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Экономическая, социальная, политическая и рекреационная география
UDK 502/504
FORMATION OF COMPENSATION AND RECOVERY FUND OF EXTRACTIVE ENTERPRISES IN THE PLACES OF RESIDENCE OF THE INDIGENOUS UNDERPOPULATION
DOI: 10.24411/1728-323X-2020-12083
A. L. Novoselov, Plekhanov Russian University of Economics, Stremyannyy Alley, 36, Moscow, Russia, 115093
I. Yu. Novoselova, Moscow State Institute of International Relations (MGIMO University), Pr. Vernadskogo, 76, Moscow, Russia, 119454 National University of Oil and Gas "Gubkin University " (Gubkin University), 63/2 Pr. Leninsky, 2723, Moscow, Russia, 119991
Extraction of minerals in the areas inhabited by indigenous small-numbered peoples leads not only to the considerable pollution of the environment, but also to a significant change in the landscape; it reduces biodiversity and leads to deterioration in the living standards. As a result, conflicts arise between extractive enterprises and the population of the region. The article proposes a system of interrelated models that allows extractive companies to form compensation projects in order to avoid such conflicts, together with the local administration and population. A simple expert procedure based on lexical estimates is suggested to assess the priority of projects, which is translated into quantitative project prioritization through fuzzy numbers. The optimal set of projects is defined using a special model given the fuzzy estimation of the limits of a compensation and recovery fund established by the extractive enterprise for the proposed projects' implementation. Solving this problem allows to find the exact volume of the compensation and recovery fund. A model for developing an optimal strategy to form the compensation and recovery fund has been developed based on the obtained information. The application of a system of logically and informatively linked models is demonstrated on a numerical example.
Keywords: environment, compensation and recovery project, conflict, population, fund.
Introduction. Formulation of the problem
Exploration and development of new deposits of oil, natural gas, rare-earth metals, polymetallic ores, non-ferrous and other mineral resources is gradually shifting towards the Arctic Zone and other little developed territories. For example, in January 2019, the area of the Arctic territories where Gazprom's oil exploration and production is carried out increased by 3018.2 km2. The development of deposits in the Arctic Zone, as well as the extraction of natural resources and their transportation, are characterized, along with technical problems, by a high risk of negative impact on the environment [1]. The mining regions located in the Arctic Zone are characterized by low assimilation potential, which leads to a long process of natural restoration of disturbed territories and threatens the extinction of the biota. Intensive development of previously unused territories leads to landscape changes, significant environmental pollution, disappearance or damage of religious buildings and decrease in biodiversity [2]. Twenty three per cent of the total area allocated for oil production by Lukoil-Western Siberia in the Khanty-Mansiysk Autonomous Okrug of Russia is the territory of traditional nature management of the Khanty and Mansi peoples. The indigenous people living in the Arctic regions are engaged in traditional fishing; their way of life is based on the natural environment, which experiences significant degradation even with very modest economic activity [3]. Thus, the Arctic mining zone requires special attention of the authorities and mining companies [4]. In the process of mining and transportation of minerals, a mining company often comes into conflict with the local population, which leads to social unrest and the need to solve the problems of interaction between mining companies and the indigenous population, preventing or eliminating the conflict (Guedes, 2015).
In the process of exploiting deposits and transporting minerals, mining companies try to take into account the interests of the local population and the administration of the region. The mining companies usually compensate indigenous people for damages (South-cott, 2015). For example, in 2018, Lukoil-Western Siberia ear-
marked about 4.8 million euros to support indigenous people. However, the payment of monetary compensation to the population does not solve the issue. Therefore, along with payments, the mining companies create free infrastructure in the tundra: communication, medical services, vehicle supply, fuels, lubricants and other material values. To reduce the severity of the conflict and to reach consensus with the local community and the region's administration, mining corporations implement compensatory and restoration work, which includes a set of social and environmental projects aimed at the restoration of the territories, reconstruction of damaged cultural sites, monetary and property compensation, etc. (Corporate Social Responsibility in Mining for APEC Economies, 2013).
Many researchers rightly point out the need for socio-economic development of the regions after the completion of mining. This ensures the growth of the economy and employment for a long period (Barbier, 2003). A special role in the socio-economic development of the regions should be given to projects related to the development of infrastructure and the creation of new enterprises. To this end, it is proposed, together with extractive companies, to implement a system of projects for the development of new industries related to food processing, traditional industries, etc. (Acharya et al., 1999). Currently, in different countries, the institutional basis for the interaction of business and the indigenous people has been created and is being improved1. The developed recommendations for conflict resolution do not cover the methods of optimal solution and project justification (A toolkit for the prevention and mediation of conflicts in the development of the mining sector, 2012).
In order to effectively implement the developed institutional framework, this article proposes a system of logically and informationally linked models that make it possible to analyze the conflict and form a set of financially secure recovery and compensation projects to mitigate or eliminate the conflict and to solve the following problems:
• Assess the priority of the proposed social and environmental projects from the standpoint of specific groups of the indigenous peoples and local administration;
• Identify a set of social and environmental projects that are of most priority and can be provided based on the size of funding offered by the corporation;
1 For example, in Canada — section 35 of the Constitution of Canada guarantees the first peoples the right to hunt, fish and gather in the territories of their traditional residence; in Russia — the Federal Law "On the Territories of Traditional Nature Management of Indigenous Minorities of the North, Siberia and the Far East of the Russian Federation", etc.
• Form a plan to set a recovery and compensation fund sufficient for the implementation of the selected social and environmental projects and ensuring the minimization of deductions to the fund during the period of minerals' extraction.
Overview of the approaches to solving problems
The justification and implementation of priority projects in terms of environmental and social factors will reduce the risks of deteriorating welfare and health, as well as polluting the territory where the indigenous population live. The environmental investment projects have been developed in the world economy for solving problems of the environmental damage compensation during the activity of mining enterprises, land restoration and recovery of disturbed territories (Novoselov et al., 2019).
Projects should be prioritized from the standpoint of the indigenous population, which is not homogeneous, and therefore the opinions of certain groups of indigenous peoples and the interests of the administration should be taken into account (Sosa and Keen-an, 2001). In practice, such an assessment is carried out as part of an ethnological examination2, the process of which is described in detail in some works (Bas-ov, 2018; Sleptsov and Petrova, 2019). Taking the above stakeholders into account necessitates multic-riteria prioritization of projects (Todumosu-Ayanu, 2014).
Quite many studies were devoted to solving the tasks of collecting and analyzing the opinions of the society with regard to projects for the territory development (Munda, 2004). At the same time, several criteria are used to identify project priorities, i.e. there is a multicriteria selection (Van Delft and Nijkamp, 1977). In order to reduce the estimation error, the examination does not require a clear numerical answer. Respondents are encouraged to give lexical assessments — for example, "very important", "important", "interesting", etc. Fuzzy numbers are currently used in most practical approaches to translate these estimates into quantitative information for subsequent processing using mathematical methods (Aly and Vrana, 2008). Fuzzy triangular numbers are used for the simplicity of perception and further algorithmiza-tion (Mahapatra and Roy, 2009). A fuzzy triangular number D is set as a triad with a minimum value Dmn, an average value Dav and a maximum value Dmax, i.e. D = (Dmin, Dav, Dmax)3. The minimum and maximum values correspond to a degree of confi-
2 Ethnological expertise is a scientific study of the impact of changes in the native habitat of small nations and the socio-cultural situation on the development of an ethnic group.
3 The minimum value is marked with the index min, average — with av (average), maximum — with max.
20 30
Quantitative estimation
Fig. 1. Graphical representation of a fuzzy triangular number
1. Questioning in the contex of stakeholders
£
2. Fuzzy prioritization of projects
3. Estimation of possible deductions to the thund as a fuzzy triangular number
0
Fig. 2. General scheme for assessing and developing a strategy for the compensation and recovery fund formation at an extractive enterprise
dence equal to zero, and the average value corresponds to a degree of confidence equal to one. Figure 1 shows a graphical representation of a fuzzy triangular number D = (10, 30, 40).
Multicriteria estimation using triangular numbers is regarded as the most reasonable approach for solving such problems (Sha, 2015).
Solution of the problem of compensation and recovery measures after the completion of mineral extraction operations in places of compact residence of indigenous peoples requires not only to determine the priority of projects, but also to find the optimal set of projects, as well as to define the optimal strategy for the formation of the required size of the compensation and recovery fund. Let us consider the sequence and methods of solving these logically and informa-tionally linked problems.
Developed scheme and methods of solution
The following projects are often offered as compensation and recovery measures: restoration of cultural monuments, restoration of pastures, riverbed restoration, biodiversity conservation, educating people to use the Internet, update of hunting gear, renewal and expansion of boats, update and expansion of snowmobiles, provision of the population with household appliances, etc. Priority evaluation of projects should involve representatives of population
groups — for example, the youth, families, and elderly people. Besides, the opinion of the local administration should be taken into account. To conduct the survey, it is proposed to answer the questions of the questionnaire about the preference for the implementation of each of the proposed projects, as well as to offer one's own project options or clarify the existing ones. As such, the total number of interested groups is four (k = 1, 2, 3, 4).
The general logical scheme regulating the sequence of solving the information and logically linked tasks is presented in Figure 2.
Task 1. Questioning in the context of stakeholders.
To solve this task, representatives of population groups (the youth, families, elderly people) and the local administration are questioned. The answers are proposed in the verbal form: 1 — little interesting; 2 — interesting; 3 — important; 4 — very important; and 5 — extremely necessary. Table 1 shows the developed scale for translating lexical answers into fuzzy triangular numbers.
Figure 3 shows graphical representation of triangular numbers corresponding to the developed scale for translating verbal responses (each triangular number provides a lexical term and a mathematical notation corresponding to Table 1).
Task 2. Fuzzy average estimate of the projects' priority.
Verbal estimates for projects i = 1, 2, ..., m received from each stakeholder group k = 1, 2, 3, 4 are translated into fuzzy triangular numbers c^ = ( cfk^, ctk , cmkax ) and averaged using the following formula:
Pi = ( pf1, pr, PD =
i
4
I '
k = 1
4
I'
k = 1
ik
4
I '
k = 1
(1)
Table 1
Scale of lexical estimates and their translation into triangular numbers
Lexical estimate of the project Fuzzy triangular number
Little interesting Interesting Important Very important Extremely necessary D1 = (0,0; 0,0; 0,25) D2 = (0,0; 0,25; 0,50) D3 = (0,25; 0,50; 0,75) D4 = (0,50; 0,75; 1,00) D5 = (0,75; 1,00; 1,00)
Little interesting
A
Di
Interesting
A
D2
Important
A
D3
Very important
A
Da
Extremely necessary
A
D5
Quantitative estimation of preferen
Fig. 3. Correspondence of triangular numbers to verbal estimates
Task 3. Estimation of possible deductions to the fund as a fuzzy triangular number.
The size of the compensation and recovery fund is defined by the Corporation's expert council, taking the project costs into account. Assume the number of experts equal to L. The experts suggest and justify the fund size b1, l = 1, 2, ..., L, then the fuzzy estimation of the fund size is found using the following formula:
B = (Bm^n, £av, bmax) =
min ( b¡) ;
l = 1,2,L
L
I bl . l = 1 .
max ( b¡ )
l = 1,2,..., L
(2)
I P ¡Uj ^ max. i = 1
(2)
Restrictions on the project costs within the estimated size of the recovery and compensation fund are as follows:
i zu < b,
i = 1
(3)
where P- is a priority of the z-th project P- = (P;-;
P?v; Pr);
Zj is costs of the z-th project implementation costs; and B is the estimated size of funding as a fuzzy estimate B = (Bl; Bav; Br).
The target value U- takes two values: 1 if the project is selected for the optimal set, or 0 if the project is not included in the optimal set of projects:
U = -j 0 , i = 1, 2, ..., m.
(4)
Task 4. Solving the optimization task of the project selection in the context of fuzzy priority and fuzzy size of financing.
The tasks of forming an optimal set of projects in the region should be solved based on the fuzzy evaluation of priorities and required project costs, as well as a fuzzy estimate of the size of the compensation and recovery fund.
The criterion of optimality is the maximization of the total usefulness of the optimal set of compensation and recovery projects for stakeholders in the region:
Fuzzy numbers should be defuzzified to solve this problem, i.e. fuzzy triangular numbers should be translated into exact values (Sahoo, 2015). It is recommended to use the method of graded mean integration representation (GMIR) proposed by Chen and Hsieh (2000). Then an exact value of the project priority and the estimated size of financing will be found using the following formulas:
mm
Pi = 0,25( Pi i = 1, 2, ..., m,
+ 2 pav + pmax),
B = 0,25(Bmin + 2Bav + Bmax).
(5)
(6)
Replacing the fuzzy triangular priority values and the estimated size of the compensation and recovery fund in model (2—4) with the exact estimates found using formulas (5—6) will lead to a deterministic optimization problem, which can be solved using one of the methods of Boolean programming — for example, the Lemke and Spielberg (1967) method. An optimal set of projects j = 1, 2, ..., m will be obtained as a result of solving the problem. Based on the optimal set
of projects, the exact required value of the compensation and recovery fund should be found:
m
W = X z,u*, (7)
i = 1
Task 5. Deciding on the optimal strategy for the fund formation.
The optimal strategy for accumulating financial resources in the compensation and recovery fund in the amount of W for the period T at a minimum amount of deductions from the profit of the extractive enterprise should be found. The criterion of optimal-ity is the minimization of deductions to the compensation and recovery fund from the profit for the period t = 1, 2, ..., T - 1:
T -1
f(X, Y, AX) = Xi + X AXt ^ min, (8)
t = 2
where Xi is the amount of deductions to the fund in year t = 1; AXt is the additional deductions to the fund t = 2, 3, ..., T - 1.
Restriction on the amount of deductions to the fund from profit is as follows:
X1 < aPh (9)
AXt < aPt, t = 2, ..., T - 1 (10)
where Pt is the profit of the project for minerals' extraction in the region of potential conflict in year t; AXt is the additional deductions to the fund t = 2, 3, ..., T — 1; and a is the permissible percentage of annual deductions from the profit of the project for minerals' extraction in the region of potential conflict.
Balance equation regulating the size of accumulated funds due to the initial deduction Xl and allocating the part of funds in the development of other enterprises of the Concern is as follows:
X2 = Xi + X Yji(1 + rj), (11)
j = 1
where Xt is the accumulated volume of deductions in year t, t = 2, 3, ... , T; Yj1 is the part of the deductions to the fund allocated to the development of the j-th enterprise in year t, t = 2, 3, ... , T - 1; and rj is the internal interest rate of investment in the j-th enterprise of the Corporation.
The balance equations that regulate the volume of accumulated funds through additional deductions AXt (t = 2, 3, ..., T — 1) and allocation of a part of the funds in the development of other enterprises of the Corporation are as follows:
Xt + i = Xt + X Yt(1 + rj) + AXt, j = 1
t = 2, 3, ..., T - 1. (12)
The equation of achievement of the target size of compensation and recovery fund by year T is as follows:
XT = W (13)
where W is the required size of the fund for the implementation of the optimal set of compensation and recovery projects.
The restriction on the maximum amount of investment required for the development of the j-th enterprise of the Corporation is as follows:
Yjt < Djt, j = 1, 2, ..., n, t = 2, 3, ..., T- 1,(14)
where Dp is the need for investment in project j in year t, j = 1, 2, ..., n, t = 2, 3, ..., T - 1.
Model (8—14) is a model of linear programming and can be solved using the simplex method.
Example of the implementation of the developed approach and discussion of the results. Diamonds are extracted in the riverbed in the region. As part of works, the enterprise breaks the bottom of the riverbed, destroys the territory along the river, disrupts habitats and migration routes of animals, and reduces the area of pastures of the deer. Part of the territory along the river is a cultural and historical place associated with the religious views of the indigenous people. The enterprise is going to implement compensation and recovery projects, for which costs three years after the commencement of works are known (Table 2).
The fuzzy estimate of the fund B = (60, 79, 86) mln euros was found at the level of the extractive enterprise. Formula (6) allows us to obtain an accurate estimate of the allocated financial resources: B = = 0,25(60 + 2 x 79 + 90) = 76 mln euros.
A matrix of verbal estimates was obtained through questioning (Table 3).
Table 2
Costs for the expected compensation and recovery projects
Project name Project costs Z, mln Euros
1. Restoration of cultural monuments 6.0
2. Restoration of pastures 12.0
3. Riverbed restoration 18.0
4. Biodiversity conservation 15.0
5. Educating people to use the Internet 9.0
6. Update of hunting gear 12.0
7. Renewal and expansion of boats 10.0
8. Update and expansion of snowmobiles 17.0
9. Provision of the population with 21.0
household appliances
Triangular numbers were obtained (Table 4) based on the found verbal estimates and the developed scale (Table 1), where the penultimate column provideB the average values of project priorities used as the coefficients of the objective function of optimization model (2—4). Since the defuzzification of fuzzy triangular numbers was required for optimization, the last column of Table 4 provided the results of defuzzification of project priorities using formula (5).
A mathematical model had been formed for the optimal selection of projects and the accurate assessment of the allocated financial resources, based on the data in Tables 2 and 4:
0,55 U1 + 0,79 U2 + 0,69 U3 + 0,84U4 + 0,73 U5 + + 0,80 U6 + 0,69 U7 + 0,69 U8 + 0,45 U9 ^ max 6U1 + 12 U2 + 18 U3 + 15 U4 + 9U5 + 12 U6 + + 10U7 + 17U8 + 21U9 < 76
Projects 2, 4, 5, 6, 7, 8 were selected in the optimal set as a result of solving this problem. Using formula 7, it is easy to find that 75 mln euros is required to implement these projects.
Additional information on the opportunities and return on investments in the development of the Corporation's enterprises, as well as the profits of the extracting enterprise that establishes a compensation and recovery fund should be used to develop a strategy for the formation of a compensation and recovery fund (Table 5). According to the condition set by the experts of the Corporation, the deductions from the profit of the extractive enterprise should not exceed one percent.
The problem of linear programming (8—14) is solved using the data in Table 5 and the previously established size of the compensation and recovery fund W = 75 mln euros. The results of solving this problem
Table 3
Estimate of the stakeholders' interest in the proposed projects
Projects Verbal estimate of projects by stakeholders
Youth Families Elderly people Administration
1 interesting important extremely necessary important
2 very important very important extremely necessary very important
3 important very important very important very important
4 extremely necessary extremely necessary very important very important
5 extremely necessary very important important very important
6 very important very important extremely necessary very important
7 important very important very important very important
8 very important very important important very important
9 important very important little interesting important
Table 4
Fuzzy estimates of preferences and their defuzzification
Project i Fuzzy estimates of project preferences by stakeholders Average value of fuzzy preferences p; bi
Youth Families Elderly people Administration
1 (0,00; 0,25; 0,50) (0,25; 0,50; 0,75) (0,75; 1,00; 1,00) (0,25; 0,50; 0,75) (0,31; 0,56; 0,75) 0,55
2 (0,50; 0,75; 1,00) (0,50; 0,75; 1,00) (0,75; 1,00; 1,00) (0,50; 0,75; 1,00) (0,56; 0,81; 1,00) 0,80
3 (0,25; 0,50; 0,75) (0,50; 0,75; 1,00) (0,50; 0,75; 1,00) (0,50; 0,75; 1,00) (0,44; 0,69; 0,94) 0,69
4 (0,75; 1,00; 1,00) (0,75; 1,00; 1,00) (0,50; 0,75; 1,00) (0,50; 0,75; 1,00) (0,62; 0,87; 1,00) 0,84
5 (0,75; 1,00; 1,00) (0,50; 0,75; 1,00) (0,25; 0,50; 0,75) (0,50; 0,75; 1,00) (0,50; 0,75; 0,94) 0,73
6 (0,50; 0,75; 1,00) (0,50; 0,75; 1,00) (0,75; 1,00; 1,00) (0,50; 0,75; 1,00) (0,56; 0,81; 1,00) 0,80
7 (0,25 ;0,50; 0,75) (0,50; 0,75; 1,00) (0,50; 0,75; 1,00) (0,50; 0,75; 1,00) (0,44; 0,69; 0,94) 0,69
8 (0,50; 0,75; 1,00) (0,50; 0,75; 1,00) (0,25; 0,50; 0,75) (0,50; 0,75; 1,00) (0,44; 0,69; 0,94) 0,69
9 (0,25; 0,50; 0,75) (0,50; 0,75; 1,00) (0,00; 0,00; 0,25) (0,25; 0,50; 0,75) (0,25; 0,44; 0,69) 0,45
Table 5
Additional economic indicators for the developing a strategy for the formation of compensation
and recovery fund
Indicators Year 1 Year 2 Year 3
Profit of the extractive enterprise, mln. Euros/year 8,000.0 25,000.0 20,000.0
maximum amount of investments required for the development of the Corporation's
enterprises, mln. Euros/year:
enterprise 1 50.0 30.0 60.0
enterprise 2 20.0 45.0 45.0
enterprise 3 18.0 60.0 40.0
internal interest rate of investments in the development of the Corporation's enterprises, %:
enterprise 1 10.0 10.0 8.0
enterprise 2 10.0 10.0 15.0
enterprise 3 12.0 12.0 12.0
Table 6
The strategy of forming a compensation and recovery fund
Indicators Values of indicators in the calendar period, mln euros
Year 1 Year 2 Year 3
Beginning of the year End of the year Beginning of the year End of the year Beginning of the year End of the year
Deductions from profits to the fund 55.80 — — — — —
Enterprise 1 37.80 41.58 1.73 1.907 60.00 64.80
Enterprise 2 — — — — — —
Enterprise 3 18.00 20.16 60 67.2 9.11 10.20
Accumulated volume of the fund 55.80 61.74 61.74 69.11 69.11 75.00
are presented in Table 6. The volume of the accumulated fund by the end of year t is equal to the volume of the fund at the beginning of the next year. The required fund size is 75.0 mln euros at the end of the third year.
The volume of the accumulated fund by the end of year t is equal to the volume of the fund at the beginning of the next year. Table 6 indicates that the accumulated part of the fund is invested in the development of enterprises 1 and 2, and the amount invested by the beginning of the year increases by the corresponding percentage by the end of the calendar year. The deductions from the profit of the extractive enterprise in the first year are 55.8 mln euros. There are no additional deductions from the profits of the extractive enterprise. The required fund size of 75.0 mln euros is achieved by the end of the third year.
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