costs result in a 7% reduction in use of hydrogen and ammonia, which is substituted
by Marine gas oil (MGO).

## 3.4 Electricity Trade

Large scale deployment of offshore wind increases the net electricity export from Norway, as illustrated in Fig. 9. However, the net electricity trade is highly dependent on both the electricity price development in Europe and the costs associated with offshore wind investments. For both cases featuring high electricity prices, Norway is a net exporter in 2050, with a 10% increase in electricity exports under the conditions of low offshore wind investment costs (LH-High). The impact is substantially greater under low European price levels, where higher technology learning of offshore wind results in a shift from net import volumes of 14 TWh (HH-Low) to net export volumes of 48 TWh (LH-Low). In this case, Norway becomes a net exporter also from an onshore perspective, thereby increasing national security of electricity supply. The volumes directly exported from offshore regions to Europe ranges from 30 TWh (HH-Low) to 56 TWh (LH-High). Consequently, the total net export volumes from onshore and offshore Norway can potentially reach 104 TWh (LH-High). Noteworthy, this study assumes political support for hybrid connections for all offshore wind areas in the southern North Sea, hence allowing electricity to be transmitted between European countries in hours of lower offshore wind production. As emphasized by (Jåstad and Bolkesjø 2023), this assumption increases the profitability of offshore wind investments.

<figure><img src="image_65.png" /><figcaption>**Fig. 9** Net export of electricity from onshore and offshore Norway in 2050. *Notes*: HH High investment cost and High demand, LH Low investment cost and High demand</figcaption></figure>
## 3.5 Sensitivity on Acceptance for Onshore Wind and Transmission Cables

The results demonstrate that acceptance of new domestic transmission cables and onshore wind affects the investments in offshore wind capacity, but mainly in the northern offshore regions. Figure 10 shows the difference in invested capacity when no new investments in transmission cables (HH-T and LH-T) and onshore wind (HH-O and LH-O) are allowed, compared to the cases with no limitations (HH and LH). The impact of limiting expansion of domestic transmission cables is negligible, with reductions of 130–640 MW. On the other hand, limiting onshore wind investments has a greater impact, with 2.6–5.3 GW increased capacity in offshore wind in all cases except HH-O-Low. In this case, the high investment cost and low European prices makes additional offshore wind investments unprofitable, and Norway relies on imports instead. In cases where offshore wind investments increase, it does not completely offset the initial investments in onshore wind, falling short of about 5–8 GW. In the high price cases (HH-O-High and LH-O-High), the maximum expansion potential assumed for offshore wind is reached at 40 GW. In low price cases (HH-O-Low and LH-O-Low), there is still additional investment potential to be utilized. The total decrease in energy production due to limitations on onshore wind range from 7 TWh (LH-O-Low) to 32 TWh (HH-O-Low). Consequently, Norway becomes increasingly more reliant on Europe in such a restrictive situation.

<figure><img src="image_66.png" /><figcaption>**Fig. 10** Difference in offshore wind capacity when limiting investments in new transmission cables (T) and onshore wind deployment (O), compared to HH and LH case. Notes: HH High investment costs, High demand, LH Low investment costs, High demand, T No new domestic Transmission, O No new Onshore wind</figcaption></figure>
# 4 Conclusion

This study analyses the role of offshore wind power in the decarbonisation of the energy system towards 2050. First, we have quantified the correlation of electricity production between the 20 identified Norwegian offshore wind areas and areas in Northern Europe. Second, we have used a long-term energy system model to analyse techno-economic investments in Norwegian offshore wind given different uncertain parameters, including European electricity prices, technology learning rates and national energy demand. Third, we have analysed how offshore wind power production influences the Norwegian energy system, including the impact on investments in other electricity generation technologies, the electricity use and emissions and the electricity trade. Finally, we have investigated how acceptance for expansion of domestic transmission cables and onshore wind power in Norway influences the techno-economic investments in offshore wind power.

To summarise the results, we answer the research questions of our chapter below.

**Research question 1** How is wind production from the 20 identified Norwegian offshore areas correlated, and how does this production align with offshore wind in the Northern European region?

The annual average wind capacity factors for the 20 areas range from 46% to 57%, with the most favourable wind conditions in areas located furthest south in Norway. On an hourly basis, the wind production varies largely across the coast, with a weak correlation below 0.2 between the north and the south. Additionally, offshore wind in Norway is weakly correlated with offshore wind production in Northern Europe. Consequently, a large share of offshore wind power distributed along the Norwegian coast and across Northern Europe can reduce the risk of energy shortages through a smoothing effect, thereby enhancing resilience while promoting a low-carbon development of the energy system.

**Research question 2** How does the cost-optimal investments in Norwegian offshore wind power depend on the development of future national demand, technology learning and subsidies, and on the European power market?

The future technology learning in offshore wind technology and the price development of the European power market has a large impact on cost-optimal investments in offshore wind in Norway. The results show that in a future European market characterized by medium to high electricity prices, the ambitions of the Norwegian government of 30 GW capacity by 2040 become economically viable without the necessity of subsidies. With lower European prices, techno-economic investments are lowered to 0–12 GW unless subsidies or sufficient technology improvements are realized. The largest investments are made in the southern and western parts of Norway, where energy demand is the largest and the distance to Europe is closest. Moreover, lower national energy demand generally decreases investments in offshore wind with a higher concentration of capacity in the south. Limitations on onshore wind expansion have the opposite effect, increasing offshore
investments in most cases. Finally, expansion of domestic transmission capacity has
a negligible impact on investments in offshore wind.

**Research question 3** How does Norwegian offshore wind power contribute to the Sustainable development goals (SDGs) on energy and climate, economy, and industry?

Norwegian offshore wind can contribute to reducing both Norwegian and European climate emissions (SDG 13) by providing green electricity (SDG 7). This study shows that offshore wind investments increase Norwegian green hydrogen production from electrolysis, enabling emission reductions in Norway due to the substitution of blue hydrogen and MGO. Moreover, results indicate that the flexible Norwegian hydropower is a facilitator for increased offshore investments, providing an advantage that many other European energy systems are lacking. Offshore wind can also be beneficial in ensuring a diverse energy mix, in which the complementarity between offshore wind and PV enables mitigation of the cannibalization effect of PV occurring during mid-day hours. As shown by the results, offshore wind can lower the average electricity price in Norway by 6-8%, and even more on a regional basis, and hence contribute to affordable electricity for both individuals and businesses.

Norwegian offshore wind can further facilitate the green transition in Europe (SDGs 7&13), however, the supply of electricity depends largely on European electricity prices and technology development. Low investment costs play an important role in altering net-import situations that could occur in futures characterized by low electricity prices. In general, results show that offshore wind can help to ensure the security of electricity supply in Norway while simultaneously providing electricity to Europe in hours of high production. Noteworthy, this study is limited to the optimization of the Norwegian energy system, and we are therefore not able to quantify the impact on emission reductions as a result of Norwegian offshore wind export to Europe. Nevertheless, the analysis finds a weak correlation to Europe, indicating that offshore wind power from Norway could reduce dependency on fossil fuels when wind conditions are limited in Northern Europe. Further work will aim to quantify the impact and support of European climate targets by integrating European and national energy system models.

Norwegian offshore wind can also enable the electrification of industry and new industry development (SDG 9). In this study, two levels of future activity level have been analysed. Results indicate that even in cases with high national demand, large volumes of electricity export could be realized. Consequently, offshore wind investments could enable even larger economic growth than assumed in this study. Further, Norwegian offshore wind can contribute to transform the Norwegian petroleum-based economy to be a value-creating and cost-competitive low-emission economy. To quantify the effect on the economy, it is an option to link energy system models to general equilibrium models.

There are several other **further research needs** related to providing robust insights on the role of offshore wind in the energy transition. This includes analysing the robustness of the investment strategy given a high temporal resolution, different
climate years, climate change effects, and forecast errors. Different regulatory frameworks governing offshore wind will also impact the deployment. To evaluate how Norwegian offshore wind can contribute to a fair green transition, the implications of the recently proposed Norwegian amendments to the regulatory processes of the Offshore energy act should be analysed.

**Acknowledgements** The authors are grateful to the Research Council of Norway for funding this work as part of the projects "FME NTRANS", grant number: 296205, and "NCS 2030", grant number: 331644. In addition, the authors wish to acknowledge the Norwegian Water and Resource Directorate (NVE) for providing detailed cost data on current and future offshore wind and transmission cable investments.

## References

Aamodt Espegren K, Haaskjold K, Rosenberg E, Damman S, Mäkitie T, Dahl Andersen A, Moe Skjølsvold T, Pisciella P (2023) NTRANS socio-technical pathways and scenario analysis. https://doi.org/10.13140/RG.2.2.16978.32965

Aas Ø, Devine-Wright P, Tangeland T, Batel S, Ruud A (2014) Public beliefs about high-voltage powerlines in Norway, Sweden and the United Kingdom: a comparative survey. Energy Res Soc Sci 2:30–37

Afewerki S, Aspelund A, Bjørgum Ø, Hanson J, Karlsen A, Kenzhegaliyeva A, Normann HE, Steen M, Sæther EA (2019) Conditions for growth in the Norwegian offshore wind industry. Centre for Sustainable Energy Studies (CenSES). Oslo

Andersson I, Andresen AM, Haugen T, Holth MT, Mindeberg SK, Nettum NG, Ramtvedt AN, Schei AM, Ødegård A (2023) Innspill til forvaltningsregime for havvind—anbefaling om ny prosess for utvikling av havvind og strømnett til havs. Norwegian Water Resources and Energy Directorate. https://publikasjoner.nve.no/rapport/2023/rapport2023_29.pdf

Backe S, Skar C, del Granado PC, Turgut O, Tomasgard A (2022) EMPIRE: an open-source model based on multi-horizon programming for energy transition analyses. SoftwareX 17:100877

Durakovic G, del Granado PC, Tomasgard A (2023) Powering Europe with North Sea offshore wind: the impact of hydrogen investments on grid infrastructure and power prices. Energy 263:125654

Energi 21 (2022) Strategy 2022 national strategy for research development and commercialisation of new climate-friendly technology. https://www.energi21.no/siteassets/energi21-strategy-2022-en-lr-2.pdf

Environment Norway (2022) Norwegian greenhouse gas emissions. https://www/environment.no/topics/climate/norwegian-green-house-gas-emissions/

European Commission (2023) Non-binding agreement on goals for offshore renewable generation in 2050 with intermediate steps in 2040 and 2030 for priority offshore grid corridor Northern Seas offshore grids (NSOG) pursuant to Article 14(1) of the TEN-E Regulation (EU) 2022/869. https://energy.ec.europa.eu/system/files/2023-01/NSOG_non-binding_offshoreFINAL.pdf

Haaskjold K, Pedrero RA (2023) Long-term optimization of the Norwegian energy system under the influence of the European power market. In: 19th international conference on the European energy market, Lappeenranta

Haaskjold K, Rosenberg E, Seljom PMS (2023) Documentation of IFE-TIMES-Norway v3. https://ife.brage.unit.no/ife-xmlui/bitstream/handle/11250/3058964/Documentation+of+IFE-TIMES-Norway+v3+%28ID+57346%29+%28003%29.pdf?sequence=2
IEA-ETSAP (2023) IEA-ETSAP optimization modeling documentation. https://iea-etsap.org/index.php/documentation

International Renewable Energy Agency (IRENA) (2019) Future of wind: deployment, investment, technology, grid integration and socio-economic aspects. International Renewable Energy Agency, Abu Dhabi

Jåstad EO, Bolkesjø TF (2023) Offshore wind power market values in the North Sea—a probabilistic approach. Energy 267:126594

Koivisto M, Gea-Bermúdez J, Kanellas P, Das K, Sørensen P (2020) North Sea region energy system towards 2050: integrated offshore grid and sector coupling drive offshore wind power installations. Wind Energ Sci 5:1705–1712

Korsnes M, Loewen B, Dale RF, Steen M, Skjølsvold TM (2023) Paradoxes of Norway's energy transition: controversies and justice. Clim Pol 23:1132–1150

Linnerud K, Dugstad A, Rygg BJ (2022) Do people prefer offshore to onshore wind energy? The role of ownership and intended use. Renew Sust Energ Rev 168:112732

Löffler K, Hainsch K, Burandt T, Oei P-Y, Kemfert C, Hirschhausen CV (2017) Designing a model for the global energy system—GENeSYS-MOD: an application of the open-source energy modeling system (OSeMOSYS). Energies 10:1468

Malka L, Bidaj F, Kuriqi A, Jaku A, Roçi R, Gebremedhin A (2023) Energy system analysis with a focus on future energy demand projections: the case of Norway. Energy 272:127107

Miljødirektoratet (2023) Norge har lovfestet et mål om å bli et lavutslippsamfunn i 2050. https://miljostatus.miljodirektoratet.no/miljomal/klima/miljomal-5.4

Norwegian Water Resources and Energy Directorate (2023a) Beregning av kraftproduksjon for de identifiserte områdene. https://veiledere.nve.no/havvind/identifisering-av-utredningsomrader-for-havvind/metode-og-vurderinger/beregning-av-kraftproduksjon/

Norwegian Water Resources and Energy Directorate (2023b) Identifisering av utredningsområder for havvind. https://veiledere.nve.no/havvind/identifisering-av-utredningsomrader-for-havvind/

Norwegian Water Resources and Energy Directorate (2023c) Kostnader for kraftproduksjon. https://www.nve.no/energi/analyser-og-statistikk/kostnader-for-kraftproduksjon/

Norwegian Water Resources and Energy Directorate (2023d) Nye områder for havvind. https://veiledere.nve.no/havvind/identifisering-av-utredningsomrader-for-havvind/nye-omrader-for-havvind/

Regjeringen (2022a) Ambitious offshore wind initiative. https://www.regjeringen.no/en/aktuelt/ambitious-offshore-wind-power-initiative/id2912297/

Regjeringen (2022b) Havvind. https://www.regjeringen.no/no/tema/naringsliv/gront-industriloft/havvind/id2920295/

Regjeringen (2022c) Major initiative to promote offshore wind power. https://www.regjeringen.no/en/aktuelt/major-initiative-to-promote-offshore-wind-power/id2900436/

Reulein D, Tiwari S, Hestvik B, Kvannli A, Pinel D, Andresen CA, Farahmand H (2023) Large-scale offshore wind development and decarbonization pathways of the Norwegian energy system. In: 2023 19th international conference on the European energy market (EEM), Lappeenranta, pp 1–5

Shields M, Beiter P, Nunemaker J (2022) A systematic framework for projecting the future cost of offshore wind energy. National Renewable Energy Laboratory, Golden

Statnett (2022) Increased consumption results in a negative Norwegian energy balance from 2027. https://www.statnett.no/en/about-statnett/news-and-press-releases/news-archive-2022/increased-consumption-results-in-a-negative-norwegian-energy-balance-from-2027/

Voldsund M, Reyes-Lúa A, Fu C, Ditaranto M, Nekså P, Mazzetti MJ, Brekke O, Bindingsbø AU, Grainger D, Pettersen J (2023) Low carbon power generation for offshore oil and gas production. Energy Convers Manage: X 17:100347

Wiese F, Bramstoft R, Koduvere H, Pizarro Alonso A, Balyk O, Kirkerud JG, Grytli Tveten Å, Folsland Bolkesjø T, Münster M, Ravn H (2018) Balmorel open source energy system model. Energ Strat Rev 20:26–34
**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

<figure><img src="image_67.png" /></figure>
# Will the Nordics Become an Export Hub for Electro Fuels and Electricity?

<figure><img src="image_68.png" /></figure>

Check for
updates

Kenneth Karlsson, Andrea Marin Radoszynski, Marie Münster, Lissy Langer, Ioannis Kountouris, Johannes Felipe Giehl, Jenny Westerberg, Sidsel A. Nissen, Alexander Daabech, Mathias Bache, Lars Hedemann, Julia Kirch Kirkegaard, and Julia Hansson

**Abstract** The Nordics have a huge renewable energy potential, mainly in the form of onshore and offshore wind as well as biomass potentials and can deliver some of the lowest electricity prices in Europe. They could export large amounts of electricity and hydrogen, supplying mainland Europe and abroad. But where and when should wind, PV and green fuel production capacity be built, and what kind of infrastructure is needed? Within the Nordics—who can/will become a net exporter of electricity and green fuels?

K. Karlsson (✉) · A. M. Radoszynski
Energy Modelling Lab, Copenhagen, Denmark
e-mail: kenneth.karlsson@energymodellinglab.com; Andrea@energymodellinglab.com

M. Münster · L. Langer · I. Kountouris
DTU Management, Kgs. Lyngby, Denmark
e-mail: maem@dtu.dk; lilang@dtu.dk; iokoun@dtu.dk

J. F. Giehl
Copenhagen Business School, Copenhagen, Denmark
e-mail: jfg.eco@cbs.dk

J. Westerberg
Profu, Stockholm, Sweden
e-mail: jenny.westerberg@profu.se

S. A. Nissen · A. Daabech · M. Bache · L. Hedemann
Brinckmann, Århus, Denmark
e-mail: san@brinckmanngroup.com; ada@brinckmanngroup.com; mba@brinckmanngroup.com; lhe@brinckmanngroup.com

J. K. Kirkegaard
DTU Wind, Roskilde, Denmark
e-mail: jukk@dtu.dk

J. Hansson
IVL Swedish Environmental Research Institute, Chalmers University of Technology, Göteborg, Sweden
e-mail: Julia.hansson@ivl.se
To avoid sub-optimal solutions, some overall analysis and planning may be needed to secure societal benefits and reduce the overall cost. By comparing plans and visions for the build-out of electrolyser capacity, electricity and gas infrastructure, and wind and solar projects, we discuss consistency (or lack thereof) in the project pipelines. Furthermore, the future role of the Nordics is discussed by comparing scenario analysis from different modelling teams to identify robust conclusions and critical uncertainties.

The impact goes beyond SDG 13 (Climate action) and supports e.g., SDG 8 (Decent work and economic growth) by implementing a more sustainable economic system and also offers the opportunity to provide affordable and clean energy (SDG 7).

## Key Messages

* The Nordics are in a position to become a major exporter of hydrogen and electricity—this conclusion is robust under the analysed situations.

* Methodology significantly influences hydrogen demand modelling conclusions. Back-casting tends to overestimate in the short and medium term, while demand-side optimization may underestimate.

* The implementation of hydrogen and electro fuels has direct positive impacts on several sustainable development goals (SDGs 7 on energy, 9 on industry, 11 on cities, and 13 on climate) but also indirect positive effects (on SDGs 8 on work and economic growth, and 12 on efficient use of resources).

* Renewable energy projects in the Nordic countries face local resistance, potentially causing delays in Power-to-X (PtX) development. Public concerns, including safety issues and the fair distribution of costs and benefits, are anticipated.

# 1 Introduction

The Nordics have a huge renewable energy potential, mainly in the form of hydro-power, onshore and offshore wind, and can deliver some of the lowest electricity prices in Europe (Sovacool 2017). They could export large amounts of electricity, hydrogen, and green fuels, supplying Europe and abroad (Ikäheimo et al. 2018). However, studies show that aspects like the regional availability and diversity of renewable energy sources (Gea-Bermúdez et al. 2023; Ikäheimo et al. 2018; Karlsson and Meibom 2008) or infrastructure for imports and exports (Pedersen et al. 2022) influence the feasibility and demand for hydrogen and green fuels. With the expected limited global supply of sustainable biomass (Haberl et al. 2010), e-fuels based on hydrogen derived from electrolysis may play an important role (Mortensen et al. 2020), particularly for the chemical industry, and heavy long-haul transport such as aviation and shipping. But where and when should wind, PV, and electrolyser capacity be built, and what kind of infrastructure is needed? Can the Nordics compete with countries in the South that have abundant solar potentials?
Within the Nordics—who can/will become a net exporter of electricity and green fuels? Here, green fuels are defined as fuels based on carbon neutral electricity or sustainable biomass.

In a race to decarbonize and fulfil the net zero target, EU members adopted national Power-to-X strategies and targets as green hydrogen was seen as the renewable energy carrier that could decarbonize hard-to-abate industries. As of writing this, nineteen EU countries have adopted a national hydrogen strategy, one has implemented a roadmap, and Italy has produced preliminary guidelines. Among the Nordic countries, Denmark is the most ambitious, targeting between 4 and 6 GW of electrolyser capacity by 2030 according to Danish Ministry of Climate, Energy and Utilities (Klima-, Energi- og Forsyningsministeriet 2021) The Danish Energy Agency (DEA) has also set up a “Power-to-X taskforce” to ensure alignment with related resource and consumption sectors and to nurture the development of a market. Sweden follows Denmark in terms of ambitions, targeting 5 GW according to the Swedish Energy Agency (Energimyndigheten 2022), while Norway does not specify a target in its national strategy. Germany, in turn, boasts a 10 GW electrolyser capacity target, also reflecting the country’s larger size according to the Federal Government of Germany (Bundesregierung 2023). The plans and ambitions on the role of green hydrogen and e-fuels presented in the early 2020s were reignited by the decision to end dependency on Russian natural gas in early 2022. For countries to achieve these ambitious targets, however, two overall prerequisites must be met (1) enough renewable electricity, and (2) a willingness-to-change.

To avoid sub-optimal solutions, some overall analysis and planning may be needed to secure societal benefits and reduce the overall cost. The future role of the Nordics is discussed by comparing scenario analysis from different modelling teams to identify robust conclusions and critical uncertainties.

Combating climate change through renewable energies provides extensive, sustainable economic potential. Climate protection measures offer the potential to improve global development prospects and contribute to the Sustainable Development Goals of the United Nations.

The impact goes beyond SDG 13 “Climate action” and supports e.g., SDG 8 “Decent work and economic growth” by implementing a more sustainable economic system. It also offers the opportunity to provide “affordable and clean energy” (SDG 7).

## 2 Who Will Be the Future Net Exporter of Power and Green Fuels?

### 2.1 Hydrogen in the Context of the Nordics

The Nordics offer great potential for the integration of hydrogen into the overall energy system. The European Union sees hydrogen as a key element of its strategy to
reduce greenhouse gas emissions, and the Nordics are often identified as role models for the energy transition. Their high potential of renewable energy sources offers the opportunity to become a major player in the European “hydrogen economy” and export green fuels to Central European countries (Ikäheimo et al. 2018).

However, the development of a hydrogen economy depends on several factors, such as economic feasibility, future demand and supply, and the idea of becoming a hydrogen export region is subject to uncertainties. The availability of renewable energy sources for hydrogen and green fuel production is an important factor for the success of the energy transition. Different expansion pathways result in huge differences regarding the capacity of electrolysis. The status quo shows different regional conditions but high potential in Norway, Finland, Sweden, and Denmark (Pedersen et al. 2022). Gea-Bermúdez et al. (2023) show a range of 6–357 GW of offshore electrolysis capacity by countries in the North Sea and Baltic region. Gea-Bermúdez et al. (2023), Ikäheimo et al. (2018), and Karlsson and Meibom (2008) state that wind (onshore and offshore) and hydropower are the most relevant renewable energy sources on a Nordic scale to produce hydrogen and green fuels.

The national conditions reveal differences within the Nordics. Child and Breyer (2016) analyse the decarbonization of the Finnish energy system. Onshore wind energy and PV will be the main contributors, and Finland is the only country in the Nordics with a share of nuclear power in the long term. Bramstoft and Skytte (2017) focus on the decarbonization of the Swedish energy sector and Lund et al. (2022) of the Danish energy sector. Both countries offer good onshore wind conditions with relevant contributions of biomass. In the long term, a limited offshore wind contribution is possible while the nuclear capacity in Sweden decreases. Norway has a different starting position as the power sector is already renewable due to abundant hydropower capacity (Pedersen et al. 2022) and an expansion of offshore wind and PV (Haaskjold and Pedrero 2023).

The investigations by Lund et al. (2022), Karlsson and Meibom (2008) and Bramstoft and Skytte (2017) highlight the opportunity to use the biomass potential in the production of synthetic fuels. Biomass offers either the chance to process hydrogen by adding biomass-based CO₂ or directly in biofuel production. Drysdale et al. (2019) and Lester et al. (2020) show uncertainty about the future contribution of biomass and discuss the competition with hydrogen for renewable fuel production.

Pedersen et al. (2022) discuss infrastructure as a prerequisite for exports or imports of energy. The realisation of the potential to export renewable energy is uncertain. The existing interconnections between the Nordics and Europe limit the chance to import or export hydrogen and require further investments. The Swedish infrastructure lacks a country-wide gas transmission grid. The gas grid in the south-west is linked to Denmark and is seen as the starting point for further development. Norway, as a major source of European natural gas supply, already has an interconnected energy infrastructure that generates opportunities for future hydrogen exports. Together with Denmark, the country offers potential storage capacities for hydrogen in the form of salt caverns. The Danish natural gas grid has existing connections to central Europe and can function as a link for the Nordics to supply
European demand (Pedersen et al. 2022). In this context, the Danish grid operator Energinet participates in the current discussions on a hydrogen backbone (Creos et al. 2021).

## 2.2 Example 1: Analysis with DTU Balmorel Europe

The Balmorel model is a technology rich, large-scale energy system model, which has been applied in many parts of the world (Wiese et al. 2018). The model is driven by exogenous inputs on demands in different sectors for electricity, heat, and fuels, including hydrogen and other alternative fuels. For some sectors, the hydrogen demands are given directly, for others the demands are determined endogenously based on competition with other energy carriers, and associated technologies and infrastructures, e.g., for the generation of peak electricity. For the analysis presented below, a least-cost optimization has been applied for the entire EU with investments allowed in electricity and hydrogen transmission grids and storages, as well as generation units. Technology costs on investment and operation are mainly from the technology catalogues provided by the Danish Energy Agency (DEA 2021). Fuel costs, as well as a uniform CO₂ tax is based on the World Energy Outlook's Net Zero Energy scenario (IEA 2023).

### 2.2.1 EU Hydrogen Infrastructure in 2050

Kountouris et al. (2023) investigate the development of EU hydrogen infrastructure in a pan-European case study from 2030 to 2050. They analyse three main scenarios: (1) *Blue*, which allows for hydrogen production using steam methane reforming from abated and unabated natural gas as well as via electrolysis and imports of green hydrogen; (2) *Green*, which only allows hydrogen production from electrolysers and imports; and (3) *-Imports*, which also only allows hydrogen production from electrolysers but prohibits hydrogen imports from outside EU. The first two scenarios consider imports from Tunisia & Algeria, Morocco, and Ukraine. In this section, we consider *grey* hydrogen as hydrogen produced using steam methane reforming (SMR) from unabated natural gas, *blue* hydrogen as produced using SMR from abated natural gas using CCS, and *green* hydrogen produced by electrolysers using electricity from the grid. Since we focus on the long-term developments in 2050, for simplicity, we do not consider the additional restrictions on electricity sourcing introduced by the Delegated Act on RFNBOs (EC 2023). In addition, the study examines how different cost assumptions for electrolysers and natural gas prices affect the balance between blue versus green hydrogen production. In the following, we extract the most important results for the hydrogen infrastructure in the Nordics. Iceland was not part of the study.
### 2.2.2 Hydrogen Demand Assumptions

The Balmorel model used in Kountouris et al. (2023) assumes a certain amount of hydrogen as model input. This demand needs to be fulfilled and drives the model’s investments in a mixture of new hydrogen grids, repurposed natural gas grids, and electricity transmission. Therefore, it is important to carefully evaluate which and to what amount hydrogen demands are included.

They consider an EU hydrogen demand of about 10 Mt. (332 TWh) in 2030 and 53 Mt. (1767 TWh) in 2050. The exogenous demand input is based on the Hydrogen Backbone report (2021) and includes applications for ammonia synthesis, liquid fuels and high-value chemicals, high-temperature industrial process heat, and iron ore reduction using hydrogen directly (but not for residential heating). The Nordics are assumed to have a relatively low hydrogen demand compared to other Central and Western European countries with more industry.

Figure 1 shows the input of exogenous hydrogen demands in the left column, which are an assumption based on other reports and an input to the model, and in the right column, the endogenous hydrogen demand, which is additional demand derived within the Balmorel model. The endogenous hydrogen demand can be used for synthetic fuels for buses and trains and for peak power. Thus, for the Nordics in 2050, the model assumes a total demand of 3.3 Mt. (110 TWh) of hydrogen.

### 2.2.3 EU Self-Sufficiency

In the context of the recent political discussion about the heavy dependence on Russian natural gas (especially in Germany), Kountouris et al. (2023) investigate the

<figure><img src="image_69.png" /><figcaption>**Fig. 1** Hydrogen demand in the Nordics in 2050. *Note:* The left column is based on exogenous input values and the right column results from the model. The values are identical for all three scenarios</figcaption></figure>
<figure><img src="image_70.png" /><figcaption>**Fig. 2** Selected generation in the Nordics in 2050. Note: Scenario blue allows for hydrogen production using steam methane reforming (SMR) from abated natural gas, green only allows production using electrolysers, and -Imports disallows imports from outside EU (Color figure online)</figcaption></figure>

impact of a self-sufficient EU without reliance on hydrogen imports from outside EU. They find that prohibiting hydrogen imports seems to have only marginal effects on the infrastructure of the Nordics (Fig. 2), whereas the main effects are seen in the South of Europe. For example, Italy that used to become a hydrogen corridor for imports from Northern Africa to Germany, significantly expands its hydrogen production capacity if imports are prohibited. Since the *Green* and *-Imports* scenario results are relatively similar, in the following, we illustrate only the *Blue* and *Green* scenarios.

### 2.2.4 Blue Hydrogen Production

The impact of blue versus green hydrogen production is still relevant in the context of the recurring debate on the role of carbon capture and sequestration (CCS) considering the importance for the Nordics, especially in Norway, and the large amount of 1.400 Mt/year of captured CO₂ that the European Commission considers in their modelling exercises using their JRC-EU-TIMES model (Blanco et al. 2018). We hence explore this, even though the European Commission with their Delegated Act (EC 2023) has for now settled on the use of renewable electricity and electrolysers to produce renewable hydrogen.

### 2.2.5 Generation and Storage Capacities

When hydrogen production is allowed by using steam methane reforming (SMR), the dynamics in the Nordics change significantly. Due to a carbon tax in the model
<figure><img src="image_71.png" /><figcaption>**Fig. 3** Selected capacities in the Nordics in 2050. Note: Scenario blue allows for hydrogen production using steam methane reforming (SMR) from abated natural gas, and green only allows production using electrolysers (Color figure online)</figcaption></figure>

(about 190 €/t CO₂ in 2050), only abated natural gas using CCS technologies is used in the Nordics. Kountouris et al. (2023) assumes a natural gas price of 10.87 €/MWh in 2050 based on the $3.8/MBtu of the Net Zero emissions scenario of the World Energy Outlook 2022 (IEA 2022).

Figure 3 shows that with small SMR capacities, especially in Norway, substantial amounts of blue hydrogen replace what would have been green hydrogen using electrolysers (Fig. 2). In addition, this also substantially reduces the need for renewable energy and hydrogen storage (Fig. 3). Most importantly, it shifts the centre of hydrogen production within the Nordics from Denmark to Norway, strengthening the role of natural gas producers in the energy transition. Due to the lower capacity factors of solar PV compared to wind (see generation in Fig. 3) and the assumptions of limited onshore wind potentials based on public acceptance, the strongest impact is on (relatively expensive) offshore wind investments, which decrease significantly, especially in Denmark and Sweden (Fig. 3).

### 2.2.6 Transmission Infrastructure

Figure 4 shows that the *Blue* scenario also affects the transmission infrastructure, namely electricity transmission lines (EL) and hydrogen pipeline infrastructure (H2). The *Blue* scenario decreases the overall need for hydrogen infrastructure in the Nordics from 45 to 32 GW. On the other hand, the need for electricity transmission lines remains approximately the same in both scenarios, at 68 GW.

### 2.2.7 Electricity and Hydrogen Exports

The Nordics mainly provide hydrogen to central Europe by exporting to Germany (DEU) and the Netherlands (NLD) via Denmark (DNK) or Norway (NOR). Figure 6
<figure><img src="image_72.png" /><figcaption>**Fig. 4** Selected electricity (EL) and hydrogen (H2) transmission infrastructure in the Nordics in 2050. Note: Scenario blue allows for hydrogen production using steam methane reforming (SMR) from abated natural gas, and green only allows production using electrolysers. **DNK** Denmark, **FIN** Finland, **NOR** Norway, **SWE** Sweden, **BLT/POL** Baltic countries + Poland, **DEU** Germany, **GBR** Great Britain, **NLD** Netherlands (Color figure online)</figcaption></figure>

<figure><img src="image_73.png" /><figcaption>**Fig. 5** Electricity and hydrogen exports from and within the Nordics in 2050. Note: Scenario blue allows for hydrogen production using steam methane reforming (SMR) from abated natural gas, and green only allows production using electrolysers. **DNK** Denmark, **FIN** Finland, **NOR** Norway, **SWE** Sweden, **BLT/POL** Baltic countries + Poland, **DEU** Germany, **GBR** Great Britain, **NLD** Netherlands (Color figure online)</figcaption></figure>

also shows considerable trade flows within the Nordics and within the Nordic countries considering the trade between the different bidding zones of each country. In the *Blue* scenario, Norway becomes the main exporter to Germany and significantly increases its hydrogen exports (Fig. 5). At the same time, in the *Blue* scenario, Denmark reduces its exports to Germany, the Netherlands, and Sweden. Overall, however, the hydrogen exports from the Nordics stay approximately the same, around 71 TWh in 2050.
On the other hand, the electricity exports from the Nordics are considerably higher than the hydrogen exports, with around 265 TWh in all scenarios (Fig. 5). The *Blue* scenario also affects electricity exports as more electricity is exported from Norway and Sweden. The dynamics of the *Green* scenario are driven by the additional offshore wind generation (Fig. 3) which is mainly added in Denmark and to some extent in Sweden.

## 2.3 Example 2: Analysis with EML TIMES-NEU Model

The TIMES Northern Europe (TIMES-NEU) is based on the open source ON-TIMES (https://github.com/NordicEnergyResearch/NCES2020) The number of green fuels and production technologies have been expanded, heavy industry is more detailed, with more green options and trade connections to surrounding countries (Fig. 6).

### TIMES-NEU Characteristics

* Partial equilibrium least cost system optimising and investment model.

* Main driver to the model is official economic projections on sector level and projections of population.

* 56 time slices a year, representing different seasons, day/night, and critical load situation—solved in 5-year steps until 2050.

* In Norway, Denmark, Sweden, Germany, and Poland all sectors included—internal trade with eight most important energy carriers among these core countries.

* With the surrounding countries (light blue) power trade between connected regions are modelled with price profiles to main model countries.

* UK, Belgium, and Netherlands—also trade with the eight energy carriers based on price profiles and potentials.

* Main model countries (darkest blue in the map) can also trade more than 40 energy carriers (fossil fuels, bio- and electro fuels, biomass, and electricity) with the global market represented with projected global market fuel prices (Fig. 7).

Important assumptions are as follows:

* National climate targets are assumed respected in the five main countries.

* Existing and projected CO₂- and energy taxes are included for the five main countries.

* Exogenous electrolyser capacity introduced according to “realistic” plans.

* Stop for import of green fuels and biomass to the main model area from 2040 (excluded from this is ammonia and hydrogen) to reflect impact from possible future EU regulation, e.g., extended EU Carbon Border Adjustment Mechanism (CBAM).
<figure><img src="image_74.png" /><figcaption>Fig. 6 Geographical area and model regions of TIMES-NEU</figcaption></figure>

* Reduction over time in available biomass for energy. Biomass resource in the main model countries is reduced by around 20% in 2030 and 40% in 2050.

* External/exogenous green hydrogen demand projections for UK, Belgium, and Netherlands

* For non-energy purpose (mainly chemical industry) fossil fuels are assumed to be faced out linearly from 2030 to 2050.
<figure><img src="image_75.png" /><figcaption>**Fig. 7** Illustrative structure of the TIMES-NEU. The model includes primary supply of fuels, upstream sector, power and heat sector, and detailed modelling of all end-use sectors</figcaption></figure>

## 2.3.1 Future Demand for Green Fuels

The demand for green fuels found in TIMES-NEU is a result of the competition between the different fuels, technological options in each sector and a global market with competing prices on all energy carriers. In the full modelling period, the five main countries can trade eight energy carriers (electricity, e-methanol, b-methanol, e-ethanol, b-ethanol, ammonia, wood chips, and wood pellets), among each other, only limited by transmission capacity (pipes, power lines etc.) With the UK, Belgium, and Netherlands the main countries can also trade the eight energy carriers and with the rest of the surrounding countries electricity can be traded between countries with shared borders. All +40 energy carriers can be traded with global market, restricted by the implemented trade constraints.

In Fig. 8 it is very clear when the import constraint on biomass and biofuels kicks in (2040), this means biofuels are replaced by e-fuels. The demand for green hydrogen as a fuel is only manifested at the end of the scenario period when the steel industry is converting.

The demand for green hydrogen in the UK, Belgium and Netherlands are based on an external analysis of their own possibility for producing fuels and their expected demand (Brinckmann 2023). The model then sees a potential export option to these countries based on their net demand and a related price. In particular, Belgium and Netherlands have a large chemical industry with a growing demand for green fuels in the future and there are big international harbours in the Netherlands which are imagined becoming fuelling hubs for international shipping.

Figures 9 and 10 shows which countries are producing the hydrogen used and traded among the countries in the model (and to the global market). The role Denmark potentially can play in this market is huge because of the wind resources in the North Sea and the central position with short distances to large consumers. So, this analysis shows that Denmark could deliver almost 50% of the needed hydrogen
<figure><img src="image_76.png" /><figcaption>**Fig. 8** Modelled demand for green fuels (bio and electro) in the fully modelled countries (Denmark, Sweden, Norway, Germany, Poland)—not including UK, Netherlands, and Belgium</figcaption></figure>

<figure><img src="image_77.png" /><figcaption>**Fig. 9** Expected direct hydrogen demand in the larger model area. *Note:* This includes direct use in industry. The demand is endogenous decided by the model in Denmark, Norway, Sweden, Germany, and Poland while it is exogenous added to the model for Netherlands, Belgium, and UK</figcaption></figure>

in the model area. Any mismatch between demand and production in the model area is balanced out by trade on the global market.

Surprisingly, to many, the model shows Germany as net exporter of green hydrogen until 2040 (Fig. 11) mainly due to already planned German electrolyser
<figure><img src="image_78.png" /><figcaption>**Fig. 10** Expected hydrogen production in the larger model area. Note: The production is endogenously decided by the model in Denmark, Norway, Sweden, Germany, and Poland while it is exogenously added to the model for the Netherlands, Belgium, and the UK</figcaption></figure>

<figure><img src="image_79.png" /><figcaption>**Fig. 11** Germany's trade with green hydrogen</figcaption></figure>

capacity and it is only when the steel industry switches from coal to green hydrogen and electricity in 2045 that their own production cannot keep up. This means a total replacement of the old steel plants with new HYBRIT plant types.

The Danish export of green hydrogen flows in two directions (Fig. 12). South, as expected beforehand, not in the beginning to Germany but to Belgium. Another flow is to Sweden which has a growing demand for green hydrogen for their chemical industry and for the green transition of their steel industry. Sweden cannot produce
<figure><img src="image_80.png" /><figcaption>**Fig. 12** Danish trade with green hydrogen</figcaption></figure>

hydrogen as cheap as Denmark can in the North Sea and the model finds it cheaper to establish a hydrogen pipeline from Denmark to Sweden. The Swedish hydro power resources are limited, and Swedish offshore wind parks will have less FLH than wind parks in the Danish part of the North Sea, hence the production price of hydrogen is higher in Sweden.

These modelling results perfectly illustrate the uncertainty in the future market for green fuels. In this case the driver for Danish green hydrogen exports is an exogenous demand projection for UK, Belgium, and Netherlands not dynamically considering the competition from electrification and other fuels to green hydrogen in these countries. Another driver for hydrogen demand is the production of ammonia for the shipping and chemical industries and in the presented scenario the model imports all fossil and green ammonia from the global market hence no local production. The prices the model sees for production (including transport and handling) of ammonia in the Middle East, Northern Africa and Australia are cheaper than production in the model area. If this balance was switched and green ammonia was being locally produced, then the demand for hydrogen in the model area would almost double.

### 2.3.2 Development of Infrastructure

Taking a closer look at the infrastructure development needed to fulfil the energy demands, we should pay special attention to the energy islands. The TIMES-NEU model has the option to invest in three **energy islands** for Denmark in the North Sea, plus utilizing the nearshore resources of the island Bornholm. According to the agreement from the Danish Parliament from June 2020, the first phase of the North Sea islands will have a minimum capacity of 3 GW of offshore wind power, with potential for expansion to 10 GW offshore wind. The strategic environment
assessment also includes a second phase in which a total of at least 10 GW of offshore wind power is established (phase one and two), but with the possibility of establishing a total of up to 40 GW (phase one and two) within the same area if the power per km² is increased.

The ambitious goal of building these energy islands is still under political discussion and will determine the position of Denmark as a hub for green hydrogen in Northern Europe.

The Nordic power sector will have to face a major transformation to accommodate the high electricity demand needed to produce green fuels in the future. The resulting power capacity of the Nordics more than doubles, from around 90 GW to 232 GW in 2020, as a result of the electrification of key industrial sectors and the increase in the use of green fuels in Europe.

These capacities will also be highly dependent on the position that the Nordics would take in the global market of green fuels. A key question to understand the needed expansion of the power system is: would the Nordics become self-sufficient to fulfil their energy needs or would they become net exporters of green fuels to central Europe?

The modelling results of TIMES-NEU shows that the high wind potential of the North Sea is utilized to produce hydrogen, mostly at the energy islands, and export it to central Europe. The offshore wind installed capacity in Denmark by 2050 reaches 10 GW mainland and 44 GW in the energy islands, summing to a total of 54 GW, almost reaching the renewable potential set in the model to 57 GW. The same case applies for Norway, that almost utilizes their full renewable potential of 9 GW, setting up 8.6 GW of wind offshore by 2050. Due to the less competitive wind resources in Sweden, the model only installs 10 GW of the potential 20 GW, and imports the hydrogen directly from Denmark, Norway, or the global market, as shown in Fig. 13.

Figure 13 shows the evolution of the offshore wind capacity and Fig. 14 the electrolyser installed capacity in the TIMES-NEU modelled regions. These results show that it is more profitable to build the electrolyses directly in the energy islands, where the wind potential is high, and then build pipelines to transport the hydrogen to the main consumption hubs. This result is highly dependent on the cost assumptions of building pipelines versus building power lines and setting up the electrolyses in the mainland.

Another key result is the first-mover effect. The country that makes the first move in installing enough electrolyser capacity will determine the market share it can take in the first years. The results from TIMES-NEU show that the buildout of the energy islands with the electrolyses, makes Denmark a first mover, taking a high share of the global market, competing with Germany until the German industry demands its own domestic production.
<figure><img src="image_81.png" /><figcaption>**Fig. 13** Offshore wind capacity per country and energy islands. Note: “IslandBornholm”, “EnergyIsland1,2” and “Island 3+4” are Danish islands, “DE EnergyIslands” are German islands</figcaption></figure>

<figure><img src="image_82.png" /><figcaption>**Fig. 14** Electrolyser capacity per country and energy islands. Note: “IslandBornholm”, “EnergyIsland1,2” and “Island 3+4” are Danish islands, “DE EnergyIslands” are German islands</figcaption></figure>

## 2.4 Take Aways from the Two Modelling Examples

Conclusions are clearly affected by the chosen methodology; are you back-casting demand for hydrogen from a fully decarbonised future or are you modelling the competition between energy carriers moving towards the same future? Back-casting tends to over-estimate the demand for hydrogen in short and mid-term (Balmorel case), while optimisation of the demand side tends to do the opposite, because it is not capturing early investments in plants (TIMES case).

The model area and boundary conditions are also important factors; what kind of trade options to the outside world has been established in the modelling framework?

Balmorel covers the power production and transmission system in all of Europe and potential production and export of green hydrogen from Northern Africa. This
makes the Balmorel model suitable for analysing the competition in production of green hydrogen between different geographical areas and hence where to build hydrogen pipelines and where to expand renewable power capacity and transmission lines. The TIMES model has a detailed modelling of all end-use sectors and is therefore strong in analysing which sectors are going to use green hydrogen (and other green fuels), for what and from when. Obviously, combining the two models in one consistent analysis would improve the results and strengthen conclusions. This has not been possible to do for this chapter but there is an ongoing Danish research project wherein it will be realised.

## 3 How Is the Green Fuel Revolution in the Nordics Affecting SDGs?

The green fuel revolution has obvious advantages from a climate point of view by contributing to replacing fossil fuels. But what impacts, positive and negative, can the development of renewable electro fuels have on other sustainability goals and targets in a system perspective? To answer this question the United Nations Sustainable Development Goals (SDGs) framework has been used. There are 17 SDGs, and each SDG has several targets (in total 169 targets) The SDG Impact Assessment Tool (GMV and Chalmers 2023) has been used to screen the most relevant sustainability aspects affected by electro fuel production and their replacement of fossil fuels. The results are shown in Table 1. Biomass based fuels are not included in the assessment.

The most obvious direct **positive impact** of electro fuels is the potential reduction of greenhouse gas emissions through the replacement of fossil fuels (SDG 7 and 13). Although the production of electro fuels comes with upstream emissions, these can be low compared to the fossil fuels they replace. As an example, Hansson et al. (2023) calculated greenhouse gas performance of several renewable transport fuels, including electro fuels. The results show emission factors of 0–20 g CO₂e/MJ compared to 94 g CO₂e/MJ for their fossil counterparts. The emission factors of fossil fuels are based on the reference given by the EU Renewable Energy Directive. The emission factor of electro fuels varies depending on for example, the type of electro fuel, and assumed emission factor for electricity. The estimates above assume that the electro fuels meet the criteria set out in the EU delegated acts on methodology for renewable fuels of non-biological origin (RFNBOs), which means a very low electricity emission factor. To assure a climate benefit of electro fuels, the electricity must have very low climate impact. Other direct positive impacts include upgrading the industry to increased sustainability (SDG 9), transition of the transport sector and reduced environmental impact of cities (SDG 11). Indirect positive impacts include job creation in the Nordic countries (SDG 8) and potentially more efficient use of natural resources (SDG 12).
<table><caption>**Table 1** Summary of relevant SDGs for electro-fuel production</caption><thead><tr><td>SDG</td><td>Overall impact</td><td>Motive</td><td>Link to energy system model results</td></tr></thead><tbody><tr><td>1. No poverty</td><td>Indirect negative</td><td>Risk that industrial countries exploit resources in developing countries</td><td>Development in total system cost of the energy system and its share of GDP</td></tr><tr><td>6. Clean water and sanitation</td><td>Direct negative</td><td>Electro fuels will require large amounts of clean water (for hydrogen production through electrolysis of water)</td><td>Amount of electro fuels produced and used</td></tr><tr><td>7. Affordable and clean energy for all</td><td>Direct positive</td><td>Mainly positive impacts. Renewable fuels replace fossil fuels. At the same time a lot of electricity is required which may lead to direct and indirect negative environmental impacts as well as competition of energy for other purposes</td><td>Projection of green fuel and electricity prices</td></tr><tr><td>8. Decent work and economic growth</td><td>Indirect positive</td><td>The production of renewable fuels will create new and potentially more jobs in the Nordics (although there is extensive refinery industry currently, the production of green fuels may cover a larger part of the global fuel demand and thus lead to more jobs)</td><td>Amount of installed energy related capacities in the period</td></tr><tr><td>9. Industry, innovation, and infrastructure</td><td>Direct positive</td><td>The purpose of the green fuel revolution is to upgrade industry to increased sustainability</td><td>New synergies between energy sector, industries, and other end-users</td></tr><tr><td>11. Sustainable cities and communities</td><td>Direct positive</td><td>Renewable fuels support the transition of the transport sector to increased sustainability and reduce the environmental impacts of cities. Conflicting targets may be potentially increased energy prices</td><td>Speed of transition in the different economic sectors</td></tr><tr><td>12. Responsible consumption and production</td><td>Indirect positive</td><td>Primarily assumed positive impact on efficient use of natural resources due to reduced demand for fossil resources. There may also be negative effects on specific targets</td><td>Projected future use of biomass and other resources for energy and fuel production</td></tr><tr><td>13. Climate action</td><td>Direct positive</td><td>Renewable fuels will replace fossil fuels and thus contribute to reduced climate impact (provided that the electricity required has low climate impact)</td><td>Development in GHG emissions from the energy sector</td></tr><tr><td>14. Life below water</td><td>Indirect negative</td><td>Probably low impact. The water demand for electrolysis might affect this SDG, although more relevant for SDG 6</td><td>Water consumption for energy plants</td></tr></tbody></table>

*Note*: There may be both positive and negative impacts on a specific SDG. The table shows the impact that was considered most important in the screening process
The production of electro fuels requires large amounts of clean water, which has a direct **negative impact** on SDG 6. In areas with scarce water resources there may be solutions such as desalination of sea water or cleaning of wastewater. Other negative impacts are mostly indirect and related to risks that industrial countries exploit resources (e.g. critical materials) in developing countries (SDG 1), negative environmental impacts due to increased electricity demand (SDG 7), competition of energy for other purposes (SDG 7) and potentially increased energy prices when electricity demand increases (SDG 11).

# 4 Key Challenges and Social Concerns

For the Power-to-X visions to become reality, a willingness-to-change is needed. The key challenges can be summarized into (1) the need for bridging a price gap and (2) a deviating focus, from energy-transition into decarbonization, with consequences for the build-out and social acceptability of PtX.

## 4.1 Price Gap: A Battle of Colours

With the normalization of natural gas prices, blue hydrogen is currently (2023) more than 50% cheaper than green hydrogen (Bhashyam 2023). While green hydrogen is expected to outcompete grey hydrogen by 2030 due to taxation on emissions, blue hydrogen will be more difficult to out-battle as the impact of carbon taxation is small. Green hydrogen is expected to become cost-competitive with blue hydrogen given various impact factors, like technology maturity, build-out of renewable energy, the EU methane regulation EU ETS, and tax regimes, at least within the EU.

The future of green hydrogen consumption in the EU has been determined by the Renewable Energy Directive III, where the hydrogen consumption in the industry must fulfil a minimum requirement of 60% renewable hydrogen by 2035. When looking at other continents, the picture looks different. In the US, blue and green hydrogen seem to be considered as renewable hydrogen and subsidies target both technologies (upon decision of the Treasury department by the end of 2023). Looking to Asia, specifically China, where natural gas is scarce, green hydrogen is expected to be the most cost-effective. Chinese technology could also become one of the key drivers for technology advancement within electrolysers (IEA 2023).

## 4.2 Delays and Challenges

The combination of an un-bridged price gap and the intensified focus on decarbonization has resulted in general delays for announced PtX Projects. Figure 15
<figure><img src="image_83.png" /><figcaption>Fig. 15 Commercial Operation Date (COD) based on the announced project pipeline of developers compared to the expected pipeline PtX capacity of analyst when incorporating offtake markets, country of development and development consortium (Brinckmann 2023)</figcaption></figure>

outlines forecasted delays in the realization of European PtX projects in comparison to announced realization date, considering key parameters for commercial assessment of project feasibility (Brinckmann 2023).

Another reasoning for project delays can be found in the missing offtake commitment from the industry. Although the offtake potential has been thoroughly investigated, and national plans and strategies for PtX have been initiated (e.g., the Danish PtX Strategy 2021), there is still a lack of commitment from targeted off takers, e.g., in the steel industry. While the political ambitions and targets in the EU are getting more and more clear, the political landscape in other parts of the world is more uncertain, e.g., in the US with the upcoming presidential election.

Finally, local opposition adds a further layer of complexity and uncertainty. Local community concerns over PtX are still under-researched, but likely to emerge with the prospect of accelerated PtX upscaling. Renewable energy facilities already face considerable local resistance in the Nordic countries (European Commission et al. 2022; Energy Monitor 2022; MX Underwriting Europe et al. 2022; Kirkegaard et al. 2020). This can delay the build-out of PtX. Further, PtX facilities are likely to produce public concerns over their siting: For instance, PtX facilities are so far classified as industrial facilities in Denmark, and therefore to be placed in industrial zones. Since the Danish PtX Strategy encourages renewable energy and PtX facilities to be co-located to make best use of the electricity network, this has potential to create local community concerns in urban areas (Klima-, Energi- og Forsyningsministeriet 2021). At the same time, the potential of PtX facilities to turn rural areas into industry-like areas, if sited there, is foreseen to produce local concerns due to a disrupted ‘sense of place’. Eventually, public concerns are also likely to emerge around safety (e.g., explosion risks), the distribution of costs and benefits (e.g., compensation schemes). Meanwhile, there is potential to also create local value, e.g., through job-creation, compensation for loss of value of property,
and funds to develop local communities. There is therefore an urgent need to strengthen public engagement and participation of local communities in the plans for PtX, to foster **social acceptance**.

## 4.3 Renewable Electricity: It Needs to Happen Sooner Rather than Later

A large amount of renewable electricity is needed to produce green hydrogen in the targeted amounts. Additional renewable energy installations are not an option but a prerequisite for realizing the PtX targets. The Danish Energy Agency has, for instance, projected the consumption of electricity in 2050 will be five times as much as it is today. This is almost entirely due to PtX and this electricity needs to come from renewable sources (DEA 2023). While additional capacities are needed, it is also beneficial for countries to add capacity to existing electricity-generating assets. With an estimated timeline of 3-4 years for a 1 GW electrolyser facility to go from financial investment decision (FID) to commercial operation, additional renewable electricity is needed sooner rather than later.

## 4.4 Willingness-to-Change: The Carrot and the Stick

Although PtX technology is not a new invention, it has failed to achieve a commercial breakthrough. Much of this is due to the fact that it is an expensive way to produce hydrogen and its derivatives compared to current production methods, which are predominantly based on natural gas. For the vast potential to be realized, it requires political will through regulation, funding, and technological maturity. Here the tendency in the EU is to use the stick rather than the carrot, which is reflected in the regulations governing the area, as outlined below:

### 4.4.1 Political Willingness

In recent years, the EU has worked hard to create an environment encouraging investment in sustainable solutions (the ‘carrot’) After years of waiting for a definition of renewable hydrogen, it finally arrived in June 2023 (Commission 2023), including rules for electricity generating assets, geographical location, and time correlation between electricity sourcing and hydrogen production. While this publication has been welcomed, the primary EU legislation on renewable energy in the industrial and transport sectors is still pending, and the final transposition of EU legislation into national law will not be completed until the summer of 2025. The
lack of certainty in the legislation creates poor conditions for investment, which is one of the reasons why only a few projects are taking a final investment decision.

### 4.4.2 Funding

Funding for green hydrogen-based products has slowly started to pick up. The EU invests in PtX production through the European Hydrogen Bank and the Innovation Fund (CINEA 2023) programs. At a national level, Germany is leading the way in funding projects with several initiatives, to support hydrogen production and stimulate the off-take sector, e.g., the CCfD mechanism and H2Global (BMWK 2023). Denmark has been focusing on supporting the production side (but not the demand side), e.g., through the Danish tender for Power-to-X capacity. The winners have a period of four years to develop and install their PtX equipment and to start the production of green hydrogen. The total amount of support available was 170 Mio. EUR (Energistyrelsen 2023). While European countries have made it clear that they are keen on PtX, the process has a long timeline with uncertainties regarding budget and scale, and a vast funding gap, which remains to be filled. The current funding budget for PtX in Europe is ~EUR 22 billion, which, according to the industry, is far from the funding required to meet the targets.

### 4.4.3 Technology Maturity

The offtake market has expressed interest and a positive attitude towards hydrogen-based products since the products will be required to meet the sustainability requirements placed on all industries by the Renewable Energy Directive III, ReFuelEU Aviation, and FuelEU Maritime. However, PtX is an expensive production method, which requires some industries to invest in new technology and equipment to adapt to hydrogen. This leads to a possible lock-in scenario for off-takers when investing in equipment. Many industry players are waiting for others to take the first step and lead the way.

## 5 Conclusion

This chapter has been diving into the uncertainties of predicting the future demand for green hydrogen and other green fuels and what role the Nordic countries could play as their supplier. The role of hydrogen depends on the competition with other options for delivering energy services. In many cases hydrogen comes in as the last option because it is energy inefficient and so far, much more expensive than the close competitors: Electrification, biofuels, and blue hydrogen.
**5.1 Export Potential from the Nordics**

The Nordics offer great conditions to become an export region for hydrogen and green fuels. However, the region faces challenges regarding the ramp up of the hydrogen economy.

In all scenarios the Nordics become a net-exporter of hydrogen and electricity. If blue hydrogen is accepted then Norway will be the main producer, while if there is no market for blue hydrogen, then Denmark is in a leading position to become the main producer of green hydrogen. However, it is uncertain how this export market will split between Germany, Belgium, and Netherlands. The Nordic countries' export options seem *not* to be sensitive to import of hydrogen from northern Africa or southern Europe as these will rather out compete electrolysers in south and central Europe.

**5.2 Need for Ramping Up Wind, PV, Electrolysers, and Infrastructure**

A key aspect is the expansion of renewable capacities. The focus will be on offshore wind and PV as the expansion of onshore wind is restricted, e.g., due to public acceptance. The whole North Sea and Baltic region offers the potential of 3–357 GW of electrolysis, but this requires an expansion of offshore wind and PV production with a factor 20 compared to today.

**5.3 Political Decisions and Regulation**

The success of green e-fuel production in the Nordics depends on future policies for import of biomass and biofuels to the EU and the ETS securing a phase out of fossil fuels in all sectors. The end-use sectors need an early price signal to invest in green fuel-based processes.

Some key policies that can drive the demand for e-fuels:

* Will the countries ratify and respect EU's Fit for 55 package and their national climate targets?

* What level will the CO₂ price reach and how fast?

* Will governments or the EU create subsidy schemes for green fuels?

* How big will the market be for PPAs and other offtake agreements in the coming years?

* Will there be any restriction on imports of biomass and biofuels to the EU?
## 5.4 Sustainable Development Goals

The implementation of hydrogen and electro fuels has direct positive impacts on several sustainable development goals (SDGs 7, 9, 11 and 13) but also indirect positive effects (on SDGs 8 and 12) There are however also potential negative impacts that need to be addressed (mainly linked to water demand for hydrogen production i.e., SDG 6 and linked to electricity production, i.e., SDGs 1, 7, 11).

## 5.5 Social Concerns

Renewable energy projects in the Nordic countries are facing local resistance, potentially causing delays in PtX development. Public concerns regarding safety issues and the fair distribution of costs and benefits, are anticipated. Despite challenges, PtX initiatives offer opportunities for local value creation, such as job opportunities and community development funds. Urgent action is needed to strengthen public engagement, ensuring the inclusion of local communities in PtX plans to foster social acceptance.

## References

* Bhashyam A (2023) 2023 hydrogen levelized cost update: green beats gray. BloombergNEF. https://about.bnef.com/blog/2023-hydrogen-levelized-cost-update-green-beats-gray/

* Blanco H, Nijs W, Ruf J, Faaij A (2018) Potential for hydrogen and power-to-liquid in a low-carbon EU energy system using cost optimization. Appl Energy 232:617-639. https://doi.org/10.1016/j.apenergy.2018.09.216

* Bramstoft R, Skytte K (2017) Decarbonizing Sweden's energy and transportation system by 2050. Int J Sustain Energy Plan Manag 14:3-20. https://doi.org/10.5278/IJSEPM.2017.14.2

* Brinckmann (2023) Proprietary database on announced PtX projects in Europe. Brinckmann, Aarhus. https://brinckmanngroup.com/

* Bundesregierung (2023) Fortschreibung der Nationalen Wasserstoffstrategie. NWS 2023. Bundesministerium für Wirtschaft und Klimaschutz (BMWK) As of July 2023, Berlin

* Child M, Breyer C (2016) Vision and initial feasibility analysis of a recarbonised Finnish energy system for 2050. Renew Sust Energ Rev 66:517-536. https://doi.org/10.1016/j.rser.2016.07.001

* Creos, DESFA, Elering, Enagás, Energinet, Eustream, FGSZ, Fluxys Belgium, Gas Connect, Austria, Gasgrid Finland, Gasunie, Gaz-System, Gas Networks Ireland, GRTgaz, National Grid, NET4GAS, Nordion Energi, OGE, ONTRAS, Plinovodi, Snam, TAG, Teréga (2021) Analysing future demand, supply, and transport of hydrogen, European hydrogen backbone. Gas for Climate, Guidehouse, Germany

* Danish Government (2021) The Government's strategy on Power-to-X. https://ens.dk/sites/ens.dk/files/ptx/strategy.pdf

* DEA (2021) Technology data for renewable fuels. Energistyrelsen. Danish Energy Agency. https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels
DEA (2023) Analyseforudsætninger til Energinet, J nr. 2023-2106. Danish Energy Agency. Microsoft Word - AF23 - Sammenfatningsnotat.docx (ens.dk)

Drysdale D, Mathiesen BV, Paardekooper S (2019) Transitioning to a 100% renewable energy system in Denmark by 2050: assessing the impact from expanding the building stock at the same time. Energ Effic 12:37-55. https://doi.org/10.1007/s12053-018-9649-1

Energimyndigheten (2022) Förslag till Sveriges nationella strategi för vätgas, elektrobränslen och ammoniak. Eskilstuna. Sweden. ISBN 978-91-7993-004-8

Energy Monitor (2022) How to overcome local opposition to wind projects, 18 May 2022. Accessed 6 Nov 2023. https://www.energymonitor.ai/corporate-strategy/how-developers-can-overcome-local-opposition-to-onshore-wind-projects/

European Commission (2023) Delegated regulation on a Union methodology for the production of renewable fuels of non-biological origin (RFNBOs). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32023R1184

European Commission, Directorate-General for Energy, Tallat-Kelpšaitė J, Brückmann R, Banasiak J et al (2022) Technical support for RES policy development and implementation—Simplification of permission and administrative procedures for RES installations (RES simplify). Interim report. Publications Office of the European Union. https://data.europa.eu/doi/10.2833/239077

Gea-Bermúdez J, Bramstoft R, Koivisto M, Kitzing L, Ramos A (2023) Going offshore or not: where to generate hydrogen in future integrated energy systems? Energy Policy 174:113382. https://doi.org/10.1016/j.enpol.2022.113382

GMV and Chalmers (2023) SDG impact assessment tool. Gothenburg Centre for Sustainable Development and Chalmers Institute of Technology. https://sdgimpactassessmenttool.org. Downloaded November 2023

Haaskjold K, Pedrero RA (2023) Long-term optimization of the Norwegian energy system under the influence of the European power market. In: 2023 19th international conference on the European energy market (EEM) presented at the 2023 19th international conference on the European energy market (EEM). IEEE, Lappeenranta, pp 1-6. https://doi.org/10.1109/EEM58374.2023.10161972

Haberl H, Beringer T, Bhattacharya SC, Erb K-H, Hoogwijk M (2010) The global technical potential of bio-energy in 2050 considering sustainability constraints. Curr Opin Environ Sustain 2:394-403. https://doi.org/10.1016/j.cosust.2010.10.007

Hansson J, Nojpanya P, Ahlström J, Furusjö E, Lundgren J, Gustavsson Binder T (2023) Costs for reducing GHG emissions from road and air transport with biofuels and electrofuels. IVL report C770, June 2023

IEA (2022) World energy outlook 2022. International Energy Agency, Paris, France

IEA (2023) World energy outlook 2023. International Energy Agency, Paris, France

Ikäheimo J, Kiviluoma J, Weiss R, Holltinen H (2018) Power-to-ammonia in future North European 100% renewable power and heat system. Int J Hydrog Energy 43:17295-17308. https://doi.org/10.1016/j.ijhydene.2018.06.121

Karlsson K, Meibom P (2008) Optimal investment paths for future renewable based energy systems—using the optimisation model Balmorel. Int J Hydrog Energy 33:1777-1787. https://doi.org/10.1016/j.ijhydene.2008.01.031

Kirkegaard JK, Cronin T, Nyborg S, Karnøe P (2020) Paradigm shift in Danish wind power—the (un)sustainable transformation of a sector. J Environ Policy Plan 23(1):97-113. https://doi.org/10.1080/1523908X.2020.1799769

Klima-, Energi- og Forsyningsministeriet (2021) Regeringens Strategi for PtX. https://kefm.dk/ Media/637751860733099677/Regeringens%20strategi%20for%20Power-to-X.pdf

Kountouris I, Bramstoft R, Madsen T, Gea Bermúdez J, Münster M, Keles D (2023) A unified European hydrogen infrastructure planning to support the rapid scale-up of hydrogen production. https://doi.org/10.21203/rs.3.rs-3185467/v1

Lester MS, Bramstoft R, Münster M (2020) Analysis on electrofuels in future energy systems: a 2050 case study. Energy 199:117408. https://doi.org/10.1016/j.energy.2020.117408
Lund H, Thellufsen JZ, Sorknæs P, Mathiesen BV, Chang M, Madsen PT, Kany MS, Skov IR (2022) Smart energy Denmark. A consistent and detailed strategy for a fully decarbonized society. Renew Sust Energ Rev 168:112777. https://doi.org/10.1016/j.rser.2022.112777

Mortensen AW, Mathiesen BV, Hansen AB, Pedersen SL, Grandal RD, Wenzel H (2020) The role of electrification and hydrogen in breaking the biomass bottleneck of the renewable energy system—a study on the Danish energy system. Appl Energy 275:115331. https://doi.org/10.1016/j.apenergy.2020.115331

MX Underwriting Europe, Wind Europe, Fieldfisher, LEIRA (2022) Report “barriers to wind”, Vienna, Austria

Pedersen JL, Pade L-L, Søyland S, Håkansson NC, Grue J, Christensen JD, Diab M, Georgiev I, Krogh A, Ivarsson Á, Meinert Cardel O, Schøn T, Jákupsstovu S, Ritonummi T, Partanen T, Pørvaldsson JAH, Fuglerud M, Hallonsten P (2022) Hydrogen, electrofuels, CCU and CCS in a Nordic context. Nordic Energy Research, Oslo

Sovacool B (2017) Contestation, contingency, and justice in the Nordic low-carbon energy transition. Energy Policy 102:569–582. https://doi.org/10.1016/j.enpol.2016.12.045

Wiese F, Bramstoft R, Koduvere H, Pizarro Alonso A, Balyk O, Kirkerud JG, Tveten ÅG, Bolkesjø TF, Münster M, Ravn H (2018) Balmorel open-source energy system model. Energ Strat Rev 20:26–34. https://doi.org/10.1016/j.esr.2018.01.003

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
<figure><img src="image_84.png" /></figure>

Check for
updates

# Transition Pathways for a Low-Carbon
Norway: Bridging Socio-technical
and Energy System Analyses

Miguel Chang, Kari Espegren, Kristina Haaskjold, Eva Rosenberg, Sigrid Damman, Tuukka Mäkitie, Paolo Pisciella, Allan Dahl Andersen, and Tomas Moe Skjølsvold

**Abstract** This study presents an interdisciplinary approach to analyze different transition pathways towards the sustainable development of a low-carbon society, focusing on Norway as a case. The study bridges a socio-technical perspective on sustainability transitions with techno-economic energy systems and regional-economic modelling analyses. Incorporating a socio-technical perspective in the scenario design allows us to envision pathways considering causal processes of technological and socio-institutional change, and potential transition bottlenecks.

M. Chang · K. Espegren (✉) · K. Haaskjold · E. Rosenberg

Institute for Energy Technology (IFE), Kjeller, Norway

e-mail: Miguel.Chang@ife.no; Kari.Espegren@ife.no; kristina.haaskjold@ife.no;

Eva.Rosenberg@ife.no

S. Damman

Department of Technology Management, SINTEF Digital, Trondheim, Norway

e-mail: Sigrid.Damman@sintef.no

T. Mäkitie

Department of Technology Management, SINTEF Digital, Trondheim, Norway

TIK Centre for Innovation, Technology and Culture, University of Oslo, Oslo, Norway

e-mail: Tuukka.Makitie@sintef.no

P. Pisciella

Department of Industrial Economics and Technology Management, NTNU - Norwegian

University of Science and Technology, Trondheim, Norway

e-mail: Paolo.Pisciella@ntnu.no

A. D. Andersen

TIK Centre for Innovation, Technology and Culture, University of Oslo, Oslo, Norway

Department of Food and Resource Economics, University of Copenhagen,

Copenhagen, Denmark

e-mail: Allanda@tik.uio.no

T. M. Skjølsvold

Department of Interdisciplinary Studies of Culture, NTNU, Trondheim, Norway

e-mail: Tomas.Skjolsvold@ntnu.no
The resulting scenarios are used in the techno-economic energy system analysis to show cost-optimal energy system configurations, including varying levels of new renewable capacity needed, new conversion technologies, and fuel substitutions across all sectors leading to different decarbonization pathways for the Norwegian energy system by 2050. The regional-economy analysis addresses the impacts of these pathways on general economic growth and labor. The results show that higher levels of decarbonization are possible for Norway; however, potential bottlenecks can slow down the transition, while trade-offs in economic growth and development must be balanced out with decarbonization ambitions.

## Key Messages

* Linking socio-technical perspectives with energy systems & economic modelling analyses helps providing a holistic framework to assess the feasibility of the energy transition.

* Transition bottlenecks—such as the maturity of technology options, feasibility of novel innovations and infrastructure, and policy developments—are identified across four envisioned scenarios for Norway.

* Cost-optimal energy system designs show varying levels of decarbonization potential.

* Trade-offs between SDG targets, namely economic growth and decarbonization, emerge when considering the degree of socio-technical change under different transition pathways of the energy system.

# 1 Introduction

The decarbonization of the energy system is expected to play a major role in achieving global climate action targets and contributing to the development of a more sustainable society (IPCC 2022). As part of the global Sustainable Development Goals (SDGs), nations worldwide have committed to providing access to affordable and clean energy (SDG 7) as well as promoting sustainable economic growth (SDG 8), industrialization and innovation (SDG 9), ensuring responsible consumption and production patterns (SDG 12) and undertaking climate action (SDG 13) (UN 2023). Reaching these goals by mid-century will require a rapid and comprehensive net-zero transition which likely involves adopting novel technologies and infrastructures, innovations and reorientation in companies, building new low-carbon value chains, and deep changes in behavior and culture (Andersen et al. 2023a).

Intrinsically, understanding how this transition will take place also requires increased knowledge of how technical developments in national energy systems are affected by the underlying societal, institutional, and organizational structures and policies. Exploring these aspects can therefore provide new insight into the different challenges occurring as the transition unfolds and outlooks of potential
pathways for a low-carbon society. Therefore, quantifying the impacts of these
elements along the transition can enhance quantitative analyses of the energy
system, facilitate planning, and provide better and practical decision support that
would not necessarily be considered solely under a techno-economic perspective
(Bolwig et al. 2019).

The process of planning the transition towards a sustainable energy system relies on mathematical models to quantify the impacts of different energy transition pathways while capturing the complex interactions within the energy system (Prina et al. 2020; Chang et al. 2021). These modelling tools often have a techno-economic perspective that captures the technical details and flows from supply technologies to end-use sectors.

However, recent studies emphasize the need to better integrate the social dimension in energy system modelling approaches (Trutnevyte et al. 2019; Krumm et al. 2022; Süsser et al. 2022). In turn, this can improve the relevance of modelled transition pathways and more adequately capture the social dynamics that drive or constrain the required changes in the energy system. Indeed, including a socio-technical perspective can also provide a more realistic understanding of the transition with practical implications of technological innovation, and societal and institutional change as society moves towards a sustainable future (Köhler et al. 2019). The integration of quantitative modelling and socio-technical studies, as argued by Turnheim et al. (2015), can thus provide a richer and more robust analytical approach to inform and provide guidance to decision-making.

Previous studies have investigated linking socio-technical transition research and energy system modelling. For instance, Li et al. (2015) highlight the need for more integrative approaches as socio-technical factors are often not captured in quantitative modelling. A systematic review by Hirt et al. (2020) outlines that only a small fraction of studies (~12%) considered the whole energy system, while sectoral models are more frequently aligned with socio-technical transition models.

Other recent studies also follow integrative approaches while looking more coarsely at the whole energy system rather than integrating bottom-up technological details. For example, these studies align socio-technical transition insights with integrated assessment models (IAMs) for European analyses (van Sluisveld et al. 2020), or propose socio-technical scenarios to IAMs for analyzing the energy transition in the UK (Freeman and Pye 2022).

However, as suggested by Geels et al. (2016), IAM's are not always sufficiently suited to provide national and local insight, due to their large global coverage and simplified representation of the energy system. Tailored knowledge accommodating sectoral detail and insight on the economy is needed, along with a necessary understanding of transition dynamics in order to support climate action and to address knowledge needs of policymakers at the national level. Some recent work partly addresses this, bridging energy system modelling and transition studies at the European level but without capturing the broader impacts on the economy (Hainsch et al. 2022). Although studies linking energy system and economic models can be found in the literature (Chang et al. 2023), e.g. linking bottom-up ESMs and CGE models of Norway (Helgesen et al. 2018), these do not purposefully align with
socio-technical transition theories (Markard et al. 2012) in their study designs. On the other hand, other studies which align quantitative modelling with socio-technical research do not take a holistic view of the entire energy system, but rather explore the transition in specific sectors, such as the power (Rogge et al. 2020) or heating sectors (Nilsson et al. 2020).

The present study addresses these gaps, providing an approach that bridges socio-technical research and bottom-up analysis of the energy system including all end-use sectors and the economy, taking Norway as a case. This approach allows for a recursive dialogue between models and qualitative storylines, to fine-tune and provide complementary insight from both quantitative and qualitative methods. In turn, the different scenarios provide potential outlooks of sustainable energy system transitions, while capturing different drivers for change and bottlenecks along the transition pathways and their impact on economic development in line with sustainable development goals.

This chapter presents an applied interdisciplinary study, taking Norway’s energy system as a case. We combine a socio-technical transition perspective with scenario design applied to techno-economic energy systems and regional economic analyses. Socio-technical transition research is used to envisage contrasting transition pathways for Norway’s energy system as well as to evaluate the socio-technical feasibility of transition pathways in terms of governance (Turnheim and Nykvist 2019). The resulting pathways are quantified and incorporated as scenarios into both a long-term energy system model (ESM) in the IFE-TIMES-Norway model (Haaskjold et al. 2023), and in the computable general equilibrium (CGE) model REMES-Norway (Werner et al. 2017). Respectively, the analyses in these models provide a bottom-up representation of the energy system and a representation of the wider effects of the different energy transition pathways on economic development.

The remaining of this chapter presents the following: Section 2 describes the overarching approach and the methods used. Section 3 presents the envisaged transition pathways, providing both a qualitative and quantitative description of the different scenarios. Section 4 presents the results of the analysis, followed by a discussion on these and other general implications in Sect. 5. Finally, Sect. 6 presents the conclusions of the study.

## 2 Approach and Methods

This section presents the key methods used in the scenario design and quantitative analyses with energy systems and economic models. The analyses were developed as part of the work in the Norwegian Centre for Energy Transition Strategies (FME NTRANS n.d.) and consist of a 10-step approach bridging socio-technical research with techno-economic analyses. The basis of this 10-step approach and the analyses are presented in further detail by Espegren et al. (2023).
## 2.1 Envisioning Socio-technical Transition Pathways

The scenario development was based on identifying pathways for the Norwegian energy system with contrasting degrees of disruption to the existing socio-technical regime and its central institutions. As suggested by Andersen et al. (2023b), the depth of system change can be distinguished in two dimensions: socio-institutional and technological. Thus, the scenarios considered combinations of minor and major system changes across these dimensions resulting in four pathways:

* Incremental Innovation (INC) pathway: minor system change in both dimensions

* Technological Substitution (TECH) pathway: major technological change and minor socio-institutional change

* Social Change (SOC) pathway: major socio-institutional change and minor technological change

* Radical Transformation (RAD) pathway: major change in both dimensions

Each of the proposed pathways is thus associated with a different type of system change. These pathways manifest in different ways. Minor technological change is linked to decarbonization mainly through core technologies that are largely compatible with the existing value chains, including biofuels, electrification, and energy-efficiency, while major technological change features novel technologies such as hydrogen, ammonia and carbon capture and storage (CCS), requiring novel value chains. Meanwhile, the socio-institutional dimension considers, e.g., the degree of change in population's values and lifestyles. The kind of system change in this pathway is also linked, for example, to changes in the actor networks in energy systems, and the kind and depth of institutional change (regulations, norms, and cognitions). Moreover, transformative pressures at a wider societal level are embedded in these pathways—for example, long-term trends related of demographics, projected demand for energy services, climate change, and societal preferences. The four pathways are elaborated in Sect. 3.

Based on the visions and qualitative descriptions of the four different pathways, key quantifiable factors were mapped to be used as base assumptions in the modelling analyses. The quantification of the scenarios covered factors such as projected energy demand developments per sector, supply and end-use technology data, limitations on energy production and transmission, resource availability, CO2 costs and targets, and energy prices. Further refinement of these pathways included recursive inputs from the modelling analyses and project partners.

## 2.2 Energy-Economy Modelling

To assess the impacts of the different transition pathway scenarios considered, the IFE-TIMES-Norway (Haaskjold et al. 2023) and REMES-Norway (Werner et al. 2017) models were used. The IFE-TIMES-Norway provides a long-term cost
optimal bottom-up representation of the energy system designs for Norway, providing investment decisions to meet energy demands in all use sectors. Meanwhile, REMES-Norway is a CGE model capable of detecting the impacts of changes in the energy sector in the overall economy.

### 2.2.1 Energy System Analysis with IFE-TIMES-Norway

IFE-TIMES-Norway is a technology-rich bottom-up model of the Norwegian energy system (Haaskjold et al. 2023), based on the TIMES modelling framework (Loulou et al. 2016). The model represents Norway’s energy system as five regions corresponding to the current electricity market areas and includes the different end-use sectors and their corresponding demands for energy services. The model provides operational and investment decisions starting from the year 2018 to 2050 in five-year periods from 2020 to 2050. To capture operational variations in energy generation and end use, each model period is divided into 96 sub-annual time slices (24 hours for a representative day in each of the four seasons).

IFE-TIMES-Norway minimizes the total discounted system costs of the energy system, including investments in supply and demand technologies, storages and transmission capacity, operation and maintenance costs, and costs of net electricity imports. The main model inputs include fuel prices, electricity prices from countries with transmission capacity connected to Norway, renewable resources, and technology characteristics such as costs, efficiencies, potentials, and technology learning curves using the Norwegian kroner (NOK) as monetary unit (exchange rate of 1 NOK = 0.1 EUR). As outputs, the model provides the optimal mix of supply capacity, and use of energy carriers and end-use technologies to meet energy service demands.

A sensitivity analysis was included in the study to address uncertainty in key input assumptions associated with the quantification of the pathways and scenarios. These uncertainties also characterize to an extent potential transition bottlenecks.

For example, uncertainty in future technology costs can have an apparent impact in modelling results, while simultaneously portraying challenges in technology deployment and consequent technology learning rates along the transition. Intrinsically, these considerations align with transition bottlenecks, further explained in Sect. 3.2, related to the maturity of options and the fit of innovations in the socio-technical system and its infrastructure. Likewise, analyzing the sensitivity of biofuel import prices or CO₂ price assumptions addresses the parametric uncertainty of these inputs while also portraying the potential effect of transformative pressures at a societal level regarding global resource availability and institutional preferences related to adopting policy measures. As such, these assumptions expand the perspectives of the societal and political feasibility of decarbonization options, illustrating how biofuel availability and CO₂ pricing act as potential transition bottlenecks. In the energy system analysis conducted with IFE-TIMES-Norway, the sensitivity analysis included the following parameters:
* Biofuel price increases in SOC and INC, which rely most heavily on biofuel replacements.

* Higher investment costs in later years, representing slower technology learning, in the TECH and RAD scenarios which have higher learning rates and adoption of new technologies.

* High CO₂ prices development (based on values from Regjeringen 2022a) across all four scenarios.

### 2.2.2 Regional Economic Analysis with REMES-Norway

The CGE model REMES-Norway (Werner et al. 2017) has been used to assess the macro-economic impacts of the various transition pathways. The REMES-Norway model provides a multi-regional multi-sectoral representation of the economy, spanning from the year 2018 to 2050. REMES-Norway is used to analyze the possible responses of the economy to policy measures or technology innovations.

In the current study, eight main factors characterizing the development of the economy were used as basis for capturing the impacts of the four scenarios considered. These factors included: population, productivity, technology, energy intensity, resource deployment, resource export, shift to a circular economy, and transportation development (Espegren et al. 2023). The model results provide a view of projected trends such as GDP development, labor change and value added across key sectors of the economy, and price and demand indices for energy commodities. The energy use by technologies has been often defined as “external”. This means that the projections of the energy mix in the economy towards 2050 are included in REMES-Norway as input data. This data is obtained by the output of the IFE-TIMES-Norway model in energy units (GWh/year). Nevertheless, the dataset used in the REMES-Norway model is measured in Million Euro per year. This means that a harmonization process is needed to make the data as compatible as possible. From the resulting outputs, no additional data feedback is provided back to the IFE-TIMES-Norway energy system model.

## 3 Socio-technical Scenarios Definition, Quantifications and Bottlenecks

This section presents a brief overview of the four transition pathways considered in the scenario analysis. These scenarios portray pathways with contrasting degrees of socio-institutional and technical change, as illustrated in Fig. 1. In the following sections, further description of the different scenarios is provided as well as an overview of key parameters quantified for each scenario. Further detail of these scenarios and quantification of input assumptions is provided in the project report for NTRANS (Espegren et al. 2023).
<figure><img src="image_85.png" /><figcaption>**Fig. 1** Type of change and challenges at the system level in relation to the four scenarios. *Note:* Adapted from Espegren et al. (2023) and Andersen et al. (2023b)</figcaption></figure>

## 3.1 Scenario Storylines

### 3.1.1 Incremental Innovation Pathway (INC)

The INC pathway depicts a scenario with gradual system change following the current technological and socio-institutional patterns. This pathway is not associated with major leaps in the developments of prospective technologies and could be seen as a continuation of current climate and energy policies, with steady population growth as per current projections, and societal focus on economic growth.

Resources used for energy production include global oil and gas (O&G), renewable energy, and biomass. There is an increasing energy demand, but also increased energy efficiency. Transport demand increases, with a focus on electrification and biofuels. The decarbonization of industry will largely depend on energy efficiency measures and electrification, with existing incumbents in the energy and industry sectors maintaining a central role.

Despite increasing awareness, incentives for environmental behavior remain weak, and people largely stick to their current lifestyles in terms of consumption, travel, and energy use. There are potential controversies and conflicts related to land use, sustainability concerns, capital and technology participation, and distributional and recognitional justice.

### 3.1.2 Technological Substitution Pathway (TECH)

The TECH pathway is characterized by a sudden pressure for change at the broader societal level, leading to development and deployment of new core technologies, but
less change in lifestyles. Moreover, population growth increases and the demands for energy and transport increase. Despite global O&G being used as feedstock for blue hydrogen production, renewable energy sources like floating offshore wind and biofuel production grow further. Alternative technologies and energy carriers like hydrogen, ammonia, batteries, and carbon capture and storage (CCS) become more available, opening routes for regionalization of existing industries.

Norway has niches in hydrogen, electrification, CCS, and hydrogen maritime technologies, providing an opportunity for the country to lead in these areas. However, potential social tensions, related to e.g., land use, sustainability contestation, and technology acceptance, may arise also in this scenario. Participation may also depend on capital and technology, raising issues of distributional and recognition justice.

### 3.1.3 Social Change Pathway (SOC)

The SOC pathway is associated with global conflict and unstable energy markets at the broader societal level and involves institutional changes reorienting from primarily economic growth towards sustainable well-being. There is a decrease in population growth due to less immigration. Due to social innovation and adoption of circular economy technologies and practices, more localized production networks and symbiotic innovations like automation become prevalent, reducing the demand for energy and global transport.

Core technologies are not replaced by new solutions to a large extent, hence emission reduction technologies such as energy efficiency, biofuels, battery-electric cars and vessels are implemented widely. Power generation experiences limited growth, and renewable energy and community-based solutions will be important. Existing incumbents in the industry face sharply increased CO₂ taxes and stronger disruptive policy measures than in INC and TECH.

The increased deployment of smart ICT-based solutions will be associated with energy use and lead to growth in e.g., data centers. However, Norwegian O&G production is expected to slow down and be shut down completely by 2034 due to climate concerns.

Potential Norwegian niches include smart transport solutions and digitalization. Circular bioeconomy innovation is also associated with green growth in some regions. The SOC pathway is characterized by increased environmental consciousness, leading to major changes in lifestyles, including less consumerism, less private ownership, more sharing and public services in transport, and a stronger focus on welfare and self-sufficiency.

### 3.1.4 Radical Transformation Pathway (RAD)

The RAD pathway is characterized by external shocks that trigger cascading disruption on multiple dimensions, involving major system change in both
technological and social dimensions. The economy shifts its focus to sustainable development and well-being, with global collaboration expected to decrease and regionalization becoming more prominent. System reconfiguring innovations, such as circular economy, integrated and flexible power systems, and local production, are highlighted.

Like the TECH pathway, the RAD pathway sees a strong increase in maturity and availability of alternative technologies and carriers, such as floating offshore wind, hydrogen, ammonia, batteries, and CCS. This opens routes for regionalization of existing industry, including electrification, hydrogen, and CCS use. Advanced bioenergy/biofuel production based on Norwegian resources are also in place.

The primary energy supply will mainly consist of renewable energy, and O&G production is phased out by 2050. The demand for energy and food stabilizes due to more sustainable lifestyles and increased focus on self-sustenance and circularity. There will be reduced demand for transport due to local production and less travel, as well as changing land use and densification in cities. The road sector sees less transport, more shared electric vehicles, and increased use of public transport, bikes, and walking. Potential Norwegian niches in the Radical Transformation Pathway include renewable hydrogen, Industry 5.0, CCS, and smart & digital solutions.

## 3.2 Transition Bottlenecks

A central idea in combining techno-economic modelling with socio-technical analysis for assessing feasibility of scenarios is to identify ‘transition bottlenecks’, which are tensions between scenarios developed by models and current developments analyzed with a sociotechnical transition perspective (Geels et al. 2020; Wachsmuth et al. 2023). This analytical exercise provides a broader socio-technical check on the scenarios and allows to provide additional insights on the conditions for specific transition scenarios, and thus their feasibility. Turnheim and Nyqvist (2019) suggests four dimensions where the theoretical potentials revealed by modelling may collide with the dynamics of real-world systems.

First, maturity of options points to whether an innovation in question is developed-enough at a given time to be able to perform the role suggested by a modelled scenario. Different decarbonization technologies have different degrees of maturity (e.g., the maturity of LNG vs. relative immaturity of ammonia as alternative fuels in shipping). However, the pace of development of yet immature solutions is not pre-determined but rather dependent on the unfolding systemic processes, such as various actors’ continued efforts to explore cost-quality improvements, market formation, and availability of resources for further development of innovations (Hekkert et al. 2007).

Second, novel innovations must fit with the other socio-technical systems and infrastructure (for example, power grids, transport infrastructure such as roads and ports, etc.). Actors may have to either design innovations to fit with the existing
systems and infrastructure, or the existing systems and infrastructure have to be fitted to innovations (Smith and Raven 2012; Bach et al. 2021), or more realistically, find a middle-ground between the two extremes. Such substantial change processes in large technical systems can be significant hurdles for innovation and time-demanding to carry out.

Third, innovations outlined by scenarios require *societal acceptability* to be adopted and to contribute to social sustainability. Acceptance may hinge on, e.g., the desirability of the modelled scenarios for the population, the perceived legitimacy of the actors pursuing the implementation of the scenarios, and the actual implementation of the scenarios (Turnheim and Nykvist 2019).

Fourth, fulfillment of scenarios may hinge on their *political feasibility*. This is related to, e.g., whether the kind of change in scenarios matches with the interests of powerful actors in politics, industry and civil society, and their vested interests (Normann 2015; Turnheim and Nykvist 2019).

## 3.3 Quantification of Socio-technical Transition Pathways

Based on the storylines provided in Sect. 3.1, key factors across each of the scenarios were mapped and parametrized as input assumptions in the energy system modelling analysis. The quantifications included technology specifications, limitations on energy production and transmission, energy demand developments per sector, availability of renewable fuels and end-use technologies, and varying investment costs corresponding to high or low technology learning rates.

An overview of the differences between the key assumptions in each scenario is provided in Fig. 2. For example, as mentioned in Sect. 3.1, lower technology learning is expected in INC and SOC and is portrayed with a lower bar, while high values are assigned to TECH and RAD since these pathways assume higher technology learning rates. These differences are further shown in the radar chart in Fig. 2 for other key input parameters, portraying the relative scale of the assumed values. In the case of hydrogen export, this is only seen under the TECH scenario which assumes that hydrogen export volumes are allowed, while the zero value in the other three scenarios denotes that no hydrogen exports are allowed. Further detail regarding the quantified values is provided in the NTRANS report (Espegren et al. 2023).

## 4 Results

This section describes the modelling results from the energy system and regional economic analysis applied to the scenarios priorly described in Sect. 3.
<figure><img src="image_86.png" /><figcaption>**Fig. 2** Overview of differences between IFE-TIMES-Norway scenario assumptions. *Note*: The radar charts portray the varying levels relative to each of the key input assumptions in the scenario (i.e., low levels covering the innermost concentric axis, and high levels reaching the outermost concentric axis, and null values not displayed). The colors denote the inputs' category. Based on the 2050 quantifications from NTRANS (Espegren et al. 2023) (Color figure online)</figcaption></figure>

# 4.1 Techno-Economic Analysis

## 4.1.1 Power Generation and Trade

The power generation mix in Norway for each of the modelled scenarios is presented in Fig. 3. In the scenarios where energy service demands are projected to increase (i.e., INC and TECH), there's a corresponding increase in total power generation. In these two scenarios, the onshore wind potentials (about 48 TWh) are fully utilized by 2050. Moreover, the increase in demand also drives up investments for other VRES, especially in the TECH scenario where electricity from offshore wind production covers the largest shares of the total power supply by 2050 (approximately 43%).

The generation mix sees increasing shares from offshore wind in both scenarios even though the two scenarios are characterized by contrasting technology learning
<figure><img src="image_87.png" /><figcaption>**Fig. 3** Power generation (TWh/year) by technology for the four scenarios</figcaption></figure>
rates to capture potential bottlenecks in technological development, with corre-
spondingly higher investment cost assumptions in the INC scenario and lower
costs in TECH. Due to common assumptions related to limited hydropower poten-
tial, the production volumes remain relatively constant across all four scenarios.

In contrast, the scenarios with low energy service demand—SOC and RAD—
only see modest increases in the additional power generation required. In these
scenarios, new production is expected mainly from PV applied in buildings covering
about 11% of the supply mix by 2050, and only reinvestments in existing wind
capacity. Offshore wind production does not play a significant role in the supply mix
of either of these two scenarios, even in the case of the RAD scenario where high
technology learning rates are assumed.

4.1.2 Transport Fuels

The energy consumption by carrier for the transport sector is presented in Fig. 4,
showing the results for the four scenarios and two different CO2 price assumptions.
For all scenarios, a large share of the fuel consumption in 2030 consists of fossil
fuels across different transport segments. In both the INC and SOC scenarios, fossil
fuels are replaced mainly with biofuels by 2050, since no limitations in biofuel
imports are considered in these two scenarios. The remainder of the transport sectors
is decarbonized by electrifying the existing vehicle fleet. In TECH and RAD,
electrification plays a larger role in replacing fossil fuel consumption with battery
electric vehicles utilized when possible due to the high efficiency and technology
learning. Meanwhile bioenergy is consumed as an intermediate replacement, show-
ing less consumption by 2050 due to higher prices on biofuel imports. Emerging
fuels like ammonia (NH3) and hydrogen (H2) also contribute to the sector's
decarbonization, with increased uptake by 2050.

As seen in Fig. 4, imposing higher CO₂ prices accelerates the decarbonization of the sector. In the INC and SOC scenarios, this translates into earlier decarbonization achievable by 2040. Similarly, in the TECH and RAD scenarios, a faster uptake of carriers replacing fossil fuels is also projected, although a full decarbonization of the transport sector is not seen until 2050. Notably for both TECH and RAD, the high CO₂ prices enable a fully decarbonized transport sector, as opposed to the reference CO₂ price assumptions.

Given the dependency of biofuels in the INC and SOC, reaching a full
decarbonization of the sector can be contingent to bioenergy availability and import
prices as potential transition bottlenecks. As presented in Fig. 5, an increase of
biofuel import prices comparable to the levels considered in the TECH and RAD
scenario (i.e., an increase of 5 times relative to the reference value) limits the extent
of the decarbonization even when considering the case of high CO2 prices. Under a
high biofuel price case, bioenergy replacements fall short while electrification and
the use of hydrogen compensate as alternative fuel replacements.
<figure><img src="image_88.png" /><figcaption>**Fig. 4** Energy use in transport for the four scenarios considering different CO₂ prices (Reference -Ref- and a High CO₂ price). *Note*: It presents the values for fossil fuel (FOS), bioenergy (BIO), electricity (ELC), hydrogen (H₂) and ammonia consumption (NH₃)</figcaption></figure>
<figure><img src="image_89.png" /><figcaption>**Fig. 5** Energy use in transport, considering different CO₂ prices (Ref. & High), and biofuel import prices</figcaption></figure>

### 4.1.3 Hydrogen Supply

In the INC and SOC scenarios, hydrogen supplies about 5 TWh/year to cover demands in the industry sector with little change from 2030 to 2050. In contrast, the TECH and RAD scenarios see higher levels of hydrogen consumption—progressively increasing towards 2050—to cover end-use demands in industry and transport.

The TECH and RAD scenarios consider as base assumptions high levels of technology learning, which consequently contribute to a higher uptake of hydrogen utilization relative to the other scenarios due to lower investment costs in new capacity. However, bottlenecks in upscaling capacities and technological maturity might limit the uptake of hydrogen and new renewable capacity and are critical for these two scenarios. Therefore, lower technology learning (TL) rates—translating in higher investment costs—were considered as part of the sensitivity analysis. In Fig. 6, the results for the TECH and RAD scenarios are further explored.

Figure 6 shows how lower technology learning rates across the TECH and RAD scenario impact the production of both green and blue hydrogen, leading to lower overall hydrogen production levels. In 2050, this represents a reduction of about 34% and 9% for the TECH and RAD scenarios, respectively.
<figure><img src="image_90.png" /><figcaption>**Fig. 6** Hydrogen supply by production technology from both central and local plants. *Note:* It includes alkaline and proton exchange membrane (PEM) electrolysis, and blue hydrogen from autothermal reforming (excluding Blue H₂ produced for export)</figcaption></figure>

### 4.1.4 CO₂ Emissions

By 2050, all four scenarios present significant CO₂ emission reductions compared to 2018 (42.76 Mton/y), spanning a range of around 87–96% considering the respective reference assumptions for each scenario and CO₂ price assumption. As seen in Fig. 7, the industry sector has the highest emission contributions in 2050 for all scenarios, while only some emissions remain from district heating plants. For the TECH and RAD scenarios, which include larger CCS options and diverse green fuel replacements, the emissions from industry are decreased further than in the INC and SOC scenarios with less potential for industrial CCS or hydrogen.

The transport sector is fully decarbonized in the INC and SOC scenarios, due to unlimited use of bioenergy products at a moderate cost. Given the higher bioenergy costs in the TECH and RAD, the reference CO₂ cost applied in the scenarios is not sufficient to make it profitable to import bioenergy or achieve full replacement of fossil fuels with other technologies. Hence, emissions remain in the sea and air transport segments. However, when considering higher CO₂ prices, the decarbonization is accelerated across all scenarios. In the TECH and RAD scenario, additional levels of decarbonization are also unlocked leading to lower emissions in industry and driving further fuel replacements in sea and air transport towards a full decarbonization of the sector.
<figure><img src="image_91.png" /><figcaption>**Fig. 7** CO₂ emissions by end-use for each scenario, considering different CO₂ price assumptions (Ref., & High CO₂ prices)</figcaption></figure>

**4.2 Regional Economic Analysis**

The typical economic growth pattern of the NTRANS low carbon scenarios, as compared to a reference scenario, considers an initial decline in GDP growth due to
<figure><img src="image_92.png" /><figcaption>**Fig. 8** Development of GDP per capita under the reference scenario and the alternative scenarios</figcaption></figure>

the decline in value added for the O&G sectors, followed by a gradual improvement of the growth achieved due to the greater availability of capital, leading to large investments in industry and services. This is showcased in Fig. 8, as GDP per capita for all scenarios. The INC scenario mostly aligns to the reference scenario including continuous O&G extraction, presenting only marginally smaller differences. Economic growth decreases towards 2050 due to stricter decarbonization requirements and higher costs for sectors to reduce emissions.

The TECH scenario has a more pronounced decrease in O&G exports, with oil extraction decreasing by around 90% domestically and gas usage increasing in chemicals and blue hydrogen production. This faster phase-out leads to a greater decrease in GDP in the short run. However, due to improvements in industrial productivity, widespread adoption of hydrogen and the implementation of carbon capture and storage (CCS) in industry, the scenario aligns closer with the INC scenario.

In contrast, the SOC and RAD scenarios have a different economic focus based on societal change, and thus lower GDP levels compared to the Reference scenario. These two scenarios' weaker growth is primarily the result of lower labor productivity and a strong phase-out of O&G. Productivity loss under the RAD scenario is slightly mildened by the increase in industrial productivity and the use of CCS in industry.

The two largest contributors to the GDP are the industry and services segments (shown in Fig. 9). For industry, the construction sector represents its main value driver, performing particularly weakly under the SOC scenario. Growth in the construction sector slows due to reduced labor productivity, higher labor costs, and stagnant demand from a non-growing population leading to increased production costs and lower demand. Despite expensive labor and lower demand growth, the construction sector's reliance on less costly energy and materials allows it to expand,
<figure><img src="image_93.png" /><figcaption>**Fig. 9** Value added for the industry and service segments under the reference scenario and the alternative scenarios</figcaption></figure>

albeit less than in the Reference scenario. Other industrial sectors, with a significant portion of costs tied to energy, face more substantial impacts due to decarbonization costs and rising wages on top of inherently declining demand. Notably, under SOC and RAD scenarios, the shift towards a circular economy model further reduces consumption. As a result, industrial value-added peaks around 2040, falling thereafter. Meanwhile, the INC and TECH scenarios, present a trend closer to the reference with a more moderate decline towards 2050.

The service segment's reaction to the changes in the SOC and RAD scenarios is less severe than that of industry. Within services sectors, the lower general economic growth decreases the speed of capital formation and investments in some sectors, like administrative services, by 2050. Lower economic activity under SOC and RAD leads to reduced labor demand compared to INC and TECH.

Under these scenarios, sectors capable of substituting labor for capital take advantage of lower wages to increase workforce and reduce capital dependence. Increased household consumption of services, driven by interest in a circular economy, softens the demand decrease due to the stagnating population and the general lack of focus on growth, allowing these sectors to maintain higher prices, albeit demand remains below the Reference scenario levels. In contrast, the TECH and INC scenarios, see an increased service production and labor demand.

In the majority of the sectors, the demand for labor under each scenario is anticipated to be lower than under the reference scenario (Fig. 10). When it comes to industries that make up the largest portion of employment and contribute significantly to overall growth, like industry or services, the TECH scenario manages to maintain an employment level that is comparable to the one in the Reference scenario.
<figure><img src="image_94.png" /><figcaption>Fig. 10 Labor demand change compared to 2018 under the different scenarios</figcaption></figure>

# 5 Discussion

The developments across the four scenarios facilitate different degrees of decarbonization towards 2050 in direct alignment with developing a clean energy system (SDG 7) and undertaking climate action (SDG 13). The scenarios, also portray the buildup of new infrastructure and emerging technical innovations (in line with SDG 9). As embodied by the RAD scenario, a combination of both major technological and socio-institutional change is required to enable the highest degrees of decarbonization and meet national targets (Regjeringen 2022b), i.e., 55% emission reductions by 2030 relative to 1990 levels (which are reported at around 51.5 million tons of CO2). Enabling more ambitious reductions, as explored in the results, could require setting higher CO2 prices, or other measures, to be well within said targets.

As mentioned, these reductions will also be conditioned by changes in behavior and lifestyle (aligned to responsible consumption, i.e., SDG 12), and emphasis on self-sustenance, circularity and local production and consumption (SDG 9). Intrinsically, the degree of change in those dimensions will yield different levels of
economic growth and labor (SDG 8), as observed in the results of the economic analysis. Thus, the tradeoffs between prioritizing specific targets and development goals must be considered and put into perspective as part of the scenario analysis, which can then provide insight for distinct policy and societal preferences.

The scenarios may be slowed down by transition bottlenecks related to the crucial feasibility dimensions identified in Sect. 3.3: (1) the maturity of options, (2) system integration and infrastructure requirements, (3) societal acceptability, and (4) political feasibility (Turnheim and Nykvist 2019). The large-scale development of onshore and especially offshore wind in the TECH scenario is likely to meet challenges in terms of societal acceptance, related to “not in my backyard” (NIMBY) attitudes, competition with other marine activities such as fishing, shipping, leisure, and/or military activities. Although the need for a green transition may result in prioritization of windfarms, a legal solution for these tensions has not yet been found. Moreover, there may be potential conflicts with indigenous rights and the increasing focus on biodiversity and conservation in sustainable development policies. Distribution to shore and on land, as well as infrastructure for export, may also be associated with controversies.

Another issue in TECH, and to some extent in RAD scenario, is that some of the core solutions, such as hydrogen or floating offshore wind, are immature and the pace of their development is uncertain, due to yet underdeveloped industrial structures, technological knowledge, and institutions, e.g., in terms of regulations and cultural perceptions. This calls for adding time, cost and uncertainty while assessing the development and deployment of said technologies. This was captured in the techno-economic analysis, e.g., for hydrogen, by adapting the corresponding assumptions which yielded lower production volumes of blue and green hydrogen. Naturally, this has broader implications regarding potential exports and the need for diversifying the fuel supply with other alternatives like biofuels, electrification, or small shares of fossil fuels which themselves can be subject to additional constraints.

Likewise, the emergence of multiple, partly synergetic and partly competing solutions, creates uncertainty among actors, who fear lock-in to first generation technologies and in some cases postpone investment decisions in anticipation of stronger policy or market signals. In turn, uncertainty regarding supply and demand balance is associated with so-called chicken-or-the-egg dilemmas. Moreover, the development of necessary renewable energy infrastructure to power the production of alternative fuels may be considered as a transition bottleneck. Even though huge power grid investments are planned towards 2030 and measures to reduce the lead time have been proposed, it may take several years to get the capacity needed to electrify, set up, or expand alternative fuel production in a specific location. These factors may slow down transitions considerably. Similarly, Norway and Europe's reliance on import of critical raw materials required for the development and upscaling of new technologies should be considered and addressed, with a view to the current geo-political context.

A crucial bottleneck, especially for SOC, but also for the RAD scenario, is the lack of political will to shut down oil and gas in Norway. Contrary to the recommendation from a government-appointed climate committee (Klimautvalget 2023)
the present government will not provide a closing strategy for the petroleum sector, but rather facilitate continued investments (Dagavisen 2023). Another critical factor (also relevant for the INC scenario) is the geo-spatial distribution and overall availability of biomass for energy. While multiple national initiatives to produce sustainable biofuels may reduce Norway's current biofuel imports, the costs are high; and, in a global long-term perspective, the demand for sustainable biomass is likely to exceed the supply (Kircher 2022). As explored in the techno-economic analysis, even higher costs, and limited availability of biofuels in INC and SOC, would imply relying on other alternatives such as electrification, hydrogen, but also hindering decarbonization efforts by extending fossil fuel use.

Meanwhile, both the SOC and RAD scenarios are dependent on significant demand reductions, linked to circularity, localization, and underlying lifestyle changes towards sufficiency. These may be politically difficult to foster actively unless strong grassroots movements and changes in public opinion form.

Despite shifts in technologies and changes in demand trends, all four scenarios show sustained levels of GDP and value-added growth as per the regional-economic analysis. However, higher levels of economic growth under certain scenarios, are not always aligned to higher degrees of decarbonization as illustrated by the RAD and SOC scenarios with contrasting metrics in these two dimensions. This highlights the need for developing transition pathways, that balance both ambitious decarbonization strategies and policy with economic growth for society.

While the scenario storylines include elements and quantifications specific to the Norwegian energy transition, the embedded considerations related to the degree of change and general challenges at the system level can be applied and contextualized to other areas. Meanwhile, analogous national modelling analyses can also be found in the ever-growing field of energy planning. Thus, the overall approach and methodology taken in the present study—linking socio-technical transition research in the scenario design with modelling analyses—could be replicated and adopted in other countries or regions.

# 6 Conclusion

This study presents an interdisciplinary approach to holistically analyze potential pathways for Norway's energy transition. The analyses link socio-technical transition research and modelling of the Norwegian energy system and the economy. These complementary perspectives provide valuable insight by capturing key considerations affecting the feasibility of the scenarios, and identifying critical issues that could slow down the transition towards a low-carbon future. Furthermore, the scenarios also illustrate that to reach ambitious levels of decarbonization across all sectors, a high degree of change will be needed in society and technological development, which will be accompanied by varying degrees of economic growth.

The energy system analysis shows that in the scenarios with minor socio-institutional change, where higher energy demands and electricity trade is expected,
new additional renewable capacity will be needed for power generation despite potential bottlenecks affecting technology costs. However, the uptake of other new emerging technologies and fuel replacements (e.g., hydrogen, ammonia) across key sectors will be more likely when considering the scenarios with major technological change. On the other hand, minor technological change leads to decreasing but continued use of fossils and higher reliance on electrification and fuel replacements with biofuels. The role of these technologies and carriers will, however, be susceptible to the degree of technological maturity, resource availability, and policies in place.

Finally, the regional economic analysis shows that across all four scenarios—despite a slowdown compared to the reference case—economic growth can be expected. The growth in GDP and labor demand is closer to the reference in the cases with minor socio-institutional change, and lower in the scenarios with major change in this dimension. This illustrates a key tradeoff in the transition pathways, where higher degrees of decarbonization with sustainable energy sources can be realized, while balancing economic growth and societal development.

**Acknowledgements** This work received funding from FME NTRANS, grant number 296205, supported by the Research Council of Norway.

## References

Andersen AD, Geels FW et al (2023a) Faster, broader, and deeper! Suggested directions for research on net-zero transitions. Oxford Open Energy 2:oiad007. https://doi.org/10.1093/ooenergy/oiad007

Andersen AD, Markard J et al (2023b) Architectural change in accelerating transitions: actor preferences, system architectures, and flexibility technologies in the German energy transition. Energy Res Soc Sci 97:102945. https://doi.org/10.1016/j.erss.2023.102945

Bach H et al (2021) Blending new and old in sustainability transitions: technological alignment between fossil fuels and biofuels in Norwegian coastal shipping. Energy Res Soc Sci 74: 101957. https://doi.org/10.1016/j.erss.2021.101957

Bolwig S et al (2019) Review of modelling energy transitions pathways with application to energy system flexibility. Renew Sust Energ Rev 101:440–452. https://doi.org/10.1016/j.rser.2018.11.019

Chang M et al (2021) Trends in tools and approaches for modelling the energy transition. Appl Energy 290:116731. https://doi.org/10.1016/j.apenergy.2021.116731

Chang M et al (2023) Perspectives on purpose-driven coupling of energy system models. Energy 265:126335. https://doi.org/10.1016/j.energy.2022.126335

Dagavisen (2023) Støre avviser plan om utfasing av olje i Norge, 11 May. https://www.dagsavisen.no/rogalandsavis/nyheter/2023/11/05/store-avviser-plan-om-utfasing-av-olje-i-norge/. Accessed 11 Jul 2023

Espegren KA et al (2023) NTRANS Socio-technical pathways and scenario analysis

FME NTRANS (n.d.) NTRANS – Norwegian Centre for Energy Transition Strategies. https://www.ntnu.edu/ntrans/ntrans. Accessed 18 Sept 2023

Freeman R, Pye S (2022) Socio-technical modelling of UK energy transition under three global SSPs, with implications for IAM scenarios. Environ Res Lett 17(12). https://doi.org/10.1088/1748-9326/aca54f
Geels FW, Berkhout F, Van Vuuren DP (2016) Bridging analytical approaches for low-carbon transitions. Nat Clim Chang 6(6):576–583. https://doi.org/10.1038/nclimate2980

Geels FW, McMeekin A, Pfluger B (2020) Socio-technical scenarios as a methodological tool to explore social and political feasibility in low-carbon transitions: bridging computer models and the multi-level perspective in UK electricity generation (2010–2050). Technol Forecast Soc Chang 151:119258. https://doi.org/10.1016/j.techfore.2018.04.001

Haaskjold K, Rosenberg E, Seljom P (2023) Documentation of IFE-TIMES-Norway v3. IFE/E:2022/013. https://hdl.handle.net/11250/3058964

Hainsch K et al (2022) Energy transition scenarios: what policies, societal attitudes, and technology developments will realize the EU Green Deal? Energy 239:122067. https://doi.org/10.1016/j.energy.2021.122067

Hekkert MP et al (2007) Functions of innovation systems: a new approach for analysing technological change. Technol Forecast Soc Chang 74(4):413–432. https://doi.org/10.1016/j.techfore.2006.03.002

Helgesen PI et al (2018) Using a hybrid hard-linked model to analyze reduced climate gas emissions from transport. Energy 156:196–212. https://doi.org/10.1016/j.energy.2018.05.005

Hirt LF et al (2020) A review of linking models and socio-technical transitions theories for energy and climate solutions. Environ Innov Soc Trans 35(March):162–179. https://doi.org/10.1016/j.eist.2020.03.002

IPCC (2022) Climate change 2022 – mitigation of climate change – summary for policymakers – working group III contribution to the sixth assessment report of the intergovernmental panel on climate change. www.ipcc.ch

Kircher M (2022) Economic trends in the transition into a circular bioeconomy. J Risk Fin Manag 15(2):44. https://doi.org/10.3390/jrfm15020044

Klimautvalget (2023) Omstilling til lavutslipp – Veivalg for klimapolitikken mot 2050

Köhler J et al (2019) An agenda for sustainability transitions research: state of the art and future directions. Environ Innov Soc Trans 31:1–32. https://doi.org/10.1016/j.eist.2019.01.004

Krumm A, Süsser D, Blechinger P (2022) Modelling social aspects of the energy transition: what is the current representation of social factors in energy models? Energy 239:121706. https://doi.org/10.1016/j.energy.2021.121706

Li FGN, Trutnevyte E, Strachan N (2015) A review of socio-technical energy transition (STET) models. Technol Forecast Soc Chang 100:290–305. https://doi.org/10.1016/j.techfore.2015.07.017

Loulou R et al (2016) Documentation for the TIMES model. IEA-ETSAP. https://iea-etsap.org/docs/Documentation_for_the TIMES Model-Part-I_July-2016.pdf

Markard J, Raven R, Truffer B (2012) Sustainability transitions: an emerging field of research and its prospects. Res Policy 41(6):955–967. https://doi.org/10.1016/j.respol.2012.02.013

Nilsson M et al (2020) A bridging framework for studying transition pathways—from systems models to local action in the Swedish heating domain. Technol Forecast Soc Chang 151: 119260. https://doi.org/10.1016/j.techfore.2018.04.003

Normann HE (2015) The role of politics in sustainable transitions: the rise and decline of offshore wind in Norway. Environ Innov Soc Trans 15:180–193. https://doi.org/10.1016/j.eist.2014.11.002

Prina MG et al (2020) Classification and challenges of bottom-up energy system models – a review. Renew Sust Energ Rev 129:109917. https://doi.org/10.1016/j.rser.2020.109917

Regjeringen (2022a) Karbonprisbaner for bruk i samfunnsøkonomiske analyser. https://www.regjeringen.no/no/tema/okonomi-og-budsjett/statlig-okonomistyring/karbonprisbaner-for-bruk-i-samfunnsokonomiske-analyser/id2878113/. Accessed 21 Sept 2023

Regjeringen (2022b) Norway's climate action plan for 2021–2030. Norwegian Ministry of Climate and Environment. https://www.regjeringen.no/contentassets/a78ecf5ad2344fa5ae4a394412ef8975/en-gb/pdfs/stm202020210013000engpdfs.pdf
Rogge KS, Pfluger B, Geels FW (2020) Transformative policy mixes in socio-technical scenarios: the case of the low-carbon transition of the German electricity system (2010–2050). Technol Forecast Soc Chang 151:119259. https://doi.org/10.1016/j.techfore.2018.04.002

Smith A, Raven R (2012) What is protective space? Reconsidering niches in transitions to sustainability. Res Policy 41(6):1025–1036. https://doi.org/10.1016/j.respol.2011.12.012

Süsser D et al (2022) Why energy models should integrate social and environmental factors: assessing user needs, omission impacts, and real-word accuracy in the European Union. Energy Res Soc Sci 92:102775. https://doi.org/10.1016/j.erss.2022.102775

Trutnevyte E et al (2019) Societal transformations in models for energy and climate policy: the ambitious next step. One Earth 1(4):423–433. https://doi.org/10.1016/j.oneear.2019.12.002

Turnheim B, Nykvist B (2019) Opening up the feasibility of sustainability transitions pathways (STPs): representations, potentials, and conditions. Res Policy 48(3):775–788. https://doi.org/10.1016/j.respol.2018.12.002

Turnheim B et al (2015) Evaluating sustainability transitions pathways: bridging analytical approaches to address governance challenges. Glob Environ Chang 35:239–253. https://doi.org/10.1016/j.gloenvcha.2015.08.010

UN (2023) The sustainable development goals report. United Nations (UN). https://unstats.un.org/sdgs/report/2023/

van Sluisveld MAE et al (2020) Aligning integrated assessment modelling with socio-technical transition insights: an application to low-carbon energy scenario analysis in Europe. Technol Forecast Soc Chang 151:119177. https://doi.org/10.1016/j.techfore.2017.10.024

Wachsmuth J et al (2023) Co-creating socio-technical scenarios for net-zero emission pathways: comparison of five national case studies. Renew Sustain Energy Trans 4:100064. https://doi.org/10.1016/j.rset.2023.100064

Werner A et al (2017) REMES – a regional equilibrium model for Norway with focus on the energy system. http://hdl.handle.net/11250/2434516

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

<figure><img src="image_95.png" /></figure>
Part III

**Informing Energy Security with Energy System Models**
# Modelling of Demands of Selected Minerals and Metals in Clean Energy Transition with 1.5–2.0 °C Mitigation Targets

<figure><img src="image_96.png" /></figure>

Check for
updates

Tiina Koljonen, Antti Lehtilä, Kirsikka Kiviranta, Kati Koponen, and Lassi Similä

**Abstract** Tackling climate change and the transition to a low-carbon energy system will especially increase the demand for certain minerals and metals, such as cobalt, copper, lithium and rare-earth elements. This raises questions about possible constraints to a clean energy system transition. Environmental and social concerns have also been raised about mining for these minerals in the Global South region. This chapter explores the future demand for selected minerals and metals in long-term scenarios for the global energy system until 2100. The climate policy pathways follow UNFCCC Nationally Determined Contributions (NDCs), extrapolated until 2100, and immediate action towards limiting warming to 1.5–2 °C. The scenarios have been modelled with the TIMES-VTT Integrated Assessment Model, which includes data on metal demands for renewable energy technologies, carbon capture and storage (CCS) power plant technologies (both fossil and bioenergy with CCS), nuclear power, battery technologies, electrolyzers, and electric vehicles. Our results suggest that to ensure affordable and clean energy access for all (SDG7) along with climate action (SDG13) the demand for mining activities in the Global South will increase rapidly, which raises concerns about inequalities between countries (SDG10). We need socially inclusive solutions and public-private partnerships to make sure that everyone benefits throughout the value chains. Human rights, safe working conditions, and the protection of the local environment in the Global South must be constantly audited to ensure a sustainable transition to clean energy systems.

## Key Messages

* In the clean energy transition, cobalt and dysprosium may be among the most critical metals in terms of resource sufficiency. Global cumulative consumption

T. Koljonen (✉) · A. Lehtilä · K. Koponen · L. Similä
VTT Technical Research Centre of Finland Ltd., Espoo, Finland
e-mail: tiina.koljonen@vtt.fi; antti.lehtila@vtt.fi; kati.koponen@vtt.fi; lassi.simila@vtt.fi

K. Kiviranta
VTT Technical Research Centre of Finland Ltd., Oulu, Finland
e-mail: kirsikka.kiviranta@vtt.fi
of cobalt overshoots its identified resources by 2100 without substantive recycling and substitution.

* Integrated Assessment Modelling can help assess the demands of the most critical minerals and metals in the clean energy transition and evaluate direct risks in climate action, affordable clean energy, and reduced inequalities in the Global South region, where the largest resources exist.

* The novelty of this study is its long-term perspective (2100) and the consideration of mineral and metal demands for a wide technology portfolio, including negative emissions technologies.

* Rapid expansion of battery metal demands calls for immediate actions to address human rights issues in global mineral supply chains (SDG10).

# 1 Introduction

Global material extraction, including biomass, fossil fuels, metal ores and minerals, has been deeply coupled with the growth of global gross domestic product (GDP). Between 1970 and 2017, per capita GDP more than doubled and material use grew by around two-thirds (UNEP 2019). The OECD (2019) has estimated that global primary materials use may almost double from 89 gigatonnes (Gt) in 2017 to 167 Gt in 2060. Non-metallic minerals represent the largest share, but metal use is projected to grow even more rapidly. Many recent studies (Watari et al. 2019; The World Bank 2017, 2020; Carrara et al. 2020; IEA 2021a, 2023b; Gielen 2021) have shown that the transition towards global climate change mitigation targets increases the demand for different minerals and metals because of the replacement of old infrastructures and the increased metal intensities of clean energy technologies. According to IEA (2021a), since 2010 the average amount of metallic minerals needed for a new unit of power generation capacity has increased by 50% as the share of renewables has risen. Claderon et al. (2024) highlighted the lack of analysis on the ability to meet these demand estimates. However, in most of the climate change mitigation scenarios the supply and demand of these materials are typically neglected, including the most recent assessment report of the Intergovernmental Panel on Climate Change (IPCC 2022).

Environmental, social, and supply risk concerns have been raised about mining and processing of minerals and metals in the Global South needed for the clean energy transition. For example, above 70% of the global cobalt is mined in the Democratic Republic of Congo (DRC), where severe human rights and environmental challenges in mining operations have been reported (IEA 2023a; UNCATD 2020). IEA (2023a) has also reported concerns due to China's increasing role in supply. For example, China produces 70% and processes 90% of rare earth metals. For cobalt, China is the major processor with over 75% of refined cobalt output in 2022.

In this chapter, the impacts on the global demand and supply of minerals and metals critical to climate change mitigation are studied using the global TIMES-VTT
Integrated Assessment Model (IAM). The analysis is based on our earlier studies on global mitigation scenarios (Lehtilä and Koljonen 2018; Koljonen and Lehtilä 2012). In this study, we have updated the TIMES-VTT database with the most recent literature and data on mineral and metal intensities of clean energy technologies. The raw materials of interest are selected based on literature and our earlier studies (Grandell et al. 2016). Many of the earlier studies on mineral and metal demands have primarily focused on electrification of the energy system with renewable energy technologies (Gielen 2021; IEA 2021a, 2023a). The added value of this study is its effort to explore the impacts brought by negative emissions technologies and practises (NETPs) as a novel element for the assessments to reach the 1.5–2.0 °C mitigation target. As an outcome, new assessments on metal demands compared with critical mineral resources by 2100 are modelled with a wider technology portfolio under study. The results for climate action (SDG13) and affordable clean energy (SDG7) are highlighted against the concerns on inequalities (SDG10), clean water (SDG7), and responsible consumption and production (SDG12).

## 2 Overview of Input Data Formulation on Minerals and Metals and Modelling Approach

### 2.1 Data Formulation for Demands of Selected Minerals and Metals

The following technologies have been selected for the critical raw materials evaluation: wind power, solar photovoltaics, concentrating solar power, geothermal, hydropower, solid and gaseous fossil fuel- and biomass based combustion with and without carbon capture and storage (CCS), biofuels, nuclear power, electric vehicle batteries and motors, and electrolysers. For bioenergy with carbon capture and storage (BECCS) we have used the metal demand data for fossil energy with CCS due to limited information on metal demands of BECCS. In the scenario modelling, direct air capture of carbon dioxide and storage (DACCS), biochar produced by pyrogenic carbon capture and storage (PyCCS), reforestation/afforestation, soil carbon sequestration, enhanced weathering, and ocean liming are included in the modelling, but without specific metal demand assessments. However, their application can indirectly impact the metal demands, e.g., trough reduced demand for other mitigation measures or increased energy inputs. The mineral and metal intensity data is typically expressed by the amount of minerals and metals needed to build a gigawatt of capacity, in tonnes per gigawatt (t/GW). The metal intensity data used in this study is based on mineral and metal intensity estimates collected from several publicly available studies. In addition to the metal demand of the assessed technologies, the shares of potential sub-technologies, metal intensity improvements and plant lifetimes have been taken into account in the scenario modelling. Table 1 describes the summary of the selected technologies in our analysis and the reference sources for their mineral and/or metal intensities.
<table><caption>**Table 1** Covered technologies in scenario analysis and reference sources for their mineral and/or metal intensities</caption><thead><tr><td>Technology</td><td>References</td></tr></thead><tbody><tr><td>Wind power</td><td>Carrara et al. (2020), IEA (2021a)</td></tr><tr><td>Solar photovoltaics (PV)</td><td>Carrara et al. (2020), IEA (2021a)</td></tr><tr><td>Concentrated solar power (CSP)</td><td>Watari et al. (2019)</td></tr><tr><td>Geothermal</td><td>Moss et al. (2011)</td></tr><tr><td>Hydropower</td><td>Ashby (2013)</td></tr><tr><td>Biomass-based combustion and biofuels</td><td>Ashby (2013), Moss et al. (2011)</td></tr><tr><td>Solid and gaseous fossil fuel combustion</td><td>Ashby (2013), Moss et al. (2013)</td></tr><tr><td>Nuclear power</td><td>Moss et al. (2011)</td></tr><tr><td>Bioenergy with carbon capture and storage (BECCS)</td><td>Moss et al. (2011) data on fossil-based combustion with CCS</td></tr><tr><td>EV batteries</td><td>Assumptions based on Volkswagen (2021)</td></tr><tr><td>EV motors</td><td>Assumptions based on IEA (2021a) and Månberger and Stenqvist (2018)</td></tr><tr><td>Electrolysers</td><td>IEA (2021a)</td></tr></tbody></table>

Besides using virgin natural resources for minerals, we can also use recycled materials after their expected lifetime. In the scenario assessments we have made rough estimates for the recycling rates of the selected metals in the selected technologies, which will reduce the demand for virgin natural resources. However, there is very little information on future potentials for recycling and therefore these estimates include large uncertainties. Besides recycling, other metals can substitute for some metals in certain applications but this information is even more lacking and therefore not considered. However, innovation and improvements of existing technologies, such as batteries, are considered, and the technology data includes estimates for metal intensity development in the future, which naturally also includes large uncertainties.

## 2.2 TIMES-VTT Model Description

The TIMES-VTT model is a global multi-region model based on the ETSAP TIMES modelling framework. The model itself is a derivative of the global ETSAP TIAM model (TIMES Integrated Assessment Model, see Loulou 2008; Loulou and Labriet 2008). The methodology can be characterized as bottom-up, technology rich partial equilibrium modelling, and the model is usually run in perfect foresight mode. The model covers all sectors but focuses on energy and emissions, with all Kyoto gases included (e.g., carbon dioxide, methane, nitrous oxide, fluorinated gases). The regional composition includes 19 regions with the most detail for Europe but
covering all countries of the world, some of which are thus quite large aggregates (e.g. Africa, Central and South America).

The model is driven by a set of demands for energy services in all sectors: agriculture, residential, commercial, industry and transport. The construction of the exogenous demands for energy services may be done by using the results from general equilibrium models, which can provide a set of coherent drivers for each region and for the world as a whole, such as population, households, GDP, and sectoral outputs (e.g. output of manufacturing industry by main branch, output of public and commercial services, output of agriculture, forestry and fishery). The decoupling factors between the drivers and the demands for useful energy services account for phenomena such as saturation and suppressed markets and are in part empirically based. Most of these final demands have economic growth as their key driver. However, the demands for all other commodities (e.g. electricity, heat, various fuel commodities, emission allowances, carbon dioxide geological storage services) in the system are endogenously determined by the model according to their supply-demand equilibrium, which must always satisfy various resource and sustainability constraints.

For supporting global integrated assessment modelling of climate change mitigation, the TIMES framework also incorporates an integrated climate module with a three-reservoir carbon cycle for carbon dioxide ($CO_2$) concentrations and single-box decay models for atmospheric methane ($CH_4$) and nitrous oxide ($N_2O$) concentrations, and the corresponding functions for radiative forcing. The forcing functions for $CO_2$, $CH_4$ and $N_2O$ follow the non-linear formulations presented in the IPCC Fifth Assessment Report (Myhre et al. 2013) but are linearized around user-defined points. If necessary, using an iterative approach the accuracy of the linearization can be improved to an arbitrary level. Additional forcing induced by other natural and anthropogenic causes is considered by means of exogenous projections. The changes in the global mean temperature are simulated for two layers, surface, and deep ocean (Loulou et al. 2016). When modelled, the emissions of fluorinated gases (HFCs, PFCs and $SF_6$) can also be accounted for in the climate model by converting them into equivalent $CO_2$ emissions. Although the carbon cycle and the concentrations of $CH_4$ and $N_2O$ are represented by quite simple models, the radiative forcing from anthropogenic greenhouse gas (GHG) emissions is reasonably well approximated by the TIMES climate module and is calibrated to reproduce historical levels.

The model has been previously used to study global, regional and national mitigation pathways to reach 1.5–2 °C mitigation targets and for impact assessments of national, Nordic, and EU level climate and energy policies. TIMES-VTT model has been the core tool in formulating and analysing the impacts of Finland's climate and energy strategies and policies, including climate neutrality target by 2035 (Lehtilä et al. 2021; Koljonen et al. 2021). A detailed description of the TIMES methodology can be found in the documentation (Loulou et al. 2016). Below, the modelling of metal demands is briefly described in three scenarios to reach 1.5–2.0 °C mitigation target by 2100, with two alternative storylines for 1.5 °C mitigation scenarios.
## 2.3 Description of Scenario Formulation with 1.5–2.0 °C Mitigation Target

For the modelling experiment, we selected eight metals to be included in the TIMES-VTT model for assessing the impact of clean energy transition on the demand for the primary extraction of these metals and resource sufficiency. The selection of metals was based on recent literature (e.g. IEA 2021a; Gielen 2021; IEA 2023a) and on our earlier studies (Grandell et al. 2016), which show the potential risks in the supply of these metals compared with their rapidly increasing demands. For each of these metals, we identified all the main energy technologies where the metals are needed and estimated the consumption in terms of unit of installed capacity. After the technical lifetime of each of these technologies, the materials are assumed to be released for scrapping, and can be recycled into new products within an assumed average delay of 5 years from scrapping to a building new installation. The model thereby produces the annual flows representing the amounts of metals stored in the new installations, and the annual recycling flows after the end of their product lifetimes (EoL). Even though the supply of minerals and their processing to these eight metals is not explicitly modelled, the resources per extraction (R/E) numbers shown in Table 2 indicate the potential constraints of these metal supplies. The recycling rates are based on assumed present rates and are expected to increase over time. The approximation of EoL recycling is described in Kiviranta et al. (2022).

The scenarios modelled are long-term scenarios for the global energy system until 2100. For the scenario formulation, we have used the key characteristics of mitigation pathways reported in the IPCC AR6 WG3 (2022). The pathways follow the Nationally Determined Contributions (NDC) until 2030 and immediate action towards limiting warming to 1.5–2 °C. Immediate action refers to the adoption of climate policies before 2025, while in the NDC scenario, deep mitigation efforts are seen after 2030. In the scenarios, GHG trajectories are modelled both at the global level and for each TIMES-VTT region as follows:

* **NDC (Reference scenario):** The global and European GHG emissions reduction trajectory is taken from the EN_INDCi2030_1400f scenario results of the REMIND-MAgPIE 2.1–4.2 model in the IIASA database (IIASA 2022). This scenario describes the impact of NDCs and long-term strategies (LTS) on the annual GHG emission trajectories, on the global scale and by region, which lead to a temperature increase of about 2 °C by 2100.

* **1.5C-Tec ("Advanced technology and global markets"):** This storyline focuses on optimistic technology development and their market-based implementation. The global temperature change is limited to 1.5 °C by 2100, but the minimum regional GHG emissions reduction trajectories are as in the NDC scenario, such that in each model year the net regional GHG emission reductions must be larger than or equal to those in the NDC scenario. Interim overshoot is allowed. World population growth is stably slowing down, reaching about 9.8 billion by 2100. Economic growth drivers are modelled according to SSP2 storyline from IPCC AR6 report (IPCC 2022).
<table><caption>**Table 2** Summary characteristics of the metals selected for consideration and assumed net EoL (end of lifetime) recycling rates (net of losses)</caption><thead><tr><th rowspan="2">Metal<sup>a</sup></th><th rowspan="2"></th><th rowspan="2">Unit<sup>b</sup></th><th rowspan="2">Reserves</th><th rowspan="2">Identified resources</th><th rowspan="2">Largest Reserves</th><th colspan="2">Current extraction</th><th colspan="2">EoL recycling, net</th></tr><tr><th>R/E Years</th><th>Current demand<sup>c</sup></th><th>2020 (%)</th><th>2050 (%)</th><th>2100 (%)</th></tr></thead><tbody><tr><td rowspan="2">Silver</td><td>Ag</td><td>kt</td><td>550</td><td>750</td><td>Peru, AUS, POL</td><td>25</td><td>30</td><td>33</td><td>52</td><td>63</td><td>80</td></tr><tr><td>Cobalt</td><td>Co</td><td>Mt</td><td>8.3</td><td>25</td><td>DRC, AUS, Indonesia</td><td>0.19</td><td>132</td><td>0.20</td><td>35</td><td>52</td><td>80</td></tr><tr><td rowspan="2">Copper</td><td>Cu</td><td>Mt</td><td>890</td><td>2100</td><td>Chile, AUS, Peru</td><td>21</td><td>100</td><td>30</td><td>45</td><td>62</td><td>80</td></tr><tr><td>Dysprosium</td><td>Dy</td><td>kt</td><td>..</td><td>1600</td><td>China, Vietnam, Russia</td><td>2</td><td>800</td><td>2</td><td>7</td><td>18</td><td>50</td></tr><tr><td rowspan="2">Lithium</td><td>Li</td><td>Mt</td><td>26</td><td>98</td><td>Chile, AUS, Arg.</td><td>0.13</td><td>750</td><td>0.13</td><td>6</td><td>15</td><td>43</td></tr><tr><td>Manganese</td><td>Mn</td><td>Mt</td><td>1700</td><td>17,000</td><td>S-Africa, Brazil, AUS</td><td>20</td><td>850</td><td>21</td><td>23</td><td>41</td><td>75</td></tr><tr><td rowspan="2">Neodymium</td><td>Nd</td><td>kt</td><td>..</td><td>29,000</td><td>China, Vietnam, Russia</td><td>30</td><td>960</td><td>33</td><td>8</td><td>20</td><td>50</td></tr><tr><td>Nickel</td><td>Mi</td><td>Mt</td><td>100</td><td>300</td><td>Indonesia, AUS, Brazil</td><td>3</td><td>100</td><td>4</td><td>62</td><td>71</td><td>85</td></tr></tbody></table>

<sup>a</sup>Main data source for reserves, resources and demands: USGS (2022), Liu et al. (2023)

<sup>b</sup>Applies to all the columns except R/E (Resources per extraction). R/E indicates the number of years the resources can cover the consumption based on the resources and the current consumption numbers of the commodity

<sup>c</sup>Current demand includes total global consumption for all the end use sectors
* **1.5C-Env** ("Nature conservation and biodiversity"): This storyline focuses on global environmental sustainability and lifestyle changes not to overshoot planetary boundaries. The global temperature change is limited to 1.5 °C by 2100, but the minimum regional GHG emissions reduction trajectories are as in the NDC scenario, such that in each model year the net regional GHG emission reductions must be larger than or equal to those in the NDC scenario. Interim overshoot is allowed. World population growth is stably slowing down until 2100, and economic growth drivers are modelled according to SSP4 storyline from IPCC AR6 report (IPCC 2022), assuming a slowdown of global economic growth, reflecting enhanced environmental awareness and circular economy. Methane emission reductions due to assumed dietary changes are also taken into consideration in the modelled agricultural GHG emissions.

# 3 Key Results of Mineral and Metal Demands in the Clean Energy Transition

## 3.1 Scenario Results for Energy Systems and Greenhouse Gas Mitigation

The global primary energy supply (TPES) has been increasing steadily throughout the 2000s, with an increase of over 40% between 2000 and 2019 (IEA 2021c). Such high growth obviously cannot continue, but many studies have been projecting the total primary energy consumption may be roughly doubling from the present levels by 2100, although the range of different projections is quite large (e.g., IIASA 2022). While electrification and the expanding use of renewable electricity generation tend to reduce growth in primary energy (IRENA 2022; Murphy et al. 2020), the transition to a post-fossil economy may also increase energy losses in some parts of the energy system, notably in storage systems, hydrogen and power-to-X conversion systems to produce synthetic fuels and other products, and due to the application of CCS, biochar or DACCS for climate change mitigation. All these various effects are reflected in our modelling results. Figure 1 illustrates the development of global primary energy supply.

In our scenario experiment, the growth in total energy supply remains quite moderate until 2050 (about 10% from 2020), but the growth becomes higher in the latter half of the century, with TPES reaching about 970 EJ in 2100 in the NDC case (Fig. 1). Some additional growth in the 1.5C-Tec scenario is consistent with the efficiency losses due to certain negative emission technologies (NETs), like DACCS and ocean liming. However, to some extent we may be underestimating the potential technology advances beyond 2050 (technology parameters are often estimated only up to 2050), as well as future changes in consumption patterns and driver elasticities for some energy service demands. Lower assumptions for economic growth in the 1.5C-Env scenarios are reflected in slower growth in global total primary energy
<figure><img src="image_97.png" /><figcaption>**Fig. 1** Development of global total primary energy supply (TPES) in the scenario variants, including non-energy uses</figcaption></figure>

supplies, reaching a considerably lower level of 800 EJ in 2100, corresponding to a 36% increase from 2020.

Among the most important energy sources, solar energy becomes the dominant source for primary energy in the latter half of the century in all scenarios. On a global scale, solar would leave wind behind already before 2040, even though wind power also continues to expand significantly. Larger scale deployment of offshore wind power could also be possible but would require heavy investments into infrastructure. One major uncertainty related to future energy sources is the sustainable potential of bioenergy supply in the longer term, having a direct link to the prospects for BECCS deployment. Like in IAM models in general, in the TIMES-VTT model the use of limited resources is exogenously constrained to sustainable potentials estimated from the literature. In 2020, the global primary production of primary biomass for energy (excluding the biomass fraction of municipal waste) was about 60 EJ/a, of which about 35 EJ/a wood fuels, about 15 EJ/a agricultural residues and 7–10 EJ/a energy crops. In the 1.5 °C mitigation scenarios, by 2050 the global primary solid biomass use for energy increases to about 97–98 EJ/a in the 1.5C-Tec scenario and to about 86 EJ/a in the 1.5C-Env scenario. According to IPCC (2022) the range of recent estimates for the technical bioenergy potential by 2050, when constrained by food security and environmental considerations, is 5–50 EJ/a for residues and 50–250 EJ/a for dedicated biomass production system respectively. In the IEA (2021b) scenarios, sustainable bioenergy potentials have been constrained to 100 EJ/a. However, it is recognised that thus avoiding the risk of negative impacts on biodiversity, freshwater systems, and food prices and availability there is a high degree of uncertainty over the future sustainable bioenergy potentials.
<figure><img src="image_98.png" /><figcaption>**Fig. 2** Development of global total net electricity supply in the scenario variants, excluding power plants own consumption</figcaption></figure>

The electrification of the global energy systems, as well as the expanding hydrogen economy, electro-fuels and decarbonized industrial systems, all increase electricity consumption, which may approach 180 PWh by 2100 according to the scenario results of the 1.5C-Tec scenario (Fig. 2). On the other hand, in the 1.5C-Env scenario with lower economic growth assumptions, electricity consumption stabilizes at a lower level of 140 PWh by 2100. The cost reductions of solar PV systems that have already taken place, and the projected further technical developments, can make solar power highly competitive on a large scale within the next few decades. Despite the additional flexibility required due to the variable nature of solar generation, the model results suggest that by 2100 60–70% of global electricity generation would be solar-based, with the highest contribution being reached in the technology-optimistic 1.5C-Tec scenario.

As expected, fossil fuel-based electricity generation is phased out almost completely by 2100, with natural gas-fired power remaining on a somewhat notable level until 2080. Bioenergy-based generation will not gain significant overall market share but will nonetheless be important in some regions and globally with respect to the negative emissions achieved though BECCS power plants. In absolute terms, nuclear power also increases notably in the scenarios but loses some share of total global generation in all the scenarios. Despite its high capital costs, nuclear power has the benefit of providing stability in the power grids under high variable power integration.

Until 2050, the global electricity supply is well in line with that in the IEA NetZero by 2050 scenario (IEA 2021b, 2023b). The total supply is about 60 PWh in
2050 and 80 PWh in 2060 in the NDC case, and around 70 PWh in 2050 and 95-98 PWh in 2060 in the 1.5 °C scenario variants. Beyond 2050, the growth in the supply may appear large, but is well explained by high electrification being the key factor behind the growth, which can also be understood by observing the moderate growth in the primary energy consumption shown in Fig. 1. High scale of electrification is enabled by the use of several types of energy storages in the model, such as batteries, power-to-X technologies, hydropower, and pumped-storage hydropower. The additional electricity consumption of DACCS plants becomes very significant beyond 2050 in the 1.5C scenarios highly reliant on this technology. At their peak deployment around 2070, the DACCS plants consume about 11% of global electricity in the 1.5C-Env case, and about 5% in the 1.5C-Tec case. The electricity supply figures are quite well in line with the IEA NZE scenario (IEA 2021b, 2023b), where the total supply was 71-77 PWh in 2050, as well as with the JRC GECO projections, where the gross supply reached 90 PWh by 2060 in their NDC case, and 99 PWh in their 1.5 °C scenario (Keramidas et al. 2022).

In the NDC scenario, the total CO₂ emissions approach zero only in 2100, and the total GHG emissions remain above 10 Gt CO₂ eq./a until 2100 (Fig. 3). The 1.5 °C mitigation scenarios follow considerably steeper decreasing emission paths, reaching the temperature target of 1.5 °C in 2100 after intermediate overshooting. Even though the global net CO₂ emissions (including LULUCF) fall to zero around 2050, the temperature has risen to 1.6 °C by that time and would keep rising unless substantial amounts of additional negative emissions are produced during the latter half of the century to fully reaching the climate target of a maximum of 1.5 °C by 2100.

<figure><img src="image_99.png" /><figcaption>**Fig. 3** Development of greenhouse gas emissions (Kyoto gases) in the scenario variants. *Note*: The red and blue lines represent the total net emissions of GHGs and CO₂, respectively, and the vertical bars show the gross emissions (positive) and removals (*BECCS* bioenergy with CCS, *DACCS* direct air carbon capture and storage, *SCS* soil carbon sequestration, *EW + OL* enhanced weathering and ocean liming) (Color figure online)</figcaption></figure>
The results indicate that fossil energy technologies with carbon capture (FECCS, including capture of related process emissions) would be employed on a large scale within the power generation sector, energy-intensive process industry, and other energy transformation sector. While in the power sector, the role of fossil-fuel based power plants would soon be decreasing, CCS would gain additional importance in the upstream fuel transformation sector, most notably in hydrogen production. Additional policies could, of course, be introduced to accelerate the transition to renewable energy, for example electrolyzers in hydrogen production, to reduce reliance on CO₂ capture and storage.

## 3.2 Overview of the Demands of Selected Metals for Selected Technologies

The total cumulative net demand for the metals included in the energy systems modelling is presented in the NDC, 1.5C-Tec, and 1.5C-Env scenarios, which is compared with identified resources (Table 3). The cumulative values represent only the modelled demands related to clean energy transition, and not all uses other than for energy sector, like in electronics or medical technologies. The cumulative uses have been compared with the identified resources reported by the US Geological Survey (USGS 2022). According to the results, out of the selected metals considered, the most critical in terms of sufficiency are cobalt and dysprosium. Above 90% of the cumulative use of these metals in the energy sectors are used in cars, which reflects the urgency for radical changes in our mobility and transport. Also 85–90% of cumulative use of Manganese (Mn) and Nickel (Ni) in energy sector is used for cars and the rest is used for wind power and power plants with CCS. The projected cumulative primary cobalt use would be 27–28 Mt., exceeding the identified resources without substantive recycling and substitution. For dysprosium, the projected primary metal requirements correspond to 80% of the identified resources,

<table><caption>**Table 3** Summary of the modelled cumulative net demand for primary production of metals</caption><thead><tr><th rowspan="2">Metal</th><th rowspan="2">Unit</th><th rowspan="2">Resources</th><th colspan="6">Modelled cumulative primary metal use by 2100</th></tr><tr><th>NDC</th><th>%</th><th>1.5C-Tec</th><th>%</th><th>1.5C-Env</th><th>%</th></tr></thead><tbody><tr><td>Ag</td><td>kt</td><td>750</td><td>130</td><td>17</td><td>150</td><td>20</td><td>120</td><td>16</td></tr><tr><td>Co</td><td>kt</td><td>25,000</td><td>18,600</td><td>74</td><td>29,740</td><td>119</td><td>29,910</td><td>120</td></tr><tr><td>Cu</td><td>Mt</td><td>2100</td><td>640</td><td>30</td><td>720</td><td>34</td><td>650</td><td>31</td></tr><tr><td>Dy</td><td>kt</td><td>1500</td><td>930</td><td>58</td><td>1290</td><td>81</td><td>1310</td><td>82</td></tr><tr><td>Li</td><td>kt</td><td>89,000</td><td>27,200</td><td>28</td><td>40,660</td><td>41</td><td>40,950</td><td>42</td></tr><tr><td>Mn</td><td>Mt</td><td>17,000</td><td>100</td><td>1</td><td>130</td><td>1</td><td>130</td><td>1</td></tr><tr><td>Nd</td><td>kt</td><td>11,200</td><td>6400</td><td>22</td><td>6910</td><td>24</td><td>7020</td><td>24</td></tr><tr><td>Ni</td><td>Mt</td><td>300</td><td>60</td><td>20</td><td>90</td><td>30</td><td>90</td><td>30</td></tr></tbody></table>

*Note*: Percentage indicates the cumulative use of metal in energy technologies compared with its identified resources
and for neodymium they correspond to almost 60%. Moreover, even though the modelled cumulative consumption of copper remains below 40% of the identified resources, one should point out that copper has significant other uses that were left out of the estimation, including electricity transmission networks that have been projected to create significant additional demand for copper (IEA 2021a).

The cumulative use of silver corresponds to 20% of the identified resources but it is notable that above 90% of the energy sector's use is for solar power installations. Overall, the results indicate the pivotal impact of the electrification of transportation on the demand for selected metals for consideration. Apart from silver, electric and hybrid vehicles would appear to contribute to 60–96% of the modelled demands for all other selected metals in clean energy technology applications. For silver, solar energy systems would however remain the most important technology cluster based on the assumed trends in the technology mix, where currently crystalline silicon cells are dominating the solar PV market (Fraunhofer 2022). The analysis for each selected metal is given below.

### 3.3 Sufficiency of Cobalt, Copper, Dysprosium, Lithium, Neodymium, Silver, and Nickel in Climate Change Mitigation

Cobalt (Co) has significant uses in lithium-ion and other types of batteries, the manufacture of magnetic, wear-resistant, and high-strength alloys, for electroplating, and in chemical industries as catalysts and as drying agents for paints and inks. Primary cobalt is obtained mainly as a by-product from the mining of nickel, silver, lead, copper and iron. According to USGS (2023), the current annual global mine production of cobalt is about 190,000 tonnes and the identified resources are 25 Mt. According to the World Economic Forum (2020), 15–30% of cobalt production in DRC comes from artisanal and small-scale mines, which represents the second largest cobalt mining sector globally after large-scale industrial mining in DRC. Most of the cobalt minerals are refined and used in China with about 80% of its consumption used by the battery industry. The modelled cumulative primary cobalt consumption due to energy technologies already exceeds identified resources. Above 95% of that use would be attributable to batteries, primarily in electric vehicles but to some extent also in stationary battery systems. Some sources estimate a significant reduction in the cobalt requirements of new battery chemistry options by 2050. Gregoir and Acker (2022) also claim that historical trends in reserve and resource development support the conclusion that there will be enough minerals and metals available for global needs. Since 2012, global resources of cobalt have doubled and reserves nearby doubled. On the other hand, the consumption of metals in other than light duty cars may also be underestimated in our modelling experiment.
Currently, the main uses of lithium (Li) are batteries (over 70%), ceramics and glass (about 14%) and various other industrial applications (about 12%) (USGS 2022). Total annual extraction amounts to about 130 kt/a, and the global identified resources are about 98 Mt. (USGS 2023), showing a 9 Mt. increase compared with the previous year (Table 3). With these estimates, the reserves per extraction (R/E) ratio would be 750 years, which appears quite high. The largest producer of lithium is Australia followed by Chile and China. However, in the scenarios presented, the modelled cumulative primary metal use alone would increase to above 40% of the identified resources. Like with cobalt, above 95% of lithium is used in batteries in cars by 2100. Nonetheless, assuming that the demands excluded from the modelling would not increase significantly, the resource sufficiency may be considered adequate, bearing in mind that there are large other unaccounted resources of lithium e.g. in seawater. Since 2012, both the reserves and resources have increased thousands of folds indicating also that Li resources might not appear to be so critical (Gregoir and Acker 2022).

Copper (Cu) is also used for various applications within all sectors of the economy. The current annual total copper demand is about 30 Mt., annual extraction is about 21 Mt. and the identified terrestrial resources amount to 2100 Mt., translating into a R/E ratio of 100 years. The production of copper is rather diversified between countries and regions. However, in 2022 the largest mine producers were Chile, Peru, China and DRC putting more pressure to Global South countries' natural environment. About 26% of copper is currently consumed by building construction, about 17% into infrastructure (including electricity transmission), about 13% into transportation vehicles, and the remaining 44% into various industrial and consumer equipment (IWCC 2023). The current end-of-life recycling rate of copper is about 45% (IEA 2021b). In the modelling experiment, the modelled cumulative net primary copper consumption (after recycling) was 640 Mt. in the NDC (2 °C) scenario and 650-720 Mt. in the 1.5C-Tec and 1.5C-Env scenarios. Above 50% of the modelled copper in the energy sector use are attributable to road vehicles and nearby 40% to solar power systems, of which a large part would be genuinely additional consumption. Moreover, one should note that the expanding power generation with large amounts of distributed generation would require remarkable additional investments also into transmission networks requiring most likely much more copper than currently. The IEA (2021a) has estimated that the annual copper consumption for transmission networks would increase to 10 Mt. by 2040. By extrapolating this projection to 2100, one may estimate the cumulative consumption at around 1400 Mt. during 2020-2100, which would cause significant additional demand of primary copper even with high recycling rates. Based on these results, the sufficiency of primary copper resources may indeed become critical during the current century, and substantial efforts to further improve the recycling rates would appear justified.

Dysprosium (Dy) and Neodymium (Ny) are rare earth minerals (REEs) and mainly used for permanent magnets, which are used especially in wind power plants and electric motors (e.g. road vehicles). Neodymium is also used to smaller extent in metallurgical, ceramics and other industries, which should bear in mind also when
considering resource sufficiency. In 2022, about 70% of REEs were produced in China, which also has the largest identified resources. The current annual demand for dysprosium is about 2000 metric tonnes, and the identified resources have been estimated at 1600 kt, which translates into a reasonably high R/E ratio of about 800 years. However, the demand dysprosium is projected to increase quite rapidly, and the results suggest that by 2100 the cumulative primary metal use in the applications modelled might account for over 80% of the identified resources. The total demand for neodymium is currently about 30 kt/a, and the identified resources have recently been estimated at 29,000 kt (Liu et al. 2023), giving a R/E ratio of nearly 1000 years. The modelling suggests that even with the rapidly increasing demand in electric motor and wind power applications, the modelled cumulative primary metal use of neodymium would amount only to about 25% of the identified resources by 2100, provided that beyond 2050 the average EoL recycling rate can be increased to 50%. It should be noted that also for the REEs considered most of the cumulative consumption is attributable to road vehicles, while for the permanent magnets of wind power plants the cumulative use is only 5-10% of the total use in the energy sector.

Until recently, the recycling rates of most REEs have been very low. According to a UNEP report (2011), the end-of-life recycling rate of dysprosium was estimated to be 1% or below. The low rates are mainly due to the low content of the metal in the recycled products, which may make the recycling process costly. In the modelling experiment, the results on primary metal requirements were obtained assuming steadily increasing recycling rates reaching 50% in 2100, the same level as for neodymium (Table 3). Therefore, the results clearly indicate that dysprosium may be among the most critical metals in terms of resource sufficiency and the need for enhanced recycling.

Silver (Ag) is a precious metal used for jewellery, silverware, coins and bars. Industrial applications are, however, increasingly significant, particularly electrical and electronic industries (TSI 2023). Among energy technologies, solar power systems have gained a notable role in the total consumption of silver. According to the USGS (2022), the annual mine production is about 25 kt. The production of silver is rather diversified between countries and regions, while the largest silver producers are Peru, Australia, China. The current global silver reserves are about 550 kt, which would mean exhaustion of the global reserves in 22 years assuming just the current level of mining. The total resources are larger, but according to an ultimately recoverable resources (URR) analysis (Sverdrup et al. 2014), the remaining recoverable silver resources were estimated at only about one million tonnes. That would translate into the exhaustion of the global silver resources within the current century, with peak primary production estimated to occur already by 2040. The modelling results indicate that transition to carbon neutrality would create about 200 kt of additional demand by 2100, where above 90% comes from PV systems. Here, one should note that the specific silver consumption of PV systems is projected to reduce significantly by 2050, and these reducing metal requirements have been considered in the modelling. Bearing in mind that most of the current silver demand is not related to energy technologies, and that main part of the demand
can be assumed reasonably stable, the additional demand would inevitably cause accelerated exhaustion of the remaining silver resources and additional pressure on maximizing the recycling rates.

Nickel (Ni) is used in significant amounts in stainless steel and other alloys to make them stronger and withstand extreme temperatures and corrosion. Moreover, it has considerable uses in plating and battery chemistry applications. The total identified resources of nickel are estimated at about 300 Mt. giving a low R/E ratio of 130 years. Between 2021 and 2022 global nickel mine production increased by 20% to 3.3 Mt. reflecting the increasing demand. Indonesia is the largest producer of nickel with Chinese joint venture companies. However, nickel recycling can still be enhanced from the current levels. According to Gregoir and Acker (2022) the projected growth of European pure nickel secondary supply would result in a decline of primary demand post 2040 and reduce the expected 2020–2050 primary demand.

The results given in Table 3 show that the differences in cumulative uses of metals under study are rather small between the two 1.5 °C mitigation scenarios. On the other hand, in the NDC scenario the cumulative use of cobalt, dysprosium, and lithium is considerably lower than in the 1.5 °C mitigation variants. The summary of cumulative metal uses in the 1.5C-Tec scenario (Fig. 4) shows that 35% of cobalt resources are used by 2050 indicating the urgency for action regarding its use in cars. Moreover, we conducted a sensitivity analysis for the 1.5C-Tec scenario where we removed DACCS from the technology options for climate change mitigation while at the same time limiting the deployment of BECCS to the scale in the original results of the scenario. According to the results, the cumulative metal requirements increased for all the metals under study by 3–60% by 2100 when the DACCS

<figure><img src="image_100.png" /><figcaption>**Fig. 4** Estimated cumulative primary requirements of selected minerals due to clean energy technologies in the 1.5C-Tec scenario within 2105–2100, in proportion to identified total global resources</figcaption></figure>
options were not available. The increases were the largest in the cobalt and lithium uses, mainly due to the increased needs for stationary electricity storage systems, but also the added consumption of dysprosium, neodymium and nickel were significant, between 7% and 18%. However, a thorough and credible sustainability analysis would require more comprehensive data both on the direct and indirect material use, and land use impacts. As an example, indirect metal use of DACCS must be considered due to its high energy consumption but closing out DACCS from technology portfolios can increase the total cumulative metal use considerably. Claderon et al. (2024) also highlighted that there are lacking analysis on the ability to meet these demand estimates.

## 4 Linking SDGs with the Clean Energy Transition

Clean energy transition with the phase-out of fossil fuels is undoubtedly needed to mitigate climate change to a safe level (SDG13) and to ensure affordable and clean energy access for all (SDG7). Electrification of the energy system is a key climate measure, which would rapidly increase the investments in such clean energy technologies, which metal intensities are much higher than for conventional technologies using fossil fuels. Minerals needed for clean energy transition are largely mined in the Global South region and processed to metals in China, where the mining industry has been associated with human rights violations, environmental contamination, and biodiversity losses (IEA 2023a; Gregoir and Acker 2022; World Economic Forum 2020).

It is evident that transition calls for sustainable consumption and production (SDG12) patterns with resource decoupling and impact decoupling from economic growth. Compared with fossil fuel production, critical and other minerals production is much more concentrated geographically. China has a dominant position not only for production of REEs but also for processing of cobalt and lithium (IEA 2023a). Major production and identified reserves are located especially in the Global South, like in DRC and Chile, which raises concerns about the potential impacts on inequalities (SDG10) especially due to artisanal and small-scale mining. Large-scale industrial mining can also cause severe environmental problems. In addition to potential environmental and biodiversity risks, mining industries use large amounts of water, which can worsen the access to clean water (SDG6). The amount of water withdrawal should especially be considered at locations where there are concerns on water availability. As an example, production of one tonne of lithium can require approximately 1.9 million litres of water and in Chile's Salar de Atacama, where lithium and other mining activities consumed 65 per cent of the region's water. That is having a big impact on indigenous communities that have lived in the Andean region of Chile, Bolivia and Argentina and local farmers (UNCTAD 2020). Consumption of water is also high for REE and cobalt mining (Gregoir and Acker 2022). It is thus important that socially inclusive solutions are suited to local needs in mineral production (SDG12).
There are many ways to tackle the critical sustainability challenges on climate action, affordable and clean energy for all, and reduced inequalities. As shown in our modelling experiment, the impacts of increased energy and material efficiencies, recycling, and metal substitution can be assessed with Integrated Assessment Modelling but there are still lacking data and knowledge especially on potentials for recycling and substitution. Our storyline 1.5C-Env included an assumption of slightly lower GDP growth compared with the other two. However, due to stricter constraints for bioenergy and BECCS implementation, the demands of some metals even increased, which reveals the complexity of the system level assessments. However, without radical changes in our behaviour towards more responsible consumption (SDG12) there is a risk of overshooting planetary boundaries and increased inequalities. We can argue that there is an urgent need for in-depth analysis and modelling on energy sufficiency and how this will impact on demand projections for minerals and metals.

# 5 Conclusion

Energy system modelling is a useful tool for assessing not only energy but also material demands for a clean energy transition. In our modelling experiment, TIMES-VTT IAM was used to analyse the demands of selected minerals and metals, which are considered critical to reach 1.5–2 °C mitigation targets. Our modelling results indicate that the raw material availability adds an extra layer of constraints and uncertainties to the clean energy transition, which has been recognized by several earlier studies. In this scenario modelling exercise, the analysis for demands of minerals and metals was expanded with the impacts brought by negative emissions technologies and practices (NETPs) as a novel element for the assessments to reach 1.5–2.0 °C mitigation target. Unfortunately, we couldn’t find any data for metal demands of NETPs except for fossil CCS, which was used for modelling of metal demands of BECCS. Based on our modelling results, metal demands for BECCS seem to be very low. However, it should be noted that large investments in DACCS will increase renewable electricity demand with high metal intensities indicating that also indirect metal use should be considered.

Based on our analysis, the most critical metals are cobalt and dysprosium, which were mostly used for batteries. As the direct demands of minerals and metals for NETPs seems to be very low, the constraints in battery metals supplies may increase the demand for NETPs. Critical metals are also used outside energy sector putting more pressure on security of supply of these metals. However, there is a lack of analysis on the ability to meet these demand estimates. We need a better understanding of recycling and substitution of critical metals, analysis of future critical mineral and metal demands outside of the energy sector, and a realistic analysis of new mine development. In our modelling exercise, even with optimistic assumptions on recycling, global demands of minerals and metals are increasing so rapidly that
without extensive opening of new mines there are increasing resource and supply security risks.

The Global North is putting more emphasis on their own mining and metal industries to decrease the supply risks of critical metals. Due to rapid growth in metal demands, we can expect that supply of critical minerals and metals for clean energy transition are still largely mined and processed in the Global South. Constant auditing and increased transparency are thus needed to monitor human rights, safe working conditions and protection of the local environment in Global South. The processed metals are shipped from Global South to Global North, which is responsible for major demand growth of these metals. In addition to responsible production of metals, we need also put more emphasis on responsible consumption. Based on our results, sustainable transformation of the transport sector with moderate growth will have a pivotal role. On the other hand, mining operations have the potential to bring economic benefits to the communities and governments where the operations take place. Thus, we need socially inclusive solutions and public-private partnerships to make sure that everyone benefits throughout the value chains.

**Acknowledgement** The authors want to acknowledge funding from EU H2020 project NEGEM (869192), and from VTT Technical Research Centre of Finland Ltd.

## References

Ashby MF (2013) Metals for low-carbon power. Mater Environ 2:349-413. https://doi.org/10.1016/b978-0-12-385971-6.00012-9

Calderon JL et al (2024) Critical mineral demand estimates for low-carbon technologies: what do they tell us and how can they evolve? Renew Sustain Energy Rev 189:113938. https://doi.org/10.1016/j.rser.2023.113938

Carrara S et al (2020) Raw materials demand for wind and solar PV technologies in the transition towards a decarbonised energy system. EUR 30095 EN, Publication Office of the European Union, Luxembourg https://data.europa.eu/doi/10.2760/160859

Fraunhofer (2022) Photovoltaics report. Fraunhofer Institute for Solar Energy Systems. https://www.ise.fraunhofer.de/content/dam/ise/de/documents/publications/studies/Photovoltaics-Report.pdf

Gielen D (2021) Critical minerals for the energy transition. International Renewable Energy Agency, Abu Dhabi. ISBN: 978-92-9260-366-3 https://www.irena.org/Technical-Papers/Critical-Materials-For-The-Energy-Transition

Grandell L et al (2016) Role of critical metals in the future markets of clean energy technologies. Renew Energy 95:53-62. https://doi.org/10.1016/j.renene.2016.03.102

Gregoir L, Acker vK (2022) Metals for clean energy: pathways to solving Europe's raw materials challenge. KU Leuven, Belgium. https://eurometaux.eu/media/rqocjybv/metals-for-clean-energy-final.pdf

IEA (2021a) The role of critical minerals in clean energy transitions. International Energy Agency, Paris. https://www.iea.org/reports/the-role-of-critical-minerals-in-clean-energy-transitions

IEA (2021b) Net zero by 2050. A roadmap for the global energy sector. International Energy Agency, Paris

IEA (2021c) World energy balances. https://www.iea.org/data-and-statistics/data-product/world-energy-balances
IEA (2023a) Critical minerals market review 2023. International Energy Agency, Paris. https://www.iea.org/reports/critical-minerals-market-review-2023

IEA (2023b) Net zero roadmap. A global pathway to keep the 1.5 °C goal in reach. 2023 Update. International Energy Agency, Paris

IIASA (2022) AR6 scenario explorer and database hosted by IIASA. https://data.ene.iiasa.ac.at/ar6/

IPCC (2022) In: Shukla PR, Skea J, Slade R, Al Khourdajie A, van Diemen R, McColllum D, Pathak M, Some S, Vyas P, Fradera R, Belkacemi M, Hasija A, Lisboa G, Luz S, Malley J (eds) Climate change 2022: mitigation of climate change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge/New York. https://doi.org/10.1017/9781009157926.

IRENA (2022) Smart electrification with renewables: driving the transformation of Energy Services. ISBN 978-92-9260-367-0. IRENA_Smart-Electrification_Renewables_2022.pdf

IWCC (2023) IWCC statistics and data series. International Wrought Copper Council. https://www.coppercouncil.org/iwcc-statistics-and-data

Keramidas K et al (2022) Global energy and climate outlook 2022: energy trade in a decarbonised world. EUR 31355 EN, Publications Office of the European Union, Luxembourg. ISBN 978-92-76-60-498-3. https://doi.org/10.2760/863694.

Kiviranta K et al (2022) Report on assessment of impacts on key non-renewable resource chains: case study on global demand, supply and trade-offs for selected metals and minerals in global mitigation pathways. Deliverable D3.9. https://www.negemproject.eu/

Koljonen T, Lehtilä A (2012) The impact of residential, commercial, and transport energy demand uncertainties in Asia on climate change mitigation. Special issue in Energy Economics 34(3): S410–S420. https://www.sciencedirect.com/science/article/pii/S0140988312001053

Koljonen T et al (2021) Carbon neutral Finland 2035—measures and impacts of the climate and energy policies. Prime Minister's Office, Helsinki. ISBN 978-952-383-257-2 (In Finnish, English summary)

Lehtilä A, Koljonen T (2018) Pathways to post-fossil economy in a well below 2°C world. In: Giannakidis G, Karlsson K, Labriet M, Gallachóir B (eds) Limiting global warming to well below 2 °C: energy system modelling and policy development. Lecture Notes in Energy vol 64. Springer, Cham. https://doi.org/10.1007/978-3-319-74424-7_3

Lehtilä A et al (2021) Development of the energy system and greenhouse gas emissions. Carbon neutral Finland 2035—impact assessments of climate and energy policies and measures. Prime Minister's Office, Helsinki. ISBN 978-952-383-318-0 (In Finnish, English summary)

Liu S-L et al (2023) Global rare earth elements projects: new developments and supply chains. Ore Geol Rev 157:105428. https://doi.org/10.1016/j.oregeorev.2023.105428

Loulou R (2008) ETSAP-TIAM: the TIMES integrated assessment model. Part II: mathematical formulation. Comput Manag Sci 5(1–2):41–66

Loulou R, Labriet M (2008) ETSAP-TIAM: the TIMES integrated assessment model. Part I: model structure. Comput Manag Sci 5(1–2):7–40

Loulou R et al (2016) Documentation for the TIMES model. Energy Technology Systems Analysis Programme (ETSAP). IEA-ETSAP | Optimization Modeling Documentation

Månberger A, Stenqvist B (2018) Global metal flows in the renewable energy transition: exploring the effects of substitutes, technological mix and development. Energy Policy 119:226–241. https://www.sciencedirect.com/science/article/pii/S0301421518302726

Moss et al (2011) Critical metals in strategic energy technologies: assessing rare metals as supply-chain bottlenecks in low-carbon energy technologies. European Commission Joint Research Centre (JRC). Publication Office of the European Union, Luxembourg. https://data.europa.eu/doi/10.2790/35716

Moss et al (2013) Critical metals in the path towards the decarbonisation of the EU energy sector: assessing rare metals as supply-chain bottlenecks in low-carbon energy technologies. European Commission Joint Research Centre (JRC). Publication Office of the European Union, Luxembourg. https://data.europa.eu/doi/10.2790/46338