Global J. Environ. Sci. Manage. 6(4): 481-496, Autumn 2020

Global Journal of Environmental Science and Management (GJESM)

Homepage: https://www.gjesm.net/

ORIGINAL RESEARCH PAPER

Impact of forced migration on the sustainable development of rural territories

V. Shcherbak1*, I. Bryzhan2, V. Chevhanova3, L. Svistun3, О. Hryhoryeva3

1Department of Entrepreneurship and Business, Kyiv National University of Technologies and Design, Kyiv, Ukraine 2Office of the Project, Integrated Development in Ukraine, Poltava, Ukraine 3National University, Yuri Kondratyuk Poltava Polytechnic, Poltava, Ukraine

ARTICLE INFO ABSTRACT This study provides a comprehensive scientific analysis of contemporary problems Article History: Received 05 February 2020 resulted from the forced migration of the Ukrainian population and its impact on Revised 24 April 2020 the sustainable development of 47 host communities of Poltava region. By means Accepted 25 May 2020 of cluster analysis 4 clusters of 26 rural territories were identified. They differ in the size of local budgets and the involvement level of forcedly displaced population into Keywords: the local economy. Factor analysis showed that the involvement level of forcedly Cluster analysis displaced population in the region’s economy is determined by 2 groups according Local community to 10 indicators. 8 indicators of the first factor determine 2/3 of the dispersion Refugees of refugees’ impact on rural economy. The first factor reduces the gross regional Rural resources product by 61.75%. The indicators of the second factor shows a positive impact and Socio-economic mechanism determines 15% of the dispersion. The use of game theory to identify conflicts of interest between refugees and host communities was justified. The reasonability to use the taxonomy method to construct a map of positioning rural areas according to the size of local budgets and the degree of integration of refugees is justified. The use of the created map identified the “growth points” in particular clusters. As a result of the implementation of the proposed conflict resolution mechanism between refugees and host communities, the budget of the rural areas of the first cluster increased by 18%, the second cluster by 14.5%, the third cluster by 13%, the fourth by 8%, refugee participation by 30%.

DOI: 10.22034/gjesm.2020.04.05 ©2020 GJESM. All rights reserved.

NUMBER OF REFERENCES NUMBER OF FIGURES NUMBER OF TABLES 39 9 5

*Corresponding Author: Email: [email protected] Phone: +38(099) 9687135 Fax: +38044-280 05 12 Note: Discussion period for this manuscript open until January 1, 2021 on GJESM website at the “Show Article. V. Shcherbak et al.

INTRODUCTION technical development and globalization (Adamson et al., Important factors in the achievement of a 2019). Sustainable development of the world’s sustainable socio-economic development of any economy is manifested in the decolonization, country include demographics and the quality of world’s territory reconstruction (Demirdjian, 2005; human resources potential of the rural areas. Most Panizzon et al., 2019), strengthening of social capital of the world’s population resides in rural areas. (Arango, 2000) and capitalization of social processes Migration processes have always played a decisive (Simianescu et al., 2017). Any social and political role in the human development, having an influence destructive process serves as a catalyst for such on the creation of states and cities, formation of cycles and demonstrates the degree of responsibility races, ethnicities and nations (Demirdjian, 2005; of relations between the state and society (Danko Panizzon et al., 2019). The history of civilization et al., 2020; Sardak et al., 2018). Examples of such was marked by the great migration of peoples and destructive processes include wars, political and great geographical discoveries. For example, USA, social revolutions (Bucholski, 2015), terrorism, AIDS, Canada and Australia are migrant based countries and natural disasters (Petrushenko et al., 2014). (Bil et al., 2018). Today, over 190 million people The following socio-political factors determine (3% of the Earth’s population) do not live in their current migration processes in Ukraine countries of origin. There are several social reasons § Geo-economic factors are determined by the used for the migration, particularly economic, political, resources and location. They are created artificially: religious, national and environmental (Greussing relevant financial flows, agglomeration effect, et al., 2017). From this perspective, migration human capital (Sardak et al., 2018), infrastructure, flows can be classified according to the term of type of economy and management (Sassen, 2005). displacement (seasonal, temporary, permanent, The reasons that force people to migrate under the or one-way) (Aleshkovski, 2017), size (single and influence of the above mentioned factors are the numerous); range (intercontinental and inland) search of self-protection, self-fulfillment, primary (Castles, 2002); directions (external and internal); or extra income and comfort (Zhitin et al., 2016); motives (forced and voluntary) (Arandarenko et § Social factors: social stratification of population, al., 2020); organization principles (organized and interest groups, social roles, priority and status initiative) (Adamson, 2006); legal status (legal and in the society, social codes (Castles, 2002), illegal) (Nitschke, 2019). The reasons and forms of intellectual and cultural resources of the nation, migration processes in Ukraine today are mostly maturity of territorial communities (Morales et conditioned by the war conflict (Balueva et al., 2018). al., 2015). Under the influence of these factors, Social reality of the hybrid war in Ukraine defines people migrate in search of understanding and the necessity to study migration processes within public support in order to integrate into the local the Ukrainian society (Stukalo et al., 2018) and communities (Bezugly et al., 2019); the impact of forced migration on the economy of § Political and information factors: governmental different regions (Shymanska et al., 2017). It is rather organization and the development of civil society problematic to analyze the forced displacement (Betts, 2012; Bisong, 2019), transparency of and its impact on sustainable development of rural elections and justice system, maturity of the areas (Kolodiziev et al., 2018). This is conditioned political structure (Bank, 2015); the extent to which by the undefined legal status of forcedly displaced the political rights and freedoms of citizens are population (Arakelova, 2017), the ongoing military protected (Lavenex, 2019); conflict in Donbas since 2014 (Pogorelov et al., 2017), § Psychological factors: values, principles, motivation the ineffective Anti-Terrorist Operation (hereinafter of the displaced population (Arcarazo et al., 2015). - ATO) (Pylypenko, 2018), the lack of a state policy At present time, there are 3 million refugees in on the protection of migrants’ rights (Vasyltsiv et Ukraine (Roskladka et al., 2020). Since the beginning al., 2019). Military conflicts constantly develop in of the military conflict in the eastern Ukraine (2014), different regions all over the world (Bucholski, 2015). half of Donbas residents have become refugees The recent trend in the cycles human civilization (Stukalo et al., 2018). According to the international development includes bipolarity, strengthening of organizations (Arakelova, 2017), the non-Ukrainian

482 Global J. Environ. Sci. Manage., 6(4): 481-496, Autumn 2020 territories are inhabited by 2.7 million people. They helped to create a community-based development left their homes in 2014–2019 and moved either plan with the involvement of FDP in rural economy. to other regions of Ukraine (Bezugly et al., 2019) This plan took into account the interests of both FDP or to Russia. More than 1 million 780 thousand and host communities. The concept of the applied inhabitants of Donetsk, Lugansk regions and Crimea methods is as follows. The purpose of factor analysis have become forcedly displaced population (Bogush, is to, identify indicators that describe forced migration 2018). All these displacements of Ukrainian citizens and generalize the characteristics of these indicators. have an impact on the economic status of the The generalizing characteristics of the indicators is territorial communities where they currently reside called a factor. The initial information was obtained (Drakokhrust et al., 2019). A literature review has from the Chief Statistics Department of Poltava shown that the structure of world migrations has region, the Unified Information FDP Database, the changed in recent years. In low developed countries, State Employment Service of Poltava Region. The external and forced migration prevails. In Ukraine, indicators for the analysis were chosen through a forced internal migration has prevailed since 2014. survey of Heads of host communities, employment A large number of Ukrainian refugees put a strain in service specialists. Factor analysis was carried out social and economic terms on the host communities.. using the STATISTICA program. The methodology of There is also a need to study the possibility of their factor analysis is as follows: the main results of factor socio-economic integration into host communities. analysis are presented as a table of factor loadings. Research objective is to analyze the impact of forced The table includes: 1) number of rows equal to the migration on sustainable development of Ukrainian number of indicators, 2) number of columns equal rural areas. The study area of research is the level to the number of obtained factors. Factor loadings of rural social and economic development under are the correlation coefficients of indicators with the influence of forced migration. The objectives of factors. A positive sign of the factor loading indicates the study are 1) to analyze the status and dynamics a direct correlation of the indicator with the factor, of migration processes in Ukraine; 2) to study the while a negative sign indicates the opposite. Factor reasons and consequences of forced migration; 3) weights (factor scores) are quantitative values of to determine the impact of forced migration on connection of the selected factors with the object sustainable development of the host rural areas and under study. The object under study is the level of opportunities for their socio-economic integration. rural territory development (local budget size). The This study has been carried out in Poltava region of greater the factor weight, the stronger influence of Ukraine in 2020 on the basis of 2019 survey data. the factor on the value of the object under study. The cumulative percentage of dispersion shows how fully MATERIALS AND METHODS the phenomenon under study was described with the Four methods were used to determine the impact help of obtained factors. In general, the dependence of forced migration on sustainable development of the object under study on the factors is determined of rural areas, including integrated territorial using Eq. 1. communities. These methods include cluster n GDP³j= ∑ F (1) analysis, factor analysis, game theory and taxonomy j =1 method. The goal of factor analysis is to identify the most significant indicators of forced migration on the Where, GDPi is a gross domestic product of і-th level of economic development of rural and united rural area; Fj is the j factor; n is the number of factors Where, GDPi is a gross domestic product of і-th rural area; Fj is the j factor; n is the number of factors territorial communities (UTС). Cluster analysis was obtained. The value of each factor is determined obtained. The value of each factor is determined using Eq. 2. applied to classify rural and UTС groups according to using Eq. 2. their gross regional product, local budget revenues, and the number of forcedly displaced population (2) (2) (FDP) living and working within the community. The 𝑗𝑗 1 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝐹𝐹 = ⁄ 𝑗𝑗 ∑ 𝑎𝑎 × 𝑉𝑉𝑉𝑉𝑉𝑉 game theory identified the essence of the conflict of Where,𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 Expl.Var. 𝑉𝑉𝑉𝑉𝑉𝑉 j is the factor scores j-th factor; aij Where, Expl.Varj is the factor scores j-th factor; aij is the factor loadings of the Varij indicator; Varij is the interests between FDP and rural areas and developed is the factor loadings of the Var indicator; Var is the ij-th ij ij the strategy of its resolution. The taxonomy method ij-th indicator.indicator.

The cluster analysis method was used to classify rural areas depending on the intensity of forced 483migration and the level of host communities development. The classification was done using the k-means method as follows: 1) many all elements of a vector space were divided into a predetermined number of clusters k; 2) the center of mass for each cluster obtained at the previous stage is recalculated at each iteration; 3) the algorithm ends when there is no change in the intra-cluster distance at any iteration. The calculation algorithm is to minimize the total quadratic deviation of cluster points from the centers of these clusters.

(3) 𝑘𝑘 2 𝑉𝑉 = ∑𝑖𝑖=1 ∑𝑥𝑥∈𝑆𝑆𝑖𝑖(𝑥𝑥 − 𝜇𝜇𝑖𝑖) Where, k is the number of clusters, Si is the obtained clusters, i=1.2,..., k; µi are the mass centers of all x vectors from the Si cluster. standard deviations (RMS) were calculated using the most significant indicators identified at the first stage (factor analysis). The game theory method was used at the third stage. The game theory is a mathematical theory of optimal behavior in a conflict situation. The subject of its study is a formalized model of conflict or the so-called "game". The main task of game theory is to determine the optimal strategies of behavior of participants. A conflict situation or a "conflict" is defined as the existing several opposite goals and strategies of participants to achieve these goals. In our case, the conflict is presented in the form of incompatibility of goals and opposing strategies to use the resources of host communities and refugees. The game theory method helped to identify conflicts of interest between FDP and rural areas and the UTC. Their conflict of interest is outlined as follows. The interest of rural areas and the UTC is the intention to obtain the maximum possible amount of regional gross product and revenues to the local budget at the lowest possible amount of welfare payment for the FDP living on this territory. That is, this problem can be solved using maximin. The FDP’s interest is the intention to minimize their contribution to the territory's economy while receiving the highest possible social benefits. Consequently, the solution to this problem is to implement a minimax principle.

Task 1 Task 2 f1(x) = x1 + x2 +…+ xn → max f2(у) = y1 + y2 +…+ ym → min a11x1 + a12x2 + …+ a1nxn ≤ 1 a11у1 + a12y2 + …+ am1ym ≤ 1 a21x1 + a22x2 + …+ a2nxn ≤ 1 a21y1 + a22y2 + …+ am2ym ≤ 1 am1x1 + am2x2 + …+ amnxn ≤ 1 a1ny1 + a2ny2 + …+ amnym ≤ 1 xi ≥ 0 yj ≥ 0 (i = 1, 2, …, n) (j = 1, 2, …, m) where f1 (x) is local budget revenues; where f2 (x) is the cost of social support for FDP; xj - contribution of the j-th category of FDP to local budget yi - social expenditures of the i-th territory for the revenues and i–th territory (j = 1, ... n) maintenance of the j-th category of FDP (i = 1, .., m).

In order to determine the optimal strategies for resolving conflicts of interest in particular rural areas and 4 Where, GDPi is a gross domestic product of і-th rural area; Fj is the j factor; n is the number of factors obtained. The value of each factor is determined using Eq. 2.

(2)

𝑗𝑗 1 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝐹𝐹 = ⁄𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸. 𝑉𝑉𝑉𝑉𝑉𝑉𝑗𝑗 ∑ 𝑎𝑎 × 𝑉𝑉𝑉𝑉𝑉𝑉 Where, Expl.Varj is the factor scores j-th factor; aij is the factor loadings of the Varij indicator; Varij is the ij-th indicator.

The cluster analysis method was used to classify rural areas depending on the intensity of forced migration and the level of host communities development. The classification was done using the k-means method as follows: 1) many all elements of a vector space were divided into a predetermined number of

Where, GDPi is a gross domestic product of і-th ruralclust area;ers k;Fj 2)is thethe jcenter factor; of n ismass the fornumber each ofcluster factors obtained at the previous stage is recalculated at each obtained. The value of each factor is determined usingiteration; Eq. 2. 3) the algorithm ends when there is no change in the intra-cluster distance at any iteration. The calculation algorithm is to minimize the total quadratic deviation of cluster points from the centers of these clusters (2) .

𝑗𝑗 1 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝐹𝐹 = ⁄𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸. 𝑉𝑉𝑉𝑉𝑉𝑉𝑗𝑗 ∑ 𝑎𝑎 × 𝑉𝑉𝑉𝑉𝑉𝑉 (3) Where, Expl.Varj is the factor scores j-th factor; aij is the factor loadings of the Varij indicator; Varij is the 𝑘𝑘 2 ij-th indicator. 𝑉𝑉 = ∑𝑖𝑖=1 ∑𝑥𝑥∈𝑆𝑆𝑖𝑖(𝑥𝑥 − 𝜇𝜇𝑖𝑖) Where, k is the number of clusters, Si is the obtained clusters, i=1.2,..., k; µi are the mass centers of all x The cluster analysis method was used to classifyvectors rural areas from thedepending Si cluster. on the intensity of forced migration and the level of host communities developmentstandard. The deviations classification (RMS) was were done calculatedusing the k -usingmeans the most significant indicators identified at the first method as follows: 1) many all elements of a vectorstage space (factor were divided analysis). into a predetermined number of clusters k; 2) the center of mass for each cluster obtainedThe game at thetheory previous method stage w asis recalculatedused at the atthird each stage. The game theory is a mathematical theory of iteration; 3) the algorithm ends when there is no changeoptimal in thebehavior intra-cluster in a conflict distance situation. at any iteration. The subject of its study is a formalized model of conflict or the The calculation algorithm is to minimize the total quadraticso-called deviation "game". ofThe cluster main pointstask of from game the theory centers is to determine the optimal strategies of behavior of of these clusters. participants. A conflict situation or a "conflict" is defined as the existing several opposite goals and strategies of participants to achieve these goals. In our case, the conflict is presented in the form of incompatibility (3) of goals and opposing strategies to use the resources of host communities and refugees. 𝑘𝑘 2 The game theory method helped to identify conflicts of interest between FDP and rural areas and the 𝑉𝑉 = ∑𝑖𝑖=1 ∑𝑥𝑥∈𝑆𝑆𝑖𝑖(𝑥𝑥 − 𝜇𝜇𝑖𝑖) Where, k is the number of clusters, Si is the obtainedUTC. clusters, Their conflicti=1.2,..., of k; interest µi are the is outlinedmass centers as follows of all .x The interest of rural areas and the UTC is the intention vectors from the Si cluster. to obtain the maximum possible amount of regional gross product and revenues to the local budget at standard deviations (RMS) were calculated using thethe mostlowest significant possible amountindicators of identifiedwelfare payment at the first for the FDP living on this territory. That is, this problem Wherestage, GDP (factori is aanalysis). gross domestic product of і-th ruralcan area; be solved Fj is the using j factor; maximin. n is The the FDP’snumber interest of factors is the intention to minimize their contribution to the obtained.The game The theoryvalue ofmethod each factorwas used is determined at the third using stage.territory's Eq. The 2. gameeconomy theory while is areceiving mathematical the highest theory possible of social benefits. Consequently, the solution to this The role of refugees in host communities optimal behavior in a conflict situation. The subjectproblem of its study is to is implement a formalized a minimax model of principle conflict .or the so-called "game". The main task of game theory is to determine (2) the optimal strategies of behavior of Theparticipants. cluster A analysis conflict methodsituation wasor a used"conflict" to is defined as the existingTask several 1 opposite goals and Task 2 classify rural areas depending on the intensity of 𝑗𝑗 strategies1 of participants𝑖𝑖𝑖𝑖 to 𝑖𝑖achieve𝑖𝑖 these goals. In ourf1(x) case, = x 1the + x conflict2 +…+ x nis → presented max in the form of f2(у) = y1 + y2 +…+ ym → min forced𝐹𝐹 = ⁄migration𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸. 𝑉𝑉𝑉𝑉𝑉𝑉 and𝑗𝑗 ∑ 𝑎𝑎the ×level𝑉𝑉𝑉𝑉𝑉𝑉 of host communities Whereincompatibility, Expl.Varj is of the goals factor and scoresopposing j-th strategies factor; aij to isuse the thea 11factorx resources1 + a 12loadingsx2 + of …+ host aof1n xcommunitiesthen ≤ 1Var ij indicator; and refugees. Varij is the a11у1 + a12y2 + …+ am1ym ≤ 1 development. The classification was done using the ij-thThe indicator game .theory method helped to identify conflicts of interest between FDP and rural areas and the k-means method as follows: 1) many all elements of a21x1 + a22x2 + …+ a2nxn ≤ 1 a21y1 + a22y2 + …+ am2ym ≤ 1 UTC. Their conflict of interest is outlined as follows. The interest of rural areas and the UTC is the intention a vector space were divided into a predetermined am1x1 + am2x2 + …+ amnxn ≤ 1 a1ny1 + a2ny2 + …+ amnym ≤ 1 to obtain the maximum possible amount of regional gross product and revenues to the local budget at numberThe cluster of clusters analysis k; 2) method the center was of massused forto eachclassify rurxali ≥ areas0 depending on the intensity of forced yj ≥ 0 the lowest possible amount of welfare payment for the FDP living on this territory. That is, this problem clustermigration obtained and the at the level previous of host stage communities is recalculated development (i =. The1, 2, classification …, n) was done using the k-means (j = 1, 2, …, m) can be solved using maximin. The FDP’s interest is the intention to minimize their contribution to the atmethod each iteration; as follows: 3) 1)the many algorithm all elements ends when of athere vector spacewhere f1were (x) is dividedlocal budget into revenues; a predetermined numberwhere of f2 (x) is the cost of social support for FDP; territory's economy while receiving the highest possible social benefits. Consequently, the solution to this isclust noers change k; 2) the in the center intra-cluster of mass distancefor each atcluster any obtainedxj - contribution at the ofprevious the j-th category stage isof FDPrecalculated to local budget at eachyi - social expenditures of the i-th territory for the problem is to implement a minimax principle. revenues and i–th territory (j = 1, ... n) maintenance of the j-th category of FDP (i = 1, .., m). iteration.iteration; 3) the algorithm ends when there is no change in the intra-cluster distance at any iteration. The calculation algorithm is to minimize the total The calculation algorithmTask is to 1 minimize the total quadratic deviation of clusterTask 2 points from the centers quadraticof these clusters deviation. of cluster points from the centers In order to determine the optimal strategies for resolving conflicts of interest in particular rural areas and of these f1(x)clusters. = x1 + x2 +…+ xn → max f2(у) = y1 + y2 +…+ ym → min 4 a11x1 + a12x2 + …+ a1nxn ≤ 1 a11у1 + a12y2 + …+ am1ym ≤ 1 a21x1 + a22x2 + …+ a2n x n ≤ 1 (3) a 21 y(3)1 + a22y2 + …+ am2ym ≤ 1 𝑘𝑘am1x1 + am2x2 + …+ 2amnxn ≤ 1 a1ny1 + a2ny2 + …+ amnym ≤ 1 𝑉𝑉 = ∑𝑖𝑖=1 ∑𝑥𝑥∈𝑆𝑆𝑖𝑖(𝑥𝑥 − 𝜇𝜇𝑖𝑖) WhereWhere,, xki ≥is 0k the is thenumber number of clusters, of clusters, Si is Sthei is obtainedthe clusters,yj ≥ i=0 1.2,..., k; µi are the mass centers of all x obtainedvectors (ifrom clusters, = 1, 2,the …, i=S n)i1.2,..., cluster. k; µi are the mass centers (j = 1, 2, …, m) of all x vectors from the S cluster. standardwhere deviationsf1 (x) is local budget (RMS)i revenues; were calculated using thewhere most f2 (x) significant is the cost of indicatorssocial support identifiedfor FDP; at the first xj - contribution of the j-th category of FDP to local budget yi - social expenditures of the i-th territory for the stagestandard (factor deviations analysis). (RMS) were calculated using the mostrevenues significant and i–th territory indicators (j = 1, identified ... n) at the first maintenance of the j-th category of FDP (i = 1, .., m). The game theory method was used at the third stage. The game theory is a mathematical theory of stage (factor analysis). optimal behavior in a conflict situation. The subject of its study is a formalized model of conflict or the TheIn order game to theory determine method the optimalwas used strategies at the third for resolvingIn conflictsorder to ofdetermine interest in the particular optimal rural strategies areas andfor stage.so-called The "game".game theory The ismain a mathematical task of game theory theory of is resolvingto determine conflicts the of optimal interest instrategies particular of ruralbehavior areas of 4 optimalparticipants. behavior A conflict in a conflict situation situation. or a "conflict" The subject is anddefined FDP, as the the maximin existing problem several has opposite been solved.goals and ofstrategies its study ofis aparticipants formalized modelto achieve of conflict these orgoals. the InAccording our case, to theit, the conflict first player is presented (maximization in the of formlocal of so-called “game”. The main task of game theory is FDPbudget the revenues, maximin roblem minimization has been of solved.social costsccording for FDP to it the irst layer maximiation o local budget incompatibility of goals and opposing strategies to userevenues the resources minimiation of host o social communities costs or FDP and maintenance refugees. chooses a strategy in accordance ith a to determine the optimal strategies of behavior of maintenance) chooses a strategy in accordance with The game theory method helped to identify conflictsmaxim of interestum rincile between using . FDP . and rural areas and the participants. A conflict situation or a “conflict” is a maximum principle, using Eq. 4. UTC. Their conflict of interest is outlined as follows. The interest of rural areas and the UTC is the intention defined as the existing several opposite goals and to obtain the maximum possible amount of regionalFDP gross the maximin productn roblem nand revenueshasn been solved. to the ccording local tobudget it the irst at layer maximiation o local budget strategies of participants to achieve these goals. revenuesmax pi min minimi(ai1хiation,a i2oхi ...socialain costsхi ) or FDP maintenance (4) chooses a strategy in accordance ith a i=1 i=1 i=1  Inthe our lowest case, possible the conflict amount is of presented welfare paymentin the form for maximthe FDPum rin livingcile on using this . territory. . That is, this problem The second layer minimiation o contribution into the local budget revenues o rural areas ofcan incompatibility be solved using of maximin.goals and Theopposing FDP’s strategies interest is theThe intention second playerto minimize (minimization their contribution of contribution to the maximiationn o socialn beneitsn  chooses a strategy in accordance ith a minimax rincile using . . toterritory's use the economy resources while of hostreceiving communities the highest andpossible intomax socialmin the( localbenefits.a х , budgeta х Consequently... a revenuesх ) , of t he rural solution areas, to this pi  mi1 i  i2mi  in i m  i=1 i=1 i=1  refugees.problem is to implement a minimax principle. maximizationmin pi max(a jof1 у jsocial,a j2benefits)у j ...a jn уchoosesj ) a strategy in The game theory method helped to identify Theaccordance second j layer=with1 a minimiminimaxj=1 ation principle,j=1 o contribution using Eq. in 5.to the local budget revenues o rural areas maximiation o social beneits chooses a strategy in accordance ith a minimax rincile using . . conflicts of interest between FDP and rural areas here xi is the robability o choosing the ith strategy o rural behavior local budget revenues yj is the Task 1 m m Taskm 2  robability o choosing the j-th strategy o FDP behavior social beneits. olution o this maximin tin and the UTC. Their conflict of interest is outlined as min pi max(a j1 у j ,a j2 у j ...a jn у j ) (5) f1(x) = x1 + x2 +…+ xn → max f2(у) = y1 + y2 +…+ ym → min follows. The interest of rural areas and the UTC is the challenge illj=1 alloj= 1to develoj=1 an otimal strategy to solve the conlict o interest or each rural area or a11x1 + a12x2 + …+ a1nxn ≤ 1 here xi isa the11у robability1 + a12y2 + o …+ choosing am1y mthe ≤ i1th strategy o rural behavior local budget revenues yj is the community andx FDP. t ill also tae into account thei interests o both arties. intention to obtain the maximum possible amount Where, i is the probabilityj- of choosing the -th a21x1 + a22x2 + …+ a2nxn ≤ 1 Therobability taxonomya o21 choosingy1 method + a22 ythe2 as + …+th used strategy am2 aty mthe o≤ FDP1ourth behavior stage. soc Thisial method beneits .allos olution inding o this out maximin satial tin ineuality o of regional gross product and revenues to the local challengestrategy illof rural allo behaviorto develo (localan otimal budget strategy revenues), to solve theyj conlict o interest or each rural area or FDP settlement and regional asymmetry o rural develoment. The algorithm o taxonomic analysis budgetam1 x at1 + the am2 lowestx2 + …+ possible amnxn ≤ 1 amount of welfare communityis the probabilitya and1ny 1FDP. + a of 2nt illychoosing2 + also …+ tae a themn intoy mj- th≤account 1strategy the interestsof FDP o both arties. paymentxi ≥ 0 for the FDP living on this territory. That is, containsThebehavior taxonomy the y (socialj ≥ method olloing0 benefits). as stages used at Solution ormationthe ourth ofstage.o thisthe This observation methodmaximin allos matrix inding the out satialmost signiicantineuality o indicators this problem(i = 1, 2, can…, n)be solved using maximin. The FDP’s determinedFDPtwin settlement challenge(j =at 1, thand 2,e willstage regional…, m) allow o actoasymmetry tor analysis develop o rural standardiation an develoment. optimal o The the algorithmmatrix element o taxonomic values analysis identiication o interestwhere f1 is (x)the is intention local budget to revenues;minimize their contribution thecontainsstrategywhere vector f2 the to(x)etalon solveisolloing the thecostdetermination stages conflictof social ormation supportof ointerest the foro distance theFDP;for observation each beteen rural matrix individual the observations most signiicant and indicatorsthe vector etalon toxj the - contribution territory’s ofeconomy the j-th categorywhile receiving of FDP to the local highest budget calculationdeterminedareayi - orsocial community at o expendituresth thee stage taxonomic o and acto of FDP.r develomentanalysisthe It willi-th standardiation alsoterritory coeicient. take for into o the The matrix indicators element obtaine valuesd identiication at the stage o o actor possiblerevenues social and i –benefits.th territory Consequently, (j = 1, ... n) the solution to analysistheaccountmaintenance vector are theetalon determined interestsof the determination j-th of category using both .o parties. othe f FDP. distance (i = 1, beteen .., m). individual observations and the vectoretalon this problem is to implement a minimax principle. calculationThe taxonomy o the taxonomic method develoment was used coeicient. at the fourth The indicators obtained at the stage o actor analysis are determined using . . In order to determine the optimal strategies for resolving conflicts of interest in particular rural areas and |х𝑖𝑖𝑖𝑖−х̂𝑗𝑗| . 4844х ̂𝑖𝑖𝑖𝑖 = |𝑚𝑚𝑚𝑚𝑚𝑚/𝑚𝑚𝑚𝑚𝑚𝑚х𝑗𝑗−х̂ 𝑗𝑗 | |х𝑖𝑖𝑖𝑖 −х̂𝑗𝑗| here are. the best or orst estimates or each indicator max/min x are the extremes o the indicators х ̂𝑖𝑖𝑖𝑖 = |𝑚𝑚𝑚𝑚𝑚𝑚/𝑚𝑚𝑚𝑚𝑚𝑚х𝑗𝑗−х̂𝑗𝑗| using . . here х̂𝑗𝑗are the best or orst estimates or each indicator max/min x are the extremes o the indicators using . . 𝑗𝑗 max/ minх̂ x =min x, если max/ minmin x x =min =mах x, еслиx, если . . |𝑚𝑚𝑚𝑚𝑚𝑚х𝑗𝑗 − х̂𝑗𝑗| > |𝑚𝑚𝑚𝑚𝑚𝑚х𝑗𝑗 − х̂𝑗𝑗| max/ min x =mах x, если . . . . |𝑚𝑚𝑚𝑚𝑚𝑚х𝑚𝑚𝑚𝑚𝑚𝑚𝑗𝑗 − х𝑗𝑗̂𝑗𝑗| >̂𝑗𝑗|𝑚𝑚𝑚𝑚𝑚𝑚х𝑗𝑗𝑚𝑚𝑚𝑚𝑚𝑚− х̂𝑗𝑗𝑗𝑗| ̂𝑗𝑗 To ran rural areas it is necessary. х − toх |determine≤ |. х the− х dimensional| vector using . . 𝑚𝑚𝑚𝑚𝑚𝑚х𝑗𝑗 − х̂𝑗𝑗| ≤ |𝑚𝑚𝑚𝑚𝑚𝑚х𝑗𝑗 − х̂𝑗𝑗| To ran rural areas it is necessary to determine the dimensional vector using . .

|х𝑖𝑖𝑖𝑖−х̂𝑗𝑗| 𝑖𝑖 𝑚𝑚 |х𝑖𝑖𝑖𝑖−х̂𝑗𝑗| . 𝑆𝑆 = ∏𝑚𝑚𝑗𝑗=1 |𝑚𝑚𝑚𝑚𝑚𝑚/𝑚𝑚.𝑚𝑚𝑚𝑚х𝑗𝑗−х̂𝑗𝑗| 𝑆𝑆 𝑖𝑖 = ∏𝑗𝑗=1 |𝑚𝑚𝑚𝑚𝑚𝑚/𝑚𝑚𝑚𝑚𝑚𝑚х𝑗𝑗−х̂𝑗𝑗| This valuevalue characteri characterieses in inexemlary exemlary ashion ashion the extentthe extent to hich to hichreugees reugees have an have imact an on imact the rural on the rural economy because the stronger the indicators dier rom the benchmar the larger the Si value ill be economy because the stronger the indicators dier rom the benchmar the larger the Si value ill be and vicevice versa. versa. aning aning o orural rural areas areas is achieved is achieved by calculating by calculating the dimensional the dimensional vector D° vector(n). ector D°(n) o. ector o dierence D° D°(n)(n) shos shos the the degree degree o remotenesso remoteness closeness closeness o rural o areas rural rom areas the rom ideal the rate ideal – bench rate – bench marable metric metric using using . . . .

𝑚𝑚 2 𝑖𝑖 𝑗𝑗=𝑚𝑚1 𝑖𝑖𝑖𝑖 𝑗𝑗 2 𝑑𝑑𝑖𝑖 = √∏ 𝑗𝑗=(1х 𝑖𝑖𝑖𝑖− х̂)𝑗𝑗 𝑑𝑑To= integral√∏ (indicatorsх − х̂) ere calculated using the taxonomy method integral indicator o rural To integral indicators ere calculated using the taxonomy method integral indicator o rural FDP the maximin roblem has been solved. ccording to it the irst layer maximiation o local budget revenues minimiation o social costs or FDP maintenance chooses a strategy in accordance ith a FDP the maximin roblem has been solved. ccording to it the irst layer maximiation o local budget maximum rincile using . . revenues minimiation o social costs or FDP maintenance chooses a strategy in accordance ith a maxim um rincile using . . n n n   max piFDPmin the( maximinai1хi , roblemai2 хi ... hasa inbeenхi ) solved. ccording to it the irst layer maximiation o local budget in=1 in=1 in=1  FDPmax therevenuesmin maximin( aminimi roblemх , ationa hasх ...o been sociala solved.х costs) ccording or FDP maintenance to it the irst layerchooses maximiation a strategy in o accordance local budget ith a The pisecond layeri1 i  iminimi2 i  inationi  o contribution into the local budget revenues o rural areas revenuesmaxim minimiumi=1 rinationcilei=1 o using social .i= 1costs . or FDP maintenance chooses a strategy in accordance ith a maximFDPThemaximi the secondum maximin ationrin cilelayer o roblem socialusing minimi. beneits has . beenat ion solved.chooses o ccordingcontribution a strategy to it in thein accordanceto irst the layer local maximiationith budget a minim revenues oax local rincile budget o rural using areas. . m n n m n m  revenuesmax minimimin( aationх , oa хsocial... a costsх ) or FDP  maintenance chooses a strategy in accordance ith a maximi ationpi n on sociali1 i  nbeneitsi2 i  in i chooses a strategy in accordance ith a minimax rincile using . . min pi max(i=a1 j1 у j ,i=1 a j2 уi=1j ...a jn у j ) maximmax minum( rina mcileх , a usingх ...m .a х.)  m Global J. Environ. Sci. Manage., 6(4): 481-496, Autumn 2020 pi  ij1=1i  i2 i j=1 in i j=1  The secondi=1 i=layer1 i=minimi1  ation o contribution in to the local budget revenues o rural areas min pi max(a j1 у j ,a j2 у j ...a jn у j ) Thehere secondmaximi xi isn layer ationthej=1 robability o n minimisocialj=1 beneitsn ation o choosingjo= chooses1 contribution a the strategy i thinto strategyin theaccordance local o budgetrural ith abehavior minimrevenuesax rincile localo rural budget using areas . revenues . yj is the max min( a х m, a х ...m a х ) m stage.maximirobabilitypi ation This oi1 method socialchoosingi  beneitsi2 i allows thein chooses ji- th finding strategy a strategy oout in FDP accordance spatialbehavior ithData soc a descriptionminimial beneitsax rincile . olution using . o. this maximin tin here xi iis=1 the robabilityi=1 i=1 o choosing the i th strategy o rural behavior local budget revenues yj is the min pi maxm (amj1 у j ,a j2mу j ...ajn у j ) inequalitychallenge ofill FDP alloj=1 settlement toj= 1develo andj=1 an regional otimal  asymmetry strategy to solveThe the forced conlict displacement o interest in or Ukraine each ruralin 2014 area began or Therobabilitymin pisecondmax( olayera j 1choosingу j , aminimij2 у j ...theat ajion-jnthу j )strategyo contribution o FDP in behaviorto the local soc budgetial beneits revenues. olution o rural o thisareas maximin tin of rural development.j=1 j=1 Thej=1 algorithm of taxonomic after the annexation of the Crimea, outbreak of an maximichallengecommunityhereation ill x andio is allothesocial FDP. robability beneitsto t develo ill oalso chooses choosing an tae otimal a theintostrategy i thaccount strategy strategy in accordance theo to rural solveinterests behavior ith the a minimconlicto local both axbudget oarties.rincile interest revenues using or y .jeach is the. rural area or analysishererobability x i is contains them robability o choosing them o following thechoosingm j-th strategy the stages: ith o strategy FDP formation behavior o rural socbehaviorarmedial beneits local conflict. budget olution in revenues the o thiseastern maximin yj is theUkraine, tin and temporary communityThe taxonomy and method FDP. t illas also used tae at the into ourth account stage. the interestsThis method o both allos arties. inding out satial ineuality o ofrobabilitymin thepichallengemax observation (o choosing aillj1 у jallo, thea toj2 у jdevelo-matrixjth... strategya jn -an у j )otimal theo FDP most behaviorstrategy significant to soc solveial beneits theoccupation conlict. olution o interestof oa partthis or maximin eachof the rural country’s tin area or territory. A new TheFDP taxonomysettlementj=1 method andj=1 regional asj= used1 asymmetry at the ourth o ruralstage. develoment. This method allos The algorithm inding out o satial taxonomic ineuality analysis o indicatorschallengecommunity ill determined allo and to FDP. develo att illthe an also stage otimal tae of into strategy factor account analysis;to solve the interests the conlictphenomenon o both o interest arties. orof each “sudden rural area poverty” or has emerged herecontains xi isthe the robabilityolloing ostages choosing ormation the ith strategy o the o ruralobservation behavior localmatrix budget the revenues most signiicant yj is the indicators standardizationcommunityFDP Thesettlement taxonomy and FDP. and method t of ill regional also theas tae used asymmetrymatrixinto at theaccount ourth element the o stage. interestsrural This values;develoment. methodo bothin Ukraine arties.allos inding The (Pylypenko, algorithm out satial ineuality 2018o taxonomic). Theo middle-class analysis Therobability taxonomy o methodchoosing as the used j-th atstrategy the ourth o FDP stage. behavior This method social allos beneits inding. olution out satial o this ineuality maximin o tin identificationcontainsdeterminedFDP settlementthe at olloing th e of andstage theregional stages o vector-etalon; acto asymmetry ormationr analysis o determination ruralstandardiationo the develoment. observation population oThe the matrixalgorithm matrix suddenly theoelement taxonomicfound most values themselvessigniicant analysis identiication atindicators the extreme o FDPchallenge settlementcontains ill allothe and olloing toregional develo stages asymmetry an otimal ormation o strategyrural o thedeveloment. to observation solve the The conlict matrix algorithm o theinterest omost taxonomic or signiicant each analysisrural indicators area or ofdeterminedthe thevector distanceetalon at th e between stagedetermination o acto individualr analysiso the observations distance standardiation beteen of poverty oindividual the matrix because observations element of the valuesarmed and the conflictidentiication vector (Arakelova,etalon o containscommunitydetermined the and olloing FDP. at th et stagesstageill also o ormationacto taer intoanalysis oaccount the standardiation observation the interests matrixo othe both matrix the arties. mostelement signiicant values identiicationindicators o andcalculation the vector-etalon; o the taxonomic calculation develoment of the taxonomic coeicient. 2017 The). indicators Among the obtaine “suddenlyd at poor”the stage were o theactor FDP determinedThethe taxonomyvectorthe vector atetalon th methodetalone stage determination determinationoas acto usedr analysis at the o o theourth standardiationthe distance distance stage. beteen This beteeno method the individual matrix individualallos element observations inding values observations out and identiicationsatial the vector ineuality and etalonothe vectoro etalon developmenttheFDPcalculationanalysis vector settlementcalculation areetalon o determined coefficient. otheand determination the taxonomicregional taxonomic using asymmetryThe o develoment thedeveloment.indicators distance . o rural beteen coeicient.obtained coeicient. develoment. individual at The andindicators The observations The the indicators algorithm populationobtaine andd obtaineo theat taxonomic thevector affected staged etalonat otheanalysis actor by stage the o conflictactor in two thecalculationcontainsanalysis stageanalysis theare ofo arefactorolloingdeterminedthe determined taxonomic analysis stages using usingdeveloment are ormation ..determined . . coeicient.o the using observation The Eq. indicators easternmatrix obtaine regions:thed most at the Donetsk signiicant stage o and actorindicators Luhansk. During 2014 6. and the first half of 2015 (Bogush, 2018; Romaniuk analysisdetermined are determined at the stage using o acto . r analysis. standardiation o the matrix element values identiication o the vectoretalon determination o the distance beteen individual et al., observations 2016 ), there and wasthe vector a deep etalon economic crisis in |х𝑖𝑖𝑖𝑖−х̂𝑗𝑗| calculation o |хthe𝑖𝑖𝑖𝑖. −х ̂𝑗𝑗taxonomic| develoment coeicient. (6) The indicators Ukraine, obtaine some of d atits theeconomic stage o potential actor was lost, and 𝑖𝑖𝑖𝑖 𝑚𝑚𝑚𝑚𝑚𝑚/𝑚𝑚𝑚𝑚𝑚𝑚 𝑗𝑗 . ̂ 𝑗𝑗 х̂ = 𝑖𝑖𝑖𝑖| |х𝑖𝑖𝑖𝑖−х̂х𝑗𝑗|−𝑗𝑗х |𝑗𝑗 analysisх̂ are=|х𝑖𝑖𝑖𝑖|−𝑚𝑚𝑚𝑚𝑚𝑚 determinedх̂𝑗𝑗|/𝑚𝑚𝑚𝑚𝑚𝑚х −х̂| using . . the standard of living of the population reached rock . . 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖here are the𝑗𝑗 ̂𝑗𝑗 best or orst estimates or each indicatormax/min x max/min x are the extremes o the indicators х ̂ ==|𝑚𝑚𝑚𝑚𝑚𝑚|here𝑚𝑚𝑚𝑚𝑚𝑚/𝑚𝑚/𝑚𝑚𝑚𝑚х𝑚𝑚𝑗𝑗𝑚𝑚−areхх̂𝑗𝑗|− theх | best or orst estimates or each indicator are the extremes o the indicators Where, are the best (or worst) estimates for bottom. In 2014-2015, the GDP of Ukraine decreased usingherehereusing . are ..are the . the best best or orst or orst estimates estimates or each orindicator each indicatormax/min x are themax/min extremes x are othe the extremes indicators o the indicators each indicator;𝑗𝑗 𝑗𝑗 x are the extremes of the by 15.8% (Panizzon et al., 2019), the volume of using . .х̂ х̂ max/min using .𝑗𝑗 . indicators,х̂|х𝑗𝑗𝑖𝑖𝑖𝑖 using−х̂𝑗𝑗| Eq. 7. industrial production - by 21.8%, the growth rate of max/х̂ min. x =min x, если х max/̂𝑖𝑖𝑖𝑖 = | 𝑚𝑚𝑚𝑚𝑚𝑚min/𝑚𝑚 x𝑚𝑚𝑚𝑚 =minх𝑗𝑗−х̂𝑗𝑗| x, если consumer prices increased by 60.9%, the average max/ max/min x min=min x =mx, еслиах x, если . . 𝑚𝑚𝑚𝑚𝑚𝑚 𝑗𝑗 𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚 𝑗𝑗 𝑗𝑗 max/min x max/here min are x =min=m theах best x, x, если orесли orst | хestimates. − х̂| > |or eachх −. indicatorх ̂ | (7) monthly are wage the extremes decreased o the by indicators23.3% (Ineli-Ciger, 2019). max/ min x =mах x, если . 𝑚𝑚𝑚𝑚𝑚𝑚. 𝑗𝑗 ̂𝑗𝑗 . 𝑚𝑚𝑚𝑚𝑚𝑚. 𝑗𝑗 ̂ 𝑗𝑗 using . . |𝑚𝑚𝑚𝑚𝑚𝑚х|𝑗𝑗 −𝑚𝑚𝑚𝑚𝑚𝑚х̂.𝑗𝑗х|𝑗𝑗 >−−|х̂𝑚𝑚𝑚𝑚𝑚𝑚х𝑗𝑗| |≤х>𝑗𝑗|𝑚𝑚𝑚𝑚𝑚𝑚−| х̂𝑗𝑗х|𝑗𝑗 .−хх̂𝑗𝑗−| х | As a result of the armed conflict, Ukraine has lost a max/To min х̂ran𝑗𝑗 x rural=mах areas x, если it is necessary . . to determine. the. dimensional vector using . . 𝑚𝑚𝑚𝑚𝑚𝑚|𝑚𝑚𝑚𝑚𝑚𝑚𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚х̂𝑗𝑗 𝑗𝑗− х̂𝑚𝑚𝑚𝑚𝑚𝑚𝑗𝑗̂|𝑗𝑗>𝑗𝑗 |𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚̂𝑗𝑗 х𝑗𝑗 𝑗𝑗− х̂𝑗𝑗̂|𝑗𝑗 To ranranTo rankrural rural areasrural areas itareas, is necessaryit is it necessary хis −necessary toх. х |determine≤−| toх | determinetoх≤ the−determine|х dimensional| . х the− х dimensional| vectorsignificant using .vector part . of using its economic . . and export potential max/ min x =min x, если 𝑚𝑚𝑚𝑚𝑚𝑚 𝑗𝑗 ̂𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚 𝑗𝑗 ̂𝑗𝑗 the dimensional vector, using Eq.х 8.− х | ≤ | х − х | (Pogorelov et al., 2017), besides eastern Donetsk and max/To ran min rural x =m ахareas x, если it is necessary . to determine . the dimensional vector using . . |х𝑖𝑖𝑖𝑖−х̂𝑗𝑗| 𝑚𝑚𝑚𝑚𝑚𝑚 𝑗𝑗 ̂𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚 𝑗𝑗 ̂𝑗𝑗 Lugansk regions have lost a significant part of human 𝑚𝑚 . | х − х | > | х − х | 𝑖𝑖 𝑗𝑗 ̂𝑗𝑗 . . 𝑆𝑆 = |∏х𝑖𝑖𝑖𝑖𝑗𝑗=−1х̂|𝑗𝑗𝑚𝑚𝑚𝑚𝑚𝑚| /𝑚𝑚𝑚𝑚𝑚𝑚х −х |𝑚𝑚𝑚𝑚𝑚𝑚 𝑗𝑗 ̂ 𝑗𝑗 𝑚𝑚𝑚𝑚𝑚𝑚 𝑗𝑗 ̂ 𝑗𝑗 (8) and material resources. The most common reasons 𝑚𝑚 𝑖𝑖𝑖𝑖 . 𝑗𝑗 х − х | ≤ | х − х | 𝑆𝑆 To𝑖𝑖 =ran∏This𝑗𝑗= 1rural |value𝑚𝑚𝑚𝑚𝑚𝑚| /хareas𝑚𝑚 𝑚𝑚characteri−𝑚𝑚хх𝑗𝑗̂−|х ̂it𝑗𝑗| is necessary es in exemlary to determine ashion the the dimensional extent to hich of vector forced reugees using displacement have . .an imact was on the “the rural desire to save 𝑖𝑖 𝑚𝑚 . This 𝑆𝑆 = valueeconomy∏𝑗𝑗= 1characteri|𝑚𝑚𝑚𝑚𝑚𝑚|х𝑖𝑖𝑖𝑖 because−/𝑚𝑚х̂𝑗𝑗𝑚𝑚|𝑚𝑚хes𝑗𝑗− theinх̂𝑗𝑗 |exemlary stronger the ashion indicators the extent dier torom hich the reugeesbenchmar have the an larger imact the on S ithe value rural ill be This𝑚𝑚 value characterizes. in exemplary fashion the one’s life and prevent harm to one’s health” (82.2% economy 𝑖𝑖 and𝑗𝑗= becausevice1 𝑚𝑚𝑚𝑚𝑚𝑚 versa./𝑚𝑚 the𝑚𝑚𝑚𝑚 aning𝑗𝑗 stronger̂𝑗𝑗 o the rural indicators areas is achieveddier rom by the calculating benchmar the dimensionalthe larger the vector Si value D°(n) ill. ector be o extent𝑆𝑆This= value∏ to which| characteri refugeesх − х |es have in exemlary an impact ashion on the ruralthe extent of to refugees), hich reugees the “desire have to an save imact the liveson the of rural loved dierence D°𝑗𝑗 (n) shos the degree o remoteness closeness o rural areas rom the ideal rate – bench andThis vice value versa.|х characteri𝑖𝑖𝑖𝑖− хaninĝ | oes rural in exemlaryareas is achieved ashion by calculating the extent the to dimensional hich reugees vector D° have(n). ector an imact o S i on the rural economy𝑚𝑚 becausebecause. thethe strongerstronger thethe indicatorsindicators differ dier romones” the (70%) benchmar (Bogush, the 2018; larger Romaniuk the value et al., ill 2016 be). dierence𝑆𝑆 𝑖𝑖 = ∏marable𝑗𝑗=1 | 𝑚𝑚𝑚𝑚𝑚𝑚D°(n)/𝑚𝑚 metricshos𝑚𝑚𝑚𝑚х𝑗𝑗−х̂ 𝑗𝑗 theusing| degree . . o remoteness closeness o rural areas rom the ideal rate – bench fromeconomyand vice the versa.benchmark,because aning the the stronger o larger rural thethe areas indicatorsS valueis achieved will dier be by rom calculatingThe the actual benchmar the forced dimensional displacementthe larger vector the ofS iD° value the(n). ector population ill be o marableThis value metric characteri usinges . in . exemlary ashioni the extent to hich reugees have an imact on the rural anddierence vice versa. versa. D°(n) aningshos Ranking the o of degreerural rural areas areas o remoteness is is achieved achieved closenessby calculatingof Ukraine o therural 2014-2018 dimensional areas rom can vector bethe conditionallyideal D°(n) rate. ector – bench divided o economy because the stronger the indicators dier rom the benchmar the larger the Si value ill be D° et al., bydierencemarable calculating metric D°(n) shosthe using thedimensional . degree . o vector remoteness (n). Vector closeness into the o following rural areas stages rom (waves) the ideal (Drakokhrust rate – bench and vice versa.𝑚𝑚 aning o2 rural areas is achieved by calculating the dimensional vector D°(n). ector o of difference 𝑖𝑖 𝑗𝑗 =D°1 𝑖𝑖𝑖𝑖 shows 𝑗𝑗 the degree of remoteness 2019), (Table 1). dierencemarable 𝑑𝑑 = √ D°metric∏(n) shos((n)х using− theх̂) .degree . o remoteness closeness o rural areas rom the ideal rate – bench (closeness)To𝑚𝑚 integral of rural indicators 2areas from ere thecalculat idealed rate using – benchthe taxonomy method integral indicator o rural 𝑑𝑑 marable𝑖𝑖 = √∏𝑗𝑗 =metric1(х𝑖𝑖𝑖𝑖 − usingх̂𝑗𝑗) . . markable metric, using Eq. 9. § The First wave: February - July 2014 (Petrushenko To integral indicators ere calculat ed using the taxonomy method integral indicator o rural 𝑚𝑚 2 et al., 2014 ); 𝑖𝑖 𝑗𝑗=1 𝑖𝑖𝑖𝑖 𝑗𝑗 𝑑𝑑 = √∏𝑚𝑚 (х − х̂ ) 2 (9) § Second wave: August-November 2014; 𝑖𝑖 𝑗𝑗=1 𝑖𝑖𝑖𝑖 𝑗𝑗 𝑑𝑑To= √integral∏𝑚𝑚 ( хindicators− х̂2 ) ere calculated using the taxonomy§ Third wave: method December integral 2014 - indicatorMarch 2015; o rural 𝑑𝑑 To𝑖𝑖 = √integral∏𝑗𝑗=1(х𝑖𝑖𝑖𝑖 indicators− х̂𝑗𝑗) ere calculated using the taxonomy method integral indicator o rural ToTwo integral integral indicators indicators ere calculat were ed calculated using the usingtaxonomy § methodFourth wave: integral January-February indicator o rural 2016; the taxonomy method: 1) integral indicator of rural § Fifth wave: 2017-2018. development by the degree of integration of FDP A visualization of the concentration of FDP’s into the local economy; 2) integral indicator of rural placement as per early 2020 is shown in Fig. 1. development by the size of local budgets. Map of According to Fig. 1, the typical region in terms positioning of rural territories by size of local budgets of FDP’s is Poltava region. According to the Unified and level of integration of FDP into local economy of Displaced Persons Information Database (Balueva, Poltava region was drawn up in the coordinate system et al., 2018), 22546 persons (16213 families) are of these two integral indicators. registered in the region, among them 7,1 thousand

485 V. Shcherbak et al. Table 1: Ranking of regions by FDP adopted in 2014-2018 (forced displacement localization profile) (Habchak et al., 2019) Table 1: Ranking of regions by FDP adopted in 2014-2018 (forced displacement localization profile) (Habchak et al., 2019)

Rank 1 place 2nd place 3rd place 4th place 5th place 6th place 7th place Year 2014 Kharkiv Donetsk Zaporozhye Kiev Dnipropetrovsk Lugansk Odessa 2015 Donetsk Lugansk Kharkiv Kiev Zaporozhye Dnipropetrovsk Poltava 2016 Donetsk Lugansk Kharkiv Kiev Zaporozhye Dnipropetrovsk Kiev 2017 Donetsk Lugansk Kyiv Kharkiv Dnipropetrovsk Kiev Zaporozhye 2018 Donetsk Lugansk Kyiv Kharkiv Dnipropetrovsk Zaporozhye Odessa

Fig. 1:Fi g.Concentration 1: Concentration of FDP’s of regionalFDP’s regional placement placement by regions by of regionsUkraine ofas Ukraineper early 2018as per(Roskladka early 2018 et al ., 2020) (Roskladka et al., 2020)

35000 30000 25000 20000 15000 10000 5000

Number of refugees, persons 0 2014 2015 2016 2017 2018 2019 Years Fig. 2: Dynamics of the number of forcedly displaced population in the Poltava region (Roskladka et al., 2020) Fig. 2: Dynamics of the number of forcedly displaced population in the Poltava region (Roskladka et al., 2020) of disabled persons, 3,9 thousand of children, 1,3 share of rural population exceeds 50%. The dynamics thousand of persons with disabilities, 8,6 thousand of of the number of forcedly displaced population retired persons. Most of them live in rural settlements. registered with the social services bodies in Poltava Poltava region has relatively stable indicators - the region is shown in Fig. 2.

486 Global J. Environ. Sci. Manage., 6(4): 481-496, Autumn 2020

1000

12000 orkable 10000

8000 cilren 000 persons 000 persons it 2000 isabilities mber of refgees 0 pensioners 201 201 201 201 2018 201 years

Fig. 3: Number of forcedly displaced population in Poltava region by category (Roskladka et al., 2020) Fig mber of forcely isplace poplation in Poltaa region by category osklaka et al 2020

FiFig.g 4. Displacement Displacement of FDP of in FDP rural in areas rral and areas territorial an communities territorial commnitiesof Poltava region of (Roskladka Poltaa et region al., 2020) osklaka et al 2020

The number of FDP increased between 2014 and Most FDP moved to Poltava region from the industrial 2015 and made 41%. In 2016, the number of FDP region. Therefore, finding a job in a rural area was decreased by 2%. In 2017 and 2018, FDP decreased very difficult. At the same time, disabled persons by 13% and 12%, respectively. The maximum number and retired persons do not need employment and of FDP in Poltava region was recorded in May 2016 - are provided with social benefits (Roskladka et al., 31,251 people, and by the end of 2018 the number 2020) in accordance with the legislation in force. of FDP decreased by 19% to 25,174. The largest The location of FDP in rural areas and territorial number of FDP are working-age persons and their communities of Poltava region is shown in Fig. 4. minor children (Pylysenko, 2018). Between 2016 and Fig. 4 shows that FDP are localized in a different way 2018, the number of such persons decreased by 28%. in rural areas and territorial communities of Poltava In comparison, for the same period, the number of region. Such placement creates additional tension retired persons decreased by 18% and disabled in the labor market in the industrial cities of Poltava persons by 15% (Fig. 3). region: Poltava, Kremenchuk, Lubny, Myrhorod. The The number of FDP was influenced by the inability Perspective Plan for the Development of 57 Unified to find a job appropriate to their professional skills. Territorial Communities of Poltava Region requires

487 The role of refugees in host communities the creation of new enterprises and jobs (Bezugly et where Var1 – the number of unemployed FDP al., 2019) and the systematic integration of FDP into able to work; Var2 – the number of FDP receiving the activities of territorial communities. The future employment services; Var3 – the number of FDPs plan to develop Poltava region’s communities is based receiving unemployment benefits; Var4 - the number on the national and regional programs (Roskladka of FDP who have been re-employed; Var5 – the et al., 2020). This plan takes into account “growth number of retired FDP; Var6 – the number of disabled points”, which include measures to stimulate the FDP; Var7 – the number of FDP receiving vocational arrival of migrant workers (from the FDP category), training; Var8 - the number of FDP employed under to the territories where their workforce is required. the civil-law contracts or self-employed outside the settlement; Var9 - the number of FDP receiving a RESULTS AND DISCUSSION child care allowance, retiree health care benefit or Research findings in terms of the dependence of a sick care allowance; Var10 - the number of FDP rural economic development on the forced migration who participated in public and other temporary (Bogush, 2018; Romaniuk et al., 2016) show that work; Var11 - the number of underage FDP; Var12 in the short term the impact of decentralization – the number of FDP employed by the enterprises on migration will be insignificant. In terms of the of the given rural area; Var13 - the number of FDP territorial structure of migration movements, no entrepreneurs who have started new businesses / change is expected, consequently the migration created new jobs. The data in Table 2 indicates that indicators (Danko et al., 2020; Sardak et al., 2018) will two factors explain the impact of forced migration on reflect the volume of the retrospective period. Fig. the level of development of rural and urban economy 5 shows the dependence of migration growth (or its in Poltava region. The first factor has the greatest reduction) on population at the expense of FDP and impact on the economy of the region. All significant own revenues of local budgets of rural territories in indicators of the first factor are marked with red colour. 2014-2018. They are negative. The dispersion of the first factor is Fig. 5 shows a questionable impact of forced 61.7496%. That is, as of the beginning of 2020 the migration on rural economies (Sassen, 2005). involvement of FDP in the rural economy of Poltava The results of factor analysis are presented in region has a negative nature, – their maintenance Table 2. (payment of social benefits) reduces local budget

Fig. 5: Migration growth (reduction) of population and own revenues of local budgets in rural areas of Poltava region(Roskladka et al., 2020) Fig igration grot rection of poplation an on reenes of local bgets in rral areas of Poltaa region 488

– – – – – – – – – – – – – – – Global J. Environ. Sci. Manage., 6(4): 481-496, Autumn 2020 1 1 𝐹𝐹 =⁄ (− 0,996093 ∙ 𝑉𝑉𝑉𝑉𝑉𝑉1 − 0,995298 ∙ 𝑉𝑉𝑉𝑉𝑉𝑉2 − practically by 2 times. The dispersion of the second The8 ,027448magnitude of the impact of the second factor 0 ,992515 ∙ 𝑉𝑉𝑉𝑉𝑉𝑉3 − 0 ,991698 ∙ 𝑉𝑉𝑉𝑉𝑉𝑉4 − 0,993497 ∙ 𝑉𝑉𝑉𝑉𝑉𝑉5 − factor is 15.0167%. The effect of the indicators of the on0,991565 the level∙ 𝑉𝑉𝑉𝑉𝑉𝑉6 − of0 ,981163 development ∙ 𝑉𝑉𝑉𝑉𝑉𝑉 7 − 0 of,979545 rural ∙ 𝑉𝑉𝑉𝑉𝑉𝑉 economy8) is second factor is positive: all significant indicators of determined using Eq. 11. the second factor are positive. According to Table– 2, the magnitude of the influence of the first factor on (11) the economy of the region is determined using Eq. 2 𝐹𝐹 = 1⁄ (0,858041 ∙𝑉𝑉𝑉𝑉𝑉𝑉12 + 0,909189 ∙ 𝑉𝑉𝑉𝑉𝑉𝑉13) 1,952176 10. Three indicators: Var9, Var10, Var11 do not affect the state of rural economy. These indicators are marked with black colour on the STATISTICA 10 methodology is to use cluster analysis to differentiate rural areas and UTС. The clusters obtained differ 1 1 (10) listing (Table 2). The social maintenance of these 𝐹𝐹 = ⁄8,027448 (−0,996093 ∙ 𝑉𝑉𝑉𝑉𝑉𝑉1 − 0,995298 ∙ 𝑉𝑉𝑉𝑉𝑉𝑉2 − categories of FDP is the most economical rating for 0,992515 ∙ 𝑉𝑉𝑉𝑉𝑉𝑉3 −Т0abl,991698e 2: Results∙ 𝑉𝑉𝑉𝑉𝑉𝑉 of4 − factor0,993497 analysis.∙ 𝑉𝑉𝑉𝑉𝑉𝑉 The5 − impact of forced migration on rural economy of Poltava region 0,991565 ∙ 𝑉𝑉𝑉𝑉𝑉𝑉6 −0 ,981163 ∙ 𝑉𝑉𝑉𝑉𝑉𝑉 7 − 0 ,979545 ∙𝑉𝑉𝑉𝑉𝑉𝑉8) (STAT I STICA 10 listing) Тable 2: Results of factor analysis. The impact of forced migration on rural economy of Poltava region (STATISTICA 10 listing)

Factor Loadings (Unrotated) (data) Extraction: Principal components Variable 2 1 (Marked loadings are >,700000) 𝐹𝐹 = ⁄1,952176 (0,858041 ∙ 𝑉𝑉𝑉𝑉𝑉𝑉12 + 0,909189 ∙ 𝑉𝑉𝑉𝑉𝑉𝑉13) Factor 1 Factor 2 Var1 - 0,996093 -0,004777 Var2 -0,995298 -0,006704 methodologyVar3 is to use cluster analysis to differentiate rural-0,992515 areas and UTС. The clusters obtained differ0,024983 Var4 -0,991698 0,021135 Var5 -0,993497 0,023030

Var6 -0,991565 0,017140 Var7 -0,981163 -0,043433 Var8 -0,979545 -0,052663 Var9 0,096448 -0,199993 Var10 0,322435 -0,338269 Var11 0,204980 -0,477776 Var12 0,137791 0,858041 Var13 0,096858 0,909189 Expl.Var 8,027448 1,952176 Prp.Totl 0,617496 0,150167

1200 nit

1000

800 conent

00 clster 1 clster 2 00 clster 200 clster 0 ar 1 ar 2 ar ar ar ar ar ar 8 ar 12 ar 1 tanar eiations Plot of eans for ac lster conent nit Fig. 6: Results of cluster analysis of the impact of forced migration on rural economy of Poltava region (STATISTICA 10 listing)

Fig eslts of clster analysis of te489 impact of force migration on rral

economy of Poltaa region 10 listing

V. Shcherbak et al. host communities. The next stage of the developed The data in Fig. 6 shows that based on the findings methodology is to use cluster analysis to differentiate of cluster analysis rural territories of Poltava region rural areas and UTС. The clusters obtained differ in are divided into 4 clusters. The first cluster includes: the level of economic development and the degree of Poltava and Kremenchug urban areas (Table 3). Plot active participation of current FDP. The results of the of means for each cluster. cluster analysis are presented in Fig. 6. The clusterization of these territories into a

Table 3: The first cluster composition (STATISTICA 10 listing) Table 3: The first cluster composition (STATISTICA 10 listing)

Members of Cluster Number 1 (data) and Distances from Respective Cluster Center Case No. Cluster contains 2 cases Distance С_22 110,2933 С_23 110,2933

Table 4: The second cluster composition Table 4: The second (clusterSTATISTICA composition 10 listing (STATISTICA) 10 listing)

Members of Cluster Number 2 (data) and Distances from Respective Cluster Center Cluster contains 4 Case No. cases Distance С_2 28,09933 С_24 22,26145 С_25 80,43748 С_26 48,24292

Table 5: Strategy matrix for conflict resolution between rural areas, UTC and FDP Table 5: Strategy matrix for conflict resolution between rural areas, UTC and FDP

FDP category Cluster Unemployed Employees, entrepreneurs Retired, disabled people rural area

Q11 = – 32,7 + 0,11Var1 + Q12 = – 17,6 + 0,03Var1 + Var2 Q13 = – 12,8 + 0,02Var1 + 1 cluster: 0,43Var2 + 2,63Var3 – + 0,9Var3 – 0,3Var4 + 0,06Var2 + 0,7Var3 – 0,03Var4 Poltava and Kremenchug 59,54Var4 – 3,17Var5 + 0,8Var6 0,29Var5 + 0,18Var6 + + 0,03Var5 + 0,13Var6 + urban areas + 0,32Var7 –0,28Var8 0,13Var7 – 0,11Var8 0,04Var7 – 0,44Var8 2 cluster: Hadyatskyi Q21 = – 37,1– 0,28Var1 + Q22 = – 21,2 – 0,28 Var1 + Q23 = – 17,5 + 0,32Var1 + district, Lubensky, 0,29Var2 + Var3 + 0,04Var4 – 0,29Var2 + Var3 + 0,04 Var4 – 0,3Var2 + 0,4Var3 – 0,53Var4 + Myrhorodskyi, 0,01Var5 – 0,22Var6 – 0,31Var7 0,01Var5 – 0,09Var6 – 0,43Var5 – 0,01Var6 - Gorishniplavnivskyi urban – 0,01Var8 0,03Var7 – 0,023Var8 0,021Var7 + 0,001Var8 areas 3 cluster: Velikobagachansky, Globinsky, Zinkivsky, Q31= – 32,7 + 0,11Var1 + Q32 = – 18,7+ 0,3Var1 + Q33 = – 13,9 + 0,22Var1 + Karlovsky, Kobelyatsky, 0,43Var2 + 2,63Var3 – 0,29Var2 + Var3 + 0,04Var4 + 0,96Var2 + 0,33Var3 – Kotelevsky, Lokhvytsky, 59,54Var4 – 3,17Var5 – 0,24Var5 + Var6 – 0,23Var7 + 0,33Var4 + 0,33Var5 + Var6 + Novosarzhsky, Orzhitsky, 0,17Var6 + 0,14Var7 + 0,05Var8 0,23Var8 0,04Var7 – 0,01Var8 Piryatinsky, Reshetilovsky, Shyshatskiy rural areas 4 cluster: Grebinkiv, Q41= – 32,7+ 0,19Var1 – 0,5Var2 Q42 = – 18,6+ 0,22Var1+ Var2 + Q43 = – 18,8 + 0,02Var1 + Dykansky, Kozelshchyna, + 0,13Var3 + 0,14Var4 + 0,4Var3 – 0,09Var4 + 0,13Var5 0,06Var2 + 0,7Var3 – 0,03Var4 Mashevsky, Semenovsky, 0,36Var5 –0,1Var6 + 0,15Var7 + – 0,22Var6 – 0,28Var7 + + 0,03Var5 – 0,03Var6 – Khorolsky, Chornokhinsky, 0,17Var8 0,29Var8 0,3Var7 + 0,29Var8 Chutovsky rural areas *Clustering of rural areas was carried out using the K-means clustering method at the second stage of the study.

490 Global J. Environ. Sci. Manage., 6(4): 481-496, Autumn 2020

ntegrate inicators of mie strategies

Fig. 7: Combined rankingFig of mixed ombine rural, UTC ranking and FDP strategies of mie (On the rral left - Umixed anrural strategies,FDP strategies UTC; on the right are mixed FDP strategies) n te left mie rral strategies U on te rigt are mie FDP strategies particular cluster is explained by the fact that most enterprises in the rural areas of the third cluster of the enterprises located on these territories are belong to the fuel and energy complex of the region. industrial and create a large number of jobs. This Most of 26 oil fields are located in this part of Poltava stimulates FDP to choose these areas for their region. Enterprises are engaged in drilling, oil and gas permanent residence and employment. The second production, refining and gas processing. Besides, the cluster includes the following rural territories: third cluster include agro-industrial enterprises. The Hadyatskyi district, Lubensky, Myrhorodskyi, fourth cluster includes the following rural territories: Gorishniplavnivskyi urban areas (Table 4). Grebinkiv, Dykansky, Kozelshchyna, Mashevsky, The clusterization of these territories into a Semenovsky, Khorolsky, Chornokhinsky, Chutovsky particular cluster is explained by the same reasons districts. The rural areas of the fourth cluster belong as for the first cluster. They are located near large to the northeastern, eastern and southeastern cities. The only difference is that manufacturing regions of Poltava region. They are mostly agrarian. enterprises on these territories are mainly engaged Their main types of activity include crop production in the processing of agricultural products. The (cultivation of cereals and industrial crops, potatoes third cluster includes the following rural territories: and vegetables), animal husbandry (livestock, pig Velikobagachansky, Globinsky, Zinkivsky, Karlovsky, breeding, poultry farming). Kobelyatsky, Kotelevsky, Lokhvytsky, Novosarzhsky, The results of the solution of double maximin/ Orzhitsky, Piryatinsky, Reshetilovsky, Shyshatskiy minimax problem are determined using Eqs. 4 and 5 districts. The rural territories of the third cluster (Table 5). belong to the developed southwestern, central and 12 regression equations were obtained for northwestern regions of Poltava region. Most of the each segment of the strategic matrix (Table 4).

491 V. Shcherbak et al.

They describe the dependence of the revenues to to the size of local budgets and the level of FDP’s local budgets in rural areas on the costs of social integration into the local economy of Poltava region maintenance of various categories of FDP. The was created in the coordinate system of two integrated correlation coefficient is 0.8227, the determination indicators. On the “OX” axis there is “Integral indicator coefficient is 0.6768, which indicates a close of integration level of FDPs into the local economy”. relationship between these factors and explains The “Integral indicator of rural development by the 67.7% of the level of the dependent variable. This size of local budgets” is located on the “OУ” axis. Both strategic matrix was calculated to determine the indicators were obtained using the taxonomy method. optimal ratio of local budget revenues in rural areas Positioning of rural areas on the map (Fig. 8) indicates to the expenditures for social maintenance of various the following. In the upper right corner there are rural categories of FDP. The strategy to choose the types of areas with a large local budget and a high level of social support and integration of FDP was analyzed integration of refugees into the socio-economic life of using the matrix Table 5). The calculations using the the host communities. In the lower left corner there obtained strategic matrix are as follows (Shymanska are rural areas with a low local budget and a low level et al., 2017). The integrated indicators of mixed of refugee involvement in the territorial economy. strategies, calculated by the method of game theory, This visualization shows the leaders of the integration are presented in Fig. 7. process and identifies “points of growth” in the rural The positioning of rural areas according to the size areas. The practical implementation of the proposed of local budgets (Vasyltsiv et al., 2019) and the level methodology to determine the impact of forced of integration of FDP into the local economy is shown migration on sustainable development of rural areas in Fig. 8. offers a method for its implementation (Stukalo et al., Map of rural territories positioning according 2018). This method is shown in Fig. 9.

Fig. 8: Map of rural territories positioning according to the size of local budgets and the level of of FDP’s integration into the local economy of Poltava region Fig 8 ap of rral territories positioning accoring to te sie of local bgets an te leel of

of FDP’s integration into the492 local economy of Poltava region

Global J. Environ. Sci. Manage., 6(4): 481-496, Autumn 2020

ective o enhance the ositive imact an hallenges to fin an otimal moel for increasing roctivity of FDP on the eveloment of rral the fulfillment of FDP’s potential as a factor of territories an commnities to ensre the fll territory eveloment in line ith socioeconomic imlementation of FDP otential as a riving stats an rral eveloment strategy of the force of sstainale rral eveloment commnities

riteria for monitoring goal achievement ensring fll imlementation of FDP laor ecational social an Proctive imact of entrerenerial otentials FDP on the fll emloyment of aleoie olation eveloment of rral roviing conitions for ecent or on the rinciles of social stice areas territorial an noniscrimination ase on the stats of FDP an integrate activation of entrerenerial investment an innovation activity territorial increase in local get revenes commnities eveloment of consmer maret an social infrastrctre groth of social laor an hman caital of rral territories territorial commnities

Directions for enhancing easres to integrate FDP an esorce sfficiency the roctive imact of accelerate the hman transformation of FDP stimlating the eveloment of rral territories otential into the availale implementation of FDP’s ase on social rral resorces otential on rral cohesion eveloment

Fig. 9: Methods for enhancing the productive impact of FDP on the development of rural territories and territorial communities Fig ethos for enhancing the roctive imact of FDP on the eveloment of rral territories an territorial commnities Fig. 9 shows the mechanism for identifying stage, a factor analysis has been carried out. The and activating “growth points” in rural areas, tools factor analysis made it possible to identify the most for activating refugees to participate in the socio- significant indicators affecting the economy of rural economic life of host communities. For research areas: 10 out of 13 indicators under consideration. At practice, the study has identified all possible threats the second stage, a factor analysis was carried out. and opportunities for sustainable development of The use of cluster analysis allowed classifying rural rural areas under the influence of forced migration. areas into separate groups depending on their level The proposed mechanism for resolving conflict of economic development and the level of integration between host communities and refugees makes it of FDP: 26 rural areas of Poltava region into 4 clusters. possible to identify not only “growth points”, but also At the third stage, the conflict between refugees strategies for resolving it. and host communities was identified through game theory and optimal conflict resolution strategies were CONCLUSION developed. At the fourth stage, a positioning map of Forced migration in Ukraine affects the size of four quadrants was constructed using the taxonomy the population within individual regions: between method. The first quadrant contains rural areas with 2014 and 2019 the number of refugees reached large local budgets and a high level of integration of almost 3 million. The study has shown that forced refugees into the host community economy: 8% of migration reduces the economy of host communities the total (2 out of 26). The fourth quadrant contains by more than 60%. The State’s impact on internal rural areas with a low local budget and a low level displacement can be achieved through socio- of integration of refugees into the host community economic mechanisms. One such mechanism is economy: 31% of the total (8 out of 26). The to use the developed methodology. At the first second and third quadrants are at an average level

493 V. Shcherbak et al. of development. Mapping rural areas according to ABBREVIATIONS local budgets and the level of integration of refugees allows us to identify “growth points” in the particular % Percentage clusters of rural areas, justifying mechanisms to Acquired immune deficiency AIDS implement the potential and economic activity of syndrome refugees for rural development. As a result of the AR of Crimea Autonomous Republic of Crimea implementation of the proposed mechanism to stimulate the development of “points of growth”, the ATO Anti–terrorist operation budget of rural areas of the first cluster has increased DPR Donetsk People’s Republic by 18%, the second cluster by 14.5%, the third cluster Eq. Formula of calculation by 13%, the fourth cluster by 8%. The possibility to Expl.Var Explanatory Variable be engaged in entrepreneurship will facilitate the integration of migrants into the local community. FDP Forcedly displaced population Integration of FDP into local communities will be Fig. Figures stimulated through allowing FDP to access the GDP Gross domestic product government information on business opportunities (with the help of the state and municipal institutions). km Kilometer This information support has to ensure that FDP has LPR Lugansk People’s Republic access to business resources of the local communities OOS Joint Forces Operation and their businesses are integrated into the local Separate areas of Donetsk and entrepreneurial community. These factors confirm ORDLO Lugansk regions the need for a comprehensive mechanism to integrate Percentage of the total variance FDP into the rural economy. Prp.Totl explained AUTHOR CONTRIBUTIONS RMS standard deviations V. Shcherbak conducted methodology, research, Statistical analysis software STATSTICA validation, conceptualization and supervision. I. package Brizgan reviewed the literature and collected the TС Territorial Community data. V. Chevhanova has implemented the software UAH hryvnya and wrote the original project plan. O. Hryhoryeva provided a graphic presentation of the material. L. USA United States of America Svistun calculated the models. UTС United Territorial Community Var Variable ACKNOWLEDGEMENT С_ХХ Case of UTС or rural area The authors would like to thank the National University “Yuri Kondratyuk Poltava Polytechnic”, REFERENCES Office of the Project “Integrated Development in Adamson, F., (2006). Crossing borders: international migration and Ukraine” in Poltava and their guidance in the financial national security. Int. Secur., 31(1): 165–199 3( 5 pages). and organizational support of this research. Adamson, F.; Tsourapas, G., (2019). The migration state in the global south: nationalizing, developmental, and neoliberal CONFLICT OF INTEREST models of migration management. Int. Migration Rev., XX(X): 1–30 (30 pages). The authors declare no potential conflict of Aleshkovski, I., (2017). Globalization of international migration: interest regarding the publication of this work. In social challenges and policy implications. RUDN J. Soc., 17(2): addition, the ethical issues including plagiarism, 213–224 (11 pages). informed consent, misconduct, data fabrication and, Arakelova, I., (2017). The influence of internally displaced persons on the social and economic development of regions in Ukraine. or falsification, double publication and, or submission, Baltic J. Econ. Stud., 3(5): 6–12 (7 pages). and redundancy have been completely witnessed by Arandarenko, M.; Corrente, S.; Jandrić, M.; Stamenković, M., the authors. (2020). Multiple criteria decision aiding as a prediction tool for

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AUTHOR (S) BIOSKETCHES Shcherbak, V.G., D.Sc. in Economics, Professor, Department of Entrepreneurship and Business, Kyiv National University of Technologies and Design, Kyiv, Ukraine. Email: [email protected] Bryzhan, I.A., D.Sc. in Economics, Professor, Office of the Project, Integrated Development in Ukraine, Poltava, Ukraine. Email: [email protected] Chevhanova, V., Ph.D., Professor, National University, Yuri Kondratyuk Poltava Polytechnic, Poltava, Ukraine. Email: [email protected] Svistun, L., Ph.D., Associate Professor, National University, Yuri Kondratyuk Poltava Polytechnic, Poltava, Ukraine. Email: [email protected] Hryhoryeva, О., Ph.D., Associate Professor, National University, Yuri Kondratyuk Poltava Polytechnic, Poltava, Ukraine. Email: [email protected]

COPYRIGHTS ©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

HOW TO CITE THIS ARTICLE Shcherbak, V.G.; Bryzhan, I.; Chevhanova, V.; Svistun, L.; Hryhoryeva, О., (2020). Impact of forced migration on sustainable development of rural territories. Global J. Environ. Sci. Manage., 6(4): 481-496.

DOI: 10.22034/gjesm.2020.04.05 url: https://www.gjesm.net/article_39873.html

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