Model-based scenarios are widely used to guide energy planning and climate policy decisions. While the mathematical and physical foundations of many techno-economic models assume universal truth and objectivity, their application to explore a yet unwritten future demands a more nuanced understanding of these concepts. Although modellers' beliefs about the certainty and universality of knowledge may influence how they present their findings to decision-makers, the matter has received little empirical attention to date. Here, we address this gap and investigate modellers' epistemic beliefs concerning energy modelling and scenarios, as well as their perspectives on the communication of model outputs and expert authority. To that end, we conducted a survey with over 160 experts from a broad range of geographical regions and disciplines. Our results show significant polarisation in the participants' beliefs, revealing the two stylised profiles of a Positivist and a Postpositivist Modeller. While there are few differences in the respondents' attitudes based on educational level and background or model usage, we find significant variation particularly based on geographic location. In an effort to overcome this polarisation, we consider our study a call for diversity in modelling teams and argue for fostering the discussion of epistemic beliefs within the broader modelling community. Finally, we recommend incorporating key topics beyond technical aspects into the training and education of future modellers.

Electricity- and hydrogen-based sector coupling contributes to realizing the transition towards greenhouse gas neutrality in the European energy system. Energy system and integrated assessment models show that, to follow pathways compatible with the European policy target of net-zero greenhouse gas emissions by 2050, large amounts of renewable electricity and H₂ need to be generated, mostly by scaling-up wind and solar energy production capacity. With a set of such models, under jointly adopted deep decarbonisation scenario assumptions, we here show that the ensuing direct penetration of electricity and H₂ in final energy consumption may rise to average shares of around 60% and 6%, respectively, by 2050. We demonstrate that electrification proves the most cost-efficient decarbonisation route in all economic sectors, while the direct use of H₂ in final energy consumption provides a relatively small, though essential, contribution to deep decarbonisation. We conclude that the variance observed across results from different models reflects the uncertainties that abound in the shape of deep decarbonisation pathways, in particular with regard to the role of H₂.

The European Union's goal of achieving climate neutrality by 2050, outlined in the European Green Deal, is supported by numerous studies providing insights into pathways and emission reduction strategies in the energy sectors. However, model comparisons of such pathways are less common due to the complex nature of climate and energy modelling. Our study brings together integrated assessment models and energy system models under a common framework to develop EU policy scenarios: a Current Trends scenario reflecting existing policies and trends and a Climate Neutrality scenario aligned with the EU's emission reduction target. Both scenarios project reduced final energy consumption by 2050, driven by increased electrification and decreased fossil fuel usage. Electricity consumption increases driven by electrification despite the improved efficiency of electrified technologies. Models align on a shift toward renewables but diverge in technology and fuel choices, reflecting various approaches to reach net-zero energy systems. Furthermore, trade-offs between energy demand and supply mitigation strategies, as well as between renewable energy, e-fuels, and CCS technologies are identified. Considering these model variations, our study highlights the importance of consistent model comparison to offer reliable recommendations to policymakers and stakeholders. We conclude that model diversity is a valuable asset when used sensibly.

Energy system optimization models (ESOMs) can be used to guide long-term energy transitions but often overlook environmental impacts and the diversity of solutions close to the cost-optimal one. Here, we combine an ESOM using Modelling to Generate Alternatives (MGA) with Life Cycle Assessment (LCA) to evaluate 260 near-optimal and technologically diverse carbon-neutral energy system designs for Portugal in 2050 across five environmental indicators: climate change, land use, water use, ecotoxicity, and materials. Using the Calliope energy modelling framework and ENBIOS for environmental assessment, we find that system designs whose cost is within 10 % of the minimum feasible cost provide up to 50 % lower environmental impacts. Our results reveal a trade-off between technological diversity and environmental performance, showing that while diversity enhances resilience, this may come with a significant increase in environmental drawbacks. Solar photovoltaic and battery technologies dominate the environmental impacts, particularly in water consumption and critical material use. This study shows that traditional cost-optimal energy system designs may not be environmentally optimal. Exploring near-optimal alternatives reveals lower-impact solutions and supports more inclusive planning for energy transitions.

Energy transition policies can be translated into narratives about how energy systems should change (e.g., towards a centralised or decentralised system). These narratives tend to reflect expectations, priorities, and perceptions on feasibility and the social acceptability of different policy options, as well as long-term goals and trade-offs, all of which influence policy criteria. Taking as its case study Portugal and the implementation of European directives there, this study aims to characterise energy transition narratives (e.g. a swift transformation to renewables) and interrelated policy criteria (e.g., participation of local communities), focusing on expectations for a socially engaging and democratic energy transition. The analysis builds on the results of a Delphi survey with 10 expert stakeholders, a citizens’ survey (n=500), and a workshop with 19 participants. It identifies the most relevant criteria to stakeholders, as well as the importance of different underlying expectations, meanings, and attitudes shaping narratives about energy system futures. The findings indicate that criteria interrelated to narratives which highlight a promise of democratic energy governance may be less important for energy transition policies, and therefore undermine energy democracy goals. The conclusion highlights suggestions for policy and future research more likely to foster sociopolitical acceptance.

Bioenergy from energy crops is a source of negative emissions and carbon-neutral fuels in many 1.5/2 ∘C IPCC pathways. This may compete with other land uses. In contrast, ancillary biomass like by-products and waste is not primarily grown for energy and thus without land/food/feed competition. Here, we examine the availability and environmental impacts of ancillary bioenergy from agricultural sources under 190 circular agroecological strategies using the global food-system model SOLm for the year 2050. We find that there is a diverse option space for the future food and energy system to meet both global warming targets (1.5 ∘C) and food system sustainability (medium to highly organic) – a similar range of ancillary bioenergy global potential (55–65 EJ)from very different food systems (50%–75% organic agriculture and various levels of waste and concentrate feeding reduction). We find three trade-offs between food system sustainability and ancillary bioenergy provision. First, there is a clear trade-off between nutrient recycling and negative emissions potential. 1.4–2.6 GTCO2eq of negative emissions supplied through ancillary bioenergy with carbon capture and storage comes at the cost of nutrient deficits and resulting incompatibility with even a medium degree of organic farming. Second, reducing feed from croplands increases the ancillary bioenergy production with low shares of organic agriculture and reduces it for high shares. Third, food waste reduction reduces ancillary bioenergy provision. Hence, the sustainable transformation of the food system towards a less animal-based diet and waste reduction may conflict with a higher ancillary bioenergy provision, especially when the organic share is high as well. The policy implication of our results is that ancillary bioenergy can provide a similar range of future bioenergy as foreseen in IPCC AR6 illustrative pathways (±10% ) without additional land use or compromising food availability. However, higher ancillary bioenergy provision or additional negative emissions compete with food system sustainability; hence, we recommend policymakers consider aligning energy system planning with the compatibility of sustainable food systems simultaneously.

Correction to: Nature Energyhttps://doi.org/10.1038/s41560-023-01341-5, published online 14 September 2023. In the version of this article initially published, there was a typographical error in the third term of equation (2) in the Methods section, which now reads “S ^* = 100 + 7T, W ^* = 4.5 – 0.025T, H ^* = e ^1.1+0.06T, T ^* = 16”, where e ^1.1+0.06T appeared originally as e ^1.1+0.6T. This error was in presentation only and does not affect the results or source code. The equation has been amended in the HTML and PDF versions of the article.

The main task of the Intergovernmental Panel on Climate Change (IPCC) is to provide comprehensive assessments of climate science. However, there are accusations of bias toward certain research fields based on limited empirical evidence. By analyzing the evidence base of Working Group 3 (WG3) reports, we show that integrated assessment modeling (IAM) research was influential in all six assessments, and overrepresented in the Summary for Policymakers (SPM). Further, we show that a small number of men working in Western Europe and the USA dominate IAM research. Thus, global climate negotiations and science may have historically prioritized mitigation solutions suggested by an unrepresentative scientific sample and missed solutions from other perspectives like those of females and non-Western cultures. However, we also show that IAM research influence decreased in AR6, implying a leveling playing field between research fields. But more effort is needed to ensure a comprehensive assessment.

Novel wind technologies, in particular airborne wind energy (AWE) and floating offshore wind turbines, have the potential to unlock untapped wind resources and contribute to power system stability in unique ways. So far, the techno-economic potential of both technologies has only been investigated at a small scale, whereas the most significant benefits will likely play out on a system scale. Given the urgency of the energy transition, the possible contribution of these novel technologies should be addressed. Therefore, we investigate the main system-level trade-offs in integrating AWE systems and floating wind turbines into a highly renewable future energy system. To do so, we develop a modelling workflow that integrates wind resource assessment and future cost and performance estimations into a large-scale energy system model, which finds cost-optimal system designs that are operationally feasible with hourly temporal resolution across ten countries in the North Sea region. Acknowledging the uncertainty on AWE systems' future costs and performance and floating wind turbines, we examine a broad range of cost and technology development scenarios and identify which insights are consistent across different possible futures. We find that onshore AWE outperforms conventional onshore wind regarding system-wide benefits due to higher wind resource availability and distinctive hourly generation profiles, which are sometimes complementary to conventional onshore turbines. The achievable power density per ground surface area is the main limiting factor in large-scale onshore AWE deployment. Offshore AWE, in contrast, provides system benefits similar to those of offshore wind alternatives. Therefore, deployment is primarily driven by cost competitiveness. Floating wind turbines achieve higher performance than conventional wind turbines, so they can cost more and remain competitive. AWE, in particular, might be able to play a significant role in a climate-neutral European energy supply and thus warrants further study.

Energy system models do not represent natural processes but are assumption-laden representations of complex engineered systems, making validation practically impossible. Post-normal science argues that in such cases, it is important to communicate embedded values and uncertainties, rather than establishing whether a model is 'true' or 'correct'. Here, we examine how open energy modelling can achieve this aim by thinking about what 'a model' is and how it can be broken up into manageable parts. Collaboration on such building blocks—whether they are primarily code or primarily data—could become a bigger focus area for the energy modelling community. This collaboration may also include harmonisation and intercomparison of building blocks, rather than full models themselves. The aim is understandability, which will make life easier for modellers themselves (by making it easier to develop and apply problem-specific models) as well as for users far away from the modelling process (by making it easier to understand what is qualitatively happening in a model—without putting undue burden on the modellers to document every detail).

This study examines the impacts of both CO2 emission restrictions and energy import/export policies, on the Italian transport sector using Euro-Calliope model, i.e. an integrated model of transport and energy systems at the European level.

Indonesia has large renewable energy resources that are not always located in regions where they are needed. Sub-sea power transmission cables, or island links, could connect Indonesia's high-demand islands, like Java, to large-resource islands. However, the role of island links in Indonesia's energy transition has been explored in a limited fashion. Considering Indonesia's current fossil fuel dependency, this is a critical knowledge gap. Here we assess the role of island links in Indonesia's full power sector decarbonisation via energy system optimisation modelling and an extensive scenario and sensitivity analysis. We find that island links could be crucial by providing access to the most cost-effective resources across the country, like onshore photovoltaics (PV) and hydropower from Kalimantan and geothermal from Sumatera. In 2050, 43 GW of inter-island transmission lines enable 410 GWp of PV providing half of total generation, coupled with 100 GW of storage, at levelised system costs of 60 US$(2021)/MWh. Without island links, Java could still be supplied locally, but at 15% higher costs due to larger offshore floating PV and storage capacity requirements. Regardless of the degree of interconnection, biomass, large hydro, and geothermal remain important dispatchable generators with at least 62 GW and 23% of total generation throughout all tested scenarios. Full decarbonisation by 2040 mitigates an additional 464 MtCO2e compared to decarbonisation by 2050, but poses more challenges for renewables upscaling and fossil capacity retirement.

Climate change and geopolitical risks call for the rapid transformation of electricity systems worldwide, with Europe at the forefront. Wind and solar are the lowest cost, lowest risk, and cleanest energy sources, but their variability poses integration challenges. Combining both technologies and integrating regions with dissimilar generation patterns optimizes the trade-off between maximizing energy output and minimizing its variability, which respectively give the lowest levelized cost and lowest integration cost. We apply the Markowitz mean-variance framework to a rich multi-decade dataset of wind and solar productivity to quantify the potential benefits of spatially integration of renewables across European countries at hourly, daily and monthly timescales. We find that optimal cross-country coordination of wind and solar capacities across Europe's integrated electricity system increases capacity factor by 22% while reducing hourly variability by 26%. We show limited benefits to solar integration due to consistent output profiles across Europe. Greater wind integration yields larger benefits due to the diversity of regional weather patterns. This framework shows the importance of considering renewable projects not in isolation, but as interconnected parts of a pan-continental system. Our results can guide policymakers towards strategic energy plans that reduce system-wide costs of renewable electricity, accelerating the clean energy transition.

Wind power has considerable potential to decarbonise electricity systems due to its low cost and wide availability. However, its variability is one factor limiting uptake. We propose a simple analytical framework to optimise the distribution of wind capacity across regions to achieve a maximally firm or load-following profile. We develop a novel dataset of simulated hourly wind capacity factors (CFs) with bias correction for 111 Chinese provinces, European countries and US states spanning ten years (∼10 million observations). This flexible framework allows for near-optimal analysis, integration of demand, and consideration of additional decision criteria without additional modelling. We find that spatial integration of wind resources optimising the distribution of capacities provides significant benefits in terms of higher CF or lower residual load and lower variability at sub-, quasi- and inter-continental levels. We employ the concept of firmness as achieving a reliable and certain generation profile and show that, in the best case, the intercontinental interconnection between China, Europe and the US could restrict wind CFs to within the range of 15%–40% for 99% of the time. Smaller configurations corresponding to existing electricity markets also provide more certain and reliable generation profiles than isolated individual regions.

Background: Energy system models based on linear programming have been growing in size with the increasing need to model renewables with high spatial and temporal detail. Larger models lead to high computational requirements. Furthermore, seemingly small changes in a model can lead to drastic differences in runtime. Here, we investigate measures to address this issue. Results: We review the mathematical structure of a typical energy system model, and discuss issues of sparsity, degeneracy and large numerical range. We introduce and test a method to automatically scale models to improve numerical range. We test this method as well as tweaks to model formulation and solver preferences, finding that adjustments can have a substantial impact on runtime. In particular, the barrier method without crossover can be very fast, but affects the structure of the resulting optimal solution. Conclusions: We conclude with a range of recommendations for energy system modellers: first, on large and difficult models, manually select the barrier method or barrier+crossover method. Second, use appropriate units that minimize the model’s numerical range or apply an automatic scaling procedure like the one we introduce here to derive them automatically. Third, be wary of model formulations with cost-free technologies and dummy costs, as those can dramatically worsen the numerical properties of the model. Finally, as a last resort, know the basic solver tolerance settings for your chosen solver and adjust them if necessary.

Accurate modelling of the weather’s temporal and spatial impacts on building energy demand is critical to decarbonizing energy systems. Here we introduce a customizable model for hourly heating and cooling demand applicable globally at all spatial scales. We validate against demand from ~5,000 buildings and 43 regions across four continents. The model requires limited data inputs and shows better agreement with measured demand than existing models. We use it first to demonstrate that a 1 °C reduction in thermostat settings across all buildings could reduce Europe’s gas consumption by 240 TWh yr−1, approximately one-sixth of historical imports from Russia. Second, we show that service demand for cooling is increasing by up to 5% per year in some regions due to climate change, and 5 billion people experience >100 additional cooling degree days per year when compared with a generation ago. The model and underlying data are freely accessible to promote further research.

Energy models are used to study emissions mitigation pathways, such as those compatible with the Paris Agreement goals. These models vary in structure, objectives, parameterization and level of detail, yielding differences in the computed energy and climate policy scenarios. To study model differences, diagnostic indicators are common practice in many academic fields, for example, in the physical climate sciences. However, they have not yet been applied systematically in mitigation literature, beyond addressing individual model dimensions. Here we address this gap by quantifying energy model typology along five dimensions: responsiveness, mitigation strategies, energy supply, energy demand and mitigation costs and effort, each expressed through several diagnostic indicators. The framework is applied to a diagnostic experiment with eight energy models in which we explore ten scenarios focusing on Europe. Comparing indicators to the ensemble yields comprehensive ‘energy model fingerprints’, which describe systematic model behaviour and contextualize model differences for future multi-model comparison studies.

Given the urgent need to devise credible, deep strategies for carbon neutrality, approaches for ‘modelling to generate alternatives’ (MGA) are gaining popularity in the energy sector. Yet, MGA faces limitations when applied to state-of-the-art energy system models: the number of alternatives that can be generated is virtually infinite; no realistic computational effort can discover the complete technology and spatial option space. Here, based on our own SPORES method, a highly customisable and spatially-explicit advancement of MGA, we empirically test different search strategies – including some adapted from other MGA approaches – with the aim of identifying how to minimise redundant computation. With application to a model of the European power system, we show that, for a fixed number of generated alternatives, there is a clear trade-off in making use of the available computational power to unveil technology versus spatial dissimilarity across alternative system configurations. Moreover, we show that focussing on technology dissimilarity may fail to identify system configurations that appeal to real-world stakeholders, such as those in which capacity is more spread out at the local scale. Based on this evidence that no feasible alternative can be deemed redundant a priori, we propose to initially search for options in a way that balances spatial and technology dissimilarity; this can be achieved by combining the strengths of two different strategies. The resulting solution space can then be refined based on the feedback of stakeholders. More generally, we propose the adoption of ad-hoc MGA sensitivity analyses, targeted at testing a study's central claims, as a computationally inexpensive standard to improve the quality of energy modelling analyses.