This study investigates the global allocation of hydrogen and synfuels in order to achieve the well below 2 °C, preferably 1.5 °C target set in the Paris Agreement. For this purpose, TIMES Integrated Assessment Model (TIAM), a global energy system model is used. In order to investigate global hydrogen and synfuel flows, cost potential curves are aggregated and implemented into TIAM, as well as demand technologies for the end use sectors. Furthermore, hydrogen and synfuel trades are established using liquid hydrogen transport (LH₂), and both new and existing technologies for synfuels are implemented. To represent a wide range of possible future events, four different scenarios are considered with different characteristics of climate and security of supply policies. The results show that in the case of climate policy, the renewable energies need tremendous expansion. The final energy consumption is shifting towards the direct use of electricity, while certain demand technologies (e.g., aviation and international shipping) require hydrogen and synfuels for full decarbonization. Due to different security of supply policies, the global allocation of hydrogen and synfuel production and exports is shifting, while the 1.5 °C target remains feasible in the different climate policy scenarios. Considering climate policy, Middle East Asia is the preferred region for hydrogen export. For synfuel production, several regions are competitive, including Middle East Asia, Mexico, Africa, South America and Australia. In the case of security of supply policies, Middle East Asia is sharing the export volume with Africa, while only minor changes can be seen in the synfuel supply.

This paper presents the potentials and costs of synthetic fuels (synfuels) produced by renewable energy via PEM water electrolysis and the subsequent Fischer–Tropsch process for the years 2020, 2030, 2040, and 2050 in selected countries across the globe. The renewable energy potential was determined by the open-source tool pyGRETA and includes photovoltaic, onshore wind, and biomass. Carbon dioxide is obtained from biomass and the atmosphere by direct air capture. The potentials and costs were determined by aggregating minimal cost energy systems for each location on a state level. Each linear energy system was modelled and optimised by the optimisation framework urbs. The analysis focused on decentralised and off-grid synthetic fuels’ production. The transportation costs were roughly estimated based on the distance to the nearest maritime port for export. The distribution infrastructure was not considered since the already-existing infrastructure for fossil fuels can be easily adopted. The results showed that large amounts of synthetic fuels are available for EUR 110/MWh (USD 203/bbl) mainly in Africa, Central and South America, as well as Australia for 2050. This corresponds to a cost reduction of more than half compared to EUR 250/MWh (USD 461/bbl) in 2020. The synfuels’ potentials follow the photovoltaic potentials because of the corresponding low levelised cost of electricity. Batteries are in particular used for photovoltaic-dominant locations, and transportation costs are low compared to production costs.

The required decarbonization of the energy system is a complex task, with ambitious targets under the Paris Agreement, and related policy analysis should consider possible impacts on the economy and society. By coupling the energy system model TIMES PanEU with the impact assessment model EcoSense and the computable general equilibrium model NEWAGE, we present an integrated assessment toolbox for the European energy system capable of internalizing health damage costs of air pollution while simultaneously accounting for demand changes in energy services caused by economic feedback loops. The effects of each coupling step are investigated in a scenario analysis. Additionally, CO₂ decomposition analysis is applied to identify the main drivers to decarbonize the energy system. Our results show that integrating externalities forces the system to take early action, which provides benefits on the societal level. Including macro-economic variables has a negative effect on energy service demands and generally reduces the need for structural change, which are still the main drivers of decarbonization. The tighter the models are coupled, the fewer the iterations needed and the lower the CO₂ prices resulting from the carbon cap and trade system. In this aspect, an integrated view can provide valuable insights to determine efficient and effective decarbonization paths.

It is a broadly accepted fact that a clear reduction of global GHG emissions is required to limit the increase of global warming to a tolerable level. A key issue in this context is the optimal breakdown of reduction targets among different world regions or even countries. Using the European Commission-funded PLANETS project, cost-optimal global burden sharing to reach global GHG reduction targets was analysed, and an optimal allocation of GHG reductions was identified, relative to the global target, to the commitments of different world regions and the trade possibilities for emission certificates. Specifically, it is evaluated how Europe can contribute in a cost-optimal way to keeping the global concentration of GHGs in the atmosphere below 530 parts per million equivalent (ppme) or below a stricter global reduction target of 500 ppme. Based on the energy system model TIMES PanEU, the potentials for emissions reduction in the different energy sectors and EU Member States and the role of key technologies are analysed. The most cost-effective potentials for GHG reductions in Europe are in the conversion/production, residential and industrial sectors. Substantial reductions in the transport sector occur only under very stringent reduction targets. Achieving ambitious reduction targets requires considerable contributions from all EU Member States until 2050.

The Green Deal of the European Union defines extremely ambitious climate targets for 2030 (−55% emissions compared to 1990) and 2050 (−100%), which go far beyond the current goals that the EU member states have agreed on thus far. The question of which sectors contribute how much has already been discussed, but is far from decided, while the question of which countries shoulder how much of the tightened reduction targets has hardly been discussed. We want to contribute significantly to answering these policy questions by analysing the necessary burden sharing within the EU from both an energy system and an overall macroeconomic perspective. For this purpose, we use the energy system model TIMES PanEU and the computational general equilibrium model NEWAGE. Our results show that excessively strong targets for the Emission Trading System (ETS) in 2030 are not system-optimal for achieving the 55% overall target, reductions should be made in such a way that an emissions budget ratio of 39 (ETS sector) to 61 (Non-ETS sector) results. Economically weaker regions would have to reduce their CO₂ emissions until 2030 by up to 33% on top of the currently decided targets in the Effort Sharing Regulation, which leads to higher energy system costs as well as losses in gross domestic product (GDP). Depending on the policy scenario applied, GDP losses in the range of −0.79% and −1.95% relative to baseline can be found for single EU regions. In the long-term, an equally strict mitigation regime for all countries in 2050 is not optimal from a system perspective; total system costs would be higher by 1.5%. Instead, some countries should generate negative net emissions to compensate for non-mitigable residual emissions from other countries.

This paper seeks to identify bottlenecks in the energy grid supply regarding different market penetration of battery electric vehicles in Stuttgart, Germany. First, medium-term forecasts of electric and hybrid vehicles and the corresponding charging infrastructure are issued from 2017 to 2030, resulting in a share of 27% electric vehicles by 2030 in the Stuttgart region. Next, interactions between electric vehicles and the local energy system in Stuttgart were examined, comparing different development scenarios in the mobility sector. Further, a travel demand model was used to generate charging profiles of electric vehicles under consideration of mobility patterns. The charging demand was combined with standard household load profiles and a load flow analysis of the peak hour was carried out for a quarter comprising 349 households. The simulation shows that a higher charging capacity can lead to a lower transformer utilization, as charging and household peak load may fall temporally apart. Finally, it was examined whether the existing infrastructure is suitable to meet future demand focusing on the transformer reserve capacity. Overall, the need for action is limited; only 10% of the approximately 560 sub-grids were identified as potential weak points.