Development of a model of the influence of tourist flows on the stability of the tourist territory (region)

Kopyrin Andrey Sergeevich PhD in Economics Head of Department, Department of Information Technology, Sochi State University 354000, Russia, Krasnodarskii krai, g. Sochi, ul. Plastunskaya, 94 kopyrin_a@mail.ru Other publications by this author

Vidishcheva Evgeniya Vladimirovna PhD in Economics Associate Professor, Department of Finance, Credit and World Economy, Sochi State University 354000, Russia, Krasnodarskii krai, g. Sochi, ul. Plastunskaya, 94-1 evgenia-vv@mail.ru

Introduction

The development of the tourism sector of the economy is one of the priorities set by the leadership of the Krasnodar Territory and the Russian Federation. However, the economic development of the region is accompanied by an increase in anthropogenic load on the territory and causes environmental problems. Thus, the construction of a model of the influence of tourist flows on the stability of the tourist territory is very relevant.

Modeling the stability of a tourist territory is a rather laborious and non-trivial process.

The global COVID-19 pandemic, border closures, lockdowns and the subsequent economic crisis, on the one hand, marked new challenges for the tourism industry, and on the other hand, opened a window of opportunity for domestic tourist destinations. In these conditions, it is especially important to plan the development of the industry in such a way as to minimize possible environmental, social and economic risks, as well as lay the foundations for sustainable growth in the medium and long term. Thus, the process of building the model presented in this study is important for building a comprehensive decision support system in tourism.

The object of the study is the interconnected economic, social and ecological system of the resort destination. The subject of the study is the interaction of key indicators affecting the sustainability of a tourist destination and tourist flows. The work is aimed at building an integrated computer model that can be used for:

- studies of the interaction of key variables;

- scenario analysis and modeling to determine the possible consequences of management decisions.

The information base of the study is open statistical data of Rosstat, the Administration of the Krasnodar Territory, as well as publications in the open press.

Materials and methods

Overview of existing models

Mathematical models and methods should be used to comprehensively solve the problems of sustainable tourism development. In Russia, such studies and the practical application of models and methods of managerial decision-making in the tourism industry are practically not represented and are not of a systemic nature.

Of considerable interest is the model of the relationship between sustainable and unsustainable tourism (often identified with mass tourism). Let's look at some models.

For example, the authors ^{[1, 2]} and ^[3], who argue that there is no way to draw a clear line between sustainable and unsustainable forms of tourism. The first introduces the types of degrees (stages) of sustainable tourism related to various aspects of tourism (attractions, transport, accommodation, product). The latter, in turn, argues that mass tourism (closer to unstable tourism) is a kind of continuum of alternative tourism (closer to sustainable tourism), so they cannot be considered as separate, opposite categories.

The authors ^[4] previously studied various approaches to assessing the sustainable development of resort regions, studied the methods of the UNWTO and the Government of the Russian Federation. It was also necessary to develop a system of indicators taking into account the existing international experience and the peculiarities of the economic, social and environmental conditions of the Russian Federation. The model proposed for development should be based on this set of indicators.

There are very few models in the domestic and foreign literature devoted to the consideration of sustainable tourism issues. Let's look at some of them in more detail.

Leszek Butowski's sustainable tourism models ^[5] represent the essence of sustainable tourism development under the following assumptions:

-                    Two groups of beneficiaries interact on the territory of the tourist destination: tourists visiting the recreational area to meet tourist needs, and the population living in the recreational area or participating in the service of the tourist sector, using the tourist flow.

- To ensure the sustainability of the destination, it is necessary to maintain a balance in meeting their needs (benefits). It should be noted that an increase in the benefits received by tourists and residents from tourist activities leads to an increase in the level of degradation of natural and socio-cultural resources. In this sense, environmental degradation can be considered as an inevitable cost of tourism development in the territory.

R. Casagrandi and S. Rinaldi ^[6] in 2002 proposed a theoretical model of tourism development that combines the main environmental and social factors. The model describes the interaction between three variables

- the number of tourists ? ( ? ) present in the region at a given time ?;

- quality of natural resources of the environment ?(?);

- funds ? ( ? ) for the creation of infrastructure for tourist accommodation and entertainment facilities.

The dynamic model of sustainable tourism development by R. Johnston and T. Tyrrell ^[7] describes the mutual influence of two indicators. The first characterizes the long-term goal of the tourism industry - the amount of long-term (sustainable) profit and represents the amount of discounted profit for future periods

The second equation characterizes the dependence of environmental quality on tourism activity. Tourists are the cause of environmental degradation.

The authors tried to develop a single synthetic model that combines the social, economic and environmental aspects of the subject of the study. The model is based on a set of indicators and the concept of net adjusted savings ^[8,9]. This method has been adapted for its application at the level of the municipality (tourist territory), and not the country.

The model assumes that the calculated "true savings index" will characterize the rate of savings accumulation in the tourist territory after taking into account the depletion of natural recreational resources and damage from environmental pollution and is measured as a percentage of gross territorial income. A positive level of true savings will lead to an increase in well-being, while negative values of this indicator will indicate an "anti-stable" type of development.

The methodological basis of the research is a systematic approach, and the main methods in the research process are analysis and synthesis based on it. Expert methods of obtaining and systematizing information and a multi-criteria approach adapted to the specifics of the object of research were also used. The technological basis of the work is a variety of modeling that combines system dynamics, event and agent modeling.

Cognitive model

According to the algorithm for constructing the model presented by the authors, it is necessary to perform the following steps:

1. Systematization and formalization of data.

2. Building cognitive models.

3. Construction of software computer models and refinement of regression formulas and equations.

4. Checking the received model.

5. Sensitivity analysis and optimization of the model structure.

6. Simulation modeling and conducting computational experiments.

7. Analysis of simulation results.

The first two stages have already been performed by the authors earlier, and as a result, a diagram of a cognitive model for assessing the sustainability of a tourist destination was obtained (Fig. 1). For a more detailed description of variables and feedback loops, see ^[10]

Fig. 1. Scheme of cause-and-effect relationships

Results and discussion

Building a flowchart

Let's move on to the next steps of the model construction algorithm. System dynamics was chosen as the basic modeling technology ^[11], which involves the separation of variables into two types: speeds and levels, as well as the construction of a flowchart.

Based on the cognitive model, we will create a flowchart. Let's highlight the variable levels, that is, the aggregate indicators (see Table 1)

Table 1. Model levels

 The presented levels are influenced by the rates of auxiliary means shown in Table 2.

 Table 2. Variable speeds

 Table 3 describes the independent variables of the model

 Table 3. Independent variables of the model

 The general block diagram is shown in Figure 2.

Fig. 2. Flow chart

Figure 2 shows a general flow diagram of the model in system dynamics notation. The next step in the formalization of the domain description is the compilation of formulas for each of the variables. To do this, it is necessary to collect and evaluate the available statistical data.

  Collection and evaluation of statistical data on variables

The next step in building a model is to determine the equations for each element of the flowchart and, if necessary, adjust the model. The equations of variables are a numerical representation of the relations between them. These equations can be constructed using correlation and regression analysis of retrospective statistical data or using the method of expert assessments.

Some indicators of the model are directly displayed in statistical reports, such as population, health, education, number of tourists, etc. The remaining variables are obtained by calculation. As an example, we give an estimate of the GMP parameter. This indicator is not included in municipal statistics. For its calculation, open statistical data is used ^[12,13], the integrated programming environment Rstudio is technologically used.

We will evaluate it by allocating the appropriate part from the GRP of the Krasnodar Territory. The distributed share will be calculated dynamically depending on the volume of goods actually produced and services rendered. We will upload the source files for evaluation.

First, the GRP data of the region was uploaded. After that, the variables gmp1 and gmp2 were created for data on the volume of services and goods sold before and after 2017 (as statistics are collected). The variables were combined and a percentage of the total amount was calculated for each district or municipality. Rows related to resort cities were selected from all the data (rows 39, 41 and 44). The estimate of the gross municipal product (on the example of the resort city of Sochi) was calculated by multiplying the GRP by the corresponding percentage. The listing of the calculation program is shown in Fig. 3.

Fig. 3. Listing of the calculation program

Other variables that do not have an exact value in statistics were evaluated in the same way.

Development of model formulas

The model equations were obtained using correlation and regression analysis. For example, according to the flowchart, the GMP variable depends on the P level and the tour variable. Let's find the parameters of the linear function for this dependence. To determine the numerical characteristics of variables and perform calculations, the R programming language and the RStudio integrated programming environment were used. A list of the results of calculating linear regression indicators is shown in Fig.4.

Fig. 4. Building a program for calculating linear regression indicators

 As can be seen from the example, the equation of the GMP variable has the form:

GMP = 0,001314*P-5,267*10^-6 *tour - 0,005075

Moreover, the presented model has considerable accuracy. The figure shows that the multiple correlation coefficient R is 0.8344, which indicates the high strength of the revealed relationship, the significance level is 0.17. This leads us to the illogical conclusion that in the short term, the current trend in the volume of tourist traffic has a weak negative impact on the volume of municipal product. Most likely, this is due to the fact that the services provided to tourists go to the shadow sector of the economy, and the increase in tourist flow leads to the uncompetitiveness of other sectors of the economy.

After determining the type and coefficients of the equations, their validation procedure is carried out. For this purpose, the model calculated indicators of variables are compared with the actual statistical values. As an example, here is a graph (Fig. 5) comparing model and actual values for the GMP variable.

Fig. 5. Graph of comparison of model and actual GMP values

           The graph shows that the modeling error is about 9%, which indicates the high accuracy of the model.

Conclusion

Based on the conducted research, the following conclusions can be formulated.

1. A comprehensive model of the impact of tourist flows on the sustainability of a tourist destination has been developed, which is evaluated using an adjusted net savings indicator. This model allows us to study the trends in the development of the tourism industry, as well as to conduct a scenario analysis of the consequences of various management decisions.

2. Using this tool for medium- and long-term planning will give the decision-maker more information in conditions of uncertainty, which will avoid many managerial mistakes.

3. The accuracy of the model is based on publicly available statistics. Unfortunately, these data are incomplete and do not include many indicators, the values of which in the model are obtained by the method of expert assessments.

The result of the study is the development of a comprehensive model of the impact of tourist flows on the sustainability of a tourist destination, which is evaluated using an adjusted net savings indicator. This model allows us to study the trends in the development of the tourism industry, as well as to conduct a scenario analysis of the consequences of various management decisions. The practical value of the study is due to the fact that the developed model allows us to study the trends in the development of the tourism industry, as well as to conduct a scenario analysis of the consequences of various management decisions. Using this tool for medium- and long-term planning will give the decision-maker more information in conditions of uncertainty, which will avoid many managerial mistakes.

Scope of the results: forecasting the impact of tourist flows on the sustainability indicators of the resort region to form the information basis for the development of management and administrative programs.

In the future, it is planned to refine and adjust the model using new statistical data; to conduct computational experiments to identify economic trends in the impact of integrated programs and scenarios for the development of the tourism sector.