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# Emission Free Energy Carriers and the Impact of Trade to Achieve the 1.5 °C Target: A Global Perspective of Hydrogen and Ammonia

Felix Lippkau, Ganesh Deepak Rupakula, and Markus Blesl

**Abstract** To achieve the global 1.5 °C target and realize a climate-neutral energy system, decarbonizing all sectors of final energy consumption is crucial. Green hydrogen and green ammonia emerge as pivotal in decarbonizing, particularly in the industrial sector by 2100, given their potential as emission-free energy carriers. These can be efficiently produced in regions abundant in renewable resources, with lower production costs and then exported to high-demand areas. This study explores the most cost-effective global trade routes for supplying these energy carriers, considering future electricity production, the role of renewables by 2100, global emissions, final energy consumption, and the subsequent production and logistics of hydrogen and ammonia. We introduce four scenarios: Business-as-Usual (BAU), a 1.5 °C scenario (1_5D) aligning with specific CO₂ budgets, and two variants of the 1_5D scenario (SoS1 and SoS2) with varying restrictions on imports and domestic production. Employing the TIMES Integrated Assessment Model (TIAM), our findings suggest a future energy landscape dominated by the electricity sector, with solar PV contributing over 50% of green electricity by 2100. Hydrogen demand could reach 13,500 TWh compared to 155,000 TWh global energy demand. Without import constraints, Middle East Asia, due to its renewable resource richness and strategic location, could fulfill the global demand for green hydrogen and ammonia.

## Key Messages

* Without climate policies, no trades in low-carbon fuels are observed.

* Middle East Asia has the lowest green hydrogen costs with abundant potential and could be the main exporter if no trade restriction policies are applied.

* Considering cost efficiency, Europe is import dependent and could import hydrogen and ammonia from Africa and Middle East Asia.

F. Lippkau (✉) · G. D. Rupakula · M. Blesl

Institute of Energy Economics and Rational Energy Use, Stuttgart, Germany

e-mail: felix.lippkau@ier.uni-stuttgart.de; ganesh-deepak.rupakula@ier.uni-stuttgart.de;

markus.blesl@ier.uni-stuttgart.de
* Net zero emission target is possible by 2050 in the scenarios considering strong climate policies and negative emissions starting from 2040.

# 1 Introduction

The global trade in high-carbon fuels has been established for years. According to (BP 2022) the daily trade in crude oil in 2022 was reported to be 66.95 million barrels. This is a 25% increase compared to 2010 when it was 53.51 million barrels (BP 2011). Converting this volume to energy, the daily trade equates to 109 TWh in 2021 which has a carbon content of 28.9 Mt. if used energetically or 10.5 Gt considering the entire year. In 2015, the Paris Agreement was announced which aims to limit global warming to well below 2 °C (United Nations 2016). According to Rogelj et al. (2018), this means a net global carbon budget of approximately 420 Gt in order to satisfy the 1.5 °C goal with the highest chance of success. Against this backdrop, in 2022, global CO2 emissions were reported by GlobalCarbonAtlas (2023) as 36 Gt, which means 8.5% of the net carbon budget remains. Based on the high quantity of carbon fuels that additionally include natural gas and coal, new low-emission fuels must be established in order to achieve the 1.5 °C target. There has already been research that investigated options for the energy system transition that are in line with the 1.5 °C target. Staffell et al. (2019) described options for transitioning various sectors from fossil fuels to low-carbon alternatives like hydrogen. Germany was deeply covered by recent studies that investigated the whole energy system transition considering all relevant sectors to decarbonize (BCG 2021; Deutsche Energie-Agentur GmbH 2021; Luderer et al. 2021; Öko-Institut e.V. et al. 2021). Green hydrogen and ammonia, both carbon-free, are promising fuels for a wide range of applications requiring replacement (IRENA 2022).

The aim of this research is to investigate the global export/import allocation of hydrogen and ammonia, considering climate policies. It aligns with the global CO2 budget set for achieving the 1.5 °C target, with additional consideration of trade restrictions. This research also aims to provide an overview of the future exchange of the emission free energy carriers until 2100 considering different driving factors for each country such as GDP, population, industry, and renewable energy capacities. In addition, the work also attempts to contribute to the Sustainability Development Goals, SDG 7: "Affordability and Clean Energy" and SDG-13: "Climate Action". It aims to find out the final energy consumption and electricity mix of renewable and non-renewable energy needed to achieve the 1.5 °C for reaching SDG-13. It also explores the production and trade of the clean energy carriers- green hydrogen and ammonia in every world region to contribute to SDG-7.

The investigation is an extension of our work (Lippkau et al. 2023) where ammonia as a low-carbon fuel is also considered here in addition to only hydrogen and synfuels exchange for the global energy system in the previous paper.
## 2 Research to Date

Most of the previous studies chose specific export and import countries for the techno-economic analysis of the trade routes. Export countries are the places where renewable sources, like sun hours and wind, are abundant to produce green hydrogen and its derivates and simultaneously have lower production costs. These include Morocco, Tunisia, Algeria, Chile, Argentina, Australia, and Denmark. Import countries have a high demand for hydrogen but have less renewable sources and/or have high costs for their own production; for instance, Germany. van der Zwaan et al. (2021) have analyzed the export of hydrogen from North African countries to Europe and found that hydrogen will be the predominant clean energy carrier imported to Europe and is expected to progressively rise. Brändle et al. (2020) provided a more generalized report on hydrogen production routes for 94 countries on 6 continents. Their important finding was that green hydrogen from renewable-powered electrolyzers will be cost-competitive starting in 2030. Another finding was that hydrogen transport via pipelines will be cost-effective only until 2000 km distance; for longer distances, transport via ship will be economical. They concluded that Germany could produce green hydrogen at a lesser price on-site compared to green hydrogen imported from the MENA region to Germany.

Hampp et al. (2023) investigated nine different trade routes: both ships and pipelines for the transport of green energy carriers like hydrogen, methane and ammonia to Germany. The export countries selected were Spain, Denmark, Morocco, Argentina, Australia, Saudi Arabia, and Egypt. The main findings are that for pipeline export, green hydrogen from Denmark has the lowest levelized cost-75 €/MWh (2.5 €/kgH₂) along with 83 €/MWh from Egypt and Spain. On the other hand, for ship export, Argentina offers the lowest levelized cost- 95 €/MWh to Germany. It performs the best compared to Egypt, Morocco, and the other EU countries as well. The authors concluded that importing green hydrogen directly to Germany is more economical than importing its derivatives and converting them back to hydrogen. In similar research, Teichmann et al. (2012) explored green hydrogen transport options via LOHC (Liquid Organic Hydrogen Carrier), LH₂ (liquid H₂) and High-voltage Direct Current (HVDC) from North Africa to Europe. They also reported that hydrogen production via HVDC import is the most economical option followed by LOHC and LH₂ import is the most expensive in terms of production and transport. Steam-Methane Reforming (SMR) is the only economical option for on-site hydrogen production. Johnston et al. (2022) developed an open-source model for shipping costs of hydrogen and derivatives- LNG, methanol and ammonia from Australia, Chile, USA, Algeria and South Africa to Germany (Rotterdam port), Japan(Tokyo port) and China (Shanghai port). They found out that Ammonia is the cost-effective hydrogen carrier with levelized costs ranging from 0.56 to 0.82 $/kgH₂ and liquid hydrogen is the most expensive chemical for shipping with costs in the range of 2.09–2.19 $/kgH₂. Additionally, they concluded that the ship's capital and fuel costs play a major role in evaluating the levelized costs. Galimova et al. (2023) also performed a study for the regions Chile, Morocco,
Germany and Finland provided a similar conclusion. They have also found that hydrogen transport by pipeline is economical up to 2400 km and sea shipping is preferable for long distances with transportation costs constituting 29–63% of total import costs. However, additional results state that imported green hydrogen from Chile and Morocco is more expensive than local production in Germany and Finland.

Egerer et al. (2023) researched Ammonia export extensively from Australia to Germany. They have focused on the entire value chain from renewable energy generation, green hydrogen production, ammonia from hydrogen, export, and cracking hydrogen back from ammonia at the final destination. The final levelized cost of hydrogen was estimated at 159.18 €/MWh with 59.4 €/MWh for green hydrogen production in Australia and 109.39 €/MWh for green ammonia production and transport to German harbor. The major cost component in this case is electricity price (81%). In other research, Sagel et al. (2022) focused on Power-to-Ammonia-to-Power (P2A2P) technology for two islands: Curaçao and Viti Levu for year-round green electricity supply. They have explored two options for green ammonia: own production with wind energy and import from Brazil, Australia, Chile, Trinidad and Tobago. They also concluded that the import of green ammonia is cheaper than on-site production with levelized cost of electricity (LCOE) = 0.11–0.37 $/kWh due to the high investment costs required for island operation of the P2A2P system.

Most of the studies selected a few specific countries as export and one to three countries as import locations. Table 1 provides a summary of the literature findings and the regions covered for the trade. Few studies emphasized green hydrogen while other studies focused on green ammonia costs. Focus was mainly placed on Germany as an import country due to high industrial demand and low renewable resources. Moreover, these studies assume hydrogen and other low-carbon fuels to be available as import with no further description of import/export structure and the quantity that is available by the export region.

To the author’s knowledge, no study exists that covers all the regions of the world for the production and trade of both green hydrogen and ammonia. This work aims to investigate the production and the export potential of the two energy carriers for all world regions under different scenarios until 2100. The regions, the countries under these regions and the scenario definition will be defined in detail in the next section.

## 3 Optimization Model and Approach

This investigation employs the TIMES Integrated Assessment Model (TIAM) (Mousavi 2019; Loulou 2008). TIAM is a global energy system model that maps 16 regions (Fig. 1) for a time horizon of 2015–2100. Regions are either referred to as countries (e.g. USA, Germany etc.) or aggregated regions (e.g. Africa, Middle East Asia etc.). For each of the regions all final energy sectors- industry, households, commercial, transport and agriculture are mapped as shown in Fig. 2. The objective of the model is to minimize the overall system cost (Net present value: NPV) for all
<table><caption>**Table 1** Overview of green hydrogen and green ammonia costs from literature</caption><thead><tr><th rowspan="2">Reference</th><th colspan="4">Continents covered</th><th rowspan="2">Green hydrogen costs (LCOH)</th><th rowspan="2">Green ammonia costs (LCOA)</th></tr><tr><th>Africa</th><th>Asia</th><th>North America<sup>a</sup></th><th>South America</th><th>Australia</th><th>Europe</th></tr></thead><tbody><tr><td>Hampp et al. (2023)</td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>2.5 €/kg from Denmark to Germany</td><td>—</td></tr><tr><td>Johnston et al. (2022)</td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>✓</td><td>2.09 $/kgH<sub>2</sub> from Rotterdam to Australia</td><td>0.56 $/kgH<sub>2</sub> from Rotterdam to Australia</td></tr><tr><td>Teichmann et al. (2012)</td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td><td>2.03 $/kgH<sub>2</sub> LOHC from Iceland to Germany</td><td>—</td></tr><tr><td>Galimova et al. (2023)</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>1.5 €/kg from Morocco to Germany and 1.7 €/kg from Chile to Finland in 2050</td><td>—</td></tr><tr><td>Egerer et al. (2023)</td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td>—</td><td>109.39 €/MWh from Australia to Germany</td></tr><tr><td>Sagel et al. (2022)</td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>—</td><td>0.12 $/kWh for Curacao<br>1.1 $/kWh for Viti Levu</td></tr></tbody></table>

<footnote><sup>a</sup>North America was not mentioned in the corresponding studies</footnote>
<figure><img src="image_103.png" /><figcaption>Fig. 1 TIAM regions and selected harbors (red dots)</figcaption></figure>

<figure><img src="image_104.png" /><figcaption>Fig. 2 Reference energy system for the TIAM model</figcaption></figure>

years and regions (Eq. 1). Therefore, the NPV is calculated by using the sum over all regions *r* and all years *y* in the time horizon multiplying the annual cost (ANcost) with the discount rate *d*<sub>*r*,y*</sub> based on the year of consideration (*refy*-y).
$$NPV = \sum_{r=1}^{R} \sum_{y \in years} (1 + d_{r,y})^{refy - y} * ANcost(r, y) \quad (1)$$

The model considers a microeconomic technology competition. Therefore, perfect foresight is used, meaning that all current and future costs, technologies, and demand projections are known at the beginning of 2015 as the base year.

In detail the model maps the supply of primary energy to final energy with the transformation sector in between (Fig. 2). The model is based on the IEA energy balance (IEA 2023) and calibrated to the base year of 2015. The demand is set exogenously by using drivers as the gross domestic product (GDP) or the population.

The cost for technologies is split by capital expenses (CapEx) and operational expenses (OpEx), while the OpEx is further divided into fixed OpEx (cost per year) and variable OpEx (cost per energy unit used). Apart from using national resources, the model also has the option to import resources (Fig. 2). Therefore, trades between TIAM regions are mapped in the TIAM model.

Within the model, hydrogen can be used as input for final energy sectors such as industry (process heat, non-energy demand), households and commercial (room heat), transport (shipping, aviation, and road transport) as well as for agriculture (machines). The demand for hydrogen is endogenously modeled, as it is set by the processes of the industry, commercial, residential, agriculture and transport sector to satisfy the demand for energy services and materials. In comparison the ammonia demand is set both endogenously and exogenously. This means that there is a fixed demand mainly caused by fertilizer production in all regions (USGS 2023). Besides this the ammonia demand as fuel is modeled endogenously within the model.

Both hydrogen and ammonia can be traded in the TIAM model. For hydrogen, LH₂ is used while ammonia can be stored under high pressure. After transport, hydrogen can be used via regasification and ammonia can either be used for satisfying the demand for ammonia itself or for back cracking to satisfy the demand for hydrogen. Overall, hydrogen and ammonia are in direct competition for transport. The CapEx and OpEx for regasification of hydrogen and back cracking of ammonia can be found in Table 2.

To map the hydrogen and ammonia trades, harbors have been selected based on size to ensure to handle the trade (Fig. 1 and Table 3). Each region has one representative harbor except Africa having two harbors, because of the size of the region and therefore investment decisions would be distorted by only using one

<table><caption>**Table 2** Technologies for after trade</caption><thead><tr><th></th><th>Regasification LH<sub>2</sub></th><th>Back cracking ammonia</th></tr></thead><tbody><tr><td>CapEx [M€]</td><td>165</td><td>1062</td></tr><tr><td>Capacity Unit</td><td>KW</td><td>t/a</td></tr><tr><td>Lifetime [a]</td><td>20</td><td>25</td></tr><tr><td>OpEx [% of CapEx]</td><td>2.5</td><td>4.3</td></tr><tr><td>Source</td><td>(IRENA 2022)</td><td>(Ishimoto et al. 2020)</td></tr></tbody></table>
<table><caption>**Table 3** Harbor selection for the TIAM model</caption><thead><tr><td>Region</td><td>Port Name</td><td>Latitude</td><td>Longitude</td></tr></thead><tbody><tr><td>AFR</td><td>Port of Walvis Bay</td><td>-22.94438616</td><td>14.48237595</td></tr><tr><td>SKO</td><td>Port of Busan</td><td>35.10370188</td><td>129.0414886</td></tr><tr><td>CSA</td><td>Port of Buenos Aires</td><td>-34.56909495</td><td>-58.38273273</td></tr><tr><td>AUS</td><td>Fremantle Ports</td><td>-32.0529628</td><td>115.7408536</td></tr><tr><td>CAN</td><td>Port of Quebec</td><td>46.82265707</td><td>-71.20249041</td></tr><tr><td>CHI</td><td>Port of Shanghai</td><td>30.63068515</td><td>122.0847303</td></tr><tr><td>GER</td><td>Port of Hamburg</td><td>53.5410807</td><td>9.986766343</td></tr><tr><td>WEU</td><td>Port of Birmingham</td><td>53.63207459</td><td>-1.85406508</td></tr><tr><td>IND</td><td>Mundra Port</td><td>22.74104731</td><td>69.7157146</td></tr><tr><td>JPN</td><td>Port of Keihin</td><td>35.42669641</td><td>139.6843441</td></tr><tr><td>AFR</td><td>Port of Benghazi</td><td>32.110470309</td><td>20.0423606</td></tr><tr><td>MEX</td><td>Puerto De Manzanillo</td><td>19.7070599</td><td>-71.7447426</td></tr><tr><td>ODA</td><td>Port of Karachi</td><td>24.83708206</td><td>66.98086793</td></tr><tr><td>EEU</td><td>Port of Gdynia</td><td>54.5360318</td><td>18.53554754</td></tr><tr><td>FSU</td><td>Port of Saint Petersburg</td><td>59.88860049</td><td>30.18117919</td></tr><tr><td>MEA</td><td>Port of Jeddah</td><td>21.49503622</td><td>39.1551345</td></tr><tr><td>USA</td><td>Port of Los Angeles</td><td>33.72839414</td><td>-118.2402335</td></tr></tbody></table>

<table><caption>**Table 4** Techno-economic data for hydrogen and ammonia transport technologies</caption><thead><tr><td></td><td>LH<sub>2</sub></td><td>Ammonia</td></tr></thead><tbody><tr><td>CapEx [M$/ship]</td><td>412</td><td>85</td></tr><tr><td>Capacity [t]</td><td>11,000</td><td>53,000</td></tr><tr><td>Lifetime [a]</td><td>30</td><td>30</td></tr><tr><td>OpEx [% of CapEx]</td><td>4</td><td>4</td></tr><tr><td>Speed [km/h]</td><td>30</td><td>30</td></tr><tr><td>Max operation days [–]</td><td>330</td><td>330</td></tr><tr><td>Fuel Use [MJ/km]</td><td>1487</td><td>2500</td></tr><tr><td>Boil-off rate [%/day]</td><td>1.3</td><td>0</td></tr><tr><td>Source</td><td>(IEA 2020)</td><td>(IEA 2020)</td></tr></tbody></table>

harbor. Moreover, the distance has influence on the cost of transport as well. The techno-economic data for the hydrogen and ammonia transport are shown in Table 4.

The hydrogen costs are calculated by cost potential curves (Table 5) that are stated in Franzmann et al. (2023). A high spatial and temporal resolution GIS model was used to capture the area-specific conditions to further calculate the LCOH based on local energy system models using CapEx and OpEx based on Table 6. A deeper insight into the hydrogen and ammonia modeling based on the cost potential curves is given based on a reference energy system (Fig. 3).

The full methodical approach of this investigation is shown in Fig. 4. Each of the 16 TIAM regions has the production route of hydrogen via electrolyzers and other hydrogen production technologies (SMR etc.) included. Hydrogen can either be used for local demand or liquefaction to receive LH₂ for trade. Hydrogen is also the
<table><caption>**Table 5** Cost potential curve for hydrogen supply (2050–2100)</caption><thead><tr><th></th><th></th><th>Step 1</th><th>Step 2</th><th>Step 3</th><th>Step 4</th><th>Step 5</th><th>Step 6</th><th>Step 7</th><th>Step 8</th></tr></thead><tbody><tr><td rowspan="2">AFR</td><td>H<sub>2</sub> Export [PWh]</td><td>7.67</td><td>14.00</td><td>24.14</td><td>47.58</td><td>44.07</td><td>22.65</td><td>7.97</td><td>20.05</td></tr><tr><td>Cost [EUR/MWh]</td><td>28.6</td><td>29.5</td><td>30.3</td><td>31.3</td><td>32.8</td><td>35.6</td><td>38.1</td><td>41.7</td></tr><tr><td rowspan="2">AUS</td><td>H<sub>2</sub> Export [PWh]</td><td>130.9</td><td>36.66</td><td>42.63</td><td>43.44</td><td>28.12</td><td>100.6</td><td>26.38</td><td>24.43</td></tr><tr><td>Cost [EUR/MWh]</td><td>31.2</td><td>35.6</td><td>36.6</td><td>36.4</td><td>32.9</td><td>36.4</td><td>43.7</td><td>43.9</td></tr><tr><td rowspan="2">CAN</td><td>H<sub>2</sub> Export [PWh]</td><td>13.23</td><td>5.68</td><td>8.36</td><td>4.67</td><td>4.57</td><td>4.49</td><td>4.84</td><td>31.07</td></tr><tr><td>Cost [EUR/MWh]</td><td>39</td><td>47.5</td><td>44.9</td><td>60.9</td><td>58.2</td><td>55.6</td><td>64.2</td><td>87.4</td></tr><tr><td rowspan="2">CHI</td><td>H<sub>2</sub> Export [PWh]</td><td>26.40</td><td>9.57</td><td>11.57</td><td>12.82</td><td>11.68</td><td>8.81</td><td>12.75</td><td>60.86</td></tr><tr><td>Cost [EUR/MWh]</td><td>46.5</td><td>53.8</td><td>50.3</td><td>45.7</td><td>43.4</td><td>42.9</td><td>44.8</td><td>32.1</td></tr><tr><td rowspan="2">CSA</td><td>H<sub>2</sub> Export [PWh]</td><td>24.34</td><td>29.99</td><td>24.28</td><td>44.60</td><td>13.27</td><td>7.12</td><td>4.99</td><td>4.99</td></tr><tr><td>Cost [EUR/MWh]</td><td>30.1</td><td>32.7</td><td>34.3</td><td>36.1</td><td>41.1</td><td>47.8</td><td>55.7</td><td>55.7</td></tr><tr><td rowspan="2">GER</td><td>H<sub>2</sub> Export [PWh]</td><td>0.17</td><td>0.10</td><td>0.09</td><td>0.06</td><td>0.05</td><td>0.02</td><td>0.02</td><td>0.02</td></tr><tr><td>Cost [EUR/MWh]</td><td>27.7</td><td>30.4</td><td>31</td><td>33.9</td><td>36.1</td><td>44</td><td>45.1</td><td>45.1</td></tr><tr><td rowspan="2">EEU</td><td>H<sub>2</sub> Export [PWh]</td><td>0.59</td><td>0.18</td><td>0.10</td><td>0.09</td><td>0.05</td><td>0.03</td><td>0.03</td><td>0.07</td></tr><tr><td>Cost [EUR/MWh]</td><td>31.1</td><td>30.6</td><td>28.6</td><td>40.8</td><td>47.2</td><td>38.4</td><td>42.4</td><td>37.4</td></tr><tr><td rowspan="2">FSU</td><td>H<sub>2</sub> Export [PWh]</td><td>28.88</td><td>0.46</td><td>0.14</td><td>0.10</td><td>9.32</td><td>7.71</td><td>1.61</td><td>55.61</td></tr><tr><td>Cost [EUR/MWh]</td><td>38.9</td><td>47.5</td><td>30.4</td><td>37.6</td><td>40.3</td><td>50.1</td><td>49.6</td><td>83.1</td></tr><tr><td rowspan="2">IND</td><td>H<sub>2</sub> Export [PWh]</td><td>12.62</td><td>4.73</td><td>2.13</td><td>0.46</td><td>0.46</td><td>0.46</td><td>0.46</td><td>0.46</td></tr><tr><td>Cost [EUR/MWh]</td><td>31.6</td><td>37.8</td><td>45</td><td>52.7</td><td>52.7</td><td>52.7</td><td>52.7</td><td>52.7</td></tr><tr><td rowspan="2">JPN</td><td>H<sub>2</sub> Export [PWh]</td><td>0.28</td><td>0.09</td><td>0.02</td><td>0.02</td><td>0.02</td><td>0.02</td><td>0.02</td><td>0.19</td></tr><tr><td>Cost [EUR/MWh]</td><td>32.2</td><td>29.3</td><td>35.1</td><td>35.1</td><td>35.1</td><td>41.7</td><td>48.8</td><td>79.2</td></tr><tr><td rowspan="2">SKO</td><td>H<sub>2</sub> Export [PWh]</td><td>0.07</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.00</td><td>0.04</td></tr><tr><td>Cost [EUR/MWh]</td><td>30.3</td><td>24.3</td><td>26.3</td><td>32.3</td><td>32.3</td><td>34.2</td><td>44</td><td>79.5</td></tr><tr><td rowspan="2">MEA</td><td>H<sub>2</sub> Export [PWh]</td><td>15.10</td><td>22.95</td><td>22.73</td><td>32.22</td><td>24.89</td><td>24.94</td><td>24.83</td><td>28.10</td></tr><tr><td>Cost [EUR/MWh]</td><td>24.2</td><td>26.4</td><td>29.7</td><td>30.6</td><td>32.4</td><td>33.4</td><td>34.2</td><td>35.5</td></tr><tr><td rowspan="2">MEX</td><td>H<sub>2</sub> Export [PWh]</td><td>13.03</td><td>8.56</td><td>6.33</td><td>6.61</td><td>7.58</td><td>3.84</td><td>3.84</td><td>3.84</td></tr><tr><td>Cost [EUR/MWh]</td><td>37.8</td><td>42.8</td><td>43.2</td><td>42.6</td><td>46.4</td><td>40.9</td><td>40.9</td><td>40.9</td></tr><tr><td rowspan="2">ODA</td><td>H<sub>2</sub> Export [PWh]</td><td>6.94</td><td>12.89</td><td>2.12</td><td>0.50</td><td>0.50</td><td>0.50</td><td>0.50</td><td>0.50</td></tr><tr><td>Cost [EUR/MWh]</td><td>30.1</td><td>30.6</td><td>31.3</td><td>41.4</td><td>41.4</td><td>41.4</td><td>41.4</td><td>41.4</td></tr><tr><td rowspan="2">USA</td><td>H<sub>2</sub> Export [PWh]</td><td>35.74</td><td>23.75</td><td>28.58</td><td>11.36</td><td>17.71</td><td>3.74</td><td>3.74</td><td>3.74</td></tr><tr><td>Cost [EUR/MWh]</td><td>38.5</td><td>42.2</td><td>43.6</td><td>40</td><td>48.5</td><td>56.8</td><td>56.8</td><td>56.8</td></tr><tr><td rowspan="2">WEU</td><td>H<sub>2</sub> Export [PWh]</td><td>0.75</td><td>0.59</td><td>0.46</td><td>0.32</td><td>0.59</td><td>0.43</td><td>0.30</td><td>0.39</td></tr><tr><td>Cost [EUR/MWh]</td><td>23.7</td><td>26.4</td><td>33.8</td><td>40.4</td><td>59.7</td><td>66.5</td><td>63.8</td><td>84.9</td></tr></tbody></table>
<table><caption>**Table 6** Techno-economic data for electricity sector and hydrogen production</caption><thead><tr><th rowspan="2">Technology</th><th colspan="4">CapEx [EUR/kW]</th><th rowspan="2">OpEx [% CapEx/ a]</th><th rowspan="2">Efficiency [%]</th><th rowspan="2">Lifetime [a]</th><th rowspan="2">Source</th></tr><tr><th>2020</th><th>2030</th><th>2040</th><th>2050</th></tr></thead><tbody><tr><td>Onshore Wind</td><td>1257</td><td>1137</td><td>987</td><td>923</td><td>3</td><td>100</td><td>25</td><td>Pietzcker et al. (2021)</td></tr><tr><td>Solar PV</td><td>703</td><td>395</td><td>340</td><td>326</td><td>1</td><td>100</td><td>25</td><td></td></tr><tr><td>Biomass</td><td>2037</td><td>1954</td><td>1892</td><td>1826</td><td>3.5</td><td>100</td><td>25</td><td>Ausfelder et al. (2022), IEA (2018)</td></tr><tr><td>Hydro Power</td><td>—</td><td>—</td><td>—</td><td>2718</td><td>2</td><td>100</td><td>100</td><td>Ausfelder et al. (2022), IEA (2021)</td></tr><tr><td>Offshore-Wind</td><td>—</td><td>—</td><td>—</td><td>1496</td><td>3</td><td>100</td><td>25</td><td>IEA (2021)</td></tr><tr><td>PEM</td><td>900</td><td>700</td><td>575</td><td>450</td><td>1.5</td><td>64, 69, 72, 74</td><td>19</td><td>International Energy Agency (2020)</td></tr><tr><td>Turbine</td><td>220</td><td>220</td><td>220</td><td>220</td><td>2</td><td>100</td><td>20</td><td>—</td></tr><tr><td>Compressor</td><td>1100</td><td>1100</td><td>1100</td><td>1100</td><td>2</td><td>87</td><td>20</td><td>Schmidt et al. (2017), Nel ASA (2020)</td></tr><tr><td>Desalination</td><td>43</td><td>43</td><td>43</td><td>43</td><td>2</td><td>—</td><td>20</td><td>A-ETSAP and IRENA (2012), FICHTNER (2011)</td></tr><tr><td>DAC</td><td>800</td><td>350</td><td>270</td><td>220</td><td>3.7</td><td>—</td><td>25</td><td>Schemme (2020)</td></tr></tbody></table>

starting point for ammonia which can be produced using the Haber-Bosch (HB) process. Same as for hydrogen, ammonia can either be shipped or used locally to satisfy the demand. This methodology approach is applied to each of the 16 TIAM regions in the same manner. The 16 TIAM regions are connected for trade using the specific route distance for the selected harbors (Table 3) and transport technologies (Table 4).

# 4 Scenario Analysis

## 4.1 Scenario Definition

For this investigation, four different scenarios are considered to have a variety of possible future events mapped to the model (Table 7). Firstly, the business as usual
<figure><img src="image_105.png" /><figcaption>Fig. 3 Reference energy system for hydrogen and ammonia production and trade</figcaption></figure>

<figure><img src="image_106.png" /><figcaption>Fig. 4 Methodology approach in the global energy system model TIAM</figcaption></figure>

(BAU) scenario is used as a reference case which does not include any climate policy as a carbon price or a carbon budget. The other three scenarios used in this investigation make use of a carbon budget of 420 Gt that limits global warming to preferably 1.5 °C (Rogelj et al. 2018). The second scenario, 1_5D does not include any further restrictions except the carbon budget according to Rogelj et al. 2018. It provides evidence on possible hydrogen and ammonia allocations without
<table><caption>**Table 7** Scenario description</caption><thead><tr><td>No.</td><td>Scenario name</td><td>Description</td></tr></thead><tbody><tr><td>1</td><td>BAU</td><td>No climate policy</td></tr><tr><td>2</td><td>1_5D</td><td>1.5 °C conform scenario based on a given CO<sub>2</sub> budget of 420 Gt for 2015–2100 (Rogelj et al. 2018)</td></tr><tr><td>3</td><td>SoS1</td><td>Based on 1_5D scenario. At least 50% of the demand must be satisfied by domestic production. No limitations of trading partners</td></tr><tr><td>4</td><td>SoS2</td><td>Based on 1_5D scenario. Demand can be satisfied by a 100% import, but with at least 5 different trading partners. No trading partner can supply more than 20% of the total hydrogen and ammonia demand</td></tr></tbody></table>

consideration of trade restrictions. To reduce the risk of supply failures, two trade restriction scenarios are considered. The third scenario, SoS1 permits a maximum of 50% of the ammonia and hydrogen demand being satisfied by imports. Therefore at least 50% need to be produced locally. As half of the demand could be satisfied by only one exporter, the fourth scenario imposes further restrictions. In order to have a variety of possible exporters, the SoS2 scenario only permits 20% of the total ammonia and hydrogen demand to be satisfied by one exporter. If there is no local production, at least five exporters are needed to fully supply the ammonia and hydrogen. All of these scenarios provide an overview of different allocation structures for global hydrogen and ammonia supply to achieve the 1.5 °C target. In all four scenarios, the trade of synfuels and bioenergetic fuels is not limited to any restrains. Furthermore, all four scenarios consider cost optimization with the same pool of technologies for investment decisions.

## 4.2 Scenario Results

### 4.2.1 Electricity Production

The rise of the global population and increasing wealth lead to higher energy service demands which simultaneously leads to a higher production of electricity globally (Fig. 5). Starting in 2015, the global electricity was approximately 21,000 TWh and mainly uses energy carriers such as coal, gas, nuclear as well as hydro energy. Solar photovoltaics (Solar PV) and wind energy only play a minor role with 194 TWh and 123 TWh.

Electricity was used in the final energy demand, mainly for industry, residential and commercial, with roughly 21,000 TWh (Fig. 6). Coal, gas, and oil take the main shares. Oil is mainly used in transport as well as residential and commercial heating. Gas takes a major share in the industry for process heat and residential and commercial heating. Coal is mainly used in the industry sector. To be aligned with the climate targets, electricity becomes more important in order to decarbonize the
<figure><img src="image_107.png" /><figcaption>Fig. 5 Global electricity production</figcaption></figure>

global energy system as all of the fossil fuels need to be replaced, but hydrogen is also an option for possible fuel switches.

According to the optimization results, the electricity production grows to approximately 22,800 TWh for BAU scenario and 30,000 TWh for 1_5D, SoS1 and SoS2 scenarios. There is an 83% increase in electricity production for BAU in 2050 and the scenarios with climate policy (1_5D, SoS1 and SoS2) show an increase of almost 130%. While there is still a share of fossil fuels in the electricity mix in 2030, mainly caused by the existing capacities as of 2015, it clearly shows a tremendous switch in 2050 (Fig. 5). Nuclear energy grows as a possible non-carbon source to approximately 2900 TWh in 2050, while it is fading out in the BAU scenario. Besides this effect, there is a clear trend in growing solar PV and wind energy which contributes to more than half of the electricity production for all scenarios and even 70% for all three climate policy scenarios.

The electricity demand is growing up to almost 120,000 TWh in 2100 where over 80% of the electricity is produced using solar PV and wind energy in the three scenarios.

Using the carbon budget approach (1_5D, SoS1 & SoS2), the global fade out of unabated coal for electricity production is shown in 2030 (Fig. 5). Without climate policy, the electricity production of coal rises to 8500 TWh which means a 12.4% share of the total electricity production.

Starting in 2050, biomass with carbon capture and storage (BECCS) is stated in the optimization solution with up to 3600 TWh for scenario 1_5D, and 2500 TWh for scenarios SoS1 and SoS2 in electricity production. Furthermore, biomass without carbon capture and storage (CCS) is used with an additional 600 TWh in 2050 for
scenario 1_5D and 1150 TWh for scenarios SoS1 and SoS2. The peak of BECCS is observed in 2050. In 2100, SoS2 has the highest production with approximately 1040 TWh.

While there is still up to 1200 TWh of electricity out of unabated gas according to the solution of the optimization results in 2050, it fades out in 2060. Throughout the TIAM model’s time horizon, the electricity sector sees no usage of hydrogen. As solar PV and wind onshore are volatile in production, gas CCS plants are built up to satisfy the peak demand. Starting 2040, approximately 2400 TWh in scenarios 1_5D, SoS1 and SoS2 are produced, peaking in 2070 with 2800 TWh in 1_5D, and 2400 TWh in SoS1 as well as 2350 TWh in SoS2.

Overall, the electricity demand rises to 120,000 TWh in 2100 for the 1_5D scenario followed by approximately 118,000 TWh for SoS1 and SoS2. The BAU scenario however peaks at 67,000 TWh which means a 55% share of the electricity production compared to 1_5D.

### 4.2.2 Final Energy Consumption

The effects of the high electricity demand can be explained by the final energy consumption of the different fuels used (Fig. 6). In order to achieve net zero greenhouse gas emissions, fossil energy carriers, such as coal, oil and gas must be replaced. According to the optimization results of the TIAM model, this is not achieved without climate policies as the BAU scenario indicates (Fig. 6). Roughly, 66,000 TWh are still fossil fuels which lead, in combination with electricity

<figure><img src="image_108.png" /><figcaption>**Fig. 6** Final energy consumption. BAU top left, 1_5D top right, SoS1 bottom left, SoS2 bottom right</figcaption></figure>
production, to total net CO₂ emissions of 28.32 Gt in 2100 (Fig. 7). Even without climate policies, electricity use grows up to almost 70,000 TWh, as the technology using it becomes more cost-efficient over time. This means a growth of almost 250% in comparison to 2015, while the final energy demand is rising by roughly 70% in comparison to 2015. Using climate policy, fossil fuels start fading out, due to the diminishing carbon budget. Nevertheless, there is still unabated gas in the final energy consumption in 2100 (1_5D, SoS1 and SoS2), as process heat does not completely switch to low carbon fuels- hydrogen and synfuels. The results indicate that using BECCS for negative emissions (Fig. 7) is more cost-efficient in comparison to biofuels for process heat in the final energy consumption. Hydrogen is mostly used in the industry and transport sector. For industry, hydrogen is used in the iron and steel sector as well as for chemicals. Considering the transport sector, aviation and international shipping start to switch to hydrogen in order to achieve climate targets. Overall, hydrogen has a final consumption of approximately 13,000 TWh in all scenarios with climate action (1_5D, SoS1 and SoS2), while synfuels are reported at roughly 5000 TWh. Therefore, synfuels and hydrogen account for 11% of the final energy consumption in 2100 for the scenarios with climate policy (1_5D, SoS1 and SoS2).

Considering the 1_5D, SoS1 and SoS2 scenarios, the difference in the final energy consumption is small. On a global scale, this means that the energy allocation works no matter of trading partners for hydrogen and ammonia (Fig. 6).

### 4.2.3 Global CO₂ Emissions

Besides the transformation in the final energy demand, the transformation for the global emissions is also well aligned (Figs. 6 and 7). As global electricity production is leading towards renewable energies, the emissions in case of climate policies are declining. In 2050; net zero is possible by using 6.8 Gt of negative emissions with BECCS. BECCS is also used in the following years with a smaller absolute value of approximately 3.5 Gt of CO₂. Considering the BAU case, the emissions remain at the same level. In 2050, there are 25 Gt of CO₂ emissions, even with high shares of Solar PV and wind energy in electricity production and a rising share of electricity in the final energy demand. In 2100 the emissions are roughly 28 Gt which is approximately the same amount compared to 2015. This means, that the efficiency improvements result in a higher share of using electricity in the final energy demand, but with rising demand, this leads to an overall stagnation in emission reduction for the BAU scenario considering no climate action. In comparison, the climate policies applied to the 1_5D, SoS1 and SoS2 scenarios show a fast decline in global emissions and the use of BECCS and CCS to efficiently use the remaining carbon budget.
<figure><img src="image_109.png" /><figcaption>Fig. 7 Global CO₂ emission balance</figcaption></figure>

### 4.2.4 Production and Export of Hydrogen and Ammonia

Considering the fade out of high fossil fuels-coal, oil and gas (Fig. 6), the hydrogen (as a part of the compensation) must be available in the regions. In the case of no climate policy (BAU), there is no export of hydrogen (Fig. 8). This behavior changes in the climate policy scenarios (1_5D, SoS1 and SoS2). In the 1_5D scenario Middle East Asia shows clearly as the main exporter of hydrogen. There are two reasons. At first, the levelized cost of hydrogen (LCOH) is the lowest due to high sun hours and secondly, the route distance to dependent regions such as Europe, is close that makes it cost attractive for trade. Therefore 1636 TWh of hydrogen are exported in 2100, mainly to Europe. In comparison to the SoS1 scenario, where only 50% of the demand can be satisfied by trade, the amount of hydrogen for trade is higher. The reason for this is caused by the reduction in ammonia trade (Fig. 10). Therefore, Middle East Asia is exporting to almost all neighboring regions.

This behavior is changing by applying the SoS2 scenario where the demand can be completely satisfied by at least five exporters or local production. Africa, Central and South America, the USA and Australia show up as possible export regions. Germany does not start to produce local hydrogen and uses the import strategy by trading with the five mentioned regions.

The differences in hydrogen production are mostly in line with the export behavior. For the 1_5D and SoS1 scenarios, Middle East Asia is not only the largest exporter, it also has the highest production in hydrogen with 5406 TWh (1_5D) and 5730 TWh (SoS1) (Fig. 9). Applying trade restrictions in the SoS1 scenario, there is
<figure><img src="image_110.png" /><figcaption>**Fig. 8** Global hydrogen export in 2100 (BAU top left; 1_5D top right; SoS1 bottom left, SoS2 bottom right)</figcaption></figure>

<figure><img src="image_111.png" /><figcaption>**Fig. 9** Global hydrogen production in 2100 (BAU top left; 1_5D top right; SoS1 bottom left, SoS2 bottom right)</figcaption></figure>

a shift visible as India remains a self-producer with an amount of 3210 TWh of hydrogen.

Furthermore, it clearly shows that Europe and Canada do not start hydrogen production in any scenario. In the case of trade restrictions, especially for SoS2, Central and South America, as well as Australia become more cost attractive for hydrogen production.

In the absence of climate policies, the production of fossil-free hydrogen is nonexistent, as depicted by the BAU scenario (Fig. 9).
<figure><img src="image_112.png" /><figcaption>**Fig. 10** Global ammonia export in 2100 (BAU top left; 1_5D top right; SoS1 bottom left, SoS2 bottom right)</figcaption></figure>

Besides the hydrogen export, the ammonia export could also play a major role towards net zero. It also shows that there is no export of ammonia in case missing of climate action (BAU) (Fig. 10). Furthermore, there is also no production of fossil-free ammonia, as the SMR option is cheaper without CO₂ prices.

In comparison to the hydrogen exports, the ammonia export shows a different behavior. Lower cost of shipping results in a higher use of export for ammonia which is shown for the 1_5D scenario where no trade restrictions are applied. This is the opposite behavior compared to the 1_5D scenario for the hydrogen export (Fig. 8).

Middle East Asia is also the leading exporter which is the same pattern as shown in the hydrogen export (Fig. 8). This is explained by the LCOH which makes the most cost of the ammonia production. Therefore, ammonia is cheaper if hydrogen is cheaper.

By applying trade restrictions, the amount of exported ammonia is decreasing. In the SoS2 scenario, Middle East Asia and Africa are the least cost exporters. Central and South America, the USA and Australia are also an option if trade restrictions are considered. China also has export options according to the results if five exporters are permitted to compete with local production.

The production of ammonia shows clear trends that are already visible in the export structure. The ammonia demand is fully produced locally in case of no climate policy (BAU) (Fig. 11). In contrast, the overall demand for ammonia is satisfied by Middle East Asia if there are no trade restrictions applied (1_5D). In the 1_5D scenario, it is evident from the production patterns that Middle East Asia is the exclusive producer of ammonia (Fig. 11).

In the case of trade restrictions, the production structure is changing as 50% of the demand must be satisfied by local production. Therefore, almost half of the overall demand is still satisfied by Middle East Asia (Fig. 11). This pattern only starts to change if another level of security is applied by considering the SoS2 scenario. In
<figure><img src="image_113.png" /><figcaption>**Fig. 11** Global ammonia production in 2100 (BAU top left; 1_5D top right; SoS1 bottom left, SoS2 bottom right)</figcaption></figure>

this case Africa, Central and South America, the USA, Australia, and China start to expand their production to start ammonia trades.

# 5 Discussion

In the process of achieving Net zero by 2050 and addressing SDG goal 13- Climate action, electricity will have the major share of final energy consumption and PV will play the key role in producing more than 50% of the electricity as shown in the scenarios 1_5D, SoS1 and SoS2. Moreover, the production and trade of clean energy carriers, green hydrogen and ammonia, also play an important role in achieving the Net Zero target. Four different scenarios are introduced here for this energy system analysis. The scenarios SoS1 and SoS2 are specially designed to contribute to SDG Goal 7—Affordable and Clean Energy. These scenarios aim to ensure that all countries can affordably produce and import clean energy carriers. For both green hydrogen and green ammonia, the Middle East Asia region has the highest share of production and export in the scenarios 1_5D and SoS1. By imposing a maximum of 20% export per country constraint and avoiding monopoly, the scenario SoS2 shows other potential regions for export. These primarily include Africa, Central and South America, USA, Australia, and India. Europe is found to be the region with the least production and export of energy carriers in achieving the Net Zero goal. This shows that Europe will be heavily dependent on imports of green hydrogen and ammonia. These results are in line with the findings of the literature research where most of them considered Germany or Europe, in general, as the import region with export from regions in Middle East Asia, Central and South America and Australia. China,
while having significant production potential for both green hydrogen and ammonia, exhibits limited export potential due to large domestic industries and high self-consumption.

This study utilizes the global TIAM model to assess future energy flows through a least-cost optimization framework, which introduces some limitations to the investigation due to the following assumptions:

* Every region (except Africa) has 1 export/import port. The global trade of hydrogen and ammonia is highly sensitive to the shipping route distance. This might cause allocation errors, as there are shorter routes available. This investigation only considers the major ports of current maritime transport as they can be used for hydrogen and ammonia trades in the future.

* Pipeline transport for hydrogen and ammonia is a promising option but not considered in this investigation as the aim of this investigation is on trade restrictions. Pipelines are characterized higher in CapEx but once built the additional cost are lower in comparison, but this only works if pipelines are the main option for transport which is not in line with our investigation.

* In this investigation the cost potential curves derived by Franzmann et al. (2023) are used as this is one of the most recent studies on hydrogen cost on a global scale and also in line with the TIAM regions. This study primarily focuses on solar PV and onshore wind for hydrogen production. Wind offshore is not considered, which could be preferable for certain regions.

* This investigation does not consider subsidies or tax credit programs such as the Inflation Reduction Act (IRA) in the USA (Senate of the United States 2022) or other national programs. Instead, a full least-cost approach is used for this assessment in order to investigate the global allocation structure based on the possible LCOH.

* The demand for end-use commodities (e.g. steel, cement etc.) is not modeled using elasticities. Therefore, higher costs do not result in reduced demand, a phenomenon that could emerge in reality.

# 6 Conclusion and Outlook

In this research, an initial investigation was done on final energy consumption and electricity supply globally for each final energy sector until 2100 by selecting four possible scenarios aligned with climate policies. It is shown that electricity demand has the major share of energy consumption in 2100 for all the scenarios. Even without climate policies, solar PV and wind energy contribute to over 50% of the global electricity production. In the other scenarios with climate policies, solar PV and wind energy contribute to over 70% of the global electricity production. Hydrogen and synfuels account for 11% of the final energy consumption considering the climate policies. Hydrogen plays a major role in decarbonizing the global energy system and the demand is strongly increasing over time. Based on these results, CO2
emissions are evaluated for each scenario until 2100. It shows that net zero is
possible in 2050 among different scenarios that consider climate policies. In contrast
to this, the total net CO₂ emissions without climate policy (BAU scenario) remain at
a level of approximately 28 Gt. A further analysis was performed on the trade of
emission-free energy carriers such as green hydrogen and ammonia among various
regions globally until 2100. In the scenario without climate policies applied (BAU),
it is shown that there is no trade of hydrogen and ammonia. Depending on the level
of trade restrictions, the export structure is changing, if climate policies are taken into
consideration. Without restrictions on trading, it clearly shows that Middle East Asia
becomes the leading producer and exporter of these low-carbon fuels. By consider-
ing trade restrictions (Scenario SoS2), exports from Africa, Central and South
America as well as Australia, for both hydrogen and ammonia are potentially
possible.

In this trade analysis, Europe is shown as import dependent. Even with the highest level of trade restrictions applied, imports are cheaper than local production in ammonia and hydrogen. Other regions, such as India and Other Developing Asia become self-sufficient along all scenarios.

Another finding from our research was that the ammonia exports are used directly for the ammonia demand associated with each TIAM region. Therefore, ammonia is not used to satisfy the hydrogen demand by using back cracking technologies. Instead, LH₂ transport is preferred in comparison to ammonia transport if hydrogen is demanded.

The overall structure of the global low-carbon fuel trade shows similarities to
common high-carbon fuel trades today. This could lead to a switch in fuels with the
remaining export regions as they could maintain their business model in the future
and export cheaper low-carbon fuels in comparison to other regions.

This investigation has shown that the Net Zero target is feasible in different
scenarios considering climate policies and trade restrictions. If the export structure is
changing (due to trade restrictions) this leads to higher overall system costs as other
exporters are considered with more distance in shipping route and higher LCOH in
production. However, there is no significant change in fuel use as shown in the final
energy consumption. In the future, a reduction in the demand for hydrogen and
ammonia is also possible as a result of material efficiency measures or a circular
economy in all the sectors. This leads to lower net exports and simultaneously,
positive effects on global emissions.

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<figure><img src="image_114.png" /></figure>
# Net-Zero Transition in Ukraine:
## Implications for Sustainable Development
## Goal 7

<figure><img src="image_115.png" /></figure>

Check for
updates

Maksym Chepeliev, Oleksandr Diachuk, Roman Podolets, Andrii Semeniuk, and Galyna Trypolska

**Abstract** In this study, we use a TIMES-Ukraine energy system-wide model to explore net-zero transition in Ukraine. The assessment considers the potential implications of the ongoing war in the country and analyzes how the achievement of ambitious mitigation goals could impact the indicators related to the Sustainable Development Goal on energy, SDG7 (Ensure access to affordable, reliable, sustainable and modern energy). Results suggest that the net-zero transition would help improve several SDG7 indicators, including increased share of renewables in total final energy consumption, and increased energy affordability in the long run. However, in the medium run, increasing energy prices and rising investment needs might challenge the net-zero transition in the country. Overall cumulative investment needs increase by around 23% over the analyzed time horizon with the major portion being concentrated within the 2030–2045 timeframe. Major efforts need to be made to ensure the availability of a wide range of options for clean and green energy financing in Ukraine, including a reduction of the bond yield rates, creation of the specialized funds to support the ‘green’ transition, as well as the creation of a more competitive domestic environment through increasing mitigation ambition. If successfully implemented, these will allow Ukraine to rebuild the domestic energy system in a more technologically advanced and climate-friendly way, at the same time further supporting the country’s sustainable development agenda.

M. Chepeliev (✉)

Purdue University, West Lafayette, IN, USA

e-mail: mchepeli@purdue.edu

O. Diachuk · R. Podolets · A. Semeniuk · G. Trypolska

Institute for Economics and Forecasting, Kyiv, Ukraine

e-mail: oadyachuk@ukr.net; r.podolets@gmx.de; semeniuk.and.i@gmail.com;

g.trypolska@gmail.com
## Key Messages

* Net-zero transition would help improve several SDG7 dimensions in Ukraine— increase electricity supply and share of renewables, but it would have a very limited effect on the energy intensity and energy dependency of the economy.

* Energy affordability would improve in the long run, but growing electricity and heat prices could challenge the ambitious climate mitigation efforts in the medium run.

* Increased international financing, a more competitive domestic environment, and higher carbon prices are required to close the major financing gap for achieving the net-zero transition.

* The assessment captures economic and demographic aspects of the Russian-Ukrainian war and provides additional support for the 'green' and sustainable recovery of Ukraine.

# 1 Introduction

Since the adoption of the Paris Agreement on climate change in December 2015 (UNFCCC 2015), many countries have further increased the level of their climate ambition. While in their original contributions, only several countries, such as Costa Rica, Bhutan and Sweden, announced net-zero greenhouse gas (GHG) emission pledges (Höhne et al. 2021), as of 2023, a total of 79 countries, including Ukraine, have communicated net zero goals in either policy documents (52 countries) or laws (27 countries) (Energy & Climate Intelligence Unit 2023). Achieving such ambitious mitigation targets require a departure from the historically observed developmental trajectory, implying drastic transformations for the energy system (Azevedo et al. 2021; Rogelj et al. 2015), changes in behavioral patterns, such as dietary shifts toward less meat-oriented food systems (Clark et al. 2020; Theurl et al. 2020), as well as major infrastructural upgrades in many cases requiring an early retirement of the existing fossil fuel and energy-intensive capital assets (Tong et al. 2019).

An important aspect of climate mitigation policies is their interaction with various dimensions of the sustainable development agenda. This includes a better understanding of synergies and tradeoffs between emission reductions and access to affordable, reliable, sustainable and modern energy (Sustainable Development Goal 7) (e.g. Fujimori et al. 2020; Von Stechow et al. 2016; Fuso Nerini et al. 2019).

In the case of Ukraine, there have been no earlier studies that explicitly analyzed the synergies and tradeoffs between stringent climate mitigation action and SDG7. However, several studies have provided an assessment of the selected aspects of the energy system functioning under ambitious mitigation pathways and varying technological options. Chepeliev et al. (2021) explored two ambitious climate mitigation scenarios achieving 68% and 83% in GHG emissions reduction in 2050 relative to the 2010 level and focused on the role of bioenergy in Ukraine. Authors find that under the most ambitious mitigation scenario, the share of modern renewable energy sources (excluding nuclear) substantially increases over time reaching over 38% of
the total primary energy supply in 2050, with over half of this being supplied by biofuels. Achievement of the ambitious mitigation goals (83% reduction in GHG emissions in 2050 relative to the 2010 level) increases overall energy system costs by 1.8% relative to the reference scenario with no targeted mitigation efforts. Constraining the bioenergy supply at the reference scenario level could more than double the mitigation costs compared with the unconstrained case. In the context of SDG7, these results provide a quantification of potential tradeoffs between access to clean and renewable energy sources, and energy affordability.

Child et al. (2017) analyze the role of storage technologies in the process of Ukraine's transition to a 100% renewable energy system by 2050. Authors find that the adoption of low-cost renewable energy generation might reduce the levelized cost of electricity over time compared to the current level, thus making it more affordable to the intermediate and final users. At the same time, the authors note several barriers that might prevent a country from succeeding in such a transition, including modernization of the existing energy infrastructure, improvements in energy efficiency, as well as stability of the investment environment and of the legislation system.

Finally, Chepeliev et al. (2023b) take into account the impacts of the war in Ukraine in terms of changes in the future energy supply and demand and explore the role of nuclear power generation under an ambitious climate scenario. Overall, the authors find that the war in Ukraine could reduce the long-term energy demand and emissions under the reference scenario compared to the pre-war forecasts, which could provide additional incentives for implementing stringent mitigation efforts. Results suggest that the achievement of a net-zero emission target in 2050 is associated with a 13% increase in the total system costs compared to the reference (no-mitigation) case with a larger amount of investments needed during the 2030–2035 period. Such transition, however, is associated with a substantial increase in the share of carbon-free energy sources, as by 2050 the primary supply of both coal and gas declines by 98% compared to the 2020 level.

In summary, existing literature provides useful insights into challenges and opportunities for implementing ambitious mitigation efforts in the country with some preliminary findings about the post-war recovery of the country's economy and energy system. However, a detailed assessment of the interactions between net-zero transition in Ukraine and the achievement of SDG7 has not been provided in the earlier studies. In addition, the context of the war in Ukraine provides an important aspect for designing future mitigation policies and assessing their impact on the affordability, reliability and sustainability of the energy supply in the country. In the current study, we aim to comprehensively address these important aspects.

The rest of the chapter is organized as follows. Section 2 provides an overview of the state of the progress on achieving the SDG7 targets in Ukraine. Section 3 discusses the methodological framework that is employed in this study, including the TIMES-Ukraine energy system-wide model and the developed scenarios. Section 4 provides an overview of the key results across the analyzed scenarios. Section 5 complements the scenario assessment part with a discussion of financing options that could enable the implementation of ambitious climate mitigation policies in Ukraine. Finally, Sect. 6 concludes.
## 2 SDG7: State of the Progress in Ukraine

Achievement of the SDGs in Ukraine has been supported by the Presidential decree from 2019 (President of Ukraine 2019), taking into account Ukraine's national development strategy outlined in the 2017 National Baseline Report "Sustainable Development Goals: Ukraine" (Ministry of Economic Development and Trade of Ukraine 2017). The latter introduces a system of 86 targets with the corresponding quantitative indicators and monitoring mechanisms. Governmental Regulations updated at the end of 2020 (CMU 2020) made the Sustainable Development Goals an integral part of Ukraine's policy planning.

Table 1 provides a summary of the historically observed values of the SDG7 indicators in Ukraine as reported by the UN (2023). In terms of the population's access to electricity (SDG 7.1.1) and reliance on clean fuels and technology (SDG 7.1.2), prior to the war Ukraine achieved sufficient progress. However, recent and ongoing attacks on the country's power plants might have impacted these metrics. The share of renewable energy has increased by over 6 times between 2000 and 2020 reaching 8.7% of total final energy consumption, however, it was still far below the EU average share of 21.8%, as well as substantially lower than that of all Eastern European EU member states (UNECE 2023a). Substantial progress over the past two decades has been also achieved in terms of reductions in energy intensity of GDP, which has declined by over two times over this period and reached 7.0 MJ/USD2017 (in purchasing power parity—PPP). Though as in the case of the renewable energy share, Ukraine is also lagging behind its Eastern EU neighbors, having an energy intensity of GDP two times higher than Poland and almost three times higher than Romania (UNECE 2023b), with much more substantial progress needed in the future. Finally, in terms of attracting international financial flows to support net-zero transition in the country, while the situation has somewhat improved since 2015, substantial progress is needed, as major financing barriers still remain, as discussed in more detail in Sect. 5. During the 2000–2021 period, Ukraine

<table><caption>**Table 1** Stocktaking of historical SDG7 indicators for Ukraine</caption><thead><tr><th>SDG indicator/year</th><th>2000</th><th>2005</th><th>2010</th><th>2015</th><th>2020</th><th>2021</th></tr></thead><tbody><tr><td>7.1.1 Proportion of population with access to electricity, %</td><td>99.1</td><td>99.9</td><td>100</td><td>100</td><td>100</td><td>100</td></tr><tr><td>7.1.2 Proportion of population with primary reliance on clean fuels and technology, %</td><td>91</td><td>93</td><td>94</td><td>&gt;95</td><td>&gt;95</td><td>&gt;95</td></tr><tr><td>7.2.1 Renewable energy share in the total final energy consumption, %</td><td>1.3</td><td>1.3</td><td>2.9</td><td>4.2</td><td>8.7</td><td>NA</td></tr><tr><td>7.3.1 Energy intensity, megajoules per USD of GDP in USD 2017 PPP</td><td>15.2</td><td>11.1</td><td>9.9</td><td>8.1</td><td>7.0</td><td>NA</td></tr><tr><td>7.a.1 International financial flows in support of clean energy research and development and renewable energy production, millions of EUR 2020</td><td>0</td><td>115.6</td><td>1.1</td><td>10.1</td><td>35.9</td><td>235.6</td></tr></tbody></table>

*Source:* Based on UN (2023)
attracted a total of around 1.9 billion EUR of international financial flows, which is less than 2% of the country's 2020 GDP.

The Sustainable Development Report 2023 (Sachs et al. 2023) assesses the overall Ukraine's SDG Index Rank as 38 out of 166 countries scoring 76.5 points out of 100 with a spillover score of 96.7 out of 100 indicating that Ukraine causes more positive and fewer negative spillover effects on neighboring countries. At the same time, the report also indicates that regarding the progress on the SDG7 in Ukraine "significant challenges remain" and that the country's "score (has been) moderately improving, insufficient to attain (the) goal" (Sachs et al. 2023).

National stocktaking of the SDG indicators outlines a somewhat different set of the SDG7 dimensions compared to those reported in Table 1 above, reflecting country-specific goals set by the Ukrainian government (SSSU 2021; Ministry of Economic Development and Trade of Ukraine 2017) (Fig. 1). Additional indicators include dimensions of import dependency and energy security, such as the

<figure><img src="image_116.png" /><figcaption>**SDG 7.1.2.** Electric power distribution losses %</figcaption></figure>

<figure><img src="image_117.png" /><figcaption>**SDG 7.1.3.** Heat losses in heat networks, %</figcaption></figure>

<figure><img src="image_118.png" /><figcaption>**SDG 7.2.2.** Share of one supplier on the nuclear fuel market, %</figcaption></figure>

<figure><img src="image_119.png" /><figcaption>**SDG 7.2.1.** Maximum share of imported primary energy (excl. nuclear) from one source, %</figcaption></figure>

<figure><img src="image_120.png" /><figcaption>**SDG 7.3.1.** Share of energy produced from renewable sources in TFEC, %</figcaption></figure>

<figure><img src="image_121.png" /><figcaption>**SDG 7.4.1.** Energy intensity of GDP, Kgoe/2011 USD PPP</figcaption></figure>

<figure><img src="image_122.png" /></figure>

**Fig. 1** Historical and targeted SDG7 indicators for Ukraine. (Source: Based on SSSU (2021) and Ministry of Economic Development and Trade of Ukraine (2017))
maximum share of imported energy from one source (for various fuels)—SDGs 7.2.1 and 7.2.2; electric power and heat networks losses (SDGs 7.1.1 and 7.1.2); and overall power generation volumes (SDG 7.1.1), though the latter might be adjusted based on the changes in the future electricity demand (Fig. 1). Overall, while the country is on track to achieve 2030 goals for the two SDG7 indicators out of the seven reported below—GDP energy intensity (SDG 7.4.1) and the maximum share of one supplier on the nuclear fuel market (SDG 7.2.2) due to the partnership with Westinghouse Electric, achievement of the remaining SDG7 indicators remains a challenging task complicated by the ongoing war, as well as a number of institutional and market barriers, as further discussed in Sects. 5 and 6. In this context, it is important to understand what role the net-zero transition in Ukraine could play in helping to achieve sustainable development goals and what synergies and tradeoffs that may arise in this regard.

# 3 Methodology

## 3.1 TIMES-Ukraine Energy System Model

To explore the net-zero transition in Ukraine, we use the TIMES-Ukraine energy system-wide model (Chepeliev et al. 2023a; EBRD 2020; Diachuk et al. 2017). This is a linear programming optimization model of energy flows. The energy system of Ukraine is represented in the TIMES-Ukraine model as a single region and consists of seven sectors: the energy supply sector (production, imports, exports, international bunkers, stock changes, and the production of secondary energy resources—petroleum products, briquettes, etc.); electricity and heat production; industry; transport; residential users (household); trade and services; and agriculture (including fishing). The energy system in the model covers the IPCC sectors “Energy” and “Industrial processes and product use” till 2060.

The model has one region which represents the entire Ukraine with 96 annual time slices. The latter is obtained by the combination of the following dimensions: 4 seasonal (winter, spring, summer, autumn) and 24 daily levels (hours). TIMES-Ukraine takes into account international trade in energy, including electricity imports/exports from/to the European Network of Transmission System Operators for Electricity (ENTSO-E). Assumptions regarding net transfer capacities between Ukraine and the EU are implemented based on the conservative case scenario by Ukrenergo (REKK et al. 2024). Figure 2 provides an overview of the energy system representation in the TIMES-Ukraine model.

TIMES-Ukraine model provides a granular coverage of the Ukrainian energy system, representing 2050 processes and 823 commodities. A detailed overview of the underlying technological and cost assumptions of the TIMES-Ukraine model used in this study is available in Chepeliev et al. (2023b). Several updates have been introduced in terms of technological parametrization in the current study. First, the availability factor for new large nuclear power plants (NPPs) is set at 88% and for
<figure><img src="image_123.png" /><figcaption>**Fig. 2** Schematic representation of the energy system in the TIMES-Ukraine model. *Notes*: The figure provides a schematic representation of the energy system in the TIMES-Ukraine model. An actual representation of the energy system within the modeling framework is much more detailed and includes over 2000 technologies. For a more detailed description of the TIMES-Ukraine model, an interested reader is referred to Chepeliev et al. (2023b), EBRD (2020), and Diachuk et al. (2017). “RE” stands for renewable energy. (Source: Reproduced with permission from Chepeliev et al. (2021))</figcaption></figure>

small modular reactors at 90%. Second, availability factor for onshore wind is set at 32%, while offshore wind has been introduced to the model—with capital expenditures (CAPEX) ranging between 2120 and 1640 EUR/kWe across years, declining over time, and availability factor of 42%. Third, an updated parametrization of storage capacities has been introduced to the model with lifetime across storage types ranging between 20 and 30 years, and CAPEX ranging between 1042 EUR/kWe in 2020 and 255 EUR/kWe in 2050. Finally, the model has an updated representation of hydrogen technologies, including three types of electrolyzer (alkaline, proton-exchange membrane and solid oxide electrolyzer cell).

An overall approach to the energy policy assessment within the TIMES-Ukraine modeling framework, which is applied in the current study, includes several steps. First, data on the underlying drivers of the future energy supply and demand are collected. These include macroeconomic and sectoral value-added forecasts, demographic forecasts, energy price forecasts, etc. Second, a reference scenario is
developed, based on the assumptions of future energy demand, macroeconomic and demographic forecasts, technological assumptions, price forecasts, etc. *Third*, policy (e.g. net-zero transition) scenario is designed by imposing additional constraints or targets for energy system development. For each scenario (reference and policy scenarios) the model estimates the least-cost trajectory of the system, i.e. energy supply and demand by sector and fuel type, energy prices, the optimal technology mix, etc. Based on such data, the cost of the selected policy pathways, most efficient technologies, as well as required energy system transformations can be identified and analyzed. TIMES-Ukraine provides an intertemporal optimization based on perfect foresight. The next section provides a discussion of the reference and policy scenarios.

## 3.2 Scenario Framework

The reference scenario (REF) is developed to achieve the targeted energy demand in a least-cost way, where the specific technological solutions are chosen from the list of available options to minimize the total energy system costs over the analyzed time horizon. The specification of energy demand is driven by exogenously estimated drivers provided to the model, including GDP forecast, value-added across sectors, industrial output forecasts, as well as demographic changes. Our reference scenario assumes that the GDP grows by 3.5% in 2024 and 6.8% in 2025 following the forecast by the National Bank of Ukraine (NBU 2023). It is assumed that Ukraine's territorial integrity will be restored starting from 2026 and that during the 2026–2030 period, the GDP will be growing at 5% per year. The growth rate is assumed to decline over time, reaching 2–3% during the 2031–2060 period.

No major structural transformations are assumed within the reference scenario as the share of services moderately increases over time. Supporting the country's economic recovery major capital generation activity—the construction industry more than doubles its share in the country's value-added over the analyzed period. It is assumed that the agricultural sector sustains its important role in the national economy, while the share of manufacturing activities moderately declines over time.

The demographic forecast takes into account the projected movements of refugees. As of May 2022, 6.4 million refugees fled Ukraine (UNHCR 2022), although 79% of them intend to return (Razumkov Center 2022). For the post-2022 period, the population growth rate follows the medium scenario from PIDSS (2022). It should be noted that the current assessment does not take into account the impacts of the energy infrastructure damage caused by the war, which could influence energy policy decisions in the short term. The data on the energy infrastructure and power plants' damage is classified and not publicly available.

To explore the impacts of the ambitious mitigation efforts in Ukraine, we develop a GHG emissions reduction scenario consistent with reaching Net-Zero Emissions (NZE) in IPCC “Energy” and “Industrial processes and product use” sectors in 2050 and preservation of net-zero emissions through the end of the simulation horizon.
Emissions in agricultural and forestry sectors are not explicitly represented in the model (except those from fuel combustion or energy use). Thus, to achieve economywide net-zero emissions additional mitigation efforts would be needed in these sectors. The developed net-zero GHG emissions scenario is consistent with Ukraine's fair contribution of limiting global warming well below 2 °C (Fig. 3) based on the equity principles applied within the Climate Action Tracker (CAT 2020). The choice of an alternative equity approach might result in a different stringency of the mitigation targets for Ukraine, although as shown in Chepeliev et al. (2021), an approach used in this study results in a more ambitious mitigation target for Ukraine than the average over five IPCC equity categories discussed in Robiou du Pont et al. (2017). It should be noted, however, that neither of the equity approaches reported above take into account the implications of the war and can be considered feasible in the long run, i.e. assuming that Ukraine's territorial integrity is restored and the economy has recovered from the impacts of the war.

<figure><img src="image_124.png" /></figure>

**Fig. 3** GHG emission trajectories across scenarios and Ukraine's fair contribution to mitigating global warming. **Notes:** Quantification of the mitigation targets for Ukraine are based on the CAT (2020) estimates that include the impact of COVID-19 but do not include the impacts of the war. The emission level in 2020 is indexed to 100. Considering that CAT (2020) reports emissions for 2030 and 2050 years, 2040 levels were estimated using linear approximation between 2030 and 2050; emissions for 2060 have been constructed using linear approximation of emission changes between 2040 and 2050. "Role model" corresponds to the case of mitigation that is more ambitious than limiting global warming to 1.5 °C. (Source: Developed by authors based on CAT (2020) and TIMES-Ukraine model)
# 4 Results

## 4.1 Implications for the Energy System

Achievement of ambitious mitigation targets within the NZE scenario sees major transformations of the national energy system. While in the reference scenario fossil fuels constitute a substantial share of the final energy consumption (FEC) even in the long-run—around 34% in 2050 (Fig. 4a), net-zero emissions scenario sees almost complete elimination of the fossil fuels in FEC post-2045 (Fig. 4b). Such transition is also accompanied by a substantial increase in electrification rates, as the latter grows from 37% of FEC under the REF scenario in 2050 to almost 61% in the NZE scenario (Fig. 4a, b). As further discussed below, electricity generation mix under the NZE scenario is also becoming cleaner. Increasing electrification rates and penetration of more advanced technologies, such as hybrid and electric vehicles, heat pumps, etc., combined with energy efficiency improvements, lead to an overall reduction in FEC within the NZE scenario, as it declines by around 15% compared to the REF scenario in 2050 (Fig. 4a, b). Such energy efficiency improvement channel reduces the pressure on the household energy bills within the ambitious climate mitigation scenario.

Despite substantial reduction in the share of fossil fuels in final energy consumption, such use is not completely eliminated under the NZE scenario even in the long-run. In particular, this is the case for industrial processes, such as production of cement, fertilizers and lime (with total emissions of around 16–18 MtCO₂eq. post-2045), as well as supply sector (with emissions around 3–7 MtCO₂eq. post-2045). To compensate for these emissions, in order to achieve net-zero target, selected negative emission technologies are adopted by the model. Between 2 and 5 MtCO₂eq. of GHGs across years are captured by the carbon capture and storage (CCS) and direct air capture (DAC) technologies in industry. The remaining negative emissions—between 10 and 22 MtCO₂eq. of GHGs depending on the year—are contributed by electricity and heat generation sector, in particular bioenergy with CCS (BECCS). Combined heat and power (CHP) gas plants with

<figure><img src="image_125.png" /><figcaption>**Fig. 4** Final energy consumption by fuels under the reference (a) and net-zero emissions (b) scenarios, Million tonnes of oil equivalent (Mtoe)</figcaption></figure>
<figure><img src="image_126.png" /><figcaption>**Fig. 5** GHG emissions across key sectors under the reference (a) and net-zero emissions (b) scenarios, Million tonnes of CO₂ equivalent (MtCO₂eq)</figcaption></figure>

CCS are being deployed by the model starting from 2030–2035 and contribute around 19% of all heat generation in 2060. Decarbonization of hydrogen also plays an important role in the NZE scenario. While in the REF scenario all hydrogen is produced from natural gas, within net-zero transition by 2045 natural gas-based hydrogen is fully substituted by biomass-originated (56% of total production) and alkaline electrolytic hydrogen (44% of overall production). Post-2050 the share of electrolytic hydrogen increases to 75%. Around half of the produced hydrogen (52–53%) is used in the fertilizers’ production process, over one-third in the biomethane production, with the remaining allocated to biokerosene, industrial processes and buildings (Fig. 5).

While no explicit carbon pricing has been implemented within the NZE scenario discussed here, an estimate of the shadow price of CO₂ generated by the model can be used as a proxy for the level of overall mitigation efforts (carbon prices) needed to achieve the state targets. Results suggest that the shadow price of CO₂ stays in a range of 30–60 EUR per tCO₂eq. until 2030 and then increases over 230 EUR per tCO₂eq. starting from 2035 reaching the peak of around 450 EUR per tCO₂eq. in 2050 (Fig. 6). For comparison, the European Investment Bank assumes the shadow price of carbon to be 250 EUR per tCO₂eq. in 2030 and 800 EUR per tCO₂eq. in 2050 (EIB 2020). However, it should be noted that such estimates represent the cost of all mitigation measures, including energy efficiency regulations, emission standards, etc. In reality, a combination of carbon pricing and other policies is usually used, therefore the costs are distributed between these various instruments, which would result in a lower level of actually applied carbon prices compared to the full shadow price of CO₂.

## 4.2 Power Generation

The results suggest that the power generation (SDG 7.1.1, Table 1), substantially increases throughout the modeling horizon both under the reference and net zero
<figure><img src="image_127.png" /><figcaption>**Fig. 6** Shadow price of CO₂ under net-zero emissions scenario, EUR per tonne of CO₂ equivalent (EUR per tCO₂eq)</figcaption></figure>

<figure><img src="image_128.png" /><figcaption>**Fig. 7** Electricity generation in the reference (a) and net-zero emissions (b) scenarios (TWh)</figcaption></figure>

scenarios (Fig. 2). In the latter case, as the economy sees higher electrification rates with increasing penetration of electric vehicles, heating and cooking appliances, by 2040 the electricity generation is over 50% higher under the NZE scenario compared to the REF case (Fig. 7). In terms of generation mix, both in the REF and NZE scenarios, wind power is by far the main contributor (Fig. 7). In the REF scenario wind power generation reaching its maximum potential being cost-competitive with other generation sources, including fossil-based. In the NZE scenario, wind power generation expands primarily due to the off-shore wind generation (not active in the REF scenario), which by the end of the modeled period constitutes around 20% of the total wind power generation. Compared to some earlier studies of the climate mitigation policies in Ukraine that relied on the TIMES-Ukraine modeling framework (e.g. Chepeliev et al. 2021; Chepeliev et al. 2023a, b), a higher share of wind
power generation in the reference scenario estimated here could be explained by improved sophistication of the power sector representation, including dispatching constraints and storage for renewable energy. Overall, these refinements have benefited wind power generation compared to other renewable generation sources, including solar power. Volumes of nuclear power generation decline over time in the REF scenario as the existing large NPPs are being decommissioned. At the same time, within the mitigation scenario, new large NPPs and small modular reactors (contribute around 23% of all nuclear power generation in 2060) are being constructed post-2035, replacing fossil-based power generation and in response to growing electricity demand. The share of nuclear power in generation mix substantially declines over time in the NZE scenario—from around 51% in 2020 to 21% in 2060. The share of solar power generation, on the other hand, more than triples between 2020 and 2060 with key increases coming from the solar roof panels.

In terms of SDG targets, the targeted value for 2030 of 182 TWh (Fig. 1) is not being reached as in the medium-term overall electricity demand is still being substantially impacted by the war and the overall reduction in economic activity and the electricity generation volumes are in a range on 167–169 TWh across the two scenarios. This implies the need for potential revision of the SDG targets in Ukraine taking into account the impacts of the war and future recovery efforts. In terms of the electricity generation mix, the NZE scenario sees substantially higher shares of nuclear power and biomass, as well as moderate (in relative terms) expansion in hydro, solar and wind generation. Wind power generation sees a major expansion in the reference scenario, even without the implementation of ambitious mitigation efforts, as this source of energy becomes more competitive with declining levelized cost of electricity (LCOE) over time. Coal-based power generation is completely eliminated in the NZE scenario starting from 2035.

## 4.3 Energy Affordability

To assess the energy affordability aspect, we estimate the marginal price for electricity and district heating in the residential sector. As discussed in Sect. 2, in Ukraine 100% of the population have access to electricity and >95% of the population have primary reliance on clean fuels and technology, therefore these two aspects of access to energy are not a concern in the country. Economic affordability of energy is a more important aspect in the context of net-zero transition and therefore is the focus of our assessment. In cases of both electricity and heat prices, an overall tendency is that the price in the NZE scenario is higher compared to the REF scenario during the first half of the simulated period, i.e. until 2035–2040, and becomes lower toward the end of the analyzed period. This temporal distribution of price impacts is driven by the fact that higher upfront investments in the generation capacities are required during the first half of the period, as further discussed in Sect. 5, while resulting fuel savings reduce electricity and heat prices over time thus benefiting consumers in the long run. Higher demand for electricity within the NZE
<figure><img src="image_129.png" /><figcaption>**Fig. 8** Electricity (a) and heat (b) marginal prices in the residential sector. Notes: “R”, “S”, “T” and “W” labels correspond to spring, summer, fall and winter seasons respectively (case of heat prices reporting). Electricity prices reporting additional distinguishes two representative periods of the day—midday (“h12” label) and midnight (“h24” label). Linear trends for each of the scenarios are reported for illustrative purposes to show the overall trend in prices over time</figcaption></figure>

scenario combined with emission constraints also leads to an expansion in selected (relatively more expensive) technologies, which are either absent or present in the very limited volumes in the reference scenario. Examples of such technologies include offshore wind, BECCS and new large NPPs. On average electricity prices in the NZE scenario are around 25% higher compared to the REF scenario at the beginning of the period and then lower post-2045 (Fig. 8a).

Heat prices show an even more substantial relative gap across scenarios—with prices under NZE substantially higher than under the REF case, especially during the 2030–2045 period when natural gas is being phased out and substituted by bioenergy sources and air-sourced heat pumps. At the same time, combined heat and power gas plants are used in combination with CCS, which adds additional generation costs (compared to the conventional CHP without CCS used in the REF scenario). In the post-2045 timeframe, however, heat prices under the NZE scenario are substantially lower than under the REF pathway (Fig. 8b).

Before the war, around 22% of households in Ukraine could not afford to keep their homes adequately warm, while 19% were struggling to pay their utility bills (Goncharuk et al. 2021). COVID pander has put further pressure on energy poverty in Ukraine (Goncharuk et al. 2021). Observed tradeoffs between net-zero transition and energy price increases in the medium run could put additional pressure on the energy poverty situation in Ukraine.

## 4.4 Import Dependency and Energy Intensity

To assess the *Energy security and import dependency* aspect we estimate a share of energy imports in Total Primary Energy Supply (TPES) (Fig. 6a). It should be noted that imports of nuclear fuel are accounted in the mining category following
conventional energy balance definitions. Results suggest that under both REF and NZE scenarios import dependency substantially declines by 2025—from around 31% to under 15% (Fig. 9a). Such a change is driven by the reduction in both coal and gas imports following lower demand due to the war. Due to the large number of refugees (approximately 8 million in 2022) and significant destruction of industry and buildings, the domestic energy demand fell by around one-third in 2022 compared to 2021. At the same time, even during the post-war recovery period (2026 and onward) overall energy demand stays well below pre-war levels (by around 10–20 Mtoe lower than 2020 levels) and thus imports of fossil fuels do not reappear. An expansion in domestic renewable energy generation (primarily bioenergy and wind) takes place, taking into account the substantial potential of the renewable energy sources in Ukraine, as well as the need to retire many of the older fossil-based power plants, reducing the need for imported fossil fuels. Overall, in the long-run, no major differences in import dependency are observed across scenarios as imports of fossil fuels decrease substantially in both cases.

To assess the progress on SDG 7.4.1 we further estimate the energy intensity of GDP across scenarios (Fig. 9b). Results suggest that under the REF scenario, the energy intensity of GDP declines by around 14% between 2020 and 2030, which is not sufficient to achieve the SDG 7.4.1 target of 18% decline in the corresponding indicator. NZE scenario, however, results in a 24% reduction in the GDP energy intensity between 2020 and 2030, thus fulfilling the SDG target and suggesting substantial synergies in this regard. In the long run, both scenarios show very similar reduction in the energy intensity as the latter declines by around 63% in the REF scenario (in 2060 w.r.t. 2020) and by over 67% under the NZE pathway (Fig. 9b). It should be noted that a major reduction of the energy intensity under the REF scenario reflects a substantial energy efficiency potential of the Ukrainian economy, suggesting that even without major additional mitigation efforts, it is cost-efficient to rely on a new and more efficient energy technologies rather than maintain the older (existing) ones.

<figure><img src="image_130.png" /><figcaption>Fig. 9 Import share in TPES (a) and energy intensity of GDP (b) across scenarios</figcaption></figure>
## 4.5 Clean Energy

The share of energy produced from renewable sources in total final energy consumption (TFEC) (SDG 7.3.1.) moderately increases under the REF scenario reaching 16% in 2030 however it is not sufficient to achieve the target of 17.1% (Fig. 10c). NZE scenario shows major synergies between emission reductions and growing share of renewable energy in TFEC over-achieving the 2030 target by around 10 percentage points (Fig. 10c). Renewable energy sources share in power generation converges in both scenarios in the long run reaching over 77% in 2060 (Fig. 10a), indicating that the power sector possesses a great economic feasibility for decarbonization, even in the absence of climate policies. Within the NZE scenario, coal power plants are fully replaced by renewable energy sources and nuclear power generation, while a small share of electricity generation from natural gas (primarily equipped with CCS) is still observed in 2060 under the net-zero transition (Fig. 7b). It should be noted, however, that while the shares of renewable energy in electricity generation in 2060 are very similar in the REF and NZE scenario, the share of carbon-free electricity is substantially higher in the latter case due to an expansion in the nuclear power generation (not considered renewable). In the case of the heat generation sector, the REF scenario shows a substantially lower share of renewable sources by the end of the period (50% in 2060) when compared to the NZE case (92% in 2060) (Fig. 10b).

# 5 Investment Needs and Financing Options for the Net-Zero Transition

To further evaluate the economic feasibility of the analyzed net-zero emissions pathway in Ukraine we assess overall investment needs across years and technologies (Fig. 11). Overall, the NZE scenario requires 23% more cumulative investments

<figure><img src="image_131.png" /><figcaption>**Fig. 10** Share of renewable energy (RE) in electricity generation (a), heat generation (b) and TFEC (c)</figcaption></figure>
<figure><img src="image_132.png" /><figcaption>**Fig. 11** Total investments by years and sectors under the reference (a) and net-zero emissions (b) scenarios (billion EUR). *Notes*: “CHP” stands for combined heat and power plants</figcaption></figure>

over the analyzed time horizon, however, the temporal distribution of investments suggests that major additional funds will be needed during the 2030–2045 period (Fig. 11b). In 2035 a large share of additional investments are associated with the transportation sector, power plants and energy storage, while in 2040 an increase in investments is primarily driven by the construction of new nuclear reactors. Nuclear power plants in Ukraine produce electricity and heat, which is true both for existing and newly-constructed power plants represented in the model and therefore are classified under the CHP technology in Fig. 11b. During the 2035 period, only additional investments are in the range of 80 billion EUR, when compared to around 2 billion EUR of the cumulative foreign investments in the clean energy technologies attracted by Ukraine over the 2000–2020 period (UN 2023), implementation of the ambitious mitigation efforts might present a major policy challenge.

To address these, Ukraine needs to continue the development of a financial system capable of resisting domestic and external risks, as well as tailoring energy policies making clean energy viable and financially attractive. Recovery challenges and related investment needs further complicate the situation. Based on the assessment from March 2023 (before the destruction of the Kakhovka dam), the cost of reconstruction of Ukraine was assessed at around EUR 390 billion (WB 2023).

Over the past decade, the majority of the climate finance in Ukraine was directed to mitigation rather than adaptation measures. In 2021, total international climate finance flow in Ukraine was around EUR 953 million. Before the war, the environment for foreign direct investments (FDI) was rather unfavorable as the latter reached only 4% of GDP (OECD 2021). During the 2009–2020 period, Ukraine managed to attract more than EUR 9 billion in renewable energy, EUR 4.9 billion was invested in electricity generation, EUR 530 million in heat generation, and EUR 340 million in domestic solar PV (SAEE 2020). When these numbers are compared to the estimated investment needs within the NZE scenario—around EUR 106 billion over 2025–2040 period for the cases of CHP, power plants and batteries—this showcases the risk of a major financing gap in implementing net-zero transition in Ukraine.
In terms of post-war clean energy technology development, Ukraine has prioritized 11 pillars, requiring at least EUR 114 billion by 2032 (National Recovery Council 2022). These funding needs are broadly consistent with the estimates of additional investments by 2035 within the NZE scenario discussed here—around EUR 118 billion.

In pre-war times lending through international financial institutions was the most popular financing option for clean energy projects. It is important to restore this option after the end of war provided the availability of limited equity guarantees through war-risk insurance. Other measures to mobilize private finance could include establishing donor guarantees and the introduction of war insurance. The situation is complicated by the fact that Ukraine had and continues to have a very high cost of capital. During the war, the borrowed capital is difficult to obtain even at rates above 25%, which is a major prohibitive factor for the development of low-carbon technologies (Lin and Bai 2023). Therefore, in the post-war period, efforts should focus on lowering the interest rates, both with the help of international donors and domestic financial institutions.

In the EU, there is a new mechanism to finance clean energy projects within the EU Green Deal framework, when the EU member states can finance renewable energy sources (RES) projects in other countries ensuring technological spillovers, as specified in the Directive (EU) 2018/2001. Energy production must be more cost-effective than in the EU. This mechanism could benefit Ukraine, as developers may potentially bring their own finance, ensuring job creation and infrastructural development in Ukraine. The funding within such a scheme is distributed through a tender mechanism or a grant. The grant will cover either investment (installation of capacity) or actual energy generation.

Given the focus of the EU policies on decarbonization and the corresponding obligations of Ukraine, the post-war recovery should include a substantial share of investments directed toward clean energy technologies, consistent with the concept of “build back better”. The Ukraine Energy Support Fund, organized by the Energy Community, currently covers the needs of energy companies to purchase fuel, technological inputs and other necessities. After the war, this program could be repurposed toward targeted low-carbon development support. The “RebuildUkraine” Facility was suggested by the EU as a mixture of grants and loans, embedded in the EU budget.

The State Decarbonization and Energy Transformation Fund which will finance clean energy projects, was established in Ukraine in 2023 and is expected to start operation in 2024 (VRU 2023). The source of the Fund's revenue is the CO₂ tax, which as of 2023 was UAH 30/t (EUR 0.72/t) of CO₂. The 2024 State Budget anticipates that UAH 759.2 million (EUR 18.19 million) would be allocated from this Fund for the decarbonization measures. As suggested by the estimates of the shadow carbon prices within the NZE scenario (Fig. 6), ambitious climate mitigation efforts in Ukraine might require substantial increase in the level of carbon pricing in the upcoming years, which could provide additional source of ‘green’ finance. However, as indicated by earlier studies, it is important to design a comprehensive set of the carbon revenue recycling measures—aimed at both supporting low-carbon
transition, as well as providing compensations to the most-vulnerable households (Muth 2023).

As of 2023, Ukraine has primary and secondary legislation in place for issuing green bonds. They can be issued by state/government agencies, as well as by individual companies. Green bonds could provide access to the Green Climate Fund (GCF), which, inter alia, finances clean energy technologies. In 2021, EBRD subscribed to around EUR 63 million of the Ukrainian Green and Sustainability-Linked Eurobond (SLB) issued out of Ukraine and listed by TSO Ukrenergo. The funds were to be directed to the SO “Guaranteed Buyer” so that the latter could pay to the producers of “green” electricity. In addition, according to the COP27 Resolution, countries may sell their emissions reduction units (UNFCCC 2022). Ukraine might also potentially use this mechanism to ensure a ‘green’ post-war recovery.

# 6 Conclusions

In this study, we use the TIMES-Ukraine energy system-wide model to explore net-zero emissions scenario in Ukraine. The assessment considers the potential implications of the ongoing war, capturing both economic and demographic aspects of the Russian-Ukrainian war, and analyzes how the achievement of ambitious mitigation goals could impact a broad range of the developmental energy-related indicators within the Sustainable Development Goals (SDG) framework with a particular focus on SDG7 (Ensure access to affordable, reliable, sustainable and modern energy).

Overall, while we find that the implementation of net-zero emissions scenario in Ukraine has major synergies with several SDG dimensions, the availability of both domestic and international finances to support such transition might be a concern, especially considering the lack of progress in this area prior to the war. In this regard, major efforts need to be made to ensure the availability of a wide range of options for clean energy financing in Ukraine, including a reduction of the bond yield rates, creation of the specialized funds to support net-zero transition, lending through the international financial institutions, exploring the financing opportunities within the EU Green Deal framework aimed at supporting technological spillovers in the non-EU member states, as well creation of a more competitive domestic environment through ramping mitigation ambition, in particular, by increasing carbon prices. If successfully implemented, these will allow Ukraine to rebuild the domestic energy system in a more technologically advanced and environmental-friendly way, while supporting the country's sustainable development agenda.

At the same time, it is important to consider the broader socio-economic context when analyzing the net-zero emissions scenarios in Ukraine. While the modeling framework used in this study provides a detailed representation of the energy sector and can be used to inform the decision-making representing various technological assumptions, it does not explicitly capture the interaction of the energy sector with the rest of the economy. At the same time, impacts on domestic producers and
consumers, changes in the country's competitiveness on international markets, impacts of considered policies on employment, wages and income distribution are all important aspects of the net-zero transition. Assessing such aspects requires the application of economy-wide modeling frameworks, such as computable general equilibrium or integrated assessment models, which could be used in combination with the energy system models (e.g. Chepeliev et al. 2024).

For instance, while reducing a dependency on imported fossil fuels, net-zero transition could lead to other forms of import dependency—technological (imported technologies) or financial (foreign borrowings). When evaluating energy transition scenarios, it is important to consider whether the associated fuel and technological supply chains could be developed domestically or would they be primarily of import origin. Creating and supporting supply chains with a high share of domestic inputs (equipment and parts, energy services, etc.) would be vital both from the economic, as well as energy security points of view. Assessing such tradeoffs would be an important extension of the analysis presented here.

Finally, it should be noted that while based on the plausible set of assumptions, an analysis presented here is of an exploratory nature. Many uncertainties underlie the future of the Ukrainian energy sector, including the cost of capital, global energy prices, costs and availability of technologies, as well as economic recovery pathways. A comprehensive analysis of these uncertainties, while beyond the scope of this paper, would provide important input to the future decision-making process.

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# Align Algeria's Energy Diversification Strategies with Energy and Climate Sustainable Development Goals

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Naïma Chabouni, Charlène Barnet, Edi Assoumou, Nadia Maïzi, and Sandrine Selosse

**Abstract** Algeria belongs to a set of nations with a model of socio-economic development that has created a high dependence on hydrocarbon exports, leaving them vulnerable to fluctuations in international market prices. Additionally, its economy has a structural imbalance, marked by weaknesses in agricultural and non-hydrocarbon-oriented secondary sectors. To overcome the pitfalls of hydrocarbon-centered development, we develop three alternative energy pathways designed for Algeria to explore the contributions to SDG 7 (energy) and SDG 13 (climate). These scenarios analyze Algeria's future power system pathways and focus on the country's national energy policies related to integrating renewable energy and developing hydrogen production. The TIMES-DZA model was used to assess technological configurations of the power and hydrogen sectors by 2070 and demonstrate these strategies' impact on CO₂ emission reduction, aligning with sustainable development goals, specifically SDG 7 and SDG 13. The results emphasize the critical role of renewable energy and hydrogen in achieving decarbonization in the power sector, advocating for a more inclusive approach that incorporates low-carbon technologies to help Algeria reach a resilient and low-carbon energy future.

## Key Messages

* The reference scenario, excluding climate policies, describes the deepening of Algeria's reliance on hydrocarbon resources which poses an environmental challenge and conflicts with the principles of SDGs 7 (energy) and 13 (climate).

N. Chabouni (✉) · C. Barnet · E. Assoumou · N. Maïzi · S. Selosse
Centre for Applied Mathematics, MINES Paris, PSL Research University, Sophia Antipolis, France
e-mail: naima.chabouni@minesparis.psl.eu; charlene.barnet@minesparis.psl.eu; edi.assoumou@minesparis.psl.eu; nadia.maizi@minesparis.psl.eu; sandrine selosse@minesparis.psl.eu
* Enforcing Algeria's policy targets on renewable and hydrogen leads to a more diversified energy supply, aligning with SDG 7, but without reducing sufficiently the greenhouse gas emissions, thus not fully aligning with SDG 13.

* The addition of a climate target to the current policy targets raises concerns about the feasibility of achieving CO₂ emissions reduction and meeting the gas domestic demand and export requirements.

* The development of a TIMES-DZA model helped shape Algeria's future climate policies.

# 1 Introduction

Algeria, historically rich in hydrocarbons, has relied heavily on these abundant and low-cost resources for development, leading to significant carbon emissions and environmental issues. With growing climate awareness, there is increased attention on transitioning to sustainable energy sources.

The Sustainable Development Goals (SDGs) framework established by the United Nations (UN) appears to be particularly relevant for guiding the development paths of emerging countries while addressing their multiple socioeconomic and environmental challenges. However, integrating SDGs into public policy, particularly long-term energy planning, is challenging due to the interlinkages and conflicts between various SDGs and inherent uncertainties.

In this chapter, we explore the contributions to SDG 7 and SDG 13 offered by three alternative energy pathways designed for Algeria using TIMES-DZA, a bottom-up energy systems optimization model widely employed for informing public policy decisions related to energy planning. We discuss Algeria's energy challenges, strategic targets for the diversification of its energy sector, and the potential contribution of bottom-up prospective modeling to evaluate energy trajectories in alignment with SDGs. The methodology of TIMES-DZA and the conceived scenario are introduced, followed by analysis of key results.

# 2 The Algerian Energy System: Many Challenges Ahead

## 2.1 The Advent of the Rentier State

Before 1958, Algeria's economy was agrarian, but the establishment of its national oil company Sonatrach in 1963 marked a shift to oil and gas production, leading to OPEC membership in 1969 (Mekideche 2009).

Industrialization efforts in hydrocarbons transformation, chemicals and steel began, alongside nationalization of the oil industry in 1971. Planning programs in 1970 and 1974 aimed to boost development but faced challenges due to the overreliance on oil leading to economic setbacks in non-oil sectors (Andreff and
Hayab 1978; Addi 1995; Gasmi and Laourari 2017; UNECA 2017; Ouchene and Moroncini 2018). The mid-1980s oil shock, marked by oil prices plummeting from $USD 30/barrel in 1982 to $USD 12/barrel in 1988, worsened Algeria's economic situation. This period saw the budget deficit reaching 13.7% in 1987, highlighting the country's vulnerability to hydrocarbons.

The 1990s, known as the “black decade”, witnessed political, socio-cultural, and economic turmoil. Algeria turned to international financial institutions for assistance, implementing International Monetary Fund structural adjustment programs in 1994–95. This neoliberal policy led to a deregulation and privatization movement in the energy sector, aiming to foster competition, attract investments, and improve efficiency. While stabilizing the economy, these measures failed to bring about substantial restructuring (Auty 2003). By the end of the decade, Algeria remained heavily reliant on hydrocarbons, with 30% of GDP, 60% of government revenues, and 95% of exports still attributed to the hydrocarbons sector (Oliva 2000).

In the 2000s, Algeria continued liberalization, ending the state monopoly on hydrocarbons in 2001 (Akacem 2004). Despite a surge in oil prices benefiting the state budget, economic diversification remained elusive. From 2000 to 2014, over 65% of the budget and 97% of exports were still tied to hydrocarbons, mainly used for sustaining subsidies (Safir 2019).

Algeria's economy reflects the characteristics of a rentier state, with rent primarily originating from external sources like oil sales (Beblawi 1987; Machín Álvarez 2010; Fuinhas and Marques 2013). Despite wealth from oil booms, socio-economic indicators remain unsatisfactory, lagging behind the Middle East and North Africa (MENA) average in terms of GDP per capita and electricity generation per capita (World Bank 2022; EIA 2023). In 2021, Algeria's GDP per capita (PPP) was $1228, compared with $17,282 for the MENA zone, the lowest country being Yemen with $3437 per capita and the highest Qatar with $102,018 per capita (World Bank 2022). In 2022, Algeria's electricity generation per capita was 1770 kWh versus 3464 kWh for the MENA region (EIA 2023). This suggests Algeria has not successfully navigated the pitfalls associated with rentier dynamics.

## 2.2 Oil and Gas: A Blessing or a Curse?

In 2022, Algeria was among the top 15 oil producers and the top 10 gas producers globally. It is the fifth-largest exporter of natural gas via pipelines and the sixth-largest exporter of liquified natural gas, making it Africa's largest gas exporter (BP 2022). Additionally, it is the fourth biggest African oil exporter after Nigeria, Libya, and Angola. However, the heavy reliance on oil and gas presents challenges, with the hydrocarbon sector contributing significantly to GDP (19%), exports (93%), and budget revenues (38%) from 2016 to 2021, highlighting a lack of economic diversification (World Bank 2022). In comparison, manufacturing contributed only 5.45% to the GDP in 2016 (Gasmi and Laourari 2017).
Algeria's over-reliance on oil and gas exposes it to market price fluctuations, causing uncertainty in fiscal revenues (World Bank 2022). For instance, the sharp decline in oil prices from US$115 per barrel in June 2014 to less than US$50 in January 2015 led to a fiscal deficit surge from 1.4% to 15.7% of GDP by 2016 (World Bank 2015, 2016, 2018). This financial strain, coupled with escalating inflation and weakening currency, contributed to the *Hirak* national protest movement in 2019 and the resignation of President Abdelaziz Bouteflika. On the other hand, periods of high oil prices, such as those following the COVID-19 crisis, can alleviate pressure on public finances and enable positive social measures like controlling the price of essential commodities and supporting employment (Serrano 2022). These examples highlight how the stability and level of hydrocarbon revenue influence social peace in rentier states like Algeria, where reliance on oil and gas for budget stability can be both a blessing and a curse depending on global economic conditions.

In addition to market price volatility, the Algerian government grapples with challenges affecting future oil and gas exports and associated revenues. Over the past decade, outputs have declined due to technical challenges, inadequate infrastructure, and insufficient investment in recovery technologies. Furthermore, bureaucratic procedures delay project development (Aissaoui 2016; Ouki 2019; BP 2022). Increasing domestic demand, particularly for gas, driven by population growth and subsidies, further strains hydrocarbon export prospects. In 2021, 67% of Algeria's primary energy consumption came from natural gas and 32% from oil, with about 98% of electricity produced from natural gas (BP 2022). Domestic gas consumption surged by 70% from 2008 to 2018 (Ouki 2019), with projections indicating a potential 17–77% increase by 2032, compared to the 2021 consumption level of 48.61 Bcm (CREG 2023). Consequently, gas exports decreased from a peak of 64 Bcm in 2005 to 55 Bcm in 2021 due to diminishing supply and rising domestic consumption.

The environmental impact of hydrocarbon-driven development has become a growing concern in Algeria since the late 1990s (Ouchene and Moroncini 2018). As the fourth largest emitter of greenhouse gases (GHGs) in Africa, Algeria's total emissions reached 284 million tons (Mt) in 2022, a two-fold increase since 1990 (Allali et al. 2015; Crippa et al. 2023). As a Mediterranean country, Algeria faces frontline impacts of climate change, including water scarcity, desertification and drought. In 2022, fuel exploitation contributed nearly 35% of Algeria's total emissions, with power and heat generation accounting for 15% (Crippa et al. 2023). This underscores the country's overreliance on hydrocarbons, particularly gas for electricity production, responsible for half of its emissions.

The structural fragility resulting from excessive dependence on hydrocarbons, coupled with uncertainties about future production and export levels, compels the Algerian government to seek income diversification. Challenges posed by global warming and the imperative of energy transition add to the uncertainty surrounding the future of hydrocarbons worldwide. Despite several attempts to diversify the economy, success has been limited. However, the ongoing transformation of global energy systems presents a new opportunity for Algeria to succeed in diversifying its economy, particularly the energy sector.
## 2.3 Getting Away from the Rentier Economy Model: Diversification and SDGs

Algeria faces numerous socio-economic and energy-related challenges, notably the need to transition from relying on abundant hydrocarbon resources for low energy prices to reserving these resources for exports to maintain revenues, given the current limited diversification of exports. Moreover, the widening gap between energy supply and consumption threatens energy security in the medium term. Diversification has long been advocated as one of the solutions to these issues. Economic diversification, as defined by the World Bank Group, involves transitioning to a more varied domestic production and trade structure to boost productivity, create job, and achieve sustained, poverty-reducing growth (OECD/ WTO 2019). Energy plays a pivotal role in these processes, contributing significantly to manufacturing, job creation, transportation, and poverty reduction. Therefore, ensuring reliable and affordable energy supply is imperative for economic in developing nations (International Crisis Group 2018; Akrofi 2021).

For hydrocarbon-exporting nations like Algeria, the challenges of economic diversification, energy transition and energy security are deeply intertwined. The Algerian government's Economic Recovery Plan, introduced in 2021 post-COVID-19, identifies the energy sector as a strategic driver of economic development. Traditional energies are seen as a fundamental for constructing a sustainable Algeria, while renewable energy (RE) is envisioned to ensure the country's energy security through a more balanced energy mix capable of meeting long-term domestic demand without depleting exportable hydrocarbon resources. The plan envisions a "low-carbon, rational and efficient in its energy consumption" Algeria of the future, with hydrogen highlighted as part of the energy transition package. This focus on hydrogen, emerging since 2019, has garnered increasing interest over the years, as evidenced by recent announcements. The plan also underscores the importance of developing promising industrial sectors including mechanical engineering, electronics, appliance production, and pharmaceuticals (Services du Premier Ministre 2021). With 80% of Algeria's industrial energy mix relying on subsidized natural gas supply (Ouki 2019), the need to diversify the energy mix with REs becomes evident, playing a crucial role in shaping the country's economic future. Given these considerations, this chapter will explore potential energy diversification pathways and their associated CO₂ emissions.

On the international stage, the objectives of energy diversification and transition are reflected in the frameworks of the Nationally Determined Contributions (NDCs) and the SDGs. The 2030 Agenda for Sustainable Development, devised by the UN, places the 17 SDGs at its core, providing a comprehensive framework to improve the quality of life for all individuals while addressing environmental challenges. Of particular relevance to our subject are SDG 7, which aims to ensure access for all to reliable, sustainable, modern, and affordable energy services, and SDG 13, which focuses on combating climate change and its repercussions.
In a 2019 progress report, the Algerian Committee responsible for monitoring the implementation of the SDGs identified the significant increase in domestic energy consumption and the resulting pressure on gas resources which should be allocated for exports, as a challenge to be tackled within the framework of SDG 7. The report highlights the development of RE and the promotion of energy efficiency as key responses to these challenges (targets 7.2 and 7.3 respectively). Regarding SDG 13, the Committee underscores Algeria's vulnerability to climate change, particularly in relation to water stress. As for this goal, a crucial lever related to the energy sector is the commitment stated in Algeria's 2015 NDC to reduce GHG emissions by 7-22% by 2030, contingent on the level of support provided by international actors (Interministerial Committee for the follow-up of the SDGs 2019).

The most straightforward indicator to assess the achievement of target 7.2 is the rate of electricity generation from RE sources or the installed renewable capacities. In 2015, Algeria set an ambitious goal to have 17,475 MW of RE capacities installed by 2035, accounting for 27% of the electricity mix. However, as of 2022, the share of RE in primary energy consumption remains low, at only 0.26%, with renewable capacities barely reaching 599 MW (BP 2022; IRENA 2023a). Regarding SDG 13 and within the scope of the energy sector, key mechanisms to be activated include reducing flaring and GHG emissions and establishing of a reporting system for emissions. Algeria has made progress in reducing the flaring rate, which decreased from 78.6% in 1970 to 8% in 2016. However, despite this improvement, Algeria's GHG emissions have continued to increase, rising from 260 Mt CO2eq/yr in 2015 to 284 Mt CO2eq/yr in 2022 (Crippa et al. 2023). Substantial and rapid reduction of GHG emissions will not be achieved without a deep transformation of the power and fuel exploitation industries. These sectors are indeed contributing significantly to Algeria's GHG emissions, as detailed in the previous section.

Combining long-term energy security, the maintenance of a fiscally sustainable level of energy products export and SDGs is complex and necessitates considering numerous parameters and anticipating developments, given the extended lead times required for introducing new renewable capacities or initiating new production fields. Long term, bottom-up, prospective modeling tools, has proven to be relevant for studying SDG 7 and SDG 13 (Barbero Vignola et al. 2020). In this chapter, we use such a modeling tool to examine the sustainable development and diversification of the Algerian energy sector. The objective is twofold. First, to measure, by the year 2070, the impact of integrating RE and hydrogen development targets on the future energy system and to assess whether these technological configurations can alleviate pressure on oil and gas resources for domestic demand while enhancing the fulfillment of SDG 7. Second, to examine the impact of associating these strategies with a CO2 emission reduction constraint that aligns with SDG 13. Before presenting the model TIAM-DZA, we will delve further into Algeria's ambition regarding its energy transition, with a special focus on hydrogen which has been on the spotlight over the past few years, and for which Algeria harbors great ambition.
# 3 Algeria's Ambitions Regarding Its Future Energy Sector

## 3.1 Renewable Energies Development

Although the share of RE in Algeria's energy mix is currently marginal, the government implemented policies and support mechanisms, such as the feed-in tariffs, to stimulate the growth of RE. In 2020, Algeria established a new ministry for energy transition and RE, releasing an ambitious roadmap. The "National Program for the Development of Renewable Energies" targets a 30% RE share in the energy supply by 2035. The ministry, focusing mainly on solar projects, aims to install 1000 MW of solar electricity in small power plants across eleven sites in the first phase. The long-term goal is to achieve 15,000 MW by 2035.

## 3.2 Hydrogen Development in Algeria

The concept of transporting solar energy from North Africa to Europe has been under consideration for more than two decades. Initially, the DESERTEC project aimed to transmit solar electricity from the MENA region to Europe through high-voltage direct current transmission (Trieb and Müller-Steinhagen 2007). However, this ambitious project has faced partial abandonment, due to challenges in developing electricity infrastructures between the two continents. Instead, a growing interest in the alternative approach of converting solar energy into hydrogen for export has emerged (Cherigui et al. 2009). As part of its REPowerEU plan, the European Union (EU) aims to use 20 Mt of renewable hydrogen<fnref n="1" /> annually by 2030, with half of this amount expected to be imported (EC 2022). North African countries, given their extensive solar potential and geographical proximity to Europe, are considered prime candidates to meet this demand. Algeria, in particular, is connected to Europe through three natural gas pipelines—the Enrico Mattei (TransMed), the Maghreb-Europe Gas (MEG), and the Medgaz pipeline—that can potentially be retrofitted to transport hydrogen at a manageable cost. The SoutH2 Corridor, a 3300 km long hydrogen pipeline linking North Africa, Italy, Austria, and Germany, primarily entails repurposing existing gas pipelines and is anticipated to be operational by 2030. This ambitious project holds promise and could provide Algeria with a strategic advantage in the competition to meet the growing European demand for H₂.

Keen to capitalize on its gas resources as a first step and then diversify beyond hydrocarbons by exploiting its renewable potential, the Algerian government fixed

<footnote n="1">¹According to the European Commission guidelines, hydrogen must be produced from RE sources and achieve at least 70% reduction in GHG emissions to be considered as renewable. Two other criteria—the additionality requirement and the criteria on temporal and geographical correlation—must be completed for hydrogen to be renewable. When renewable hydrogen comes from RE other than biomass, then it falls under the category of Renewable Fuels of Non-Biological Origins (RFNBOs), otherwise it is considered as a biomass fuel.</footnote>
the target to produce and export to the EU, 30–40 TWh of gaseous and liquid
hydrogen (which is equivalent to around 1 Mt of hydrogen) by 2040. The goal
will be achieved through a mix of blue hydrogen—produced from natural gas
through the Steam Reforming process associated with a Carbon Capture Use and
Storage (CCUS) facility—and green hydrogen, generated by electrolysis using the
country's abundant solar resources or wind when relevant. To support this export
strategy, Algeria has launched a national program structured in three phases to
develop and produce hydrogen, starting from now and extending beyond 2040
(Aouissi and Haggmark 2023). Algeria also aspires to replace grey hydrogen,
presently used in local industries, with green hydrogen, thereby contributing to the
decarbonization of the national industrial sector. As for CCUS, no official target or
guidelines have been released by the government so far. Nevertheless, Algeria is
known to hold some underground CO₂ sequestration potential (Aktouf and Bentellis
2016).

# 4 Long-Term Bottom-Up Energy Systems Modeling in Support of Evaluating Algeria's Energy Ambitions Alignment with SDGs

## 4.1 Energy Modeling in Algeria

Algeria currently lacks extensive long-term, bottom-up prospective models for
comprehensive energy systems analysis. A partial equilibrium simulation modeling
framework known as EnerNEO has been applied to Algeria, primarily for commer-
cial purposes at present (Hadjer et al. 2023). Additionally, Umaru formulated an
energy strategy aligned with Algeria's SDGs based on a MESSAGE model (Umaru
2021). Neither of these models incorporates the production and export of hydrogen
as a means of promoting economic diversification. Prospective analysis exploring
possible energy future for Algeria and related challenges exists, but they are not
model-based (Supersberger and Führer 2011; Isaiah and Stambouli 2017; Zahraoui
et al. 2021). To the best of our knowledge, the MESSAGE model developed by
Umaru and the TIMAS—DZA model developed by Chabouni (2022), are the only
academic prospective bottom-up optimization models applied to Algeria that exist so
far (Chabouni 2022).

## 4.2 TIMES-DZA

TIMES (The Integrated MARKAL-EFOM System) is a bottom-up model generator
for energy systems analysis (Loulou et al. 2016). Developed by Chabouni (2022) at
the Centre for Applied Mathematics of Mines Paris PSL in France, the TIMES-DZA
model focuses on Algeria. The model is part of the TIMES family, which consists of
<figure><img src="image_135.png" /><figcaption>Fig. 1 Simplified RES assumed in the TIMES-DZA model</figcaption></figure>

cost optimization partial equilibrium models. These models are designed to explore and optimize interactions within energy systems, including energy consumption, technologies, trade, system costs, and the marginal costs of environmental policies (Loulou et al. 2016; Wright et al. 2016).

Figure 1 displays the simplified Reference energy system (RES) that describes the structure and energy flows within TIMES-DZA. This system comprises three major parts. The supply side incorporates energy sources such as natural gas, shale gas and renewable resource potential but not petroleum products which are not used today for power production in the Northern interconnected grid (RIN) region. TIMES-DZA covers only RIN, the biggest grid in Algeria that comprises more than 80% of the country's population. The conversion part includes fuels production and conversion technologies, such as H₂ production and various types of power plants. It also encompasses transmission and distribution infrastructures. The demand section covers power and gas demand across different sectors, as well as the quantities of natural gas and H₂ exported. Energy-related CO₂ emissions are also tracked within the model.

The underlying model data for this base year is calibrated to the 2016 energy balances of the Ministry of Energy in Algeria and other international reports (IRENA 2015; Bertelsmann Stiftung 2018; Enerdata 2020; BP 2022). The model is run in 5-year increments up to 2070. The TIMES-DZA model accounts for an 8% discount rate, the average rate set by the CREG (CREG 2016).

## 4.3 TIMES-DZA Model Assumptions

### 4.3.1 Demand Projection

The application of TIMES-DZA presented in this chapter focuses on the optimization of the hydrogen and gas supply, and uses exogenously fixed demands for power and gas, defined as follows.
Energy demand models analyze the energy-economy relationship, incorporating factors like population, GDP, and socio-economic changes to estimate demand (Saiah and Stambouli 2017). On a side exercise, we developed an econometric model employing Ordinary Least Squares (OLS) technique to link these variables, enabling us to project future electricity and gas demand. We developed a long-run scenario based on likely future growth rates of relevant economic and demographic drivers.

Regression models were separately built for the electricity and gas sectors, incorporating past consumption patterns and relevant explanatory indicators. We developed a “Trend scenario” for electricity and gas demand spanning 2018–2070 (Table 1 and Fig. 2). The demand scenarios in the economic sectors simulates consistent GDP and added value growth in each sector, reflecting Algeria’s GDP trends from 2018 to 2020 at 1.3%. These scenarios do not consider significant reforms that could reshape the economy in the future.

As for the residential sector, population growth is considered a driving factor, which shows a moderate to downward trend, aligning with the UN’s low scenario projected for 2100, as updated in 2019 (UN 2019). Over the last five years, the Algerian population has increased by 2.1%, reaching 41.4 million in 2017. UN projections suggest a relatively steady progression, averaging around 2%, gradually declining to a negative rate of −0.2% by 2070.

As part of its National Energy Efficiency Program (2016–2030), Algeria adopted rationalization measures for energy-intensive sectors like residential, industry, and transport. In the transport sector, the plan aims to promote clean fuels while ensuring an attractive quality/price ratio for customers. One of the initial steps involves annually converting 145,000 vehicles (diesel and gasoline) to liquefied petroleum gas (LPG) since 2019 (Siouani 2023). In this chapter, we consider this additional LPG demand from the transport sector by constructing a low scenario from 2016 to 2070. Using a simple regression model, we estimate the evolution of the car fleet based on GDP per capita scenarios. From this, we derive the car ownership factor, which is projected to increase from 0.15 in 2016 to 0.317 by 2070 in Algeria. We then employ extrapolation to develop three scenarios for the number of vehicles converted to LPG by 2070, aligning with Algerian government policies. Algeria aims to convert approximately one million vehicles to LPG by 2023, reaching a 30% share of converted vehicles by 2030. We calculate the number of converted vehicles by 2030 by applying the 30% share to our fleet scenario and projecting the annual growth rate from 2019 to 2030. For the subsequent years up to 2070, we maintain the same annual number of converted vehicles as in 2030.

### 4.3.2 Export Projection

Given the key role of hydrocarbons in the country’s fiscal and commercial structure, our TIMES-DZA model maintains a steady natural gas export level of 50.5 Bcm throughout the entire study period until 2070 (Table 2 and Fig. 3), reflecting the average export value observer from 1990 to 2019. This decision aims to ensure
<table><caption>**Table 1** Electricity and gas demand projections by sector in the “trend scenario”</caption><thead><tr><th colspan="8">Electricity demand scenario by sector (PJ)*</th></tr><tr><th>Sector</th><th>Agriculture</th><th>Construction</th><th>Hydrocarbons</th><th>Industry</th><th>Mining</th><th>Services</th><th>Transport</th><th>Residential</th><th>Total</th></tr></thead><tbody><tr><td>2016</td><td>1.2</td><td>1.2</td><td>12.3</td><td>55.2</td><td>1.2</td><td>14.6</td><td>2.7</td><td>110</td><td><strong>198</strong></td></tr><tr><td>2050</td><td>1.4</td><td>2.6</td><td>2.9</td><td>4.2</td><td>31.4</td><td>22.1</td><td>284</td><td>465</td><td><strong>814</strong></td></tr><tr><td>2070</td><td>1.8</td><td>3.7</td><td>4.1</td><td>5.7</td><td>40.7</td><td>30</td><td>500</td><td>597</td><td><strong>1183</strong></td></tr><tr><th colspan="8">Natural gas demand scenario by end-use sector (PJ)*</th></tr><tr><th>Sector</th><th>Residential sector</th><th>Industrial Consumers</th><th>Transport (LPG)</th><th>Total</th></tr><tr><td>2016</td><td>330</td><td>439</td><td>2.5</td><td><strong>770</strong></td></tr><tr><td>2050</td><td>1660</td><td>1420</td><td>120</td><td><strong>3198</strong></td></tr><tr><td>2070</td><td>2425</td><td>2550</td><td>205</td><td><strong>5190</strong></td></tr></tbody></table>

<footnote>* It is noteworthy, this scenario is built on a series of the assumptions of the previous mentioned drivers</footnote>
<figure><img src="image_136.png" /><figcaption>**Table 2** Natural gas and hydrogen exports assumptions (in PJ)</figcaption></figure>

<table><thead><tr><th></th><th>2016</th><th>2035</th><th>2040</th><th>2050</th><th>2070</th></tr></thead><tbody><tr><th>Natural gas exports</th><td>1819</td><td>1819</td><td>1819</td><td>1819</td><td>1819</td></tr><tr><th>H<sub>2</sub> exports</th><td>0</td><td>0</td><td>144</td><td>144</td><td>144</td></tr></tbody></table>

<figure><img src="image_137.png" /><figcaption>**Fig. 3** Visualization of natural gas and H2 exports in TIMES-DZA model</figcaption></figure>

economic stability by securing a reliable income stream and allocating export revenues to finance investments in energy transition and low-carbon technologies progressively over time. In domestic gas resources are too low to satisfy both domestic demand and exports, the model is able to import gas.
This study assumes that natural gas will continue to play a role in specific application where alternative low-carbon technologies face implementation challenges, such as industrial processes, heating, or backup power generation, despite the ongoing energy transition in Europe focused on decarbonation. Additionally, Algeria's natural gas exports to Europe could be utilized in hydrogen production, utilizing methods like natural gas with CCUS, among other potential applications.

Our model incorporates the target of annually exporting 40 TWh of gaseous and liquid H₂ to the European market, maintained throughout the year 2070 (Table 2), and aligning with the government's stated objectives.

### 4.3.3 Shale Gas Development

To address the depletion of Algeria's natural gas reserves and ensure sustained supply for exports and future demands, the government is contemplating the development of shale gas resources, aligned with Sonatrach's strategic plan (SH2030). The aim is to achieve an unconventional gas production level of 20 Bcm by 2030.

Incorporating shale gas development into our TIMES-DZA model, we have formulated assumptions regarding its future trajectories. A maximum additional capacity level for shale gas has been fixed for the entire period (2030–2070).

Due to limitation in expertise and infrastructure, Algeria faces constraints in shale gas extraction. Aligning with the SH2030 target, we derived a scenario projecting additional shale gas capacity based on the country's natural gas production growth rate (2011–2021), set at 2.4% in the latest BP Statistical Review of World Energy (BP 2022). By extrapolation, shale gas capacity could reach around 48 Bcm (1797 PJ) by 2070 (Table 3).

In our modeling approach, we have assumed a moderate shale gas extraction cost of 5€/GJ, in line with relevant literature (Orthofer et al. 2019).

### 4.3.4 Future Technological Options

This chapter explores the prospects of shale gas development by investigating the future applications of Combined Cycles Gas Turbine (CCGT) for electricity generation while excluding CCGT with carbon capture and storage (CCS).

Algeria's strategic location offers ideal conditions for RE development, with abundant solar PV potential exceeding 27,000 TWh and wind energy potential surpassing 32,000 TWh (IRENA 2023b). Hence, TIMES-DZA includes solar PV and onshore wind as future investment options.

<table><caption>**Table 3** Evolution of additional shale gas capacity from 2030 to 2070</caption><thead><tr><th>Horizon</th><th>2030</th><th>2035</th><th>2040</th><th>2050</th><th>2060</th><th>2070</th></tr></thead><tbody><tr><td>Shale gas additional capacities (PJ)</td><td>747</td><td>840</td><td>945</td><td>1185</td><td>1470</td><td>1797</td></tr></tbody></table>
For hydrogen production, two options are considered: electrolyzers for green hydrogen and steam methane reforming with CCS (SMRCCS) for blue hydrogen (IRENA 2023c). However, the model limits the maximum capacities of these technologies to 5 GW/year, equivalent to approximately 34 electrolyzing facilities of 150 MW/year.

### 4.3.5 Electricity Generation Technologies in TIMES-DZA

To construct TIMES-DZA, we compiled data from various sources to establish a comprehensive database on Algeria's power and gas systems. Extensive review of national international reports was conducted for this purpose (BP 2022; MEM 2022; IRENA 2023a; EIA 2023).

Tables 4 and 5 report the techno-economic characteristics of Algeria's electricity generation technologies required for this study (IRENA-ESTAP 2013; Cannone et al. 2021; IEA-ETSAP 2024).

The study accounts for decreasing costs of renewable technologies, particularly solar and wind energy. Additionally, it assumes consistent fixed costs for emerging hydrogen technologies, acknowledging their current status as relatively immature technologies (Table 6).

<table><caption>**Table 4** Current and future power capacity by technology</caption><thead><tr><th colspan="2">Power plants</th><th>2019 Stock (GW)</th><th>Committed</th><th>Planned (GW)</th><th>Possibility of future investment</th></tr></thead><tbody><tr><td rowspan="5">Thermal plants</td><td>Steam turbine</td><td>2.4</td><td>/</td><td>/</td><td>No</td></tr><tr><td>Gas turbine</td><td>11.3</td><td>/</td><td>/</td><td>Yes</td></tr><tr><td>Combined cycles</td><td>4.3</td><td>8.2</td><td>/</td><td>Yes</td></tr><tr><td>Mobile gas turbine</td><td>1.3</td><td>/</td><td>/</td><td>No</td></tr><tr><td>Diesel generators</td><td>0.4</td><td>/</td><td>/</td><td>Yes</td></tr><tr><td>Nuclear</td><td>Nuclear reactor</td><td>16</td><td>/</td><td>2.4</td><td>Yes</td></tr><tr><td colspan="6">Renewable energies</td></tr><tr><td>Hydro</td><td>Hydropower</td><td>0.227</td><td>/</td><td>/</td><td>No</td></tr><tr><td rowspan="2">Solar</td><td>PV solar</td><td>0.219</td><td>1 (2023)</td><td>13.5 (2030)</td><td>Yes</td></tr><tr><td>CSP</td><td>2</td><td>/</td><td>2</td><td>Yes</td></tr><tr><td rowspan="2">Wind</td><td>On-shore</td><td>0.01</td><td>/</td><td>5</td><td>Yes</td></tr><tr><td>Off-shore</td><td>/</td><td>/</td><td>/</td><td>No</td></tr><tr><td>Bioenergy</td><td>Biomass</td><td>1</td><td>/</td><td>/</td><td>No</td></tr><tr><td rowspan="2">Hydrogen</td><td>SMRCCS</td><td>/</td><td>/</td><td>40 TWh</td><td>Yes</td></tr><tr><td>Electrolysis</td><td>/</td><td>/</td><td>/</td><td>Yes</td></tr></tbody></table>
<table><caption>**Table 5** Techno-economic parameters of electricity generation technologies</caption><thead><tr><th></th><th></th><th>Capital cost (€/kW in 2020)</th><th>Fixed cost (€/kW in 2020)</th><th>Operational life (year)</th><th>Efficiency</th><th>Average capacity factor</th></tr></thead><tbody><tr><td>Thermal plants</td><td>Combined cycles</td><td>1200</td><td>35</td><td>30</td><td>0.48</td><td>0.85</td></tr><tr><td>Nuclear</td><td>Nuclear reactor</td><td>6137</td><td>184</td><td>50</td><td>0.33</td><td>0.85</td></tr><tr><td rowspan="2">Solar</td><td>PV</td><td>1378</td><td>17.9</td><td>24</td><td>1.0</td><td>0.35</td></tr><tr><td>CSP</td><td>4058</td><td>40.6</td><td>30</td><td>1.0</td><td>0.45</td></tr><tr><td>Wind</td><td>On-shore</td><td>1489</td><td>59.6</td><td>25</td><td>1.0</td><td>0.21</td></tr><tr><td rowspan="2">Hydrogen</td><td>Electrolyzer</td><td>1866</td><td>93.3</td><td>20</td><td>1.0</td><td>—</td></tr><tr><td>SMRCCS</td><td>265</td><td>13.2</td><td>20</td><td>0.9</td><td>—</td></tr></tbody></table>

<table><caption>**Table 6** Renewable and hydrogen technologies costs assumptions</caption><thead><tr><th>EUR/kW</th><th>INVCOST~2030</th><th>INVCOST~2040</th><th>INVCOST~2050</th><th>INVCOST~2070</th></tr></thead><tbody><tr><td>Wind</td><td>967</td><td>830</td><td>830</td><td>830</td></tr><tr><td>Solar</td><td>869</td><td>802</td><td>727</td><td>727</td></tr><tr><td>SMRCCS</td><td>265</td><td>265</td><td>265</td><td>265</td></tr><tr><td>Electrolyzer</td><td>1867</td><td>1867</td><td>1867</td><td>1867</td></tr></tbody></table>

## 4.4 Scenario Definition

This chapter aims to evaluate the impact of current policies on RE integration, and hydrogen development on the future energy system. It also seeks to assess technological configurations by 2070 and the impact of associating these strategies with a CO₂ emission reduction constraint aligned with SDG 13.

Three long-term scenarios are proposed and analyzed using the TIMES-DZA model, as described below and in Table 7.

**The reference scenario “All fossil fuels (BAU)”** explores conventional and unconventional resources while following Sonatrach's 2035 strategy. It assumes no climate policies or pledged actions but allows for investments in RE sources. The goal is to assess the impact of an unrestricted fossil fuel trajectory on Algeria's power system by 2070.

**The Renewable + Hydrogen scenario “ReN + H₂”** incorporates exploration of conventional and non-conventional resources, focusing on RE development (solar PV and wind power) and hydrogen production (blue and green hydrogen). It evaluates the feasibility of achieving Algeria's REs and hydrogen production targets and their role in shaping a more balanced energy mix, aligning with SDG 7 principles. Moreover, it provides insights for policymakers on crafting comprehensive and effective strategies for a sustainable energy future.

**The Renewable + Hydrogen + CO₂ bound scenario “ReN + H₂ + CO₂ bound”**. Building upon the BAU scenario, this scenario aims to achieve gradual
<table><caption>**Table 7** Scenarios assumptions summary</caption><thead><tr><th></th><th>All fossil fuels (BAU)</th><th>ReN + H<sub>2</sub></th><th>ReN + H<sub>2</sub> + CO<sub>2</sub>_bound</th></tr></thead><tbody><tr><td>Electricity and gas demand</td><td>Trend scenario</td><td>Trend scenario</td><td>Trend scenario</td></tr><tr><td>Fossil-fuel based generation</td><td>Allowed</td><td>Allowed</td><td>Allowed</td></tr><tr><td>Development of solar and wind</td><td>Allowed</td><td>Allowed</td><td>Allowed</td></tr><tr><td>Limit on RE potential use</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Natural gas Export target (PJ/year)</td><td>1819 (fixed for all periods)</td><td>1819 (fixed for all periods)</td><td>1819 (fixed for all periods)</td></tr><tr><td>Shale gas development limit</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>H<sub>2</sub> export targets (PJ/year)</td><td>No</td><td>144 (same for all periods beginning 2040)</td><td>144 (same for all periods beginning 2040)</td></tr><tr><td>Upper bound constraint for H<sub>2</sub> production technologies (GW/year)</td><td>No</td><td>5</td><td>5</td></tr><tr><td>Lower bound on RE share in total generation (in %)</td><td>No</td><td>30% (starting 2035)</td><td>30% (starting 2035)</td></tr><tr><td>CO<sub>2</sub> reduction target</td><td>No</td><td>No</td><td>Gradual CO<sub>2</sub> emissions reduction (−25% by 2035, −35% by 2050 and −50% by 2070, compared to BAU)</td></tr></tbody></table>

CO₂ emissions reduction, reaching 50% reduction by 2070 compared to the projected level under the BAU scenario. It maintains the parameters of the “ReN + H₂” scenario and assesses the impact of ambitious climate action on Algeria’s 2070 power system configurations, aligning with SDG 13.

## 4.5 Results

### 4.5.1 All Fossil Fuels Scenario

Figure 4 depicts the primary energy supply in the BAU scenario from 2016 to 2070. By 2070, the total energy supply reaches 11.7 EJ, marking a 2.5-fold increase compared to 2016, primarily driven by population growth. This study does not consider energy efficiency measures such as energy-efficient lighting or smart building technologies. The findings suggest a shift in energy supply dynamics over time, with declining natural gas reserves leading to increased reliance on
<figure><img src="image_138.png" /><figcaption>Fig. 4 Primary energy supply under the BAU scenario</figcaption></figure>

shale gas and a growing contribution from renewables, especially after 2050. Fossil fuels maintain a significant role, highlighting Algeria's dependence on hydrocarbons. Meanwhile, the share of renewables (solar+ wind) rises, reaching 24% of the total energy supply by 2070 (from 4% in 2040).

Figure 5 illustrates that in the BAU scenario, fossil-fuel-based power plants (Combined cycles without CCS) dominate power generation, exceeding 70% until 2040 and maintaining significant presence through 2070. This dominance is closely tied to the development of shale gas, serving domestic demand and fueling international gas exports. By 2050, renewables will surpass fossil fuels, constituting 60% of the power mix by the end of the projection period. This significantly elevates the share of renewables in total power generation from 0.4% (297 GWh) in 2016 to 57% (166 TWh) in 2050 and 56% (240 TWh) in 2070, reflecting a more decarbonized trajectory. However, the outcomes of the BAU scenario slightly deviate from Algeria's National Renewables policy, which targets 30% renewable power generation by 2035. Instead, our results show integration of renewables from 2040 at 25%. Several factors contribute to this delay, including over-reliance on natural gas that hinders the transition. Additionally, substantial investments in renewable projects are required, although the renewable sector may not be technologically mature or economically competitive enough compared to the gas industry by 2035.

The contributions of wind and solar PV are balanced until 2050, after which solar PV emerges as the primary contributor through 2070. Projected installed capacity for solar PV and wind power in 2070 are 92.1 GW and 42.1 GW, respectively. These findings closely align with those reported by IRENA in the “Planned scenario”, indicating that natural gas remains the dominant contributor to electricity generation in North Africa in 2040. Specifically, Algeria’s power generation mix for 2040 comprises 74% natural gas, 15% solar PV, and 11% wind power.

Figure 6 illustrates the final gas usage in the BAU scenario from 2016 to 2070. In the long term, domestic natural gas will meet demand across residential, industrial, and LPG-transport sectors. By 2070, these sectors collectively account for 63% of
<figure><img src="image_139.png" /><figcaption>**Fig. 5** Power generation by sources under the BAU scenario</figcaption></figure>

<figure><img src="image_140.png" /><figcaption>**Fig. 6** Final natural gas usage in the BAU scenario</figcaption></figure>

the total gas consumption. There is a drop and recovery in export levels between 2050 and 2060, driven by the shift from domestic natural gas to domestic shale gas and the depletion of domestic natural gas resources (Fig. 3). Imports of gas are then needed to satisfy the required exports when domestic resources are not sufficient. Due to the high cost of importing natural gas, the model opts to halt exports until domestic reserves recover, resuming exporting by 2060. This fluctuation might have economic, social, and geopolitical repercussions, requiring Algeria's resilience in adapting to changing market conditions. Exports of domestic gas are expected to rebound, approaching the 2016 levels (2 EJ) by 2070 (1.8 EJ).
<figure><img src="image_141.png" /><figcaption>Fig. 7 CO₂ emissions related to gas use by the power and end-use sectors in the BAU scenario</figcaption></figure>

In Fig. 7, CO₂ emissions from sectors consuming only natural gas and/or electricity experience a significant increase from 2016 levels, reaching 420 Mt of CO₂ by 2070. The breakdown of emissions reveals a nearly equal contribution from both the industrial and residential gas sectors to the overall increase of CO₂ emissions in Algeria. Their respective shares have increased from 18% and 24% in 2016 to 32% and 34% by 2070. In contrast, the power sector continues to emit CO₂; however, its share has halved by 2050, attributed to the increased utilization of renewables for electricity generation. By the end of the studied period, this sector contributes 18% to the total CO₂ emissions.

### 4.5.2 Comparison of BAU and ReN + H₂ Scenarios

The ReN + H₂ scenario shares the same assumptions as the BAU concerning shale gas development while aligning with Algeria's current renewable targets and enabling hydrogen production. This new feature does not decrease CO₂ emissions from power and gas sector except for the year 2035, due to renewable energy development. This is attributed to the utilization of SMRCCS for hydrogen production. It's worth mentioning that electrolyzers were not used in this scenario due to their high cost. Consequently, the overall trends in CO₂ emissions by sector in the ReN + H₂ scenario closely mirror those of the reference case (Fig. 8).

The results in terms of electricity generation are presented in Fig. 9. The difference between both scenarios comes from the assumption of enforcing a minimum 30% share of renewables by 2035, as stipulated in Algeria's energy policy. The outcomes reveal that this specified share would exclusively derive from solar PV sources, contributing approximately 51.7 TWh to the overall power generation.
<figure><img src="image_142.png" /><figcaption>**Fig. 8** CO₂ emissions related to gas uses by the power and end-use sectors under the BAU and ReN + H₂ scenarios</figcaption></figure>

<figure><img src="image_143.png" /><figcaption>**Fig. 9** Differences in power generation by sources between BAU and ReN + H₂ scenarios</figcaption></figure>

Overall, the ReN + H₂ scenario entails a reduced deployment of fossil fuel generation.

### 4.5.3 Decarbonization Pathway: ReN + H₂ + CO₂-Bound Scenario

The analysis of the decarbonization scenario for Algeria's energy system has unveiled an expected shortfall in gas and hydrogen supply by 2070. These shortages pose challenges in meeting final energy requirements, as depicted in Fig. 10.
<figure><img src="image_144.png" /><figcaption>**Fig. 10** Primary energy supply under the ReN + H₂ + CO₂-around scenario</figcaption></figure>

<figure><img src="image_145.png" /><figcaption>**Fig. 11** Final energy demand under the ReN + H₂ + CO₂-around scenario</figcaption></figure>

Bounding CO₂ emissions highlighted some limitations, revealing that by 2070, approximately 25 Bcm of residential gas demand remained unsatisfied, and hydrogen exports were halted by 2060 (Fig. 11).

These supply deficiencies could potentially lead to various challenges for Algeria, encompassing economic repercussions, power outages, and social unrest. Nevertheless, as part of efforts to mitigate CO₂ emissions and align with SDG 13 aimed at combating climate change, our model proposes a strategy to replace a share of the gas industry demand with hydrogen starting from 2050. This initiative results in a significant surge in hydrogen production, utilizing both SMRCCS and electrolysis at
<figure><img src="image_146.png" /><figcaption>Fig. 12 Hydrogen production under the ReN + H₂ + CO₂-bound scenario</figcaption></figure>

full capacity (5GW/year/technology). By 2070, the production levels reach 175 TWh and 158 TWh, respectively, as illustrated in Fig. 12.

Due to the imposed CO₂ emissions constraint, the proportion of renewables in Algeria's power generation is projected to experience substantial growth, surpassing the targeted share outlined in the policy by 2035 in the decarbonization scenario compared to the ReN + H₂ scenario. The key factor driving this divergence is the substantial increase in the installed capacity of renewable power.

By 2040, electricity generation from renewable power is projected to nearly triple in the decarbonization scenario, reaching 200 TWh, in contrast to the ReN + H₂ scenario's 68 TWh, primarily attributed to green hydrogen generation. According to IRENA's report (IRENA 2023b), Algeria possesses significant (theoretical) renewable energy potential, with solar resources estimated at 27,904 TWh/year and wind resources at 30,155 TWh/year. By 2070, power generation is 100% renewable, with 700 TWh from solar PV and 120 TWh from wind. This suggests that the achieved generation represents only a fraction of the theoretical potential.

Despite a seemingly modest goal to reduce CO₂ emissions in this exercise, the findings (Fig. 12) highlight that Algeria will encounter various challenges, including substantial upfront economic investments, technical hurdles in grid integration, financial constraints, and the need for clear policies to ensure a smooth transition. Furthermore, incorporating substantial amounts of renewables poses an additional challenge related to the intermittent nature of solar and wind power. Effectively harnessing these resources hinges on the implementation of a robust storage system. The outcomes of our scenarios underscore this imperative, revealing a notable surge in storage capacity. By 2070, the storage infrastructure is projected to expand significantly, reaching more than 220 TWh.

Figure 13 presents the CO₂ emissions associated with Algeria's power and gas sectors under the decarbonization scenario. The results reveal a continuous upward trajectory in total CO₂ emissions until the year 2070, notably driven by emissions emanating from the gas sector. In contrast, CO₂ emissions related to the power sector are projected to decline considerably to reach zero emissions by 2050, coinciding with 100% of electricity generation from renewables. These findings underscore the
<figure><img src="image_147.png" /><figcaption>Fig. 13 Power generation by sources under ReN + H₂ and ReN + H₂ + CO₂-bound cases</figcaption></figure>

<figure><img src="image_148.png" /><figcaption>Fig. 14 CO₂ emissions related to gas uses by the power and end-use sectors under ReN + H₂ with CO₂-bound scenario</figcaption></figure>

imperative for Algeria to intensify its efforts in addressing emissions from other gas consuming sectors, particularly the residential and industrial sectors. Achieving alignment with the SDG targets necessitates a dedicated commitment to addressing emissions in these sectors (Fig. 14).

# 5 Discussion and Concluding Remarks

This study used the TIMES-DZA model to investigate Algeria's future energy system. The results reveal that in the absence of actions taken to fight climate change (BAU scenario), Algeria's reliance on hydrocarbon resources is projected to
intensify, leading to a structural imbalance within its economy and potentially triggering a recession stemming from the decline in natural gas resource exports. This scenario poses threats to both energy security and the environment, conflicting with the core principles of SDG 7 and 13.

To address the impact of rent dependency, the study introduces the Algerian government's renewables energy and hydrogen exports targets in 2035 and 2040 respectively (ReN + H₂ scenario). This strategic shift allows for a significantly more diversified power supply, aligning with SDG 7. Notably, the share of RE in electricity increases from 2% in 2035 in the BAU scenario to 30% in 2035 in the ReN + H₂ scenario, indicating a more sustainable energy mix based on modern low-carbon sources. However, the study finds that setting a renewable penetration target alone may not be sufficient to effectively reduce GHG emissions in the long run, thereby falling short of alignment with SDG 13.

Our findings suggest that achieving significant CO₂ emissions reduction by 2070 in Algeria necessitates a substantial and extensive development of RE. Decarbonizing the Algerian energy sector involves the installation of an additional 54 GW of solar and wind capacity by 2070, compared to the ReN + H₂ scenario. It's noteworthy that for the year 2022, less than 0.6 GW of renewable energies were reported, underscoring the substantial scale-up required to achieve the ambitious decarbonization goals. This indicates that the current targets set by Algeria for RE development may be insufficient to make a substantial impact on lowering the country's GHG emissions, a key aspect of SDG 13. Furthermore, the study highlights a potential challenge to the decarbonization of Algeria's domestic energy system posed by the country's hydrogen strategy. The production of green hydrogen requires additional renewable capacity. Therefore, achieving this export strategy would demand a rapid acceleration in the rate of installing new renewable capacity, raising questions about the feasibility of implementing this strategy while decarbonizing the domestic energy system.

Bottom-up models like TIMES-DZA play a crucial role in offering policy makers detailed insights into energy system trajectories aligned with specific targets, such as GHG emissions reductions. They provide valuable information that serves as a foundation for making informed decisions regarding technology choices and long-term investments in the energy sector, which is pivotal in the global effort to combat climate change. While bottom-up, technology-rich, optimization models excel in providing detailed energy pathways and informing technology and investment decisions, it's acknowledged that they may not inherently provide insights into the broader economic repercussions of these energy pathways. Nevertheless, the TIMES-DZA model, by contributing valuable insights aligned with current policy objectives, can play a significant role in fostering a sustainable and resilient energy future for Algeria.

This study applied TIMES modeling approach to optimize the power and hydrogen production of Algeria. The use of exogenous fixed demands by end-uses constitutes a limitation. Further analyses with endogenous substitution between fuel uses may provide additional insights to the achievement of SDG 7 and 13. Further improvements of TIMES-DZA could also encompass a greater proportion of
low-carbon energy sources. Another alternative technology that could be added is fossil fuels with CCS, a crucial tool to decarbonize the energy system. Finally, these results tend to advocate for more solar power than wind. Algeria has abundant resources for both renewable energy sources and future analyses could better investigate this trade-off.

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Part IV

**Engaging with Policy-Makers on Energy
System Models**
# Integrating Sustainable Development Goals to Assess Energy Transition Scenarios in Municipalities of Northern Sweden

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Check for
updates

Parvathy Sobha and Anna Krook-Riekkola

**Abstract** Local governments face significant challenges in implementing the United Nations Sustainable Development Goals (SDGs). To address these challenges, the study adopts a model-based system analysis approach for integrating sustainable development principles into the local energy transition, examining (i) what to include and vary across scenarios, (ii) what to extract from the model results, and (iii) what to discuss with the local governments. An energy system optimisation model, based on the TIMES modelling framework, is employed to represent the municipal energy system of Gällivare in northern Sweden and its potential transition pathways. The study also provides a comparative data-driven assessment to examine whether the energy transition pathways converge or diverge from the SDGs. In addition, a set of sustainability indicators has been identified/developed and are implemented in the model to assess sustainability across different pathways. The challenge inherent in such quantitative comparisons, particularly those of a complex nature such as sustainability, lies in selecting appropriate indicators for municipalities that align with the model's framework. The analysis shows both convergent and divergent pathways. For instance, electrification in the identified transition pathways reduces fossil fuel use (converges towards SDG13 on climate), but electrification also introduces complexities with increased material usage for electric vehicles (diverges from SDG12 on responsible consumption and production).

## Key Messages

* Application of the TIMES-based energy system optimisation model to systematically explore how local energy systems can meet future societal needs sustainably.

* Development of an inventory on integrating each SDG while assessing energy transition scenarios in the municipalities of northern Sweden.

P. Sobha (✉) · A. Krook-Riekkola
Luleå University of Technology, Luleå, Sweden
e-mail: parvathy.sobha@ltu.se; anna.krook-riekkola@ltu.se
* Introduction of a heatmap including indicator values in different future energy pathways as an effective tool for evaluating and communicating the sustainability of such pathways.

* Assistance in highlighting synergies between SDG7 (on energy), SDG11 (on cities), and SDG13 (on climate) while featuring trade-offs between SDG7 (on energy affordability), SDG12 (on responsible consumption and production), and SDG13 (on climate).

# 1 Introduction

Northern Sweden is undergoing an accelerated green transition in response to market demand and the urgent need for sustainable societies (Arctic Center of Energy 2023). *Green transition* refers to the shift from an economy heavily reliant on fossil fuels and other environmentally harmful practices to one that is characterised by sustainable and environmentally friendly practices. The latter include energy transitions and the development of new industrial establishments. Municipalities in northern Sweden are at the centre of emerging mega-projects aimed at positioning the country at the forefront of green industries (Nordic Cooperation 2023). Construction and planning are in full swing in northern Sweden as they prepare for the associated energy transition and its concomitant industries. The projected workforce for these emerging industries is estimated to reach 20,000 employees, along with an additional 30,000 workers for associated services. This cumulative effect is expected to increase the region's population by approximately 100,000, leading to a 20% growth in the region's population (Smart City Sweden 2023).

In 2015, the United Nations (UN) introduced the Sustainable Development Goals (SDGs) to stimulate action over the next 15 years in areas of critical importance according to its “shared blueprint for peace and prosperity for people and the planet, now and into the future” (UN 2015a, b). Energy is considered the cornerstone of sustainable development (ESCAP 2023); hence, it can also interfere with other sustainability goals (UN 2002). Local authorities play a crucial role in the green transition, being responsible for developing and maintaining economic, social and environmental infrastructure as well as appropriate planning and policymaking. Therefore, their active involvement is essential for accelerating sustainable development at local level (UNCED 1992; Graute 2016). Combining new green industries with existing energy systems creates social and environmental sustainability challenges for local government, particularly considering the complexity of such systems and the interconnectedness of the SDGs (Fuso Nerini et al. 2019). In northern Sweden, for instance, electrification plays a major role in the green energy transition: the location of new green industries is driven by cheap electricity prices (Node Pole 2022), access to renewable energy, reliable power grids, abundant land, and a stable political system (Garbis et al. 2023).

Several studies have explored SDG implementation in cities, addressing sustainable green transition frameworks, governance conditions and policies for achieving
sustainability. Cases in point include Kutty et al. (2020), who developed a concep-
tual model that captures multifaceted interactions of policy, technology, and societal
factors to guide the green transition to smart sustainable cities. Viale Pereira and
Schuch de Azambuja (2022) developed a research-based road map and practical
guidelines for building smart sustainable city initiatives. Giuliodori et al. (2023)
examined the trade-offs among the economic, social and environmental dimensions
of sustainability in cities, proposing smart governance as a crucial factor in smart
sustainable cities. Liu et al. (2023) used open-source big data analysis to assess SDG
progress in cities, highlighting the utility of big data in tracking SDG performance.
However, the literature reveals a gap in addressing local green transitions. This
research gap underscores the need for new methods that are driven by societal needs.
Such methods could dictate the outcomes and objectives of the local energy system,
for example, while shaping the direction and priorities of the local green energy
transition.

Numerous other studies highlight the necessity of a holistic perspective to
understand the complex and interconnected nature of sustainability and the SDGs
(Cuello Nieto 1997; Barrett and Grizzle 1998; Nilsson et al. 2016; Stafford-Smith
et al. 2017). The current study aligns with this idea by adopting an applied system
analysis approach to gain a holistic perspective, while identifying the challenges
local governments face in implementing relevant SDGs at local level. The current
study uses an energy systems optimisation model (ESOM) based on The Integrated
MARKAL-EFOM System (TIMES) model generator (Loulou et al. 2016). The
ESOM considers a range of factors, including energy resource potentials, demand
patterns, and economic and environmental issues, which are employed to systemat-
ically represent the given municipality’s current energy system and its potential to
change. To assess the sustainability of different energy transition scenarios, the study
introduces a set of sustainability indicators to quantitatively compare sustainability
across those scenarios.

The remainder of this paper is organised as follows: Sect. 2 offers an overview of the local context, while Sect. 3 describes methods applied. Section 4 explores SDGs relevant for local energy transition and proposes a set of sustainability indicators. In Sect. 5, the ESOM and scenarios defined are introduced. Section 6 discusses the model results, after which Sect. 7 concludes the paper.

## 2 Local Context: The Case of the Municipalities in Northern Sweden

The municipalities of northern Sweden are at the forefront of a global green
transition being driven by the area's wealth of natural resources. These include
energy, ore, forests and water, in addition to surplus clean electricity and a power
grid with ample capacity. The municipalities of Gällivare and Kiruna are home to
some of the largest iron mines in Europe (operated by state-owned mining company