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.

This study contributes to the Climate, Land, Energy, and Water system (CLEWs) framework by developing an integrated model for Kenya capturing the interdependencies between climate, land, energy, and water systems. Focusing on cooking and crop production, it examines their contributions to land use changes, mainly deforestation, and emissions. We evaluate three scenarios-BAU, SC1, and SC2- that target clean cooking transitions and reduced crop imports, covering seven crops representing 72 % of Kenya's cultivated area. We detail the challenges of gathering data to populate such a model through document examination and literature review, and we identified uncertain input parameters. Results show that forest loss from cooking varies with the fraction of non-renewable biomass (fNRB). Under BAU, forest cover loss could range from 300 km2 at an fNRB of 0.3 to 900 km2 at 0.9. Scenarios SC1 and SC2 mitigate these impacts through cleaner cooking solutions. By 2050, under the clean cooking scenario (SC2), LPG stoves could achieve up to 96 % penetration, reducing CO2 emissions to 8.3 MTon and PM2.5 to 0.8 kTon, compared to high emissions in the BAU scenario dominated by wood and charcoal stoves. In agriculture, land use expands by 56 %, 69 %, and 33 % across the scenarios, while fossil fuel use rises from 2.46 PJ to 5.9 PJ by 2050, increasing CO2 emissions, from 183 kTon to 436 kTon. The findings highlight the need for integrated policies promoting clean cooking, sustainable agriculture, and deforestation mitigation. This integrated CLEWs approach provides actional insights for reducing deforestation and emissions in energy and agriculture sectors.

This study applies Global Sensitivity Analysis (GSA) to an optimization model for Kenya based on the Climate, Land, Energy, and Water systems (CLEWs) framework. The model provides insights into the interdependencies among energy, land, and water systems, and the sensitivity analysis allows to evaluate its ability to capture sectoral interactions and trade-offs while identifying key influential parameters shaping the overall system behavior. Using the Morris screening method, the analysis emphasizes the key role of discount rates—both global and technology- specific—influencing the energy sector, biomass use for cooking, renewable energy sources penetration, and forest land cover. The findings also show that all inputs influence forest cover, demonstrating the CLEWs model’s capacity to capture system interactions. By using a fully open methodology, this work enhances CLEWs model transparency and provides actionable insights for policymakers to address trade-offs and support resilient resource management.

Achieving the Sustainable Development Goals (SDGs) constitutes a global commitment that necessitates the development of innovative strategies to integrate research, policy, and practice effectively. In the European Union (EU), multi-stakeholder engagement has become a vital strategy for tackling complex climate and energy research challenges. This approach is crucial to establishing research priorities that effectively address SDGs 7 and 13. Despite its recognized importance, the existing literature offers no comprehensive overview and guidance on effective multi-stakeholder engagement in EU-funded climate and energy research. This study shows that a scoping review, combined with stakeholder co-design workshops, can reveal key gaps and inform guidelines for robust multi-stakeholder engagement. A systematic review of 23 published articles using criteria drawn from the broader stakeholder engagement literature found that engagement terminology is rarely defined and often used interchangeably, indicating a gap between the literature and its real-world application. This study also provides guidelines for conducting effective stakeholder engagement, drawing upon the broader stakeholder engagement literature, the outcomes of the scoping review, and lessons learned during the European Climate and Energy Modelling forum project. Three co-design workshops engaging 85 stakeholders conducted in 2021 and 2022 uncovered 83 research priorities centred on policy, regulation, and using energy and climate models to inform policymaking. These research priorities are provided as an open data set. The findings of the study underscore the need for standardized engagement practices to enhance the impact of EU-funded climate and energy research and guide future policy and research initiatives.

When investigating access to electricity in Sub-Saharan African countries such as Kenya and Benin, which have access rates of 71% and 42% respectively (2020), modelers are faced with limited computational resources, poor data availability, and energy systems undergoing dramatic changes in the supply, transmission and distribution, and demand sides. This paper explores the relative importance of path-dependent modeling decisions, such as spatial and temporal resolution, and parametric uncertainties, such as final energy demand, discount rate, and capital costs. We use global sensitivity analysis to find that electricity demand, discount rate, and temporal and spatial resolution significantly influence the assessed results parameters. Results differ between countries showing that the model setup must use spatial and technological parameter values appropriate for the context. The results show that especially when modeling the expansion of the network, spatial and temporal resolution should be customizable and changed, even between scenarios, depending on the research question.

The upscaling of novel carbon dioxide removal, such as bioenergy carbon capture and storage (BECCS), to gigatonne scales is an urgent priority if global warming is to be limited to well below 2 °C. But political, economic, social, technological, environmental and regulatory uncertainty permeates BECCS projects and deters investors. To address this, we explore options to improve the robustness of BECCS deployment strategies in the face of multi-dimensional uncertainties. We apply Dynamic Adaptive Planning (DAP) through expert interviews and Robust Decision Making (RDM) through exploratory modelling, two decision making under deep uncertainty methods, to the case of Stockholm Exergi, an early mover aiming to deploy BECCS at a combined heat and power plant in the capital of Sweden. The main contributions of the research are to 1) illustrate how a quantification of robustness against uncertainty can support an investment decision to deploy BECCS 2) comprehensively cover uncertain vulnerabilities and opportunities of deploying BECCS, and 3) identify critical scenarios and adaptations to manage these uncertainties. The main conclusions are: investing in BECCS is relatively robust if assessing performance across many scenarios and if comparing the worst-cases of either investing, or not doing so. Not investing could miss out on up to € 3.8 billion in terms of net present value. The critical uncertainties of BECCS can be managed by strengthening biomass sustainability strategies and by gaining support for negative emission trading regulation on carbon markets, e.g., voluntary or Paris Agreement Article 6. Even in vulnerable scenarios of average electricity prices above 82 €/MWh, if trading regulation is implemented before 2030 and if negative emission prices exceed 151 €/CO2, investing in BECCS performs better than not doing so in 96% of cases. We suggest that facility-level parameters and cost-reductions are of little importance for BECCS investments and upscaling. It is regulatory certainty of operating revenues, e.g., through negative emission markets, that needs to be provided by policymakers.

Located in Southern Europe, the Drina River Basin is shared between Bosnia and Herzegovina, Montenegro, and Serbia. The power sectors of the three countries have an exceptionally high dependence on coal for power generation. In this paper, we analyse different development pathways for achieving climate neutrality in these countries and explore the potential of variable renewable energy (VRE) and its role in power sector decarbonization. Weinvestigate whether hydro and nonhydro renewables can enable a net-zero transition by 2050 and how VRE might affect the hydropower cascade shared by the three countries. The Open-Source Energy Modelling System (OSeMOSYS) was used to develop a model representation of the countries' power sectors. Findings show that the renewable potential of the countries is a significant 94.4 GW. This potential is 68% higher than previous assessments have shown. Under an Emission Limit scenario assuming net zero by 2050, 17% of this VRE potential is utilized to support the decarbonization of the power sectors. Additional findings show a limited impact of VRE technologies on total power generation output from the hydropower cascade. However, increased solar deployment shifts the operation of the cascade to increased short-term balancing, moving from baseload to more responsive power generation patterns. Prolonged use of thermal power plants is observed under scenarios assuming high wholesale electricity prices, leading to increased emissions. Results from scenarios with low cost of electricity trade suggest power sector developments that lead to decreased energy security.

Europe and North America account for 32 % of current carbon emissions. Due to distinct legacy systems, energy infrastructure, socioeconomic development, and energy resource endowment, both regions have different policy and technological pathways to reach net zero by the mid-century. Against this background, our paper examines the results from the net zero emission scenarios for Europe and North America that emerged from the collaboration of the European and American Energy Modeling Forums. In our analysis, we perform an inter-comparison of various integrated assessments and bottom-up energy system models. A clear qualitative consensus emerges on five main points. First, Europe and the United States reach net zero targets with electrification, demand-side reductions, and carbon capture and sequestration technologies. Second, the use of carbon capture and sequestration is more predominant in the United States due to a steeper decarbonization schedule. Third, the buildings sector is the easiest to electrify in both regions. Fourth, the industrial sector is the hardest to electrify in the United States and transportation in Europe. Fifth, in both regions, the transition in the energy mix is driven by the substitution of coal and natural gas with solar and wind, but to a different extent.

There is growing recognition of the need for openness in the governance and management of long-term energy systems planning, including improving data accessibility to inform the planning process. Open data principles offer a way to manage and govern this process more collaboratively and transparently, but they are challenging to implement particularly in resource-constrained and decentralised planning contexts like low- and middle-income countries. For this reason, this paper assesses the viability of open data practices for enhancing transparency and collaboration in energy planning, using Kenya as a case study. Through qualitative analysis of policy documents and stakeholder interviews, this study evaluates the alignment and divergence between internationally accepted values and principles of open science and open data and Kenya's energy planning needs. What emerges is a contrasting picture. The results show that, while open approaches to energy data are theoretically promising for addressing current energy data challenges in Kenya, stakeholders show limited agreement or understanding of practical implementation pathways. These findings aim to support Kenyan stakeholders and decision-makers involved in the ongoing long-term planning process under the Integrated National Energy Plan.

At the time of writing, 759 million people (2019) still lack access to electricity globally. It is important for energy planning to describe plausible pathways to achieve national goals, using tools such as energy systems models to explore scenarios and provide insight. Until recently, modelling energy access in countries with a low electrification rate was conducted at low spatial (e.g., national) and/or temporal resolutions (e.g., annual time slices or ‘overnight’ electrification). In this paper, we develop methods in an open-source computational workflow with high spatial resolution in an open-source energy systems optimisation model. We use Kenya as our case application where approx. 16 million people still lack access to electricity (2019). One reference scenario and two diagnostic hypothetical scenarios are developed to assess the model. The spatial resolution of approximately 40 by 40 km cells leads to 591 demand cells split between electrified and un-electrified population. The results show that in the reference scenario, the optimal supply option for the unelectrified population is PV panels and batteries. At the same, an oversupply of the planned power plants is observed. The model can capture dynamics between spatially explicit supply options and central power plants in one model.