中国沙漠地区旅游业与旅客运输的空间格局及动态响应研究

doi:10.12118/j.issn.1000–6060.2021.04.27

Abstract

Abstract:

With the wide distribution of deserts throughout northwest China, desert area tourism products are located at a distance from eastern tourist-intensive cities and visitors primarily rely on civil aviation transport to reach scenic desert spots. Based on indices of civil aviation transport, such as the number of tourists, income, passenger throughput, number of airlines, and navigation cities of desert areas from 2009 to 2019, this paper evaluated the space-time development relationship between desert tourism in China and civil aviation passenger transport using the spatial adjacency, analyses of the buffer zone, and the panel vector autoregression model. The three major conclusions are as follows. (1) Distributed along the centerline of the Silk Road Economic Belt, tourism resources in China’s desert areas are denser in the north-central and northwestern areas. (2) Of the 45 desert scenic spots covered by airports with civil aviation passenger transport, 86.7% have superior accessibility, while only 6 have inferior accessibility according to this study. This result shows that the development of desert tourism is strongly influenced by civil aviation passenger transport. Moreover, the buffer distances between most of the desert scenic spots and the civil aviation passenger transport airports are medium-range, with short distance and medium-short distance buffers accounting for 73.3% of the total, reflecting the high adjacency, namely, the high spatial dependency, between tourism in desert areas and civil aviation passenger transport. (3) The findings of the co-integration test show that there is a long-term and stable equilibrium between tourism in desert areas and civil aviation passenger transport. According to the impulse response and variance decomposition, it can be concluded that, in the short run, desert tourism has positive and negative interactivity with civil aviation passenger transport, while in the long run, desert tourism will promote the development of the latter. In return, civil aviation passenger transport will boost desert tourism by expanding the number of navigation cities and the market for passenger resources. For the future development of tourism and civil aviation passenger transport in desert areas, we can improve via the following three aspects: first, continuously improving the air transport capacity of desert areas; second, continuously promoting the development of tourism by seizing the strategic opportunity of the Silk Road Economic Belt, integrating existing desert tourism resources, and tapping deep into cultural connotations to instill content and soul in the product of desert tourism to build a well-known desert tourism brand in China and worldwide; and third, adhering to the integrative development strategy of “tourism plus transport”. By taking advantage of low altitude sightseeing by short distance civil aviation passenger transport and giving full play to the link-up of civil aviation transport, an integrative development brand and pattern of desert tourism and transport can be built.

Key words: desert tourism, passenger transportation, spatial pattern, panel vector autoregression (PVAR) model, dynamic response

ZHANG Yang,JIN Xue,GONG Xianjie. Spatial pattern and dynamic response between tourism and passenger transportation in desert areas of China[J].Arid Land Geography, 2021, 44(4): 1153-1163.

Figures/Tables 13

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References 25

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民航机场	服务景区数量/个	平均距离/km	距离排序
石河子花园机场	1	15	1
阿拉善右旗机场	1	76	8
榆林榆阳机场	2	18	2
中卫沙坡头机场	2	18	3
乌海机场	2	41	4
巴彦淖尔天吉泰机场	2	73	7
金昌金川机场	2	103	12
喀什机场	2	153	14
吐鲁番交河机场	2	160	15
克拉玛依机场	3	66	6
赤峰玉龙机场	3	135	13
鄂尔多斯伊金霍洛国际机场	4	80	9
阿拉善左旗机场	4	82	10
敦煌莫高国际机场	4	86	11
额济纳旗机场	5	42	5

变量	LLC		ADF
变量	统计量	P值	统计量	P值
TN	14.49620	1.0000	0.49106	1.0000
∆TN	-0.95635	0.1694	23.93540	0.1572
∆²TN	-10.37310	0.0000	97.80860	0.0000
TI	12.67110	1.0000	0.16240	1.0000
∆TI	-0.71523	0.2372	21.04670	0.2771
∆²TI	-11.00480	0.0000	96.74610	0.0000
PT	10.03340	1.0000	1.30080	1.0000
∆PT	-1.86147	0.0313	27.16450	0.0760
∆²PT	-12.65920	0.0000	115.13400	0.0000
RN	9.64575	1.0000	1.39363	1.0000
∆RN	-0.90475	0.1828	33.15150	0.0160
∆²RN	-9.60373	0.0000	88.19150	0.0000
NC	8.69586	1.0000	0.95720	1.0000
∆NC	-3.14952	0.0008	34.45140	0.0111
∆²NC	-13.13960	0.0000	116.21600	0.0000

变量	检验结果	Panel v	Panel rho	Panel PP	Panel ADF	Group rho	Group PP	Group ADF
旅游人数与民航客运指标	统计量	-2.5568	0.2602	-2.4504	-2.6186	2.5156	0.6387	-2.1302
旅游人数与民航客运指标	P值	0.9947	0.6027	0.0071	0.0044	0.0041	0.0385	0.0166
旅游收入与民航客运指标	统计量	-2.5545	0.7217	-1.7098	-2.9800	1.7877	-3.5452	6.8413
旅游收入与民航客运指标	P值	0.9947	0.7648	0.0436	0.0014	0.0031	0.0002	0.0000

PVAR模型	滞后阶数	LogL	LR	FPE	AIC	SC	HQ
旅游人数和民航客运	0	115.0796	NA	1.92×10^-7	-4.11406	-3.96673	-4.05724
	1	309.7320	353.25800	2.57×10^-10	-10.73081	-9.99415^*	-10.44671
	2	331.2138	35.80297	2.12×10^-10	-10.93384	-9.60785	-10.42246
	3	356.2382	38.00013	1.56×10^-10	-11.26808	-9.35277	-10.52942
	4	365.0314	12.04988	2.14×10^-10	-11.00116	-8.49652	-10.03522
	5	404.3821	48.09520^*	9.81×10^-11*	-11.86600^*	-8.77203	-10.67278^*
旅游收入和民航客运	0	150.2769	NA	5.21×10^-8	-5.41766	-5.27033	-5.36084
	1	367.3054	393.86650	3.05×10^-11	-12.86316	-12.12650	-12.57906^*
	2	381.3863	23.46812	3.31×10^-11	-12.79208	-11.46609	-12.28070
	3	409.9643	43.39620^*	2.13×10^-11*	-13.25794	-11.34262^*	-12.51927
	4	425.0283	20.64327	2.32×10^-11	-13.22327	-10.71862	-12.25733
	5	441.9765	20.71455	2.44×10^-11	-13.25839^*	-10.16442	-12.06517

期数	方差贡献率	∆²TN	∆²PT	∆²RN	∆²NC
2	0.056761	92.35141	6.943634	0.096063	0.60889
4	0.070589	87.70865	5.383833	1.405993	5.50152
6	0.076131	84.83131	4.649286	1.966472	8.55294
8	0.084798	76.32061	4.034594	2.110090	17.53470
10	0.091662	70.13224	3.715256	1.909879	24.24262
12	0.095734	66.87106	3.550654	1.876782	27.70150
14	0.098626	64.74838	3.440676	1.888071	29.92287
16	0.100444	63.58143	3.381164	1.907688	31.12971
18	0.101617	62.86770	3.338663	1.936415	31.85722
20	0.102394	62.41877	3.307314	1.970104	32.30381

Spatial pattern and dynamic response between tourism and passenger transportation in desert areas of China

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期数	方差贡献率	∆²TI	∆²PT	∆²RN	∆²NC
2	0.016242	77.35197	14.68834	0.112632	7.84706
4	0.020782	66.92748	9.34729	1.636461	22.08877
6	0.022522	62.02068	10.02843	2.573322	25.37757
8	0.024289	59.74629	13.13849	2.488893	24.62633
10	0.026023	57.87297	13.92427	2.357463	25.84530
12	0.027955	52.51975	14.68688	2.239894	30.55348
14	0.029649	49.19169	16.42298	1.996189	32.38914
16	0.031344	45.75465	18.07754	1.791866	34.37594
18	0.032998	42.57980	18.72436	1.635458	37.06038
20	0.034554	39.98929	19.69515	1.517314	38.79825

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