LKAB). Iron pellets are transported to Narvik or Luleå (municipalities in northern Norway and Sweden, respectively), where they are either shipped out or refined further. A green industry will now refine the pellets locally at a plant using the new Hydrogen Breakthrough Ironmaking Technology (HYBRIT). The method entails employing green hydrogen produced from renewable electricity as a primary energy source, thus replacing blast furnaces both in Sweden and Norway. Another fossil-fuel-free steel production plant (H2 Green Steel) is being established in the Municipality of Boden. Furthermore, the Luleå Municipality is home to both traditional industries, such as steel production (global steel company SSAB, which uses blast furnaces) and manufacturing (global truck and bus manufacturers Scania), and modern ventures such as the Facebook Luleå Data Center (Facebook's first outside the USA). Other new green industries being established in Luleå include phosphorus and rare earth refinement (by LKAB) and green ammonia and fertiliser production (by Fertiberia). In addition, the Skellefteå Municipality is home to a copper smelting plant (Boliden Rönnskär, a world leader in recycling electronics) and a new gigafactory producing electric vehicle (EV) batteries, amongst others (Northvolt).

The green transition represented by these industries entails a significant increase in electricity supply and the installation of new power lines. Among several municipal energy companies that have taken the lead in this regard is Skellefteå Kraft, the fifth largest electricity producer in Sweden with an almost 100% renewable energy portfolio, and Luleå Energi. Luleå Energi is introducing innovative methods to identify and avoid users overstating their capacity needs. The green transition also demands that new infrastructure such as housing and roads be developed. In addition, more professionals for essential public services such as healthcare, education, police, social services and public transportation need to be recruited.

This study focuses on the Gällivare municipality, which has two hydropower plants and a wind power plant supplying and exporting electricity. A biomass-based combined heat and power (CHP) plant is the primary district heating source. Industrial electricity demand constitutes most consumption, followed by the transportation and residential sectors. Gällivare has also been chosen as the location for establishing HYBRIT, due to the municipality's proximity to iron ore mines locally as well as in Kiruna. Furthermore, expansion of these mines has brought significant changes to Gällivare and Kiruna, including moving, constructing and demolishing houses and other buildings, which has resulted in a high share of fly-in-fly-out work culture. These factors are expected to significantly impact Gällivare's energy system by way of increasing the demand for energy-intensive services and goods. Finally, Gällivare is also home to a significant Sami population and their cultural heritage. Their presence is integral to the municipality's diverse socio-economic and political landscape. Thus, the municipality is taking strong efforts to ensure Gällivare is socially sustainable too.
## 3 The Methodological Framework: From Modelling to Policy Implications

An applied system analysis approach can enhance the understanding of energy transition and support decision-makers as they navigate through it (Krook-Riekkola 2015). The approach employed here, namely the system analysis approach using ESOM, can be described as a four-step process, as depicted in Fig. 1. Step 1 entails simplifying and conceptualising the ‘real system’ (current system as well as potential future systems) into a model. This step includes making assumptions about the future and deciding what to vary between scenarios. In Step 2, all assumptions are organised into a mathematical model and the corresponding algorithms are solved. Step 3 involves interpreting the model results and drawing conclusions about the energy future for which to aim—preferably by working together with governments, citizens and other stakeholders. Finally, Step 4 identifies the actions and policies that are needed today to ensure the development of a sustainable energy system in the future.

The evolution of the energy system depends on dynamic factors, including energy service demands, external trends, and local actions. Consequently, the trajectory of the local energy system is influenced by the needs of municipal citizens and companies, national and international activities, and actions taken by local governments. The ESOM approach offers a holistic and comprehensive means of identifying and assessing the potential energy transition pathways of the local energy system in focus.

In this study, the aim was to support a local municipal body (in our case Gällivare) in its transition to a sustainable energy system in compliance with relevant SDGs. Such an energy system is not an isolated entity: it is highly integrated with meeting other sustainability targets. Thus, the sustainability targets being aimed for will influence the choice of energy to be considered to achieve them. Similarly, the kind of energy system to be developed will determine what sustainability targets can be achieved. Understanding these connections is essential, as SDG7 (on energy) on

<figure><img src="image_151.png" /><figcaption>**Fig. 1** System analysis approach using energy system modelling (Krook-Riekkola 2015)</figcaption></figure>
the energy system is influenced by other SDGs and, in turn, defines the conditions for achieving them. It is, therefore, imperative to understand and consider the impacts of each SDG when one assesses Gällivare's energy transition—and an ESOM is perfectly suited for such an assessment.

The studied transition is also driven by our societal needs, which dictate the outcomes of the energy system and determine the direction of its green transition. To effectively assess these connections, the following steps are proposed, grounded in a system analysis approach:

* Identify SDG targets and indicators relevant for the energy transition of northern municipalities;

* Apply an ESOM to develop energy transition pathways;

* Evaluate the sustainability of each pathway using identified indicators.

**3.1 Identify SDG Targets and Indicators Relevant for the Energy Transition of Northern Municipalities**

SDGs and their associated political declaration emphasise the importance of national interpretations, wherein each country is expected to establish its own goals, targets, and priorities for implementing SDGs based on their specific national conditions and capacities (Weitz et al. 2015). However, it is important to recognise that not all of these national interpretations may directly apply to every local government; hence, a process of localisation is necessary to tailor SDGs to fit the local realities of each specific region or municipality (Croese et al. 2021). Some Swedish municipalities' official websites explicitly mention their prioritised SDGs, while others detail sustainability measures such as recycling and waste management. However, references to SDGs differ across such websites, and a few do not mention SDGs at all. Additionally, local-level information may not always be available. Hence, to ensure a unified and coherent approach, the study begins by identifying Sweden's nationally relevant SDG targets and assessing their relevance to the municipalities in focus and to energy transition per se, before identifying their existing—or defining their new—corresponding indicators.

**3.2 Apply an ESOM to Develop Energy Transition Pathways**

An ESOM is employed to represent potential future options for the energy system in focus. Different scenarios are defined in the ESOM, while energy transition pathways for the municipality concerned are obtained from the model run of these scenarios. Each pathway illustrates how the energy system in question could evolve to meet future energy service demands while operating within the constraints of defined measures, targets, and prevailing trends. Scenarios are defined by varying
key parameters and/or targets over which the municipality has control, such as climate targets, air pollution reduction objectives, or the strategic integration of renewable energy sources. In this study, various scenarios were defined based on discussions with the Gällivare municipality.

### 3.3 Evaluate the Sustainability of Each Pathway Using Identified Indicators

Finally, model results are analysed and interpreted with respect to the sustainability of each defined scenario. A defined set of indicators is then used to make a quantitative comparison across scenarios to evaluate the sustainability of each pathway. The data visualisation method known as *heatmap* is used to illustrate how each scenario performs. The heatmap accomplishes this by representing the magnitude of the indicators' quantitative values as a colour showing the level of sustainability, allowing for a comparative assessment of such variation across different scenarios.

## 4 Identify SDG Targets and Indicators Relevant to an Energy Transition by Northern Municipalities

The SDG targets and indicators relevant to an energy transition by Sweden's northern municipalities (NMs) were identified by progressing through three phases (Table 1). The table also shows the outcome of each phase of identification.

**Phase 1** Start by considering globally recognised SDGs and their corresponding targets (UN 2015a). At this initial stage, details regarding indicators were not considered. Subsequently, all SDGs and their corresponding targets relevant to Sweden were identified, utilising sources such as the Organisation for Economic Co-operation and Development Library (OECD 2022a), which provided insights

<table><caption>**Table 1** Identified number of SDGs, targets and indicators of relevance for different scopes</caption><thead><tr><th>Phase of identification</th><th>Nature of relevance of SDG</th><th>SDGs identified</th><th>Targets identified</th><th>Indicators identified</th></tr></thead><tbody><tr><td rowspan="2">Phase 1</td><td>Global relevance (defined by UN)</td><td>17</td><td>169</td><td>231</td></tr><tr><td>Relevant to Sweden</td><td>17</td><td>74</td><td>109</td></tr><tr><td rowspan="2">Phase 2</td><td>Relevant to energy transition in Sweden</td><td>17</td><td>70</td><td>104</td></tr><tr><td>Relevant to energy transition in NMs</td><td>15</td><td>39</td><td>57</td></tr><tr><td>Phase 3</td><td>Suitable for inclusion in ESOM scenario analysis (values in brackets indicates ones included in model + ones discussed with NMs)</td><td>11 (6 + 5)</td><td>19 (9 + 10)</td><td>6 (6 + 0)</td></tr></tbody></table>
into Sweden's progress towards achieving these SDG targets, and Statistics Sweden (2023a), which offered data on indicators included in the national reports. In instances where data was lacking, published literature on Sweden's performance in respect of its sustainable practices was used (e.g. Anselmi et al. 2023; Weitz et al. 2015). Once the sources had been considered, data from the OECD Library was prioritised as data from Statistics Sweden lacked comprehensive information on certain targets and did not provide a clear status of target attainment.

**Phase 2** In this phase, each target identified in Phase 1 was examined to identify its connection, if any, to the energy transition. Published literature exploring interlinkages between SDGs and energy was employed for this. For example, the analysis primarily draws on the work of Fuso Nerini et al. (2018), who mapped synergies and trade-offs between energy and SDGs. To augment the credibility of findings, additional scholarly works were also consulted. These included McCollum et al. (2018), who examined interlinkages between SDGs and energy; Aboul-Atta and Rashed (2021), who analysed the relationship between sustainable development indicators and renewable energy consumption; and Gjorgievski et al. (2022), who quantified synergies and trade-offs between national climate actions and SDGs. SDGs that were relevant to the energy system of the municipalities in focus were then identified, taking the local context into account.

**Phase 3** This phase aimed to understand the UN indicators defined for the chosen SDG targets, and tried to match or identify corresponding indicators within the energy system model. In certain instances, UN indicators aligned with model parameters, enabling direct assessments. In cases where the UN indicators did not match the model parameters, new indicators were defined. In some other cases, due to model scope and/or a lack of contextual data, it was not possible to define suitable indicators. In these instances, model results were used in combination with discussion topics to gain insights. This was carried out outside the model (Step 3 in Fig. 1) and involved discussions with municipality representatives to identify key measures that were vital for the identified sustainable energy transition (Step 4 in Fig. 1). Such measures included investments in infrastructure and/or measures that altered energy prices and investment costs to favour critical technologies or actions.

Table 2 presents an inventory on how to integrate each SDG when assessing energy transition scenarios in municipalities of northern Sweden. The following SDGs were excluded from the inventory because their association with energy in the case study was limited:

* SDG3 (on good health): Air pollution is estimated to cause 6700 premature deaths in Sweden yearly, along with chronic respiratory diseases (IVL 2022). Air pollution is directly linked to energy conversion and use (IEA 2016). But this is integrated into SDG11 (on cities).

* SDG4 (on education): In Sweden, the proportion of people achieving higher education is higher in metropolitan municipalities compared to smaller municipalities like GÄL (OECD 2022b; Statistics Sweden 2023b). Sweden has universal electricity access (World Bank 2021), making energy access concerns irrelevant to this study, despite its impact on educational attainment.
<table><caption>**Table 2** Mapping the relevance of SDG targets and indicators for assessing the energy transition in focus</caption><thead><tr><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>SDG</td><td>National and regional/local relevance of SDG targets (T) for Sweden, with a focus on its northern municipalities (NMs) and Gällivare (GÅL)</td><td>When to address relevant target(s) in model-based analysis*</td><td>Defined result indicators for measuring SDG progress and/or topics to discuss with GÅL</td></tr><tr><td>SDG1 (no poverty)</td><td><strong>T1.3 Social protection system:</strong> This is generally comprehensive in Sweden (Statistics Sweden 2019: 25)<br/><strong>T1.4 Equal rights to economic resources and access to basic services:</strong> These can be ensured by implementing social tariffs on energy services or measures to prevent disconnection from electricity and heating services (Dobbins et al. 2016), which are already in place in Sweden. Even though the target is considered achieved or is on track (OECD 2022a), access to capital for energy transition influencing purchase of EVs and energy efficiency improvements in houses is relevant</td><td><strong>T1.4: While interpreting and discussing the results</strong> (Step 3)</td><td><strong>T1.4 Discussion topic:</strong> To enhance a just energy transition, where citizens are not left behind, discuss the results with a view to addressing the impacts of high energy prices on low-income groups at risk, who are left with inefficient housing/ vehicles due to their limited investment capacity for measures with high upfront costs (e.g. retrofitting of houses, EVs). Such measures are cost-efficient from a system perspective but require access to capital</td></tr><tr><td>SDG2 (zero hunger)</td><td><strong>Agriculture in northern Sweden is practised for fewer days a year owing to its relatively colder climate in relation to the rest of the country</strong> (Jordbruksverket 2023)<br/><strong>T2.3 Small-scale food producers and livelihood of indigenous people:</strong> NMs have indigenous Sami populations practising traditional reindeer herding. Their pastures are being affected by</td><td><strong>T2.3 &amp; T2.4: When defining scenarios</strong> (Step 1) and/or while discussing the results (Step 3)</td><td><strong>T2.3 &amp; T2.4 Discussion topic:</strong> Discuss the results with respect to potential competition between energy and reindeer husbandry and/or local food production. Support the discussion with the model results (such as installed capacity of wind power, solar parks and thermal power plants) as well as insights from increased electricity use/demand</td></tr></tbody></table>

(continued)
<table><caption>**Table 2** (continued)</caption><thead><tr><th></th><th>National and regional/local relevance of SDG targets (T) for Sweden, with a focus on its northern municipalities (NMs) and Gällivare (GÄL)</th><th>When to address relevant target(s) in model-based analysis*</th><th>Defined result indicators for measuring SDG progress and/or topics to discuss with GÄL</th></tr></thead><tbody><tr><td>SDG</td><td>climate change (Johnsen et al. 2023) and by alternative land use that includes wind farms and mining activities (Österlin and Raitio 2020). Tsegaye et al. (2017) demonstrate that, through careful planning, reindeer husbandry and wind energy development can coexist with minimal effects on reindeer spatial use. Thus, there is no competition between energy and reindeer husbandry per se, but the risk of such conflict emerging needs consideration<br><b>T2.4 Ensure sustainable food production systems:</b> IRENA (2015) highlights the target's water-food-energy nexus by integrating the provision of electricity from carbon-neutral sources</td><td></td><td>which could potentially lead to the construction of new power grids</td></tr><tr><td>SDG5 (gender equality)</td><td><b>All targets:</b> In Sweden, notable gender differences exist in choices of transportation (public transport, cars, biking, etc.), with women often selecting more sustainable options. Including gender perspectives are therefore also important for evaluating effective measures in the sector (Swedish Public Transport 2022:26)</td><td><b>All targets:</b> While considering which scenarios to apply (Step 1) and while discussing the results (Step 3)</td><td><b>All targets -discussion topic:</b> Discuss and understand the features of public transportation in sparsely populated areas (such as deploying smaller buses and on demand) and then define scenarios including mode shift to public transport. Discuss regarding the ancillary benefits for SDG5 in respect of having good public transportation</td></tr></tbody></table>

(continued)
<table><caption>**Table 2 (continued)**</caption><tr><td>SDG</td><td>National and regional/local relevance of SDG targets (T) for Sweden, with a focus on its northern municipalities (NMs) and Gällivare (GÄL)</td><td>When to address relevant target(s) in model-based analysis*</td><td>Defined result indicators for measuring SDG progress and/or topics to discuss with GÄL</td></tr><tr><td>SDG7 (affordable and clean energy)</td><td>T7.1 Access to affordable, reliable and modern energy services: In Sweden, this has usually been taken for granted. However, price hikes over the past few years in Sweden and the EU have made it a topic again<br>T7.2 Substantially increased share of renewable energy: This currently already comprises 75% of final energy consumption in Sweden and 70-80% in NMs (Swedish Energy Agency 2022)<br>T7.3 Energy efficiency: This is generally high in Sweden (IEA 2024) but can still be improved to ease the energy transition</td><td>T7.1-3: While extracting the model results (Step 2)</td><td>while evaluating the cost of having public transportation<br>T7.1-3 Discussion topic: Discuss the following result indicators and their implications:<br>T7.1.1 Result indicator: Marginal cost of electricity and district heating<br>T7.2.1 Result indicator<br>T7.2.1: Renewable energy share in total final energy consumption<br>T7.3.1** Result indicator: Distance to the EU target for primary energy and final energy consumption</td></tr><tr><td>SDG8 (decent work and economic growth)</td><td>T8.1 Sustain per capita economic growth: New green industries are being established, which are securing economic growth<br>T8.2 Raise economic productivity: Even though industrial innovation and diversification are of concern for GÄL as its economy is based on a few large industries, this target is considered less significant for the energy transition and it therefore excluded from the study</td><td>T8.1: While discussing the results (Step 3)<br>T8.4: While extracting the model results (Step 2)</td><td>T8.1 Discussion topic: The impacts of new green establishments on future energy needs<br>T8.4 Result indicator: The material footprint associated with energy transition (see SDG12 on responsible production and consumption)</td></tr></table>

(continued)
<table><caption>**Table 2** (continued)</caption><thead><tr><th></th><th>National and regional/local relevance of SDG targets (T) for Sweden, with a focus on its northern municipalities (NMs) and Gällivare (GÄL)</th><th>When to address relevant target(s) in model-based analysis*</th><th>Defined result indicators for measuring SDG progress and/or topics to discuss with GÄL</th></tr></thead><tbody><tr><td>SDG</td><td><strong>T8.3 Promote development-oriented policies:</strong> Government Offices of Sweden (2021) state that this target has been achieved<br><strong>T8.4 Global resource efficiency:</strong> The material footprint of new industrial establishments is important to consider as many of them rely on resource extraction, yet less significant for the energy transition as this target pertains to the material life cycle and not to energy use perse</td><td></td><td></td></tr><tr><td>SDG9 (industry, innovation and infrastructure)</td><td><strong>All targets:</strong> These are highly relevant for NMs considering their current industrial transition and as they welcome entrepreneurs and support new businesses. New establishments lead to increased and diversified energy demands, driven by both infrastructural needs and the influx of people</td><td><strong>All targets:</strong> While estimating the demand projections (Step 1)</td><td><strong>All targets—discussion topic:</strong> Discuss the various scenarios with new establishments in NMs, assessing their implications for energy demand, incoming residents, and lifestyle influences (impacting the future need for household electricity, heating and transportation)</td></tr><tr><td>SDG10 (reduced inequalities)</td><td><em>With the influx of immigrants coming to work with green establishments, their integration and resettlement as well as the management of their cultural diversity are important to consider (Koopmans 2010).</em> Additionally, it is vital to incorporate diverse stakeholder perspectives to ensure that the green</td><td><strong>T10.2, T10.3 &amp; T10.7:</strong> While estimating the demand projections and defining scenarios (Step 1)</td><td><strong>T10.2, T10.3 &amp; T10.7 Discussion topic:</strong> Introduce the three specified targets to NMs to consider (i) how societal needs evolve (heating houses, transportation, etc.) and (ii) the opportunities for energy communities</td></tr></tbody></table>

(continued)
<table><caption>**Table 2** (continued)</caption><thead><tr><th></th><th>National and regional/local relevance of SDG targets (T) for Sweden, with a focus on its northern municipalities (NMs) and Gällivare (GÄL)</th><th>When to address relevant target(s) in model-based analysis*</th><th>Defined result indicators for measuring SDG progress and/or topics to discuss with GÄL</th></tr></thead><tbody><tr><td>SDG</td><td>transition accommodates the needs and concerns of all individuals and communities. This also includes the energy transition<br><b>T10.2 Social, economic and political inclusion:</b><br>Relevant to NMs<br><b>T10.3 Equal opportunity and reduce inequalities of outcome:</b><br>Relevant to NMs<br><b>T10.7 Facilitate orderly, safe, regular and responsible migration and mobility of people:</b> Relevant to NMs</td><td></td><td></td></tr><tr><td>SDG11 (sustainable cities and communities)</td><td><b>T11.1 Adequate, safe and affordable housing and basic services:</b> The population of northern Sweden and the resultant demand for housing are increasing rapidly (Norran 2023). The construction of cheaper, temporary houses with low-energy building standards is therefore being discussed. However, these constructions will result in significantly higher energy demands during the long and cold winters—making it more challenging to meet T11.1, without low energy costs<br><b>T11.3 Enhance inclusive and sustainable urbanisation and capacity:</b> This could be interpreted as</td><td><b>T11.1:</b> While extracting (Step 2) and discussing the results (Step 3)<br><b>T11.3:</b> While communicating the results and making them understandable to citizens (Step 3)<br><b>T11.6:</b> (see SDG3 on good health)</td><td><b>T11.1 Discussion topic:</b> Discuss scenarios results with different building codes to assess the impact of houses with different energy performance (energy efficiency).<br><b>T11.1 Result indicator:</b> Marginal cost of space heating, electricity and district heating, other end use services<br><b>T11.3 Discussion topic:</b> Broadening the accessibility of the analysis to a wider audience</td></tr></tbody></table>

(continued)
<table><caption>**Table 2 (continued)**</caption><thead><tr><th></th><th>National and regional/local relevance of SDG targets (T) for Sweden, with a focus on its northern municipalities (NMs) and Gällivare (GÄL)</th><th>When to address relevant target(s) in model-based analysis*</th><th>Defined result indicators for measuring SDG progress and/or topics to discuss with GÄL</th></tr></thead><tbody><tr><td>SDG</td><td>emphasising the importance of an inclusive energy transition, i.e. involving citizens in deciding which path to take. To achieve this inclusive approach, it is critical that citizens are informed about the consequences of the different pathways <b>T11.6 Air quality: (see SDG3 on good health)</b></td><td></td><td></td></tr><tr><td>SDG12 (responsible consumption and production)</td><td><b>All targets:</b> Although these are difficult to measure quantitatively due to the cross-cutting nature of available resources and their extraction, production, and consumption (Chan et al. 2018), the International Energy Agency highlights the importance of evaluating the material footprint within energy transitions</td><td><b>T12.2:</b> While defining Technology parameters by including material footprint (Step 1) and analysing the result (Step 2)</td><td><b>T12.2 Result indicator:</b> At present, material footprint in terms of critical material requirement per vehicle is used. This does not impact the optimisation but can be extracted as a result indicator</td></tr><tr><td>SDG13 (climate action)</td><td><b>T13.2 Integrate climate change measures into national policies, strategies and planning:</b> Sweden has a net zero GHG emissions goal for 2045</td><td><b>T13.2:</b> While defining scenarios (Step 1) and extracting results (Step 2)</td><td><b>T13.2 Discussion topic:</b> Key measures to achieve the assessed climate targets and which alternative climate targets to analyse. <b>T13.2 Result indicators:</b> Annual GHG emissions in total and per sector</td></tr><tr><td>SDG15 (life on land)</td><td><b>T15.1 Ecosystems:</b> This topic is important but not easy to measure <b>T15.2 Sustainable management of forests:</b> Forestry in Sweden is</td><td><b>T15.2:</b> While defining scenarios (Step 1) and/or while discussing results (Step 3)</td><td><b>T15.1 &amp; T15.2 Discussion topic:</b> Discuss the potential of forestry residues to be defined in scenarios (such as lower outtake)</td></tr></tbody></table>

(continued)
<table><caption>**Table 2** (continued)</caption><thead><tr><th>SDG</th><th>National and regional/local relevance of SDG targets (T) for Sweden, with a focus on its northern municipalities (NMs) and Gällivare (GÄL)</th><th>When to address relevant target(s) in model-based analysis*</th><th>Defined result indicators for measuring SDG progress and/or topics to discuss with GÄL</th></tr></thead><tbody><tr><td></td><td>mainly for material use (timber, pulp and paper), while the forestry residues and by-products (from these industries) are used for energy purposes. Biomass accounts for around 30% of total energy supply (Swedish Energy Agency 2022). The outtake of forestry residues can impact biodiversity</td><td></td><td><b>T15.1 & T15.2 Discussion topic:</b> Discuss regarding the biomass usage (such as whether its high or low)</td></tr></tbody></table>

*This either refers to the four-step system analysis approach described in Sect. 3 herein or indicates target of non-relevance

**The UN defines energy intensity as an indicator for T7.3, but both energy efficiency and intensity calculations come with flaws (US Department of Energy 2024). Since Sweden is an EU member, the study adopts the energy efficiency indicator defined by the European Commission (2024)

* SDG6 (on water): Energy use is currently not foreseen to compete with fresh water and thus is excluded from the study.

* SDG14 (on life below water): National indicators for Sweden show scope for improvement, while not related to energy use. SDG14 is also not relevant to landlocked regions like Gällivare.

* SDG16 (on peace, justice and strong institutions) and SDG17 (on partnerships) are important but beyond the scope of local energy transition.

# 5 Apply an ESOM to Develop Energy Transition Pathways

This section briefly introduces the ESOM and its application in scenario analysis, incorporating the chosen SDG indicators to develop energy transition pathways for NMs.
## 5.1 Background to the Model

This case study employs the TIMES-City-GAL model, an ESOM tailored to suit Gällivare's energy system based on TIMES-City. TIMES-City—developed within the Surecity (Sustainable and Resource Efficient Cities) project, hosts a modelling structure to support cities in achieving their sustainability targets (Pardo-García et al. 2019). The model includes two energy supply sectors (ELC&DH and supply) and six demand sectors (residential, commercial, municipal, transportation and industry) (Krook-Riekkola et al. 2018). ELC&DH sector includes existing and potential district heating and electricity production units. In the Supply sector, the potential import and export of electricity and other energy commodities are defined. Today, Gällivare is a major exporter of electricity (hydro, wind and a CHP plant connected to the district heating grid)—which may change with new green establishments. The model is given the opportunity to exchange electricity with the national power grid.

The model optimises net system cost to meet the given demand using available resources, technologies, and the given constraints. The time frame over which the model operates spans from 2018 to 2050, with each year being divided into 12 time-slices. Demand is seen as useful energy or as a useful energy service, e.g. persons-kilometre travelled in cars, or space heating in residential houses with A (best) to G (worst) energy efficiency gradings. Emission factors are defined either as tailpipe or upstream. *Tailpipe emissions* are those from energy use within the municipality; they are defined across the system from energy conversion and distribution to end use. *Upstream emissions*, on the other hand, are those derived from extracting, converting, and distributing energy outside the municipality. *Tailpipe emissions* are defined for three greenhouse gases (GHG), namely carbon dioxide, methane and nitrous oxide, in addition to five different air pollutants: nitrogen oxides, particulate matter (PM2.5, PM10), sulphur dioxide and volatile organic compounds. *Upstream emissions* are only defined for the GHGs.

When indicators were integrated into the model, some aligned directly with the model parameters, such as air pollutant emissions (SDG11 on cities) and share of renewables (SDG7 on energy). For indicators without direct model counterparts, new indicators were defined, such as energy efficiency (SDG7 on energy). In certain cases, additional analysis of model outputs was carried out due to the lack of model parameters. For example, the new investments were used to assess the material footprint for SDG12 (on responsible production and consumption). The model yielded investment figures for various vehicle types, including EVs and internal combustion engines. By applying an estimate of critical materials per vehicle, the net material footprint was obtained; this outcome enabled scenario comparisons even without precise figures.
## 5.2 Defining the Scenarios

Eight scenarios were defined to analyse the energy system transition as outlined in Table 3. The scenarios were developed by varying parameters (fuel prices) and three targets (Table 3). In these scenarios, high fuel prices were assumed to be 30% more elevated in comparison with low fuel prices for all fuels (gasoline, biofuels, etc.). The target for renewables in the Renewables Scenario was defined as the share of renewable energy in final energy consumption within the municipality in focus. Climate (all GHGs) and air pollutant (PM2.5, PM10) targets only included emissions related to energy use within the municipal border. For targets which were set to be achieved by 2045, it was assumed that progress to date would continue in the years to follow. In the case presented, factors such as population, energy technology choices, technology stock, and energy demand remained consistent across all scenarios, ensuring a standardised basis for comparison.

# 6 Evaluate the Sustainability of Each Pathway Using Identified Indicators

The sustainability of each of the eight pathways was assessed using the chosen result indicators. Table 4 shows the indicator values in different scenarios for the year 2050. The data in Table 4 was normalised for use in the heatmap in Fig. 2. Normalised values were identified based on the minimum and maximum values of indicators in different scenarios.

<table><caption>**Table 3** Scenarios defined to analyse the energy transition</caption><thead><tr><th>Scenario description (scenario name in brackets)</th><th>Fuel prices</th><th>Share of renewables in final energy consumption</th><th>Climate target (all GHGs)</th><th>Air pollutant target (PM2.5 and PM10)</th></tr></thead><tbody><tr><td>No-target, low prices (NT-LP)</td><td>Low</td><td rowspan="2">No target</td><td rowspan="2">No target</td><td rowspan="2">No target</td></tr><tr><td>No-target, high prices (NT-HP)</td><td>High</td></tr><tr><td>Renewables-scenario, low prices (RS-LP)</td><td>Low</td><td rowspan="2">100% by 2050</td><td rowspan="2">No target</td><td rowspan="2">No target</td></tr><tr><td>Renewables scenario, high prices (RS-HP)</td><td>High</td></tr><tr><td>Climate scenario, low prices (CS-LP)</td><td>Low</td><td rowspan="2">No target</td><td rowspan="2">Net zero by 2045</td><td rowspan="2">No target</td></tr><tr><td>Climate scenario, high prices (CS-HP)</td><td>High</td></tr><tr><td>Sustainability scenario, low prices (SS-LP)</td><td>Low</td><td rowspan="2">100% by 2050</td><td rowspan="2">Net zero by 2045</td><td rowspan="2">65% reduction by 2045</td></tr><tr><td>Sustainability scenario, high prices (SS-HP)</td><td>High</td></tr></tbody></table>
<table><caption>**Table 4** Indicator values and their corresponding units of measurement in eight different scenarios</caption><thead><tr><th rowspan="2">Indicator</th><th rowspan="2">Unit</th><th colspan="8">Type of scenario</th></tr><tr><th>NT-LP</th><th>NT-HP</th><th>RS-LP</th><th>RS-HP</th><th>CS-LP</th><th>CS-HP</th><th>SS-LP</th><th>SS-HP</th></tr></thead><tbody><tr><td>Fossil energy share in total final energy consumption</td><td>Percentage</td><td>20</td><td>20</td><td>7</td><td>7</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Fossil energy share in electricity supply</td><td>Percentage</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Fossil energy share in transport sector (final energy consumption)</td><td>Percentage</td><td>98</td><td>98</td><td>31</td><td>31</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Distance to energy efficiency target in transport sector</td><td>TJ*</td><td>65</td><td>65</td><td>9</td><td>9</td><td>54</td><td>54</td><td>54</td><td>54</td></tr><tr><td>Marginal cost—electricity</td><td>€'000/TJ</td><td>~0</td><td>~0</td><td>~0</td><td>~0</td><td>~0</td><td>~0</td><td>~0</td><td>~0</td></tr><tr><td>Marginal cost—travel (by car)</td><td>€'000/TJ</td><td>75.0</td><td>75.0</td><td>77.6</td><td>77.6</td><td>77.8</td><td>77.8</td><td>78.5</td><td>78.8</td></tr><tr><td>Material footprint—transport sector</td><td>t</td><td>17</td><td>17</td><td>17</td><td>17</td><td>19</td><td>19</td><td>19</td><td>19</td></tr><tr><td>Air pollutant emissions (PM2.5 + PM10)</td><td>kt</td><td>0.25</td><td>0.25</td><td>0.38</td><td>0.38</td><td>0.02</td><td>0.01</td><td>0.02</td><td>0.01</td></tr><tr><td>GHG emissions (all GHGs)</td><td>kt</td><td>33.7</td><td>33.7</td><td>11.1</td><td>11.1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>

<figure><img src="image_152.png" /><figcaption>**Fig. 2** Heatmap representing normalised values of SDG indicators (on the Y axis) across different scenarios (on the X axis) defined in the ESOM. *Note*: Shades of red indicate relatively less sustainability, while shades of blue denote relatively higher sustainability (Color figure online)</figcaption></figure>

<footnote>*Negative values represent consumption below target</footnote>
In all scenarios, regardless of fuel price fluctuations, the share of fossil energy in electricity supply remained constant. This result is explained by the fossil-free status of northern Sweden's electricity production. No-target Scenarios (NTs) uses cheap fossil fuels leading to low marginal cost of travel (by car) and increases air pollutant and GHG emissions. The indicator, distance to energy efficiency target identified for the transport sector, follows a similar pattern to that of fossil share, which implies lower energy savings (i.e. a greater distance to the target) with a higher fossil-fuel share. In the Renewables Scenarios (RSs), there is a clear shift towards renewable energy sources in pursuit of SDG7, which results in a reduced share of fossil-fuel energy and lower levels of GHG emissions. However, air pollutant emissions are significant, due to the choice of biofuels. The marginal cost of travel by car is high in NTSs, owing to the increased biofuel cost.

Climate Scenarios (CSs) and Sustainability Scenarios (SSs) show similar trends across indicators. The CSs defined by climate action result in a reduction of air pollutants and an increased share of renewables—without explicit targets. Thus, the fulfilment of SDG13 (on climate) has reinforced the attainment of SDG7 (on energy) and SDG11 (on cities), showing synergies between the SDGs. Nonetheless, the material footprint (which does not account for reusing or recycling) in the transport sector increases notably in CSs and SSs, primarily due to a greater share of EV use. This shift toward EVs contributes to reduced emissions and an improvement in energy efficiency. The indicator, distance to energy efficiency in the transport sector shows higher energy savings with lower fossil share. However, the marginal cost of travel by car is also increased due to the upfront EVs cost as well as increased biofuels prices. Here, even though shifting to EVs reinforces goals regarding air pollution in, for example, SDG11 (on cities), SDG13 (on climate), and SDG7 (on energy efficiency), it slows down goals like SDG7 (on affordable energy) and SDG12 (on responsible production and consumption). These instances show the trade-offs and synergies between climate targets and SDGs and underscore the complexity and multifaceted nature of the studied energy transition and sustainability.

# 7 Conclusion

The study has illustrated the role of applying a model-based system analysis approach for integrating sustainable development principles into local energy transition. A data-driven comparative assessment was also done to examine whether the energy transition pathways converged or diverged from the SDGs. Insights derived from the analysis aligned closely with the study's aim of assessing sustainability across different energy transition pathways. A critical learning point was the identification and definition of sustainability indicators tailored to gauge progress across various energy transition pathways. When such indicators and pathways were illustrated in a heatmap, they offered valuable information that could be used in planning because the map allows stakeholders to easily identify and compare the
impacts of different energy scenarios. Furthermore, employing the system analysis approach enables the assessment of certain aspects of targets and indicators which are outside the model's scope. Cases in point are discussing the impact of high energy prices on low-income groups, or the potential competition of reindeer husbandry with other land uses. Overall, the entire analysis facilitates informed decision-making by highlighting key areas where interventions are needed and helps to strategise for a more sustainable energy future in alignment with both local and broader sustainable development perspectives.

The chosen indicators facilitated a detailed quantitative assessment, which allowed the discernment of not only the achievements, but also the challenges inherent in aligning with the UN SDGs. The study findings also specifically draw attention to the complex interplay between the pursuit of sustainability and the mechanisms of energy transition. The study further reveals that having ‘tunnel vision’ in respect of energy transition can lead to undesired impacts on the energy system. For instance, while the increased use of renewable energy supports urban sustainability (SDG11 on cities) and climate change mitigation (SDG13 on climate), it also raises concerns about energy affordability (SDG7 on energy) and material consumption (SDG12 on responsible production and consumption). These interdependencies which are evident from the heatmap underline the inherent trade-offs that must be managed.

The study highlights that achieving sustainable goals as well as a successful energy transition requires a holistic and integrated perspective. While the study focused on Gällivare, the method on how to identify and define (i) what to include and vary across scenarios, (ii) what to extract from the model results—and which result indicators to focus on, and (iii) what to discuss with the local government can be applied to other municipalities. Other local governments can adapt developed approach to their own contexts, energy profiles and specific challenges or goals to assess and guide their targets, measures and practices. In addition, the heatmap proves to be an effective tool for presenting and comparing result indicators. Notably, certain indicators and factors that were not prioritised for Gällivare due to regional specificity might hold relevance for areas elsewhere (e.g. water requirements for electricity generation and biofuel crop production with respect to SDG6 on water).

The TIMES-City modelling framework used in the study falls short by not including social issues during the assessment of SDG targets and energy transition capacities. The scope of the model could be broadened to encompass environmental factors such as land and water use within and outside the geographical scope, where required (SDG6 on water, SDG14 on life below water, and SDG15 on life on land), and to include material flows to look more closely at SDG8 (on economic growth) and SDG12 (on responsible production and consumption). The model, which currently focuses on energy flows within a defined geographical boundary, could also be expanded or merged with other models and methods. Models/Methods that enables a deeper analysis of social issues related to SDG1 (on poverty), SDG2 (on hunger), SDG5 (on gender equality), SDG8 (on economic growth), SDG9 (on industry and innovation) and SDG10 (on reduced inequalities).
**Acknowledgements** The authors wish to express their gratitude for the financial support received from the Swedish Energy Agency [grant number 46240-1] and the Formas Research Council [grant number 2019-01550].

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<figure><img src="image_153.png" /></figure>
# Translating Research Results into Policy Insights to Underpin Climate Action in Ireland

<figure><img src="image_154.png" /></figure>

Check for
updates

Brian Ó Gallachóir, Paul Deane, and Fionn Rogan

**Abstract** This chapter presents innovative processes that have been developed and used to bridge the interface between the research ecosystem and policy-making ecosystem. It focuses on a specific case study, namely how energy systems modelling has been used to inform energy and climate mitigation policies in Ireland. We trace the development of energy systems modelling tools and capacity in Ireland over the past 15 years, and the key role it has played in addressing important policy questions related to delivery of a number of UN Sustainable Development Goals (SDGs). We also outline the parallel evolution of novel research communications methods, proactive engagement programmes with policy practitioners and co-production processes. Specific examples of energy and climate mitigation policies are highlighted to demonstrate how the research results have been used together with the communication and engagement methods not only to inform but also to underpin policy developments. The contributions of this work to the SDGs are highlighted, in particular SDG 13 on climate and SDG 7 on energy, but also SDG 12 on sustainable consumption and production and SDG 17 on partnerships. We conclude with a proposed seven stage approach, for energy modellers who wish to successfully bridge between the research and policy eco-systems, namely (1) undertake scientifically robust research, making methods and results openly and publicly available (2) frame research questions that respond to specific policy needs, (3) translate research results into policy insights (4) improve communications of research findings including through use of infographics (5) engage actively with policy

<footnote>B. Ó Gallachóir (✉)
MaREI Centre, Sustainability Institute, University College Cork, Cork, Ireland
e-mail: b.ogallachoir@ucc.ie</footnote>

<footnote>P. Deane · F. Rogan
MaREI Centre, Sustainability Institute, University College Cork, Cork, Ireland</footnote>

<footnote>School of Engineering and Architecture, University College Cork, Cork, Ireland
e-mail: jp.deane@ucc.ie; f.rogan@ucc.ie</footnote>
practitioners and policy makers (6) co-produce policy with policy practitioners and (7) build absorptive capacity in the policy system.

## Key Messages

* Innovative processes, including co-production and building absorptive capacity, successfully bridged the interface between the energy research ecosystem and policy-making ecosystem.

* Energy systems modelling played a key role in advancing delivery of SDG 13 (climate), SDG 7 (energy), SDG 12 (sustainable consumption and production) and SDG 17 (partnerships) in Ireland.

* Transitioning from traditional energy modelling approach to co-producing policy is challenging but fulfilling.

* A seven stage approach is proposed for energy modellers who wish to successfully bridge between the research and policy eco-systems.

# 1 Introduction

Using research to inform, improve and influence policy decisions is not new. Although many academics have personal experience, or have attended impact training, there is a limited empirical evidence base to inform academics wishing to create impact (Olivier and Carney 2019).

The purpose of this chapter is to present a specific case study on research results which were translated into policy insights and how these insights were then used to underpin policy decisions. It draws on specific experiences over 15 years from Ireland, where University College Cork has established energy systems modelling capacity. From the outset, the team had a focus not only on developing models and undertaking future energy systems scenario analysis, but also on using the results and insights gained to directly influence Ireland's energy and climate policies. We make an important distinction between energy system *models*, which need to be continually updated, and energy system *modellers*, who need to continually reflect on the impact, usefulness and value of their research findings and insights. This chapter will describe how, over a 15 year period, the modellers experimented with and learned from a range of different research dissemination and engagement practices.

The methods section charts the development of the energy systems models, namely the TIMES Ireland Model that was developed in the period 2018–2023, and its predecessor the Irish TIMES model that was developed in the period 2008–2018. This section also discusses the scenario analyses undertaken, which connect the model capabilities with specific policy questions. Finally, this methods section also presents a number of approaches employed for translating research results into policy insights, in particular policy briefs, web portals and infographics.
The policies section highlights a number of specific energy and climate policies that have been informed, improved or influenced by the research and analyses undertaken, detailing which processes were used in each case and the lessons learned and challenges encountered. In most cases, the policies affected contribute to Sustainable Development Goal (SDG) 13: Take urgent action to combat climate change and its impacts and SDG 7: Ensure access to affordable, reliable, sustainable and modern energy for all. In some cases, the policies also contribute to SDG 12: Ensure sustainable consumption and production patterns and SDG 17: Strengthen the means of implementation and revitalize the global partnership for sustainable development.

The recommendations section provides guidance to energy modelling teams in other countries and institutions who wish to actively increase the evidence base for policy decisions but also ensure that the knowledge generated has the best opportunity to influence policy decisions. This contributes directly to SDG target 17.9 Enhance international support for implementing effective and targeted capacity-building in developing countries to support national plans to implement all the sustainable development goals.

# 2 Methods: From Energy Models to Communication Tools

The key methods developed and employed in this case study were the models developed, the scenarios analysed, and the communication tools employed.

## 2.1 TIMES Models

The Irish TIMES model and its successor the TIMES Ireland Model are both partial equilibrium models of Ireland's energy system, built using the TIMES model generator, a techno-economic modelling tool developed by International Energy Agency Energy Technology Systems Programme (IEA-ETSAP) (Chiodi et al. 2015a). TIMES models are currently used by approx. 200 energy systems modelling teams in 70 countries globally. TIMES is a linear programming model generator, which provides a technology-rich basis for estimating energy dynamics over a long-term, multiple-period time horizon. It is usually applied to the analysis of the entire energy sector of a country or a region, but may also be applied to study single sectors (e.g. the electricity sector) in detail. It maximises the total surplus, equivalent to minimising the total discounted energy system cost, over the entire time horizon while respecting environmental and many technical constraints.

There are clear limitations that need to be borne in mind when interpreting the results from TIMES models—most notably, these results are not attempts to forecast the future. The scenarios are based on different policy assumptions, and the results from one scenario are best interpreted by comparing them with the results from other
scenarios, rather than as absolute results. Regarding the absolute results, they clearly depend on the robustness of future projections of economic growth and fuel prices that drive the model. In addition, as the focus of this model is on technology choice, the representation of behavioural effects is currently represented in only a limited manner.

## 2.2 Irish TIMES Model: 2008–2019

The Irish TIMES model was a mono-regional model of the entire Irish energy system that was originally extracted from the Pan European TIMES (PET) model (Ó Gallachóir et al. 2012). It was updated and expanded by the Energy Policy and Modelling Group in University College Cork and used to build a range of energy and emissions policy scenarios to explore the dynamics behind the transition to low carbon energy systems (Chiodi et al. 2013a; Chiodi et al. 2013b) (i.e. contributing to SDG 13), to analyse energy security (Glynn et al. 2014, 2017) i.e. contributing to SDG 7, to assess impacts of limited bioenergy resources (Chiodi et al. 2015b; Czyrnek-Delêtre et al. 2016) i.e. contributing to SDG 12 and to explore interactions between the energy system and power system (Deane et al. 2012, 2015a, b), the energy system and the economy (Glynn et al. 2015) and the energy system and agriculture system (Chiodi et al. 2015c; Deane et al. 2016).

The core model contained a database of 1350 energy supply-side and demand-side technologies, which contained technical data (e.g. thermal efficiency, capacity), environmental data (e.g. emission coefficients) and economic data (e.g. capital costs) that varied over the entire time horizon (2005–2050). The exogenous model inputs were energy supply and energy service demands. On the supply side, these included indigenous energy resource availability, primary energy (mostly fuel) prices and available energy imports. On the demand side, separate energy service demand projections were derived and input from macro-economic projections of the economy to 2050.

The Irish TIMES model provided a range of future energy system configurations for Ireland that varied according to a range of policy constraints for the period out to 2050, but in each case delivering projected energy service demand requirements optimised to least cost. The least cost optimisation feature came to be one of the most valued features of the Irish TIMES model by Irish policy-makers. The Irish TIMES model provided a means of testing energy policy choices and scenarios, and assessing the implications for: (i) the Irish economy (technology choices, prices, output, etc.), (ii) Ireland's energy mix and energy dependence, and (iii) the environment, focusing mainly on greenhouse gas (GHG) emissions. It was used to both examine baseline projections, to assess the implications of emerging technologies, and of alternative policy choices, such as meeting renewable energy targets and carbon-mitigation strategies. The real value of the Irish TIMES model was in the insights (Ó Gallachóir et al. 2020a) it provided into some of the key challenges and
decisions facing Ireland in terms of energy and climate policy. More details about the particular insights, challenges and decisions are discussed later on in the chapter.

While many changes were made to the Irish TIMES model between 2008 and 2019, because some parts of the original model (e.g. the base year structure) were fixed and unchangeable, the UCC modelling team decided that the benefits of a more flexible and transparent model would justify the effort of building an entirely new TIMES model. This decision also coincided with the cessation of funding from Ireland's EPA for Irish TIMES and the commencement of funding for the development of a new TIMES model from that government agency's parent department, i.e. the Department of Communications, Climate Action and Environment.<fnref n="1" />

## 2.3 TIM (TIMES Ireland Model): 2019–2023

The TIMES Ireland Model (TIM) is the successor model to the Irish TIMES Model and was developed to address specific limitations of Irish TIMES in informing updated and more ambitious near-term climate mitigation policy, in particular a government commitment to reduce greenhouse gases by an average of 7% yr. – 1 in the period to 2030 and a net-zero target for 2050, underpinned by a series of 5-year carbon budgets (Balyk et al. 2021). TIM also took advantage of new advances in energy systems optimisation modelling techniques and new data that enabled an increased temporal and geographic resolution relative to Irish TIMES.

Unlike in the Irish TIMES model, which had a fixed base year model structure, in TIM a special spatiotemporal approach was taken in the RES base year specification and scenario file data structures to allow flexible regional definitions and temporal resolution in TIM. This enabled TIM to run in multiple modes with multiple configurations of regional and temporal resolution, ranging from a single region national model at a single annual time slice, all the way to 26 counties at hourly resolution where supply–demand data are available at that spatiotemporal granularity (electricity, gas, and transport).

High temporal granularity is needed to appropriately model energy futures with high variable renewable energy systems integration, especially in scenarios with high levels of electrification of end-use demands. At the same time, high temporal granularity can be computationally expensive and can significantly increase the time required for model development and testing. This issue is addressed in TIM by constraining all time slice model input data to a single file generated with a specific temporal resolution. A time slice tool is used to aggregate raw time series data and create a file in the required format (Balyk 2022).

High spatial granularity is required to give greater policy clarity on optimal investment needs based on region- and county-specific characteristics to enable the counteraction of socioeconomic challenges such as energy poverty and

<footnote n="1">¹As of 2024, named Department of Environment, Climate and Communications</footnote>
<figure><img src="image_155.png" /><figcaption>**Fig. 1** Reference Energy System for TIM</figcaption></figure>

infrastructure development within an optimisation framework (Aryanpur et al. 2019). This is addressed in TIM by creating model input data structures that allow specifying data and formulating constraints on both national and county levels. Internal file switches, and user shell options, can then be used to apply TIM on either of the levels. This was used for example in a specific high spatial resolution analysis focussing on transport decarbonisation (Aryanpur et al. 2022) and for a separate analysis in decarbonising residential energy use (McGuire et al. 2023).

Figure 1 shows a simplified Reference Energy System in TIM that describes the structure and energy flows including two major parts, i.e. the supply side and demand side. The former comprises energy resources, fuel production and conversion technologies (e.g. biorefineries, hydrogen production, and different power plants), transmission, and distribution infrastructure (e.g. gas pipelines and power grid). The latter covers end-use sectors (e.g. transport and residential) and the corresponding energy service demands (i.e. passenger, freight, and hot water). Energy resources incorporate both domestic fossil-based fuels and renewable resource potential. These fuels are processed and then distributed across the country. End-use technologies consume energy commodities to meet energy service demands. GHG emissions from fossil fuels combustion and process-related emissions in industry are tracked with the fuel supply module, electricity generation technologies, and sectoral consumption levels.

Energy service demands in end-use sectors are driven by growth in the population and in the economy. The model is set up to allow for alternative scenarios for these drivers, resulting in different energy service demand projections in the end-use sectors. For example, the impact of low energy demands was explicitly considered
in one scenario analysis and a complement to more typical supply side focussed studies (Gaur et al. 2022).

## 2.4 Scenario Analyses

The scenario analyses undertaken with the Irish TIMES and TIM models have always been carefully structured to respond to specific policy needs at the time of the analysis. As Ireland's climate mitigation policy evolved over time, new scenarios were built, and where necessary the models were further developed in order to inform the new policy questions and requirements that arose.

Ireland's key climate mitigation policy focus in 2013 was two-fold. The short term focus was to meet mandatory EU greenhouse gas emissions (GHG) targets to be achieved by the year 2020 for each EU Member State. These targets were for GHG emissions that fall outside of the EU Emission Trading Scheme and were specified in an EU Effort Sharing Agreement for non-ETS GHG emissions (EU 2009). In order to inform how Ireland might be able to meet this target, we built scenarios where we imposed separate emissions reduction targets for ETS and non-ETS related emissions and explored the energy technology systems pathways and associated costs to achieve Ireland's target to achieve a 20% reduction in non-ETS emissions by 2020 relative to 2005 levels (Chiodi et al. 2013b).

The long term Government policy focus in 2013 was to set a 2050 target for GHG emissions that aligned with the overall EU ambition for that time horizon. The European Union perspective was that industrialized countries should contribute to an overall 50% reduction in GHG emissions target by reducing GHG emissions by between 80% and 95% by the year 2050, relative to 1990 levels (European Council 2009). Against this context we built emissions reduction scenarios for Ireland focusing on a reduction in GHG emission of between 80% and 95% relative to 1990 levels and published the first ever long term energy scenario results for Ireland (Chiodi et al. 2013a).

There was a significant increase in climate ambition in Ireland in 2018/2019 in line with the growing ambition globally, and in the EU in particular, and also reflective of a changing political response nationally to climate change. This increase in ambition was both informed and underpinned by the scenario analysis we undertook and by how we translated the results into policy insights and engaged with the policy practitioners (i.e. civil servants) and policy makers (i.e. elected representatives). In this was we contributed directly to SDG target 13.2 namely *Integrate climate change measures into national policies, strategies and planning and SDG target 17.14 Enhance policy coherence for sustainable development*.

We also responded to the increase in ambition by building new scenarios (shown in Fig. 2) that fully stretched the capacity of the Irish TIMES model. These scenarios downscaled a number of global carbon budgets to Ireland and outlined different energy system pathways in line with different probabilities of achieving a 2 °C and 1.5 °C target. These represented the first deep decarbonization scenarios for Ireland.
<figure><img src="image_156.png" /><figcaption>Fig. 2 Scenarios for deep decarbonization for Ireland based on equitable carbon budgets</figcaption></figure>

that were consistent with the Paris Agreement and above current national EU mitigation targets using equitable carbon budgets (Glynn et al. 2019). These results showed that a net zero energy system for Ireland was technically feasible.

Ireland's Climate Action & Low Carbon Development (Amendment) Act 2021 established new targets for Ireland to reduce GHGs by 51% by 2030 relative to 2018 levels, and to achieve climate neutrality by 2050. Taking advantage of the model improvements in the TIMES Ireland Model, we developed a range of scenarios (Glynn et al. 2019) that combined both of these ambitions, and made a range of assumptions for what targets might be established for emissions reduction in agriculture (responsible for 34% of Ireland's GHG emissions).

Here the core scenario sets imposed different emissions reductions on CO2 emissions depending on a range of pre-set possible (methane and nitrous oxide) emissions reductions imposed on the agriculture sector. The five core scenarios were:

* A25E65: 25% reductions from agriculture by 2030, 65% reductions from energy & industry

* A33E61: 33% reductions from agriculture by 2030, 61% reductions from energy & industry

* A40E57: 40% reductions from agriculture by 2030, 57% reductions from energy & industry

* A51E51: Equal mitigation, 51%, from agriculture and from energy & industry
* **A55E49**: 55% reductions from agriculture by 2030, 49% reductions from energy & industry

In addition to these core scenarios, a number of additional scenario variants were also included to take account of some key uncertainties. The variants were:

* **Early Action** where climate action is front-loaded via emissions constraints in the early years

* **Late Action** where delays are allowed to take advantage of declining costs

* **LED** where Low Energy Demand variants are achieved via amended energy service demands

* **Tech-Optimism** where faster RES, bioenergy / hydrogen imports and early CCS are assumed

## 2.5 TIM Results Web APP

In order to provide a bridge between the research ecosystem and policy-making ecosystem, University College Cork developed a web app in 2021 to share results with policy practitioners for the period 2021–2050 for a range of different scenarios that were modelled with the TIMES Ireland Model (TIM). The TIM Carbon Budgets 2021 Web App (Daly et al. 2021a) visualises most of the scenarios included in the github (Daly et al. 2021b) repository that contains these energy system scenarios for Ireland meeting decarbonisation targets for 2030 and 2050. Up to two scenarios can be selected for comparison and the difference can be computed. In addition to the scenario results visualisation, documentation in the TIM model and scenario descriptions are also available from the web app. The app has proved to be a very effective communication tool when discussing scenario results with policy makers and with other stakeholders (Fig. 3).

## 2.6 Policy Briefs and Infographics

As another bridging mechanism between science and policy, we explored a number of different ways to effectively translate research results into policy insights. The UK usefully defines a policy brief as a short document that summarises a project or key research findings and their policy implications (NCCPE 2023). When invited to present evidence to the Oireachtas (Irish Parliament) Committee on Climate Action in 2018 UCC (2018) prepared a policy brief and added it to the written and verbal testimony. In this policy brief we included a 1-page written summary of some key research findings and policy insights and accompanied the text-only 1-page summary with an infographic as presented in Fig. 4 to present which constituted headline messages (e.g. “The time to act is now”) and reinforcing imagery (designed by a graphic designer) to present the key findings in a more accessible manner.
<figure><img src="image_157.png" /><figcaption>**Fig. 3** Sample output from TIM Result Web App highlighting results differences for transport for two different scenarios, one with a 65% reduction in energy related CO2 by 2030 and a second with a 49% reduction and also assumed low energy demand</figcaption></figure>

The focus on this analysis and policy engagement was primarily on SDG target 13.2, namely *Integrating climate change measures into national policies, strategies and planning*, but also the parts of SDG target 13.3 relating improving institutional capacity on climate change mitigation in addition to Ireland's contribution to SDG target 7.2 increasing substantially the share of renewable energy in the global energy mix and SDG target 7.3, doubling the global rate of improvement in energy efficiency.

Other examples of policy briefs published in include one on Ireland's carbon budgets, highlighting their role as an important bridge between climate ambition and climate action (Ó Gallachóir 2021), insights on carbon capture and offsetting that was included in evidence to the Oireachtas (Parliament) Committee Pre-Legislative Scrutiny of the Climate Action and Low Carbon Development (Amendment) Bill 2020 (Glynn 2020).
<figure><img src="image_158.png" /><figcaption>**Fig. 4** Infographic as part of Policy brief to Oireachtas Committee translating research results into policy insights</figcaption></figure>
These policy briefs have also been extended to include other areas of climate mitigation research, for example citizen engagement and dialogue (Ó Gallachóir et al. 2020b), contributing to SDG target 12.8 namely ensuring that people everywhere have the relevant information and awareness for sustainable development and lifestyles in harmony with nature.

Infographics can also be used separately from policy briefs. There is of course a danger that the information presented can be an oversimplification of the research results but it can be a very effective and accessible way of communicating. Figure 5 summarises insights relating to a 7% per annum reduction in GHG emissions in Ireland over the period 2021–2030 based on scenario analysis using the TIM model. The timing in 2020 was important as political parties in Ireland were trying to form a coalition Government after a general election and one of the red-line issues in the negotiations was a 7% reduction per annum in GHG emissions. We developed this infographic that accompanied a newspaper article (Daly et al. 2020) providing a text version of the same key insights from the scenario analysis. Given that this helped develop a cross-political party programme for Government, it contributes directly to SDG target 17.14, enhancing policy coherence for sustainable development.

# 3 Modelling to Inform Policy

Over the past ten years in Ireland there has been a significant increase in the amount of energy and climate mitigation policies introduced. National energy policies were infrequent prior to that and prompted by specific needs, for example in 1978 in response to the oil crises (Department of Industry Commerce and Energy 1978) and in 1999 when the first significant sustainable energy policy was developed (Department of Public Enterprise 1999).

## 3.1 2015: Climate Action and Low Carbon Development Act 2015

The Irish government was planning to legislate for climate action and low carbon development and published a Heads of Bill (General Scheme of a Climate Action and Low Carbon Development Bill) in 2013 (DECLG (Department of Environment, Community and Local Government) 2013). This raised key questions regarding the evolution of Ireland's future energy system to enable the transition to a low carbon future. The Irish TIMES model had the capacity to address these key questions.

In the period June–December 2013, the Department of the Environment, Community and Local Government commissioned UCC and the ESRI to produce a Low Carbon Energy Roadmap for Ireland to 2050 using the Irish TIMES model (Deane et al. 2013). This involved framing research questions that respond to specific policy
<figure><img src="image_159.png" /><figcaption>**Fig. 5** Infographic highlighting insights from analysis of a 7% per annum reduction in GHG emissions for Ireland in the period 2021–2030</figcaption></figure>
needs. The specific focus of this analysis was on technological changes in the energy system and the associated implications. A key policy question underpinning the analysis focused on the dynamics of the energy system moving towards a low carbon economy for two key time horizons, i.e. to 2050 and to 2030. The process involved modelling analysis and regular meetings and discussions with a number of government departments, providing technical advice and guidance on the development of a long-term strategy for Ireland. The purpose of the roadmap is to explore possible routes towards decarbonisation of the energy system, with a focus on achieving this at least cost to the economy and to society. The key issue is making well-informed policy choices. Hence, this roadmap does not stipulate which policies are necessary to achieve the transition; instead, it focuses on the key drivers and their implications for the energy system of moving to a low carbon economy.

The analysis focused on identifying ways of achieving 80% and 95% reductions in energy-related CO2 emissions. The analysis also included the creation of a “business-as-usual” baseline (assuming no further policy actions), in order to enable comparisons between options and to help quantify the scale of the transition required. The analysis also presented results in terms of the mitigation potential for each sector (Fig. 6). All scenarios focused on the period to 2050, building on the EPA’s projections that cover the period to 2020. The researchers spent significant time designing and refining graphics that could be easily understood by a non-technical audience. This analysis was also presented to the Oireachtas Committee considering the Bill (Ó Gallachóir 2013), which was subsequently enacted in legislation in December 2015 (Government of Ireland 2015). The evidence provided highlighted the techno-economic feasibility of deep decarbonisation, which provided confidence to the policy system to legislate for climate action.

<figure><img src="image_160.png" /><figcaption>**Fig. 6** Waterfall graph showing sectoral reduction in emissions in 2050 in an 80% CO2 emissions reduction scenario relative to a Business as Usual Scenario (Deane et al. 2013)</figcaption></figure>
## 3.2 2015: White Paper on Energy Entitled Ireland's Transition to a Low Carbon Future 2015-2030

The White Paper on Energy, Ireland's Transition to a Low Carbon Energy Future 2015-2030 (DCENR (Department of Communications, Energy and Natural Resources) 2015), was launched in December 2015. It provided, at that time, a complete energy policy update of actions that the government intended to take in the energy sector, with a particular focus on the period up to 2030. The Irish TIMES model was used to provide technical assistance (Ó Gallachóir et al. 2015) to inform the White Paper.

The process of bridging between science and policy in this case was different and iterative involving more direct interactions between the research team and the policy practitioners within the Department of Energy. It involved the researchers having many meetings with department officials, in particular over the period April-June 2015. The analysis undertaken not only focused on the energy transition to a low carbon future but also addressed a number of other key areas of policy, including the potential role of bioenergy in electricity generation, an assessment of the extent of energy poverty and how this might be addressed, and new approaches for quantifying energy security. In this way, the analysis addressed a number of SDGs, namely elements of SDG 13 on climate mitigation, SDG 7 on energy security and SDG 1 on addressing poverty.

The process focussed on drawing out the key policy questions of interest by actively listening to a wide range of policy makers queries and mapping these questions to specific areas or sectors of the energy system (for example residential, power sector, industry etc). Often there was a cross cutting dimension to questions with overlapping impact for a number of sectors, (for example electrification of heat, retrofitting and power sectors impacts). Once the query was well formulated and the specific part of the energy system identified, energy modelling analyses was used to examine a specific impact and this impact was compared in magnitude to a counterfactual where no change was made. This allowed policy makers to understand the isolated impact of single policy decision. Results were communicated using simple infographics, diagrams and uncomplicated narratives where the impact was clearly explained but without a detailed explanation of the theory and mathematics behind it. This type of communication and practice allow a singular focus on a specific issue and provided numbers to put with the narratives of policy decisions with formed the basis for active and insightful discussion.

This form of deep engagement went beyond informing policy with a report and tended more towards policy co-production, while acknowledging the distinct roles of the researchers and the policy practitioners. In addition, through the iterative interactions modelled on a workshop approach, we also built the capacity of the policy system to better understand the results of the modelling, along with the strengths and limitations of modelling on addressing particular policy questions. The difference in approach was also reflected in the depth of analysis that underpinned Ireland's 2015
White Paper on Energy entitled Ireland's Transition to a Low Carbon Future 2015–2030.

## 3.3 2017: National Mitigation Plan 2017

The Department of Communications, Climate Action and Environment (DCCAE) commissioned the SFI MaREI Centre at University College Cork (UCC) and the Economic and Social Research Institute (ESRI) to provide technical and economic advice and guidance on the development of National Mitigation Plan (NMP) for Ireland, as required under the Climate Action and Low Carbon Development Act (2015).

The National Policy Position on Climate Action and Low Carbon Development (DCCAE 2014) set an ambition of achieving at least an 80% reduction in CO2 emissions relative to 1990 levels. In addition, the EU has proposed a 2030 emissions reduction target for Ireland. The period to 2030 is critical on the journey to achieving this long-term objective; different levels of ambition in the medium term have implications for the long-term pathway and, conversely, the long-term ambition affects the choice between medium-term options. It is important to consider these interactions to develop a coherent decarbonisation strategy.

UCC undertook this analysis with partners in 2017 (Curtin et al. 2017) to inform the development of Ireland's first NMP. We developed a NMP scenario that brings together the medium-term mandatory obligations proposed by the EU and the longer-term national ambition.

We compared this with a counterfactual business-as-usual case to explore the scale of the challenge. The NMP scenario is therefore based on the following assumptions:

* overall CO2 emissions are 80% below 1990 levels by 2050;

* for ETS emissions, a carbon price rises to €40/t by 2030 and emissions decline 2.2% per annum thereafter to 2050;

* Ireland meets its mandatory non-ETS target for 2030 agreed by the EU.

We noted that these assumptions reflect the difficulty of determining how a national target can be achieved within the context of an EU-wide ETS. National governments have little final influence on whether ETS emissions reductions occur on their territory unless a carbon price floor (such as that in the UK) is considered; ultimately, this depends on decisions taken by the installations covered and the ETS carbon price. A key feature of the Irish TIMES model was its ability to separately and coherently model all the sectors of the economy (e.g. transport, electricity, buildings, etc) and to present the results in terms of the ETS and non-ETS sectors (Fig. 7).
<figure><img src="image_161.png" /><figcaption>**Fig. 7** NMP scenario developed to inform the National Mitigation Plan, distinguishing between energy related CO2 emissions that fell within the scope of the EU Emissions Trading Scheme (ETS emissions) and remaining emissions (non-ETS emissions)</figcaption></figure>

## 3.4 2021: Climate Action and Low Carbon Development (Amendment) Act 2021

In the period from 2018–2021, there was a significant increase in climate ambition and this stretched the capacity of the Irish TIMES model. The Climate Action Plan (2019) focussed on delivering the short term targets to 2030 in line with an 80% long-term emissions reduction target. However, the Plan also listed a specific action to evaluate in detail the changes required to adopt a more ambitious commitment of net zero greenhouse gas emissions by 2050. We responded to this need by building new scenarios and carrying out new analysis (Ó Gallachóir et al. 2019) to focus on achieving net-zero CO2 emissions by 2050 (using carbon budgets over the period to 2050). We compared these scenarios with the pre-existing ambition to reduce emissions by 80% relative to 1990 levels by 2050, as illustrated in Fig. 8.

In 2020, the Government published new proposed legislation, namely the Climate Action and Low Carbon Development Amendment Bill 2020, proposing not only an ambition for Ireland to achieve climate neutrality by 2050, but also a doubling of the short term ambition for emissions reduction, namely to achieve on average a 7% per annum reduction in GHG emissions by 2030.

During this period, we developed a successor model, the TIMES Ireland Model (TIM) to take advantage of TIMES modelling improvements and new policy needs, as summarised in the Methods Section. In parallel with these modelling improvements, we continued to use the Irish TIMES model in order to provide continued modelling analyses to the policy makers. Against that context, we provided a scoping analysis on the 7% per annum ambition in late 2020 (Rogan et al. 2020) and preliminary results from the new TIM model in Q2 2021 (Daly et al. 2021c).
<figure><img src="image_162.png" /><figcaption>**Fig. 8** Comparison of net-zero scenario (responding to increased climate ambition) with previous 80% emissions reduction scenario</figcaption></figure>

In addition to supporting the policy practitioners (i.e. civil servants) in this way, we also provided evidence to policy makers (i.e. politicians) at Parliamentary hearings discussing the new legislation (referenced in Committee Report (Houses of the Oireachtas 2020). The legislation was enacted on 23 July 2021 (Houses of the Oireachtas 2021). The results and insights we presented provided confidence to the policy system to legislate for the increased ambition and put in place firm targets and governance methods. Part of this confidence came from being open and transparent about the model strengths and weaknesses and communicating to policy makers where results were robust or uncertain. This was helped by running many scenarios to isolate key variables that had an outsized impact on results or insight. This allowed policy makers to focus on key drivers of change. These insights were communicated in clear uncomplicated short briefs with non-technical explanations to the provision of results.

The Climate Act places on a statutory basis “the National Climate Objective”; that “the State shall, so as to reduce the extent of further global warming, pursue and achieve, by no later than the end of the year 2050, the transition to a climate resilient, biodiversity rich, environmentally sustainable and climate neutral economy”. The Climate Act also provides for a 51% reduction in greenhouse gases by 2030 compared to 2018 levels, and puts in place a rigorous governance structure, including a system of carbon budgeting, sectoral emissions ceilings, a national adaptation framework, sectoral adaptation plans, and annually updated Climate Action Plans, to ensure that Ireland achieves its national, EU and international climate commitments in the near- and long-term.
In addition to engaging with the policy eco-system, we also collaborated with other stakeholder (including from industry) focussing on decarbonising different sectors, including how to halve transport GHG emissions by 2030 together with the Irish Bioenergy Association (Deane 2021a), how to achieve zero emissions in electricity generation together with the Electricity Association of Ireland (Mehigan and Deane 2020) and the role of wind energy in achieving climate neutrality in Ireland by 2050 together with Wind Energy Ireland (Deane 2021b). This contributes directly to SDG target 17.16 on increasing multi-stakeholder partnerships that mobilize and share knowledge and expertise and SDG target 17.17 on encouraging and promoting effective public, public-private and civil society partnerships.

## 3.5 2022: Oireachtas Approval of Carbon Budgets 2022

Ireland's legislation (Climate Action and Low Carbon Development (Amendment) Act 2021 established a 5 yearly carbon budget process for the periods 2021–2025 and 2026–2030. The Act specified that the Climate Change Advisory Council would propose carbon budgets for these first two periods.

UCC's research team had two distinct roles regarding the decision making on carbon budgets. Firstly, two UCC researchers were invited members of the Carbon Budgets Committee established by the Climate Change Advisory Council to assist them in quantifying Ireland's carbon budgets. In this capacity we assessed available analysis and data and collectively produced the Council's proposed carbon budgets and presented these proposed budgets to the Joint Oireachtas Committee on Climate Action in January 2022 (Houses of the Oireachtas 2022).

Secondly, part of the evidence considered by the Carbon Budgets Committee was developed by UCC via mitigation scenarios with TIM to explore different options for energy system mitigation for consideration by the Council, focusing on the implications for energy system evolution, technology choices and demand pathways. This analysis formed a significant part of the evidence used by the Climate Change Advisory Council to propose the first set of carbon budget recommendations (Climate Change Advisory Council 2021).

The Oireachtas Committee recommended that the carbon budgets proposed be adopted, they were then approved by the Irish Government in February 2022 and then adopted by both Houses of the Oireachtas in May 2022 without the need for a vote, attesting to the cross party political support. This was a good example of how effective co-production of policy can take place where the scientific and policy communities work together on a shared challenge, in this case determining appropriate carbon budgets that take many, sometimes conflicting goals into account simultaneously, including deep decarbonisation, climate justice, economic growth, increased biodiversity, etc.
# 4 Conclusion and Recommendations

The key findings from this case study of 15 years energy of bridging between the research eco-system and policy eco-system are that it is very challenging but can be highly impactful. The benefits of effectively bridging between science and policy would appear obvious and the COVID-19 pandemic clearly demonstrated this. Yin et al. found that policy documents in the COVID-19 pandemic substantially access recent, peer-reviewed, and high-impact science (Yin et al. 2021).

An ex-post assessment of the science policy interfaces during the pandemic highlights the need to build and strengthening knowledge translation (i.e. interaction among the producers and users of research, removing the barriers to research use, better of tailoring information to different target audiences so that effective interventions are used more widely), a shift in the existing ‘incentive’ system of researchers (publish or perish) and building capacity in equity-centred evidence-informed decision-making (Kuchenmüller et al. 2021).

The challenges are significant however and should not be downplayed. The transition from the more traditional research approach of publishing results in a journal paper and then moving on to addressing the next research question is very far removed from the co-production approach of working with policy practitioners collaboratively on policy development. There are of course a number of different steps and approaches available to closing the gap between the science and policy communities. From our experience over a 15 year period, motivation is critically important in order to overcome the challenges and to take on the extra effort to move beyond the traditional research process towards any or all of: actively informing, influencing, underpinning and co-producing policy. Researchers that have an ambition to achieve positive policy impact from the research are more likely to succeed. Another key part of our experience is trial and error (and sometimes success) in finding methods to effectively translate research findings into policy relevant information. Actively engaging with the policy eco-system is a non-trivial challenge. Ireland being a relatively small country, with a relatively small civil service, made it easier to locate the key policy practitioners to approach, and also relatively easy to physically get to their location. Access to policy practitioners and policy makers is also aided by a culture of informal engagement being acceptable.

It is also useful to tease out some of the lessons learned from the different policy engagements over the past 15 years, a sub-set of which are touched on in this chapter. Journal papers and detailed reports provide a strong and necessary evidence base but are not sufficient without a significant effort in translating the results into policy relevant insights. Engagement is not about communicating research findings, but critically also about listening to the policy practitioners needs, and developing a clear understanding of the policy making process, which is significantly different from the research process. Rather than pushing messages and findings from research it is vital to listen and learn what is useful from their perspective. Building trust with policy practitioners can take a lot of time and effort, but is hugely important. This includes developing personal relations respecting their role, their position, and when
conversations are confidential in nature (especially when this not explicitly stated). Regular staff turnover in the civil service (as they move to different departments across the Government system to improve promotion prospects) makes this a particular challenge.

Based on this experience, coupled with the examples provided our approach can be summarised in a seven step plan that other energy systems modelling teams may find useful, in particular those who wish to bridge between the research and policy eco-systems:

1. **Undertake scientifically robust research**, submit it for peer review, publish it in scientific journals and make methods and results openly and publicly available (e.g. TIMES Ireland Model has peer reviewed paper, model code openly available and scenario results web interface)

2. **Frame research questions that respond to specific policy needs**, and then submit the results and insights to policy practitioners to inform policy (e.g. energy modelling analysis specifically undertaken to inform the National Mitigation Plan 2017)

3. **Translate research results into policy insights**—including through the use of 'policy briefs' (e.g. policy brief submitted to the Joint Oireachtas Committee on Climate Action in 2018, focusing on insights for 2030, for 2050 and on citizen engagement and dialogue)

4. **Improved communications** of research findings through the development of **infographics** (e.g. pathways to a net zero energy system)

5. **Engage actively with policy practitioners and policy makers**—this is critical to move beyond informing and towards influencing policy, mindful of the different roles and responsibilities of each (e.g. the frequent (fortnightly) meetings with Department officials during the summer of 2015 to work on the White Paper on Energy)

6. **Co-production of policy**—co-production is challenging but can be very successful (e.g. invited members of the Carbon Budgets Committee established by the Climate Change Advisory Council to assist them in quantifying Ireland's carbon budgets. These proposed budgets adopted by both Houses of the Oireachtas in May 2022 without the need for a vote.

7. **Building absorptive capacity in the policy system**—This is a further recommendation that arises from the many interactions with the policy system. In addition to the researchers improving their communication of policy insights from the research results, the focus here is on equipping the policy makers to understand the strengths and limitations of the modelling approaches used, and improved interpretation of the scenario results generated. This requires dedicated time and resources to build policy makers' capacity to better absorb the insights arising from the energy modelling analyses. This can be readily extended via a safe space for 'basic' questions, capacity building, secondments in both directions, etc.
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# Retrospective of Prospective Exercises: A Chronicle of Long-Term Modelling and Energy Policymaking in France

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Nadia Maïzi, Edi Assoumou, and Ariane Millot

**Abstract** The French prospective aims to influence the shaping of the future by exploring different scenarios and evolution, so that long-term challenges are considered in current decision-making. In practice realizing this linkage between modellers and policy makers is itself a process and comes with its own challenges. This chapter reflects on modelling to inform various energy policy assessments in France aligned with SDGs 7 (energy), 8 (economic growth), 9 (industry), 11 (cities) and 13 (climate), across several exercises around the reduction of greenhouse gas emissions, the nuclear phase-out, the implementation of a 100% renewable power system with its reliability issues and the assessment of carbon value. It is based on our practical experience at the Centre for Applied Mathematics (MINES Paris—Université PSL) where TIMES has been used as a bottom-up optimization approach to offer insights, within extended committees, to French policymakers. The model we developed is the TIMES-FR model where FR stands for France. Our experience reveals that policymakers do not fully harness the wealth of technical insights provided by researchers, as they often prioritize short or mid-term challenges. While policy perspective does not seem to need the precision and comprehensive-ness of technical modelling, we show that the insights tend to penetrate at some point in the public debate and policy-making sphere, which emphasizes the relevance of using prospective and energy system models to address climate change.

N. Maïzi (✉) · E. Assoumou

Centre for Applied Mathematics, MINES Paris, PSL Research University, Sophia Antipolis, France

e-mail: nadia.maizi@minesparis.psl.eu; edi.assoumou@minesparis.psl.eu

A. Millot

Department of Chemical Engineering, Imperial College London, London, UK

e-mail: a.millot@imperial.ac.uk
## Key Messages

* Enhancing the dialogue between modellers and policy makers is a continuous process during which the technicality of models is often hard to convey as synthetic policy message.

* Our TIMES modelling experiences in France illustrate how TIMES models can support decision making and catalyse discussions on demands, technical choices and systemic interactions.

* Using the model in prospective assessments, we show that TIMES models are relevant tools to explore SDGs 7 (energy), 8 (economic growth), 9 (industry), 11 (cities) and 13 (climate).

* In the context of an acute climate crisis, it is crucial to strengthen the relation between modelling teams that shed light on possible futures and decision-makers, so that they effectively leverage the insights from the scenario results.

# 1 Introduction

Every decision involves the future. This is undoubtedly the reason why all decision-makers are obsessed with their relationship with the future, which has led them to turn to oracles since the dawn of time. The first divinatory technique, the Yi Ching, is attributed to the founder of the Chinese Empire around the fourth century BC. Like Arabic geomancy, it is a technique that involves constructing stories of the future from randomly drawn passages in sacred books. This desire to guess the future is part of a fatalistic credo in which everything is written in advance, the “mektoub” so dear to the Mediterranean.

In our modern societies, forecast, prediction, foresight, prospective or scenario analysis are new related and nuanced expressions of this attempt to make sense of the future. This chapter reviews a practical experience of interactions with the decision-making sphere. Over the past two decades, we have been involved in several country-level prospective exercises for France using our TIMES-FR model to answer different types of energy system-related questions for various stakeholders. These were genuine occasions to confront the short-term, time-constrained agenda of working groups, which included a large range of stakeholders, involving the open and continuous improvement of model mindset of modellers.

Were decision-makers able to understand and make use of the information we provided? Did we learn anything useful from a modelling perspective? Can these scenarios serve the Sustainable Development Goals (SDGs)? This chapter examines this experience to derive some insights into the difficulties encountered as well as the few successes we achieved and the uncertainty about the effectiveness of the dialogue. It shows how long-term analysis results can be handled to build relevant energy policies and strategies for the future, relying on a weak but persistent line of communication and at least some recognition of the contribution of each community.

First, we explicit the specific relation that is established between decision-makers and the future through prospective exercises, whereby prospective is conceived as a
way of shedding light on the future, enabling its construction. Then we underline the interest of using TIMES to provide long-term scenarios. Finally, we draw specific lessons from past exercises for France, related to SDGs 7, 8, 9, 11 and 13, illustrating the pitfalls of dialogue with policy and decision-makers.

The debate on SDGs opens a new phase or, at least, a new challenging chapter in this dialogue. It offers an opportunity to question the type of broader societal aspirations that have been addressed or were omitted in these past experiences.

## 2 French Prospective: Evolution of the Decision-Making Philosophy in France

To predict is always to run up against the unforeseeable, making any forecast perilous. The energy sector, for example, is full of examples of failed forecasts: unpredictable oil shocks in the 1970s, and more recently the advent of rock gas, which has turned world markets upside down, are all cataclysmic events that experts were unable to anticipate and that have caused complete market disruption. In the same vein, all attempts to forecast oil prices have been unsuccessful: none of the exercises carried out by various renowned international agencies has ever consistently tracked fluctuations in the actual price of crude.

This section resituates the French approach to the future for decision-makers in its historical context.

### 2.1 A Vision Driven by Technical or Operational Efficiency

In the aftermath of the Second World War, the United States used futurology, the science of forecasting to define its relationship with the future. Preoccupied with military issues in the context of the Cold War, the United States deployed technological forecasting methods to anticipate what future inventions of military equipment might look like. The work was carried out by the RAND Corporation (RAND for Research and Development), which in fact had a much broader mission of: "[...] furthering and promoting scientific, educational, and charitable purposes for the public welfare and security of the United States." (RAND 2023).

Ultimately, RAND researchers had to think in terms of several possible futures: to envision the military equipment of the future, they had to consider all the factors that could determine decisive technical developments and evaluate any potential geostrategic developments that this equipment would have to respond to. To do so, they developed a now well-known method, the Delphi method, based on a back-and-forth process that solicits expert opinion on a list of probable innovations, and whose limitations include neglecting the relationship between these innovations.
## 2.2 A Philosophically Motivated Vision of Prospective

Around the same time, in France, a new attitude emerged that laid the foundations for the French-style *prospective*. The main difference with the American approach was that prospective did not give technology the same central position in predetermining the future. For some, the impact of technology on society seemed more like a threat to the social order, or even to the survival of humanity. We should bear in mind that the context was the industrial reconstruction of the country after the Second World War, featuring national planning driven by the government.

To design these reconstruction plans, the relationship with the future had to be rethought. In particular, the preparation of the Third State Plan (1958–1961) saw a clear shift from a short-term focus to a long-term focus.

This is because the *Commissariat Général au Plan* (French General Planning Commission), in collaboration with the *Service des Etudes Economiques et Financières* (French Economic and Financial Studies Department), considered it necessary to draw up an economic projection with the aim to also capture social and cultural developments. Similarly, to prepare the Fourth Plan, projections extended to 1975. The evolution from planning to prospective had started, with the aim of producing scenarios to help arbitrate between different choices for society. This development had a profound impact on French society, with prospective becoming an integral part of central government departments (all ministries had their own prospective units), as well as in the industrial sector. Beyond the organisational level, on the theoretical level, the contribution of several intellectual figures was crucial.

## 2.3 Gaston Berger's Legacy

Although the contribution of the RAND is better known, it is worth looking at a pioneer figure of this more philosophical French school of thought, and his unique relationship with the future: Gaston Berger. Born in 1896, Berger began his career at the head of a flower fertiliser company. Curious by nature, eager to learn, and passionate about literature and philosophy, he began studying at the University of Aix-en-Provence late in life, where he forged his conviction that decision-makers needed help to apprehend the future (Berger et al. 2007). He considered himself as “a philosopher in action”: he thought that action was the very raison d’être of decision-makers, who, in order to act, needed to be resolutely turned towards the future. Berger was aware of living in an accelerating world in which the future would not be magically revealed to him: he had to build it.

By the time the 1950s arrived, Berger found himself living in an even more complex, fast-paced world, which he described as follows: “If he is sixty years old, one of our contemporaries has lived in three worlds; if he is thirty years old, he has experienced two... It took thousands of years for man to move from the speed of his own race to that of a galloping horse. It took him twenty-five or thirty centuries to
cover a hundred kilometres an hour. It only took him fifty to exceed the speed of sound.” (Berger et al. 2007).

For Gaston Berger, this very rapid change in society, driven by an unprecedented scientific and technical shift, justified the need to take an interest in the distant future, not as “something that has already been decided, and which we will gradually discover for ourselves, but as something to be done”— the total opposite of the forecasting approach. His prospective approach is summed up in this metaphor: “Our civilisation is like a car driving faster and faster down an unfamiliar road after dark. Its headlights have to reach further and further if we are to avoid disaster.” (Berger et al. 2007).

## 2.4 Production of Long-Term Scenarios

While the practice of prospective is disproportionately discussed regarding its quantitative performance today, not all long-term scenarios currently widely debated and submitted to the public, decision-makers and negotiators are based on models. Thus, the same climate and energy issues can be fuelled by scenarios that:

* tell a story (often one we believe in a priori) and back it up with descriptions and visions: this is what is known as storytelling based on expert opinion;

* involve backcasting, i.e., once you have set the objective you are trying to achieve, you go back in time by simulating and consolidating the trajectory that would enable you to achieve this goal, including intermediate stages;

* combine the imagination of science fiction writers with our scientific expertise, for instance in the case of the scenarios produced by the RED TEAM (2021) for the French Ministry of Defence in association with PSL Research University (Paris Sciences et Lettres).

To explore the long term, climate prospective can also rely on models that are developed to provide the closest possible match with the ‘real world’ and convey its complexity. This is the approach to the future that is widely discussed in this volume and that we are collectively exploring within the community of the Energy Technology Systems Analysis Programme (ETSAP), in which the Centre for Applied Mathematics of l'École Nationale Supérieure des Mines de Paris has been operating as reference laboratory for France since 2008.

In this diversity of frameworks to question the future, our experience illustrates that as part of larger deliberative processes, TIMES models can:

* readily contribute to SDGs 7 and 13, which are clearly energy-related;

* help describe the SDGs 8 and 9 challenges in terms of infrastructure;

* contribute to the SDG11 social dimension by questioning the end-use demands.
# 3 Case Study: Chronicle of the Dialogue Between TIMES and Policymaking in France

This third section provides a brief chronicle of our experience of long-term model-
ling as a component of the scientific policy debate in France since 2005.

## 3.1 TIMES as a Useful ‘Mathematical’ Tool . . . That Merely Approximates Reality

Our model is derived from the work initiated by the International Energy Agency (IEA) through ETSAP after the two oil crises of the 1970s. This initiative aimed to develop a tool capable of envisioning the long-term future of the energy system and, ultimately today, the associated climate issues.

The underlying hypothesis adopted by TIMES models features a single benevo-
lent planner that induces centralised decisions based on an optimality criterion. It is
justifiable to strongly call into question this hypothesis. However, optimisation is a
key method that is used by scientists and essential for decision-makers. Sometimes,
it is important to solve a problem optimally; at other times, optimality is not the
crucial issue, either because a near-optimal solution is sufficient, or because it is
difficult to associate the real problem with a simple criterion enabling judgement of
the solution. Even in these cases, optimisation is useful because it gives the modeller
a way of testing their thinking (Maïzi 2012). If the optimal solution is not feasible, or
even ridiculous, then this may underline the need to refine the model and the thinking
around it, while respecting a principle of coherence with respect to the problem to be
addressed (Colasse et al. 1995) because “caution, the natural companion of igno-
rance, can sometimes lead to the use of an amended or different criterion” (p. 165
Massé 1991).

The static nature and the linearity property adopted by the optimal formulation of
the TIMES models place the underlying optimisation within the framework of
mathematical programming (Dantzig 1963). This branch of operational research
emerged in 1940 in the context of scheduling or planning activities such as produc-
tion planning or stock management led by large organisations and military opera-
tions. Scheduling, in the sense of planning, consists in developing a programme to
determine the value of a variable (known as a decision variable) representing a
quantity or a level of activity. This programme mathematically describes the restric-
tions inherent in planning as a set of equations or inequalities involving these
variables. An acceptable plan is one that considers all these constraints. In TIMES,
both the objective function and the equality or inequality constraints are linear.
Although attractive, it is sometimes unrealistic to retain the linearity hypothesis to
reflect the specificity of the optimal question being asked.

However, in the TIMES family of models, the optimisation hypothesis, although
unrealistic in a competitive, individualistic and decentralised world, provides
valuable insights. Indeed, the results obtained through this approach correspond to the best achievable technological offer satisfying the evolution of demand that we envisage for the future. Even if this best response, this optimal response, leads to unsustainable greenhouse gas emissions, or if the model does not produce a solution, it nevertheless provides a valuable indication. It tells us that the set of hypotheses we are considering leads to undesirable or unfeasible futures. We then have to go back to the drawing board and consider different options or levers in order to change the trajectory and build a future that is both plausible and desirable (Maïzi 2012). Prospective does not aim to foresee, but rather to shed light on possible futures in order to offer “a prosthesis to the blind decision-maker seeking a calculated adventure”, as Pierre Massé urges us to do (Massé 1991).

## 3.2 Evolution of French Legislation on Energy and Climate

In 2024, the transition to a low-carbon energy system is one of the official objectives of French energy policy, and the main commitments set out in the law can be summarised as follows:

* -40% GHG by 2030 compared to 1990 and carbon neutrality by 2050;

* -50% of the final energy consumption by 2050 compared to 2012 (20% by 2030);

* -40% fossil fuel consumption by 2030 compared to 2012;

* 33% of renewable energy in final energy consumption by 2030 and 40% for electricity production;

* cancellation of the previous target of 50% nuclear power in electricity generation by 2035.

The 2030 objective for GHG reduction is due to be updated soon to 50% to align with the new goal set in the European Climate Law enacted in 2021 (reduction of 55% by 2030 compared to 1990 levels for the European Union) and the recent recommendation to reduce net emissions by 90% by 2040.

The adoption and subsequent definition of these objectives, set out in successive bills since the early 2000s (Fig. 1), has evolved, notably aligning France with international and European commitments showing that the challenges of SDG13 have now become a major element of French energy policy.

<figure><img src="image_165.png" /><figcaption>**Fig. 1** Evolution of plans and laws in France from 1990 to 2021. (From A. Millot's PhD thesis 2019). *Note*: DNTE stands for National Debate on Energy Transition</figcaption></figure>
We here look back over this chronology (Millot 2019), focusing on the legislative context of the three committees in which we participated. This serves to illustrate, at the French level, how the climate-constrained energy scenarios that we have drawn up have enabled discussions on the implications of choices and options, in terms of industrial policy, imports, costs, deployment rates of various means of production (SDGs 8 and 9), technical feasibility (SDG7), lifestyles and behaviour (SDG11) etc.

## 3.3 The POPE Law (Energy Policy Orientation Programme)

In 1998, the Kyoto Protocol led to the adoption of an 8% reduction target for GHG emissions by the European Union. The ensuing burden sharing approach led France to pledge to maintain its emissions at 1990 levels, which went on to be included in 2000 in the first *Plan national de lutte contre le changement climatique* (PNLCC: National Plan to Combat Climate Change).

The *Plan Climat* (Climate Plan) adopted in 2004 was designed to meet both national planning needs and European Union reporting requirements (Virlouvet 2015) by strengthening measures to combat climate change, with for example the introduction of penalty and reward schemes in the transport sector and the implementation of local climate plans.

The following year, 2005, saw the adoption of the *Programme d'orientation de la politique énergétique* (POPE: Energy Policy Orientation Programme), known as the Factor 4 law. This legislation set the target of dividing greenhouse gas emissions by a factor of 4 by 2050 compared with 1990 levels and improving energy efficiency by 2% a year starting from 2015. This law therefore introduced two novel elements: a long-term target for emissions and an energy efficiency target. Consideration was given to breaking down this target by sector, with the creation of a *Commission Énergie* (Energy Committee) within the *Centre d'Analyse Stratégique* (CAS: Centre for Strategic Analysis attached to the Prime Minister's office) to assess medium- and long-term trajectories, i.e. for 2020 and 2050 (Syrota 2008). This committee was composed of researchers including members from modelling teams, representatives of trade unions, the private energy sector, and various ministries (industry, transport, agriculture, energy, and economy). The same structure was subsequently replicated in other committees in which we participated. This gave us the opportunity to bridge the gap between the various stakeholders involved in the issues raised by SDGs 7, 8, 9, 11 and 13 as different working groups defined the macroeconomic framework, the housing and mobility demand and the policy measures.

To provide relevant insights, we carried out, using our TIMES-FR model (Maïzi and Assoumou 2014), a series of five prospective exercises for this commission, in order to identify the major trends for 2050. These exercises evaluated different reduction scenarios by varying the hypotheses of the baseline scenario. The scenario had to comply with the laws and commitments already made by France, the 2005 law and the 2015 objectives featured in the PPE (multiannual energy plan). The first version was based on the following assumptions:
* high fossil fuel prices;

* maximum deployment of nuclear potential (in terms of feasibility and eligibility);

* technological progress without disruption;

* renewable resources limited to available potential;

* compliance with legislation;

* compliance with international commitments;

* maximum exploitation of insulation potential.

Four variants of this baseline scenario then reconsidered a number of elements that we used to carry out a sensitivity analysis. These involved:

* diminishing the voluntarist character of the first scenario by reducing the exploitation of insulation potential in the residential sector successively from 100% to 50% and then to 25%, with the effect of increasing useful demand in the residential sector;

* revising mobility demand downwards and lowering gas prices;

* testing, for the industrial sector, the hypothesis of an expansion of the services sector in French industry (or of relocation) and that of maintaining a certain level of industrial growth;

* varying the level of nuclear penetration, initially unlimited and then limited respectively to 90GW and 65GW of new potential installed capacity.

These new scenarios were constrained in terms of their CO₂ emissions (relative to 1990 levels, restricted to energy use and excluding emissions from international traffic). Whatever the hypotheses used, it was established that in these different scenarios, and to make conservative hypotheses on potential technological choices, the Factor 4 constraint was unattainable. Indeed, the following technologies were not allowed in our exercises to meet demand: CO₂ capture and sequestration, deep-rock geothermal, offshore wind, hydrogen vector. Renewable source potentials were limited by expert opinion. This minimalist vision of technological progress enabled us to clarify the future in the sense of a lower threshold, which could only be improved if it turned out that one of the discarded technologies could be deployed with an acceptable cost/effectiveness ratio.

For two of our exercises, the factor 4 constraint is only possible at the expense of irrational choices, such as stopping industrial production in the steel, glass and ammonia industries. In our TIMES modelling exercise, this corresponds to an optimal solution which favours importing the quantities needed to satisfy useful demand in order to externalise CO₂ emissions so as to satisfy the Factor 4 constraint. This impossibility of determining a technological choice that satisfies the constraint is reflected in the marginal costs associated with one tonne of CO₂ avoided, which in all these exercises, often from 2020 onwards, exceed several thousand euros, indicating the failure to find a coherent solution (Table 1).

It then became clear that the trend would not allow France to achieve a CO₂ emissions reduction factor in line with the law's objective without a significant change in direction, even when varying a voluntary useful demand, as we had envisaged for the transport and residential sectors, and maintaining the industrial
<table><caption>**Table 1** Evolution of the marginal cost of abatement per tonne of CO₂ avoided with a division by 2 (F2), by 3 (F3), by 4 (F4) of CO₂ emissions regarded as 1990 levels in 2050 for the BAU scenario</caption><thead><tr><th>Marginal Cost €/tCO<sub>2</sub></th><th>2015</th><th>2020</th><th>2025</th><th>2030</th><th>2035</th><th>2040</th><th>2045</th><th>2050</th></tr></thead><tbody><tr><th>F4</th><td>44</td><td>82</td><td>490</td><td>1318</td><td>5094</td><td>9916</td><td>19,700</td><td>29,627</td></tr><tr><th>F3</th><td>45</td><td>110.2</td><td>456</td><td>505</td><td>1578</td><td>2443</td><td>5124</td><td>9927</td></tr><tr><th>F2</th><td>41</td><td>103.5</td><td>255</td><td>257</td><td>228</td><td>176</td><td>229</td><td>449</td></tr></tbody></table>

*Note:* It assumes limited nuclear with 58 PWR = 90 GW, residential: insulation potential 75%, transportation: greater efficiency = road freight 30%, aviation 20%, modal transfers

sector at a reasonable level of competitiveness. Furthermore, given the set of hypotheses we had agreed to restrict ourselves to, our analyses show that factor 2 appears to be a potential target in terms of reducing CO₂ emissions. One of the scenarios considered (Table 1), with marginal costs of the order of a few hundred euros, offers options for:

* the electricity generation mix, with the penetration of wind power and biomass (wood-fired power stations) balancing out a reduced nuclear capacity;

* the transport sector halving its emissions thanks to technologies based on biofuels and NGVs (Natural Gas Vehicles), which will gain significant ground from 2020 onwards;

* the residential/tertiary and energy sectors achieving a factor of 3;

* the industrial sector halving its emissions, notably by substituting electricity for non-specific gas uses.

From our recommendations, the Commission reported the following in 2008: "By 2050, given the need for growth, it seems difficult to achieve anything better than a factor of 2.5, unless we start integrating breakthrough technologies that were not selected because deemed very unlikely. To go beyond that, towards factor 4, we would have to, on the one hand, fundamentally change our behaviour and all our organisational processes and, on the other hand, rely on technological advances that are difficult to predict." (Syrota 2008).

This is an example of concise policy-oriented statements aiming to synthesize the insights of a long series of scenario construction and modelling. Reading this statement today clearly shows the multiple SDG industrial, economic, and societal implications that were already present. However, we also have to acknowledge that very little was concretely implemented to influence behaviour, consumption, daily life, work and production.

## 3.4 Fukushima and Nuclear Power in France

From 2007 onwards, France gradually changed its approach to energy policy (Andriosopoulos and Silvestre 2017) and the energy issue began to be increasingly taken into account in climate policy, with the opening of debates that led to the
adoption of the Grenelle 1 and 2 laws (2009 and 2010) detailing the sectoral objectives for implementing the policy to tackle climate change. Once again, many measures from previous Climate Plans that had not been implemented were recovered and integrated (Millot 2019). In addition, the content of the Grenelle laws was largely dictated by the need to comply with European legislation (Lacroix and Zaccaï 2010). In that regard, the adoption of the *Paquet Énergie Climat* (Climate Energy Package) in 2008 had a structuring role for French energy policy.

In 2011, we took part in the work of the Besson Commission on the future of the French energy system, using our France model to explore several scenarios for the development of the French energy mix at the 2050 horizon.

Here again, we produced a thorough analysis of the power sector with TIMES-FR (Maïzi and Assoumou 2007) from which elements in terms of industrial policy, operation of the power system, and consumption issues were not exploited by decision-makers. Figure 2 compared for instance the cumulated new capacity for the power sector required by 2050 in a Business-as-usual case and a politically decided progressive (PROG) and fast (FAST) nuclear phase-out. It further combines these elements with a carbon tax (t1) case or an emission limit in volume (v1).

Our contribution concluded with the following recommendations in Syrota (2008): "there are a number of findings that seem to us to be lasting and robust, whatever the technological options adopted:

<figure><img src="image_166.png" /><figcaption>**Fig. 2** New installed capacities cumulated over the horizon. Note: Cases are BAU (Business-as-usual case), a politically decided progressive (PROG) and fast (FAST) nuclear phase-out, combined with a carbon tax (t1) case or an emission limit in volume (v1)</figcaption></figure>
* the cliff effect puts the French electricity system up against the wall: sustained investment will be needed to renew an electricity generation fleet that is reaching the end of its life;

* this situation opens up opportunities for all industrial sectors, the main challenge of which will be to sustain an ambitious rate of construction of new capacity, with specific issues, including acceptability and reliability;

* at the same time, if environmental issues are to remain high on the public policy agenda, the current paradigm of increasing electricity consumption will have to be challenged in the coming decades;

* these factors suggest that the question of long-term energy policy options cannot be defined solely in terms of technological choice and must go beyond the framework of pro- or anti-nuclear opposition.

This contribution, which is mainly a technical reflection, should be part of a wider debate on social and behavioural choices. It will be essential to involve users of electric vehicles in this debate.”

However, the conclusions of this report (Syrota 2008), as widely disseminated in the press, will only be remembered for their narrow focus on the issue of the potential extension of the life expectancy of power stations to 60 years. As pointed out in a press article (Lingaard 2012), “almost a year after the Fukushima accident, the Energies 2050 report wants to turn the page on the questioning of the atomic energy industry”. Therefore, the results of the proposed prospective exercises were not used to draw up a genuine energy programming plan.

## 3.5 Carbon Tax and Quinet Commission

At the end of the National Debate on Energy Transition (DNTE) launched in 2012, the *Loi de transition énergétique pour la croissance verte* (LTECV: Law on Energy Transition for Green Growth) was enacted. The green growth concept, originating from the *Grenelle de l'environnement* summit, was adopted as the main objective of the law. Economic growth therefore remains the main indicator of public policy performance, while the environment and ecological issues are seen as growth drivers aligned with SDG8 and SDG9. There is therefore no fundamental questioning of the mode of development. From this perspective, it seems difficult to seek to change lifestyles, and the measures promoted by the ministry focus more often than not on the supply side and the technological aspect, and less on the demand for energy services. Despite this context, we conducted different studies that questioned drivers of human behaviour to address demand-side mitigation policies. Relevant lessons were drawn from these studies regarding behaviour (Cayla et al. 2011; Cayla and Maïzi 2015), degrowth options (Briens 2015), and lifestyle (Le Gallic et al. 2017; Millot et al. 2018). While similar underlying hypotheses to the ones we used were used for several alternative scenarios for achieving SDGs 8, 10 and 11, decision-makers made scant use of them.
Indeed, the main way to integrate climate change action (SDG 13) into national policies in France relied on carbon value, as shown by the implementation of a carbon tax in the LTECV, whose pathway aligned with the Quinet Commission's recommendation. A previous attempt to introduce a carbon tax in the Grenelle laws of 2009 was rejected by the *Conseil constitutionnel* (Constitutional Council). However, the Quinet Commission, comprising several experts, gathered in 2008 to assess the shadow price of carbon and produced a report in 2009 recommending a progressive carbon tax, reaching €100/tCO₂ by 2030 and €200/tCO₂ by 2050 (Quinet 2008). This carbon value was established so that all public projects and all public decisions would henceforth be arbitrated by integrating their cost for the climate, i.e. their carbon cost. We participated in this effort, providing a thorough analysis of how carbon value could be a key driver to support mitigation pledges (Assoumou and Maïzi 2011).

Ten years later, as the yellow vest movement erupted, the Quinet Commission *Deux* (two) was again called on to assess the tutelary value of carbon, used for the socio-economic evaluation of public investments (Quinet 2019). In our contribution to this report, we used our TIMES-FR model to assess the pathway of the tutelary value of carbon to achieve carbon neutrality.

The marginal abatement cost of CO₂ is illustrated in Fig. 3 for two scenarios, which both achieve carbon neutrality by 2050 for France. However, the hypothesis behind the availability of carbon sinks leads to different constraints for the energy system represented in TIMES-FR. Under the low assumption, the carbon sink amounts to 75 MtCO₂ while in the high scenario, it reaches 95 Mt. CO₂. Consequently, the two scenarios give rise to two different energy systems, resulting in different total final energy consumption (Fig. 4).

However, some scenarios assessed with our model either led to infeasibilities or to marginal cost values well above €7000/tCO₂. These scenarios reflected some

<figure><img src="image_167.png" /><figcaption>**Fig. 3** Evolution of the marginal abatement cost of CO₂ with TIMES-FR for two scenarios</figcaption></figure>
<figure><img src="image_168.png" /><figcaption>Fig. 4 Final energy consumption by 2050 with TIMES-FR for two scenarios</figcaption></figure>

constraints on the availability of some technologies or the lower potential of carbon sinks and were not selected for the final report, which focused on scenarios with more acceptable values for the marginal cost of CO₂. The final figures presented by the Quinet Commission amounted to €250/tCO₂ in 2030 and €775/tCO₂ in 2050, values accepted as consensual, taking into account the disparity in the results of the different models used.

## 4 Conclusion on the Difficulty of Getting Key Messages Across

The chronicle of the prospective exercises we have developed testifies to the various challenges raised by France around SDGs 7, 8, 9, 11 and 13, which successively sought to divide its GHG emissions by 4 by 2050 (compared to 1990 levels) in the 2007 Syrota report, considered phasing out nuclear, implementing 100% renewables and reliability issues in the 2011 Besson report, then went on to assess carbon value in the 2018 Quinet commission to establish a climate value for action.

Through our involvement in these various government committees, we have measured the gap between:

* on the one side, our point of view as modellers wanting to shed light on the future through prospective analyses based on the TIMES model used as a prospective tool to obtain normative information from energy scenario analyses. This model's explicit formulation of the input-output relationships for each technology can be
applied to a horizon of several decades and a given final demand in order to minimise a discounted overall cost and obtain the corresponding levels of activity and investment required to create a virtual economy in which the various technologies can compete. The extensive results obtained in this way make it possible, in line with the philosophy of foresight, to work on building the future by considering measures reflecting different policies, measures or incentives;

* on the other side, the position of a pragmatic, modern decision-maker who may have less lofty aspirations for the future and may simply seek to minimise any short- or medium-term dissatisfaction resulting from difficult choices on somewhat worrying technical issues among numerous societal priorities. In this quest for immediacy, such decision-makers are reluctant to take into account all the weak signals that prospective analysis can offer, and tend to focus on specific elements, neglecting the wealth of lessons that could be learned.

In terms of energy policy objectives (SDG7), this latter issue has been clearly demonstrated since the 2000s, as energy policy has followed a cycle of around 5 years. Two to three years after the adoption of a law, a new consultation lasting 1 or 2 years is launched, leading to the adoption of new objectives and a new law. The latest law adopted in 2019 enacted the target of carbon neutrality and a new law is currently under discussion (2024). As Virlouvet (2015) points out, "all national climate planning documents begin by stating that the results of the previous plan have not been achieved". Many documents also repeat old measures from previous plans. For example, the economic instruments announced in the PNLCC 2000 (National Programme to Combat Climate Change) were not implemented but were taken up again in the 2004 and 2009 Climate Plans. Regarding the process of drafting the law, the Grenelle was a turning point in the consideration of the climate issue in energy policy (Virlouvet 2015) and introduced a process of consultation with the various stakeholders. This process has been strengthened by the DNTE (National Debate on Energy Transition).

Monitoring of the objectives of the *Loi Énergie Climat* (Energy and Climate law) is scattered among several documents produced by the Ministry, which makes it difficult to understand the progress of the transition. For example, the commitments made in previous laws are not specifically monitored. As far as emissions are concerned, the 2020 target has been met, but the current reduction rate is not aligned with the rate needed to achieve carbon neutrality by 2050. On the other hand, the reduction in final energy consumption is not in line with the commitments made. In particular, the POPE law target for final energy intensity has not been met.

While we understand this difficulty of dealing with urgency combined with the idea of achieving a desirable future (namely in line with SDGs), we strongly recommend considering the use of prospective to enlighten decision-makers who take the climate issue seriously. Indeed, they can only adopt a feasible plan by adhering to this foundation of prospective disciplines, which establishes the close link between the future and action and is based on the fundamental idea that the future is not to be endured, as a fatalistic posture implies, but rather to be built. In these times of climate emergency, this is a message that needs to be revived: to look
at the long term to decide on the strategy to be deployed. In the absence of an enlightened approach, the climate issue tends to be governed by clumsy and ineffective sweeping measures.

Our prospective exercise using the TIMES model is in line with this philosophy but struggles to be adopted by decision-makers. However, in the story we have just told and beyond, we have measured that our impact (as members of the scientific community), although delayed, has sometimes penetrated the decision-making sphere. For example, the question of how the penetration of renewable energies has impacted the reliability of the electricity system (Drouineau et al. 2015), the debate on our lifestyles (Cayla et al. 2011) and the increasing scarcity of critical materials (Boubault and Maïzi 2019) are all prospective subjects that have finally found their way into the public debate and serve the related SDG7, SDG9 and SDG13. These are signs that, despite the difference in perspectives, the dialogue is constructive.

Moreover, climate prospective, since it concerns all sectors and combines many issues (economic, societal, technical, political, financial, legal, strategic, etc.), cannot be carried out employing a single type of model, nor using only models, since there are many causal loops.

Ultimately, what we are discussing here is the art of deciding, because, like French mathematician and philosopher Blaise Pascal, we believe that it is impossible to work on effective thinking without exercising climate prospective, both in order to shed light on how to envisage mitigation and adaptation options, and to ensure that the solutions of some do not become the problems of others.

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# Correction to: Aligning the Energy Transition with the Sustainable Development Goals

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Maryse Labriet, Kari Espegren, George Giannakidis, and Brian Ó Gallachóir

## Correction to:

M. Labriet et al. (eds.), *Aligning the Energy Transition with the Sustainable Development Goals*, Lecture Notes in Energy 101, https://doi.org/10.1007/978-3-031-58897-6

The book "Aligning the Energy Transition with the Sustainable Development Goals", written by Maryse Labriet, Kari Espegren, George Giannakidis, and Brian Ó Gallachóir, was originally published electronically on the publisher's internet portal on 11th June 2024 without open access. With the author(s)' decision to opt for Open Choice the copyright of the book changed on 3rd Oct 2023 to © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 and the book is forthwith distributed under a Creative Commons Attribution CC BY 4.0. ETSAP (Energy Technology Systems Analysis Program), Institute for Energy Technology (IFE), System Analysis – ETSAP, Instituttveien 18, 2027 Kjeller, Norway.

<footnote>The updated version of this book can be found at
https://doi.org/10.1007/978-3-031-58897-6</footnote>
**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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

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