The EU Delegated Act establishes criteria for certifying electrolytic hydrogen as renewable. However, adjustments to these criteria are under review to balance hydrogen’s economic viability with its sustainability. This study employs a European integrated electricity and hydrogen system model to evaluate the climate and economic impacts of relaxing additionality and spatiotemporal correlation criteria by 2030. Scenarios range from unrestricted electrolysis flexibly operating at short-run marginal electricity prices to domestic and cross-border power purchase agreements (PPAs) with newly installed renewables, under hourly or annual temporal matching. Two perspectives are explored, both applying a ‘compete’ additionality framework where hydrogen production competes with direct electrification in a co-optimized system. The first analysis constrains the country-specific renewable buildout to National Energy and Climate Plans (NECPs) using ten-year network development plan’s (TYNDP 2024) National Trend trajectories. This reflects the practical difficulty of accelerating renewable expansion by 2030, even with green hydrogen mandates. Under this constraint, additionality with spatiotemporal correlation reduces attributional emissions (i.e. emissions from grid electricity use, attributed to hydrogen production) but raises power system emissions by ∼1 kgCO _2 /kgH _2 compared to the unrestricted electrolysis scenario, as scarce new renewables are diverted to green hydrogen production, increasing fossil generation in the background electricity system. The second, exploratory perspective allows endogenous expansion beyond NECP targets to simulate policy-driven accelerated deployment, where building out green hydrogen, slightly reduces the additional power system emissions associated with hydrogen integration compared to unrestricted electrolysis. Moreover, although total system cost impacts remain modest, additionality with spatiotemporal criteria raises the levelized cost of hydrogen (LCOH) in both perspectives, with cross-country variation driven by short-run electricity prices. EU-averaged results show unrestricted electrolysis yields lower LCOH of €2–2.5/kgH _2 (assuming NECPs are met), while stringent PPA-based scenarios increase it up to €7/kgH _2 . Easing temporal correlation in PPA-based scenarios slightly raises attributional emissions but lowers LCOH. However, additionality remains the dominant cost driver. Finally, the study underscores the need for tailored exemptions from additionality and spatiotemporal correlation criteria based on factors such as nuclear share, renewable targets, and marginal clean generation frequency to refine these requirements effectively.

This study evaluates the technoeconomic impacts of direct and indirect electrification on the EU's net-zero emissions target by 2050. By linking the JRC-EU-TIMES long-term energy system model with PLEXOS hourly resolution power system model, this research offers a detailed analysis of the interactions between electricity, hydrogen and synthetic fuel demand, production technologies, and their effects on the power sector. It highlights the importance of high temporal resolution power system analysis to capture the synergistic effects of these components, often overlooked in isolated studies. Results indicate that direct electrification increases significantly and unimpacted by biomass, CCS, and nuclear energy assumptions. However indirect electrification in the form of hydrogen varies significantly, between 1400 and 2200 TWhH2 by 2050. Synthetic fuels are essential for sector coupling, making up 6-12% of total energy consumption by 2050, with the power sector supplying most hydrogen and CO2 for their production. Varying levels of indirect electrification impact electrolysers, renewable energy, and firm capacities. Higher indirect electrification increases electrolyser capacity factors by 8%, leading to more renewable energy curtailment but improves system reliability by reducing 11 TWh unserved energy and increasing flexibility options. These insights inform EU energy policies, stressing the need for a balanced approach to electrification, biomass use, and CCS to achieve a sustainable and reliable net-zero energy system by 2050. We also explore limitations and sensitivities.

The relevance of sector coupling is increasing when shifting from the current highly centralised and mainly fossil fuel-based energy system to a more decentralized and renewable energy system. Cross-sectoral linkages are already recognized as a cost-effective decarbonisation strategy that provides significant flexibility to the system. Modelling such cross-sectoral interconnections is thus highly relevant. In this work, these interactions are considered in a long-term perspective by uni-directional soft-linking of two models: JRC-EU-TIMES, a long term planning multisectoral model, and Dispa-SET, a unit commitment and optimal dispatch model covering multiple energy sectors such as power, heating & cooling, transportation etc. The impact of sector coupling in future Europe-wide energy systems with high shares of renewables is evaluated through five scenarios. Results show that the contributions of individual sectors are quite diverse. The transport sector provides the highest flexibility potential in terms of power curtailment, load shedding, congestion in the interconnection lines and resulting greenhouse gas emissions reduction. Nevertheless, allowing combinations of multiple flexibility options such as hydro for the long-term, electric vehicles and flexible thermal units for the short-term provides the best solution in terms of system adequacy, greenhouse gas emissions and operational costs.

Job creation is arguably an important socioeconomic benefit of renewable energy deployment. In turn, this employment creation may be contingent upon the influence of some key factors, including technology learning, trade effects and policies and may affect different renewable energy technologies and activities of the renewable energy value chain in different ways. This paper estimates the gross employment stemming from the deployment of three renewable electricity technologies – photovoltaics (PV), wind on-shore and wind off-shore – up to 2050 for all Member States of the European Union. It uses a novel analytical methodology which is able to capture the influence of technology learning and internal and external trade. Additionally, it provides highly disaggregated results per activity in the supply chain (manufacturing, installation and O&M), year and country for different technology and policy scenarios. The results show that the employment created by those three technologies can be significant but considerable differences across technologies, activities and countries can be observed. In the analyzed period (2014–2050), most employment will be created in the PV sector, in the operation and maintenance activities and it will be highly geographically concentrated in a few countries. However, job creation will strongly depend on the scenarios and assumptions being made. In particular our findings suggest that the availability of carbon capture and storage will have a considerable influence on the number of jobs being created. In contrast, changes in other assumptions have limited effects on the results: a variable (vs. a constant) learning rate, more restrictive emissions targets by 2050 and higher PV costs.

This Excel file contains the data behind the graphs of the following JRC report: Tsiropoulos I., Nijs W., Tarvydas D., Ruiz Castello P., Towards net-zero emissions in the EU energy system by 2050 – Insights from scenarios in line with the 2030 and 2050 ambitions of the European Green Deal, EUR 29981 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-13096-3, doi:10.2760/081488, JRC118592. The report is downloadable from: https://ec.europa.eu/jrc/en/publication/towards-net-zero-emissions-eu-energy-system-2050

All hydrogen related input data of the JRC-EU-TIMES model. The full JRC-EU-TIMES is open on Zenodo: http://doi.org/10.5281/zenodo.3544900. Associated datasets of JRC-EU-TIMES are made open as separate data sources for non-TIMES users. These datasets consist of a selection of the JRC-EU-TIMES input files with information on one specific topic. These datasets are not stand-alone models. In the file "Hydrogen Inputs JRC-EU-TIMES July 2019.xlsx", all hydrogen data are grouped. This grouping is cleaner as the subres/scenario files because all the data are in one place with additional metadata for improved understanding. You will find technologies related to hydrogen production, but also consumption and transformation. The two other files are the TIMES Subres files for hydrogen production, storage, transport and distribution. In JRC-EU-TIMES, one of the hydrogen production routes is transforming electricity surpluses with electrolysers. Different from the enclosed paper [Blanco H. et al., 2019], the electricity surpluses of the open JRC-EU-TIMES are based on a country-specific analysis with an hourly model outside JRC-EU-TIMES. Some more information on this is available in the other enclosed paper [Pavičević M., 2019].

This Excel file includes a geometry based calibration of the EU building stock for the year 2010 and is one of the building blocks of JRC-EU-TIMES. Associated datasets of JRC-EU-TIMES are made open as separate data sources for non-TIMES users. These datasets consist of a selection of the JRC-EU-TIMES input files with information on one specific topic. These datasets are not stand-alone models though.

As the EU energy system transitions to low carbon, the technology choices should consider a broader set of criteria. The use of Life Cycle Assessment (LCA) prevents burden shift across life cycle stages or impact categories, while the use of Energy System Models (ESM) allows evaluating alternative policies, capacity evolution and covering all the sectors. This study does an ex-post LCA analysis of results from JRC-EU-TIMES and estimates the environmental impact indicators across 18 categories in scenarios that achieve 80-95% CO2 emission reduction by 2050. Results indicate that indirect CO2 emissions can be as large as direct ones for an 80% CO2 reduction target and up to three times as large for 95% CO2 reduction. Impact across most categories decreases by 20-40% as the CO2 emission target becomes stricter. However, toxicity related impacts can become 35-100% higher. The integrated framework was also used to evaluate the Power-to-Methane (PtM) system to relate the electricity mix and various CO2 sources to the PtM environmental impact. To be more attractive than natural gas, the climate change impact of the electricity used for PtM should be 123-181 gCO(2eq)/kWh when the CO2 comes from air or biogenic sources and 4-62 gCO(2eq)/kWh if the CO2 is from fossil fuels. PtM can have an impact up to 10 times larger for impact categories other than climate change. A system without PtM results in similar to 4% higher climate change impact and 9% higher fossil depletion, while having 5-15% lower impact for most of the other categories. This is based on a scenario where 9 parameters favor PtM deployment and establishes the upper bound of the environmental impact PtM can have. Further studies should work towards integrating LCA feedback into ESM and standardizing the methodology.

Fuel cell electric vehicles (FCEV) currently have the challenge of high CAPEX mainly associated to the fuel cell. This study investigates strategies to promote FCEV deployment and overcome this initial high cost by combining a detailed simulation model of the passenger transport sector with an energy system model. The focus is on an energy system with 95% CO2 reduction by 2050. Soft-linking by taking the powertrain shares by country from the simulation model is preferred because it considers aspects such as car performance, reliability and safety while keeping the cost optimization to evaluate the impact on the rest of the system. This caused a 14% increase in total cost of car ownership compared to the cost before soft-linking. Gas reforming combined with CO2 storage can provide a low-cost hydrogen source for FCEV in the first years of deployment. Once a lower CAPEX for FCEV is achieved, a higher hydrogen cost from electrolysis can be afforded. The policy with the largest impact on FCEV was a purchase subsidy of 5 k€ per vehicle in the 2030–2034 period resulting in 24.3 million FCEV (on top of 67 million without policy) sold up to 2050 with total subsidies of 84 bln€. 5 bln€ of R&D incentives in the 2020–2024 period increased the cumulative sales up to 2050 by 10.5 million FCEV. Combining these two policies with infrastructure and fuel subsidies for 2030–2034 can result in 76 million FCEV on the road by 2050 representing more than 25% of the total car stock. Country specific incentives, split of demand by distance or shift across modes of transport were not included in this study.

Data on the potential generation of energy from wind, solar and biomass is crucial for analysing their development, as it sets the limits on how much additional capacity it is feasible to install. This paper presents the methodologies used for the development of ENSPRESO, ENergy System Potentials for Renewable Energy SOurces, an EU-28 wide, open dataset for energy models on renewable energy potentials, at national and regional levels for the 2010–2050 period. In ENSPRESO, coherent GIS-based land-restriction scenarios are developed. For wind, resource evaluation also considers setback distances, as well as high resolution geo-spatial wind speed data. For solar, potentials are derived from irradiation data and available area for solar applications. Both wind and solar have separately a potential electricity production which is equivalent to three times the EU's 2016 electricity demand, with wind onshore and solar requiring 16% and 1.4% of total land, respectively. For biomass, agriculture, forestry and waste sectors are considered. Their respective sustainable potentials are equivalent to a minimum 10%, 1.5% and 1% of the total EU primary energy use. ENSPRESO can enrich the results of any energy model (e.g. JRC-EU-TIMES) by improving its analyses of the competition and complementarity of energy technologies.

Power-to-Methane (PtM) can provide flexibility to the electricity grid while aiding decarbonization of other sectors. This study focuses specifically on the methanation component of PtM in 2050. Scenarios with 80-95% CO2 reduction by 2050 (vs. 1990) are analyzed and barriers and drivers for methanation are identified. PtM arises for scenarios with 95% CO2 reduction, no CO2 underground storage and low CAPEX (75 (sic)/kW only for methanation). Capacity deployed across EU is 40 GW (8% of gas demand) for these conditions, which increases to 122 GW when liquefied methane gas (LMG) is used for marine transport. The simultaneous occurrence of all positive drivers for PtM, which include limited biomass potential, low Power-to-Liquid performance, use of PtM waste heat, among others, can increase this capacity to 546 GW (75% of gas demand). Gas demand is reduced to between 3.8 and 14 EJ (compared to similar to 20 EJ for 2015) with lower values corresponding to scenarios that are more restricted. Annual costs for PtM are between 2.5 and 10 bln(sic)/year with EU28's GDP being 15.3 trillion (sic)/year (2017). Results indicate that direct subsidy of the technology is more effective and specific than taxing the fossil alternative (natural gas) if the objective is to promote the technology. Studies with higher spatial resolution should be done to identify specific local conditions that could make PtM more attractive compared to an EU scale.

Data on the potential generation of electricity from wind is crucial information for analysing the future role of this renewable energy source. In this report, a description is presented of the methodologies used for the derivation of a dataset. The dataset consists of an estimation of (1) wind speeds accounting for high-resolution effects, (2) power production accounting for a wide range of turbine types, (3) suitable areas and (4) associated cost estimates. Wind speed information is systematically derived from 30 years of meteorological data based on the MERRA reanalysis dataset, and from the high resolution geo-spatial data based on the Global Wind Atlas. Within this project, the wind potentials and techno-economic parameters are gathered and processed into input datasets for the JRC-EU-TIMES model. This allows improved modelling of the competition and the complementarity of wind with other technologies, the key functionality of the JRC-EU-TIMES model. Moreover the datasets can also be used for the analysis of policy questions relating to the availability of wind energy.

Hydrogen represents a versatile energy carrier with net zero end use emissions. Power-to-Liquid (PtL) includes the combination of hydrogen with CO2 to produce liquid fuels and satisfy mostly transport demand. This study assesses the role of these pathways across scenarios that achieve 80-95% CO2 reduction by 2050 (vs. 1990) using the JRC-EU-TIMES model. The gaps in the literature covered in this study include a broader spatial coverage (EU28 +) and hydrogen use in all sectors (beyond transport). The large uncertainty in the possible evolution of the energy system has been tackled with an extensive sensitivity analysis. 15 parameters were varied to produce more than 50 scenarios. Results indicate that parameters with the largest influence are the CO2 target, the availability of CO2 underground storage and the biomass potential. Hydrogen demand increases from 7 mtpa today to 20-120 mtpa (2.4-14.4 EJ/yr), mainly used for PtL (up to 70 mtpa), transport (up to 40 mtpa) and industry (25 mtpa). Only when CO2 storage was not possible due to a political ban or social acceptance issues, was electrolysis the main hydrogen production route (90% share) and CO2 use for PtL became attractive. Otherwise, hydrogen was produced through gas reforming with CO2 capture and the preferred CO2 sink was underground. Hydrogen and PtL contribute to energy security and independence allowing to reduce energy related import cost from 420 bln(sic)/yr today to 350 or 50 bln(sic)/yr for 95% CO2 reduction with and without CO2 storage. Development of electrolyzers, fuel cells and fuel synthesis should continue to ensure these technologies are ready when needed. Results from this study should be complemented with studies with higher spatial and temporal resolution. Scenarios with global trading of hydrogen and potential import to the EU were not included.

This paper assesses how different levels of geographical disaggregation of wind and photovoltaic energy resources could affect the outcomes of an energy system model by 2020 and 2050. Energy system models used for policy making typically have high technology detail but little spatial detail. However, the generation potential and integration costs of variable renewable energy sources and their time profile of production depend on geographic characteristics and infrastructure in place. For a case study for Austria we generate spatially highly resolved synthetic time series for potential production locations of wind power and PV. There are regional differences in the costs for wind turbines but not for PV. However, they are smaller than the cost reductions induced by technological learning from one modelled decade to the other. The wind availability shows significant regional differences where mainly the differences for summer days and winter nights are important. The solar availability for PV installations is more homogenous. We introduce these wind and PV data into the energy system model JRC-EU-TIMES with different levels of regional disaggregation. Results show that up to the point that the maximum potential is reached disaggregating wind regions significantly affects results causing lower electricity generation from wind and PV.

The optimization energy system model JRC-EU-TIMES is used to support energy technology R&D design by analysing power technologies deployment till 2050 and their sensitivity to different decarbonisation exogenous policy routes. The policy routes are based on the decarbonised scenarios of the EU Energy Roadmap 2050 combining energy efficiency, renewables, nuclear or carbon capture and storage (CCS). A "reference" and seven decarbonised scenarios are modelled for EU28. We conclude on the importance of policy decisions for the configuration of the low carbon power sector, especially on nuclear acceptance and available sites for new RES plants. Differently from typical analysis focussing on technology portfolio for each route, we analyse the deployment of each technology across policy routes, for optimising technology R&D. R&D priority should be given to those less-policy-sensitive technologies that are in any case deployed rapidly across the modelled time horizon (e.g. PV), but also to those deployed up to their technical potentials and typically less sensitive to exogenous policy routes. For these ‘no regret’ technologies (e.g. geothermal), R&D efforts should focus on increasing their technical potential. For possibly cost-effective technologies very sensitive to the policy routes (e.g. CSP and marine), R&D efforts should be directed to improving their techno-economic performance.

............................................................................................................ 1 1 Introduction ................................................................................................... 2 2 Enhancing the description of residential and non-residential buildings ..................... 3 2.1 Technology database ................................................................................. 3 2.1.1 Datasets ........................................................................................... 3 2.1.2 Database structure and criteria ............................................................ 3 2.1.3 Data quality check ............................................................................. 5 2.1.4 Refurbishment measures .................................................................... 5 2.2 Implementation of the residential buildings module in JET ............................... 5 2.3 Testing the implementation of the residential buildings module ........................ 7 2.4 Implementation of the non-residential buildings module .................................. 9 2.5 Data quality check for residential buildings ................................................... 11 2.6 Data quality check for non-residential buildings ............................................ 14 3 Heating &cooling and heat distribution technologies ............................................ 17 3.1 Datasets ................................................................................................. 17 3.2 Technology database ................................................................................ 17 3.2.1 Data source ..................................................................................... 17 3.2.2 Database architecture ....................................................................... 18 3.2.3 Quality check and manual adjustments ................................................ 19 3.3 Model SubRes .......................................................................................... 19 3.3.1 SubRes architecture .......................................................................... 20 3.3.1.1 Residential SubRes ...................................................................... 20 3.3.1.2 Commercial SubRes .................................................................... 23 3.3.2 Other model changes ........................................................................ 26 3.4 Model tests and preliminary results ............................................................. 26 3.4.1 Scenario results ................................................................................ 27 3.4.1.1 Key emissions dynamics .............................................................. 27 3.4.1.2 Key buildings energy dynamics ..................................................... 28 3.4.2 Functioning of new features ................................................................ 31 3.4.2.1 High-level comparison with previous model ..................................... 31 4 Conclusions ................................................................................................... 33 4.1 Buildings ................................................................................................. 33 4.2 Technologies for heating and cooling ........................................................... 33 List of abbreviations and definitions ...................................................................... 35 List of figures .................................................................................................... 37 List of tables ...................................................................................................... 38