發(fā)布時(shí)間：2018-07-03 10:47

  本文選題：國內(nèi)旅游需求 + 旅游氣候指數(shù)��； 參考：《浙江理工大學(xué)》2017年碩士論文

【摘要】：我國國內(nèi)旅游需求受到多種因素的影響,包括經(jīng)濟(jì)因素、政治因素、社會(huì)因素、文化因素、資源因素等,文章以已有相關(guān)研究成果為基礎(chǔ),以北京、浙江、四川、海南、廣東“四省一市”為研究對象,基于氣候資源指標(biāo)(降水量、風(fēng)速、日照時(shí)數(shù)、溫度、相對濕度)、居民消費(fèi)價(jià)格分類指數(shù)、經(jīng)濟(jì)政策不確定性、國家法定節(jié)假日天數(shù)等月度數(shù)據(jù),構(gòu)建分省市面板數(shù)據(jù)模型,分析氣候資源指標(biāo)對國內(nèi)旅游需求影響的顯著性。由模型估計(jì)結(jié)果可知,氣候資源因素對我國“四省一市”國內(nèi)旅游需求整體存在較為顯著的影響,不同氣候資源指標(biāo)對不同省市國內(nèi)旅游需求影響的顯著性不同。為實(shí)現(xiàn)旅游產(chǎn)業(yè)發(fā)展和氣候資源變化協(xié)同推進(jìn),精確預(yù)測氣候資源剛性約束下國內(nèi)旅游需求的變化趨勢,推進(jìn)我國國內(nèi)旅游需求供給側(cè)與需求側(cè)改革進(jìn)程,在把握傳統(tǒng)旅游氣候指數(shù)內(nèi)涵的基礎(chǔ)上,基于旅游氣候指數(shù)五大指標(biāo)與“四省一市”國內(nèi)旅游需求人數(shù)的彈性數(shù)值,分析旅游氣候指數(shù)各個(gè)指標(biāo)對各省市國內(nèi)旅游需求的影響機(jī)理,并指出由于省市氣候背景和地理位置等的差異,旅游氣候指數(shù)指標(biāo)對不同省市國內(nèi)旅游需求存在不同程度的影響,傳統(tǒng)旅游氣候指數(shù)的標(biāo)準(zhǔn)化構(gòu)建未考慮因地制宜等問題。根據(jù)彈性數(shù)值歸一化處理結(jié)果修正傳統(tǒng)旅游氣候指數(shù)的初始權(quán)重分配,并對傳統(tǒng)旅游氣候指數(shù)與修正旅游氣候指數(shù)進(jìn)行了測度和對比,發(fā)現(xiàn)兩者存在較大的測度差異,表明傳統(tǒng)旅游氣候指數(shù)和修正旅游氣候指數(shù)在解釋“四省一市”國內(nèi)旅游需求的變化時(shí)存在一定的不確定性,何者能作為解釋變量更精確的解釋和預(yù)測國內(nèi)旅游需求,不同省市存在一定差異。利用結(jié)構(gòu)時(shí)間序列模型,分別以傳統(tǒng)旅游氣候指數(shù)和修正旅游氣候指數(shù)為解釋變量,將以京、浙、川、瓊、粵四省一市為代表的我國國內(nèi)旅游需求時(shí)間序列數(shù)據(jù)分解為趨勢(水平和斜率)、周期、季節(jié)、無規(guī)律因子等多個(gè)因子,對我國國內(nèi)旅游需求進(jìn)行預(yù)測并分析非氣候資源剛性約束、傳統(tǒng)旅游氣候指數(shù)約束、修正旅游氣候指數(shù)約束下三條預(yù)測趨勢線的差異,對比傳統(tǒng)旅游氣候指數(shù)與修正旅游氣候指數(shù)對國內(nèi)旅游需求預(yù)測精度的影響,借助RMSE值判別具有最優(yōu)國內(nèi)旅游需求預(yù)測精度的省市旅游氣候指數(shù)權(quán)重構(gòu)造標(biāo)準(zhǔn),并在最優(yōu)旅游氣候指數(shù)標(biāo)準(zhǔn)下預(yù)測未來兩年、五年、十五年各省市國內(nèi)旅游需求及其變化趨勢。研究結(jié)果表明,一方面,傳統(tǒng)旅游氣候指數(shù)和修正旅游氣候指數(shù)情形下結(jié)構(gòu)時(shí)間序列模型對國內(nèi)旅游需求的預(yù)測精度不同,修正旅游氣候指數(shù)相對有效地提高了地區(qū)旅游需求預(yù)測精度,且白晝舒適度指數(shù)和降水指數(shù)是最重要的氣候因素;另一方面,不同省市國內(nèi)旅游需求對氣候資源剛性約束的敏感性不同,存在強(qiáng)氣候資源剛性約束和弱氣候資源剛性約束之分,氣候資源剛性約束的強(qiáng)弱對于“十三五”時(shí)期旅游需求變化趨勢的預(yù)判具有重要影響,進(jìn)而影響優(yōu)化地區(qū)旅游需求的供求政策。文章基于相關(guān)研究結(jié)論,立足供給側(cè)與需求側(cè)改革兩大視角,通過構(gòu)建氣候資源剛性約束下國內(nèi)旅游供求政策矩陣,實(shí)現(xiàn)替代性政策工具的優(yōu)化選擇和互補(bǔ)性政策工具的耦合強(qiáng)化,提出一整套優(yōu)化我國國內(nèi)旅游需求的政策組合拳。在研究視角上明確了氣候資源對我國國內(nèi)旅游需求發(fā)展的剛性約束作用;在理論觀點(diǎn)上提出了根據(jù)氣候資源剛性約束下國內(nèi)旅游需求變化趨勢因地制宜調(diào)整的旅游產(chǎn)業(yè)發(fā)展策略;在技術(shù)方法上構(gòu)建了修正旅游氣候指數(shù),并提出修正旅游氣候指數(shù)約束下部分省市國內(nèi)旅游需求預(yù)測精度更優(yōu)。相關(guān)研究重在氣候資源因素影響國內(nèi)旅游需求的實(shí)證分析及理論機(jī)理分析,重在新問題下旅游氣候指數(shù)的創(chuàng)新性修正,重在有無氣候資源剛性約束的國內(nèi)旅游需求預(yù)測及變化趨勢對比研究。
[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.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F592

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 謝慧明;強(qiáng)朦朦;沈滿洪;;中國居民旅游需求的動(dòng)態(tài)決定機(jī)制及其影響因素——一個(gè)經(jīng)濟(jì)、文化與自然環(huán)境的綜合視角[J];浙江理工大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2016年02期

2 王慶林;楊志輝;;基于GA的廣義模糊時(shí)間序列建模及其在旅游需求預(yù)測中的應(yīng)用[J];江西科學(xué);2015年05期

3 李靜;Philip L.PEARCE;吳必虎;Alastair M.MORRISON;;霧霾對來京旅游者風(fēng)險(xiǎn)感知及旅游體驗(yàn)的影響——基于結(jié)構(gòu)方程模型的中外旅游者對比研究[J];旅游學(xué)刊;2015年10期

4 邢彩盈;張京紅;劉少軍;張明潔;;基于氣候指標(biāo)評估氣候變化對海南旅游的影響[J];自然資源學(xué)報(bào);2015年05期

5 梁昌勇;馬銀超;陳榮;梁焱;;基于SVR-ARMA組合模型的日旅游需求預(yù)測[J];管理工程學(xué)報(bào);2015年01期

6 劉少軍;張京紅;吳勝安;張明潔;車秀芬;;氣候變化對海南島旅游氣候舒適度及客流量可能影響的分析[J];熱帶氣象學(xué)報(bào);2014年05期

7 張洪;程振東;王先鳳;;城市居民國內(nèi)旅游需求影響因素分析及對策研究[J];資源開發(fā)與市場;2014年06期

8 郭偉;李京;;基于改進(jìn)的優(yōu)化組合方法的旅游需求預(yù)測[J];統(tǒng)計(jì)與決策;2011年08期

9 李志龍;陳志鋼;覃智勇;;基于支持向量機(jī)旅游需求預(yù)測[J];經(jīng)濟(jì)地理;2010年12期

10 陶偉;倪明;;中西方旅游需求預(yù)測對比研究:理論基礎(chǔ)與模型[J];旅游學(xué)刊;2010年08期

相關(guān)博士學(xué)位論文 前3條

1 張巖;結(jié)構(gòu)時(shí)間序列模型在季節(jié)調(diào)整中的理論分析與應(yīng)用研究[D];南開大學(xué);2013年

2 馬麗君;中國典型城市旅游氣候舒適度及其與客流量相關(guān)性分析[D];陜西師范大學(xué);2012年

3 丁憶;中國國內(nèi)旅游消費(fèi)理論與實(shí)證研究[D];華東師范大學(xué);2011年

相關(guān)碩士學(xué)位論文 前4條

1 李鵬飛;海南島旅游氣候資源及其影響力評價(jià)[D];海南師范大學(xué);2013年

2 李京;天津市城市居民國內(nèi)旅游需求動(dòng)態(tài)演變分析及預(yù)測[D];燕山大學(xué);2010年

3 李萍;杭州市旅游氣候資源及開發(fā)利用研究[D];中南林學(xué)院;2005年

4 張運(yùn)來;我國國內(nèi)旅游需求影響因素分析及趨勢預(yù)測方法應(yīng)用研究[D];東北林業(yè)大學(xué);2002年

，

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