The Economic Aspect of Sustainability in Russian Arctic Areas

Ludmila Babkina

and Oksana Skotarenko

2,3 b

Northwestern Institute of Management, Branch of the Russian Presidential Academy of National Economy and Public

Administration under the President of the Russian Federation, 57 Sredniy Pr., Vasilievsky Island, 199178 Saint Petersburg

Murmansk Arctic State University, 57 Prospekt Lenina, 183034 Murmansk, Russia

Military Academy of Logistical Support named after General of the Army A. V. Khrulyov, 8 Naberezhnaya Makarova,

199034, Saint Petersburg, Russia

Keywords: Sustainability, National Project, Arctic Regions, Stability.

Abstract: The article explores different approaches to the term “sustainability” existing in academic literature and used

in practice. It describes three methods – two well-known ones and another one developed by the authors –

used for quantitative assessment of the degree of economic and financial sustainability in regions. The regions

include four Russian federal subjects in the Russian Arctic and three macroregions that include the aforesaid

regions. The indicators used for the assessment are grouped by the following aspects: relevance to national

projects, type of assessment scale, internal or external economic factors, and income or spending in a

consolidated regional budget. The article explores and draws a distinction between the influence of external

factors – indicators of the demographic and natural environments – on the economic sustainability of an area.

A comparative analysis is done for three Russian macroregions and four Arctic regions by degree of economic

and financial sustainability. The regions and macroregions are ranked based on the results of the comparative

analysis using the selected indicators. The ranking helps identify potential strategic vectors and their

succession as well as criteria for improvement of economic sustainability in the Russian Arctic.

1 INTRODUCTION

The relevance of the study is determined by the fact

that there are several approaches to the term

“sustainability”.

The first approach implies that sustainability is a

balanced and proportionate development of three

macro-environments – economy, demographics, and

(natural) environment – in any area. The approach

became widespread in the late 20

century after the

Rio de Janeiro UN Conference followed by the

signature of a number of environment-related

documents, including influential ones, such as the

Kyoto Protocol and the Paris Agreement. The

approach has dominated in many developed countries

with a post-industrial service-based economy

(Concept, 1993; Towards, 2018).

Russia is currently implementing its national

projects aimed at improved sustainability in its

regions, including land areas of the Russian Arctic

https://orcid.org/0000-0001-5018-0191

https://orcid.org/0000-0002-5255-5564

(On national projects, 2020). Therefore, out of all

interconnected processes, we have to focus on the

economic ones and view the achievement results of

demographic and environmental goals as external

factors to these economic processes (Bulletin, 2020).

The second approach to sustainability is used in

financial relations on both the micro level, i.e. in

business entities, and macro level, i.e. in regional

financial management systems. Therefore, financial

sustainability applies to both businesses and

territories, including regions. It is determined by

internal factors.

Consequently, economic sustainability is

determined by two groups of factors: internal and

external. In this study, factors are expressed as

indicators of annual government statistics reports.

The third approach is not based in research but

often used in practice, when sustainability is

understood as stability. Stability, in its turn, can be

understood either as a long-term stagnation or as a

distinct trend. Stagnation means lack of economic

342

Babkina, L. and Skotarenko, O.

The Economic Aspect of Sustainability in Russian Arctic Areas.

DOI: 10.5220/0010668200003223

In Proceedings of the 1st International Scientiﬁc Forum on Sustainable Development of Socio-economic Systems (WFSDS 2021), pages 342-348

ISBN: 978-989-758-597-5

growth. A trend is determined by an increasing

positive or negative change in economic indicators. A

steady-state economy is an economy with relatively

stable major indicators, such as population or

consumption, whose scope does not exceed the

carrying capacity of the ecosystem. The term often

applies to national economies but can be used to

analyse economic systems of cities, regions, or the

world.

2 THE STUDY METHODOLOGY

The study is based on the main principles of the

systemic, comprehensive, and qualimetric

methodological approaches, employing a

proportionate and balanced assessment of the

condition of, pollution levels in the environmental

components, and conservation spending. The study

uses the statistical and index methods of regional

qualimetry and the methods of financial and

economic analysis.

Developing methodological approaches to

studying regional economies. For example, academic

literature suggests using economic digitalisation tools

to identify extreme structural components of

economic potential growth in regions (Babkin et al.,

2019).

It is possible to use the qualimetric

methodological approach to address the major issue

of using a novel scholarly and methodological

framework in managing territorial processes in the

Arctic (Kozin and Plotnikov, 2019).

A number of studies are dedicated to minimising

and mitigating environmental risks in the Russian

Arctic (Bykovskaia et al., 2021).

3 RESULTS AND DISCUSSION

Regional sustainability assessment is important

because development of Arctic areas has been

declared a priority strategic activity by the Russian

Government, where the Ministry for Development of

the Russian Far East and Arctic was established in

2012 (Concept, 1992). Out of all Russian Arctic land

areas, we have chosen only four regions as objects of

study. They all have their indicators in annual

government statistics reports. It is Murmansk Oblast

and three Autonomous Okrugs: Nenets, Yamalo-

Nenets, and Chukotka (On Land Territories, 2016).

Sustainability of Russian Arctic regions will be

improved, as a whole and in terms of economic

processes, by implementation of the respective

national and federal projects adopted in 2018 and

2020. The projects contain strategic development

goals until 2024 and 2030 and their implementation

criteria in each region for both internal and external

economic factors. (On national development goals,

2020).

In particular, scholarly literature contains studies

on how and to what extent (degree) the national

projects implemented in demographics and the

environment affect the upward and downward

changes in major economic indicators (National

Projects, 2020). Those national projects include

Demographics and Ecology implemented via their

respective five and ten federal projects.

The Ecology Project has had a generally positive

effect on economic growth in Russian regions, which

was 0,05% in 2020 and projected to reach 0,06% in

2021. The projections will, however, be adjusted

because of the pandemic.

Academic studies show that the Demographics

Project has had a negative influence on regional

economies. However, experts predict that the

negative effect on economic indicators will slightly

decrease in 2021 compared with 2020 (-0,23%).

Thus, the share of the National Projects for

Demographics and Ecology in the overall Russian

economic growth was 24,34% in 2020, expected to

reach 27,26% in 2021 or 30,96% given the projected

changes.

It should be noted that the National Project for

Demographics has influenced the annual economic

growth significantly more that the National Project

for Ecology: by a factor of 4,19 in 2020 and 3,92 in

2021 or 4,21 given the projected changes.

Not one but four national projects have been

developed for improvement of economic indicators:

Productivity and Employment, Digital Economy in

Russia, Small and Medium Businesses and Support to

Private Enterprise, and International Cooperation and

Exports. The biggest increase in economic

development, equal to 0,09% in 2020, resulted from

the Project for a Digital Economy in Russia, and the

Project for Productivity and Employment accounted

for the lowest increase of 0,01%, which is different

by a factor of 15,33. Lower growth degrees of the

indicator in question were obtained from the Projects

for International Cooperation and Exports (0,07%)

and Medium Businesses and Support to Private

Enterprise (0,05%), which is lower than the top value

by 26,03% and 76,92%. In 2021, the National Project

for a Digital Economy in Russia should account for a

0,1% economic growth, but, given the changing

internal and external factors, the figure is expected to

The Economic Aspect of Sustainability in Russian Arctic Areas

343

be 0,07%, which is lower by a factor of 1,32 or by

32,43% than otherwise would in favourable

conditions. The expected growth figures in 2021 for

the other three projects – Productivity and

Employment, Small and Medium Businesses and

Support to Private Enterprise, and International

Cooperation and Exports – will be lower than in 2020

by 20%, 30%, and 23,73%, respectively, given

favourable conditions. In unfavourable conditions,

the indicator will be lower by a factor of 3, 1,44, and

1,66, i.e. by 200%, 44, and 66%, respectively.

Consequently, the most significant negative effect

on growth rates in regional economies, including

those in the Arctic, is caused by the National Project

for Demographics. The other national projects have a

less significant yet positive effect. The second place

is held by the Project for a Digital Economy in Russia,

the third by International Cooperation and Exports.

Two projects hold the fourth place: Small and

Medium Businesses and Support to Private Enterprise

and Ecology. The final, fifth place is held by the

Project for Productivity and Employment.

We believe that the classification of the national

projects by their influence reflects the spending on the

projects. The share of spending for the National

Project for Demographics amounted to 4,19% of the

government spending on its social policy. The Project

for Ecology accounted for 38,46% of the environment

conservation spending.

The largest share, equal to 2,43% of the

consolidated government spending on the national

economy, belonged to the National Project for a

Digital Economy in Russia, one of the four economic

projects. The smallest share of 0,16% was spent on

the Project for Productivity and Employment. The

National Projects for International Cooperation and

Exports and Medium Businesses and Support to

Private Enterprise accounted for their respective

shares of 1,94% and 1,36%. As a result, the

cumulative share of spending on the four projects was

12,96% in 2019.

The comparative analysis of economic indicators

in the Arctic regions has been done using the

transparency principle. All of the required absolute

and relative indicators, including those required for

calculation of specific values, are taken from the

annual government statistics reports (Regions of

Russia, 2020). The main idea behind the comparative

analysis is to identify the positions of each region in

a reporting year by the selected quantitative indicators

in order to compare and rank them relative to the other

Arctic regions. Besides, the suggested comparative

analysis involves comparing the quantitative regional

indicators with, first, the average figures in the

respective macroregion (federal district), secondly,

with the quantitative indicator values in Russia as a

whole. A similar comparison is also made for each

indicator between the macroregions (federal

districts), identifying the position of the macroregion

relative to the other ones and the indicator value in

Russia as a whole.

For further studies, the available indicators were

grouped by several aspects.

The first aspect is whether the indicator applies to

national projects, e.g. Digital Economy in Russia. By

this aspect, the group includes the indicators required

for monitoring the performance of national projects.

The second aspect is the type of assessment scale

(direct or inverse), which means that the comparative

ranking of the region depends on the meaning of the

absolute or relative indicator. The aspect can also be

called a vector of influence: positive or negative.

Positive influence means the highest indicator value

is ranked the highest, with an increase in the value

improving the position of the region. Negative

influence means that an increase in the indicator value

describes a deteriorating situation in the region. The

first (direct) scale means that the top rank is assigned

to the region with the highest (maximum) indicator

value, the other ranks to be assigned in descending

order. The second (inverse) scale means that top rank

is assigned to the lowest indicator value, the other

ranks to be assigned in ascending order.

In the annual government statistics reports,

regional economic conditions are monitored using

just one performance indicator from the National

Project for a Digital Economy in Russia. It is the share

of households with broadband Internet access. The

share of those households is 73,2% in Russia on

average. In the Northwestern Macroregion, the share

is larger and equal to 76,5% (1

rank), with the share

being 75,4% (2

rank) in the Urals and 71,2% (3

rank) in the Far East. In the regions, the largest share

of households with Internet access is in Yamalo-

Nenets AO (96,3%), ranked first. The second rank

belongs to Murmansk Oblast (82,4%), the third to

Chukotka AO (59,1%), and the fourth to Nenets AO

(56,0%). The difference (96,3–56,0) is 40,3%, with

the maximum being different from the minimum by a

factor of 1,72.

There are no monitored indicators in the other

economy-related national projects: Productivity and

Employment, Digital Economy in Russia, Small and

Medium Businesses and Support to Private

Enterprise, and International Cooperation and

Exports. We will therefore use the main

socioeconomic indicators describing the conditions in

Russian regions as well as other statistical data

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344

(Regions of Russia, 2020).

To assess the performance of the National Project

for Productivity and Employment, we will use two

indicators. The first indicator is a specific one,

calculated as the gross regional product (GRP) per

employed person. The second indicator is a statistical

one: unemployment. The calculations show that the

gross regional product per employed person was

1047,024 thousand RUB in 2018 in Russia on

average. In the Ural Macroregion, the indicator was

1,61 times higher, equal to 1680,772 thousand RUB.

In the Northwest, the indicator value was 1157,47

thousand RUB, slightly exceeding the Russian

average by a factor of 1,1 or by 10,5%. The Far

Eastern Macroregion had a lower indicator value of

962,219 thousand RUB, which is lower than the

Russian average by 8,1% and by a factor of 1,75

(74,7%) and 1,2 (20,3%) that the Ural and

Northwestern Macroregions, respectively.

A comparison of the indicator in the Arctic

regions shows that the highest value of 8694,5

thousand RUB per capita belongs to Nenets AO

rank), the lowest, 1225,048 thousand RUB per

capita, to Murmansk Oblast (4

rank). The second

rank is held by Yamalo-Nenets AO, with its GRP per

employed person being 5892,848 thousand RUB. The

third rank is held by Chukotka AO, where the

indicator value is 2063,93 thousand RUB per capita.

The respective values are lower than the maximum

value by a factor of 1,48 (47,54%) and 4,21

(321,26%). The difference is 7469,45 thousand RUB

per capita, with the ratio of the maximum value to the

minimum value being 7,1 (609,76%).

The second indicator, unemployment, was 4,8%

in Russia. The areas are ranked on an inverse scale,

and for the macroregions, it was as follows: the first

rank was held by the Northwest (3,9%), the second by

the Urals (4,7%), and the third by the Far East (6,3%).

The unemployment level was therefore lower that the

national average in two macroregions by a factor 1,23

and 1,02. In the third macroregion (Far East), it is 1,31

times higher.

A comparison of the unemployment level in the

Arctic regions has shown that the lowest indicator

value of 2,1% was observed in Yamalo-Nenets AO

rank), the highest, 8,1%, in Nenets AO (4

rank),

with the variation of 6% or 3,86 times. That lowest

unemployment level is lower than the national

average and the Ural Macroregion by a factor of 2,29

and 2,24, respectively. It is noteworthy that the

Nenets region has both the highest GRP per employed

person and the highest level of unemployment.

Chukotka AO is ranked second (3,1%) and

Murmansk Oblast third (6,8%). The lowest

unemployment level is therefore exceeded by the two

regions by a factor of 1,48 and 3,24, respectively.

The average number of (non-outsourced) small

business employees per 1,000 people annually

employed in the regional economy can be considered

a performance criterion for National Project for Small

and Medium Businesses and Support to Private

Enterprise. There are no statistical data for medium

businesses.

On average, there are 149,8 people employed by

small businesses per 1000 employed people in

Russia. In the macroregions, the following numbers

of those employees were observed: 177,36 people in

the Northwest, 140,06 people in the Urals, and 134,42

people in the Far East. Therefore, the value exceeded

the national average only in the Northwestern

Macroregion, by a factor of 1,18 (18,4%). In the Ural

and Far Eastern Macroregions, the number was lower

by a factor of 1,07 (6,95%) and 1,11 (11,44%).

In the Arctic, the largest number of people

employed by small businesses was observed in

Murmansk Oblast: 110,74 people per 1,000 employed

people (1

rank). However, it is 1,35 times lower than

the Russian average (by 35,27%) and 1,6 times lower

than that of the Northwestern Macroregion (by

60,16%). The lowest value was in Nenets AO, where

the number was 50,31 people (4

rank), which is

lower than in leading Murmansk Oblast by 60,43

people or by a factor of 2,2 (by 120,1%).

Chukotka AO had a number almost identical to

that of Nenets AO: 54,05 people (3

rank), 2,05 times

(by 104,88 %) behind the leading region and by

15,22% behind Nenets AO. The second rank belongs

to Yamalo-Nenets AO with its number of 62,49

people, lagging behind Murmansk Oblast by a factor

of 1,77 (77%).

To assess the performance of the National Project

for International Cooperation and Exports, an

indicator was calculated for exports to neighbouring

and other countries per employed person in the

regional economy. The value for Russia is 6,280

USD. In the Northwestern, Ural, and Far Eastern

Macroregions, it is 7184,4 USD, 6446,3 USD, and

7244,88 USD, respectively. Therefore, the indicator

in the Northwestern Macroregion exceeds the export

amount per employed person in Russia by a factor of

1,144 (by 14,4%). The positions of the Far Eastern

and Northwestern Macroregions are almost the same,

the difference being a mere 0,9%, with the Urals

exceeding the national average by a factor of 1,026 or

2,6%.

In the Arctic region, Murmansk Oblast was

ranked first, with its export amount of 10026,4 USD

per employed person in its economy. The second

The Economic Aspect of Sustainability in Russian Arctic Areas

345

rank was held by Yamalo-Nenets AO (7181,95 USD),

the third by Chukotka AO (4000 USD), with their

respective values below leading Murmansk Oblast by

a factor of 1,4 (39,6%) and 2,5 (150%). There are no

statistical data for Nenets AO. The variation was

6026,4 USD or 2,5 times.

Thus, as shown in Table 1, the macroregions are

ranked by five economic indicators, and the

Northwestern Macroregion has received an additive

rank of 6 points, the Ural Macroregion 9 points, and

the Far Eastern Macroregion 13 points.

The cumulative (additive) rank of the Arctic

regions by the five economic indicators is 9 points in

Murmansk Oblast and Nenets AO. The difference is

that Nenets AO was ranked by the four indicators

available in the statistical reports. Yamalo-Nenets AO

is ranked higher (7 points) and Chukotka AO lower

(11 points).

The second approach is based on the following

factors determining regional financial sustainability:

solvency of all economic agents;

good regional balance of payments;

low internal and external debt;

deficit-free regional budget.

Table 1: Positions of Russian Arctic regions in 2018 by

economic situation.

Table 2 shows a history of macroregional and

regional budget performance in the Arctic land areas

in order to find out whether there is a budget deficit

and how financially sustainable the regions are.

Table 2: Historical budget performance in Arctic regions,

As seen from the data in Table 2, regional budget

performance is defined as the proportion of the

budget income to the spending. In Russia, a federal

budget deficit existed until 2018, varying from the

maximum of 7,28% to the minimum of 0,48% within

the range of 6,8%.

In the Northwestern Macroregion, a budget deficit

also existed for the same period, albeit to a smaller

extent, from 5,56% to 1,55% within the range of

4,01%.

In Ural Macroregion, a budget deficit was

observed only once in 2017. It was 1,64%, and the

variation is therefore 0%.

The budget deficit in the Far Eastern

Macroregion was 6,09% at its highest and 2,41% at

its lowest, with no deficit in 2015 and 2017. The

variation was 3,68%. Thus, the macroregions were

more financially sustainable than Russia as a whole,

given the variation range.

However, if financial sustainability means a

budget deficit or surplus lower than 1%, i.e. applying

the principle of balance, Russia had that balance in

2017 and 2019, the Northwestern Macroregion in

2019, with no such balance observed in the Ural and

Far Eastern Macroregions.

In Murmansk Oblast and Nenets Autonomous

Okrug, both parts of the Northwestern Macroregion,

the highest budget deficit was 14,52% and 10,58%

and the lowest 0,41% and 3,27% within the respective

ranges of 14,11% and 7,31%. Murmansk Oblast had

a balanced budget in 2017 and 2018 and Nenets

Autonomous Okrug in 2019.

Similar to the Ural Macroregion as a whole,

Yamalo-Nenets Autonomous Okrug generally had a

budget surplus. A budget deficit was observed in that

Autonomous Okrug only in 2013 and 2015, its

maximum value being 12,51% and the minimum

value 0,63%. The 2015 budget was therefore

balanced. The figure varied within the range of

11,68%.

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346

The budget deficit in Chukotka Autonomous

Okrug was 34,33% at its highest and 4,6% at its

lowest within the range of 29,73%. The budget was

balanced only in 2019.

Consequently, given the variation ranges, the

Urals was the most financially sustainable

macroregion and the Northwest the least financially

sustainable one. Out of the four Arctic regions, the

first place by financial sustainability is held by Nenets

Autonomous Okrug, the second by Yamalo-Nenets

AO, the third by Murmansk Oblast, Chukotka

Autonomous Okrug holding the worst and the least

financially sustainable position.

The third approach involves studying changes in

the gross domestic product as the main indicator of

economic sustainability. In the annual government

statistics reports on Russian regions, it is the gross

regional product (GRP) (Regions of Russia, 2020).

There are, however, two essential conditions.

First, the GRP has to be adjusted for inflation.

However, the indicator shown in Russian statistical

reports is called the fixed-price index of the actual

GRP volume. We will therefore analyse the changes

in this indicator value. Instead of an inflation level

indicator, which is also unavailable, we will use two

similar indicators: consumer price index and

industrial producer price index.

It is believed that the actual price-adjusted

(inflation-adjusted) GRP volume in a sustainable

economy has to be sufficiently stable, without growth

or reduction from year to year. Put differently, this

state is called stagnation, as we have already

described above.

Secondly, additional investment amounts and

sources have to be excluded. To assess whether this

condition is fulfilled, we will use the statistical

indicator call the comparable-price index of capital

investment volumes.

In order to analyse the changes in the indicators

and identify the degree of sustainability in the

macroregions and the regions included therein, we

will use a stage-by-stage methodology.

At the first stage, we will find the maximum and

minimum deviation of the indicator in question from

100%, expressed as a positive value (growth) or a

negative value (reduction).

At the second stage, we will calculate the

variation range based on the identified growth or

reduction values. To do that, we will sum up the

extreme positive and negative values of growth and

reduction.

At the third stage, we will find the variation

interval, using a formula where a double value of the

minimum deviation from 100%, irrespective of its

positive or negative sign, is subtracted from the sum

of deviation expressed as the variation range.

At the fourth stage, we will rank the macroregions

and regions separately, assigning the first rank to the

smallest variation interval and the third rank for the

macroregions and the fourth rank for the regions to

the largest interval.

At the fifth stage, we will sum up the ranks of the

areas to get an additive rank.

At the sixth stage, we will identify the degree of

sustainability for each area relative to the others in

accordance with their additive ranks.

An analysis of the changes in the first indicator –

fixed-price index of the actual GRP volume – from

2010 to 2018 has shown that, in Russia as a whole,

the maximum growth as a deviation from 100% was

4.6% and the minimum was minus 0.6%. The

corresponding variation range was 5,2% and the

variation interval was 4,0% (4,6%+0,6%-0,6×2).

In the macroregions, the largest variation interval

(7,4%) was observed in the Urals, a slightly smaller

one (6,2%) in the Far East, and the smallest (4,2%) in

the Northwest. In the Arctic regions, the negative

leadership by the variation interval belonged to

Chukotka Autonomous Okrug (31,4%). In Nenets

and Yamalo-Nenets Autonomous Okrugs, the values

were 16,7% and 12,9%, respectively. The smallest

interval of 1,1% was in Murmansk Oblast.

By the second indicator – consumer price index –

the variation intervals from 2010 to 2019 varied from

the highest value of 10,6% in the Ural Macroregion

to the lowest value of 9,9% in the Far East. In the

Northwestern Macroregion, the value was 10,1%. In

Russia, it was 10,4%. In the Arctic regions, the

difference in the variation intervals was more

pronounced because it was 13,7% in Nenets AO and

9,0%. In Chukotka AO and Murmansk Oblast, the

variation interval was 9,7% and 10,3%, respectively.

The third indicator is the industrial producer price

index. An analysis of how it changed from 2013 to

2019 has shown that the Russian average variation

interval was 9,4%, with the deviation values in the

areas varying more significantly than those of the

consumer price index did. For instance, in the Ural

Macroregion, the value was the highest, equal to

15,7%. In the Far Eastern and Northwestern

Macroregions, the respective variation intervals were

15,7% and 13,7%, much lower than in the Urals. In

the Arctic regions, the variation intervals were even

larger, from 37,2% in Chukotka AO to 23,9% in

Murmansk Oblast. In Yamalo-Nenets and Nenets

Autonomous Okrugs, those values were 30,0% and

29,4%, respectively.

Finally, the fourth indicator is the comparable-

The Economic Aspect of Sustainability in Russian Arctic Areas

347

price index of capital investment volumes. An

analysis of positive and negative deviations from

2010 to 2019 has shown their most significant

differences. In Russia as a whole, the difference

between the maximum investment growth in 2010

(6,3%) and the minimum growth in 2015 (-10,1%)

accounted for a respective variation range of 16,4%.

The variation interval adjusted for the minimum value

was 16,0%. In the macroregions, the variation

intervals were as follows: 25,7% in the Northwest,

25,2% in the Far East, and 16,6% in the Urals. In the

Arctic regions, the negative leadership belonged to

Chukotka AO, where the difference between the

positive and negative extremes (variation range) was

122,3%, the variation interval being 122,1%. In

Murmansk Oblast, the variation range was more than

two times smaller, equal to 53,8%. The variation

interval was 51,0%. In Nenets and Yamalo-Nenets

Autonomous Okrugs, the variation range was below

50%, equal to 45,2% and 41,6%, respectively, the

variation intervals being 40,8% and 40,2%.

In accordance with the ranking rule, we will

assign ranks to the macroregions and regions, the first

rank being for the smallest variation interval. The

additive rank is a sum of the ranks for the four

indicators in question. As a result, the Northwestern

Macroregion has a rank of 6 points (1+2+1+2)

relative to the other two macroregions. The additive

ranks of the Far Eastern and Ural Macroregions had

more points, equal to 7 (2+1+2+2) and 10 (3+3+3+1),

respectively.

4 CONCLUSIONS

We can therefore conclude that the degree of

economic sustainability in the Northwestern Region

is higher than in the Urals. The Far Eastern Region is

insignificantly behind the Northwest as far as this

indicator is concerned. The highest degree of

economic sustainability in the four Arctic regions has

been identified in Murmansk Oblast (1+3+1+2) and

Yamalo-Nenets AO (2+1+3+1). These two areas

have received identical additive ranks of 7 points. A

lower degree of sustainability is found in Nenets AO

with its additive rank of 10 (3+4+2+1). The lowest

degree of economic sustainability has been identified

in Chukotka Autonomous Okrug. Its additive rank is

13 (4+2+4+3).

The study can be continued further to identify the

positions of the Russian macroregions and Arctic

regions in all national projects and their respective

incorporated federal projects.

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