface that pareto to the model
Solving surface that pareto to the model

Figure 3.

VAR model characteristic polynomial inverse root
VAR model characteristic polynomial inverse root

Figure 4.

InGDP response to InGDP shock
InGDP response to InGDP shock

Figure 5.

InGDP response to InTYC shock
InGDP response to InTYC shock

Figure 6.

InTYC response to InGDP shock
InTYC response to InGDP shock

Figure 7.

InTYC response to InTYC shock
InTYC response to InTYC shock

Test results of vector corrected error model

Explained variable Interpretation variable Coefficient estimate Standard deviation Z statistic Interaction probability (P value)
TYC ΔInGDP 2.2658 0.0758 -37.92 0.000
ΔInGDPt−1 1.3984 1.4934 1.03 0.328
ΔInTYCt−1 0.9243 0.7732 1.18 0.274
ecmt−1 -1.3026 0.9463 -1.52 0.179
cons 0.0065 0.1842 0.04 0.993
GDP ΔInTYC 0.4793 0.0187 -37.65 0.000
ΔInTYCt−1 0.0542 0.2075 0.20 0.829
ΔInGDPt−1 0.2567 0.3793 0.65 0.537
ecmt−1 -0.2526 0.5324 -0.53 0.000
cons 0.0460 0.0459 0.88 0.418

Variance decomposition of VAR model

Period InGDP variance decomposition InTYC variance decomposition
S.E. InGDP InTYC S.E. InGDP InTYC
1 0.2356 100.0000 0.0000 0.0881 51.6388 48.3612
2 0.0975 52.9653 47.0347 0.1117 52.9158 47.0842
3 0.1545 62.3246 37.6754 0.1332 55.2703 44.7297
4 0.1693 62.4273 37.5727 0.1567 57.0939 42.9061
5 0.1887 64.2145 35.7855 0.1745 58.2465 41.7535
6 0.1896 64.4615 35.5385 0.1796 57.9066 42.0934
7 0.1905 64.0687 35.9313 0.1922 57.1062 42.8938
8 0.2182 62.1418 37.8582 0.2109 57.1469 42.8531
9 0.2514 63.0612 36.9388 0.2198 57.9134 42.0866
10 0.2492 63.8279 36.1721 0.2295 58.0631 41.9369
11 0.2542 64.1278 35.8722 0.2413 58.3265 41.6735
12 0.2545 64.4675 35.5325 0.2442 58.0489 41.9511

Parameter estimation of VAR model

Variable InTYC InGDP
InTYC(–1) 0.6648 0.1759
(0.7458) (0.1926)
[0.94] [0.96]
InTYC(–2) -0.9642 -0.0674
(0.8135) (0.2082)
[-1.22] [-3.38]
InGDP(–1) 3.4278 0.7869
(3.4275) (0.8523)
[1.07] [0.98]
InGDP(–2) -0.7025 -0.0456
(1.8051) (0.4373)
[-0.39] [-0.14]
Constant term -25.0564 3.0226
(21.3515) (5.2713)
[-1.19] [0.62]
R–sq 0.9843 0.9964
F statistic 41.0548 139.8421
P value 0.0019 0.0002
AIC -6.4927 -3.1954
HQIC -7.2149 -3.8516
SBIC -6.4208 -3.1454

Pareto solution of sports leisure resource allocation optimization

Variables X11 X12 X13 X14 X15
Optimization value 162.37 41.28 201.45 305.74 9.159
Variables X16 X17 X18 X18 X110
Optimization value 13.412 29.508 69.631 24.259 43.083
Variables X111 X112 X113 X114 X115
Optimization value 26.564 58.317 28.243 21.267 30.524
Variables X21 X22 X23 X24 X25
Optimization value 501.21 324.52 2908.74 1224.32 30071
Variables X3 X4 E(X) δ(X) Dδ(X)
Optimization value 2406.32 51.718 611.45 0.0009 849.62

Stability test of sports industry added value and GDP

Variables ADF test value t statistic Stability
1% threshold 5% threshold 10% threshold
InTYC 5.042 -2.704 -1.941 -1.598 Non-stationary
InGDP 7.641 -2.704 -1.941 -1.598 Non-stationary
dInTYC -1.185 -2.704 -1.941 -1.598 Non-stationary
dInGDP -2.235 -2.704 -1.941 -1.598 Non-stationary
d2InTYC -2.934 -2.704 -1.941 -1.598 Smooth
d2InGDP -2.738 -2.704 -1.941 -1.598 Smooth
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