Renewable liquid fuels are essential for achieving emissions targets for hard-to-electrify sectors such as aviation and shipping. While biofuels and synthetic e-fuels have been well-studied, e-biofuels, produced by adding renewable hydrogen to biomass conversion to better utilise the biogenic carbon, remain understudied and lack a clear role in EU fuel regulations. In this paper, using a sector-coupled European energy system model, we find that e-biofuels are cost-effective to meet stringent emissions targets if biomass availability is limited and fossil fuels are ineligible, either due to limited carbon sequestration capacity or to high renewable fuel mandates. By directly increasing utilisation of biogenic carbon instead of synthesising fuels based on captured CO_2, there are savings from fuel production and carbon capture that reduce total system costs by up to 2.7

Long-term energy scenarios often assign a substantial role to biofuels in net-zero pathways, despite decades of policy support and limited evidence of sustained cost reductions or large-scale diffusion. Using biofuels as a critical case, this paper examines a broader problem in scenario construction: the misalignment between modelled technology pathways and socio-technical feasibility. Drawing on innovation theory, learning-curve evidence, and observed patterns of policy support and investment, it argues that biofuels are constrained by a configuration of mutually reinforcing limitations, including feedstock-dominated costs, weak learning dynamics, and permanent policy dependence. These constraints do not form a simple causal chain, but jointly limit the conditions under which large-scale competitiveness could emerge. Despite this, energy system and integrated assessment models often project extensive biofuel deployment under assumptions that abstract from political volatility and investment risk. The paper argues for greater use of empirical plausibility checks to improve the relevance of long-term energy scenarios.

Biomass is widely seen as essential for decarbonization, providing renewable energy, carbon-based fuels, and negative emissions, but rising land-use and biodiversity concerns are increasing pressure to restrict its use in Europe. Using a sector-coupled energy system model of Europe (PyPSA-Eur-Sec), we quantify the cost consequences of solid biomass scarcity under net-zero emissions targets across alternative technology futures. Restricting biomass availability from present-day levels to a severely constrained level increases total European system costs by 14–28%, with biomass used primarily for liquid fuels and industrial heat rather than electricity. The magnitude of this cost increase depends strongly on the maturity of substitute technologies: scarcity is most costly when hydrogen and electrofuel production remain expensive or inefficient, more so than when carbon storage or renewable electricity deployment is constrained. These results indicate that policies restricting biomass use should be paired with support for alternative carbon-neutral fuel pathways.

Generation diversity, renewable energy supply, and self-sufficiency are commonly cited hedges against supply disruptions and price risk in electricity markets, yet empirical validation of their effectiveness remains limited. The 2022 European energy crisis exposed electricity markets to severe price shocks driven by curtailed Russian gas imports. We use this natural experiment to test whether these factors mitigated the price shocks across 38 European bidding zones. Using robust linear regression models, we relate pre-crisis system characteristics (gen eration diversity, renewable integration, and self-sufficiency in electricity supply) to changes in average prices, extreme prices, and price variability. We find no evidence that greater generation diversity or self-sufficiency moderated price shocks during the crisis, challenging assumptions about their protective value. Larger reservoir hydro shares are the only robust predictor of smaller price increases, though we cannot fully separate the role of hydro reservoir's flexibility and low cost from that of the partial market isolation. Variable renewable energy shares do not systematically lower either average or extreme price changes, but are associated with larger in creases in day-to-day price variability. Natural gas shares show a directionally positive but imprecise association with price outcomes. The results suggest that under market coupling and marginal pricing, crisis-driven shocks transmit across bidding zones, limiting the explanatory power of zonal supply security indicators. Accordingly, assessments of price resilience should place greater emphasis on regional market dependencies rather than zonal generation structure alone.

Wind turbine output heavily depends on the wind speed at the deployment site. Energy system optimization models (ESOMs) typically allocate turbines in a cost-optimal manner, leading to siting at the windiest locations. However, such allocation methods lack an empirical base and risk overestimating the cost-competitiveness of wind power compared to real-life. This study assesses the historical siting of onshore wind turbines with respect to wind speed across 25 regions and introduces a heuristic method to represent wind power deployment patterns within ESOMs. Additionally, we conduct a comprehensive evaluation of existing wind turbine allocation methods in ESOMs. Our results show that turbines are typically sited at locations with slightly higher wind speeds than the regional mean, and new turbines within each region are consistently placed at sites with similar average wind speeds each year. The heuristics that best match historical deployment patterns tend to allocate 80-100 % of wind capacity to the 70th to 90th percentiles of the windiest areas. The cost-optimal turbine siting approach consistently favors windier locations compared to historical deployment. Overall, the findings present a promising avenue for incorporating historical data to improve the representation of wind power in future energy system modeling.

Abstract Renewable energy resources are widely available, yet they are unevenly distributed globally. In a renewable future, countries lacking high-quality renewable resources may choose to import energy from other countries. To assess the resource-dependent and techno-economic basis for global renewable energy trade and identify potential importers and exporters, this study introduces two new metrics: Renewable Export Cost Index (Cost Index) and Renewable Export Volume Index (Volume Index). These metrics are computed based on regional resource potential, domestic energy demand and varying financial costs across countries, without the need for any energy system modeling. By applying these two metrics to 165 countries/regions, we identify countries with significant potential for exporting renewable energy (e.g., the US, China) and those that lack the domestic resources to satisfy demand (e.g., South Korea, Japan). The Cost Index and Volume Index are validated through a separate analysis, employing a comprehensive energy system model for each country/region.

Previous research has highlighted concerns about week-long energy droughts in renewables-based energy systems. Reservoir hydropower could offer a viable solution to mitigate such energy shortfalls. However, current energy systems models often oversimplify hydropower by assuming it can operate continuously at maximum output. This study investigates the ability of reservoir hydropower to sustain a high output and thereby mitigate energy droughts. In contrast to most energy system models, the hydropower model used in this study includes cascading, head dependency, turbine efficiency curves and environmental constraints. We estimate that Swedish hydropower can sustain between 77% and 96% of its installed capacity for one week, with the higher end of this range achievable during spring. This range in sustained output is equivalent to about 3 GW, or about 20% of average demand in Sweden, which underscores the importance of understanding the operational limitations of hydropower. Our findings indicate that river bottlenecks, primarily due to regulations on maximum flows, are the main factor limiting hydropower's ability to sustain higher outputs. With the upcoming renewal of environmental permits for hydropower plants in Sweden, these findings provide valuable insights for policymakers. The importance of analysing hydropower's ability to sustain high outputs is not unique to Sweden; the method proposed in this study can serve as a critical tool for similar assessments in other hydro-rich countries. Moreover, the sustained output capabilities demonstrated in this study challenge the prevalent simplified representations of hydropower in energy models, highlighting the need for more sophisticated modelling approaches.

The One Sun One World One Grid (OSOWOG) initiative advocates the development of a global Super grid for sharing renewable energy, especially solar energy. This study evaluates the economic benefits of such a Super grid, which connects six large regions spanning from Australia to the US, utilizing a detailed energy system optimization model and considering heterogeneous discount rates among countries. Integrating the six regions into a Super grid reduces the electricity system cost by 3.8% compared to isolating them. In contrast, grid expansion within each region reduces the electricity system cost by 12% on average. The economic benefits of the OSOWOG initiative's global Super grid expansion seem to be rather limited. Moreover, the allowance for a Super grid consistently results in decreased investments in solar power, indicating that it is not an effective strategy for enhancing the deployment of solar power, even when transmission grids covering 18 time zones are available.

Biomass is a versatile renewable energy source that can be used in all parts of the energy system, but it is a limited resource and usage needs prioritisation. Here we use a sector-coupled European energy system model to explore the range of cost-effective near-optimal solutions for achieving stringent emissions targets. We show that provision of biogenic carbon rather than energy is the main value of biomass, with the energy system cost increasing by 20% if biomass is excluded. It is not crucial in which sector biomass is used if it is combined with carbon capture to enable negative emissions and e-fuel production. A shortage of renewable electricity or hydrogen primarily increases the value of biomass for fuel production, which appears as the marginal abatement option and is most sensitive to uncertainties. Biomass usage is significantly affected if the biomass is associated with upstream emissions.

Biomass associated with low upstream emissions offers cost-effective renewable carbon for negative emissions and production of chemicals, aviation and shipping fuels, reducing the need for more costly options like direct air capture. Policy support for sustainable biomass use alongside emerging technologies reduces energy system costs and the risk of missing emissions targets.

Capacity expansion models used for policy support have increasingly represented both the variability and uncertainty of weather-dependent generation (wind and solar). However, although also uncertain, as demonstrated by the performance of the French nuclear power fleet in 2022, uncertainty arising from nuclear power outages has been largely neglected in the literature. This paper presents the first capacity expansion model that considers uncertainty in nuclear power availability caused by unplanned outages. We propose a mathematical model that combines a scenario-based stochastic optimization approach (to deal with weather-related uncertainties) with a data-driven adjustable robust optimization approach (to deal with nuclear failure-related uncertainties). The robust model represents the bulky behavior of nuclear power plants, with large (1 GW) units that are either on or off, while at the same time letting the model decide on the optimal amount of nuclear capacity. We tested the model in a case for Northern Europe (seven nodes) with a time resolution of 1250 time steps. Our findings show that nuclear power outages do, in fact, impose a vulnerability on the energy system if not considered in the planning phase. Our proposed model performs well and finds solutions that prevent Loss-of-Load (at a price of robustness of 0.6 even in more extreme weather conditions. Robust solutions are characterized by a higher capacity of gas plants, but, perhaps surprisingly, nuclear power capacity is barely affected.

Many of the major challenges facing global society are unstructured collective harms (e.g., global warming): collective in the sense that they arise as the result of the actions of, or interactions among, multiple agents, and unstructured in the sense that there is no coordination or intention to cause harm among these agents. But how should we distribute moral responsibility for these harms? In this paper, an answer is proposed to this question. This answer builds on but develops existing proposals by drawing together literatures that speak to different aspects of the question. First, it is argued that the notion of causal contribution needs to be broadened to include the idea of causation as production. Second, it is discussed how the voluntariness and foreseeability conditions are best interpreted in this context. Third, literature on moral taint is drawn to introduce additional objective (external) criteria.

To model a future power system with high shares of variable renewables, it is essential to capture the flexibility of dispatchable technologies such as hydropower. However, the representation of hydropower is often oversimplified in energy system investment models, such that the flexibility of hydropower is significantly exaggerated. This suggests the need for improved representations of hydropower that capture physical river dynamics but are computationally efficient to maintain the tractability of large models. Here, we develop a series of hydropower optimization models for a single river with various levels of techno-physical detail to evaluate options for hydropower representations in energy system investment models. All models operate hourly over a full year with perfect foresight. We explore trade-offs between accuracy and computational time involved in including features such as the river network, head-dependent power production, and discharge -dependent turbine efficiencies. We find that the level of detail significantly affects the optimal production and confirm that a simplistic hydropower representation similar to those often used in investment models significantly overestimates the flexibility of hydropower. The most detailed nonconvex model includes a full river network, head-dependency, and turbine efficiencies and is solved in just one hour on a modern desktop computer. Furthermore, we linearize this detailed model, thereby reducing computation time to one minute while featuring production dynamics substantially more similar to the full nonconvex model than a naive linear network model. These contributions pave the way for improving hydropower representations in investment models to avoid overestimating the flexibility that hydropower may provide.

Many of the major challenges facing global society can be characterized as unstructured collective harms (for example, global warming and structural discrimination).These harms are collective in the sense that they arise as the result of the actions of, or interactions between, multiple agents, where no single agent can control the outcome, and they are unstructured in the sense that there is no coordination or intention to cause harm among the agents involved. But it is not clear how we should determine who is morally responsible for these harms. In this paper, we propose an answer to the question of who is complicit in an unstructured collective harm. Our answer builds on but develops existing proposals by drawing together literatures that speak to different aspects of the question. First, we argue that the notion of causal contribution needs to be broadened to include the idea of causation as production. Second, we draw on the literature on moral taint in order to introduce additional objective (external) grounds for complicity in unstructured harms, such as benefitting from harm. Third, we discuss how the voluntariness and foreseeability conditions are best interpreted in this context.

Long distance transmission within continents has been shown to be one of the most effective variation management strategies to reduce the cost of renewable energy systems. In this paper, we test whether the system cost further decreases when transmission is extended to intercontinental connections. We analyze a Eurasian interconnection between China, Mid-Asia and Europe, using a capacity expansion model with hourly time resolution. Our modelling results suggestthat a supergrid option decreases total system cost by a maximum of 5%, compared to continental grid integration. The maximum cost reductionis achieved when (i) the generation is constrained to be made up almost entirely by renewables, (ii) the land available for VRE farms is relatively limited and the demand is relatively high and (iii) the cost for solar PV and storage is high. This is explained by that a super grid allows for harnessing of remote wind-, solar- and hydro resources demand centers. As for low-cost storage, it represents a competing variation management option, and may substitute part of the role of the supergrid, which is to manage variations through long-distance trade. We conclude that the benefits of a supergrid from a techno-economic perspective are in most cases negligible, or modest at best.

A critical parameter in modeling studies of future decarbonized energy systems is the potential future capacity for onshore wind power. Wind power potential in energy system models is subject to assumptions regarding: (i) constraints on land availability for wind deployment; (ii) how densely wind turbines may be placed over larger areas, and (iii) allocation of capacity with respect to wind speed. By analyzing comprehensive databases of wind turbine locations and other GIS data in eleven countries and seventeen states in Australia, Canada, and the US; all with high penetration levels of wind power, we find that: i) large wind turbines are installed on most land types, even protected areas and land areas with high population density; ii) it is not uncommon with a deployment density up to 0.5 MW/km2 on municipality or county level, with rare outlier municipalities reaching up to 1.5 MW/km2 installed capacity; and iii) wind power has historically been allocated to relatively windy sites with average wind speed above 6 m/s. In many cases, allocation methods used in energy system models do not consistently reflect actual installations. For instance, we find no evidence of concentration of installations at the windiest sites, as is frequently assumed in energy system models. We conclude that assumptions made in models regarding wind power potentials are poorly reflective of historical installation patterns, and we provide new data to enable assumptions that have a more robust empirical foundation.

Abatement options for the hard-to-electrify parts of the transport sector are needed to achieve ambitious emissions targets. Biofuels based on biomass, electrofuels based on renewable hydrogen and a carbon source, as well as fossil fuels compensated by carbon dioxide removal (CDR) are the main options. Currently, biofuels are the only renewable fuels available at scale and are stimulated by blending mandates. Here, we estimate the system cost of enforcing such mandates in addition to an overall emissions cap for all energy sectors. We model overnight scenarios for 2040 and 2060 with the sector-coupled European energy system model PyPSA-Eur-Sec, with a high temporal resolution. The following cost drivers are identified: (i) high biomass costs due to scarcity, (ii) opportunity costs for competing usages of biomass for industry heat and combined heat and power (CHP) with carbon capture, and (iii) lower scalability and generally higher cost for biofuels compared to electrofuels and fossil fuels combined with CDR. With a-80% emissions reduction target in 2040, variable renewables, partial electrification of heat, industry and transport, and biomass use for CHP and industrial heat are important for achieving the target at minimal cost, while an abatement of remaining liquid fossil fuel use increases system cost. In this case, a 50% biofuel mandate increases total energy system costs by 123-191 billion euro, corresponding to 35%-62% of the liquid fuel cost without a mandate. With a negative-105% emissions target in 2060, fuel abatement options are necessary, and electrofuels or the use of CDR to offset fossil fuel emissions are both more competitive than biofuels. In this case, a 50% biofuel mandate increases total costs by 21-33 billion euro, or 11%-15% of the liquid fuel cost without a mandate. Biomass is preferred in CHP and industry heat, combined with carbon capture to serve negative emissions or electrofuel production, thereby utilising biogenic carbon several times. Sensitivity analyses reveal significant uncertainties but consistently support that higher biofuel mandates lead to higher costs.