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4. Representing Nordic greenhouse policies: setting the policy shocks 

This chapter introduces the Nordic-TERM model and presents the main modelling assumptions and scenarios involved in this research. These include a baseline run and a policy run and set out the effects of the greenhouse policies as deviations from the baseline. 

4.1. The Nordic-TERM model

Our main analytical tool for this project is the Nordic-TERM model. It is called Nordic because it focuses on the Nordic countries and TERM because the model is in the tradition of The Enormous Regional Models initially developed at the Centre of Policy Studies at Victoria University in Melbourne, Australia (Horridge 2012; Adams, Dixon, and Horridge 2015). Information on TERM models in general and the technical details of Nordic-TERM in particular are given in Appendix 2. A brief overview is provided here.
Nordic-TERM, like all TERM models, is a multi-regional computable general equilibrium (CGE) model. Nordic-TERM, which was created for this project, identifies the five Nordic countries Denmark, Finland, Iceland, Norway, and Sweden, and the Rest of Europe (RoE). Each Nordic country is split into subnational regions at the NUTS-2 level: 5 regions in Denmark; 5 in Finland; 1 in Iceland; 7 in Norway; and 8 in Sweden. With the rest of Europe (RoE) treated as a single region, Nordic-TERM has 27 regions. The regions are treated as trading economies with strong flows of capital and labour within the regions of each country. 
We chose the CGE approach for this study for four reasons:
  • First, CGE models are a practical framework for representing micro-economic policies. That is because CGE models incorporate descriptions of inputs to industries and sales to users. As explained in Section 4.3, for this project we applied ‘shocks’ to our model by making changes to the composition of inputs for the production of motor fuels and changes to the composition of expenditures by households, including a switch from motor fuels towards increased use of electricity (associated with the adoption of electric vehicles). The quantitative details of the shocks (the extent of the reduction in motor fuels, how much extra electricity etc.) are always subject to debate. Using the CGE framework, we can identify the role of each shock in contributing to key results and the effects on those results of varying the degree of the shocks within plausible ranges.
  • Second, CGE models specify explicit price-sensitive behaviour for multiple agents. Using a CGE model, we can trace how demand for various products by households, firms, exporters, and importers is affected by changes in prices. That is important for the present study, which involves policy-induced changes in commodity prices, particularly those of energy products. 
  • Third, CGE models incorporate links between different economic agents. The most obvious are input-output links in which one agent (such as the motor vehicle industry) is a customer for the product of another agent (such as the fabricated metals industry). However, CGE models also include macro-economic links through labour, capital, and foreign-currency markets: expansion of one industry in a CGE model generates costs for other industries through wage, interest, and exchange rate effects. With these links in place, a CGE model picks up not only effects of greenhouse policies on energy industries, but also on industries and households that depend on such directly affected activities.
  • Fourth, a CGE model allows for computations at a high level of disaggregation. As mentioned already, Nordic-TERM encompasses 27 regions. Within each region there are 53 industries. At this level of disaggregation, we can identify activities most relevant to greenhouse gas emissions, including: 5 distinct types of electricity generation based on different fuels; 3 agricultural activities; 3 transport modes; 4 mining activities; oil refining; and 4 categories of metal production. 
In addition to the disaggregated regional and industry results generated by the core part of Nordic-TERM, further results can be generated using downstream add-on programs. In this analysis, we have created add-on facilities to take core Nordic-TERM results and compute implications for the following: greenhouse gas emissions; employment by occupation and wage band; and living costs for households classified by income decile and location: urban (large cities); intermediate regions (towns and suburbs); and rural areas. 
CGE models can be run using various choices for the length of a period. One possibility is to perform year-on-year simulations, where the length of each period is one year. However, in this analysis, we are concerned with long-term effects. We are therefore able to simplify the simulations by using just one period of 11 years, namely 2019 to 2030. When the Nordic-Term model was developed, the latest data in the CGE database were from 2020. We selected 2019 as the reference year instead of 2020 since the global economy was disrupted by the COVID-19 pandemic in the latter year and 2020 does not therefore reflect typical economic activity patterns in the Nordic Region. Each computation starts with a database for 2019. We then apply shocks for the exogenous variables representing their movements from 2019 to 2030. The model generates results for endogenous variables showing their resulting movements from 2019 to 2030. In other words, the model starts with a picture of 2019 and generates a picture of 2030.
We conducted two types of simulation using Nordic-TERM. The first is a baseline run showing the development of the Nordic economies from 2019 to 2030 in the absence of incremental greenhouse policies. The second is a policy run showing the development of the Nordic economies with decarbonisation policies for the transport sector in place and with phasing-out of coal in the power sector. Comparison of the policy and baseline results shows the effects of anticipated policies by the Nordic countries. 

4.2. Developing the baseline scenario

The baseline run shows the evolution of the Nordic economies from 2019 to 2030 in the absence of greenhouse policies beyond those already implemented by 2019. It not only excludes policies that are yet to be agreed, but also policies that have been agreed, but are yet to be implemented. The baseline simulation covers macro variables, output projections for industries by nation, employment projections for industries and subnational regions, and emissions projections by nation. The baseline also includes projections for employment by occupation, wage band, education, and age. 
Table 2 shows percentage growth in macro variables for the Nordic countries and the rest of Europe. The results are for growth over the 11 years from 2019 to 2030. They were derived in the baseline simulation. 
Table 2. Baseline national forecasts: 2019-2030 (percentage growth for 11 years)
Real household consumption (C)
Real investment (I)
Real government consumption (G)
Export volumes (X)
Import volumes (M)
Real GDP
Aggregate employment
Average real wage
Aggregate capital stock
GDP price index
Change to Consumer Price Index (CPI)
Export price index
Import price index
(15-64 years)
In the baseline simulation, GDP and employment growth were set exogenously for each of the Nordic countries and the Rest of Europe (RoE). In determining these variables, we started with historical GDP data from the OECD (2023). We used these data to derive GDP growth for the decades 2001-11 and 2011-21. We then accessed the World Bank historical data and projections for growth in the population aged between 15 and 64 (World Bank 2023). Combining the historical data for GDP and the working-age population, we derived growth in productivity for the decades 2001-11 and 2011-21, with productivity defined as GDP divided by working-age population (15-64). For the decade 2021-31, we assumed that productivity growth in each of the five Nordic countries and RoE will be the average of the productivity growths from the two earlier decades. Finally, we assumed that employment growth in the Nordic countries and RoE in the decade 2021-31 will match the World Bank projection for growth in the working-age population. With employment growth thus projected and our productivity assumption in place, we derived GDP growth. Our calculations, alongside the details of the baseline simulation and their underlying assumptions, are given in Appendix 1. Detailed results regarding forecasted industry outputs, CO2 emissions, and employment are also available in Appendix 1.

4.3. Setting the policy shocks

Our policy run shows the development of the Nordic economies with decarbonisation policies for the transport sector in place and with phasing-out of coal in the power sector. Our analysis captures:
  • increases in the cost of motor fuels to industries (mainly the road transport industry) associated with the attainment of biofuel targets in diesel;
  • increases in the cost of motor fuels to households associated with the attainment of biofuel targets;
  • changes in the composition of inputs in the production of motor fuels (substitution of petroleum by bio-based feedstock); 
  • increases in the use of electricity by households associated with the attainment of targets for the electric vehicle (EV) share of the passenger car fleet;
  • reductions in the use of motor fuels by households associated with the reduction in the share of passenger cars accounted for by internal combustion vehicles;
  • increased expenditure by households on charging station for EVs; and 
  • loss of physical capital through scrapping of the remaining coal-fired electricity generation and its replacement by other forms of generation
In line with the selection criteria outlined in Chapter 3, these effects summarise the key impacts of Nordic ESR policies on households. Technical details on the formulation of the policy shocks and the calculation of welfare effects are given in Appendices 1 and 2 to this Report.
The main information sources for modelling climate policies in the Nordic countries are the National Energy and Climate Plans (NECPs) referenced at the foot of Table 3. Policy targets and some of the measures that the Nordic countries are adopting are reported in a broadly comparable fashion in these reports, which are updated every three years. We used data from the reports for 2019. The national reports differ in level of detail. For example, the Danish report contains a very detailed annex covering economic and energy assumptions, as does the Finnish report, which however relies more on references to research reports. What the reports have in common is that the detail on policy targets is richer than the detail on specific policy measures.
Table 3. Calculation of percentage increases in motor fuel costs in 2030
Biofuel blending
(percentage of motor fuels)
Biofuel blending
(percentage of diesel)
Cost of diesel blend in 2030 (euro per litre)
Diesel blend
Diesel share of passenger car motor fuel use
Car fuels used by house­holds
Average over all motor fuels
Target 2030
with baseline shares
Price rise as a per­centage, 2030
2020 and 2030
Price rise as a per­centage, 2030
Price rise as a per­centage, 2030
Sources for biofuel percentages in 2020 and targets for 2030:

4.4. Bio­fuels

Although important criticism has been voiced regarding the use of biofuels, particularly first-generation biofuels, as an alternative energy source for vehicles,
Despite their capacity to replace fossil fuels (Malla et al. 2022), biofuels have been blamed for increasing food prices, thereby reducing food security in developing countries, and for driving global land use change, with unintended impacts on global biodiversity, potentially contributing to a net increase in greenhouse gas emissions (see Sarwer et al. 2022 for a recent review of this issue).
all Nordic countries have set targets to increase the renewable proportion of motor fuels (including gasoline and diesel). The first two columns in Table 3 show the actual biofuel share in motor fuels as of 1 January 2020 (column 1), and the target for 2030 (column 2). The definition of the blending target varies among the Nordic countries. In most of the countries, the biofuel blending target was expressed in terms of biofuel as a share of motor fuels.
The Danish Integrated National and Climate Plan of 2019 sets a renewable energy target for the transport sector aimed at increasing the renewable share from some nine per cent in 2020 to 19 per cent in 2030, mostly through electrification. According to the Danish legislation in place by the end of 2019, suppliers should blend at least 5.75 per cent of biofuels in the transport fuel they put on the market.
These goals have since then been tightened, and in 2021, the blending requirement for gasoline, diesel, and gas supplied for land transportation was set at 7.6%, including 0.3% advanced biofuels (more information is available here: CO2e-fortrængningskrav og regler for VE-brændstoffer til transport | Energistyrelsen (ens.dk). Nonetheless, in this report, we focus on policy goals that were in place in 2019 in the Nordic countries, before the start of the pandemic.
From 1 January 2020, this also included 0.9 per cent advanced biofuels, while the actual biofuel share in motor fuels was about six per cent in that year. The biofuel target for liquid fuels in the transport sector should be in the environment of seven per cent for 2030 (Danish Ministry of Climate and Utilities 2019).
In Finland and Norway, the targets are set explicitly: 30 per cent in 2030 in both countries. For Finland, the 2020 biofuel share in motor fuels was about 11.7 per cent (Ministry of Economic Affairs 2019). The Norwegian blending requirement for 2020 was 20 per cent, which was planned to be raised by 10 per cent; with double-counting of emission reductions for biofuels of the latest generation, that would amount to 40 per cent (Norwegian Ministry of Climate and Environment 2019, 2021). 
According to the Swedish Reduktionsplikt policy defined by Law 2017:1201, the Swedish blending targets are expressed in terms of an emission reduction obligation, where the targets are defined in comparison with emissions from producing the same amount of energy with fossil fuels. This therefore amounts to a blending requirement, which is given separately for petrol and diesel in the law. In Sweden, the biofuel share in motor fuels in 2020 was about 23%. The emission reduction target for 2030 was 28% for petrol and 66% for diesel (Ministry of Climate and Enterprise 2017). Assuming that the petrol and diesel shares of 2020 remain unchanged until 2030, the emissions target would require an overall blending share for low-emission fuels in 2030 of about 63 per cent (Table 3, column 2). It should be noted that in 2022 the Swedish Parliament (Riksdag) decided to pause the gradual increase in the required reduction for petrol and diesel. That means that that the reduction levels for 2022 continued to apply in 2023. During the spring of 2023, the government announced that the required reduction for petrol and diesel would be lowered to six per cent from 2024 and remain at that level for the remainder of the current term of office. In our scenario, we do not take these developments into account. Instead, we analyse how the required reduction as defined in Law 2017:1201 would influence the Swedish economy, industries, and households if it were to be implemented as initially planned.
In Iceland's case, the biofuel share in motor fuels in 2020 was 7.6 per cent. The share of renewables was 11.4 per cent including electricity, and was expected to grow as EVs become more common. Electricity is to account for most of the increase in renewables in Iceland’s transport sector. Iceland’s biofuel target is not so much concerned with increasing the biofuel share per se, but rather with increasing the domestic production of fossil-free fuels. These fuels would be produced by combining carbon capturing and the production of hydrogen with renewable electricity (hence the term e-fuels).
Diesel fuels account for different fractions of motor fuel use by passenger car fleets in the five countries (see column 8 in Table 3) and for approximately 100 per cent in their road transport sectors. The bio shares in gasoline for these countries are already at about the maximum level that is technically compatible with the gasoline-using motors in the current generations of passenger cars and biofuels. We therefore assume that bio targets for motor fuels will be achieved by increasing the bio content of diesel fuels, with the main implications being for the cost of motor fuels used by industries, particularly the road transport industry. 
As explained in Appendix 1, we calculated implied biofuel targets for diesel fuels, assuming no increase in bio shares of gasoline for any of the countries, except Sweden, for which we increased the gasoline bio share from 12 per cent to 28 per cent in order to account for the specific targets set by its Reduktionsplikt. The results of these calculations are shown in columns (3) and (4) of Table 3. The 2020 shares and the targets for 2030 expressed in terms of bio shares for diesel do not differ greatly from the bio shares expressed in terms of motor fuels, i.e., columns (3) and (4) are similar to columns (1) and (2).

4.5. Electric vehicles (EV)

The approximate targets for increasing electric vehicles (EVs) as a share of passenger car fleets are given in Table 4. There is an explicit target for growth in the number of EVs in only two of the Nordic countries. The Danish target is to have 775,000 EVs on the road by 2030,
The Danish parliament declared a more ambitious target of a million EVs in 2020 but so far there are only policies in place for the earlier government target of 775,000 EVs by 2030.
whereas Finland is targeting a figure of 750,000. These targets imply an increase of 23.1 percentage points in the share of EVs in Denmark and 24.0 percentage points in Finland. 
Table 4. Targets for sales of electric vehicles and effects on household motor fuel consumption in 2030
EV share 2020-2030 (%)
Policy-induced percentage change in 2030 in household consumption of motor fuels
775,000 EV target
750,000 EV target
60% EV sales share
70% EV sales share
60% EV sales share
2020 EV shares
2030 targets
  • Denmark: https://www.reuters.com/article/us-denmark-climatechange-autos-idUSKBN28E23O on Danish government targeting 775000 EVs. 
  • Finland: Hiilineutraali Suomi 2035 – ilmasto- ja energiapolitiikan toimet ja vaikutukset (HIISI), Synteesiraportti – Johtopäätökset ja suositukset (valtioneuvosto.fi)
  • Sweden: Sweden’s Country report to UNFCCC 2021. Emissions from diesel cars fell by 21% from 2010 in the year 2020; the target is a reduction of 66% by 2030, thus a reduction of 43% from 2020. It is possible to derive the biofuel blending target from this and the share of diesel in 2020. Sweden’s long-term strategy for reducing greenhouse gas emissions (unfccc.int)
  • Norway’s Climate Action Plan for 2021–2030. Norwegian Ministry of Climate and Environment, Meld St. 13 (2020–2021) (regjeringen.no)
For the other Nordic countries, we assume that the EV share of total sales of passenger cars in each year from 2020 to 2030 will be in line with current expectations as expressed in the sources listed under Table 4: 70 per cent in Norway and 60 per cent in Sweden and Iceland. As explained in Appendix 1, these assumptions concerning the share of sales, combined with expected scrapping rates for existing cars, imply growth in the EV share of the Norwegian passenger car fleet from 22.1 per cent in 2020 to 62.2 per cent in 2030. For Sweden and Iceland, the EV share is expected to grow from 6.2 per cent to 48.1 per cent and from 4.6 per cent to 47.4 per cent, respectively. Further details on assumptions related to household demand for electricity, costs of EV, and home charging stations are presented in Appendix 1.

4.6. Electricity generation sector

To enable a complete decarbonisation of the electric sector, we assume that by 2030 coal will have almost ceased entirely to be used in power generation in the Nordic Region. We simulate this by scrapping 90% of the capital and investment in Nordic-TERM’s coal-fired generation of electricity (ElecCoal industry, see Appendix 1). We make a corresponding reduction in the nations’ aggregate capital stock. In our simulations, coal electricity is replaced endogenously by low or zero-carbon alternatives.