气候资源刚性约束下国内旅游需求变化趋势与对策研究

发布时间：2018-07-03 10:47

本文选题：国内旅游需求 + 旅游气候指数　；参考：《浙江理工大学》2017年硕士论文

【摘要】：我国国内旅游需求受到多种因素的影响,包括经济因素、政治因素、社会因素、文化因素、资源因素等,文章以已有相关研究成果为基础,以北京、浙江、四川、海南、广东“四省一市”为研究对象,基于气候资源指标(降水量、风速、日照时数、温度、相对湿度)、居民消费价格分类指数、经济政策不确定性、国家法定节假日天数等月度数据,构建分省市面板数据模型,分析气候资源指标对国内旅游需求影响的显著性。由模型估计结果可知,气候资源因素对我国“四省一市”国内旅游需求整体存在较为显著的影响,不同气候资源指标对不同省市国内旅游需求影响的显著性不同。为实现旅游产业发展和气候资源变化协同推进,精确预测气候资源刚性约束下国内旅游需求的变化趋势,推进我国国内旅游需求供给侧与需求侧改革进程,在把握传统旅游气候指数内涵的基础上,基于旅游气候指数五大指标与“四省一市”国内旅游需求人数的弹性数值,分析旅游气候指数各个指标对各省市国内旅游需求的影响机理,并指出由于省市气候背景和地理位置等的差异,旅游气候指数指标对不同省市国内旅游需求存在不同程度的影响,传统旅游气候指数的标准化构建未考虑因地制宜等问题。根据弹性数值归一化处理结果修正传统旅游气候指数的初始权重分配,并对传统旅游气候指数与修正旅游气候指数进行了测度和对比,发现两者存在较大的测度差异,表明传统旅游气候指数和修正旅游气候指数在解释“四省一市”国内旅游需求的变化时存在一定的不确定性,何者能作为解释变量更精确的解释和预测国内旅游需求,不同省市存在一定差异。利用结构时间序列模型,分别以传统旅游气候指数和修正旅游气候指数为解释变量,将以京、浙、川、琼、粤四省一市为代表的我国国内旅游需求时间序列数据分解为趋势(水平和斜率)、周期、季节、无规律因子等多个因子,对我国国内旅游需求进行预测并分析非气候资源刚性约束、传统旅游气候指数约束、修正旅游气候指数约束下三条预测趋势线的差异,对比传统旅游气候指数与修正旅游气候指数对国内旅游需求预测精度的影响,借助RMSE值判别具有最优国内旅游需求预测精度的省市旅游气候指数权重构造标准,并在最优旅游气候指数标准下预测未来两年、五年、十五年各省市国内旅游需求及其变化趋势。研究结果表明,一方面,传统旅游气候指数和修正旅游气候指数情形下结构时间序列模型对国内旅游需求的预测精度不同,修正旅游气候指数相对有效地提高了地区旅游需求预测精度,且白昼舒适度指数和降水指数是最重要的气候因素;另一方面,不同省市国内旅游需求对气候资源刚性约束的敏感性不同,存在强气候资源刚性约束和弱气候资源刚性约束之分,气候资源刚性约束的强弱对于“十三五”时期旅游需求变化趋势的预判具有重要影响,进而影响优化地区旅游需求的供求政策。文章基于相关研究结论,立足供给侧与需求侧改革两大视角,通过构建气候资源刚性约束下国内旅游供求政策矩阵,实现替代性政策工具的优化选择和互补性政策工具的耦合强化,提出一整套优化我国国内旅游需求的政策组合拳。在研究视角上明确了气候资源对我国国内旅游需求发展的刚性约束作用;在理论观点上提出了根据气候资源刚性约束下国内旅游需求变化趋势因地制宜调整的旅游产业发展策略;在技术方法上构建了修正旅游气候指数,并提出修正旅游气候指数约束下部分省市国内旅游需求预测精度更优。相关研究重在气候资源因素影响国内旅游需求的实证分析及理论机理分析,重在新问题下旅游气候指数的创新性修正,重在有无气候资源刚性约束的国内旅游需求预测及变化趋势对比研究。
[Abstract]:The domestic tourism demand is affected by many factors, including economic factors, political factors, social factors, cultural factors and resource factors. Based on the existing research results, the article takes Beijing, Zhejiang, Sichuan, Hainan and Guangdong "four provinces and one city" as the research object, based on the climate resources index (precipitation, wind speed, sunshine hours, temperature). Degree, relative humidity), consumer price classification index, economic policy uncertainty, national legal holiday days and other monthly data, construction of a provincial panel data model, analysis of the impact of climate resources indicators on domestic tourism demand. The results of the model estimate can be known, climate resources factors in China, "Four Provinces one city" domestic Brigade There is a significant impact on tourism demand as a whole. The impact of different climate resource indicators on domestic tourism demand in different provinces and cities is different. In order to realize the development of tourism industry and climate resources change, the change trend of domestic tourism demand under the rigid constraints of climate resources is predicted, and the supply side of domestic tourism demand is promoted. On the basis of grasping the connotation of the traditional tourism climate index, based on the five major indexes of the tourism climate index and the elastic values of the number of domestic tourism demand in the "four provinces and one city", the influence mechanism of each index of the tourism climate index on the domestic tourism demand is analyzed, and the climate background and geography of the provinces and cities are also pointed out. The index of tourism climate index has different influence on the domestic tourism demand in different provinces and cities. The standardization of the traditional tourism climate index does not consider the problems of local conditions. The initial weight distribution of the traditional tourism climate index is amended according to the results of the elastic numerical normalization, and the traditional tourism climate index is also revised. The measurement and comparison with the revised tourist climate index show that there is a large difference in measurement between the two. It shows that the traditional tourism climate index and the revised tourist climate index have certain uncertainty in explaining the changes in the domestic tourism demand of "four provinces and one city". The time series data of China's domestic tourism demand, represented by the traditional tourism climate index and the revised tourist climate index, will be decomposed into trend (level and slope), cycle, season, and irregular cause by using the structure time series model, with the traditional tourist climate index and the revised tourist climate index as the explanatory variables, which are represented by the four provinces, Beijing, Zhejiang, Sichuan, Qiong and Guangdong Province. Several factors, such as sub factors, are used to predict the domestic tourism demand and analyze the rigid constraints of non climate resources, the traditional tourist climate index constraints, the correction of the difference of three forecast trends under the constraints of the tourism climate index, and the influence of the traditional tourism climate index and the revised Tourism climate index on the prediction accuracy of domestic tourism demand, with the help of RMS. The E value discriminates the urban tourism climate index weight structure standard with the best domestic tourism demand prediction accuracy, and forecasts the domestic tourism demand and the change trend in the next two years, five years and fifteen years under the optimal tourism climate index standard. The results show that, on the one hand, the traditional tourist climate index and the revised tourist climate index are on the one hand. In the case of the structural time series model, the prediction accuracy of the domestic tourism demand is different. The correction of the tourist climate index is relatively effective in improving the prediction accuracy of regional tourism demand, and the day comfort index and precipitation index are the most important climatic factors. On the other hand, the domestic tourism demand of different provinces and cities is sensitive to the rigid constraints of climate resources. The sensitivity is different, there are rigid constraints of strong climate resources and rigid constraints of weak climate resources. The strength of rigid constraints of climate resources has an important impact on the prediction of the change trend of tourism demand in the "13th Five-Year" period, and then affects the supply and demand policy of optimizing regional tourism demand. Based on the relevant research conclusions, the article is based on supply side and needs. In the two perspectives of side reform, by constructing the policy matrix of domestic tourism supply and demand under the rigid constraints of climate resources, the optimal choice of alternative policy tools and the coupling strengthening of complementary policy tools are realized, and a set of policy combinations to optimize domestic tourism demand in China is put forward. On the basis of the rigid constraint of the demand development, this paper puts forward the tourism industry development strategy, which is adjusted according to local conditions according to the local conditions of the change of domestic tourism demand under the rigid constraints of climate resources, and constructs a revised tourist climate index on the technical method, and puts forward the domestic tourism demand of some provinces and cities under the restriction of the tourism climate index. The prediction accuracy is better. The relevant research focuses on the empirical analysis and theoretical mechanism analysis on the influence of climate and resource factors on domestic tourism demand. It emphasizes the innovation of the tourism climate index under the new problem, and focuses on the domestic tourism demand prediction and the change trend contrast research with the rigid constraints of climate resources.
【学位授予单位】：浙江理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F592

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