A study of the temporal and spatial variations in the suitability of the environment in Chinese cities for tourism and in strategies for optimizing the environment

Figures & data

Figure 1. Research area and spatial distribution of cities.

Table 1. Questionnaire results of Delphi index system building in the first stage.

Dimension	Sub-dimension	Index	Mean	Soundness
Climate conditions	Climate elements	Temperature	4.4	0.69
		Humidity	3.6	0.69
	Weather conditions	PREC	3.9	0.73
Vegetation conditions	Vegetation cover conditions	LAI	4.2	0.63
	Vegetation greenness	NDVI	3.6	0.84
Environmental risk	Smog risk	PM2.5	4.5	0.52
		PM10	2.8	0.78
	Ultraviolet radiation risk	UVB	3.7	0.67

Note: Each indicator was rated by the expert panel members using a scale ranging from strongly agree (5) to strongly disagree (1).

Table 2. Index system for evaluating the suitability of the environment for tourism with levels of expert approval for different indicators.

Dimension	Sub-dimension	Indicator	Evaluation Criteria	Evaluation Index	Basis of Evaluation	Mean	Soundness
Suitability of natural environment	Climate suitability	Suitability of temperature	Body comfort	UTCI	9–26°C is the best range; otherwise, the smaller the difference with 9°C or 26°C, the better	4.6	0.51
		Precipitation	Level of impact on tourist activities	PREC	8 mm or less is best; otherwise, the smaller the difference with 8 mm, the better	3.9	0.73
	Vegetation environment suitability	Effect of trees on the suitability of the environment	Amount of tree cover	LAIH	The higher the better	4.1	0.56
		Effect of shrubs on the suitability of the environment	Amount of shrub cover	LAIL	The higher the better	3.6	0.69
Health impact assessment	Assessment of effects on health	Effects of air quality on health		PM2.5	The lower the better	4.7	0.48
		Effects of radiation on health	Effects on skin health	UVB	The lower the better	3.8	0.63

Figure 2. Technology roadmap of the AHP–TOPSIS algorithm.

Table 3. Judgment matrix scale.

Value	Level of importance	Notes
1	Equally important	The two factors are equally important
3	Slightly more important	Factor i is slightly more important than factor j
5	Clearly more important	Factor i is clearly more important than factor j
7	Much more important	Factor i is much more important than factor j
9	Absolutely more important	Factor i is absolutely more important than factor j
2, 4, 6, 8		Values intermediate between the above listed values
Wji = 1/Wij	If the ratio of the importance of factor i to that of factor j is Wij, then that of factor j to factor i is Wji = 1/Wij.

Figure 3. Spatial distribution of the annual average suitability of the environment for tourism in China in 2019.

Figure 4. First principal component scores of 684 cities in China.

Figure 5. Number of days suitable for tourism in the 684 cities across China.

Figure 6. Seasonal proportions of the days suitable for tourism in the 684 cities across China.

Figure 7. Monthly average values of the EBS and normalized parameters (UTCI, PREC, LAIH, LAIL, PM_2.5 and UVB) for 12 selected cities.

Table 4. Ranking of 50 representative tourist cities in terms of environmental suitability in 2019.

Rank	Tourist city	Province	Latitude	Longitude	EBS score	Advantageous factor	Limiting factor
1	Kunming	Yunnan	25.04	102.42	97.67	LAIH	UVB
2	Xiamen	Fujian	24.27	118.06	97.22	LAIH	PREC
3	Lijiang	Yunnan	26.86	100.25	97.11	LAIH	UVB
4	Guangzhou	Guangdong	23.08	113.14	96.67	LAIL	PREC
5	Jinhua	Zhejiang	29.07	119.39	96.45	LAIL	PREC
6	Guilin	Guangxi	25.17	110.17	96.42	LAIL	PREC
7	Fuzhou	Fujian	26.05	119.18	96.32	LAIL	PREC
8	Nanning	Jiangsu	22.48	108.19	96.09	UTCI	PREC
9	Sanya	Hainan	18.14	109.31	96.04	PM2.5	PREC
10	Wenzhou	Zhejiang	28.01	120.39	96.03	LAIL	PREC
11	Nanchang	Jiangsu	28.40	115.55	96.00	LAIH	PREC
12	Taizhou	Zhejiang	28.41	121.27	95.93	LAIL	PREC
13	Ningbo	Zhejiang	29.52	121.33	95.86	LAIL	PREC
14	Zhangjiajie	Hunan	29.08	110.29	95.66	LAIL	PREC
15	Shenzhen	Guangdong	22.33	114.07	95.43	UTCI	LAIH
16	Huangshan	Anhui	29.43	118.18	95.01	LAIL	PM2.5
17	Chongqing	Chongqing	29.35	106.33	94.87	UTCI	PM2.5
18	Shangrao	Jiangxi	25.27	117.58	94.74	UTCI	PREC
19	Xi’an	Shanxi	34.17	108.57	94.60	LAIH	PM2.5
20	Changsha	Hunan	28.12	112.59	94.40	LAIH	PM2.5
21	Beijing	Beijing	39.55	116.24	94.38	LAIH	PM2.5
22	Zunyi	Guizhou	27.42	106.55	94.35	UTCI	PM2.5
23	Chengdu	Sichuan	30.40	104.04	94.33	UTCI	PM2.5
24	Qingdao	Shandong	36.03	120.18	94.32	LAIL	PM2.5
25	Haikou	Hainan	20.02	110.20	94.26	LAIL	PREC
26	Shijiazhuang	Hebei	38.02	114.30	94.04	LAIL	PM2.5
27	Hangzhou	Zhejiang	30.16	120.10	94.02	LAIL	PM2.5
28	Guiyang	Guizhou	26.35	106.42	93.96	UTCI	PREC
29	Wuhan	Hubei	30.35	114.17	93.85	UTCI	LAIH
30	Baoding	Hebei	38.51	115.30	93.79	LAIH	PM2.5
31	Shanghai	Shanghai	31.14	121.29	93.68	UTCI	PM2.5
32	Luoyang	Henan	34.41	112.27	93.61	UTCI	PM2.5
33	Hefei	Anhui	31.52	117.17	93.54	UTCI	LAIH
34	Jiujiang	Jiangxi	29.43	115.58	93.47	UTCI	LAIL
35	Xuzhou	Jiangsu	34.15	117.11	93.17	UTCI	PM2.5
36	Suzhou	Jiangsu	31.19	120.37	92.95	UTCI	LAIH
37	Nanjing	Jiangsu	32.03	118.46	92.78	UTCI	LAIL
38	Taiyuan	Shanxi	37.54	112.33	92.77	LAIH	PM2.5
39	Tianjin	Tianjin	39.02	117.12	92.61	UTCI	PM2.5
40	Zhengzhou	Henan	34.46	113.40	92.27	UTCI	PM2.5
41	Jinan	Shandong	36.40	117.00	91.80	UTCI	PM2.5
42	Lanzhou	Gansu	36.04	103.51	91.24	LAIH	UTCI
43	Shenyang	Liaoning	41.48	123.25	91.12	UVB	LAIH
44	Yinchuan	Ningxia	38.27	106.16	90.67	LAIH	LAIL
45	Harbin	Heilongjiang	45.44	126.36	90.32	UVB	LAIH
46	Changchun	Jilin	43.54	125.19	89.97	UVB	LAIH
47	Urumchi	Xinjiang	43.45	87.36	89.82	UTCI	PM2.5
48	Hohhot	Neimenggu	40.48	111.41	89.75	UVB	LAIH
49	Sining	Qinghai	36.38	101.48	88.99	PM2.5	LAIH
50	Lhasa	Xizang	29.39	91.08	87.06	PM2.5	UVB

Figure 8. Differences between the environmental suitability of cities at different administrative levels.

Table

Availability of data	Template for data availability statement	Policy
Data openly available in a public repository that does not issue DOIs	The UTCI, PREC, LAI and UVB data that support the findings of this study are openly available from ECMWF ERA5 at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5.	All
Data openly available in a public repository that does not issue DOIs	The ground-based PM2.5 data that support the findings of this study are openly available from the China Urban Air Quality Real Time Release Platform at http://106.37.208.233:20035/.	All

Data availability statement

Table

Availability of data	Template for data availability statement	Policy
Data openly available in a public repository that does not issue DOIs	The UTCI, PREC, LAI and UVB data that support the findings of this study are openly available from ECMWF ERA5 at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5.	All
Data openly available in a public repository that does not issue DOIs	The ground-based PM2.5 data that support the findings of this study are openly available from the China Urban Air Quality Real Time Release Platform at http://106.37.208.233:20035/.	All