RES2Go: Matching energy demand and supply in industry hubs in Europe

RES2Go: Matching energy demand and supply in industry hubs in Europe Faculty of Engineering and Architecture Departement of Electromechanical, Systems and Metal Engineering Energy & Systems Lab, Energy and Cluster Management [email protected] Volta Building, Tech Lane Ghent Science Park - Campus A Technologiepark-Zwijnaarde 131, Ghent B-9052, Belgium www.ugent.be October 2025 2/26 ABSTRACT Early 2025 the European Commission launched the Clean Industrial Deal to reinvigorate the competitiveness of Europe's industry. One of the routes focuses on providing low-carbon energy solutions whilst staying true to the Commission's climate neutrality ambition. Energy-intensive industries (EIIs) are crucial to this transition since they represent one-fifth of Europe's greenhouse gas emissions. However, since the energy crisis, energy costs are the biggest challenge for EIIs in Europe, and in addition, the transition towards net zero brings plenty of uncertainty due to forced new production routes and auxiliary services. One of the levers to reduce energy prices, in particular low-carbon energy costs, is to lower the long-term investment risk, and key to reduce energy consumption is process optimisation, not only for EIIs but also for policymakers and energy investors. To guide policy and industry decision-makers on infrastructure or business planning, there is a strong necessity to anticipate energy needs. However, assessing current and future industrial energy consumption is challenging due to confidentiality and/or lack of data. The AIDRES report (Advancing industrial decarbonisation by assessing the future use of renewable energies in industrial processes) [1] published by DG Energy in 2022, addresses this data gap by using blueprints of key process industries: steel, chemical, glass, refineries, fertilisers and cement. Industry blueprints are introduced in the H2020 project EPOS [2], providing a generic description of an industrial process associated with a final product, by calculating material and energy needs per tonne of product output. These values can be projected at the European level using production rates of industrial sites to obtain the total amount of energy or tonne of material (e.g. crude oil, biomass, hydrogen). From this database various pathways can be applied to locate energy demand hubs or plan supply services. This way the RES2Go tool responds to the request from European policymakers to address cluster scenario flexibility while offering a high level of granularity. This paper introduces the user-friendly RES2Go web tool, developed in collaboration with DG ENER under supervision of an industry reflection board, and published as JRC datasets [3]. It projects pre-made or self-designed transition pathways to support industrial sites and hubs in Europe. The paper highlights the potential of RES2Go as decision-support tool in companies, industry hubs, for policymakers and investors, by assessing energy demand/supply options across Europe. The web tool offers a wide range of features designed to meet the current and future expectations of any energy actor, with the aim of providing local energy solutions and thus supporting the competitiveness of the energy-intensive industry in Europe. 3/26 Table of Contents Abstract ....................................................................................................................... 2 Introduction ................................................................................................................. 4 1 RES2Go ................................................................................................................. 5 1.1 Backbone database: AIDRES ...................................................................................... 6 1.2 From AIDRES to RES2Go ............................................................................................ 7 1.3 RES2Go modules ....................................................................................................... 8 1.3.1 Pathway module ..................................................................................................................... 8 1.3.2 Clustering modules ................................................................................................................. 9 1.3.3 Times series .......................................................................................................................... 13 1.4 RES2Go output ........................................................................................................ 13 1.4.1 Pathway output .................................................................................................................... 14 1.4.2 Spatial and cluster output ..................................................................................................... 15 1.4.3 Local RES supply .................................................................................................................... 17 2 Discussion ........................................................................................................... 18 3 Conclusion ........................................................................................................... 20 References .................................................................................................................. 22 4/26 INTRODUCTION On 20 February 2024, the Antwerp Declaration for a European Industrial Deal [4] stressed crucial points for energy-intensive industry (EII) facing the post-2022 energy crisis [5]. Among them, energy competitiveness is of key importance since EIIs generate around 20% of the Europe’s GDP [6]. They are also involved in many strategic value chains including defence or health, as well as in the development of clean technologies such as batteries or photovoltaic cells [7] [6]. To keep the foundation industry competitive in Europe is therefore vital to the Commission, particularly in the light of reaching net zero in 2050. Within this context, energy supply has become essential to policy development, as explicitly addressed in the European Clean Industrial Deal (CID) published on 26 February 2025 [8]. It includes the Action Plan for Affordable Energy that focuses on the electricity price by encouraging an upgrade of infrastructure to enhance interconnectivity and support the European single market. In addition, the Industrial Decarbonisation Accelerator Act [9] aims to improve predictability by enhancing the use of databases by decision-makers and thus to drive infrastructure investments. In this context, the ability to predict and assess the consequences of industrial pathways is crucial for guiding investment decisions. Uncertainty plays a significant role for companies, and one way to reduce associated risks is through shared investment and collaboration. This form of cooperation is known as industry clustering, studied for 25 years at the Energy and Cluster Management research group at Ghent University, and now being highlighted by both Draghi [10] and the European Round Table for Industry (ERT) [6] as a key enabler of the energy transition. This paper addresses the above topics by focusing on industrial clusters to advance the concept of energy hubs across Europe. Indeed, clustering energy supply and demand is essential to the successful deployment of clean energy infrastructure, by offering new opportunities for industry to invest, share risks, optimise demand and call for supply [11]. For instance, the Tranzero Initiative — Port of Gothenburg, Sweden — demonstrates how common interest in energy infrastructure is leveraged in order to reduce investment needs and risks [12]. Still such initiatives mainly exist at the local level, resulting from a bottom-up process, which often prevents to focus on larger-scale investments. A European spatial database encompassing the energy demand of heavy industries, combined with a clustering approach, can be used to assess the potential for high-level industrial symbiosis. This is one of the key pillars in DG ENER's mission [13] following the Draghi report [10]. The RES2Go tool is designed to serve that purpose and is the focus of the present paper. After introducing the core database, AIDRES [14], an overview of the main modules is given, and the key outcomes as well as application opportunities of the RES2Go tool are discussed. 5/26 1 RES2GO RES2Go is an open-source, interactive web tool to support industrial emission reduction by clustering energy demand and providing low-carbon energy solutions. RES2Go assesses the need for energy in industrial processes and clusters across Europe. While independent of the source, a focus on renewable energy is easily configured and also a projected geo-localisation of cluster supply is a feature of the tool. The interface is designed with user accessibility in mind. The software is developed using Streamlit [15], which offers an intuitive interface added with open access and easy deployment. The diagram in Figure 1 shows the logic behind the RES2Go tool, with the main modules in blue boxes. Figure 1: Relationship flow chart of RES2Go tool • The pathway module defines the scope by selecting sectors and products from AIDRES or by adding new ones. The output of this module is the specific energy consumption per tonne of product for a given pathway. • The clustering module then uses these specific energy parameters in combination with spatial data from AIDRES. For newly added products that lack spatial data, their implementation is handled through the cluster-level submodule. Since the absolute consumption of energy carriers is spatially located, the user can choose among several clustering algorithms. This module provides consumption information aggregated at cluster level, which can either be visualised using maps (mapping output) or used in the clusterlevel submodule to edit and/or add new products to a selected cluster. Insights into the energy carriers shared within clusters can be visualised in the energy carriers (green box) representation. • Finally, the time series module integrates the spatial location and electricity consumption of each cluster with industrial load profiles (ELMAS), the local technical potential of renewable energy sources (ENSPRESO), and historical local hourly generation data for onshore wind and solar (EMHIRES) to estimate the renewable energy potential associated with a cluster. As ENSPRESO provides three scenarios, the user can select one to compare the impact of strong versus low renewable energy implementation. 6/26 1.1 Backbone database: AIDRES To support the spatial assessment of industrial emission reduction pathways, only a few databases offer detailed, site-specific energy demand data. Among these, only Hotmaps [16] and AIDRES provide spatially resolved information relevant to the analysis presented in this paper. While Hotmaps presents valuable insights into industrial heat demand, it lacks forward-looking pathways and process-level detail. In contrast, AIDRES focuses on both current and future industrial technologies and production processes, making it more suitable as basis for building industrial energy scenarios with spatial outcomes. Developed in 2022 by the AIDRES consortium, assigned by the European Commission, the AIDRES database includes site-level data on energy consumption, CO₂ emissions, and production capacities for 1,536 industrial sites across Europe, covering six energy-intensive sectors: steel, chemicals, refineries, fertilisers, glass, and cement. Each sector is represented by one or more generic products (e.g. olefins, polyethylene, polyethyl-acetate in the chemical sector [17]). Per product, several production routes are developed based on different feedstocks, energy carriers, and decarbonisation technologies (e.g. electrification, CCUS, hydrogen). These routes were modelled using blueprints generated in ASPEN [18] [19] and OSMOSE [20] developed for the EPOS project, under University of Ghent ECM coordination [2]. Blueprints simulate industrial processes using anonymised but realistic data, enabling the calculation of specific energy and CO₂ emission values per tonne of product — referred to here as production route parameters. AIDRES identifies 218 production routes across the six sectors. These routes serve as the building blocks for constructing pathways, which combine one or more routes across sectors to estimate aggregated energy demand and emissions. By projecting production route parameters onto individual sites (based on product type and production capacity), it becomes possible to estimate site-level and EU-wide energy and emissions profiles. In cases where multiple routes exist for one product, AIDRES applies weighted averages to reflect uncertainty in future technology adoption — an approach that improves regional comparability but limits accuracy at the individual site level. The spatial component of AIDRES is established by linking the EU ETS installation-level data (EUTL [21]) with the E-PRTR database [22], which provides coordinates, sector classification, and emissions data. After applying verification filters, installations are aggregated per industrial site, acknowledging that a single company may operate several installations at one location. Production capacities are compiled using a combination of public data from industry federations (e.g. EUROFER [23], Concawe [24]), default values for smaller sites based on Pareto analysis [25], and emission factors from the literature. 7/26 1.2 From AIDRES to RES2Go While AIDRES provides a valuable basis for modelling industrial energy scenarios, it presents several limitations that constrain the direct application and the capacity to update. Firstly, working with the database requires advanced technical skills, particularly in coding. New pathways or scenario adaptations can only be implemented by modifying SQL files [26], which limits the usability by nonexpert users and restricts the inclusion of emerging technologies or new production processes. Furthermore, the underlying assumptions of AIDRES are increasingly outdated. The reference year for all data and scenarios is 2018, which is before Europe entered a deep downturn. Over the past five years the European industry has faced a series of grand challenges, at first the COVID-19 pandemic, then the war in Ukraine, and subsequently the energy and economic crises, which significantly cut the competitiveness of Europe's foundation industry. As a result, the three scenarios developed in AIDRES — EU-MIX 2050 (climate-neutral target), and two intermediate 2030 and 2040 pathways — no longer fully reflect the current technological and economic landscape. Still the base year 2018 can be kept as reference year provided flexibility in setting scenarios and implementing (new) technologies can be guaranteed [27]. For example, back in 2020 hydrogen was designated as a strategic priority by the European Commission [28], and the AIDRES 2050 pathway accordingly integrates hydrogen-based processes for sectors such as steel, chemicals and glass. However, more recent analyses, such as the IEA’s 2024 Hydrogen Outlook [29], cast uncertainty on the pace and scale of hydrogen deployment. This highlights the challenge of maintaining scenario relevance amid evolving policies, markets, and technological trends. In addition, the structure of AIDRES restricts flexibility in modelling. The database does not currently allow for adding new production routes, nor for including novel products or installations. This limitation is especially notable in the chemicals sector, which is highly complex and includes multiple upstream and downstream processes. AIDRES represents this sector by just three products: polyethylene (PE), polyethylacetate, and olefins, therefore overlooking major emission or energy consumption sources. Another significant constraint is the lack of temporal resolution. AIDRES outputs are expressed as annual average energy values per product, which precludes any time series analysis. This is a critical limitation in energy systems increasingly reliant on variable renewable energy sources (RES), where hourly or seasonal profiles are essential for assessing system flexibility and investment needs. Temporal modelling is particularly important for evaluating mid-term agreements such as power purchase agreements (PPAs) and other energy assets [30]. Despite these limitations, AIDRES remains a robust and thorough dataset that can serve as a strong basis for more dynamic and user-centred tools. RES2Go is developed precisely to overcome these barriers — by improving accessibility, enabling scenario editing, integrating time series data, and expanding the scope of products and configurations though the modules presented in the flow chart (Figure 1) and explained below. 8/26 1.3 RES2Go modules The modules aim to effectively address how industrial clusters can contribute to achieving climate neutrality in process industries [31] as well help to enhance competitiveness as mentioned in the Draghi report [10]. The first two modules, which consist of developing a pathway and projecting the pathway to allocate and cluster energy consumption, focus exclusively on demand. These modules only provide annual energy consumption and spatial data. However, discussions with industrial stakeholders have emphasised the importance of the temporal dimension, both for industrial demand and renewable energy generation. The time series module addresses this by aligning demand with local renewable energy supply. The RES2Go modules leverage open databases, on the one hand by integrating and analysing them using insightful clustering algorithms, and on the other hand by allowing user input to adapt the results to specific requirements 1.3.1 Pathway module The configuration of pathways in RES2Go relies on a single table from the AIDRES database. It provides input data on energy, feedstock, and emissions per tonne of product for all production routes. Table 1 provides an example of a production route used for the polyethylene. From these data, a pathway can be developed in three ways: by starting from a pre-defined pathway such as the EU-MIX from AIDRES, by creating a new pathway from scratch, or by uploading a previously made pathway. The latter option allows to use a single pathway in multiple sessions. In all cases, the RES2Go interface enables users to select and add production routes across different sectors, adjust the associated weights, and edit the raw data for energy use, feedstock input, and CO₂ emissions per tonne, direct and indirect. When multiple production routes are selected for a single product, the module calculates the weighted average for all relevant parameters as highlighted in Table 2. The polyethylene (PE) route is the combination of all PE production routes, using natural gas (NG), hydrogen (H2) or electricity (EL). Table 1: Polyethylene (chemical) natural gas production route parameters from AIDRES Route name Electricity (GJ/t) Hydrogen (GJ/t) Natural gas (GJ/t) Hydrogen (t/t) Natural gas (t/t) Total emission (TC02/t) PE (NG) 0.4923 0.00597 0.29048 0.00005 0.00593 0.05004 9/26 Table 2: Weighted average energy consumption for polyethylene from AIDRES EU-MIX-2050 Route name Electricity (GJ/t) Hydrogen (GJ/t) Natural gas (GJ/t) Total emission (TC02/t) Weight (%) PE-MEA (NG) 0.50 0.0059 0.3044 0.0209 31.30 PE (H2) 0.49 0.31 0.00 0.0164 34.35 PE (EL) 0.75 0.01 0.00 0.0249 34.35 Weighted average energy PE 0.58 0.11 0.09 0.02 To create a new production route, the user can either create a new product within an AIDRES sector or create a new sector. This feature allows to include technologies or processes that are not covered in the original AIDRES database, supporting greater flexibility in defining scenarios. Once a pathway is defined, the algorithm projects the production route parameters (per tonne of product) onto corresponding industrial sites to obtain absolute values for energy consumption, feedstock use, and CO₂ emissions at the site level These pathways form the building block for the subsequent module, which projects energy and emission values per tonne of product onto industrial sites across Europe and enables clustering based on site-level characteristics. 1.3.2 Clustering modules Next to pathway scenarios for industries and sectors, RES2Go also provides the option to visualise hubs for (energy) optimisation and (resource) circularity. It is well-known that industrial clustering has significant untapped potential and combining it with renewable energy availability can strengthen the European energy system, as described by Dhondt et al. [32]. This feature uses a second key table compiled in the AIDRES project: a database of industrial sites and characteristics, such as name, location, associated products and production capacity per site within the AIDRES scope. If a production value is assigned to a site, this is interpreted as capacity and multiplied by the sectoral utilisation rate to estimate production. Subsequently, following the clustering algorithm outlined below, the energy demand per site is aggregated by cluster and rounded to two significant figures. With production estimates assigned to each site, users can select and configure a clustering algorithm to optimise inter-relations between industries and sectors in Europe. In the first version of RES2Go, four algorithms, based on DBSCAN [33] and KMeans [34], are implemented using the Scikitlearn library. They are selected for their demonstrated relevance in industrial clustering applications, as highlighted by Mendez et al. in the H4C project [35]. 16/26 Figure 8: Map output from RES2Go with DBSCAN min points = 5 and max distance = 22km for EU-MIX-2018 and EU-MIX-2050 Figure 9: Map output sites coloured by cluster from RES2Go with DBSCAN min points = 5 and max distance = 22km. Absolute values per site and per cluster can be presented in a table. This is meant to inform politicians as well as energy actors on hubs and nodes with high-potential impact on the climate and energy transition. In addition, it is possible to focus on one or several countries in case studies, for instance by downloading a CSV file including energy demand, location and industry name for the selected countries or clusters. By selecting a cluster pie chart, additional visualisations of the energy carrier distribution in the cluster can be accessed. The charts include a tree-map (Figure 10) to explicit the share per energy carrier and a Sankey diagram showing the flow of energy carriers towards the respective sectors (Figure 17/26 11). A downloadable CSV table is provided as option, listing all sites with their respective energy consumption, production levels, and utilisation rates. In addition, data on clusters, sites, and production capacities can be exported for editing with cluster level module in the Cluster – micro scale section of the tool. Figure 10: Energy carriers tree map comparison for Port of Antwerp cluster EU-MIX-2018 (3000 PJ/y) and EU-MIX-2050 (32000 PJ/y) Figure 11: Energy carriers Sankey diagram for the Port of Antwerp using EU-MIX-2050 (32000 PJ/y). The node and streams size is proportional to the corresponding energy carrier’s consumption for each sector and product. 1.4.3 Local RES supply The electricity consumption of a cluster or site may be known; however, this information is typically reported as an annual volume. As highlighted in the literature, incorporating the temporal dimension is crucial when assessing the integration of intermittent energy sources for industrial sites or clusters. Therefore, it is valuable to evaluate and compare supply and demand through time series analysis as shown on Figure 12. 18/26 Figure 12: Industrial demand & onshore wind and solar time profile with EU-MIX-2050 with production rate of 100% for all sectors. This functionality enables users to assess the proportion of cluster demand that can be met by onshore wind and solar electricity supply within a given NUTS2 or national region, both in terms of total volume and hourly generation profiles. This can support business developers in evaluating the potential need for grid, storage solutions or baseload capacity such as SMR [44]. The historical data from EMHIRES enables users to conduct deeper analyses based on years that are favourable or not for renewable energy generation. Complementary information of this kind can be found in the ‘Renewable energy resources’ section of the European state of the climate reports [45]. Finally, this feature provides a comparison between the isolated cluster and the local renewable energy generation, without accounting for other sectors or clusters within the NUTS-2 region. This implies that further analysis of the load profile is required from the user, to be matched with the energy supply data. Access to data is key to industrial symbiosis; this is granted either directly by the industry and energy sector, via public datasets or embedded in scientific tools. 2 DISCUSSION The RES2Go tool assesses and addresses the future of clean energy in industrial processes and clusters in Europe, with a focus on variable renewable energy. The tool results from expertise built over 25 years of cluster management research at Ghent University. Living in symbiosis and working in synergy to create win-win situations is of all times, in society as well as industry [46] [47]. Mutualisation is the core principle of economies of scale and scope. Starting from business park management, over eco-industrial parks, local steam networks and green port areas, to cross-sectoral and urban-industrial symbiosis, ECM gathered the knowledge and developed the skills to build models and tools in support of matching policy ambitions and industry challenges in the transition towards climate and resource neutrality [48], [49], [50]. Dynamic models are essential to answer the challenges of an economy in transition. Moreover, in times of crisis industrial realism is a key driver of any tool forecasting pathways towards neutrality in a 25-year time span. RES2Go provides the flexibility to tailor industry as well as policy objectives, the ability to customise indicators, add products and sectors, match local supply and 19/26 energy infrastructure; and it has the user-friendliness to visualise clustering potential on a local, regional or European scale. Still, the RES2Go tool has plenty of room for improvement and expansion. Three main areas are covered below. • Firstly, the underlying databases come with constraints. While AIDRES covers the highestemitting sectors with significant clustering potential, it does not include all energy-intensive industries. For example, the pulp and paper sector is missing, even though it contributes substantially to Europe's emissions (see Figure 13). This also applies to the ceramics and refractory sectors. Moreover, those sectors are crucial to Europe's competitiveness, as they are all highly productive and pro-active in developing low-carbon solutions. Figure 13: Cluster and emission shares of major industrial sectors, with AIDRES sectors shown in bold. DBSCAN clustering parameters: minimum sites = 5, minimum distance = 25 km. Emission data from JRC-EIGL [51] ETS/E-PRTR [21], [22]. The gaps also extend to renewable energy generation and industrial load profiles. These profiles are based on historical data and often don't reflect emerging or future processes. For instance, electricity use in the steel sector under the reference pathway involving electric arc furnaces (EAF) may differ significantly from alternative electrification pathways using molten oxide electrolysis (MOE). While RES2Go allows users to update inputs manually, addressing these gaps fully will depend on the release of updated data in the literature. • Secondly, the current clustering algorithms rely primarily on spatial proximity. This approach is appropriate when dealing with physical energy carriers such as hydrogen or biomass, where distance significantly affects feasibility. However, when it comes to electricity, spatial clustering becomes more complex. Electricity networks already connect multiple industrial sites, and building new transmission lines is expensive, while leveraging existing infrastructure is often more cost-effective. For this reason, it would be beneficial to incorporate grid connectivity into the clustering algorithm. For example, two nearby sites (A and B) currently assigned to different clusters (Y and Z) may be more appropriately grouped 20/26 together if they share an electrical connection, rather than being treated as separate A–Y and B–Z clusters. This is currently ongoing work with the implementation of the grid model PyPSA-Eur [52] • Thirdly, while RES2Go aims to provide agility, there is a trade-off between flexibility and user-friendliness. As more products and industrial sites are added, the tool risks becoming more complex and less intuitive [53]. To mitigate this, RES2Go allows users to download pre-defined pathway and cluster files, which can serve as templates or starting points for further customisation. 3 CONCLUSION The RES2Go tool provides an accessible interface to assess industrial energy demand under various emission reduction scenarios tailored to specific local industrial and energy settings. One of the key strengths of the tool lies in its flexibility: users can customise roadmaps by adjusting industrial pathways derived from, or extending beyond, the AIDRES database. In addition, the clustering module builds on this functionality by identifying and analysing energy hubs across Europe using multiple algorithmic approaches. These clusters can then be refined by incorporating new sites and products, or removing activities in case of reduced production, allowing for an actual representation of the industrial footprint and improving insight into local energy use. To this purpose the tool presents several key performance indicators (KPIs) to evaluate selected pathways, including CO₂ reduction potential, total energy demand, and the availability of local low-carbon energy supply — all while incorporating temporal profiles of energy use. The key ambition of RES2Go is to be agile while robust and user-friendly while holistic. The use of pre-defined pathways is an interesting feature to guide users with established scenarios (e.g. Material Economics [45], DG CLIMA [54] or National Energy and Climate Plans (NECP) [55]. For this reason, regular updates of the tool are planned to ensure the integration of new pathways and activities, for instance industrial pathway from the IEA [56] are already integrated. As an open-source platform, RES2Go is designed to evolve. Future developments could also include the integration of grid-related constraints, for example through coupling with the PyPSA-Eur model. Additionally, updating and expanding the AIDRES database, both in terms of recent data and sectoral scope, would further enhance the tool’s relevance and applicability. Other energy-intensive industries like pulp and paper, ceramics and refractory sectors are on the radar, and further collaboration with A.SPIRE Processes for Planet is initiated [57]. RES2Go aims to foster clustering and knowledge sharing across industries, both within and between hubs. This aligns with the Horizon Europe initiative Hubs for Circularity (H4C) [58] and its European Community of Practice (ECoP) [59], which seek to enhance circularity and industrial symbiosis. The strength of the tool lies in its ability to assess demand with high spatial granularity, based on the production capacity of individual industrial sites, while allowing the user to integrate and enrich the backbone data to foster clustering. An improvement would be to allocate pathways to specific areas. This functionality could be further developed, for example, by challenging the NECPs for each country and analysing demand at the national level to inform assessments of the European energy system. Moreover, the approach of allocating specific pathways to defined areas could be optimised in order to promote industrial symbiosis and assign production routes according to available resources (e.g. H₂ or 21/26 NG hubs) or the presence of emerging clean technologies (e.g. CCUS, H2 electrolysers). With this approach, pathways would not only serve as input but also become an optimised output of RES2Go, thereby better reflecting real-world conditions. Finally, the time generation module could take into account other sectors and neighbouring clusters to enable a more comprehensive comparison of the cluster impact on the local energy system. As the tool aims to reach a broad audience, including policy as well as industry, the option to import industrial load profiles is considered a particularly relevant feature. RES2Go aims to bring together several databases into one tool in order to showcase how industry clusters can support industrial symbiosis, advance energy efficiency, and foster future investments in line with the Draghi report [10] and its implementation, a particular mission of DG ENER [13]. To this end the tool targets users in all three pillars of the transition: policymakers, researchers, and industry stakeholders. 22/26 REFERENCES [1] L. Girardin, J. Valee, and J. Correa Laguna, AIDRES, “Advancing industrial decarbonization by assessing the future use of renewable energies in industrial processes”: assessment and geo mapping of renewable energy demand for technological paths towards carbon neutrality of EU energy intensive industries : methodology and results in support to the EU industrial plants database. Publications Office of the European Union, 2023. Accessed: Mar. 11, 2025. [Online]. Available: https://data.europa.eu/doi/10.2833/696697 [2] ‘EPOS Project’. 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