Decarbonising the German steel industry requires not only mature low-carbon technologies, but also framework conditions that make their deployment feasible in practice. This study identifies the most critical barriers using a mixed-methods approach: 1) a systematic review of 42 publications and 2) semi-structured interviews with experts from all German primary steel producers. The review results in a comprehensive inventory of barriers associated with feasibility dimensions, which serves as a structured basis for the interviews. Experts weighted each barrier and highlighted the most important obstacles.The resulting ranking by practitioners differs markedly from rankings inferred from literature, suggesting that frequency of mention may be a weak indicator of practical relevance. Barriers with high consensus and importance relate to energy costs and the long-term availability of low-carbon electricity and hydrogen, regulatory certainty (EU ETS, CBAM, support schemes), international competition with global overcapacities, and the creation of lead markets for green steel. Socio-cultural barriers appear comparatively minor in this sectoral context.Methodologically, the study demonstrates a structured diagnostic approach: capturing the barrier landscape via literature, refining and prioritising through expert input, and translating top barriers into actionable indicators. The results provide policymakers and energy systems modellers with an empirically grounded, prioritised set of barriers for designing more realistic and feasible decarbonisation pathways for the steel industry.

Rapid electric vehicle (EV) adoption in India raises concerns about their climate benefits, as existing studies often overlook spatiotemporal variations. This study develops a state-level hourly marginal emission factor framework derived from grid generation to evaluate EV charging emissions. Annual well-to-wheel emissions are evaluated across convenience, overnight, midday, and MEF-optimized charging. Results show that national MEFs are highest during afternoon and evening peaks and lowest during overnight, with spatial variation ranging from 0.03-1.12 kg CO₂/kWh. Convenience charging yields highest emissions at 4.59 Mt CO₂/year, while MEF-optimized charging, which aligns EV usage with lowest MEF hours, reduces emissions by 34.83%. As MEF-optimized charging adoption increases, charging emissions reduce nearly linearly, from 3.8% at 10% adoption to 35% at 100% adoption relative to baseline. Sustained renewable integration could lower MEFs by up to 91.3% by 2060. EV charging should be guided by time and location-specific MEFs, integrated into smart charging platforms.

The Middle East and North Africa region faces critical water scarcity and food security challenges that threaten economic development. Fertilizer use supports food self-sufficiency, but its production is highly water intensive. Supplying desalinated water to a decarbonized fertilizer plant offers an environmentally sustainable pathway. This study investigates co-locating a decarbonized fertilizer plant with a seawater desalination facility, optionally implementing minimum liquid discharge (MLD) to generate additional revenue through recovery of magnesium hydroxide and sodium chloride (NaCl). Three configurations were modeled: a conventional seawater reverse osmosis (SWRO)-based plant; and two MLD configurations using high-pressure RO (HPRO), osmotically-assisted RO (OARO), and crystallizers. Financial performance was assessed using a novel discounted and allocated levelized cost (DALC) method, internal rate of return (IRR), and net present value (NPV). In a Moroccan case study, the conventional configuration achieved the lowest DALC and energy consumption (0.70 USD/m3water and 3.8 KWhel/m3), with an IRR of 23.9 %. The first MLD configuration had higher costs (0.94 USD/m3water, 12.0 KWhel/m3) and a lower IRR (14.5 %), with water recovery limited to 71.4 % due to nonuse of magnesium crystallizer effluent (60.4 % in the conventional setup). Reusing this effluent in the second MLD configuration increased water recovery to 96.7 %, yet higher impurities at the NaCl crystallizer feed reduced the IRR to 9.7 %, which could be improved through financing strategies such as lowering capital costs to endorse the MLD-maximizing option. The findings emphasize advancing impurity removal methods and exploring innovative project financing strategies to enable financially and environmentally sustainable seawater desalination for decarbonized fertilizer production.

The transformation of the electricity system towards higher shares of renewable energy necessitates increased flexibility on the demand side. The industrial sector, which accounts for over 40% of Germany's total electricity demand, exhibits significant heterogeneity in terms of production processes, load characteristics and grid use patterns. Consequently, it is considered a primary source of such flexibility. This study examines the economic and regulatory implications of deploying battery energy storage systems (BESS) in industrial settings, focusing on how different grid fee policy frameworks influence operational strategies, total costs of companies as well as their impact on grid operators. Using a mixed-integer linear programming model applied to more than 800 real-world industrial load profiles, we explicitly capture the diversity of industrial electricity demand and assess three regulatory scenarios: the current framework with incentives for atypical and intensive grid usage, and two proposed reforms incorporating dynamic energy prices and revised capacity prices. The objective of the model is a minimization of each company's annual total costs, with the model deciding how many BESS modules should be built. Results show that BESS adoption leads to an average cost reduction in electricity procurement costs of 14.3%, with some companies reaching cost reductions of over 30%. At the same time companies that make use of atypical grid usage increase their maximum peak load on average by 51.7%, while the average grid fee payments are reduced by 41.6%. It is shown that by removing capacity price components of grid fees and by fully dynamizing energy price components, cost and grid fee payment reductions as well as peak loads are increased significantly. This raises concerns about unintended system effects such as increased network congestion or transformer overloading. In contrast, retaining capacity components supports more equitable cost allocation and reduces the risk of cross-subsidization, whereby smaller consumers subsidize larger, more flexible ones. These findings underscore the importance of cost-reflective grid tariffs that align network charges with underlying system costs, and incentivize grid-friendly behavior. From a policy perspective, economic efficiency ought to be balanced with potential distributional effects, taking into account temporal and locational price signals enabling the efficient deployment of industrial flexibility.

The electrification of heavy-duty road transport is crucial for climate-neutral mobility, yet it changes fleet operating costs, with energy expenditure accounting for around 17% of the Total Cost of Ownership for battery-electric trucks (BETs). We investigate "Energy-as-a-Service" (EaaS), a business model combining dynamic electricity tariffs with an Energy Management System to optimize depot charging based on price signals and vehicle data. A linear optimization model is applied to three German BET use cases under various tariffs using 2023 wholesale market data. Results show that switching from a fixed to a dynamic tariff reduces annual charging costs by 11–43%, showing EaaS's viability as a new service domain for market actors. Four bundled EaaS pricing models were derived and evaluated through an expert survey, with respondents favoring kWh-based pricing combined with an installation fee for its transparency, familiarity, and flexibility. These findings highlight EaaS's potential to reduce costs and accelerate BET adoption.

Lowering the use of fossil fuels not only mitigates climate effects by decreasing the emission of greenhouse gases, but also reduces the release of harmful air pollutants into the atmosphere. Thus, the transition to a carbon-free energy system in the upcoming years could potentially have a major impact on lower air pollutant emissions, leading to better air quality and less harmful impacts on human health and ecosystems. Currently, emissions from power plants in the energy supply sector (e.g. coal or oil) contribute strongly to total air pollutant emissions in Europe. Among others, especially emissions of sulphur oxides (SOx), nitrogen oxides (NOx) and particulate matter (PM) are highly relevant regarding air quality issues. In order to be able to make informed statements about the impact of the European energy transition and the phase-out of fossil fuels on air quality, providing detailed information on the temporal and spatial character of air pollutant emissions in the future are required. However, the future projection of air pollutant emissions from power plants poses a major challenge because it is influenced by various factors like the pace of renewable energy rollout, power line capacities and the phase-out of fossil power plants. This work aims to provide estimates of NOx, SOx and PM emissions from power plants in Europe for the year 2030 and to analyse the temporal and spatial dynamics of these emissions in differing energy transition scenarios compared to current emission characteristics. The energy system model framework REMix is used to model activities of power plants in 2030. It considers the effects of power line capacities, renewable energy capacity increase, consumption patterns and the future power plant fleet of European countries in order to simulate power plant activities in high spatial and temporal resolution. The corresponding emission projections are based on current emission factors of power plants, e.g. from emission reports and information on installed flue gas cleaning systems, and are modelled considering the implementation of European emission standards for power plants in 2030. The results show that ambitious scenarios for the energy transition cause significant changes in the spatial and temporal occurrence of the considered air pollutant emissions compared to the current emission characteristics of power plants in Europe.

Modeling energy systems typically requires multiple weather years to ensure accurate results. However, running large-scale energy system models with multiple weather years is computationally intensive. To address this, a typical meteorological year is often used, which represents the long-term characteristics of historical weather. However, relying solely on such a representative year is insufficient due to the growing frequency of extreme weather events. It is crucial to consider how energy systems perform under extreme conditions to ensure a secure supply. This study addresses this need by focusing on weather-related extreme conditions of energy systems and employing various methods to generate synthetic weather years for a sector-coupled energy system. By configuring two system scenarios, we aim to identify a robust energy system configuration capable of accommodating all historical years. Additionally, we examine the characteristics of this system configuration and identify critical factors that influence system robustness to weather variability. Our results demonstrate the important role of dispatchable generation technologies in maintaining the security of supply. Furthermore, we find that short-term extreme events, such as 17 or 18 consecutive hours of extremely high residual load, can impose significant stress on the energy system, often exceeding the impact of longer-term extreme events.

In view of the increasing volatility and uncertainty in the German electricity system, new ways of providing flexibility must be found. In particular, industrial companies could potentially play a significant role in demand-side flexibility as they are the largest consumers of electricity in Germany. This paper investigates the potential benefits of using battery energy storage (BESS), photovoltaic (PV) and dynamic electricity tariffs in industrial companies to reduce costs and how this affects electricity consumption. Realistic load profiles of small and medium-sized German companies are used as input data. The results show that the implementation of BESS, PV and the use of dynamic electricity tariffs can significantly reduce the total annual costs of industrial companies. One of the main reasons for this is a reduction in grid charges. The results indicate that by taking advantage of atypical grid usage, the total grid fee revenues received by the grid operator could be significantly reduced. Therefore, an adjustment of existing mechanisms is most likely required to avoid an unfair distribution of grid-related costs among different stakeholders.

The practical feasibility of GHG mitigation pathways is increasingly acknowledged as essential to climate scenario development. However, energy system models (ESMs) still lack a structured and comprehensive approach for integrating feasibility considerations. This study proposes a conceptual framework that addresses this gap by guiding the integration of feasibility aspects into model-based scenario studies, with a specific focus on the industrial sector.At the heart of the proposed concept lies the Feasibility Loop—the core contribution of this work. It provides a structured, visual, and process-oriented approach to systematically link existing methods, indicators, and data sources across the entire modelling process. The loop identifies key steps in a model-supported feasibility assessment, clarifies how different types of methods contribute to these steps, and supports modellers in understanding where and how feasibility aspects can be meaningfully integrated.The framework is built around three distinct types of feasibility constraints—hard, quantitative soft, and qualitative soft—which serve as a conceptual bridge between assessment content, modelling tools, and interdisciplinary knowledge. Supporting components include a 5W1H-based structuring of the research context, a typology of feasibility-relevant indicator categories, and guidance for modelling requirements such as granularity and adaptability.Rather than prescribing a fixed workflow, the proposed concept serves as a flexible toolbox, enabling tailored application depending on available resources and research goals. Finally, it aims to improve the relevance, comparability, and transparency of scenario results and support more robust decision-making in the transformation of energy-intensive industry systems.

Raw materials are essential for robust global pathways towards carbon-neutral futures. However, many raw materials are subject to geopolitical risks, meaning that potential supply bottlenecks can be an obstacle to a rapid transformation of the energy system towards carbon neutrality. In order to investigate this in more detail, we combine integrated assessment modelling, material flow analysis and a scenario-level geopolitical risk assessment in this study. We show that the total raw material demand for construction and operation of the energy and transport system decreases when considering both, fossil fuels and non-fuel raw materials for the construction of technologies. However, the expected sharp increase in demand for many raw materials in clean energy and transport technologies requires a steep ramp-up of the global raw material production to avoid supply shortages and corresponding price increases. Ambitious system transformation leads to lower total raw material costs compared to a business-as-usual scenario and-depending on assumptions on raw material price development-than today. Finally, scenario-level geopolitical supply risk factors (country concentration and weighted country risk of raw material supply) depend only weakly on the degree of defossilization of the energy and transport system. The declining raw material costs are thus the main driver for a considerable reduction in geopolitical-economic dependencies of ambitious climate protection compared to business-as-usual strategies.

Reducing greenhouse gas emissions in the transport sector is among the hardest challenges in transforming energy systems to zero emissions. Transport energy demands are driven by an interplay of social behavioral, technical factors, political decisions and economic conditions, motivating detailed transport demand modeling.In Germany, transport energy supply – increasingly from electricity – is expected to challenge the energy supply infrastructure. Recent studies assume large shares of imported clean energy carriers and proclaim global renewable fuel import potentials. Simultaneously, sustainable biofuels’ impacts on required electricity supply infrastructure is yet not well understood.We assess the impact of climate ambition, indirect electrification shares and biofuel availability on energy supply infrastructure in 8 demand scenarios. Coupling the European energy system model REMix with the biofuel allocation model BENOPTex, we calculate cost-minimal energy supply infrastructure for each scenario. This high detail of integrated transport sector and biofuel modeling is novel to energy system analysis.We find that incorporating user preferences in sales decisions clearly narrows the range of transport energy demand. As the German renewable energy potential is exhausted, higher clean fuel demand is covered by imports. Still, the use of these fuels drives the required power grid expansion, and especially electrolysis and fuel production capacities. Biofuel availability may significantly reduce e-fuel demand reducing cost-optimal hydrogen production capacity in the medium term and necessary grid expansion within Germany beyond 2030.The model outcome is limited by assumptions on costs and availability of import options. Future work should further address modal shift transport scenarios.

Urea, a globally dominant synthetic nitrogen fertilizer, presents a complex challenge for India. While promoting agricultural productivity, its production-reliant on natural gas-is projected to drive a threefold increase in India's natural gas consumption by 2050. To meet ambitious climate targets while ensuring food security, India's existing urea plants must be decarbonized. This study conducted techno-economic modeling of "blue" and "green" urea production techniques for all 34 existing urea plants in India, incorporating technologies such as electrolyzers and carbon capture. Using a mixed-integer programming approach from a central planner's perspective, we evaluated key indicators of business-as-usual and decarbonization pathways for the sector under different scenarios. The results indicate that a high level of decarbonization is economically feasible under most scenarios, with the base scenario showing a potential adoption of over 93% green urea by 2050, thus reducing the sector's current natural gas consumption intensity of 645SCM t urea by 96%. This transition also results in a lower freshwater withdrawal intensity of approximately 4 m 3 t urea , which is below India's current average of 6.4m3 t urea . The levelized costs of urea for the decarbonization pathway are more robust against external factors, ranging from 398 to 487 USD 2026 t urea , depending on the scenario. However, these costs must compete with the internationally traded urea prices, which fluctuated between 202 and 925 USD t urea from 2019 to 2024, largely driven by natural gas prices. Low future natural gas prices could be a key barrier to achieving decarbonization and reducing the water intensity of urea. This study suggests that implementing a carbon tax could serve as an effective mitigation strategy in such cases. Future research should consider the integrated modeling of hydrogen and ammonia demands, which are relevant green fuels for the energy transition.

The workplace, as a promising location for Electric Vehicle Supply Equipment (EVSE), presents a particular challenge, as different user requirements (e.g., parking and charging durations) meet a spatially and quantitatively limited offer of EVSE. However, integrating electric vehicles synergistically into the energy system of the employer can increase the profitability of the system and, correspondingly, increase the number of EVSE. For this, a deep understanding of employees’ charging behavior is key. For providing some evidence of empirical charging patterns at the workplace, this work examined a dataset of 23.9 million observations on empirical charging processes at workplaces in 2023. To identify user groups, a probabilistic model (Gaussian Mixture Model) and a K-Means clustering approach were applied and the results compared. Eight groups were identified, including full-time and part-time employees, pool vehicle users, and opportunists. The group-specific probability distributions are used to publish a synthetic dataset of parking and charging patterns at workplaces. The openly provided dataset helps to identify the right composition of EVSE in the employee context and to optimize potential fields of action.

This paper examines macroeconomic issues of technological import dependence in the expansion of renewable energy generation capacity, a key concern for policymakers amid ongoing geopolitical tensions and the urgent need for a rapid energy transition. Despite the critical importance of understanding determinants in trade of clean energy technologies, previous studies have lacked empirical evidence on supply-side determinants. Using a structural gravity model, this study analyzes the relationship between technology imports and the expansion of wind and solar photovoltaic (PV) capacities. The findings reveal significant differences in countries' development trajectories, showing that between 2000 and 2020, increases in renewable energy capacity did not substantially drive technology imports. A 100 % increase in the growth rate of wind energy capacity led to a 1.9 % increase in wind technology imports, while the same growth rate for solar PV resulted in a 6.2 % increase in PV technology imports. These findings hold even when China, the largest producer of clean energy technologies, is excluded from the dataset. Based on these results, it is recommended that policymakers continue to support renewable energy expansion, as it does not necessarily lead to higher import dependency and may offer opportunities for local industries, especially when coupled with industry-specific support measures.

Optimization-based frameworks for energy system modeling such as TIMES, ETHOS.FINE, or PyPSA have emerged as important tools to outline a cost-efficient energy transition. Consequently, numerous reviews have compared the capabilities and application cases of established energy system optimization frameworks with respect to their model features or adaptability but widely neglect the frameworks’ underlying mathematical structure. This limits its added value for users who not only want to use models but also program them themselves.To address this issue, we follow a hybrid approach by not only reviewing 63 optimization-based frameworks for energy system modeling with a focus on their mathematical implementation but also conducting a meta-review of 68 existing literature reviews.Our work reveals that the basic concept of network-based energy flow optimization has remained the same since the earliest publications in the 1970s. Thereby, the number of open-source available optimization frameworks for energy system modeling has more than doubled in the last ten years, mainly driven by the uptake of energy transition and progress in computer-aided optimization.To go beyond a qualitative discussion, we also define the mathematical formulation for a mixed-integer optimization model comprising all the model features discussed in this work. We thereby aim to facilitate the implementation of future object-oriented frameworks and to increase the comprehensibility of existing ones for energy system modelers.

Electricity demand is a crucial factor in energy system planning. Understanding future electricity demand is vital for developing effective energy and climate policies, as well as establishing a resilient and sustainable energy system. In light of these considerations, the escalating challenges posed by climate change are anticipated to have a substantial impact on electricity demand. This study, therefore, provides a comprehensive analysis delving into the dynamic nature of Temperature Response Functions (TRFs) of electricity demand across Europe. By examining various factors influencing electricity demand in residential buildings, such as thermal insulation, heating electrification, space cooling, and passive cooling, we aim to understand their collective impact on shaping future Temperature Response Functions. To project electricity demand, our study incorporates these factors into our scenario assumptions. Through a comprehensive investigation of these scenarios, our findings reveal distinctive regional influences of these factors. In regions where heating demand prevails, an initial increase in electricity demand is anticipated due to increased electrification rates. However, improved building thermal insulation is expected to substantially reduce winter electricity demand in the long run. Conversely, in regions with pronounced cooling demand, a notable escalation in electricity demand is foreseen due to increased space cooling penetration rate. Nevertheless, the application of effective passive cooling measurements is expected to mitigate and markedly diminish this increase. By highlighting the differential influences of these factors on electricity demand across Europe, our findings can offer valuable insights and guidelines first for energy system modelers for considering the change in Temperature Response Functions and second for policymakers to develop effective climate change adaptation and mitigation strategies.