TOURIST COMPASS IN THE OIL AND GAS REGIONS OF RUSSIA

ABSTRACT

This article examines the current state and future development of tourism in oil and gas regions of Russia. The key factors influencing the formation and changes in tourism demand are analyzed. The study assesses the growth opportunities for the tourism sector and offers practical recommendations for the development of sustainable tourism in these regions. They include integration of the principles of sustainable tourism development into the socio-economic development strategy of the region; protection of ecologically vulnerable regions, wildlife and biodiversity; development of transport infrastructure and a passenger transport system to different tourist destinations.

АННОТАЦИЯ

В статье рассматривается текущее состояние и перспективы развития туризма в нефтегазовых регионах России. Анализируются ключевые факторы, влияющие на формирование и изменение туристского спроса. В исследовании оцениваются возможности роста туристического сектора и предлагаются практические рекомендации по развитию устойчивого туризма в этих регионах, а именно: интеграция принципов устойчивого развития туризма в стратегию социально-экономического развития региона; защита экологически уязвимых регионов, дикой природы и биоразнообразия; развитие транспортной инфраструктуры и системы пассажирских перевозок к различным туристическим направлениям.

Keywords: tourism, oil and gas regions, extrapolation, forecast.

Ключевые слова: туризм, нефтегазовые регионы, экстраполяция, прогноз.

Introduction

Tourism within the country is an important component of economic and social development. It helps to increase income, create jobs, develop infrastructure, and attract investment [1-3]. Changing the volume of demand for domestic tourism services is an urgent topic and requires studying the factors influencing this process. The authors of the following articles consider the factors affecting tourism in Russia [4-6] but the purpose of this study is an identification, analysis and assessment of trends in the structure and volume of tourist demand in the oil and gas regions of the Russian Federation, taking into account the specifics of socio-economic development. The study aims to identify the key factors affecting tourism, as well as to develop recommendations for the development of the tourism sphere.

The main factors affecting the amount of tourists in oil and gas regions:

1. Recreational potential. The regions' recreational resources and diverse natural landscapes contribute to the development of the service sector and various types of tourism.

2. Availability of facilities for tourist routes and organization of thematic excursions. The objects for tourist trips can be “oil” towns, oil and gas producing enterprises, old drilling sites, etc. During excursions people will learn something new about the oil and gas industry, will have the opportunity to observe the work of industrial equipment, as well as get acquainted with the history of development of the oil and gas industry and the development of various fields in a particular region.

3. The interest of oil and gas companies. The participation of oil and gas companies in tourism projects is: firstly, brand advertising; secondly, career guidance (excursions can help them to attract staff); thirdly, another aspect of the companies' social programs. However, when organizing excursions at various production facilities, it is necessary to carefully select the facilities and coordinate tourist routes. If this point is not taken into account, the owners and management of these facilities may prohibit any tourist activities on the territory of the enterprises.

Materials and methods

To estimate tourist demand we can use different forecast methods, which are descried in several articles: a priori factor ranking method [7], existential logic method [8], long short-term memory model [9] but we decided to use forecast extrapolation. Extrapolation is a method of predicting values or trends based on existing data. It involves using known values to predict values outside that range. For example, if you have data on population growth in a city over the past few years, you can extrapolate that data to estimate what the population will be like in a few years. Extrapolation can be linear (assuming that changes occur uniformly) or non-linear (considering more complex relationships). However, it is worth remembering that extrapolation always involves a certain amount of risk, as it assumes that trends observed in known data will continue into the future.

The scope of the method of forecast extrapolation is quite extensive, for example volatility forecasting [10], asymmetric forecasts [11] and automobile-demand forecasting [12]. So this method can be used to assess changes in tourist demand in oil and gas regions, as well as the prospects for the development of this industry. Extrapolation is a statistical method of predicting values beyond the limits of available data, based on the continuation of the trend observed in them. The accuracy of extrapolation depends on the correctness of the assumption about the continuation of this trend and the reliability of the initial data. Therefore, this method should be used with caution.

Extrapolation can be categorized into some types, each of these has its own specific applications:

1. Linear extrapolation assumes that the trend between two known data points is a straight line. This type is easy to use and is effective for data that are uniformly increasing or decreasing.

2. Polynomial extrapolation uses polynomial functions to approximate the trend to initial data points. This type is more flexible than linear extrapolation, so it can be used to model more complex trends [13].

3. Exponential extrapolation can be applied when data follows an exponential trend: rising or falling at an increasing rate. However, this type is not applicable for data with zero or negative values.

4. Logarithmic extrapolation is well suited for data whose rate of change increases or decreases rapidly at first, but then levels off. This type can be used for both positive and negative values.

Results and discussion

For the analysis, the statistics of visits over 10 years to 11 regions with a developed oil and gas industry were considered (Picture 1). The number of tourists visited the Kamchatka Territory during this time was taken as the initial data.

Figure 1. Attendance of oil and gas regions, thousand people 

[compiled by the authors according to the Federal State Statistics Service of the Russian Federation https://rosstat.gov.ru/; Tourism information exchange system https://www.nbcrs.org/]

The table clearly shows an increase in the number of tourists by 2020, but then due to the COVID-19 pandemic, the demand for tourist services drops sharply but in 2021 it also increases sharply and continues to grow again. The dynamics of the increase can be seen more clearly in the histogram in Picture 2. Since the number of tourists in 2020 is out of the general dynamics, it can be excluded and ignored in further analysis.

Figure 2. Dynamics of changes in the number of tourists 

[compiled by the authors according to the Federal State Statistics Service of the Russian Federation https://rosstat.gov.ru/; Tourism information exchange system https://www.nbcrs.org/]

In the extrapolation method, a very important issue is the forecast lead time. There is an opinion that the forecast lead time should be equal to the value of the time interval about which reliable statistics are available. However, there is another point of view: the lead period should not exceed one third of the time interval of the flashback. On the other hand, the experience of existing forecasts in various fields using extrapolation methods shows that with their help it is possible to make forecasts even for 10-15 years ahead.

Thus, the period for which the forecast is being developed directly affects the choice of the appropriate forecasting method, the forecast lead time is one of the signs according to which forecasts are classified [2]. In this article, the forecast will be three points corresponding to 2024, 2025 and 2026.

To determine the most appropriate predictive model, a graph is constructed according to the data presented in Picture 1.

Figure 3. Dynamic series graph

[compiled by the authors according to the Federal State Statistics Service of the Russian Federation https://rosstat.gov.ru/; Tourism information exchange system https://www.nbcrs.org/]

The graph shows the presence of an increasing trend. To begin with, it would be most logical to use a linear trend:

𝑦̂ = 𝑎 + 𝑏𝑡,

where t – number of dynamic series’ parameter.

Using Excel tools, we find the trend equation and the determination coefficient, which allows us to evaluate the accuracy of the model. The linear trend equation is y = 35,587x + 35,978, and the determination coefficient r2 = 0,919. This coefficient is quite close to 1, which indicates the high accuracy of the model used.

Then, according to this trend, a graph is constructed (Picture 4).

Figure 4. Linear trend

[compiled by the authors according to the Federal State Statistics Service of the Russian Federation https://rosstat.gov.ru/; Tourism information exchange system https://www.nbcrs.org/]

According to the data obtained, the linear trend accurately reflects the initial data, but it is worth trying other nonlinear models.

Besides the linear one, the exponential trend can be tried to apply. The trend in general looks like this:

𝑦̂ = 𝑎,

where t – number of dynamic series’ parameter.

The exponential trend equation: y = 65,071e^0,209x and the determination coefficient r² =  0,814.

This determination coefficient is also quite close to 1, which indicates a high accuracy of the model, but it is lower than determination coefficient of the linear model (0,919).

According to this trend, a graph is constructed (Picture 5).

Figure 5. Exponential trend

[compiled by the authors according to the Federal State Statistics Service of the Russian Federation https://rosstat.gov.ru/; Tourism information exchange system https://www.nbcrs.org/]

It can be seen from Picture 5 that the exponential trend runs very close to the initial data, but the determination coefficient of this model is lower than the linear one.

Another model that can be used for research is a polynomial of the second degree, the general form of which is:

𝑦̂ = 𝑎 + 𝑏𝑡 + 𝑐,

where t – number of dynamic series’ parameter.

The equation of the resulting trend: y = -0,7671x² + 43,258x + 21,914 and the determination coefficient r² = 0,921. This coefficient of determination is quite close to 1, which indicates the high accuracy of the model used, and it is also higher than the coefficients of the two previous models (0,919 – for linear; 0,814 – for exponential).

According to this trend, a graph is constructed (Picture 6).

Figure 6. Polynomial trend

[compiled by the authors according to the Federal State Statistics Service of the Russian Federation https://rosstat.gov.ru/; Tourism information exchange system https://www.nbcrs.org/]

It can be seen from the graph that the second degree polynomial runs close to the original data, just as it was noted above by the coefficient of determination, this is the best model.

Table 1 shows a comparison of the determination coefficients once again.

Table 1.

Comparison of trend coefficients

[Source: compiled by the authors]

It follows from table 1 that the most suitable model is the second degree polynomial. The point forecast will be based on this model. As described earlier, the forecast will be for only 3 years. The desired values calculated using the trend equation is shown in Picture 6.

Table 2.

Predicted parameters

[Source: compiled by the authors]

To compare the received trend with the initial data, a graph is constructed. It can be seen from the graph that, according to the determination coefficient, the model accurately describes the initial dynamic series, but still has some error.

Figure 7. Forecast results

[compiled by the authors according to the Federal State Statistics Service of the Russian Federation https://rosstat.gov.ru/; Tourism information exchange system https://www.nbcrs.org/]

Conclusion

To recapitulate, extrapolation methods are successfully used for forecasting in different socio-economic spheres. Specifically, in this article, the application of forecast extrapolation methods to predict the number of tourists in the Kamchatka Territory was considered.

The following models were used for forecasting:

• The linear trend;
• The exponential trend;
• The 2 degree polynomial.

The best model, according to the determination coefficients is the model of a 2 degree polynomial. According to this model, forecasts for the next three years were fulfilled. According to the forecast, the number of tourists in the Kamchatka Territory will exceed 430 thousand people.

The following methods can be used to improve the quality of the results obtained: the idea of Random Forest which is to combine multiple trees to obtain an accurate result [14] and Shapley's additive explanation method [15].

The following recommendations exist for the sustainable development of tourism:

1. Integration of the principles of sustainable tourism development into the socio-economic development strategy of the region. Tourism should be considered as one of the key industries generating employment, attracting investment and stimulating economic growth [16, 17].

2. Protection of ecologically vulnerable regions, wildlife and biodiversity. It is necessary to avoid placing tourist infrastructure facilities in vulnerable areas, and in the process of preparing for the implementation of projects, conduct the necessary research with the involvement of relevant experts [18].

3. Development of transport infrastructure and a passenger transport system to different tourist destinations. It is also necessary to develop digital platforms to facilitate travel planning. [19-20].

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