# The Empirical Test on the Impact of Climate Volatility on Tourism Demand: A Case of Japanese Tourists Visiting Korea

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## Abstract

**:**

## 1. Introduction

## 2. Theoretical Background and Literature Review

#### 2.1. Climate Change and Tourism Demand

#### 2.2. The Relationship between Climate Volatility and Tourism Demand

#### 2.3. Control Variables

## 3. Research Model

#### 3.1. The Development of Climate Volatility Index

_{t}). The second formula is a conditional variance equation, which is defined as ARCH (${\mathsf{\epsilon}}_{\mathrm{t}-\mathrm{p}}^{2}$) with a constant term (ω) and p lag of standard error and GARCH (${\mathsf{\sigma}}_{\mathrm{t}-\mathrm{q}}^{2}$) which is a conditional variance of predicted error that has q lag. The value that refers to climate volatility is the GARCH Variance (${\mathsf{\sigma}}_{\mathrm{t}}^{2}$) that is estimated from this formula.

#### 3.2. Tourism Demand Model

_{t}refers to tourism demand and it is measured by a log transformation of the number of Japanese tourists visiting Korea at month t. lnINCOME

_{t}refers to Japanese tourists’ income and it is measured by a log transformation of the income of Japanese tourists at time t. lnRECOST

_{t}refers to the relative travel cost in Japan compared to that in Korea and it is measured by a log transformation of the relative travel cost at time t. And CVI

_{t}refers to the Climate Volatility Index of Korea at time t. All variables are monthly data.

_{t}= α + δ

_{1}CVI

_{t}+ β

_{1}lnINCOME

_{t}+ β

_{2}lnRECOST

_{t}+ ε

_{t}

_{t}= α + γ

_{1}lnTA

_{t−1}+ ∙∙∙ + γ

_{p}lnTA

_{t−p}+ β

_{1}lnINCOME

_{t}+ ∙∙∙ + β

_{q}lnINCOME

_{t−q}+

δ

_{1}lnRECOST

_{t}+ ∙∙∙ + δ

_{m}lnRECOST

_{t−m}+ θ

_{1}CVI

_{t}+ ∙∙∙ + θ

_{n}CVI

_{t−n}+ ε

_{t}

#### 3.3. Data

## 4. Results

#### 4.1. Descriptive Statistics

_{t}from January 2000 to December 2013 is 12.2618 with the lowest of 11.3292 and the highest of 12.7959. The standard deviation is 0.2303. The average of lnINCOME

_{t}is 4.6249 with the lowest of 4.4987 and the highest of 4.7741. The standard deviation is 0.0513. The average of lnRECOST

_{t}is 4.0167 with the lowest of 2.9648 and the highest of 4.8969. The standard deviation is 0.5131. The average of mGCVI

_{t}and GCVI

_{t}are 0.3805 and 0.3125 with the lowest of 0.1820 and 0.2076, and the highest of 0.6329 and 0.5053, respectively. The fluctuation of mGCVI

_{t}is greater than that of GCVI

_{t}.

#### 4.2. Analysis Results

_{t}, lnRECOST

_{t}, and climate volatility indexes, we applied t for lnINCOME

_{t}and t − 1 for lnRECOST

_{t}, and t − 2 for mGCVI

_{t}, t − 3 for GCVI

_{t}, respectively. The summary of time lags of variables is shown in Table 4.

_{t}, and the Equation (5) intends to estimate that of GCVI

_{t}on tourism demand.

_{t}= α + γ

_{1}lnTA

_{t−1}∙∙∙ γ

_{3}lnTA

_{t−3}+ β

_{1}lnINCOME

_{t}+ δ

_{1}lnRECOST

_{t}+ δ

_{2}lnRECOST

_{t−1}+

θ

_{1}mGCVI

_{t}+ θ

_{2}mGCVI

_{t−1}+ θ

_{3}mGCVI

_{t−2}+ ε

_{t}

_{t}= α + γ

_{1}lnTA

_{t−1}+ ∙∙∙ + γ

_{3}lnTA

_{t−3}+ β

_{1}lnINCOME

_{t}+ δ

_{1}lnRECOST

_{t}+ δ

_{2}lnRECOST

_{t−1}+

θ

_{1}GCVI

_{t}+ θ

_{2}GCVI

_{t−1}+ ∙∙∙ + θ

_{4}GCVI

_{t−3}+ ε

_{t}

_{t}and lnRECOST

_{t−1}is over 10, which means there exists multicollinearity. So, we modified the Equation (4) by excluding lnRECOST

_{t−1}. An autocorrelation test of error term was also conducted using the Breusch–Godfrey test, which proves the existence of the first-order autocorrelation in error term. By reflecting these test results, we excluded lnTA

_{t−2}and lnTA

_{t−3}and modified the Equation (4) into the Equation (6) to estimate the relationship between mGCVI

_{t}and tourism demand at t.

_{t}= α + γ

_{1}lnTA

_{t−1}+ β

_{1}lnINCOME

_{t}+ δ

_{1}lnRECOST

_{t}+ θ

_{1}mGCVI

_{t}+ ∙∙∙ + θ

_{3}mGCVI

_{t−2}+ ε

_{t}

^{2}of the Equation (6) with mGCVI

_{t}is 65.53 percent, and the Durbin–Watson value is 1.91, which implies that the equation fits well without serial correlation. Looking more specifically at the analysis results, tourism demand shows no significant relationship with mGCVI

_{t}and mGCVI

_{t−1}, but shows a negative relationship with mGCVI

_{t−2}. lnTA

_{t}shows a positive relationship with lnTA

_{t−1}, lnINCOME

_{t}and lnRECOST

_{t}.

_{t}and lnRECOST

_{t−1}is over 10. So, we excluded lnRECOST

_{t−1}from the Equation (5). Then, based on the Breusch–Godfrey test to detect an autocorrelation of error term, we excluded lnTA

_{t−2}and lnTA

_{t−3}, and modified the Equations (5)–(7) to estimate the relationship between GCVI

_{t}and tourism demand at t.

_{t}= α + γ

_{1}lnTA

_{t−1}+ β

_{1}lnINCOME

_{t}+ δ

_{1}lnRECOST

_{t}+ θ

_{1}GCVI

_{t}+ ∙∙∙ + θ

_{3}GCVI

_{t−2}+ ε

_{t}

^{2}of the Equation (7) with GCVI

_{t}is 64.58 percent, and the Durbin–Watson value is 1.83, which implies that the equation fits well and there is no autocorrelation. A closer look into the analysis results shows that there is no significant relationship between GCVI

_{t}and tourism demand at t. lnTA

_{t}shows a positive relationship with lnTA

_{t−1}, and both lnINCOME

_{t}and lnRECOST

_{t}show a positive relationship with lnTA

_{t}. While there shows a significant relationship between mGCVI

_{t}and tourism demand at t, we cannot find such a relationship between GCVI

_{t}and tourism demand at t. The estimation results of the Equations (6) and (7) are shown in Table 5.

## 5. Conclusions

#### 5.1. Discussion and Implications

_{t}(tourism demand) as dependent variable, and lnINCOME

_{t}(tourists’ income), lnRECOST

_{t}(relative travel cost between Japan and Korea), climate volatility index as independent variables. We selected the appropriate time lags of each variable based on AIC, and set an Autoregressive and Distributed Lags model that includes t − 3 for lnTA

_{t}, t for lnINCOME

_{t}, t − 1 for lnRECOST

_{t}, t − 2 for mGCVI

_{t}, and t − 3 for GCVI

_{t}. We also excluded variables that exhibit multicollinearity and autocorrelation in error term.

_{t−1}, lnINCOME

_{t}, lnRECOST

_{t}all have a positive relationship with lnTA

_{t}. This means that an increase in Japanese tourists’ income leads to an increase in tourism demand for Korea, and an increase in relative travel cost in Japan also has the same effect, which confirms the results of previous studies [44,45]. The coefficients of lnINCOME

_{t}measured by the industrial production index of Japan are 0.90 with mGCVI

_{t}and 0.82 with GCVI

_{t}variable, which are the largest among all the explanatory variables. This result does not deviate from our general conjecture that income is the most influential variable on tourism demand. It can be interpreted that one unit increase of income variable affects the tourism demand by 90 percent of one unit of the income variable, which is measured by the industrial production index of Japan. The coefficient of relative travel cost is 0.23 with 99 percent statistical significance when mGCVI

_{t}or GCVI

_{t}variable is included in the regression. That is, a unit change in the relative travel cost positively affects Japanese tourism demand for Korea by 23 percent of one unit of the relative travel cost variable. The 2017 International Visitor Survey by Ministry of Culture, Sports and Tourism supports these results. The Japanese tourism demand for Korea in the current period, lnTA

_{t}, also turns out to be strongly influenced by its own preceding variable, lnTA

_{t−1}.

_{t}shows a significantly negative relationship with tourism demand at t − 2. The coefficient of mGCVI

_{t−2}is −0.42, which indicates a unit increase of climate volatility in terms of mGCVI

_{t}two months prior affects Japanese tourist demand for Korea by −42 percent of a unit mGCVI

_{t−2}. It means that the climate volatility in Korea in terms of mGCVI

_{t}two months prior negatively affects Japanese tourism demand for Korea in the current period. This result is consistent with the study of Money and Crotts [36], who argue that Japanese tourists generally make travel decisions two months prior to their departure. In Cho [5], a large temperature variation in a country showed a negative impact on the total tourist arrivals, although he did not consider applying a time-lag. This result implies that an increase in climate volatility in a certain area causes tourists to avoid traveling to that destination. A tourist response to climate change generally consists of adaptation, adjustment, and avoidance [46]. Tourists adapt themselves physiologically to some ranges of climate change. For instance, tourists are likely to visit as scheduled without taking any action if climate change at the destination is expected to be low. A low variation of climate change would not affect tourists’ satisfaction even during their visits, as they are physiologically adapted. Tourists try to adjust themselves to variations of climate change by preparing some items such as an umbrella for unexpected rainfall, clothes for unexpected cold, and an alternative tour program for unexpected weather, etc. However, if tourists are highly uncertain about the weather condition of travel destination when they plan, they may change their destination to a more suitable place or delay their travel schedule. His study confirms that Japanese tourists respond to climate volatility.

_{t}shows no significant relationship with tourism demand at time t. We cautiously propose that this is attributable to the fact that Japanese tourists mostly visit large cities, and the relative importance of climate indicators that people actually perceive. While temperature and rainfall have direct impacts on tourists’ activities, humidity, insolation, sunshine, and wind speed have relatively less impacts on tourism. Another explanation might be that weather forecasts mainly focus on temperature and rainfall [35]. Since insolation, wind speed, sunshine, and humidity are not generally included in daily weather information, they rarely affect tourists’ decision making.

#### 5.2. Limitations and Further Research

## Author Contributions

## Funding

## Conflicts of Interest

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**Table 1.**Results of Volatility Estimation by Generalized Autoregressive Conditional Heteroscedasticity (GARCH).

Indicator | Precipitation | Temperature | Wind Speed | Insolation | Snowfall | Sunshine | Humidity |
---|---|---|---|---|---|---|---|

ARCH(−1)^{2} | 0.4120 ** (0.0777) | −0.0103 (0.0170) | 0.0946 (0.1176) | 0.2769 ** (0.1052) | 0.5292 ** (0.0754) | 0.1379 * (0.0703) | 0.0446 (0.0501) |

ARCH(−1)^{2}* (ARCH(−1) < 0) | −0.0585 * (0.0250) | ||||||

ARCH(−2)^{2} | −0.1513 (0.1126) | −0.4563 ** (0.0656) | −0.1104 (0.0717) | −0.0232 (0.0499) | |||

GARCH(−1) | 0.2068 ** (0.0517) | 0.8280 ** (0.3206) | 0.7821 ** (0.2059) | 0.5283 ^{†}(0.2713) | 0.8237 ** (0.0367) | 0.9607 ** (0.0314) | 0.9724 ** (0.0248) |

Adjusted R^{2} | 0.0021 | 0.0020 | 0.0020 | 0.0006 | 0.0021 | 0.0017 | 0.0016 |

Durbin-Watson | 1.8974 | 1.4789 | 2.0908 | 1.4687 | 1.78736 | 1.5988 | 1.6720 |

^{†}, *, and ** refer to 90%, 95%, 99% confidence levels, respectively.

Variables | Measurement | Data Source | |
---|---|---|---|

lnTA_{t} | Log(the number of Japanese tourists to Korea at month t) | Korea Tourism organization | |

lnINCOME_{t} | Log(Industrial Production Index at month t) | Japan’s Ministry of Economy, Trade and Industry & Korea’s Ministry of Strategy and Finance | |

lnRECOST_{t} | Log($\frac{\{\left(\frac{\mathrm{KRW}}{\mathrm{JYP}}\right)\text{}\times \text{}\mathrm{CPI}\text{}\mathrm{of}\text{}\mathrm{Japan}\text{}\mathrm{at}\text{}\mathrm{month}\text{}\mathrm{t}\}\text{}}{\mathrm{CPI}\text{}\mathrm{of}\text{}\mathrm{Korea}\text{}\mathrm{at}\text{}\mathrm{month}\text{}\mathrm{t}}$) | ||

CVI | mGCVI_{t} | CVI based on precipitation, temperature, and snowfall at month t | Author’s own elaboration |

GCVI_{t} | CVI based on precipitation, temperature, wind speed, insolation, snowfall, sunshine, and humidity at month t | Author’s own elaboration |

Variable | Mean | Std. | Min. | Max. |
---|---|---|---|---|

lnTA_{t} | 12.2618 | 0.2302 | 11.3293 | 12.7959 |

lnINCOME_{t} | 4.6249 | 0.0513 | 4.4987 | 4.7741 |

lnRECOST_{t} | 4.0167 | 0.5131 | 2.9648 | 4.8969 |

mGCVI_{t} | 0.3085 | 0.0632 | 0.1820 | 0.6329 |

GCVI_{t} | 0.3125 | 0.0633 | 0.2076 | 0.5053 |

Variable | Time Lags | Variable | Time Lags |
---|---|---|---|

lnTA_{t} | 3 | mGCVI_{t} | 2 |

lnINCOME_{t} | 0 | GCVI_{t} | 3 |

lnRECOST_{t} | 1 |

mGCVI | GCVI | ||
---|---|---|---|

Variables | Coef. | Variables | Coef. |

lnTA_{t−1} | 0.7809 ** (0.0475) | lnTA_{t−1} | 0.7648 ** (0.0483) |

lnINCOME_{t} | 0.9030 ** (0.2379) | lnINCOME_{t} | 0.8189 ** (0.2377) |

lnRECOST_{t} | 0.2331 ** (0.0766) | lnRECOST_{t} | 0.2275 ** (0.0748) |

mGCVI_{t} | 0.1844 (0.1920) | GCVI_{t} | 0.0881 (0.1974) |

mGCVI_{t−1} | 0.2558 (0.2119) | GCVI_{t−1} | −0.0903 (0.2206) |

mGCVI_{t−2} | −0.4244 * (0.1953) | GCVI_{t−2} | −0.1936 (0.1981) |

Adj R^{2} | 0.6553 | Adj R^{2} | 0.6458 |

Obs | 166 | Obs | 166 |

Durbin-Watson | 1.91 | Durbin-Watson | 1.83 |

Mean VIF | 1.37 | Mean VIF | 1.37 |

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## Share and Cite

**MDPI and ACS Style**

Hwang, Y.S.; Kim, H.S.H.; Yu, C. The Empirical Test on the Impact of Climate Volatility on Tourism Demand: A Case of Japanese Tourists Visiting Korea. *Sustainability* **2018**, *10*, 3569.
https://doi.org/10.3390/su10103569

**AMA Style**

Hwang YS, Kim HSH, Yu C. The Empirical Test on the Impact of Climate Volatility on Tourism Demand: A Case of Japanese Tourists Visiting Korea. *Sustainability*. 2018; 10(10):3569.
https://doi.org/10.3390/su10103569

**Chicago/Turabian Style**

Hwang, Yun Seop, Hyung Sik Harris Kim, and Cheon Yu. 2018. "The Empirical Test on the Impact of Climate Volatility on Tourism Demand: A Case of Japanese Tourists Visiting Korea" *Sustainability* 10, no. 10: 3569.
https://doi.org/10.3390/su10103569