Spatial and temporal evolution of climate comfort index in China and its impact on tourism

doi:10.3969/j.issn.1004-9479.2024.07.20222103

Abstract

Abstract:

Climate comfort is an important indicator of the attractiveness of tourist destinations, and its impact on the tourism industry has been paid attention to by scholars. According to the daily meteorological data of China from 1990 to 2020, the climate comfort level model was used to classify the climate comfort level, and the spatio-temporal evolution law of China's climate comfort level was explored, and then the influence of climate comfort level on domestic and inbound tourists in various provinces and regions was explained through the second-order partial correlation analysis. The results show that: ① The spatial distribution of China's climatic comfort zones shows obvious zonal distribution law, and there is also a certain degree of longitude inclination under the influence of topography, and the spatial distribution of climatic discomfort zones is basically locked by Hu Huanyong line and ladder dividing line. ② Since 1990, China's climate comfort index has not shown an obvious downward trend in most regions, and China's climate as a whole is developing in a more livable and comfortable direction. ③ Climate comfort level is an important factor affecting the tourism market in most regions, and the comfort level of Qinghai, Ningxia, Gansu, Hainan and Beijing has the most obvious influence on the number of tourists.

Key words: climate comfort, spatial and temporal pattern, number of visitors, Grey Correlation Analysis

摘要：

在全球气候变暖背景下，气候舒适度对旅游业的影响日益受到关注。根据中国1990—2020年的逐日气象数据，利用气候舒适度模型划分气候舒适区等级，探析中国气候舒适度的时空演变规律，进而通过灰色关联分析阐释气候舒适度对各省区国内和入境旅游人次的影响。研究结果表明：①中国气候舒适区的空间分布表现出明显的纬向分布规律，同时受地形影响也存在一定程度的经向倾斜，气候不舒适区的空间分布基本上受到胡焕庸线和阶梯分界线的“锁定”。②1990年以来，中国气候舒适度指数在大多数区域呈不明显下降趋势，中国气候整体上正朝着利于人居的舒适方向发展。③气候舒适度是影响大部分地区旅游市场的重要因素，青海、宁夏、甘肃、海南、北京五省市的旅游舒适度对旅游业的影响最为明显。

关键词: 气候舒适度, 时空格局, 旅游业, 灰色关联分析

Peijun RONG, Rongrong ZHANG, Xianhong ZHAO. Spatial and temporal evolution of climate comfort index in China and its impact on tourism[J]. World Regional Studies, 2024, 33(7): 139-149.

荣培君, 张荣荣, 赵现红. 中国气候舒适度时空演变及其对旅游业的影响[J]. 世界地理研究, 2024, 33(7): 139-149.

Figures/Tables 8

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级别	1级	2级	3级	4级	5级
SI	SI≤3.6	3.6<SI≤5.7	5.7<SI≤12.0	7.78<SI≤15.8	SI>15.8
人体感觉	最舒适	较舒适	正常	较不舒适	最不舒适

级别	1级	2级	3级	4级	5级
SI	SI≤3.6	3.6<SI≤5.7	5.7<SI≤12.0	7.78<SI≤15.8	SI>15.8
人体感觉	最舒适	较舒适	正常	较不舒适	最不舒适

省市	酒店床位数	4A、5A景区数量	客运总量	城镇化率	人均GDP	森林覆盖率	建成区绿化覆盖率	气候舒适度
北京	0.879	0.858	0.588	0.852	0.570	0.930	0.820	0.829
天津	0.655	0.734	0.810	0.654	0.821	0.663	0.710	0.649
河北	0.829	0.612	0.635	0.784	0.640	0.835	0.820	0.735
山西	0.760	0.783	0.788	0.701	0.811	0.693	0.708	0.685
内蒙古	0.867	0.690	0.712	0.762	0.749	0.756	0.756	0.743
辽宁	0.613	0.755	0.847	0.673	0.809	0.672	0.679	0.664
吉林	0.631	0.775	0.769	0.682	0.959	0.675	0.676	0.671
黑龙江	0.679	0.700	0.762	0.633	0.860	0.628	0.649	0.612
上海	0.739	0.830	0.742	0.760	0.906	0.929	0.788	0.742
江苏	0.752	0.803	0.759	0.700	0.862	0.723	0.675	0.657
浙江	0.657	0.723	0.822	0.622	0.747	0.614	0.638	0.605
安徽	0.720	0.754	0.866	0.717	0.818	0.699	0.712	0.690
福建	0.893	0.915	0.734	0.868	0.803	0.838	0.850	0.829
江西	0.728	0.756	0.687	0.685	0.805	0.662	0.671	0.649
山东	0.705	0.797	0.700	0.700	0.895	0.698	0.677	0.681
河南	0.689	0.771	0.821	0.701	0.907	0.674	0.679	0.644
湖北	0.817	0.904	0.717	0.804	0.955	0.813	0.805	0.790
湖南	0.796	0.850	0.721	0.792	0.918	0.766	0.765	0.747
广东	0.590	0.914	0.703	0.705	0.655	0.696	0.739	0.677
广西	0.838	0.879	0.890	0.877	0.674	0.847	0.800	0.792
海南	0.828	0.860	0.595	0.890	0.649	0.873	0.842	0.879
重庆	0.814	0.801	0.741	0.786	0.918	0.805	0.791	0.759
四川	0.661	0.722	0.782	0.689	0.696	0.663	0.715	0.641
贵州	0.850	0.898	0.643	0.814	0.771	0.831	0.793	0.766
云南	0.712	0.713	0.826	0.718	0.871	0.698	0.688	0.658
陕西	0.798	0.866	0.919	0.819	0.669	0.803	0.844	0.769
甘肃	0.718	0.611	0.646	0.855	0.665	0.838	0.755	0.881
青海	0.789	0.823	0.681	0.964	0.755	0.959	0.936	0.945
宁夏	0.953	0.870	0.651	0.940	0.794	0.946	0.948	0.924
新疆	0.829	0.612	0.635	0.784	0.640	0.835	0.820	0.735

省市	酒店床位数	4A、5A景区数量	客运总量	城镇化率	人均GDP	森林覆盖率	建成区绿化覆盖率	气候舒适度
北京	0.879	0.858	0.588	0.852	0.570	0.930	0.820	0.829
天津	0.655	0.734	0.810	0.654	0.821	0.663	0.710	0.649
河北	0.829	0.612	0.635	0.784	0.640	0.835	0.820	0.735
山西	0.760	0.783	0.788	0.701	0.811	0.693	0.708	0.685
内蒙古	0.867	0.690	0.712	0.762	0.749	0.756	0.756	0.743
辽宁	0.613	0.755	0.847	0.673	0.809	0.672	0.679	0.664
吉林	0.631	0.775	0.769	0.682	0.959	0.675	0.676	0.671
黑龙江	0.679	0.700	0.762	0.633	0.860	0.628	0.649	0.612
上海	0.739	0.830	0.742	0.760	0.906	0.929	0.788	0.742
江苏	0.752	0.803	0.759	0.700	0.862	0.723	0.675	0.657
浙江	0.657	0.723	0.822	0.622	0.747	0.614	0.638	0.605
安徽	0.720	0.754	0.866	0.717	0.818	0.699	0.712	0.690
福建	0.893	0.915	0.734	0.868	0.803	0.838	0.850	0.829
江西	0.728	0.756	0.687	0.685	0.805	0.662	0.671	0.649
山东	0.705	0.797	0.700	0.700	0.895	0.698	0.677	0.681
河南	0.689	0.771	0.821	0.701	0.907	0.674	0.679	0.644
湖北	0.817	0.904	0.717	0.804	0.955	0.813	0.805	0.790
湖南	0.796	0.850	0.721	0.792	0.918	0.766	0.765	0.747
广东	0.590	0.914	0.703	0.705	0.655	0.696	0.739	0.677
广西	0.838	0.879	0.890	0.877	0.674	0.847	0.800	0.792
海南	0.828	0.860	0.595	0.890	0.649	0.873	0.842	0.879
重庆	0.814	0.801	0.741	0.786	0.918	0.805	0.791	0.759
四川	0.661	0.722	0.782	0.689	0.696	0.663	0.715	0.641
贵州	0.850	0.898	0.643	0.814	0.771	0.831	0.793	0.766
云南	0.712	0.713	0.826	0.718	0.871	0.698	0.688	0.658
陕西	0.798	0.866	0.919	0.819	0.669	0.803	0.844	0.769
甘肃	0.718	0.611	0.646	0.855	0.665	0.838	0.755	0.881
青海	0.789	0.823	0.681	0.964	0.755	0.959	0.936	0.945
宁夏	0.953	0.870	0.651	0.940	0.794	0.946	0.948	0.924
新疆	0.829	0.612	0.635	0.784	0.640	0.835	0.820	0.735

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