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chapter 7

regional perspective on the economy

AuthorS: Sigrid Jessen, Karen Refsgaard and Carlos Tapia
DATA AND MAPS: Maria Bobrinskaya and Kamila Dzhavatova
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The region as a unit of analysis: What is meant by a regional perspective?

This section aims to shed light on some of the economy’s most important economic, social, and environmental dynamics from a regional perspective.
Regions can be classified according to various distinct yet often overlapping typologies, each of which may delineate regions along physical, cultural, economic, functional, and administrative lines. In this section, we approach regions via an administrative lens. Depending on data availability, we focus on the municipality level (Local Administrative Units (LAU) 1 - 2) and the regional level based on the Nomenclature of Territorial Units for Statistics 2–3 (NUTS2–NUTS3). When studying regional development, we not only examine trajectories within the different regions but also compare regions of the Nordic countries.
Our findings on regional inequality illustrate the importance of making both intra- and inter-regional comparisons. In recent years, numerous researchers have argued that many countries have experienced rising inter-regional inequality since the 1980s (e.g. Storper 2018). In the following, we show that the same tendency can also be observed in the Nordic countries, both between and within regions. However, the specifics of these trajectories also vary across the Nordic Region.

Economic aspects of the regional economy: Growth and regional inequality in the Nordic Region

This section analyses economic change and inter-regional inequality, as well as innovation in a regional context.
Historically, the assessment of the economic development of nations, regions and cities has relied on metrics related to output, consumption or income, such as Gross Domestic Product (GDP). GDP measures the additional economic value created by the production of goods and services in a country within a given timeframe minus the value of production process inputs (OECD, 2024).
Figure 7.1 shows GDP growth for the European countries. While their GDP grew by an average of 3.4% between 2021 and 2022, the Nordic countries’ GDP grew by 3.5%. By 2022, the average EU per capita GDP was USD 57.098, while the corresponding Nordic average was USD 80.406 (OECD, 2024). Both GDP levels and growth rates differed between the Nordic countries in 2021–2022 – Iceland had the highest increase in GDP, at 8.9%, while Finland had the lowest, at 1.3%. Norway had the highest level of per capita GDP in 2022, at USD 121.263, while Finland had the lowest, USD 62.720.
Between 2022 and 2023, the GDP growth rate fell in many EU countries. During this period, the EU growth rate average was 0.4, while the corresponding rate for the Nordic countries was 0.84 (ibid.). The individual rates for the Nordic countries varied, with Finland at -1%, Sweden -0.2%, Norway 0.5%, Denmark 1.8% and Iceland at 4.1% (Eurostat, 2024a).
Figure 7.1: Real GDP annual change in 2022 and 2023 (%)
Source: Eurostat & NSIs
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Map 7.1 shows the percentage growth in regional GDP, also called the Gross Regional Product (GRP). In Denmark, the Capital Region (including Copenhagen) had the highest growth. The Midtjylland region, which includes the second-largest city, Aarhus, was the second-fastest growing. Two regions, Northern Jutland, and Southern Jutland, both had falling GRP. These trends align with the levels of GRP in Denmark, where the Capital Region’s figure is approximately six times greater than that of Northern Jutland.
For the other Nordic countries, the regional pattern seems to be less closely linked to urbanisation. In Finland, for example, the highest growth of GRP between 2021 and 2022 is found in the northern and less populated regions. However, the GRP in the Capital Region of Finland is approximately 10.5 times higher than in Northern Lapland. The same pattern is evident in Sweden, where Middle Norrland has had the biggest GRP increase, potentially driven by the development of green economies, including the production of lithium-ion batteries (Tadaros et al. 2022). While a one-year period is too short to reach a definitive conclusion regarding persistent economic regional convergence, it will be interesting to follow and investigate this trend in greater detail in the future.
GDP and GRP are commonly used indicators for economic development, but both have been criticised for not showing how economic progress or decline is distributed across people or geographic areas (e.g. Gray et al. 2013). Nor do they consider changes in production input or non-economic characteristics, e.g. changes in the stock of natural resources or environmental quality. The following section, therefore, looks at several different innovation indicators, as well as other social and environmental indicators, to present a more holistic picture of the state of the regional economies.
Map 7.1: Gross Regional Product change in 2021–2022 (%)
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Regional accumulations of innovative activities in the Nordic regions

Several researchers (e.g. Schumpeter 1939) have long linked innovation processes to economic growth. It has been suggested that the unequal distribution and diffusion of technologies and innovation activities have been the main drivers of rising regional economic inequalities in many Western countries (including several Nordic countries) since the 1980s (e.g. Storper 2018). However, while the prevailing narrative has been that innovation was a largely urban phenomenon, both earlier (e.g. Asheim and Isaksen 1997) and more recent evidence indicate that innovation does exist outside of the main urban centres, but often in different forms and sizes (e.g. Parrilli & Heras 2016; Doloreux & Shearmur 2023). As such, in this section, we study various innovation activities across regions.
Innovation activities can be differentiated across multiple dimensions, e.g. whether they are related to incremental or radical changes, or to product or process innovation, and whether the innovation is driven by know-what and know-why as in the Science-Technology-and-Innovation (STI) or know-how and know-who as in Doing-Using-Interacting (DUI) innovation modes (Jensen et al. 2007). However, regional innovation processes are notoriously difficult to measure (e.g. Nelson et al. 2014). One typical way of approximately gauging the level and quality of innovation activity is to look at the resources used in the innovation process, including both research and development (R&D) and non-R&D expenditures. Expenditure is just one input into the innovation process and may not describe the success of the innovation process or the type of innovation that emerges from it, but it can indicate strategic decisions related to innovation activities.
Maps 7.2a, 7.2b and 7.2c show R&D expenditures in the public and business sectors as a percentage of regional GDP, along with non-R&D innovation expenditure in Small and Medium Enterprises (SMEs) as a percentage of turnover. Together, these metrics offer a comprehensive understanding of the innovation landscape and provide insights into governments’ and higher education institutions’ commitment to foundational research, as well as the competitiveness and dynamism of the business environment and SMEs’ innovation capacity. By considering investment in both R&D and non-R&D activities, these indicators illustrate a broad spectrum of innovation drivers, from basic research to market-driven initiatives, and underscore the diverse pathways through which innovation fosters economic growth and social progress.
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Map 7.2a, 7.2b, 7.2c: R&D and non-R&D expenditures in the public and private sector
Source: Regional Innovation Scoreboard, calculated as all R&D expenditure in the government sector and the higher education sector, denominated by regional GDP
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First, Map 7.2a showcases R&D expenditure in the public sector as a percentage of GDP in the Nordic countries in 2023. In that year, the European level of R&D expenditure in the public sector, as a percentage of GDP, was 0.78%. By comparison, the Nordic average was 0.9%. While the more urban regions, in general, lead the Nordic regions, this is not always the case, as shown by the variation between the frontrunners.
The leading region is Trøndelag (including Norway’s third-largest city, Trondheim), with 2.30% of regional GDP. It is in third place in the EU as a whole. The next regions are Övre Norrland with 1.77%, Northern Jutland with 1.54%, Östra Mellansverige with 1.52%, and Hovedstaden with 1.49%. A common feature of most of the top-ranking regions is that they host universities and other higher education institutions known for innovation practices. Most Nordic regions have not seen significant increases or decreases in public R&D spending between 2016 and 2023.
Map 7.2b focuses on the private sector’s investment in research and development activities and depicts R&D expenditure in the business sector as a percentage of regional GDP. A higher percentage suggests a greater emphasis on innovation by businesses, potentially leading to the creation of new products, processes, and markets. The Nordic average is 1.45% in 2023, slightly below the European average of 1.51%.
The highest percentages are found in regions with larger cities. The top three Nordic regions with the highest R&D expenditure in the business sector as a percentage of GDP are Västsverige at 4.2%, Hovedstaden at 3.1%, and Trøndelag at 2.7%. In Finland, the leading region is Etelä-Suomi with 2.55%. Iceland is represented by a national assessment, with 1.98% of GDP. In Sweden, three other regions also scored quite highly: Sydsverige with 2.5%, Stockholm with 2.45% and Östra Mellansverige with 2.4%.
Lastly, Map 7.2c depicts non-R&D innovation expenditure in SMEs as a percentage of turnover, with values normalised between 0 and 1. This perspective captures innovation activities beyond traditional R&D, such as investments in design, marketing, training, and organisational innovation within SMEs. It highlights the importance of non-technological innovation in driving competitiveness and growth, especially for SMEs that may lack the resources for large-scale R&D initiatives.
The Nordic average is 0.4, the European is 0.53. The leader for this indicator is Åland with 0.73, followed by all regions in Norway (except for the two inland ones), ranging from 0.52 to 0.6, Midtjylland in Denmark with 0.56 and Gothenburg in Sweden with 0.59. The lowest values are seen at Nordjylland in Denmark with 0.27, Norra Mellansverige in Sweden with 0.28, Östra Mellansverige in Sweden with 0.3 and Syddanmark in Denmark with 0.31.
In total, these three innovation measures show varying regional trends in innovative activities among the Nordic regions. While it is usually the most urban regions that are associated with a higher level of innovative activities across the three measures, a number of non-urban regions appear to be specialising in various types of innovation.

Social aspects of the regional economy: Inter-regional income distribution and inequality

This section looks at a social dimension of the regional economy in the Nordic countries, specifically the development of intra-regional economic inequality. For this task, we adopt the Gini coefficient index, which is one of the most widely used inequality measures. The index ranges from 0–1, where 0 indicates a society where everyone receives the same income, and 1 is the highest level of inequality, where one individual or group possesses all the resources in the society, and the rest of the population has nothing.
Figure 7.2 demonstrates the development in Gini coefficients from 2011 to 2022 for the Nordic regions, measured in terms of disposable income. Across this period, Sweden and Greenland have the highest levels of income inequality, while Norway and the Faroe Islands have the lowest. Denmark, Finland and Åland lie in the middle.
Among the two countries with the highest Gini coefficients, Greenland has experienced the lowest increase (approximately 0.029 points) over the last more than 10-year period. This contrasts with the Nordic average from 2011–2022 of 0.046 points. Previous research has argued that the high Gini coefficient in Greenland reflects both the small size of the population and the relatively large share employed in the informal economy (Andersen 2015; Ravn et al., 2023). Sweden, on the other hand, has seen an increase of approximately 0.04 points in 11 years. In 2021, Sweden reached the highest Gini coefficient level (0.33) since measurements began in 1975 (Health Europe, 2023). It fell slightly again between 2021–2022.
Figure 7.2: Development in Gini coefficients (2011–2022)
Source: NSIs & Eurostat
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Previous research suggested that the increase in inequality was due to the diminished significance of social transfers within the working-age population and the resulting decrease in relative income levels experienced by the lowest quintile (Barth et al. 2021; Greve & Hussain 2022). Other explanations probably include high immigration rates, which resulted in 16% of the population being foreign-born in 2015 (OECD, 2017).
Map 7.3 displays the Gini coefficients for Nordic municipalities in 2022, while Map 7.4 depicts the changes in Gini coefficients for Nordic municipalities between 2018–2022. The absence of data for municipalities in Iceland prevents a comparison with the rest of the Nordic Region. In 2022, the highest municipality income disparities were observed in the capital city regions of Denmark, Finland and Sweden, each of which had Gini coefficients around 0.6. Danderyd (0.64), Lidingö (0.52), and Gentofte (0.51) had the highest Gini coefficients. These municipalities also have some of the highest incomes in their respective countries.
Map 7.3: Gini coefficients for disposable income (2022)
Note: Blue areas indicate a Gini coefficient below the Nordic average. Red areas indicate a Gini coefficient above the Nordic average (0.27, excluding Greenland, as a statistical outlier). The data for the Faroe Islands is for 2021.
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Map 7.4: Percentage change in Gini coefficients at municipal level (2018–2022)
Note: Blue areas indicate a decrease in income inequality, while red areas indicate an increase in income inequality.
Map 7.4 illustrates significant variations in the change in income inequality across Nordic municipalities and regions. Between 2018 and 2022, income inequality increased in predominantly rural municipalities, notably in Jämtland, Gävleborg, Dalarna and Västerbotten in Sweden, as well as Telemark in Norway. For Denmark, the rise in inequality is mainly for the municipalities in Western Jutland.
At the same time, approximately one third of municipalities in the Nordic Region experienced a decrease in income inequality during the same period, primarily in Finland and Åland. For example, in Finland, the distribution of inequality was more varied. This trend aligns with the ongoing narrowing of the household income gap observed in many Finnish municipalities since 2011, which is mainly attributed to the economic downturn of the early 2010s, as well as demographic shifts such as outmigration and ageing (Roikonen 2022).

Environmental aspects: GHG emissions and (supra-)national policies

This section investigates environmental aspects of the regional economy via analyses of territorial greenhouse gas (GHG) emissions at national level. As EU policies have significant implications for the countries’ actions aimed at reducing GHG emissions, the impacts of the EU-level schemes are assessed alongside the Nordic ones. Finally, the GHG emissions are evaluated in the light of the GDP analysis to illuminate issues surrounding the decoupling of economic growth and emissions.
In Figure 7.3 the annual territorial GHG emissions spanning from 1990 to 2021 are shown for each of the Nordic countries. While emissions have surged in Iceland, a contrasting macro trend is evident in Denmark, Finland, and Sweden, where reductions have been consistently observed, with the pace of which has been accelerating since 2010. Norway, on the other hand, initially experienced an increase, which has started tapering off starting from in 2020. Emissions increased in Iceland and Norway between 1990 and 2021 due to a recovery in international oil prices which that affected oil-related activity in Norway, and as well as an increase in aluminium production in Iceland (Dixon et al. 2023). However, both EU policies and the national-level policies in the Nordic countries have contributed significantly to the reductions.

Box 7.1: Greenhouse gas (GHG) emissions

Production- versus consumption-based greenhouse gas emissions:
  • Production-based GHG emissions are those attributed to the location of the production of goods and services. Production-based emissions are also named territorial or land-based emissions.
  • Consumption-based emissions attribute the emissions generated in the production of goods and services based on where they are consumed.[1] These are calculated by adjusting ‘production-based’ emissions (emissions produced domestically) for trade.
  • Consumption-based emissions equal production-based emissions, minus emissions embedded in exports, plus emissions embedded in imports. This is known as the ‘carbon footprint’.
  • Greenhouse gas emissions versus CO2 emissions
  • Greenhouse gas emissions include carbon dioxide, methane and nitrous oxide from all sources, including land-use change. They are measured in tonnes of carbon dioxide-equivalents over a 100-year timescale.
Figure 7.4 shows that, since the introduction of the EU Emissions Trading System (ETS) in 2005, emissions under this scheme have decreased. In terms of a proportion of all CO2 emissions in the EU and the EEA, the Nordic countries remained around 40% from 2012 to 2020 – approximately 42% in Denmark, 35% in Sweden and 33% in Finland. As part of the Fit for 55 package, the EU institutions have adopted a new target: to reduce emissions from EU ETS sectors by 62% by 2030, compared to 2005 levels. To reach this target, all Nordic countries must speed up the reduction of GHG emissions from those sectors.

The sectors not covered by the ETS scheme are covered by policy mechanisms under the EU’s Effort Sharing Regulation (ESR), which covers around 60% of territorial EU GHG emissions for sectors like transport, housing, agriculture, small industries, and waste management (Dixon et al., 2023). All EU member states, along with Iceland and Norway, have committed themselves to this scheme.
Figure 7.4 shows the GHG emissions from ESR sectors for 2005–2020, as well as the annual targets for 2021–2030, as set out under Implementing Decision (EU) 2020/2126 and EEA Joint Committee No 269/2019. The plot also shows a tentative reduction trajectory towards the new ESR targets agreed under the Fit for 55 Package. Chapter 8 adopts a business perspective to examine in more detail the changes in GHG emissions in the different sectors.
All the Nordic countries have exceeded the EU’s overall climate goals (Flam and Hassler, 2023), with several Nordic countries on track to achieve carbon neutrality between 2035 and 2045. Finally, the Nordic national governments confirmed their commitment to ambitious climate goals in 2019 by adopting the Nordic Vision 2030 (Norden, 2019), which sets the goal of the Nordic Region being the most sustainable and integrated region in the world in 2030. This vision includes a commitment to promote the transition towards carbon neutrality.
Figure 7.3: Territorial GHG emissions in the Nordic countries 1990-2021 (% change from 1990)
Source: UNFCCC
Note: Excluding emissions from land use, land use change or forestry (LULUCF)
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Figure 7.4: Verified greenhouse gas emissions under the ETS Directive (2003/87/EC) (tonnes of CO2 equivalent)
Source: EEA EU Emissions Trading System (ETS) database
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The question of decoupling

Today’s economic and business models and modus operandi are based on ideas of infinite opportunities – in other words, a linear economy – which may not be compatible with the finite nature of natural resources. Socioeconomic systems require materials and energy for human activity, agriculture, livestock, and manufactured goods, all of which lead to emissions of greenhouse gases (GHGs), air and water pollutants and waste. Due to its wealth and purchasing power, the Nordic Region is also facing challenges related to its current consumption patterns and waste streams. As articulated in Watson et al. (2021):
Green growth forms the core of sustainable development strategies in the Nordic countries, the rest of Europe and indeed in the rest of the world. The central assumption is that economic growth can be continued while reducing resource use, environmental pressures, and impacts. [...] However, it is not clear whether absolute decoupling is possible in the long-term or whether it is simply a pipe dream that those who see economic growth as a societal priority can hang on to.”
Grafström (2023) argues that since 1990, the European economy has become 60% larger, while at the same time, emissions have been reduced by 25%. Between 1990 and 2019, the Swedish economy doubled, while GHG emissions fell by almost 29%, and the population grew by 1.6 million. Denmark’s GDP has increased almost every year since 1990, except during the financial crisis and COVID-19, while GHG emissions increased less in the early part of this period and have been decreasing since 2006. A large part of the decrease in emissions is due to the green transition, especially measures related to renewable energy and energy efficiency. In Norway, emission intensity fell by 1.6% for production and 1.7% for GDP in 2022, indicating that Norway has become more emissions effective. Since 1990, the emissions intensity for production has fallen by 56.3%, and for GDP by 49.9% (SSB 2023).
Looking at the Nordic countries if isolated from the rest of the world, the evidence for absolute decoupling seems to be the case for GHG emissions when considered from a territorial perspective for Denmark, Finland, and Sweden. However, the reduction in GHG emissions is partly due to carbon leakage, e.g. from the outsourcing of heavy industrial production to other regions abroad. Statistics Denmark (2023) reports a shift in the Danish economy away from industry and towards services. Similarly, the IEA points to deindustrialisation as the reason for around 30% of the decrease in CO2 emissions in Europe (IEA, 2024).
Alongside changes on the supply side, the demand for labour is also undergoing a profound transformation – often referred to as the fourth industrial revolution. Developments in information technology, combined with robotisation and artificial intelligence, are enabling the automation of tasks previously only done by humans (Degryse, 2016). In 2020, Nordregio conducted an analysis of automation and Nordic labour markets and concluded that 32% of jobs were at risk due to automation (Grunfelder et al., 2020). Although automation may result in the displacement of workers in particular industries in the short term, the jobs it creates are expected to more than make up for the losses in the longer term (McKinsey & Company, 2017). Nevertheless, these structural changes in Nordic industries may have significant impacts on territorial GHG emissions.
Further, corresponding changes in consumption have not been seen (Watson et al. 2021). We will investigate this from a household perspective in a later section. Haberl et al. (2020), as part of a large literature review looking at the possibility of achieving absolute decoupling in the future, argue that there is ample evidence that a continuation of past trends will not yield absolute reductions in either resource use or GHG emissions. So far, environmental and climate policies have, at best, achieved a relative decoupling of GDP and resource use – specifically, GHG emissions (Kemp-Benedict 2018, Haberl et al. 2020).
By contrast, circular economy approaches, based on a recycling of materials or resources (as described, e.g. by Gowdy and Erickson 2005; Kovacic et al. 2020), look at the economy as being constrained by environmental factors. One current example of the circular economy approach is GreenLab Skive (Salonen & Tomren 2023), a business park comprising eight companies specialising in energy storage and resource efficiency. A notable feature of the park is its provision of internally sourced renewable energy, which serves as the primary energy supply for the resident companies, all of which are engaged in the production of environmentally friendly goods, including electro-fuels, heat and other sustainable products. The system also allows companies to share their surplus resources within the park, which cuts the costs of production. The park is constructed around a circular economy framework that aims to foster innovation to benefit not only the resident companies but also the surrounding region and to promote a process of evolution towards industrial symbiosis. For example, converting agricultural waste into resources such as biogas and biomass not only reduces carbon emissions but works in conjunction with local agricultural production processes. These energy systems are also creating jobs in their regions (Green Power Denmark 2024). In Denmark, the energy industry, including wind and Power-to-X, employs 73,000 people. Between 2023 and 2030, the sector expects to need an additional 45,000 jobs on average per year for the development of more renewable energy based on electricity.
In sum, while it may not be possible to achieve decoupling on a global level, we do have attempts and practices in the Nordic Region, e.g. circular economy models, that have benefits for the environment, jobs, and the economy, and on which we can build in the future.

References

Andersen, T. M. (2021). The Greenlandic economy: Structure and prospects. In Greenland's Economy and Labour Markets, pp. 11-29. Routledge.
Asheim, B. T. & Isaksen, A. (1997). Location, agglomeration and innovation: Towards regional innovation systems in Norway? European planning studies, 5(3), pp. 299-330.
Barth, E., Moene, K. O. K. & Pedersen, A. W. (2021). Rising inequality in the egalitarian Nordics. Europe’s income, wealth, consumption, and inequality. In Europe's Income, Wealth, Consumption, and Inequality. Oxford University Press.

Bjerregaard, P., Dahl-Petersen, I. K. & Larsen, C. V. L. (2018). Measuring social inequality in health amongst indigenous peoples in the Arctic. A comparison of different indicators of social disparity among the Inuit in Greenland. SSM-population health, 6, pp. 149-157.
Degryse, C. (2016). Digitalisation of the economy and its impact on labour markets. Working Paper. ETUI Research Paper – Working Paper 2016.02, Brussels. dx.doi.org/10.2139/ssrn.2730550
Dixon, P., Rimmer, M, Wittwer, G., Honkatukia, J., Tapia, C., Sanchez Gassen, N. (eds.) (2023) What impact do climate change policies have on Nordic economies, industries, and households? Nordregio report 2023:10. http://doi.org/10.6027/R2023:10.1403-2503
Doloreux, D. & Shearmur, R. (2023). Does location matter? STI and DUI innovation modes in different geographic settings. Technovation, 119, 102609.
Eurostat (2024). Real GDP growth rate – volume. Available at: https://ec.europa.eu/eurostat/databrowser/view/tec00115/default/table?lang=en
Flam H. & Hassler J. (2023). EU versus national climate policies in the Nordics. Nordic Economic Policy Review 2023. Nordic Council of Ministers.
Gowdy, J. & Erickson, J.D. (2005). The approach of ecological economics. Camb. J. Econ. 2005(29), pp. 207–222.
Grafström, J. (2023). Klimatet vinner på tillväxt. Available at: https://timbro.se/miljo/klimatet-vinner-pa-tillvaxt/
Gray, M., Lobao, L. & Martin, R. (2012). Making space for well-being. Cambridge Journal of Regions, Economy and Society, 5(1), pp. 3-13.
Greve, B. & Hussain, M. A. (2022). Support for governmental income redistribution in Nordic countries. European Review, 30(3), pp. 426-444.
Grunfelder, J., Norlen, G. & Randall, L. (eds.) (2020) Gassen, N. S. State of the Nordic Region 2020. Nord 2020:001, Nordic Council of Ministers. https://doi.org/10.6027/NO2020-001
Haberl, H. D., Wiedenhofer, D., Virág, G., Kalt, B., Plank, P., Brockway, T., Fishman, D., Hausknost, F., Krausmann, B., Leon-Gruchalski, A., Mayer, M., Pichler, A., Schaffartzik, T., Sousa, J., Streeck, J. & Creutzig, F. (2020). A systematic review of the evidence on decoupling of GDP, resource use and GHG emissions, Part II: synthesizing the insights. Environmental Research Letters, 15: 065003.
Health–Europe, T. L. R. (2023). Sweden's economic inequality gap is widening and worrying. The Lancet Regional Health-Europe, 26. https://doi.org/10.1016/j.lanepe.2023.100610.
Jensen, M. B., Johnson, B., Lorenz, E., Lundvall, B. Å. & Lundvall, B. A. (2007). Forms of knowledge and modes of innovation. The learning economy and the economics of hope, 155.
Kemp-Benedict, E. (2018). Dematerialization, decoupling, and productivity change. Ecological Economics, 150(2018), pp. 204–216.
Kovacic, Z., Strand, R. & Völker T. (2020). The circular economy in Europe. Critical perspectives on policies and imaginaries. Oxon: Routledge.
McKinsey & Company. (2017). A future that works: the impact of automation in Denmark. The Tuborg Research Centre for Globalisation and Firms and McKinsey & Company. Available at: https://mck.co/2O2jZhU
Nelson, A., Earle, A., Howard-Grenville, J., Haack, J. & Young, D. (2014). Do innovation measures actually measure innovation? Obliteration, symbolic adoption, and other finicky challenges in tracking innovation diffusion. Research Policy, 43(6), pp. 927-940.
Norden (2019) Our vision 2030. Available at: https://www.norden.org/en/declaration/our-vision-2030
OECD (2024), Gross domestic product (GDP) (indicator). Available at: https://data.oecd.org/gdp/gross-domestic-product-gdp.htm
OECD (2017). Chapter 1. Income, wealth and equal opportunities in Sweden. In OECD Economic Surveys: Sweden 2017. https://doi.org/10.1787/19990448.
Parrilli, M. D. & Heras, H. A. (2016). STI and DUI innovation modes: Scientific-technological and context-specific nuances. Research Policy, 45(4), pp. 747-756.
Ravn, R. L. & Høgedahl, L. (2023). Employment in a post-colonial society – The case of Greenland. Journal of Industrial Relations, 65(5), pp. 640-662.
Ritchie, H., Rosado, P. & Roser, M. (2024a). CO2 and greenhouse gas emissions. Available at: https://ourworldindata.org/co2-and-greenhouse-gas-emissions
Ritchie, H., Rosado, P. & Roser, M. (2024b). Breakdown of carbon dioxide, methane and nitrous oxide emissions by sector. Available at: https://ourworldindata.org/emissions-by-sector
Roikonen, P. (2022). Income inequality in Finland, 1865–2019. Scandinavian Economic History Review, 70(3), pp. 234-251.
Schumpeter, J. A. (1939). Business cycles: A theoretical, historical, and statistical analysis of the capitalist process. New York: McGraw-Hill.
Salonen, H. & Tomrén, L. (2023). Can local value creation induce a sense of justice during green transitions? A study of six rural areas in Denmark, Finland, and Norway. Nordregio report 2023:9. https://doi.org/10.6027/R:2023:91403-2503
Statistics Denmark (2023). Tema: Klima. Available at: https://www.dst.dk/da/Statistik/temaer/klima
Storper, M. (2018). Separate worlds? Explaining the current wave of regional economic polarization. Spatial Transformations, 136, pp. 247-270.
Tadaros, M., Migdalas, A., Samuelsson, B. & Segerstedt, A. (2022). Location of facilities and network design for reverse logistics of lithium-ion batteries in Sweden. Operational Research, 22, pp. 895-915. https://doi.org/10.1007/s12351-020-00586-2
Watson D., Svendsen N. L., Kaarsberg S. & Värttö H. (2021). Is economic growth compatible with a sustainable future? A review of literature. TemaNord 2021, NCM. https://pub.norden.org/temanord2021-542/#85722