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- 검색키워드
- 저자 : Keshen Jiang (검색결과 4 건)
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Machine Learning Aided Tracking Analysis of Haze Pollution and Regional Heterogeneity
( Fangfang Gu ),( Keshen Jiang ),( Fangdong Cao ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.6
Not only can air pollution reduce the overall competitiveness of tourist destinations, but also changes tourists' travel decisions, thereby affecting the tourism flows. The study presents a machine learning method to analyze how the haze pollution puts spatial effect on tourism flows in China from 2001 to 2018, and reveals the regional differences in heterogeneity among eastern, central, and western China. Our investigation reveals three interesting observations. First, the Environmental Kuznets Curve of the impact of haze pollution on tourism flows is not significant. In the eastern and western regions, the interaction between haze pollution and domestic tourism flows as well as inbound tourism flows shows an inverted U-shaped curve respectively. Second, there is an significantly positive spillover effect of tourism flows in all of the eastern, central, and western regions. As to the intensity of spillover, domestic tourism flows is higher than that of the inbound tourism flows. Both of the above figures are greatest in the eastern. Third, the Chinese haze pollution mainly reduces the inbound tourism flows, and only imposes significantly negative direct effects on the domestic tourism flows in the central region. In the central and eastern regions, significantly negative direct effects and spillover effects are exerted on inbound tourism.
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Predicting Urban Tourism Flow with Tourism Digital Footprints Based on Deep Learning
Fangfang Gu,Keshen Jiang,Yu Ding,Xuexiu Fan 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.4
Tourism flow is not only the manifestation of tourists’ special displacement change, but also an important driving mode of regional connection. It has been considered as one of significantly topics in many applications. The existing research on tourism flow prediction based on tourist number or statistical model is not in-depth enough or ignores the nonlinearity and complexity of tourism flow. In this paper, taking Nanjing as an example, we propose a prediction method of urban tourism flow based on deep learning methods using travel diaries of domestic tourists. Our proposed method can extract the spatio-temporal dependence relationship of tourism flow and further forecast the tourism flow to attractions for every day of the year or for every time period of the day. Experimental results show that our proposed method is slightly better than other benchmark models in terms of prediction accuracy, especially in predicting seasonal trends. The proposed method has practical significance in preventing tourists unnecessary crowding and saving a lot of queuing time.
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Study on Tourism Carbon Emissions and Distribution Efficiency of Tourism Economics
Xiaoyu Cheng,Keshen Jiang 한국유통과학회 2018 Asian Journal of Business Environment (AJBE) Vol.8 No.2
Purpose - It is important to figure out the relationship between tourism carbon emissions and tourism economics for a healthy tourism development. Research design, data, and methodology - Data of this study are collected from 27 provinces (cities) of China. Tourist consumption stripping coefficient is used to calculate tourism carbon emissions. SBM-Undesirable model is used to measure the efficiency of tourism economics under the constraint of tourism carbon emissions. Results - The results show that: during the year of 2005-2015, there are obvious differences in totals and intensities of tourism carbon emissions among 27 provinces and cities which can be divided into three areas. There is a high possibility of underestimating the actual efficiency of tourism economics by leaving tourism carbon emissions out of account, and a high inefficiency caused by tourism carbon emissions will lead a low efficiency of tourism economics. Conclusions - The development of tourism should give consideration to both economic and environmental benefits, and reduce the inefficiency caused by tourism carbon emissions to improve efficiency of tourism economics by improving the level of technical efficiency and promoting technological progress.
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Study on the Influencing Factors of TFP of Low-carbon Tourism Distribution
Xiaoyu Cheng,Keshen Jiang 한국유통과학회 2017 The Journal of Industrial Distribution & Business( Vol.8 No.7
Purpose - Performance appraisal has a significant influence on the development of low-carbon tourism distribution. Research design, data, and methodology - Data of this study are collected from 27 provinces (cities) of China. SBM-Malmquist model is used to measure the TFP and its dynamic changes of low-carbon tourism distribution; TOBIT model is used to discuss the factors of TFP of low-carbon tourism distribution. Results - The results show that, there are obvious differences among regional TFP of low-carbon tourism distribution, the average change tends to grow positively in general, and the western region grows fastest on average due to the improvement of technical efficiency and technical progress, while there are technical efficiency improvement but technical regresses in eastern and central regions. The economic scale, economic strength, structure of energy consumption, location quotient and government regulation have a significant positive effect on the TFP of low-carbon tourism; energy intensity, industrial structure and opening degree have a negative effect; investments in fixed assets, intensity of R&D fund and urbanization rate have no significant influence on the TFP of low-carbon tourism. Conclusions - Improving the productivity of low-carbon tourism and reducing regional differences are effective ways to develop low-carbon tourism and enhance tourism competitiveness.
ScienceON
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