中国典型城市旅游气候舒适度及其与客流量相关性分析

【作者】 马丽君；

【导师】 孙根年；

【作者基本信息】 陕西师范大学 ， 旅游管理， 2012， 博士

【摘要】 气候与旅游是近年来国内外旅游研究的一个热点问题,国内外旅游研究者在旅游气候资源评价与开发、气候变化对旅游资源的影响、气候变化对旅游需求的影响、气候变化对目的地客流量接待及旅游流的影响等方面进行了深入的研究。旅游气候舒适度是气候与旅游研究的一个重要内容,然而相关研究多为现状的分析和评价,没有从一个较长时间尺度上考察气候舒适度的变化及其规律,且就气候论“气候”,较少将气候舒适度与客流量年内变化联系起来,探讨气候舒适度与客流量年内变化的关系。本文以典型城市为研究对象,在系统收集相关数据资料的基础上,主要完成了城市气候舒适度的现状评价及30年来城市旅游气候舒适度的变化分析、游客对气候变化的感知及其对出游行为影响的调查研究、气候舒适度与客流量、游客网络关注度时空相关关系分析、气候舒适度变化对客流量的影响分析等几个方面的研究,主要结论如下：(1)西安市居民年内气候变化感知与实际年内气候变化状况基本一致。西安市居民年内气候变化感知与出游季节偏好具有较强的一致性,气候最舒适的季节是居民最愿意出游的季节,最不舒适的季节是居民最不愿意出游的季节,说明气候舒适性是影响游客出游时间选择的重要因素。气候是影响居民旅游目的地选择的重要因素,其它条件相同的情况下,居民偏好于气候舒适的旅游目的地。气候是影响居民旅游项目选择的重要因素,不同季节居民对旅游项目的偏好不同,春季和秋季居民对不同旅游项目的偏好较为一致,有相同的变化趋势,且偏好程度相差不大,夏季和冬季居民对不同旅游项目的偏好波动较大。(2)纬度和海拔高度是影响城市旅游气候舒适度的重要因素。城市年综合气候舒适指数随纬度的降低先升高后降低,说明城市年气候舒适度随纬度的降低先升高后降低,长江流域城市年综合气候舒适指数随海拔高度的降低呈一下降趋势,城市年气候舒适度随海拔高度的降低呈下降趋势。随着纬度的降低,1-2月和12月气候舒适度升高,3-5月和10-11月气候舒适度先升高后降低,6-9月气候舒适度降低。依据城市综合气候舒适指数的年内变化,可以将46个城市划分为倒“V”形、倒“U”形、“M”形和宽“U”形4种类型。依据城市旅游气候舒适期的年内分布,可以将46个城市划分为夏适型、冬适型、春秋适型和四季型4种类型。(3)气候舒适度是影响游客网络关注度时空变化的重要因素。收集30个旅游城市游客网络关注度,分析其年内时空变化状况,按游客网络关注度年内变化,将30个城市划分为13种类型。在游客网络关注度月指数与气候舒适度指数比较的基础上,采用虚拟变量的回归分析方法,建立相关模型,分析气候舒适度与游客网络关注度的相关关系,长春、北京、西宁和海口游客网络关注度月指数的气候弹性系数分别为0.542、0.46、1.182和0.8,长白山、八达岭长城、周庄古镇、张家界武陵源游客网络关注度月指数的气候弹性系数分别为0.333、0.632、0.438、0.324。气候舒适度是影响游客网络关注度空间分布的重要因素,综合气候舒适指数每变化1个单位,游客网络关注度将增加(或减少)0.641万次。(4)气候舒适度是影响客流量时空变化的重要因素。收集2005-2007年26个城市各月入境客流量,分析其年内时空变化状况,将其年内变化分为4种类型。在客流量月指数与气候舒适度指数比较的基础上,采用虚拟变量的回归分析方法,建立了4个城市入境旅游客流量月指数模拟模型,哈尔滨、大连、北京、海口入境游客月指数气候弹性系数分别为0.666、0.372、0.625、1.227。同时收集了4个城市2005-2007年各月国内客流量以及4个景区的客流量数据,对国内客流量与气候舒适度年内变化的相关关系进行了分析,北京、海口、张家界和昆明国内游客月指数气候弹性系数分别为0.1221、1.069、0.401、0.763,九寨沟、青城山、都江堰和广汉三星堆游客月指数气候弹性系数分别为2.337、0.831、0.421、0.816。利用旅游资源丰度、经济发展水平、综合气候舒适指数3个因素,建立其与入境及国内客流量地域分布的统计关系,综合气候舒适度指数每变化1个单位,入境客流量将增加(或减少)2.17万人,国内客流量将增加(或减少)30.72万人。(5)全球气候变化使城市气候舒适度发生了改变。30年来旅游气候舒适度的变化主要受地理纬度的影响,重庆以北绝大部分城市年综合气候舒适指数上升,旅游气候舒适度升高,重庆以南绝大部分城市年综合气候舒适指数下降,旅游气候舒适度降低。纬度较高的城市温湿指数、风寒指数和综合气候舒适指数变化幅度相对较大,纬度较低的城市变化幅度相对较小。春季纬度较高的城市气候舒适度上升,纬度较低的城市气候舒适度有所下降；夏季绝大多数城市气候舒适度降低,且随纬度的降低城市气候舒适度下降幅度有所减小；秋季绝大部分城市气候舒适度下降,且随纬度的降低气候舒适度的下降幅度在增大；冬季除济南外,南昌及其以北城市气候舒适度均上升,且随纬度的降低气候舒适度上升的幅度在减小。30年来45个城市年综合气候舒适指数共上升了26.8,促进了我国旅游业的发展,但随着全球的进一步升温,年综合气候舒适指数下降的城市将进一步增多,下降的幅度将进一步增大,对旅游业的促进将逐渐转变为抑制。极端天气气候对旅游业有重大的影响,2008年雪灾对旅游业的影响,其游客损失量与客流量基数(本底值)成正比,游客损失率与2008年本底值(基数)成反比,损失量和损失率两者均与受灾程度存在一定的成正比关系。(6)气候舒适度变化对目的地客流量接待产生了影响。利用综合气候舒适指数及40个城市的相关气候和旅游客流量数据,构建国内外旅游气候模型,分析气候舒适度变化对旅游业的影响。结果显示综合气候舒适指数每变化1个单位我国入境及国内旅游客流量将增加或减少1.852万人次和35.263万人次,重庆以北绝大部分城市年接待客流量增加,重庆以南绝大部分城市年接待客流量减少,30年来40城市国内及入境旅游客流量分别增加540.7万人次和28.4万人次。本文的主要创新点有：(1)以温湿指数、风寒指数和着衣指数为基础,采用专家打分和层次分析法确定各分指数的权重,建立了一个新的旅游气候舒适性综合评价模型,该模型不仅能直接反映客流量的年内月变化,而且还有可加和等特征。(2)构建气候舒适度与客流量及游客网络关注度时空相关模型,揭示旅游气候舒适度弹性。传统的研究多以气候论“气候”,较少将气候舒适度与客流量年内变化联系起来,探讨气候舒适度与客流量年内变化的关系；本文系统收集城市客流量及游客网络关注度数据,分析其时空变化规律,并将其与气候舒适度时空变化规律相对比,构建气候舒适度与客流量及游客网络关注度时空相关模型,揭示旅游气候舒适度弹性。(3)本文揭示了极端天气气候对旅游业影响的动力机制,并借助本底趋势线理论,分析了2008年雪灾对旅游业的影响,发现2008年雪灾对旅游业的影响,其游客损失量与客流量基数(本底值)成正比,游客损失率与2008年本底值(基数)成反比,损失量和损失率两者均与受灾程度存在一定的成正比关系。(4)构建旅游气候模型,估算气候变化对旅游业的影响。国内外有关气候变化对游客旅游需求和目的地客流量接待影响的研究多以其中一种或几种气候要素为变量,通过建立相关模型定量分析气候变化对旅游业的影响,这种研究方法存在较大的缺陷,因为大多数气候要素对旅游业的影响均存在“过犹不及”的现象。本文以综合气候舒适指数为变量,构建旅游气候模型,从气候舒适度视角定量分析了气候变化对旅游业的影响。更多还原

【Abstract】 The relationship between climate and tourism is a focus in tourism research at home and abroad in recent years. The evaluation and development of tourism climatic resources, effects of climate change on tourism resources, effects of climate change on tourism demand, effects of climate change on the reception and the tourism flow and so on are well studied by domestic and foreign tourism researchers. Tourism climate comfort degree is an important research filed in the research of relationship between climate and tourism. However, most of related researches focus on the current situation and evaluation. Few people study on the change of climate comfort degree and its rule from a relatively long time scale. And they seldom link the climate comfort degree to the monthly change of tourism passengers to explain the relationship between climate comfort degree and monthly change of tourism passengers. Based on collecting relevant datas, climate comfort degree of typial cities, the change of climate comfort degree in the past30years is analyzed, tourists’perception of climate change and its impact to their travel decision-making, the relationship between climate comfort degree and passenger, tourist’s network attention, influence of climate comfort degree’s change tourist’s traffic are analyzed. The main results show that:(1) Xi’an residents’perception of climate change are basically the same with the actual climate change. Xi’an residents’perception of climate change are basically the same with the seasonal preference in traveling. The most comfortable season is the most willing to travel for residents, the most uncomfortable season is the most unwilling to travel for residents. This shows that climate comfort degree is an important factor which affects tourist’s travel time choosing. Climate is an important factor which affects resident’s destination choosing. Residents prefer to the more comfortable destination, if the other conditions are the same. Climate is an important factor which affects resident’s items choosing. Items which residents prefer to is different in different season. Resident’s preference in spring is similar with the autumn’s. Their change trends are the same, and the degree of preference to different items is alike. The degree of preference to different items is very different in summer and winter.(2) Latitude and elevation are two important factors which affect the city’s climate comfort degree. As the latitude decrease, the comprehensive climate comfort index first goes up, then goes down, it shows that climate comfort degree first goes up, then goes down as the latitude decrease. The comprehensive climate comfort index goes down as the elevation decrease along the Yangtze River, it shows that climate comfort degree goes down as the elevation decrease. As the latitude decrease, climate comfort degree of January, February and December goes up; climate comfort degree of March, April, May, October and November first goes up, then goes down; climate comfort degree of June, July, August and September goes down. The46cities are divided into four types based on the change of comprehensive climate comfort index in a year. The46cities are divided into summer comfort, winter comfort, spring and autumn comfort and four seasons comfort four types.(3) Climate comfort degree is an important factor which affects the temporal and spatial variation of tourist’s network attention. Based on the data of network attention of tourist, the temporal and spatial variation of network attention of tourist in30cities are analyzed. The30cities can be divided into three kinds by the variation of network attention of tourist in a year. Based on the comparison of monthly index of network attention and climate comfort degree and numerical valued special factors, the simulated model of the monthly index of network attention of tourist are founded. Climatic coefficient elasticity of monthly index in Changchun,Beijing,Xining, Haikou is respectively0.542、0.46、1.182and0.8. Climatic coefficient elasticity of monthly index in Changbai Mountain, the Great Wall,Zhouzhuang, Zhangjiajie is respectively0.333、0.632、0.438and0.324.Climate comfort degree is one important factor which affects the spatial distribution of network attention of tourist. The number of network attention of tourist will increase (or decrease)6,410if the comprehensive climate comfort index change one.(4) Climate comfort degree is an important factor which affects the temporal and spatial variation of tourists.The number of inbound tourists from2005to2007in the26cities are collected to analyze their spatial and temporal variation, according to that they are divided into four types. Based on the comparison of monthly index of tourists and climate comfort degree and numerical valued special factors, the simulated model of the monthly index of four cities’inbound tourists are founded. Climatic coefficient elasticity of monthly index in Haerbin, Dalian,Beijing, Haikou is respectively0.666、0.372、0.625and1.227. Meanwhile the number of domestic tourists from2005to2007in4ities and tourists from2005to2007in4scenic spots are collected to analyze the correlation of domestic tourists and climate comfort degree. Climatic coefficient elasticity of monthly index in Beijing, Haikou,Zhangjiajie, Kunming is respectively0.1221、1.069、0.401and0.763. Climatic coefficient elasticity of monthly index in Jiuzhaigou, Qingcheng Mountain, Dujiangyan, Sanxingdui is respectively2.337、0.831、0.421and0.816.The spatial distribution of inbound and domestic tourists are analyzed by the abundance of tourism resources, the level of economic development, and the climate comfort degree, their statistical relation is founded. The number of inbound tourists will increase (or decrease)21.700if the comprehensive climate comfort index change one. The number of domestic tourists will increase (or decrease)307,200if the comprehensive climate comfort index change one.(5) Cities’climate comfort degree are changed by the global warming. The change of tourism climate comfort degree in the past30years is mainly affected by geographical latitude. Comprehensive climate comfort index of most cities which located in north of Chongqing city rise, so their tourism climate comfort degree rise.Comprehensive climate comfort index of most cities which located in south of Chongqing city fall, so their tourism climate comfort degree fall. THI, WCI and CCI of cities located in higher latitude changed relatively large, THI,WCI and CCI of cities located in lower latitude changed relatively little. In spring, climate comfort degree of cities located in higher latitude rise, climate comfort degree of cities located in lower latitude fall. In summer, climate comfort degree of most cities fall, and the falling degree is reducing as the latitude decrease. In autumn, climate comfort degree of most cities fall, the falling degree is increasing as the latitude decrease.In winter, climate comfort degree of cities which located in north of Nanchang city rise, the rising degree is reducing as the latitude decrease. Comprehensive climate comfort index of45cities totally increased26.8. Development of tourism in China is promoted by the variation of climate comfort degree in the last30years. But as global warming, comprehensive climate comfort index of more and more cities will fall, its effection will gradually transform into inhibition. Tourism are often affected by extreme weather and climate.The tourists loss is proportional with the base number of tourists (number of natural trend curve) in direct proportion. Loss rate of tourists is proportional with the number of natural trend curve (base number of tourists). Both the loss rate and the loss volume exist a direct proportion with the snowstorm’s intensity.(6) From the climate comfort degree angle, models of domestic and inbound tourists are founded by comprehensive comfort index and relevant data of climate and tourism of the40cities, and the influence of climate change on tourism is quantitative analyzed. Result shows that Climate change has a heavy impact on domestic and inbound tourism, the number of domestic and inbound tourists will increase (or decrease)18,520and352,630if the comprehensive climate comfort index change one integrated. Tourists of most cities which located in north of Chongqing city rise, tourists of most cities which located in south of Chongqing city decrease. Influenced by the variation of climate comfort degree, domestic and inbound tourism traffic of40cities totally increased5.407million and28.4million.The main innovation points in this paper are concluded as follows:(1) A new comprehensive comfort index is founded based on assignment of THI, WCI and ICL. This new index not only can directly reflect the number of tourists in each month in a year, but also has some peculiarity like comparable, plus able and so on. (2) Few related researches link the climate comfort degree to the monthly change of tourism passengers to explain the relationship between climate comfort degree and monthly change of tourism passengers. Based on the data of network attention of tourist and the number of tourists, the temporal and spatial variation of network attention of tourist and the number of tourists are analyzed. Based on the comparison of temporal and spatial variation of network attention of tourist and the number of tourists and the temporal and spatial variation of climate comfort degree, the simulated model of the monthly index of network attention of tourist and the number of tourists are founded.(3) Impact mechanism of extreme weather and climate to tourism are analyzed. The influence of snowstorm in2008to tourism is analyzed by the theory of natural trend curve. The tourists loss is proportional with the base number of tourists (number of natural trend curve) in direct proportion. Loss rate of tourists is proportional with the number of natural trend curve (base number of tourists). Both the loss rate and the loss volume exist a direct proportion with the snowstorm’s intensity.(4) There are defective in most of related researches which focus on effects of climate change on tourism demand and effects of climate change on the tourists reception at home and abroad.Because impacts of most the climate elements on the tourism industry are going beyond the limit is as bad as falling short, however their models are founded by one ore several climate factors as a variable. In this paper we take comprehensive climate comfort index as a variable, construct tourism climate models, analyze the influence of climate change on tourism from the climate comfort degree angle.更多还原