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Sustainability in the Arctic regions: what, how and why?

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Abstract and Figures

We measured and analysed the level of sustainable development in 14 regions in the Arctic Europe including Norway, Sweden, Finland, and Russia. The UN Agenda 2030 of sustainable development goals adjusted to specifics of the Arctic Europe was used as a measurement framework.
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– A periodic report with insight to business activity and opportunities in the Arctic
Issue #04—March 2020
Sustainability in the Arctic regions: what, how and why?
2
BUSINESS INDEX NORTH Issue #04—March 2020
3
Contributing authors and organizations
Alexandra Middleton
Assistant Professor,
University of Oulu
alexandra.middleton@oulu.
Anders Hersinger
Professor,
Luleå University of Technology
anders.hersinger@ltu.se
Andrey Bryksenkov
Deputy Director of representative oce
« Russian State Hydrometeorological
University» in Moscow
ets-spb@mail.ru
Andrey Mineev
Researcher, High North Center at
Nord University Business School
andrey.mineev@nord.no
Elena Dybtsyna
Associate Professor,
Nord University Business School
elena.dybtsyna@nord.no
Erlend Bullvåg
Dean,
Nord University Business School
erlend.bullvag@nord.no
Jaakko Simonen
Professor,
University of Oulu
jaakko.simonen@oulu.
Ossi Pesämaa
Associate Professor,
Luleå University of Technology
ossi.pesamaa@ltu.se
Peter Dahlin
Assistant Professor, School of Business,
Society and Engineering,
Mälardalen University
peter.dahlin@mdh.se
Sissel Ovesen
Senior Advisor
Bodø Science Park
so@kpb.no
Acknowledgements
We gratefully acknowledge the basic funding for the BIN project
provided by the Norwegian Ministry of Foreign Affairs (through the
Arctic 2030 program) and Nordland County Council (through the DA
Nordland program).
We would like to thank our strategic Expert Partners for con-
tributing to the strategic development of the BIN project: The Arctic
Economic Council, the Norwegian Shipowners’ Association, MGIMO
University, Akvaplan-niva, Maritime Forum Nord, the Center for High
North Logistics.
Contacts
Chair of the BIN Project Board
Erlend Bullvåg, PhD, Dean at Nord University Business School
Erlend.Bullvag@nord.no
+47 906 49 591
BIN project coordinator
Andrey Mineev, PhD
Researcher at the High North Center for Business,
Nord University Business School
Andrey.Mineev@nord.no
+47 957 26 128
Project partners
Consortium partners responsible for R&D and technical work related to the production of BIN report:
Basic funding provided by:
Expert partners contributing to strategic development of the BIN project:
BUSINESS SCHOOL
Cover Image
Night hik ing in Tromsø for Sus tainable
development goals, August 2019.
Photo: Ole-Martin Sandness/
Scream Me dia for Norad
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BUSINESS INDEX NORTH Issue #04—March 2020
5
What is BIN?
Business Index North (BIN) is a project that contributes to sustain-
able development and value creation in the Arctic. The overall goal
is to set up a recurring, knowledge-based, systematic information
tool for stakeholders such as businesses, academics, governments
and regional authorities, as well as media, in the Arctic states. The
coordinator of the BIN project is the High North Center for Business
and Governance at Nord University Business School (Norway). The
project is implemented through the international network of partners
from Norway, Sweden, Finland, and Russia. Nordland County Council
(Norway) and the Norwegian Ministry of Foreign Affairs provide basic
funding for the project.
This is the fourth “Business Index North” periodic analytical report
that focuses on sustainable development in northern regions of
Norway (Finnmark fylkeskommune, Troms fylkeskommune, Nordland
fylkes kommune), Sweden (Norrbottens län and Västerbottens län),
Finland (Lapin maakunta, Pohjois-Pohjanmaan maakunta, Kainuun
maakunta) and North-West Russia (Murmansk oblast’, Arkhangelsk
oblast’, Republic of Karelia , Nenets Autonomous District , Komi Republic
and Yamalo-Nenets Autonomous District). These regions as statistic
units correspond to the NUTS3 classication of territorial units intro-
duced by the European Union. Hereafter in our report, we use the
English names of these regions without the word “region” from each
corresponding language (e.g. Norwegian “fylkeskommune”, Finnish
“maakunta”, Swedish ”län”, and Russian “Oblast”, District, Republic
areabandoned).
These regions are referred to collectively as the “BIN area” (gure
on the next page). Our denition of the BIN area correlates with the
EU concept of a macro-region1. The BIN area runs across national bor-
ders has common characteristics and challenges. The BIN area can
be viewed as a strategic layer across countries for future development
andcooperation.
The BIN reports provide a comprehensive analysis of sustainable
business development in the European Arctic including the north-
ern territories of Norway, Finland, Sweden, and Russia. The reports
are based on statistical data from multiple sources, using scientic
methods and provide factual and comparable indicators across
a set of topics and geographic regions. The ndings of the BIN
reports are presented through maps and gures which are easy for
most users tounderstand. You can also use oure online resources at
www.businessindexnorth.com.
Previous BIN reports
Our previous reports emphasized the value created by people who
live in or deal with the north, their livelihoods and the importance
of quality education and job creation. At the same time, success-
ful business activities and economic development are another vital
component of value creation, as highlighted in our innovation report.
Thus, BIN covers value creation activities benecial to both individuals
and legal entities. In this regard, Business Index North seeks to trace
both societal and economic developments in the Arctic and offers a
detailed considered view of how these evolve in combination. So far
we have produced three annual reports and two special issues.
The rst BIN annual report issued in 2017 focused on three large
topics: People, Business and Production. The second annual report
(2018), in addition to continuing topics of the rst report, included
chapters on Connectivity and Maritime Transportation in the Arctic.
The third annual report (2019) focused on value creation in the north
through the development of businesses, society and people, as well as
infrastructural conditions such as energy, connectivity and knowledge
infrastructure. Maritime trac and transportation infrastructure along
the Northern sea route became a topic of the rst special issue (2019).
There we have analysed drivers for development of the Northern Sea
Route and produced maps showing regional on land infrastructure in
the Arctic Europe which is and can be connected to the Northern Sea
Route. The second special issue (published at the beginning of 2020)
focused on innovative companies in the European Arctic. Based on
a detailed study of 63 innovative companies and organizations we
present implicit and explicit conditions for successful business devel-
opment in the Arctic.
1 An area including a territory om a number of dierent Member States or regions associated with one or more common features and challenges
(EU definition).
Norway 5328. 21 17.51
Nordland 243.39 6.75
Troms 167.20 6.72
Finnmark 75.87 1.66
Sweden 10230.19 25.12
Västerbotten 270.15 4.94
Norrbotten 250.50 2.58
Finland 5517.92 18.16
Kainuu 73.06 3.62
Lapland 178.52 1.93
North Ostrobothnia 412.16 11.19
Russia 146880.42 8.95
Arkhangelsk Oblast (excl. NAOs) 1111.03 2.74
Komi Republic 840.87 2.05
Murmansk Oblast 753.56 5.67
Nenets Autonomous Okrug 44.00 0.26
Republic of Karelia 622.48 4.49
Yamalo-Nenets Autonomous Okrug 538.55 0.85
Copenhagen
Oslo
Stockholm
Helsinki
Moscow
St Petersburg
NORWAY
SWEDEN FINLAND
RUSSIA
WESTERN EUROPE EASTERN EUROPE
NORTH SEA
ARKHANGELSK
OBLAST
LAPLAND
FINNMARK
NORDLAND
TROMS
VÄSTERBOTTEN
NORRBOTTEN
MURMANSK
OBLAST
KOMI
NENETS
YAMALO-NENETS
KARELIA
BALTIC SEA
KAINUU
NORTH
OSTROBOTHNIA
BARENTS SEA
BIN area
Densit y of
population
per sq. km
Population
in thousands
N
– A periodic report with insight to business activity and opportunities in the Arctic
Issue #01
—March 2017
People
Gives overview of population
structure, human capital and
employment in the BIN area
Business
Highlights the BIN area’s
innovations, business
activities andcooperation
Production
Focuses on renewable
energy and growth
in the BIN area
A periodic report with insight to business
activity and opportunities in the Arctic
People
Gives an overview of the
human dimension in the North,
including demography, education,
quality of life and work.
Business
Maps growth potential of
the BIN area and highlights
selected innovative clusters,
companies and brands.
Connectivity
Focuses on the roles of maritime
transport, digital infrastructure and
broadband availability in increasing
connectivity of the BIN area.
Issue #02 — April 2018
– A periodic report with insight to business activity and opportunities in the Arctic
Issue #03—June 2019
People
Provides analysis on
demographic and human
capital trends in the region
Business
Gives an overview of business
activity and perspective
of value creation
Development conditions
Focuses on key issues of
Connectivity, R&D in business
and Electricity production
6
BUSINESS INDEX NORTH Issue #04—March 2020
7
Executive summary
We measured and analysed the level of sustainable development in 14
regions in the Arctic Europe including Norway, Sweden, Finland, and
Russia. The United Nations Agenda 2030 of sustainable development
goals was used as a measurement framework. We used 52 indica-
tors selected from the UN framework under criteria of appropriate-
ness and data availability for the Arctic. The indicators were grouped
into ve interlinked pillars of sustainability: People, Society, Economy,
Environment and Partnership.
We see big differences between the north and the rest in the four
countries of Arctic Europe. Our analysis shows that the situation in the
Arctic areas is better only in case of 21% of the indicators. For 34% of
the indicators the situation is the same, and about 45% of the indica-
tors describe a situation in the Arctic areas worse than that prevailing
in the respective countries as a whole. Specically, performance is
worse on People, Society and Environment indicators. At the same
time, Arctic regions in Norway and Sweden are performing better than
their respective countries on economic indicators. At aggregate, with
the exceptions of the regions of North Ostrobothnia in Finland, and
Yamalo-Nenets in Russia, the Arctic areas lag behind their respective
countries in terms of sustainable development.
For a more comprehensive view we developed maps and tables
where the performance of the Arctic regions can be compared against
each other and the correspondingcountries.
People
Economic development in the Arctic does not always translate into an
improved economic situation for the local population. In many places
experiencing economic growth we observe worrying negative trends
in demographics.
Not all regions are self-sucient in producing local food. At the
same time some regions, e.g. North Ostrobothnia, are extremely e-
cient. Arctic regions can benet from shared knowledge on the devel-
opment of sustainable agriculture to support the food security of the
local populations.
There are feasible discrepancies in achieving the goal of health
and wellbeing for the Arctic population. Higher death rates due to
cancer and mental wellbeing require special attention.
The main issues that need to be addressed are the capital-periph-
ery divide, availability of medical services and preventive policies, age
structure and educational attainment prole of the population.
The Nordic BIN regions signicantly lag behind the overall coun-
try averages in attainment of tertiary education . Among the Russian
BIN regions, wide discrepancies are obser vable when it comes to
attainment of tertiary education. Since education in it self is impor-
tant for sustainable development in any region, improved education
access should be one of the main focus for increased sustainability
in theArctic.
Gender equality through effective participation and equal oppor-
tunities for leadership at all levels of decision-making in political, eco-
nomic and public life are not fully realized at either country or at the
Arctic regional level.
Demographic trends give cause for concern. In the Nordic Arctic
total population growth rate is only one third as rate at the country
level. In the Russian Arctic regions the population is declining.
Society
Special attention needs to be paid to improve the safety on roads
and to resolve deep underlying societal challenges such as limited
availability of jobs, poverty and accessibility of mental health services.
Collectively these problems explain elevated violence in the Arctic
regions measured in terms of homicide rates.
Arctic societies are experiencing a rapid demographic shift with
adecreasing population of children and young adults creating threats
to sustainably functioning and resilient societies in the future.
Population of children has decreased in most of the areas of the
Nordic Arctic during the last ten years. Conversely, the Russian Arctic
regions show diverging trends, e.g. Nenets Autonomous Okrug and
Yamalo-Nenets Autonomous Okrug showed a sound increase in the
population of children while other Russian Arctic regions experienced
either low or negative growth.
Population of the young adults has increased in most parts of the
Nordic Arctic but growth remains well below the corresponding coun-
try averages, while most of the Russian Arctic regions experienced
adecline in population of young adults (20-39 years old). Population
of elderly people increased in all Arctic regions.
Economy
The Nordic Arctic regions had a total of 29.3 TWh electricity sur-
plus in 2017. There is a need for ecient local use of electric-
ity produced predominantly from renewable sources. The Nordic
Arctic region has potential to become attractive for establishing
energy-intensiveindustries.
Business development measured in terms of stock in active enter-
prises shows growth in the sector of business activities and real estate,
and in the hospitality sector, while the number of manufacturing rms
is in decline.
The employment growth rate needs to be increased in most of
the regions apart from Yamalo-Nenets Autonomous Okrug. The unem-
ployment situation is very different across countries with challenges
persisting in Finland and Russia. And now facing Corona, it is expected
to reach record levels in all the BIN regions.
Job creation, increasing innovative potential and fostering know-
ledge economy should be on the development agendas of the Arctic
regions. Most of the Arctic regions, except North Ostrobothnia, lag
behind their countries averages in terms of knowledge infrastructure.
There is lack of large companies investing in R&D activities.
Environment
Emissions per capita are higher than the respective countries’ aver-
ages in most of the Arctic Europe regions due to differences in indus-
try structure larger presence of (mining, manufacturing, oil and gas)
and climatic conditions. Economic activit y conducive to increased
emissions needs to be viewed hand-in-hand with wellbeing in the
region. It is important to have regionally specic strategies and plans
for climate change mitigation that take into consideration all pillars of
sustainable development.
Partnership
Macro-economic indicators stimulating partnership: GDP per capita
is lower than the respective national averages for most of the Nordic
Arctic regions, but growth rate is higher. For Russia there are big dif-
ferences between regions in terms of GDP per capita. Regions relying
more on natural resources have higher GDP per capita. Given the high
inequality of incomes this is a trend limiting partnerships.
High level and growth rate of GDP in the regions is associated with
overconsumption at the macro-level, which in turn presents problems
for environment. Achieving partnerships through macroeconomic sta-
bility shall be done in conjunction to human development, sustainable
consumption and environmental sustainability.
Contributions and how this report can be used
The rst holistic report to numerically represent SDGs
status in the Arctic
To localize SDGs in the Arctic context with a set of targets
and indicators
To assist in prioritizing of SDGs
To identify risks and opportunities contributing to global
level SDGs
To identify data gaps
To provide a framework for national policy-making (as a
framework instrument please refer to the summary tables
presented in the last section of the report)
BIN’s comment on Covid-19 situation in the Arctic
In this report we assessed the sustainability of the Arctic regions
before COVID-19 pandemic hit the world. The spread of the virus and
efforts to bring it under control will denitely affect sustainability of
the Arctic regions. The scale of the impact will largely depend on the
existing conditions for sustainability and governments’ responses to
the crisis. Although COVID-19 was not the focus of this report, the
indicators presented in this report along with previous BIN reports will
help readers evaluate vulnerabilities and favourable conditions of the
Arctic regions that are now facing pandemic outbreak.
Here we seek to illustrate how indicators can be used to assess
vulnerabilities and conditions that may potentially weaken the impact
of the virus.
The Arctic regions with their low density of population and low
urbanization (apart from larger cities in the Russian part of the Arc-
tic) are less exposed to the risk of rapid virus spread. However, there
are some places with higher proximity and dense living conditions
(i.e. island communities, construction workers settlements) that pose
higher infection risks.
Vulnerabilities of the Arctic regions stem from the demographic
structure with ageing population and a high proportion of +65-year-
olds that are most at risk.
Moreover, high proportion of people with chronic diseases and
obesity, and mental health issues create additional risks. Historically,
the corresponding death rates in the Arctic were already rather high.
In the report we identied negative growth in agricultural and ara-
ble land, meaning higher dependency on food produced elsewhere.
In the case of supply chain disruptions, this may have negative impact
on food security.
Tourism in the Arctic is likely to be negatively affected due to fall
in demand and imposed travelling restrictions. In particular, hotels,
catering, restaurants, entertainment and cultural and creative indus-
tries would suffer most from the crisis. Additionally, service providers,
retailers are to be potentially negatively impacted.
In local communities depending on larger companies, negative
impact can be much stronger than in larger cities with distributed
economy in the south.
Unemployment in the Arctic regions is expected to increase dur-
ing the crisis. This will probably strain the Arctic economy. A relative
lack of access to capital in the Arctic must be taken into consideration
when designing measures for the restart of economic activity.
Broadband access shall be advanced further to meet the demand
for remote work and teaching.
As a result of pandemic outbreak as well as restrictions imposed
by the governments, the Arctic regions are potentially at risk of high
unemployment rates, lowering quality of life, depopulation, and less
attractive opportunities for investments. On the other hand side, the
Arctic regions are so far better off in terms of infection rates.
In times of the crisis, we need to build partnerships and learn
from each other. Countries have different exit strategies and support
mechanisms to re-build the economy. Decisions made as part of the
rebuilding plan will have long-lasting effects on all aspects of sustain-
ability. We therefore challenge authorities to develop a preparedness
plan on how to address interconnected risks and achieve sustainabil-
ity. Evidence from the Arctic regions can be used for targeted meas-
ures to build socially, environmentally and economically sustainable
Arctic regions during and after the crisis.
8
BUSINESS INDEX NORTH Issue #04—March 2020
9
Report approach: A tailored set of SDG targets
and indicators at the Arctic level
The United Nations Sustainable Development Goals (SDGs) were
introduced in 2015 in order to provide a roadmap to achieve a bet-
ter and more sustainable future for all by 2030. Altogether 17 SDGs
address the global challenges we face, including those related to
poverty, inequality, climate change, environmental degradation, peace
and justice. Each goal has specic targets and indicators that are
used to monitor progress towards its achievement. In total, The United
Nations dened 169 targets and 231 indicators. Understanding of how
SDGs are achieved at the Arctic level is crucial for future development.
Why
While SDGs are truly global, their achievement starts from regions and
municipalities. Localization refers to the process of selecting, adapt-
ing, implementing and monitoring the SDGs at the local level.
What kind
By focusing on SDGs in the local context we use a set of indicators
for each SDG to measure and monitor progress. SDGs can provide a
framework for local development policy, reect challenges and high-
light oppor tunities. SDGs provide a language that the whole world
understands. By using carefully selected SDGs targets and indicators
we localize UN SDGs at the Arctic level. and adopt a 5 pillar approach
to be more specic when we identify challenges and need for action.
How
By analysing SDGs achievement, this report uses a set of targets and
indicators relevant at the Arctic (BIN area). The report can be used to
provide reference in regard to:
the overall situation in the Arctic BIN regions regarding
various dimensions of sustainability
the challenges and opportunities regarding each individual
SDGs at the regional level
the performance of the BIN regions compared to the coun-
try averages and each other
which SDGs are currently being achieved and which require
more attention?
Framework for SDG analys in the Arctic
In our analysis we focus on topics People, Society, Economy,
Environment and Partnership, each including a set of targets and
indicators. We select targets and indicators according to:
1) their appropriateness for the Arctic regions
2) the availability of comparable data on the regional level
5 pillars of Arctic stainability
Arctic People
This pillar focuses on people with the goals to end poverty, hunger,
ght inequality, ensure healthy lives, knowledge & inclusion and the
empowerment of women.
Arctic Society
This pillar includes sustainable cities and communities and the peace,
justice and strong institutions that are essential for functioning and
sustainable societies.
Sustainable Economy in the Arctic
This pillar deals with sustainable business, affordable clean energy,
nance and socio-economic development, responsible con-
sumption and production, all of which in turn ser ve as input for
reducinginequalities.
Note: data on SDG 12 is not available on the regional level (hence
SDG12 is not included)
Arctic Environment
This pillar focuses on the environment, water and sanitation, sustain-
able consumption, ghting climate change, includes marine and ter-
restrial ecosystems.
Note: Data not available for SDG 6, SDG 14 and SDG15
Environmental data are typically spread across a range of agencies
and levels of government and information is often compiled for other
purposes. While some data is available on the national level, compara-
ble regional environmental statistics are lacking.
Arctic Partnership
This pillar recognizes that the road to achieving SDGs requires new
and existing working partnerships for sustainable development.
People Environment
Society Economy
Partnership
for Methodology and more details regarding the report approach,
SDGs, targets and indicators used, please refer to the Appendix in
the end of the report.
11
Contents
What is BIN? 04 / Executive Summary 06
Arctic People12 / Arctic Society28
Sustainable Economy in the Arctic 34 / The
Arctic Environment44 / Arctic Partnerships 48
Summary Tables 52
12
BUSINESS INDEX NORTH Issue #04—March 2020 Section (01) - Arctic People
Arctic People
This chapter focuses on people dedicated to ending poverty, hunger, combatting inequality,
ensuring healthy lives, knowledge and inclusion and the empowerment of women. In order
to understand Arctic people, we add demographic indicators that reect changes in human
populations on the regional level in the Arctic. Demographic analysis is essential for social and
economic sustainability. By analysing demographic trends, we can evaluate the resilience of the
Arctic regions to such phenomena as population ageing and outmigration of young adults.
Winner s of Bicycli ng competitions at Barent s summer gam es (Bodø, S eptember 2017)
Photo: Bo dø Fotoklu bb
People - Aggregate score for indicators “Arctic People”
– the BIN regions compared to their countries averages
13
0123 4 5 6 7 8 9 10
Finland
Nordland
Troms
Murmansk Oblast
Lapland
Kainuu
Finnmark
North Ostrobothnia
Norrbotten
Västerbotten
Komi Republic
Arkhangelsk Oblast (excl Nenets)
Norway
Russia
Sweden
Republic of Karelia
Nenets Autonomous Okrug
Yamalo-Nenets Autonomous Okrug
Aggregate scores are calculated for a set of indicators presented in the chapter Arctic Pe ople. This approach assumes equal
weights for the indicators. To calculate sco res and compa re the indicators across countries and regions we used a standard
scaling formula for 1-10 point scale. Higher score means better situation in a particular region, and vice-versa.
14
BUSINESS INDEX NORTH Issue #04—March 2020
15
Section (01) - Arctic People
Figure 1.1 — At risk of poverty rates, %, 2013 and 2017
Figure 1. 2 — At risk of poverty rates, %, 2013 and 2017
Average poverty rate in the BIN Nordic re-
gions was 12.1% (0.4 lower than in the whole
of Norway, Sweden, Finland) in 2017. Regional
variation is wide, e.g. in Finland, all BIN regions
have a higher at-risk of poverty rate than the
national average. Conversely, in Norway the
BIN regions are on average slightly better
than the national average. The poverty rate
in the BIN Russian regions on average was
27.8% in 2017. In the Russian BIN regions,
poverty is 2.3 times more prevalent than in
the Nordic BIN regions.
Figure 1.2 demonstrates that the at risk of poverty rate decreased in
the Russian BIN regions on average by 1.55 percentage points, while
in the Nordic BIN regions it increased by 0.3. The results demonstrate
that the policies are not ecient enough to eradicate the poverty risk
in the BIN Nordics, in the Russian BIN regions poverty rates are very
high despite a slight decrease over for the years under analysis, 2013–
2017. Natural resources extraction in Yamalo-Nenets Autonomous
Okrug does not translate into well-being in the local population, which
has some of the highest poverty risks in Russia overall. The progress is
rather slow in achieving SDG1 in the BIN area. The results indicate that
the Arctic regions require sound policy frameworks at the national, re-
gional and international levels to support poverty eradicationactions.
2013 2017
0% 5% 10% 15% 20% 25% 30%
Yamalo-Nenets Autonomous Okrug
Nenets Autonomous O krug
North-Ostrobothnia
Murmansk Oblast
Norrbotten
Lapland
Västerb otten
Republ ic of Karelia
Arkhan gelsk Ob last (excl. N AO)
Kainuu
Finland
Sweden
Russia
Komi Republic
Norway
Finnmark
Nordlan d
Trom s
-1, 5
-1, 0
0,0
0,5
BIN Nord ic Total of
Nor way,
Sweden
BIN Russ ia Total of
Russia
SDG 1 — No Poverty
Investments in agriculture are crucial to increase the
capacity for agricultural productivity and sustainable food
production systems and are necessary to alleviate hunger.
We have selected indicators that are relevant in assessing
food securit y in the BIN area. Locally grown food is essen-
tial to provide long-term food security for communities.
SDG 2 — Zero Hunger
Figure 1. 3 — Change in agricultural and arable land area, 2009 -2018
Seven regions (Nenets, Västerbotten,
Nordland, Finnmark, Troms, Murmansk Oblast
and Kainuu) had an average negative growth
(-7%) ranging from -1% to -18% in agricul-
tural land area during the years 2009–2018.
Total negative growth in agricultural land in
Sweden, Finland and Nor way was -2%, and
in Russia +1% over the same period. Komi
Republic, Arkhangelsk Oblast (excl. NAO) and
the Republic of Karelia had zero growth in
agricultural and also in arable land. Of agri-
cultural land in the BIN Nordic regions 87%
is arable land, while only 32% of agricultur-
al land in the Russian BIN regions is arable
land. Growth in arable land occurred only in
North Ostrobothnia (5%) and in Russia over-
all (1%), while in nine regions the decrease in
arable land averaged to 9% during the years
2009–2018. Agricultural land is typically land
devoted to agriculture. Arable land is a land
actually cultivated.
-20% -1 5% -10 % -5% 0% 5% 10% 15%
Kainuu
Murmansk Oblast
Finland
Västerb otten
Arkhan gelsk Ob last (excl. N AO)
Republ ic of Karelia
Nenets Autonomous O krug
Nordlan d
Sweden
Komi Republic
Norway
Trom s
Finnmark
Norrbotten
Russia
Lapland
North O strobothnia
Yamalo-Nenets Autonomous Okrug
Change in agricultural
land area 20 09–201 8
Change in a rable lan d
area 2009–2018
Agricultural land in use
Land allocated to the culti vation
of crops and animalhusbandry.
Arable land in use
The total of areas under
temporar y crops, temporary
meadows and pastures, and land
lying temporarily fallow. Arable
land is suitable for agriculture,
in other words, arable la nd is
cultivatedland.
The at-risk-of-poverty rate is the share of
people w ith an equivalised disposable inc ome
below the at-risk-of-poverty threshold, which
is set at 60% of the national median equiva-
lised disposable income.
16
BUSINESS INDEX NORTH Issue #04—March 2020
17
Section (01) - Arctic People
Figure 1. 5 — Production of milk, cattle and crops in natural units per capita, 2018
Figure 1. 4 — Arable land per 1000 population in sq km, 2018
In Norway, there are no big differences be-
tween the country average of arable land (just
under 2 sq. meters) per 1,000 population and
the BIN regions. In Sweden, Norrbotten has
the least arable land per 1,000 population.
In Finland, North Ostrobothnia has arable
land of 5 sq km per 1 ,000 population, above
the country average. In Russia, Arkhangelsk,
Karelia and Komi have similar indicators as
the Norwegian regions. Murmansk, Nenets
and Yamalo-Nenets Autonmous Region have
the least arable land per 1,000. Low numbers
are indicative of harsh climatic conditions and
the prevalence of permafrost.
Figure 1.5 demonstrates that some BIN re-
gions have very high levels of eciency in
terms of producing food from agriculture, e.g.
North Ostrobothnia and Kainuu are leaders
in milk production with three times more milk
produced per capita than in Norway, Sweden,
Finland and the Russian Federation on aver-
age. Production of cattle varies a lot, it is high
in Nordland, North Ostrobothnia and Nenets
Autonomous Okrug. North Ostrobothnia is
the top producer of crops measured as total
potatoes and barley. Overall, the Nordic BIN
regions are more ecient in producing milk
than Norway, Finland and Sweden as a whole,
mainly due to the rurality of the Nordic BIN re-
gions. In Russia, all BIN regions produce sig-
nicantly less milk, cattle and crops than the
country average per capita, mainly due to the
harsh climate and unavailability of agricultural
and arable land.
0 2
4
6 8
10
North O strobothnia
Finland
Norrbotten
Sweden
Komi Republic
Trom s
Republ ic of Karelia
Lapland
Västerb otten
Norway
Finnmark
Nordlan d
Kainuu
Arkhan gelsk Ob last (excl. N AO)
Murmansk Oblast
Nenets Autonomous Region
Yamalo-Nenets Autonomous Okrug
BIN Nordic
Total of Swede n, Nor way and Fin land
BIN Rus sia
Russia
Region Milk Cattle Crops
North Ostrobothnia 939 31 561
Kainuu 877 20 64
Lapland 512 17 26
Finland 414 16 351
Nordland 420 26 8
Finnmark 268 9 0
Trom s 186 827
Norway 286 17 144
North Sweden 286 - -
Norrbotten - 5 52
Västerbotten -10 90
Sweden 262 13 178
Republic of Karelia 98 557
Komi Republic 63 12 65
Nenets Autonomous Okrug 77 30 17
Arkhangelsk Oblast (excl. NAO) 109 317
Murmansk Oblast 25 2 6
Yamalo-Nenets Autonomous Okrug 410 17
Russia 202 38 268
Nordic BIN regions total 498 16 104
Total of Norway, Finland and Sweden 321 15 224
Russian BIN regions total 63 10 30
Crops are plants such as wheat and potatoes
that are grown in large quantities for food . In
our analys is we focus on such crops as pota-
toes and barley, which can be grown in the
High North regions
Note: crops and cattle are measured in kg, milk in litres
Figure 1. 6 — Change in production of milk, cattle, and crops per capita, 2010-2018
-60 -40 -20 020 40 60 80 100
Finland
Lapland
Norrbotten
North S weden
Nordlan d
Russia
Västerb otten
Trom s
Finnmark
Sweden
Republ ic of Karelia
Norway
North O strobothnia
Kainuu
Komi Republic
Nenets Autonomous O krug
Arkhan gelsk Ob last (excl. N AO)
Murmansk Oblast
Yamalo-Nenets Autonomous Okrug
Milk Cattle Crops
Figure 1.6 illustrates change in the production
of milk, cattle and crops (potato and barley)
per capita. The results demonstrate that from
2010 to 2018 negative growth in milk pro-
duction is observed in nearly all BIN regions
apart from Kainuu and North Ostrobothnia.
Negative trends in crops production per cap-
ita are especially pronounced in the Komi
Republic and the Republic of Karelia.
Arctic climate necessitates specialization. Lack of arable
land and the small number of crops that can be grown
in such High North areas pose challenges to agriculture.
Supply of locally sustainably produced agricultural and
dairy products is essential for the resilience, health and
well-being of the Arctic communities. Negative growth
in agricultural and arable land as well as negative trends
in the production of major food groups (milk, cattle and
crops) per capita creates threats to food security in the
Northern regions, making them more vulnerable and
dependent on imported food produce. At the same time
the analysis demonstrates that in certain regions such as
North Ostrobothnia and Kainuu food production (milk) is
ecient and higher than the total of Nor way, Finland and
Sweden, therefore, providing an example of food export-
ing regions. In the future, data on sheries and aqua-
culture would be useful to complement the analysis of
food securit y in the Arctic. given strong presence of the
North-Norwegian sheries and aquaculture industry, and
also growing potential of these industries in the North-
West Russia.
Thinking ahead, it is important to monitor the impact
of extractive industries on the state of agricultural and
arable lands in the North and support land (re)cultivation
policies. Melting permafrost creates preconditions for
increased use of land for agricultural purposes that will
be on the agenda when creating policies for achieving
SDG 2 and strengthening capacity for adapting to climate
change. Indicators reecting the use of sustainable agri-
culture on the regional level are needed for the monitor-
ing of SDG2 achievement.
18
BUSINESS INDEX NORTH Issue #04—March 2020
19
Section (01) - Arctic People
To analyse SDG 3 we chose indicators that reect health,
both physical and mental, and also the well-being of Arc-
tic communities. Selected indicators reect ageing popu-
lation in the Arctic and concerns over health care availa-
bility and accessibility.
SDG 3 — Good Health and Well-Being
Figure 1.7 — Death rates due to cancer per 10,000 capita , average 2015-2017 and change 2008-2017
The average death rate due to cancer per
10,000 population overall in Sweden, Norway,
Finland and Russia was 21.2. In eight regions,
namely Västerbotten, Finnmark, Arkhangelsk
Oblast (excl. NAO), Norrbotten, Republic of
Karelia, Nordland, Lapland, Kainuu death
rates were above the national averages
of Sweden, Norway, Finland and Russia. In
Kainuu death rates due to cancer are as high
as 27.6. In the Russian regions of Nenets
Autonomous Okrug (14.9) and Yamalo-
Nenets Autonomous Okrug (9.3), the lowest
death rates due to cancer are observed. High
death rates due to cancer are linked to age-
ing population, one-quarter of new cancer
cases are diagnosed in people aged 65 to
741. In regions with the highest proportion of
elderly people, there are more deaths due to
cancer, e.g. in Kainuu the share of those over
65 was 25.7 % and median age 50.3 years.
By comparison median age in Yamalo-Nenets
Autonomous Okrug was 33.3 years in 2017.
Deaths due to cancer have been on the in-
crease for the last 10 years in the BIN Nordic
regions (+1.38), while in Norway, Sweden
and Finland overall (-1.17) a slight decrease
was observed. Similarly, in the BIN Russian
regions deaths due to cancer increased by
0.65, while on average in Russia a decrease
of -0.17 was observed.
Finnmark
Trom s
Arkhan gelsk Ob last (excl. N AO)
Norw ay total
Russia tot al
Nordlan d
Republ ic of Karelia
Sweden t otal
Finland total
Norrbotten
Västerb otten
Kainuu
Lapland
-10
-5
0
5
10
15
20
25
30
35
Murmans k Oblast
North O strobothnia
Nenets Autonomous O krug
Yamalo-Nenets Autonomous Okrug
Komi Republic
Note: data for the Russian BIN regions for the period 2014-2017
Death rate due to cancer per 10,000 population describes
the number of people who die from cancer out of 10,000
people in one year, calculated as the average for 2015 –
2017. Cancer is the second leading cause of death globally.
Between 30 and 50% of cancers are preventable by healthy
lifestyle choices such as avoidance of tobacco consumption
and targeted public health measures. Tobacco and alcohol
consumption, unhealthy diet and physical inactivity are
amajor cancer risk. Ageing is another fundamental factor in
the development ofcancer.
Average pe r 10 000 ca pita Change 2008–2017
Figure 1. 8 — Death rates due to ischaemic heart deseases per 10,000 capita, average 2013–2017 and change 2008–2017
Average las t 3 years Change 2008–2017
The Russian BIN regions have much higher
incidents of deaths due to ischaemic heart
diseases than do the Nordic BIN regions. The
highest rates are observed in Arkhangelsk
Oblast (excl. NAO) with a rate of 44.8 per
10,000 population. In Finland Lapland
(25.5) and Kainuu (29.3) have higher rates
than the national average of 18.3. In Sweden
Norrbotten (16.4) and Västerbotten (12.8) also
have higher rates than the national average
for Sweden, 11.7. Similarly, in Norway, Finnmark
(10.1) and Norland (8.6) have slightly high-
er rates than the total for Norway, 7.4. While
population ageing contributes to death rates
due to ischaemic heart diseases, further in-
vestigation is needed into the availability of
preventive measures, e.g. mapping of hospi-
tals (medical institutions) with Percutaneous
Coronary Intervention Centres (PCI) and their
proximity to population in the Arctic regions.
The trend for the period 2008–2017 indicates
a decrease in death rates due to ischaemic
heart diseases in the Nordic BIN regions the
decrease is on par with the national averages
for Norway, Sweden and Finland, while in the
Russian BIN regions the decrease is smaller
(-6.71) than the country’s total of (-10.55).
Nenets Autonomous O krug
Norrbotten
Lapland
Västerb otten
Yamalo-Nenets Autonomous Okrug
Republ ic of Karelia
Murmans k Oblast
North O strobothnia
Komi Republic
Kainuu
Finland
Arkhan gelsk Ob last (excl. N AO)
Russia
-30
-20
-10
0
10
20
30
40
50
Finnmark
Nordlan d
Norway
Trom s
Sweden
Deaths due to ischaemic heart disease per
10,000 population. This indicator measures
the number of people who die from redu ced
blood supply to the heart out of 10,000 peo -
ple in one year, calculated as the average for
2015–2017. This indicator is part of cardio -
vascular de ceases (CVD),which are disorders
of the heart and blood vessels. Deaths due to
CVD are the main cause of deaths worldwide.
Accordin g to WHO, major causes of CDV are
unhealthy diet, physical inactivity, tobacco
use and harmful consumption of alcohol.
Underlying causes are globalization, urban-
ization and population ageing. Other deter-
minants of CV Ds include poverty, stress and
hereditaryfactors .
1 https://www.cancer.gov/about-cancer/causes-prevention/risk/age
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BUSINESS INDEX NORTH Issue #04—March 2020
21
Section (01) - Arctic People
2 https://www.i.no/en/op/hin/health-disease/copd/
Figure 1. 9 — Death rates due to chronic respiratory deseases per 10,000 capita, average 2015–2017 and change 2008–2017
Average 2015–2017 Change 2008–2017
High death rates due to chronic respiratory
diseases in Finnmark (10.9), Nordland (10.0)
and Swedish BIN regions can be attributed to
high life expectancy. The level of education
affects the risk as people with primary edu-
cation attainment only have three times high-
er risk of chronic respiratory deceases than
do people with higher education2. Therefore,
while ageing population explains higher
death rates due to CRDs in the Nordic BIN
regions, a more systematic mapping of oth-
er CRDs risk factors is needed. For instance,
data on smokers as percentage of population
reveals that in 2000-2010 smoking was on
average 12 percentage points more prevalent
in Finnmark than in the Oslo region, while in
2010-2018 the differences disappeared.
Death rate due to ch ronic respiratory diseases
per 10,000 population describes the number
of people who die from chronic respiratory
diseases (CR Ds) out of 10,0 00 people in one
year, calculated as the average for 2015 – 2017.
Chronic respiratory diseases (CRDs) are dis-
eases of the ai rways and other stru ctures of the
lung. Tobacco smoking, indoor and outdoor air
pollutio n, allerg ens, occu pational risks such as
exposure to chemicals and dust and frequent
lower respiratory infections are major risk fac-
tors for chronic respirator y diseases(CR Ds).
Kainuu
Lapland
Västerb otten
Komi Republic
Finland
Norway
Norrbotten
Arkhan gelsk Ob last (excl. N AO)
Sweden
Trom s
Republ ic of Karelia
Finnmark
Nordlan d
-4
-2
0
2
4
6
8
10
12
14
North O strobothnia
Nenets Autonomous O krug
Murmansk Oblast
Yamalo-Nenets Autonomous Okrug
Russia
Figure 1.10 — Death rates due to suicide per 10,000 capita, average 2015–2017 and change 2008–2017
3 http://apps.who.int/gho/data/node.sdg.3-4-viz-2?lang=en
4 Silviken A. Prevalence of suicidal behaviour among indigenous Sámi in northern Norway. Int J Circumpolar Health. 2009;68(3):204–11;
Hassler S, Johansson R, Sjölander P, Grönberg H, Damber L. Causes of death in the Sámi population of Sweden, 1961-2000. Int J Epidemiol.
2005;34(3):623–9.; Soininen L, Pukkala E. Mortality of the Sámi in northern Finland 1979–2005. Int J Circumpolar Health. 2008;67(1):43–55.
Average 2015–2017 Change 2008–2017
Nenets Autonomous Okrug (4.2) and Komi
Republic (3.2) have the highest death rates
due to suicide per 10,000 population. The
global average was 1.1 in 2016 (WHO) and
two times higher among men than among
women3. Studies from Arctic nations reveal
elevated suicide rates among Indigenous
populations, with substantial disparities com-
pared to non-Indigenous populations4. In
the Nordic BIN regions the average death
rate due to suicides (1.41) for the years 2015-
2017 was slightly higher than in Norway,
Finland and Sweden overall at 1. 25. despite
a slight decline in death rates due to suicide
overall (-0.15) some Nordic BIN regions, e.g.
Norland, Norrbotten, Västerbotten and North
Ostrobothnia, saw growth in suicide rate.
While all Russian BIN regions saw a decrease
(-2.0) in deaths due to suicide over the last
10 years, the overall rate for the years 2015-
2017 was still considerably higher (2.4) than
in the Russian Federation overall (1.6). Further
studies are needed to establish the reasons
for suicides in all regions with high suicide
rates. The availability and accessibility of
mental health services for vulnerable groups
need to be evaluated. Data is required on sex,
age-groups and statistics on suicide rates
in indigenous peoples. Specic data would
help create responses including preventive
measures at the individual, community and
national levels.
North O strobothnia
Nordlan d
Lapland
Norrbotten
Norway
Arkhan gelsk Ob last (excl. N AO)
Kainuu
Finland
Yamalo-Nenets Autonomous Okrug
Republ ic of Karelia
Russia
Nenets Autonomous O krug
Komi Republic
-8
-6
-4
-2
0
2
4
6
Finnmark
Trom s
Västerb otten
Murmansk Oblast
Sweden
Suicide may occur at any age throughout the
lifespan and is the secon d leading cause of
death among 15–29 year olds globally. Suicide
rates are used as a mental health indicator.
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BUSINESS INDEX NORTH Issue #04—March 2020
23
Section (01) - Arctic People
22
Figure 1.11 — Total death rate due to ischaemic heart disease, cancer, chronic respirator y diseases and suicides ,
average rate for 2015–2017 and change 2008–2017*
Average 2015–2017 Change 2008–2017
Overall, Arkhangelsk Oblast, Kanuu and the
Republic of Karelia have the highest death
rates overall, while Troms and Yamalo-
Nenents Autonomous Okrug have the lowest
total death rates. In the Nordic BIN regions
total death rate equalled 47.7 while in total
of Sweden Norway and Finland it equalled
41.4. In Russia total combined death rate is
59.1 with big discrepancies among regions,
e.g. Yamalo-Nenets Autonomous Okrug had
a combined death rate of 26.1 compared to
Arkhangelsk Oblast without NAO, where it
amounted to 75.
Norrbotten
Finnmark
Komi Republic
Västerb otten
Sweden
Republ ic of Karelia
Russian Federation
Nordlan d
Murmans k Oblast
Lapland
Finland
Arkhan gelsk Ob last (excl. N AO)
Kainuu
-40
-20
0
20
40
60
80
100
Nenets Autonomous O krug
Norway
Trom s
Yamalo-Nenets Autonomous Okrug
North O strobothnia
Figure 1.12 — Life expectancy at birth, 2017
Females
Males
Females Males
In the Russian BIN regions average male life
expectancy at birth is 66.3 years compared to
78.7 in the Nordic BIN regions. For females, in
the Russian BIN regions, average life expec-
tancy is 77 compared to 83.7 in the Nordic
BIN regions. Within the Nordic BIN regions
the gap between females and males is on av-
erage 5 years, with shorter life expectancy for
males. Within the Russian BIN regions the gap
between females and males is on average
10.7 years shorter life expectancy for males.
During the last 10 years Russia had a postive
life expectancy trend. Men’s life expectancy
was shorter in the Nordic BIN regions by 1 year
compared to the total of Nor way, Sweden and
Finland and by 1.2 years in the Russian BIN re-
gions compared to Russia’s total. There were
no signicant differences between female’s
life expectancy in the BIN regions and corre-
sponding country averages.
Poverty and education levels should be
considered in conjunction with the interpre-
tation of these numbers. Healthcare provision
systems play an important role in promoting
longer life spans. Furthermore, we need an
understanding of the major environmental
risks to health in the Arctic regions dened
as all the physical, chemical and biologi-
cal factors external to a person. e.g. pollu-
tion of air, water and soil, occupational risks,
built environments, climate and ecosystem
changerisks.
Figure 1.13 — Change in life expectancy at birth, by sex, 2008–2017
Figure 1.13 shows that life expectancy at birth
rose in all Russian BIN regions, with the great-
est increase in the Komi Republic. Life expec-
tancy increased by four years for females and
by six years for males.
65
70
75
80
85
90
Finland
Kainuu
Lapland
North O strobothnia
Finnmark
Nordlan d
Trom s
Arkhan gelsk Ob last (excl. N AO)
Komi Republic
Murmansk Oblast
Nenets Autonomous O krug
Republ ic of Karelia
Yamalo-Nenets
Autonomous Okrug
Norrbotten
Västerb otten
Russia
Sweden
Norway
55
60
0 2 4 6 8 10
Yamalo-Nenets Autonomous Okrug
Nenets Autonomous O krug
Arkhan gelsk Ob last (excl. N AO)
Komi Republic
Republ ic of Karelia
Murmansk Oblast
Russia
Note: data only available for the
Russian BIN regions
Life Expectancy at birth (years) in 2017 refers
to the mean number of years a new-born child
can expect to live if subjected throug hout his
or her life to the current mor tality co nditions .
Life expectancy is inuenced by many factors
such as socio-economic status, including
employment, income, education and economic
wellbeing. Improvements in the educational
attainment levels of the population contribute
to improvements in life expectancy.
24
BUSINESS INDEX NORTH Issue #04—March 2020
25
Section (01) - Arctic People
24
2015
2018
Figure 1.15 — Tertiary education attainment among 25 to 64 year-olds population, %, 2016, Russia
Figure 1.14 — Tertiary education attainment among 25 to 64 year-olds population, % , 2015–201 8
Russia’s total average of tertiary educa-
tion attainment among 25 to 64-year-olds
was 53.1% in 2016. According to the OECD,
Russia has one of the highest shares of
adults attaining tertiary level education out
of all OECD and partner countries, which is
19 percentage points more than the OECD
average5. Big differences are observed in the
Northern regions, with the Yamalo-Nenets
region outperforming the country average by
8.8 percentage points and the Komi Republic
underper forming by 11.4 percentage points.
Economic development due to the oil and
gas industries in Yamalo-Nenets Autonomous
Okrug potentially contributes to the demand
for a highly skilled labour force.
In 2018, Nordic BIN regions had tertiary ed-
ucation attainment of 39.9% which is four
percentage points lower than in the total of
Sweden, Finland and Nor way. On average no
growth is observed 2015–2018 due to neg-
ative growth in tertiary education attainment
rates in Norway (3.6 percentage points) and
4.1 percentage points in the North of Norway.
Education affects individuals’ quality of life in many ways; it predicts employment opportunities, earning potential and
reduces the risk of poverty. The level of education is fundamental in predicting individuals’ health and life expectancy.
SDG 4 — Quality Education
0
5
10
15
20
25
30
35
40
45
50
Finland
East and North
Finland
Sweden
North Sw eden
Norway
North N orway
010 20 30 40 50 60 70
Yamalo-Nenets Autonomous Okrug
Arkhangelsk Oblast
Nenets Autonomous O krug
Rebubl ic of Karelia
Russia
Murmansk Oblast
Komi Republic
5 Education at a Glance. OECD (2016)
Note: Total Russia data from 2014,
data on regional level in Finland
not available
Gender equality is addressed by analysing indicators of participation of women in the labour force.
SDG 5 — Gender Equality
Figure 1.16 — Employment participation rate as % of labour force aged 15–64, by gender, 2017
Figure 1.17 — Change in employment participation , by sex, 2013–2017
The results demonstrate that women are less
likely to participate in employment than men
in both Nordic and Russian BIN regions. In
the Russian BIN regions, on average, the dif-
ference is 7.2% (ranging from 2% in Nenets
Autonomous Okrug to 10.5% in Murmansk
Oblast), compared to Russia’s total of 10.5. In
the Nordic BIN regions the difference is 2.7%.
MalesFemales
0% 20% 40% 60% 80% 100%
Nenets Autonomous O krug
Komi repub lic
Nordlan d
Finnmark
Republ ic of Karelia
Sweden
Norway
Finland
Arkhan gelsk Ob last (excl. N AO)
Norrbotten
Trom s
Russia
Västerb otten
Murmansk Oblast
Yamalo-Nenets Autonomous Okrug
Females
Males
The trend in the em ployment participati on gap
between males and females help us to gauge
if women participate more in employment.
The gap in employment participation rates in-
creased from 2013 to 2017 indicating a wors-
ening situation for females in the following re-
gions: Murmansk Oblast, Arkhangelsk Oblast,
Finnmark and Komi Republic. Some regions
have reached nearly equal employment rates
in male and female employment participation.
Greater female participation in labour force is
either due to improved job availability or con-
ditions created (e.g. childcare provision, fewer
kids per mother, sharing of responsibilities for
child and elderly care between males and
females etc.). Hence a systematic analysis is
needed of the underlying factors predispos-
ing to greater participation by females in the
labour force.
-8%
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
Finland
Norrbotten
Sweden
Komi Republic
Trom s
Republ ic of Karelia
Västerb otten
Norway
Finnmark
Nordlan d
Arkhan gelsk Ob last (excl. N AO)
Murmans k Oblast
Nenets Autonomous O krug
Yamalo-Nenets Autonomous Okrug
Russia
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BUSINESS INDEX NORTH Issue #04—March 2020
27
Section (01) - Arctic People
Figure 1.18 — Change in total population in the BIN area, %, 2009–2018
Figure 1.19 — Changes in population incl. Russia, index 2009 = 100, 2009–2018
Nearly all BIN regions had population growth
below the respective country averages. The
region of North Ostrobothnia had the same
level of population growth as the rest of
Finland. Nenets Autonomous Okrug and
Yamalo-Nenets Autonomous Okrug had pop-
ulation growth larger than Russia’s with an
average of 3%. The biggest negative growth
occurred in the Russian regions of Komi
Republic (-81,448), Arkhangelsk (-93,316)
and Murmansk (-51,997) Oblast, Republic of
Karelia (-31,282) and in Finnish Kainuu (-6,173)
and Lapland (-5,226) regions. In absolute
numbers, there were 201,155 fewer people
living in the BIN area in 2018 than in 2009.
Measured as index, population change in the
BIN regions exhibits a negative trend (-3.48%)
for the whole period 2009-2018 while in
Finland, Sweden, Nor way and Russia popula-
tion continued to grow with a 3.49% increase.
Diverging trends in the Arctic regions and in
the southern regions of the corresponding
countries create challenges for demographic
resilience in the Arctic.
-10 % -8% -6% -4% -2% 0% 2% 4% 6% 8% 10%
Norway
Nenets Autonomous O krug
Komi Republic
Nordlan d
Finnmark
Republ ic of Karelia
Sweden
Finland
Arkhan gelsk Ob last (excl. N AO)
North O strobothnia
Norrbotten
Lapland
Trom s
Russia
Västerb otten
Murmansk Oblast
Kainuu
Yamalo-Nenets Autonomous Okrug
Demographic indicators
92
94
96
98
100
102
104
106
2009
2010
2012
2013
2014
2015
2016
2017
2018
Countries incl Russia BI N area incl Russia
Figure 1. 20 — Changes in population excl. Russia, index 2009=100, 2009–2018
Figure 1.20 illustrates changes in population
excluding Russian regions most affected
by negative population growth. The growth
in the Nordic BIN regions is still 3.25 times
slower than in Finland, Norway and Finland
onaverage.
96
98
100
102
104
106
108
110
2009
2010
2012
2013
2014
2015
2016
2017
2018
Countries excl Russ ia BIN area e xcl Russia
Conclions
Economic development in the Arctic does not always translate into
an improved economic situation for the local population. High lev-
els of poverty risk serve as a warning sign that the wellbeing of the
local population regarding economic situation and health requires
specialattention.
In regard to SDG 2, the results demonstrate that not all regions are
self-sucient in producing local food. At the same time some regions,
e.g. North Ostrobothnia, are extremely ecient. Arctic regions can
benet from shared knowledge on the development of sustainable
agriculture to support food security of the local populations.
There are big discrepancies in achieving the goal of health and
wellbeing for the Arctic population. The main factors that need to be
addressed are the capital-periphery divide, availability of medical ser-
vices and preventive policies, age structure and education attainment
prole of the population. Increase of death rates due to cancer and
mental wellbeing require special attention.
The Nordic BIN regions lag behind the overall country averages in
tertiary education attainment. In the Russian BIN regions, big discrep-
ancies are observable between regions in tertiary education attain-
ment. The results demonstrate that gender equality through effective
participation and equal opportunities for leadership at all levels of
decision-making in political, economic and public life are not fully real-
ized at either country or Arctic regional level. It is important to under-
stand whether the provision of public services, infrastructure and
social protection policies and the promotion of shared responsibility
is enough, especially in the regions with widening gaps in employment
participation between males and females.
28
BUSINESS INDEX NORTH Issue #04—March 2020
29
Section (02) - Arctic Society
Arctic Society
This chapter includes indicators on sustainable cities and communities and the peace, justice and
strong institutions that are essential for the functioning of sustainable societies. Furthermore, we
add such demographic indicators not currently part of the SDG framework, but of high relevance to
the Arctic. Namely, we include structural demographic indicators such as change in the population
group aged 0-19 and in the group of young adults aged 20–39 that reect societal sustainability.
City life in Oulu, Finland.
Photo: Hild a Weges / iStoc k
Aggregate score for indicators “Arctic Society”
- the BIN regions and their countries averages
012345 6 7 8 9 10
Finland
Nordland
Troms
Yamalo-Nenets Autonomous Okrug
Lapland
Kainuu
Finnmark
North Ostrobothnia
Komi Republic
Arkhangelsk Oblast (excl Nenets)
Norway
Russia
Republic of Karelia
Nenets Autonomous Okrug
Murmansk Oblast
Norrbotten
Västerbotten
Sweden
Aggregate scores are calculated for a set of indicators presented in the chapter Arctic Society. This approach assumes equal
weights for the indicators. To calculate sco res and compa re the indicators across countries and regions we used a standard
scaling formula for 1-10 point scale. Higher score means better situation in a particular region, and vice-versa.
30
BUSINESS INDEX NORTH Issue #04—March 2020
31
Section (02) - Arctic Society
SDG 11 — Sustainable cities and communities
Figure 2.1 — Death rates due to trac accidents per 10,000 capita, average 2015–2017 and change 2008–2017
The results demonstrate that the death rate
due to trac accidents is much higher in
the Russian BIN regions. In the Nordic BIN
regions (except Troms) death rates due to
trac accidents are considerably higher
than the overall average (0.38) for Sweden,
Finland and Norway. For instance, the death
rate in Norrbotten is 2.6 times higher than
Norway ’s total (0.32). Since deaths due to
trac accidents are inuenced by a great
number of factors1, e.g. physical (being young,
inexperienced, driving under the inuence of
alcohol or drugs), climate and weather con-
ditions and socio-economic factors. Crash
risk factors (failure to use seatbelts, helmets
and child restraints; poorly designed and
maintained roads, poor visibility) and post-
crash risk factors (post-crash care for injured
persons to reduce fatalities and improve out-
comes) all contribute to death rates due to
trac accidents. It is challenging to attrib-
ute higher death rates in the Arctic regions
to any particular risk factor, hence attention
should be paid to studying what particular
risk factors are prevalent in the Arctic regions
and for preventive measures to be designed
accordingly.
1 Bachani AM, Peden M, Gururaj G, et al. Road Trac Injuries. In: Mock CN, Nugent R, Kobusingye O, et al., editors. Injury Prevention and
Environmental Health. 3rd edition. https://www.ncbi.nlm.nih.gov/books/NBK525212/#
Norrbotten
North O strobothni a
Yamalo-Nenets Autonomous Okrug
Nordlan d
Finland
Arkhan gelsk Ob last (excl. N AO)
Komi Republic
Kainuu
Murmans k Oblast
Nenets Autonomous O krug
Finnmark
Republ ic of Karelia
Russia
-3,0
-2,5
-2,0
-1,5
-1,0
-0,5
0,0
0,5
1,0
1,5
2,0
2,5
Västerb otten
Norway
Sweden
Trom s
Lapland
Average 2015-2017 Chang e 2008-2017
SDG 16 — Peace, Justice and Strong Institutions
Figure 2. 2 — Intentional homicides per 100,000 capita, average 2014–2016 and change 2010–2016
The highest rates of intentional homicides are
observed in the Russian BIN regions a fairly
low in the Nordic BIN regions. The average
value for this indicator in the Russian BIN
regions was 11.7, while the average in Russia
was 7.7. In the BIN Nordic regions it was 0.66,
with no difference from the total for Sweden,
Finland and Norway of 0.67. Research has
shown that economic development, inequal-
ity and poverty are signicant predictors of
homicide rates across countries. Gini coe-
cients are used to explain intentional homi-
cide rates as a larger income gap between
poor and rich people would lead to rising
criminal behaviour.
-10 -5 0510 15 20
Finland
Republ ic of Karelia
Nenets Autonomous O krug
Lapland
North O strobothnia
Norw ay
Komi Republic
Nordlan d
Yamalo-N enets Auton omous Okr ug
Norrbotten
Murmansk Oblast
Trom s
Finnmark
Arkhangelsk Oblast
Russia
Sweden
Västerb otten
Kainuu
Average 2014-2016 Change 2010-201 6
Intentional homicide is the death of a person purposefully in icted by another person, excluding suicides
outside of astate of war. Homicide is a broader c ategory than murder, as it als o includes manslaughter.
Figure 2.3 shows that all Russian BIN regions
experienced negative population growth
in the population group aged 20–39. With
most negative changes obser ved in the Komi
Republic and Arkhangelsk Oblast (excl. NAO).
Troms region had the biggest growth, ex-
ceeding 10%, while most of the regions fell
behind the corresponding country averages.
In absolute numbers, the population of 20-
39 year-olds decreased by 237,387 people
in all BIN regions in the period from 2009
to2018.
Figure 2. 3 — Change in population aged 20–39 in the BIN area, %, 2009–2018
-25 % -20 % -15 % -10 % -5 % 0 % 5 % 10 % 15 %
Finland
Murmansk Oblast
Västerb otten
Arkhan gelsk Ob last (excl. N AO)
Norrbotten
Republ ic of Karelia
Nordlan d
Kainuu
Sweden
Komi Republic
Norway
Finnmark
Trom s
Russia
Lapland
North O strobothnia
Yamalo-Nenets Autonomous Okrug
Nenets Autonomous O krug
32
BUSINESS INDEX NORTH Issue #04—March 2020
Conclions
Special attention needs to be paid to safety on the roads and to deep
underlying societal challenges such as availability of jobs, poverty and
accessibility of mental health services that collectively explain ele-
vated violence in the Arctic measured as homicide rates. Arctic soci-
eties are experiencing a rapid demographic shift with a decreasing
population of children, and young adults and growing elderly popula-
tion creating threats to sustainably functioning and resilient societies
in the future.
33
Section (02) - Arctic Society
Figure 2. 4 — Change in population aged 0-19 in the BIN area, %, 2009–2018
Figure 2.4 shows that only few regions had
positive growth in children and youth popu-
lation aged 0–19. Nenets and Yamalo-Nenets
Autonomous Okrug had a growth in the range
of 9–11%, while of the Nordic BIN regions only
Västerbotten had a growth of 1.6% . In abso-
lute numbers, Yamalo-Nenets Autonomous
Okrug had the biggest growth, 12,268 in the
group aged 0-19. Nine regions in the BIN area
had negative growth ranging from -17.5%
in Kainuu to (-1.45) in North Ostrobothnia.
Altogether the population of 0–19 year-olds
in all BIN regions decreased by 18,422 people
from 2009 to 2018. Negative trends in popu-
lation aged 0–19 have long-lasting effects on
the societal structure in the Arctic, with fewer
people needing education and entering the
job market in the future. At the same time BIN
area has a growing ageing population with an
increase of population aged 80+ by 38,563
during 2009–2018.
-20 % -15 % -10 % -5 % 0 % 5 % 10 % 15 %
Finland
Murmansk Oblast
Västerb otten
Arkhan gelsk Ob last (excl. N AO)
Norrbotten
Republ ic of Karelia
Nordlan d
Kainuu
Sweden
Komi Republic
Norway
Finnmark
Trom s
Russia
Lapland
North O strobothnia
Yamalo-Nenets Autonomous Okrug
Nenets Autonomous O krug
34
BUSINESS INDEX NORTH Issue #04—March 2020
35
Sustainable Economy in the Arctic
This chapter deals with energy, business activities and innovative
potential and also levels of inequality at the regional level.
Yamal LNG plant
Photo: No vatek
Section (03) - Sustainable Economy in the Arctic
Aggregate score for indicators “Sustainable Economy in the Arctic”
- the BIN regions and their countries averages
0123 4 5 6 7 8 9 10
Finland
Yamalo-Nenets Autonomous Okrug
Lapland
Kainuu
North Ostrobothnia
Komi Republic
Arkhangelsk Oblast (excl Nenets)
Russia
Republic of Karelia
Nenets Autonomous Okrug
Murmansk Oblast
Norrbotten
Västerbotten
Sweden
Nordland
Troms
Finnmark
Norway
Aggregate scores are calculated for a set of indicators presented in the chapter Sustainable Economy in the Arctic. This
approach assumes equal weights for the indicators. To calculate scores and compare the indicators across countries and
regions we used a standard scaling formula for 1-10 point scale. Higher score means better situation in a particular region, and
vice-versa.
36
BUSINESS INDEX NORTH Issue #04—March 2020
37
Section (03) - Sustainable Economy in the Arctic
SDG 7 — Affordable and Clean Energy
Figure 3.1 — Balance of electricity production in TWh, 2017
Figure 3.1 demonstrates that many High
North regions in the Nordic countries have
a substantial surplus of electricity produced,
for instance, Västerbotten (11.6 TWh) and
Norrbotten (7.8 TWh) have the greatest
amount of surplus electricity produced, foll-
owed by Nordland ( 7.4 TWh). Of all electric-
ity produced in the Nordic BIN regions 85%
originates from renewable energy sources.
(The rest from termo and nuclear production).
Some Russian BIN regions have negative
surplus of electricity produced, e.g. Yamalo-
Nenets Autonmous Okrug and the Republic
of Karelia. This abundance of electricity in the
Nordic BIN regions that can potentially make
them attractive for establishing energy-inten-
sive industries, such as steel-making and bat-
tery cell production. Conversely, regions that
have a decit of electricity produced should
address energy security issues and adopt
strategies for installing capacity for generat-
ing green energy.
-25 -20 -15 -10 -5 0510 15 20 25 30
Finland
Murmansk Oblast
Västerb otten
Arkhan gelsk Ob last (excl. N AO)
Republ ic of Karelia
Nenets Autonomous O krug
Nordlan d
Kainuu
Sweden
Komi Republic
Norway
Trom s
Finnmark
Norrbotten
Russia
Lapland
North O strobothnia
Yamalo-Nenets Autonomous Okrug
SDG 8 — Decent Work and Economic Growth
Figure 3. 2 — Employment rates, 2018 and change 2013–2018
In 2018 average employment in the Nordic
BIN regions was 65.3% of the working aged
population with a slight increase of 1.3% from
2014. In 2018 average employment in the
Russian BIN regions was 61.4% of working
aged population with an average decrease of
5.4% from 2014. Yamalo-Nenets Autonomous
Okrug had consistently highest employment
rates in the range of 75% of the working age
population in 2014 and 2018. Employment
rates need to be studied in relation to pop-
ulation structure, industry structure and
availability of jobs across sectors for males
and females. Employment of elderly people
and young people needs to be studied in
moredetail.
-20 -10 010 20 30 40 50 60 70 80
Norway
North O strobothnia
Arkhan gelsk Ob last (excl. N AO)
Finnmark
Trom s
Sweden
Republ ic of Karelia
Komi Republic
Västerb otten
Yamalo-Nenets Autonomous Okrug
Lapland
Murmansk Oblast
Norrbotten
Nenets Autonomous O krug
Russia
Finland
Kainuu
Nordlan d
2018 Change 2013 -2018
Employment rates are dened as a measure of th e extent to which available labour resources
(people available to work) are being used. They are calculated as the ratio of the employed to the
working age population. At the EU level the target is to increase the em ployment rate of the popu-
lation age d 20 to 64 years to at least 75% by 2020.
38
BUSINESS INDEX NORTH Issue #04—March 2020
39
Section (03) - Sustainable Economy in the Arctic
Figure 3. 3 — Unemployment rate, 2018 and chnage 2013–2018
Figure 3. 4 — Tourism as % of regional GVA, 2017
Different patterns of unemployment can be
observed across the BIN regions. The highest
unemployment rates are in the Finnish BIN
regions averaging 11.3%. While unemploy-
ment decreased in the Finnish BIN regions
in the range of 5%, it remains high. High un-
employment rates in Finland are due to struc-
tural unemployment arising after the 1990s
recession. Structural unemployment means
that the trend for job loss is high while the
employment probability trend is low1. In the
Russian BIN regions, in 2018 unemployment
was 7.4% on average with a slight increase of
0.4 percentage points over the period 2013-
2015. The lowest unemployment rates are
in the Norwegian BIN regions at 2.1% aver-
age. High unemployment results in a loss of
income for individuals, increased pressure
with respect to government spending on
social benets and a reduction in tax reve-
nue2. Further investigation would require un-
employment data on unemployment among
young people, by sex and by proportion of
people in long-term unemployment. Korona
crisis severely hit labor markets in the spring
of 2020, increasing unemployment to levels
not seen since second world war.
Note: for country level as % of country GVA
Tourism is one of the largest and fastest
growing sectors in the world economy and
the economic achievements of tourism are
signicant. Tourism plays a key role in global
economic activity, job creation, export reve-
nue and domestic value added3. Figure 3.4
demonstrates that the regions of Lapland
(3%) and Troms (2.1%) have the highest
shares of tourism as % of GVA . The world me-
dian for travel and tourism direct contribution
to GDP was 3.5% (World Bank). Northern re-
gions have fairly active tourism sectors con-
tributing to their regional GVA, however some
regions. e.g. in the North of Russia would
need to develop and promote tourism more
in order to contribute to local job creation
and the related economic development.
-8 -6 -4 -2 0 2 46810 12 14
Finland
Murmansk Oblast
Västerb otten
Arkhan gelsk Ob last (excl. N AO)
Republ ic of Karelia
Nenets Autonomous O krug
Nordlan d
Kainuu
Sweden
Komi Republic
Norway
Trom s
Finnmark
Norrbotten
Russia
Lapland
North O strobothnia
Yamalo-Nenets Autonomous Okrug
1 Bank of Finland . https://www.boulletin.fi/en/2018/3/unemployment-rate-in-finland-close-to-structural-level/
2 https://ec.europa.eu/eurostat/statistics-explained/index.php/Unemployment_statistics#Recent_developments
3 OECD (2018), OECD Tourism Trends and Policies 2018, OECD Publishing, Paris, https://doi.org/10.1787/tour-2018-en
0,0 % 0,5 % 1,0 % 1,5 % 2,0 % 2 ,5 % 3,0 % 3, 5 %
Finland
Murmansk Oblast
Västerb otten
Arkhan gelsk Ob last (excl. N AO)
Republ ic of Karelia
Nenets Autonomous O krug
Nordlan d
Kainuu
Sweden
Komi Republic
Norway
Trom s
Finnmark
Norrbotten
Russia
Lapland
North O strobothnia
Yamalo-Nenets Autonomous Okrug
2018 Change 2013 -2018
Figure 3. 5 — Share of households with internet broadband access (in % of total households), 2009 and 2017
Figure 3.6 — Change in number of active enterprises, 2008–2016
0 % 20 % 40 % 60 % 80 % 100 %
Finland
Arkhangelsk Oblast
Komi repub lic
Eastern and Northe rn Finland
North S weden
North ern Nor way
Murmansk Oblast
Nenets Autonomous O krug
Sweden
Russia
Norway
Republ ic of Karelia
Yamalo-Nenets Autonomous Okrug
Figure 3.5 shows that on average the Nordic
BIN regions had 95% of households with
Internet broadband access in 2017. Between
2009 and 2017 the share of households in
the Nordic BIN regions with broadband ac-
cess rose by 21 percentage points. In gen-
eral, the Russian BIN regions had 77% of
households with broadband access and the
growth from 2009 to 2017 was 38%. These
statistics only reect the minimum speed of
broadbandaccess.
Figure 3.6 demonstrates that all Nordic BIN
regions saw growth in the number of active
enterprises. Some regions, e.g. Västerbotten,
had growth greater than the average for
Sweden. All Russian BIN regions (except the
Republic of Karelia) had negative growth, with
the biggest decrease in Murmansk Oblast.
Economic downturn and economic sanctions
post-2014 inuenced the decrease in the
number of active enterprises in Russia in the
period 2008–2016. See gure 3.7 for indus-
try level changes.
SDG 9 — Industry, Innovation and Infrastructure
-20 % -15 % -10 % -5 % 0 % 5 % 10 % 15 % 20 % 25 %
Finland
Västerb otten
Komi repub lic
Kainuu
Lapland
Norrbotten
North O strobothnia
Finnmark
Nordlan d
Trom s
Murmansk Oblast
Nenets Autonomous O krug
Sweden
Russia
Norway
Republ ic of Karelia
Yamalo-Nenets Autonomous Okrug
Arkhan gelsk Ob last (excl. N AO)
Arkhangelsk Oblast
2009 2017
40
BUSINESS INDEX NORTH Issue #04—March 2020
41
Section (03) - Sustainable Economy in the Arctic
In 2018, the total number of active enterpris-
es in the Nordic BIN regions was 142,237 and
124,885 in the Russian BIN regions. The high-
est growth among all regions in the Russian
and Nordic BIN regions occurred in the ac-
commodation and food sector, with 6,861
companies operating in 2006 and 8,569 in
2016. The manufacturing sector had near-ze-
ro or negative growth. The trends in mining
and quarrying are not uniform across the
BIN region with negative growth in the North
of Russia and positive growth in Troms and
Finnmark, increasing from 17 and 23 compa-
nies in 2008 to 24 and 43 companies in 2016
respectively. Since mining sector companies
tend to be big, even a relatively small in-
crease in numbers is signicant for the region.
Business activities and real estate demon-
strated the strongest growth in the years
2008-2016, growing from 48,886 companies
in 2006 to 59,220 in 2016.
Figure 3.7 — Change in number of active enterprises by industry, 2008–2016
Manufacturing
Accomodation
and food
services Construction Manufacturing
Mining and
quarrying
Wholes ale and
retail trade ;
repair of moto r
vehicles and
motorcycles Transp or t
Business
activit ies and real
estate
Finland 13 % 5 % -2,8 % 5,2 % 0,0 % -6,3 % 1 7,9 %
Kainuu 1 % 9 % -16,6 % 2,3 % 0,3 % -14 ,3 % 6, 3 %
Lapland 10 % 0 % -3,7 % 26 ,7 % -5 ,1 % -7 ,2 % 16 ,2 %
North Ostrobothnia 12 % 6 % 2 ,8 % -2, 8 % -1, 5 % -3,0 % 24 ,7 %
Russian 25 % 17 % -5,8 % 5,6 % -11,8 % 36,6 % 20,2 %
Republic of Karelia 37 % 48 % 2,7 % 1, 9 % -3, 5 % 28, 8 % 29,2 %
Komi Republic 52 % 0 % -20, 9 % -24, 1 % -11 ,4 % 1 4,0 % 7,6 %
Arkhangelsk Oblast 55 % 13 % -17,7 % -24,3 % -22,9 % 3, 5 % 1 0,6 %
Nenets Autonomous Okrug -5 % -22 % 18,6 % -50,0 % -8 ,7 % 18 ,9 % 11 ,4 %
Arkhangelsk Oblast (excl. NAO) 58 % 15 % -1 8, 4 % -1, 8 % -23,2 % 2, 9 % 10,6 %
Murmansk Oblast 23 % -1 % -41,9 % -41,1 % -33,0 % 0,1 % 1 2,3 %
Yamalo-Nenets Autonomous Okrug 68 % -26 % -41 ,0 % -23,7 % -16,6 % 41,3 % 11,2 %
Norway 1 0 % 19 % 0,7 % 14 ,3 % -4, 4 % -4, 0 % 27,6 %
Nordland 9 % 21 % 4,4 % 0,0 % -13 ,3 % -8,3 % 21 ,6 %
Trom s 19 % 13 % 6, 0 % 41,2 % -1 5,1 % - 9,2 % 22,1 %
Finnmark 5 % 8 % 10, 0 % 87,0 % -14 ,0 % -9,5 % 28, 6 %
Sweden 20 % 31 % -0,3 % 2 ,7 % 6,2 % 0,4 % 37, 7 %
Västerbotten 22 % 25 % 4 ,7 % 0,0 % 4 ,7 % 0, 5 % 41,6 %
Norrbotten 12 % 38 % 4 ,4 % 6,4 % -2,2 % -2,0 % 37, 6 %
Figure 3. 8 — Gini coecients, 2017
The Norwegian regions of Nordland (0.217),
Troms (0.219) and Finnmark (0.220) have the
lowest Gini coecients in the whole of Arctic
Europe, lower than the Norway total of 0.252.
In Russia, Gini coecients are much higher
with an average total of 0.409. At the same
time, some regions, which are more urban,
such as Karelia (0.335) and Murmansk Oblast
(0.375), have lower Gini coecients. The ur-
ban rural divide partially explains differences
in Gini coecients.4 Nenets and Yamalo-
Nenets Okrug have the highest values of
Gini coecients, these regions, dominated
by the oil and gas industry, have the widest
gap in the distribution of incomes between
oil and gas industry workers and other sector
employees. On average in the resource-ex-
tracting industr y, a worker is approximately
ve timed better paid than an education em-
ployee5. The Finnish and Swedish high north
regions have lower Gini coecients than the
national totals, yet higher than their neigh-
bouring regions in the North of Norway. At-
poverty risk rates (SDG2) provide additional
insight into Gini coecient interpretation.
What is a good Gini score? The top 12
countries6 with a clear advantage in terms of
both Human Development Index7 and Global
Innovation Index8 demonstrate a range of
Gini coecients between 0.274 (Finland and
Sweden) and 0.410 (USA, Israel). The average
Gini for the top 12 countries is 0.327. Being
placed at the top of the Human Development
Index 2018, Norway was ranked nineteenth
on the Global Innovation Index. In low ine-
quality countries, there is a potential trade-
off between human development and poten-
tial to innovate. However, a country’s values,
priorities and support mechanisms for inno-
vation should be considered when interpret-
ing these ratings.
SDG 10 — Reduced Inequalities
0,0 0,1 0,2 0,3 0,4 0,5
Finland
Västerb otten
Komi repub lic
Kainuu
Lapland
Norrbotten
North O strobothn ia
Finnmark
Nordlan d
Trom s
Murmansk Oblast
Nenets Autonomous O krug
Sweden
Russia
Norway
Republ ic of Karelia
Yamalo-Nenets Autonomous Okrug
Arkhan gelsk Ob last (excl. N AO)
Arkhangelsk Oblast
4 https://www.emera ld.com/insight/content/doi/10.1108/01443580510574805/full/html
5 Nalimov, P., & Rudenko, D. (2015). Socio-economic problems of the Yamalo-Nenets Autonomous Okrug development. Procedia Economics and
Finance, 24, 543-549.
6 Switzerland, Sweden, United States, Netherlands, United Kingdom, Finland, Denmark, Singapore, Germany, Israel, Republic of Korea, Ireland.
7 e Human Development Index is a statistical tool used to measure a country’s overall achievement in its social and economic dimensions
(http://hdr.undp.org/sites/default/files/hdr_2019_overview_-_english.pdf).
8 Global Innovation Index (https://www.globalinnovationindex.org/Home) is a composite measure of a country’s entire innovation performance.
The Gini coecient is used to measure Income inequality
among individuals in the distribution of disposable income
in a countr y or a region . The Gini co ecient is based on
the comparison of cumulative proportions of the p opu-
lation against cumulative proportions of income they receive,
and ranges b etween 0 in the case of per fect equality and
1in the case of perfect in equalit y. A higher Gini coecient
indicates greater inequality, with high income individuals
receiving much large r percentag es of the total income of
the population. In co ntrast, a lower Gini coecient indicates
asituation w here income is more equally distributed among
the population.
The Gini coecient is known as an imp ortant indi-
cator of the socio-eco nomic development of a country.
Aproper just distribution of income is a prerequisite for
improved quality of life, social jus tice and - for higher income
countries – innovativeness, economic development and high
labourproductivity.
42
BUSINESS INDEX NORTH Issue #04—March 2020
43
Figure 3.9 demonstrates that the Gini coe-
cient changes very slowly over time. However,
even a small change may indicate important
trends. For example, for Norway the total Gini
increased by 0.02 (or by 7.7%) over the last 10
years, while for Northern Norway the increase
was 11.3%. In more practical terms, this means
that 10 years ago in Norway the income lev-
el of the richest 10% of the population was
2.6 times higher than the income level of the
“poorest” 10% of the population. Nowadays
this rate is 2.8 – an increase corresponding
to a change of Gini by just 0.02 or 7.7%. For
Northern Norway the change in income lev-
els of the 10% richest compared to the 10%
”poorest” was from 2.37 to 2.63 during the last
10 years. Increases in Gini were observed in
most of the Nordic BIN regions, with initial-
ly the lowest level of inequality in 2012. The
biggest decrease was observed in the North
of Russia with initially the highest level of
inequality. The Finnish BIN regions did not
have big changes in Gini coecients. The
Nordic trend should not be considered to be
necessarily negative while the Russian trend
is positive, the best performing countries in
human development and innovations had
Gini coecients in the range 0. 274-0.410.
Note: Patent applications sent to nation-
al patent oces. Russian data average for
2013–2018. Nordic countries data average for
2008–2017.
Figure 3.10 shows the average number of
patent applications per 10,000 capita sub-
mitted to national intellectual property rights
authorities. Patenting is an important indica-
tor of innovative activity towards the commer-
cialization of new knowledge. On a national
basis, Finland has the highest level of pat-
enting activit y followed by Sweden, Nor way,
and Russia. Among the BIN regions, North
Ostrobothnia and Västerbotten demonstrate
the highest level of patenting activity. Since
the statistics shown are based on the ap-
plicant’s (owner of the invention) address, a
large number of inventions made in the re-
gions of Norrbotten and North Ostrobothnia
by local inventors are included in the total
numbers for Sweden and Finland (as such
the inventions are owned respectively by
Eriksson and Nokia). Besides Norrbotten,
Västerbotten, North Ostrobothnia, the other
BIN regions demonstrate rather low levels of
patenting activity (less than half of their re-
spective national averages). This indicates a
lack of larger companies doing R&D and also
a lack of knowledge infrastructure. This limits
the integration of the regions in the knowl-
edge-based economy. Knowledge-based
economy sustains growth through techno-
logical advantage, access to information and
know-how; to a lesser extent it depends on
natural resources and physical means of pro-
duction located in the region.
Figure 3. 9 — Change in the Gini coecients, 2012–2017
Figure 3.10 — Number of patent applications per 10,000 capita
-0,05 -0,04 -0,03 -0,02 -0,01 0,0 0 0,01 0,02
Finland
Västerb otten
Komi repub lic
Kainuu
Lapland
Norrbotten
North O strobothn ia
Finnmark
Nordlan d
Trom s
Murmansk Oblast
Nenets Autonomous O krug
Sweden
Russia
Norway
Republ ic of Karelia
Yamalo-Nenets Autonomous Okrug
Chukotka Auto nomous Okrug
Arkhan gelsk Ob last (excl. N AO)
Arkhangelsk Oblast
0,0 0,5 1,0 1,5 2,0 2,5 3,0
Finland
Västerb otten
Komi repub lic
Kainuu
Lapland
Norrbotten
North O strobothnia
Finnmark
Nordlan d
Trom s
Murmansk Oblast
Nenets Autonomous O krug
Sweden
Russia
Norway
Republ ic of Karelia
Yamalo-Nenets Autonomous Okrug
Arkhan gelsk Ob last (excl. N AO)
Conclions
The Nordic Arctic regions had a total of 29.3 TWh electricity surplus
in 2017. There is a need for ecient local use of electricity produced
predominantly from renewable sources. Hence, the Nordic Arctic
region has a potential to become attractive for establishing energy-in-
tensive industries.
Business activity measured in terms of stock in active enterprises
shows that business activities are thriving in the sector of business
activities and real estate and in the hospitality sector, while the number
of manufacturing rms is in decline.
The employment rate needs to be increased in most of the regions
apart from Yamalo-Nenets Autonomous Okrug. The unemployment
situation is very different across countries with challenges persisting
in Finland and Russia. The scale of inequality is very different between
the Nordic and Russian regions.
Creation of new jobs, increasing innovative potential and fostering
knowledge economy should be on the development agenda of the
Arctic regions.
Section (03) - Sustainable Economy in the Arctic
44
BUSINESS INDEX NORTH Issue #04—March 2020
45
The Arctic Environment
This chapter deals with SDG 13 Climate Action. Specically, we
address CO2 emissions resulting from human activity.
Hammer fest Island Melkøya , g as process ing plant.
Photo: iSto ck
SDG 13 — Climate Action
Section (04) - The Arctic Environment
Figure 4.1 — Emissions of kg CO 2 equivalent, per capita, 2017
Figure 4.1 shows emissions of kg CO2 equiv-
alent per capita in the BIN Nordic regions.
Industry accounts on average for 75 % of
all CO2 emissions. High emissions per capi-
ta in e.g. Norrbotten and North Ostrobothnia
are explained by energy-intensive industries
(steel-making, which also uses coal and coke
in the process) and relatively low population
density. For instance, 90 % of the EU ’s iron
ore extraction takes place in the Norrbotten
region, while only 2.4 % of Sweden’s popula-
tion live there1. The population density is very
low in Norrbotten with just 2.6 people per
square km, while in the whole of Sweden it is
25.1. The table below exemplies the share of
the Artic regions in the countr y’s total emis-
sions. The industrialized regions have higher
emissions than the regions with no manufac-
turing and industrial production sectors.
05 000 10 000 15 000 20 000 25 000
Norway
North O strobothnia
Finnmark
Trom s
Sweden
Västerb otten
Lapland
Norrbotten
Finland
Kainuu
Nordlan d
1 Climate and energy strategy for the county of Norrbotten
Share of the Arctic regions in the country ’s total emissions
Norway 100% Sweden 100% Finland 100%
Finnmark 3.0% Norrbotten 11.4% Lapland 14 .2%
Trom s 2.0% Väster botten 2.9% Kainuu 1.5%
Nordland 6.2% North Ostrobothnia 5.3%
Note: Data for Finland without land use and land-use change and forestry.
46
BUSINESS INDEX NORTH Issue #04—March 2020
Figure 4.3 — Pollutants emitted into the atmosphere from stationary sources, kg per capita, Russia
Figure 4.2 — Change in emissions of kg CO2 equivalent per capita, %, 2013–2017
-15% -1 2% -9% -6% -3% 0% 3% 6% 9% 12%
Norway
North O strobothnia
Finnmark
Trom s
Sweden
Västerb otten
Lapland
Norrbotten
Finland
Kainuu
Nordlan d
Figure 4.2 demonstrates that on the country
level Sweden, Norway and Finland reduced
their emissions of kg CO2 equivalent. In the
regions with increased industrial activity, e.g.
Nordland, Norrbotten, emissions grew in the
period 2013-2017.
For the Russian BIN regions we use the indi-
cator “pollutants emitted into the atmosphere
from stationary sources” since no compara-
ble data on CO2 emissions are available. For
Russia in general, about half of the pollutants
into the atmosphere are released from sta-
tionary sources. The pollutants include sol-
ids, gaseous and liquid substances: sulphur
dioxide (SO2), nitrogen oxides (NO2), carbon
monoxide (CO), hydrocarbons (without vol-
atile organic compounds), volatile organic
connections, other gaseous and liquid sub-
stances. The Yamalo-Nenets and Nenets
Autonomous Okrug have the highest values
among the indicators. all due to the hydro-
carbon projects in the area. Yamalo-Nenets
demonstrated the highest decrease of pollut-
ants, while Nenets demonstrated the highest
increase from 2012 to 2017.
-1 000 -500 0500 1 000 1 500 2 000 2 500 3 000
Russian
Yamalo-Nenets Autonomous Okrug
Arkhan gelsk Ob last (excl. N AO)
Komi Republic
Murmansk Oblast
Republ ic of Karelia
Nenets Autonomous O krug
2017 Change 2012–2017
Conclions
The Arctic regions are feeling the results of climate change with dimin-
ishing ice, permafrost melting, erosion and other negative conse-
quences. Analysis of indicators of SDG13 Climate Change should be
collaged with the demographic and societal changes in the region.
Economic activity conducive to increased emissions should be
viewed together with wellbeing in the region. It is important to have
regionally specic strategies and plans for climate change mitigation
that take into consideration all aspects of sustainable development.
47
Section (04) - The Arctic Environment
48
BUSINESS INDEX NORTH Issue #04—March 2020
49
Arctic Partnerships
In this chapter, indicators from the Macroeconomic Dashboard are analysed. These indicators are used to measure
the achievement of the goal to enhance global macroeconomic stability, including thorough policy coordination
and policy coherence. The Macroeconomic Dashboard features a set of indicators that have agreed international
standards indicative of macroeconomic stability and growth in sustainability. The indicator selection builds on existing
macroeconomic monitoring frameworks followed by countries and by international and regional agencies. A successful
sustainable development agenda requires partnerships between governments, the private sector and civil society.
Tromsø bridg e lighted by co lours of the U N 17 sustain able devel opment go als, Augu st 2019
Photo: Ørja n Aslakse n/Scream Me dia for Nora d
Section (0 5) - Arctic Partnership
Figure 5.1 — GDP, Euro per inhabitant , 2016
Figure 5. 2 — GDP (GRP), Euro per inhabitant, 2017, Russia
Gross Domestic Product (GDP) per capita
is often used as an estimate of the materi-
al prosperity of a country and well-being
too. Figure 38 shows that GDP per capita
(price adjusted) is highest in Norway and its
Arctic regions Troms, Finnmark and Nordland.
A trong economy coupled with a small pop-
ulation living in the north translates into high
values of GDP per capita in Nor way. The
lowest level of GDP per capita is observed
in Kainuu and North Ostrobothnia. The differ-
ence on the regional level between the rich-
est regions and those with lowest GDP per
capita are twofold. We observe that the Arctic
regions follow the levels of GDP per capita
on the country level. Differences between the
metropolitan and the Northern regions are
pronounced in all countries.
Figure 5.2 shows GDP (measured as Gross
Regional Product) per inhabitant in 2017. The
highest GDP per capita is in Yamalo-Nenets
and Nenets Autonomous Districts, both heav-
ily involved in the exploitation and export of
hydrocarbon natural resources. The Russian
economy still depends heavily on natural
resources and differences between GRP
across regions are signicant. This may set
limits to the development of partnerships
towards decreased cross-regional inequality
and increased innovations. A worrying trend
is the marked inequality of disposable in-
come in “rich” regions (as shown in the chap-
ter Economy, both Yamal and Nenets have the
highest Gini scores, above 0.41).
Gross domestic product (GDP) is the standard measure of the value added created through the production of goods and services in a coun-
try during a certain period. It also measures the income earned from that production or the total amount spent on nal goods and services
(lessimports).
0
10 000 20 000 30 000 40 00 0 50 000 60 00 0 70 000 8 0 000
North-Ostrobothnia
Norrbotten
Lapland
Västerb otten
Kainuu
Finland
Sweden
Norway
Finnmark
Nordlan d
Trom s
020 000 40 00 0 60 00 0 80 000 100 000
Murmansk Oblast
Arkhan gelsk Ob last (excl. N AO)
Republ ic of Karelia
Russia
Yamalo-Nenets Autonomous Okrug
Komi Republic
Nenets Autonomous O krug
SDG 17 — Partnership for the Goals
50
BUSINESS INDEX NORTH Issue #04—March 2020
Figure 5. 3 — GDP grow th, average annual growth 2008-2016
Figure 5. 4 — GDP (GRP), average annual growth, 2009–2017, Russia
Figure 5.3 illustrates annual average GDP
growth 2008-2016. The highest growth oc-
curred in Finnmark, Troms and Nordland,
where the economy is driven by extractive
industries, manufacturing, aquaculture and
construction. In Finland and Sweden growth
on the regional level was below the coun-
try average, except for Lapland, which is
much affected by the tourism industry. Slow
growth in Finland can be linked to the con-
sequences of the post-2008 recession and
shrinking exports to Russia. High growth in
the Arctic regions in Norway presents some
challenges to sustainable development due
to the growth in consumption and associated
environmentalburden.
Figure 5.4 shows the annual growth of GDP
(measured as Gross Regional Product) 2009-
2017. There is rather big difference in growth
rates ranging from -0.6% in Komi Republic to
+4.5% in Yamalo-Nenets Autonomous Okrug.
LNG and oil exports and the construction
sector are the main drivers of the economy
of Yamalo-Nenets. Arkhangelsk oblast has
astrong manufacturing industry. Major indus-
tries of Karelia are manufacturing, transport
and mining and these demonstrated unstable
economic growth. Murmansk relies on mining,
manufacturing, sheries and aquaculture. Oil
and gas are the major industries in Nenets
and Komi. The development of these regions
is limited by their remoteness and relative
lack of transport infrastructure. Growth in
Russia overall is conditioned by the largest
industries, namely manufacturing, mining and
extraction of natural resources, trade, trans-
portation and storage. We can see that the
Russian regions are very different in terms of
industrial prole, infrastructure and distances
to major transport infrastructure.
0% 1% 2% 3% 4% 5% 6% 7%
Norway
North O strobothnia
Finnmark
Trom s
Sweden
Västerb otten
Lapland
Norrbotten
Finland
Kainuu
Nordlan d
-1% 0% 1% 2% 3% 4% 5%
Murmansk Oblast
Nenets Autonomous O krug
Republ ic of Karelia
Arkhan gelsk Ob last (excl. N AO)
Russia
Yamalo-Nenets Autonomous Okrug
Komi Republic
Conclions
Macroeconomic indicators help to understand the level of economic
development and associated prosperity for the population. The Arctic
regions have very diverse proles. Regions with a high share of extrac-
tive industries, for example, demonstrate very high growth and GDP
per capita. Economic development and high GDP per capita are
linked to overconsumption. Some of the richest Arctic regions, on the
other hand, have the highest level of inequality and the worst pov-
erty rates. Achieving partnerships through macroeconomic stability
needs to be done in conjunction with improved human development,
increased sustainable consumption and increased environmental sus-
tainability. Stronger partnerships should be developed from national
government systems, in order to strengthen sustainability in the Arctic
areas, since many weaknesses can be mitigated through national initi-
ative and renewed policies for the Arctic accounting better for people
and businesses present here.
51
Section (0 5) - Arctic Partnership
52
BUSINESS INDEX NORTH Issue #04—March 2020
53
Summary tables
The four tables presented in this section compare the sustainable development
indicators of the Northern regions of Norway, Sweden, Finland and Russia
each to the overall situation in their own respective countries.
For example, the regions of Northern Norway
are compared to the overall situation in
Norway, the regions of Northern Finland are
compared to the overall situation in Finland,
etc. The tables do not assess the overall level
of sustainable development for the countries
on an international scale. Rather, the tables
describe differences within the countries,
on a national scale. And these differences,
between the north and the rest, are big for
all four countries. In general, with the excep-
tions of the regions of North Ostrobothnia
in Finland, and Yamalo-Nenets in Russia,
the Arctic areas lag behind their respective
countries in terms of sustainable develop-
ment. Prior to presenting the four tables, we
would like to illustrate the percentages of
sustainability measurements for northern
areas of each country compared that region’s
country as a whole. In general, in only 21%
of the measurement cases, do the Northern
areas of the four countries outperform their
respective countries as a whole, in 34% of
the indicators the situation is approximately
the same and about 45% of the indicators
describe a situation worse than that prevail-
ing in the country as a whole.
The indicators for each region are shown
in the four tables presented next. We use
three colours to indicate development status
– green if the situation in the region is better
than in that region’s country as a whole , yellow
if this is approximately at the same level, and
red if the situation is worse. At the same time,
where it is possible, we indicate development
trends for the indicators. Arrows pointing
upward indicate an increase in recent years,
arrows pointing downward indicate decrease.
Arrows pointing to the right, in turn, indicate
stability or stagnation1.
Section (0 6) - Summary tables
Better th an own country as a whole Worse than o wn countr y as a whole Same as own c ountry as a w hole
50%
15%
35%
31%
23%
46%
North S weden - measurements o f sustaina bility
indicators compared to Sweden a s a whole
North -West Russia - measurem ents of sustaina-
bility indicators compared to Russia as a wh ole
32%
23%
45%
24% 27%
49%
North N orway - me asurements of sustain abilit y
indicators compared to Norw ay as a whole
North Finland - measurement s of sustain ability
indicators compared to Finland as a whole
Figure 6.1 — Measurements of sustainability indicators for northern regions compared to their countries as a whole
1 Please note that the terms increase, decrease, stability (associated with the arrows) have numerical but not public value related references.
54
BUSINESS INDEX NORTH Issue #04—March 2020
55
Pillar Indicator Nordland Trom s Finnmark
People 1.2.1 At-risk-of-pover ty rate, 2017
2.4. 3 Arable land in sq . km per 100 0 people
2.4. 4 Level of crops production per cap ita, 2018
2.4. 5 Level of milk product ion per cap ita. 2018
2.4. 6 Level of cattle produ ction per c apita, 201 8
3.8 .5 Total death rate du e to ischemic h eart disease, canc er,
chronic res piratory d iseases an d suicides , average rate for 2015-201 7
3.8 .6 Life Ex pectancy a t birth for ma les (years) in 201 7
3.8 .7 Life Exp ectancy at birth for fema les (years) in 201 7
4.3 .1 Populatio n 25 – 64 aged w ith tertia ry educa tion (%)
5.5 .1 Employme nt participation rate as % of l abour force a ged 15- 64, by sex - F emales, 2 017
5.5 .1 Employme nt participation rate as % of l abour force a ged 15- 64, by sex- Males, 2017
BIN infer red indicato r: Total popul ation grow th, 2009 -2018, %
Society 11 .2.1 Death rate due to tra c accide nts per 10 00 0 population, ave rage 2015 -2017
16.1.1 Intentional homicide rate (homicides per 100 000 population)
BIN infer red indicato r: Grow th in share of you ng peopl e (0-19 years), 200 9-2018, %
BIN infer red indicato r: Grow th in share of you ng adults (20- 39 years), 200 9-2018, %
Economy 7.1.1 Surplus of e lectrici ty produc tion in T Wh per 10 0 000 capita, 2017
8.5 .1 Employme nt rate as % of worki ng population, 201 8
8.5 .2 Unem ployment ra te (% total labor forc e), 2018
8.9.1 Tour ism as % of Regi onal GVA, 2017
9.b.1 Gro wth in num ber of active e nterprise s, 2008-20 17, %
10.2 .1 Gini coecient, 201 7
Environment 13.2 .1 CO2 equivalent emissions per capita, 2017
Partnership 17.12.1.1 GDP a nnual grow th, %, 2009–201 6
17.12.1. 2 GDP, Euro per inha bitant, 201 8
9.1.1 Sha re of househo lds with Inte rnet broadb and access
in % of total hous eholds , 2017 (same for target 9.c)
Figure 6. 2 — Sustainability indicators for regions in Northern Norway compared to the situation in Norway as a whole
Better th an Norw ay as a whole
Worse than N orway as a w hole
Same as No rway as a who le
Increasing
Stable
Decreasing
Section (0 6) - Summary tables
Pillar Indicator Västerbotten Norrbotten
People 1.2.1 At-risk-of-pover ty rate, 2017
2.4. 3 Arable land in sq . km per 1 000 p eople
2.4. 4 Level of crops production per cap ita, 2018
2.4. 5 Level of milk product ion per cap ita. 2018
2.4. 6 Level of cattle produ ction per c apita, 201 8
3.8 .5 Total death rate du e to ischemic h eart disease, canc er,
chronic res piratory d iseases an d suicides , average rate for 2015-201 7
3.8 .6 Life Ex pectancy a t birth for ma les (years) in 201 7
3.8 .7 Life Exp ectancy at birth for fema les (years) in 201 7
4.3 .1 Populatio n 25 – 64 aged w ith tertia ry educa tion (%)
5.5 .1 Employme nt participation rate as % of l abour force a ged 15- 64, by sex - F emales, 2 017
5.5 .1 Employme nt participation rate as % of l abour force a ged 15- 64, by sex- Males, 2017
BIN infer red indicato r: Total popul ation grow th, 2009 -2018, %
Society 11 .2.1 Death rate due to tra c accide nts per 10 00 0 population, ave rage 2015 -2017
16.1.1 Intentional homicide rate (homicides per 100 000 population)
BIN infer red indicato r: Grow th in share of you ng peopl e (0-19 years), 200 9-2018, %
BIN infer red indicato r: Grow th in share of you ng adults (20- 39 years), 200 9-2018, %
Economy 7.1.1 Surplus of e lectrici ty produc tion in T Wh per 10 0 000 capita, 2017
8.5 .1 Employme nt rate as % of worki ng population, 201 8
8.5 .2 Unem ployment ra te (% total labor forc e), 2018
8.9.1 Tour ism as % of Regi onal GVA, 2017
9.b.1 Gro wth in num ber of active e nterprise s, 2008-20 17, %
10.2 .1 Gini coecient, 201 7
Environment 13.2 .1 CO2 equivalent emissions per capita, 2017
Partnership 17.12.1.1 GDP a nnual grow th, %, 2009–201 6
17.12.1. 2 GDP, Euro per inha bitant, 201 8
9.1.1 Sha re of househo lds with Inte rnet broadb and access
in % of total hous eholds , 2017 (same for target 9.c)
Figure 6. 3 — Sustainability indicators for regions in Northern Sweden compared to the situation in Sweden as a whole
Better th an Sweden a s a whole
Worse than S weden as a wh ole
Same as Swe den as a whol e
Increasing
Stable
Decreasing
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BUSINESS INDEX NORTH Issue #04—March 2020
57
Pillar Indicator Kainuu Lapland North-Ostrobothnia
People 1.2.1 At-risk-of-pover ty rate, 2017
2.4. 3 Arable land in sq . km per 100 0 people
2.4. 4 Level of crops production per cap ita, 2018
2.4. 5 Level of milk product ion per cap ita. 2018
2.4. 6 Level of cattle produ ction per c apita, 201 8
3.8 .5 Total death rate du e to ischemic h eart disease, canc er,
chronic res piratory d iseases an d suicides , average rate for 2015-201 7
3.8 .6 Life Ex pectancy a t birth for ma les (years) in 201 7
3.8 .7 Life Exp ectancy at birth for fema les (years) in 201 7
4.3 .1 Populatio n 25 – 64 aged w ith tertia ry educa tion (%)
BIN infer red indicato r: Total popul ation grow th, 2009 -2018, %
Society 11 .2.1 Death rate due to tra c accide nts per 10 00 0 population, ave rage 2015 -2017
16.1.1 Intentional homicide rate (homicides per 100 000 population)
BIN infer red indicato r: Grow th in share of you ng peopl e (0-19 years), 200 9-2018, %
BIN infer red indicato r: Grow th in share of you ng adults (20- 39 years), 200 9-2018, %
Economy 7.1.1 Surplus of e lectrici ty produc tion in T Wh per 10 0 000 capita, 2017
8.5 .1 Employme nt rate as % of worki ng population, 201 8
8.5 .2 Unem ployment ra te (% total labor forc e), 2018
8.9.1 Tour ism as % of Regi onal GVA, 2017
9.b.1 Gro wth in num ber of active e nterprise s, 2008-20 17, %
10.2 .1 Gini coecient, 201 7
Environment 13.2 .1 CO2 equivalent emissions per capita, 2017
Partnership 17.12.1.1 GDP a nnual grow th, %, 2009–201 6
17.12.1. 2 GDP, Euro per inha bitant, 201 8
9.1.1 Sha re of househo lds with Inte rnet broadb and access
in % of total hous eholds , 2017 (same for target 9.c)
Figure 6. 4 — Sustainability indicators for regions in Northern Finland compared to the situation in Finland as a whole
Better th an Finland as a whole
Worse than Finland as a whole
Same as Fin land as a who le
Increasing
Stable
Decreasing
Section (0 6) - Summary tables
Pillar Indicator Karelia Komi Nenets
Arkhangelsk
Oblast
Murmansk
Oblast
Yamalo-
Nenets
People 1.2.1 At-risk-of-pover ty rate, 2017
2.4. 3 Arable land in sq . km per 100 0 people
2.4. 4 Level of crops production per cap ita, 2018
2.4. 5 Level of milk product ion per cap ita. 2018
2.4. 6 Level of cattle produ ction per c apita, 201 8
3.8 .5 Total death rate due to ischem ic heart disease , cancer,
chronic res piratory d iseases an d suicides , average rate for 2015-2017
3.8 .6 Life Ex pectancy a t birth for ma les (years) in 201 7
3.8 .7 Life Exp ectancy at birth for fema les (years) in 201 7
4.3 .1 Populatio n 25 – 64 aged w ith tertia ry educa tion (%)
5.5 .1 Employme nt participation rate as % of l abour force a ged 15- 64,
by sex - Femal es, 2017
5.5 .1 Employme nt participation rate as % of l abour force a ged 15- 64,
by sex-Mal es, 2017
BIN infer red indicato r: Total popul ation grow th, 2009 -2018, %
Society 11 .2.1 Death rate due to tra c accide nts per 10 00 0 population,
average 2015-2017
16.1.1 Intentional homicide rate (homicides per 100 000 population)
BIN infer red indicato r: Grow th in share of you ng peopl e (0-19 years),
2009-2018, %
BIN infer red indicato r: Grow th in share of you ng adults (20- 39
years), 2009-2018, %
Economy 7.1.1 Surplus of e lectrici ty produc tion in T Wh per 10 0 000 capita,
2017
8.5 .1 Employme nt rate as % of worki ng population, 201 8
8.5 .2 Unem ployment ra te (% total labor forc e), 2018
8.9.1 Tour ism as % of Regi onal GVA, 2017
9.b.1 Gro wth in num ber of active e nterprise s, 2008-20 17, %
10.2 .1 Gini coecient, 201 7
Environment 13.2 .1 CO2 equivalent emissions per capita, 2017
Partnership 17.12.1.1 GDP a nnual grow th, %, 2009–201 6
17.12.1. 2 GDP, Euro per inha bitant, 201 8
9.1.1 Sha re of househo lds with Inte rnet broadb and access
in % of total hous eholds , 2017 (same for target 9.c)
Figure 6. 5 — Sustainability indicators for regions in North-West Russia compared to the situation in Russia as a whole
Better th an Russia as a w hole
Worse than R ussia as a who le
Same as Russia as a whole
Increasing
Stable
Decreasing
58
BUSINESS INDEX NORTH Issue #04—March 2020
59
Appendix
e five pillars approach: SDGs, targets and indicators ed
There are many ways to group SDGs, the most common being to group
all 17 SDGs into either three (Economy, Society and Environment) or
ve blocks (People, Planet, Prosperity, Peace and Partnership). These
both types of wording and grouping originate from the UN. According
to The UN Foundation, SDGs are a framework of interconnected goals
and progress on one block of goals must be reected and supported
in another. In this report we propose a modied approach of grouping
of SDGs into ve pillars that use labels and constructs that are more
obvious for describing corresponding phenomena. The proposed
grouping builds on the UN’s three-block and ve-block approaches.
Our proposed ve pillars are thus: People, Society, Economy, Environ-
ment and Partnership.
We select targets and indicators based on their appropriateness
to represent development towards SDGs in the Arctic. For example,
Target 1.1: Eradicate extreme pover ty. According to UN denition this
should be measured by an indicator of extreme povert y eradication, or
by 2030, eradicate extreme povert y for all people everywhere, cur-
rently measured as people living on less than $1.90 a day. As shown,
this target and indicator are not appropriate for developed societies.
Hence, we chose, Target 1.2: Reduce poverty by at least 50%.
An appropriate suitable indicator, by 2030, is to reduce at least
by half the proportion of men, women and children of all ages living
in poverty in all its dimensions according to national denitions, which
would be at risk of being in the poverty rate.
This list contains SDGs grouped by pillars, targets and indicators.
Some indicators are not the same as those provided in the UN list, this
is due to localization of SDGs for the Arctic region. Hence some of the
indicators are selected based on customization, relevance and data
availability criteria. The availability of comparable data on the regional
level appeared as a sound issue. However, we believe this is the most
comprehensive view on SDGs achievement and progress in the Arctic
area based on the ve-pillar approach.
Pillar SDG Targe t/s Indicator/s
People
SDG1 No Poverty 1.2 By 2030, reduce at least by half the proportion
of men, women and children of all ages living
in poverty in all its dimensions according to
national denitions
1.B Create sound policy frameworks at the national,
regional and international levels, based on
pro-poor and gender-sensitive development
strategies, to support accelerated investment
in poverty eradication actions
1.2.1 At-risk-of-poverty rate
SDG2 Zero Hunger 2.4 By 2030, ensure sustainable food production
systems and implement resilient agricultural
practices that increase productivity and
production, that help maintain ecosystems,
that strengthen capacit y for adaptation to
climate change, extreme weather conditions,
drought, ooding and other disasters and that
progressively improve land and soil quality
2.4.1 Agricultural land in use
2.4. 2 Arable land in use
2.4. 3 Arable land in sq. km per 1000 people
2.4. 4 Change in production of crops, milk and
cattle, %
SDG3 Good Health and
Well-being
3.8 Achieve universal health-care coverage,
including nancial risk protection , access to
quality essential health-care services and
access to safe, effective, quality and affordable
essential medicines and vaccines for all.
3.8 .1 Death rate due to ischaemic hear t disease
per 10,000 population
3.8 .2 Death rate due to cancer per
10,000population
3.8 .3 Death rate due to chronic respirator y
diseases per 10 000 population
3.8 .4 Death rate due to suicides per
10,000population
3.8 .5 Total death rate due to ischaemic heart
disease, cancer, chronic respiratory
diseases andsuicides
3.8 .6 Life Expectancy at birth (years) in 2017
SDG4 Quality
Education
4.3 By 2030, ensure equal access for all
women and men to affordable and qualit y
technical, vocational and tertiary education,
includinguniversity
4.3 .1 Population 25 – 64 aged with tertiary
education (%)
Section (0 6) - Summary tables
Pillar SDG Targe t/s Indicator/s
SDG5 Gender Equality 5.4 Recognize and value unpaid care and domestic
work through the provision of public services ,
infrastructure and social protection policies
and the promotion of shared responsibility
within the household and the family as
nationallyappropriate
5.5 Ensure women’s full and effective participation
and equal opportunities for leadership at all
levels of decision-making in political, economic
and public life
5.4.1 Employment gap, by sex
5.5.1 Employment participation rate as % of
labour force aged 15- 64, by sex ,
Demographic security Recognize and prevent depopulation of the Arctic
territories. This indicator is inferred by the BIN
project (it is not included in the UN framework)
although it is very important for the A rctic areas,
which are sparsely populated and characterized by
small communities spread over large land areas.
Change in total population (trend for the last
10years), %
Society SDG 11 Sustainable
cities and communities
1.2 By 2030, provide access to safe, affordable,
accessible and sustainable transp ort systems
for all, improving road safet y, notably by
expanding public transport, with special
attention to the needs of those in vulnerable
situations , women, children, p eople with
disabilities and older persons
11.3 By 2030, enhance inclusive and sustainable
urbanization and capacity for participatory,
integrated and sustainable human settlement
planning and management in all countries
11.2 .1 Death rate due to trac accidents per
10,000 population
11.3 .1 Death rate due to trac accidents per
10000 population
SDG 16 Peace, Justice
and Strong Institutions
16.1 Signicantly reduce all forms of violence and
related death rates ever ywhere
16.1.1 Intentional homicide rate (homicides per
100,000 population)
Societal integrity Ensure favourable structure of Arctic societies,
stimulating human development, exchange of
knowledge, good quality of life, as well as economic
sustainability. This indicator is inferred by the BIN
project (it is not speicifcally included in the UN
framework) although it is very important for the
Arctic areas, which are sparsely populated and
characterized by small communities spread over
large land areas.
Change in share of young people 0-19 years old
(trend for the last 10 years), %
Change in share of young adults 20-39 years old
(trend for the last 10 years), %
Economy SDG 7 Affordable
Clean Energy
7.1 By 2030 ensure universal access to affordable,
reliable and modern energy services
7.1.1 Electricity production from wind and
hydropower in TWh and as % of energy mi x
SDG8 Decent Work
and Economic Growth
8.5 By 2030, achieve full and productive
employment and decent work for all women
and men, including for young people and
people with disabilities, and equal pay for work
of equalvalue
8.9 By 2030, devise and implement policies to
promote sustainable tourism that creates jobs
and promotes lo cal culture and products
8.5.1 Employment rate as % of
workingpopulation
8.5.2 Unemployment rate (% total labour force)
8.9.1 Tourism as % of GVA
SDG9 Industry,
Innovation and
Infrastructure
9.1 Develop high-quality, reliable, sustainable
and resilient infrastructure, including regional
and transborder infrastructure, to support
economic development and human well-being,
with a focus on af fordable and equitable
access for all
9.b Support domestic technology development,
research and innovation in developing
countries , including by ensuring a policy
environment conducive, inter alia, to
industrial diversication and value addition
tocommodities
9.c. Signicantly increase access to information
and communication technology and strive to
provide uni versal and af fordable access to
the Internet in the least developed countries
by2020
9.1.1 Share of households with Internet
broadband access in % of total
households, in 2009 and 2017 (same
for target 9.c). In the summar y tables
indicator 9.1.1. is included in the pillar
partnerships. Digital connectivity is
important for both economic development
and for partnerships as it enables
communication, exchange of information,
mutual understanding and coordination
ofactivities.
9.b.1 Number of active enterprises
Number of patent applications per 10,000
ofpopulation
60
BUSINESS INDEX NORTH Issue #04—March 2020
61
Pillar SDG Targe t/s Indicator/s
Economy, conts S DG 10 Reduced
Inequalities
10.2 By 2030, empower and promote the
social, economic and political inclusion of
all, irrespective of age, sex, disability, race,
ethnicity, origin, religion or economic or
otherstatus
10.2.1 Gini coecient
Environment SDG 13 Climate Action 13.2 Integrate climate change measures into
national policies, strategies and planning
13. 2.1 CO2 equivalent emissions per capita
Partnership SDG 17 Partnership 17.13 Enhance global macroeconomic stability,
including thorough policy coordination and
policy coherence
17.12.1 Selected indicators from
Macroeconomicdashboard
Comparing SDG indicators for the Nor thern regions to those of
their respective countries as a whole
We use three colours to indicate development status – green if the
situation in the region is better than country as a whole, yellow if this
is approximately the same level, and red if the situation is worse. We
used +/- 10% interval to compare development status in the region to
the country as a whole. If the value of the indicator in the region differs
by more than 10% either way from the region’s country as a whole,
the difference is considered and assigned either a green or a red
marker. For example, the at-risk-of-poverty rate in Troms is 8.1, while
for Norway as a whole it is 10. That is -19% difference, and since less
poverty is better, Troms gets a green marker for this indicator – the
situation is better than in Norway as a whole. If the difference is less
than 10%, we consider that the situation in the region is approximately
the same and assign a yellow marker.
At the same time, where possible, we indicate the development
trend for the indicators. Arrows pointing upward indicate an increase in
recent years, arrows pointing downwards indicate a decrease. Arrows
pointing to the right, in turn, indicate stability or stagnation. Please
note that the terms increase, decrease, stability (associated with the
arrows) have numerical but not public value related references. For
example, an arrow pointing down does not necessarily mean that
the situation is getting worse. Instead, it just shows that the numeri-
cal value of the indicator has decreased in recent years. This may be
good for indicators like death rate, but bad for indicators like arable
land in use if we interpret them in terms of public value. Such interpre-
tations are left to the readers of this report. This mode of comparison
includes all ve pillars but shows in-country differences between the
Northern areas and rest of the respective countries. An integrated
dataset with core values for each region and indicator can be made
available to readers of this report upon request.
Comparing SDG indicators for Northern regions across countries
We use a 10-point scale to compare the indicators across countries.
Values for countries’ averages are also included in the comparisons.
For each indicator, the region or country with the best value is given
a score of 10 and the region or country with the worst value is given
a score of 1. Then all the other regions and countries are assigned
scores between 1 (the worst in a set) and 10 (the best in a set) using
a standard scaling formula. This approach assumes equal weights for
the indicators and provides aggregate scores for three pillars – Peo-
ple, Society, Economy. Aggregated scores for the pillars Environment
and Partnership are not calculated because they include indicators
which are not directly comparable on an international scale (we used
other indicators for Russia due to data availability issues). Aggregate
scores for each BIN region and country are illustrated by maps in the
report. On the maps we transform scores on 10-point scales into col-
our grades associated with the level of development. An integrated
dataset with core values for each region and indicator can be made
available to readers of this report upon request.
Section (0 6) - Summary tables
Wind turbines in Umeå Sweden
Photo: IStock/wayneknoesen
Issue #04
—March 2020
Business Index North (BIN) is a project that contributes to sustainable development and value
creation in the Arctic. The overall goal is to set up a recurring, knowledge-based, systematic
information tool for stakeholders. This is the fourth issue of the “Business Index North”
analytical report and focuses on the BIN area, including the northern regions of Norway,
Sweden, Finland, and Russia. In future issues of the report we would like to include Alaska and
the Northern Territories of Canada, Iceland and Greenland.
The BIN project is implemented through an international network of universities, research
organizations, businesses and public sector institutions. The main implementing partner is
the High North Center for Business and Governance at Nord University Business School.
Nordland County Council and the Norwegian Ministr y of Foreign Affairs provide basic funding
for the BIN project.
www.businessindexnorth.com
BUSINESS SCHOOL
ResearchGate has not been able to resolve any citations for this publication.
16.1.1 Intentional homicide rate (homicides per 100 000 population) BIN inferred indicator: Growth in share of young people (0-19 years
4.3.1 Population 25 -64 aged with tertiary education (%) BIN inferred indicator: Total population growth, 2009-2018, % Society 11.2.1 Death rate due to traffic accidents per 10 000 population, average 2015-2017 16.1.1 Intentional homicide rate (homicides per 100 000 population) BIN inferred indicator: Growth in share of young people (0-19 years), 2009-2018, % BIN inferred indicator: Growth in share of young adults (20-39 years), 2009-2018, % Economy 7.1.1 Surplus of electricity production in TWh per 100 000 capita, 2017