The Impact of Air Pollution on the Development of Inbound Tourism in Chinese Megacities: The Case Study of Beijing, Guangzhou and Chongqing

The existing studies pay more attention to the impact of public transport and other public service facilities on urban air pollution and tourism, but less on the negative effect of air pollution caused by carbon emissions of business fixed investment on inbound tourism. This article attempts to make a supplementary analysis about the above point through examining the correlation between air pollution associate with business fixed investment and the size of inbound tourism based on panel data of three megacities (Beijing, Guangzhou and Chongqing) in China over the period from 2015 to 2019. The findings of this paper show that the effects of air pollution linked with carbon emissions from business fixed investment on the number of inbound tourists (NIT) is a negative correlation, while the influence of GDP per capita and tourism revenue on NIT reveal a positive relationship by applying fixed effects model for benchmark regression and the system-GMM estimator for robustness check. Moreover, the negative influence of PM 10 on sample cities is more than PM2.5. Some different results of core variables between benchmark and sub-sample regressions don’t imply the above conclusion to be substantively changed because of different distribution and concentration of nominal inbound tourists in specific sample megacities. In order to fundamentally improve air quality and to stimulate the development of inbound tourism, the suggestion of this study is to promote new business fixed investment with clean energy of renewable and low carbon.

Dynamic Forecasting Algorithm of Inbound Ice and Snow Tourism in China Based on Improved Deep Confidence Network

Ice and snow-based tourism is getting popular around the world and it is one of the major sources of revenue for a region with required facilities. According to a report by China Daily, China was expected to witness 230 million tourist visits in 2020-2021 with a total revenue generation surpassing 390 billion yuan. In order to promote the ice and snow tourism, proper arrangements such as accommodation, transport facility, and energy provision for heating and food need to be arranged as per the demand of the visitors at a certain period of time. A tourist prediction system can help in this regard for good estimation but considering the problems of traditional ice and snow tourism systems, specifically the prediction accuracy and long forecasting time, a dynamic forecasting algorithm for ice and snow inbound tourism based on an improved deep confidence network is proposed. The system analyzes the relationship between the demand for ice and snow inbound tourism and the level of national economic development, people’s living standards, demographic characteristics, traffic conditions, and tourism supply capacity. It then takes the influencing factors of ice and snow inbound tourism demand as sample data and arranges the sample data sequence. The inbound tourism demand dynamic prediction model uses an improved deep confidence network to learn and train the prediction model, input test data into the trained model, and output the dynamic prediction value of ice and snow inbound tourism demand in the output layer to obtain the prediction result. The simulation results show that the proposed algorithm has improved accuracy in predicting the demand of inbound tourism for ice and snow, whereas the forecasting time is reduced.

The regional differences and influencing factors of tourism development on Hainan Island, China

Exploring the spatial pattern of tourism resources and tourism economy is vital to improve the utilization efficiency of tourism resources and promote sustainable tourism development. This research investigated the quantity and types of tourism resources and analyzed the spatial patterns of tourism resources on Hainan Island from the perspectives of spatial variation and spatial association. The spatial and temporal pattern of the number of tourists and tourism revenue during 2010–2019 were further analyzed. The influencing factors of tourism development were explored based on the geographic detector. The results showed that 10425 tourism resources exist on Hainan Island, and the type of buildings and facilities had the largest number of tourism resources. The geological landscape, astronomical phenomena and meteorological landscapes, buildings and facilities, ruins and remains, tourism commodities, and human activities showed significant spatial agglomeration. Domestic tourism was far more developed than inbound tourism in terms of the number of tourists and tourism revenue. However, the spatial difference of tourism resources and tourism economy was apparent on Hainan Island. Factor analysis showed that the quantity of hotels, the proportion of tertiary industry in the GDP, and the regional population were the most influential factors for the distribution of tourism resources, while the density of the road network, the quantity of hotels, the per capita GDP, the proportion of tertiary industry in GDP, the regional population, and the quantity of tourism resources showed obvious influences on the tourism economy of Hainan Island. Interactions of the factors mainly fell into three types: synergistic increases, single factor weakening, and nonlinear weakening. It is suggested that the local government should fully exploit diversity types of tourism resources on Hainan Island to attract more tourists and improve the tourism revenue; improving the inbound tourism, and to strengthen the construction of road network on Hainan Island.