In order to verify the influence of the variables listed above on the number of tourists and determine their specific direction of action, this paper constructs four variables by using the data of China's sulfur dioxide emissions from 2009 to 2020, RMB exchange rate, the number of passengers carried by aircraft and whether there is an entry quarantine policy. The policy variables are 0-1 variables. Taking 2019 as the boundary, there was no COVID-19 outbreak before 2019, and China did not implement relevant epidemic prevention and control policies. However, after 2019, the COVID-19 outbreak and the situation became severe, China implemented strict epidemic containment for epidemic prevention and control. For foreign tourists who want to travel in China, they need to be quarantined for a long period of time in advance. In addition to paying high time cost, they also need to bear a large amount of quarantined expenses, which will hinder the people who want to travel and make them delay or even give up their travel plans. It is worth mentioning that according to the outbreak timeline released by the official news, the end of 2019 is also included. Considering the accuracy of measurement, this paper assigns the epidemic policy variables of 2019 and 2020 as 1, while the other years are all assigned as 0. In order to obtain more accurate regression results, this paper centralizes policy variables and then conducts regression. The variables mentioned above are gradually added for panel data regression, and all variables are processed logarithmically during the regression. In addition, considering that the variables considered here have a certain delay effect on the number of tourists, the current situation cannot be immediately reflected in the number of tourists at that time, but only after the change of relevant factors appears for a period of time, tourists can feel the change and make a response. Therefore, in the specific regression, in addition to considering the time trend of the number of tourists, the logarithm of the number of tourists in the last period is taken as an explanatory variable for regression, other variables are also treated with first-order lag, and the final regression results are shown in the table below, with robust standard error in brackets.
The first column in the table shows the regression result of the number of tourists in the current period to the number of tourists lagging behind. The regression coefficient is significantly positive, which is also in line with our previous expectations. The regression coefficient is 0.996, indicating that on the whole, the number of tourists in the later period is slightly lower than the number of tourists in the previous period, showing a downward trend. In the regression presented in the second column, this paper controls the fixed effects from different countries in order to eliminate the influence of some country-related factors on regression. After controlling the fixed effect, the result coefficient of regression has an obvious decline, from 0.996 to 0.577, but the coefficient is still significantly positive at the 1% level. Based on the regression formula in column (1), the factors of RMB exchange rate are taken into consideration and the regression results in column (2) are obtained. Again, the RMB exchange rate is treated with a one-step lag. The obtained results show that the RMB exchange rate has a significant positive impact on the number of tourists, that is, the higher the RMB exchange rate, the more the number of tourists. Based on the above analysis, we know that the RMB exchange rate reflects the relative value of RMB, the higher the exchange rate means that one dollar can be exchanged for more RMB, the less valuable RMB is, and the more money is saved for tourists. Therefore, A rise in the yuan's exchange rate will spur an increase in tourism. (3) The centralized policy variable is added to the regression equation corresponding to column 4. The policy variable itself is not significant, but the addition of this variable reduces the regression coefficients of the previous two variables, but its significance is not affected. The author guesses that the reason why policy variables are not significant here is that the time range of data selected in the empirical study in this paper is from 2009 to 2020, with a time span of 12 years, while the scope of policy factors only covers 2019 and 2020. Compared with the overall data, the time is too short to explain the overall data changes well. So the results are not significant. Columns (4) and (5) are the results obtained after adding aircraft capacity factor and environmental pollution factor respectively. According to the final overall regression results shown in column (5), only the policy factor is not significant, while other variables all show significant influence.The previous period's RMB exchange rate positively affects the number of inbound tourists in the current period at the 5% level of significance, probably because tourists perceive the change in foreign exchange and when the RMB depreciates, it is more cost-effective for them to travel at foreign exchange rates, so the more inbound tourists there are. The number of inbound tourists in the previous period affects the number of inbound tourists in the current period at the 1% level of significance, probably because of the interaction between tourists. Infrastructure development in the previous period also positively affects the number of inbound tourists in the current period. The better the infrastructure development, the easier the access and the more flights are opened, the more favourable the arrival of inbound tourists. Air pollution in the previous period also positively affects the number of inbound tourists in the current period at the 5% level of significance, and tourist cities may have slightly lower levels of air pollution than other countries, so inbound tourists will come to visit them.
