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To meet the Paris Agreement's aim of limiting global warming to 1.5 degrees Celsius, there is an urgent need for countries to reduce their greenhouse gas emissions by transitioning their energy sectors from fossil-based to zero‑carbon sources. Despite strong climate policies and gradual emissions reductions, Germany remains the greatest emitter in the European Union. Coal, responsible for almost 35 % of the country's CO2 emissions, is not scheduled to be phased out until 2038. Wind, one of Germany's greatest sources of renewable energy, has faced challenges due to a 1000 m federal ‘rule’ between residential buildings and wind turbines. The German coal phase-out and the onshore wind phase-in are linked to questions of procedural injustice in energy, as showcased in multiple studies. In this paper we develop a comprehensive framework that introduces the concept of political inequality as a lens to examine procedural injustice in energy transition decision-making, arguing that it offers greater nuance. We apply the framework to Germany's coal phase-out and onshore wind phase-in, asking how stakeholders in the German energy transition – i.e. the Energiewende - report political inequalities of voice, representation, treatment and influence in these decision-making processes, both at the federal level and in the state of North Rhine-Westphalia. We also explore how such inequality impacts climate mitigation in Germany's energy sector and whether it slows progress. Our findings from twenty-eight semi-structured interviews with German decision-makers, civil society and activists, highlight multiple reported inequalities in these processes that point to several procedural injustices in energy transition decision-making in Germany. However, while political inequalities can indeed slow progress on climate mitigation in the energy sector, the reverse may also materialise. Further research is needed to understand how the tension between political inequality and climate mitigation unfolds in the German energy sector and in the broader energy transition.

To meet the Paris Agreement's aim of limiting global warming to 1.5 degrees Celsius, there is an urgent need for countries to reduce their greenhouse gas emissions by transitioning their energy sectors from fossil-based to zero‑carbon sources. Despite strong climate policies and gradual emissions reductions, Germany remains the greatest emitter in the European Union. Coal, responsible for almost 35 % of the country's CO2 emissions, is not scheduled to be phased out until 2038. Wind, one of Germany's greatest sources of renewable energy, has faced challenges due to a 1000 m federal ‘rule’ between residential buildings and wind turbines. The German coal phase-out and the onshore wind phase-in are linked to questions of procedural injustice in energy, as showcased in multiple studies. In this paper we develop a comprehensive framework that introduces the concept of political inequality as a lens to examine procedural injustice in energy transition decision-making, arguing that it offers greater nuance. We apply the framework to Germany's coal phase-out and onshore wind phase-in, asking how stakeholders in the German energy transition – i.e. the Energiewende - report political inequalities of voice, representation, treatment and influence in these decision-making processes, both at the federal level and in the state of North Rhine-Westphalia. We also explore how such inequality impacts climate mitigation in Germany's energy sector and whether it slows progress. Our findings from twenty-eight semi-structured interviews with German decision-makers, civil society and activists, highlight multiple reported inequalities in these processes that point to several procedural injustices in energy transition decision-making in Germany. However, while political inequalities can indeed slow progress on climate mitigation in the energy sector, the reverse may also materialise. Further research is needed to understand how the tension between political inequality and climate mitigation unfolds in the German energy sector and in the broader energy transition.

Energy sharing in distributed energy systems constitutes the pivotal strategy development for enhancing clean energy utilization and low-carbon emission achievement. However, as market mechanisms continue to improve, energy trading in distributed energy systems will shift from the traditional system-to-system model to the multi-stakeholder model. Therefore, this study constructs a two-layer energy-sharing framework that contains different stakeholders. Firstly, the energy system operator guides the energy-sharing behavior among distributed energy systems through energy transaction pricing to maximize its revenue. Then, the distributed energy systems obtain optimal energy sharing and internal operation strategies based on the energy system operator's price signals to minimize their energy costs. Additionally, this study addresses the uncertainty of renewable energy generation in distributed energy systems using the Wasserstein metric ambiguity set, and combines it with the energy sharing issue to form a distributionally robust energy trading optimization model. Finally, to solve the two-layer multi-agent distributionally robust energy sharing problem, we employ strong duality theory to transform the problem into a more solvable form. An adaptive genetic algorithm-analytical target cascading method is proposed to achieve optimal transaction pricing and energy scheduling. The case analysis results demonstrate that the proposed strategy can achieve economic benefits of 5.51 % and environmental benefits of 5.73 %, effectively balancing economic efficiency and robustness.

Energy sharing in distributed energy systems constitutes the pivotal strategy development for enhancing clean energy utilization and low-carbon emission achievement. However, as market mechanisms continue to improve, energy trading in distributed energy systems will shift from the traditional system-to-system model to the multi-stakeholder model. Therefore, this study constructs a two-layer energy-sharing framework that contains different stakeholders. Firstly, the energy system operator guides the energy-sharing behavior among distributed energy systems through energy transaction pricing to maximize its revenue. Then, the distributed energy systems obtain optimal energy sharing and internal operation strategies based on the energy system operator's price signals to minimize their energy costs. Additionally, this study addresses the uncertainty of renewable energy generation in distributed energy systems using the Wasserstein metric ambiguity set, and combines it with the energy sharing issue to form a distributionally robust energy trading optimization model. Finally, to solve the two-layer multi-agent distributionally robust energy sharing problem, we employ strong duality theory to transform the problem into a more solvable form. An adaptive genetic algorithm-analytical target cascading method is proposed to achieve optimal transaction pricing and energy scheduling. The case analysis results demonstrate that the proposed strategy can achieve economic benefits of 5.51 % and environmental benefits of 5.73 %, effectively balancing economic efficiency and robustness.

The heavy industry is often regarded as hard-to-abate due to its importance to infrastructure build-up and capital stock, its reliance on high-temperature heat requirements, and the critical role it plays in global supply chains and security. These complexities have often been invoked to justify the persistence of residual greenhouse gas (GHG) emissions from cement, steel, and chemicals production by the year of net-zero, which, in contrast, suggest the need for global-scale roll-out of carbon dioxide removal (CDR) technologies. In this study, we use the global integrated assessment model (IAM) COFFEE with a detailed representation of industrial processes to understand the role of the industrial sector in climate change mitigation scenarios with different temperature ambitions. Our findings reveal a nuanced picture. While the industrial sector presents residual emissions of 1300–7600 MtCO2yr−1 in well-below 2 °C scenarios by 2050, it also emerges as a key mitigation asset in specific subsectors (e.g. chemicals and steel) and regions (e.g. AUS, BRA, CAN CAM, SAM), depending on the level of climate ambition pursued and the availability of biomass and carbon capture scale-up. Thus, the sector's role in climate change mitigation is context-dependent, opening pathways for strategic planning and technological and regional targeted actions.

The heavy industry is often regarded as hard-to-abate due to its importance to infrastructure build-up and capital stock, its reliance on high-temperature heat requirements, and the critical role it plays in global supply chains and security. These complexities have often been invoked to justify the persistence of residual greenhouse gas (GHG) emissions from cement, steel, and chemicals production by the year of net-zero, which, in contrast, suggest the need for global-scale roll-out of carbon dioxide removal (CDR) technologies. In this study, we use the global integrated assessment model (IAM) COFFEE with a detailed representation of industrial processes to understand the role of the industrial sector in climate change mitigation scenarios with different temperature ambitions. Our findings reveal a nuanced picture. While the industrial sector presents residual emissions of 1300–7600 MtCO2yr−1 in well-below 2 °C scenarios by 2050, it also emerges as a key mitigation asset in specific subsectors (e.g. chemicals and steel) and regions (e.g. AUS, BRA, CAN CAM, SAM), depending on the level of climate ambition pursued and the availability of biomass and carbon capture scale-up. Thus, the sector's role in climate change mitigation is context-dependent, opening pathways for strategic planning and technological and regional targeted actions.

Regionalized integrated energy system models considering stakeholder inputs are uncommon in the literature. This study tested and validated an existing quantitative optimization-based OPERA regional modeling framework. Stakeholder responses to surveys resulted in multiple future scenarios and sensitivities, applied to the Dutch province of Groningen energy transition. Stakeholder reflections in a workshop confirmed the potential of the model as a strategic decision-supporting tool. The tool successfully analyzed trade-offs, compromises, and complementarities regarding the different choices of stakeholders. The study reflected on the modest role of solar photovoltaics, which supplied 6.6–17.5 % of the primary energy, in comparison to policies and stakeholder assumptions. Biomass energy, at 18.2–28.5 %, was more prominent than expected. Similarly, choosing a scenario close to the current policy implied a strong dependency on imports, with net imports constituting 50 % of the energy supply. On the other hand, regional self-sufficiency implied spatial implications beyond stakeholder expectations. For example, land use associated with onshore wind energy was ∼13 % of the provincial land. The stakeholder interaction process highlighted capacity investments via other harmonized model linkages and the importance of the science-policy interfaces. Compared with contemporary models, the major advancements are spatial interfacing and the inclusion of land-use planning and policy constraints.

Regionalized integrated energy system models considering stakeholder inputs are uncommon in the literature. This study tested and validated an existing quantitative optimization-based OPERA regional modeling framework. Stakeholder responses to surveys resulted in multiple future scenarios and sensitivities, applied to the Dutch province of Groningen energy transition. Stakeholder reflections in a workshop confirmed the potential of the model as a strategic decision-supporting tool. The tool successfully analyzed trade-offs, compromises, and complementarities regarding the different choices of stakeholders. The study reflected on the modest role of solar photovoltaics, which supplied 6.6–17.5 % of the primary energy, in comparison to policies and stakeholder assumptions. Biomass energy, at 18.2–28.5 %, was more prominent than expected. Similarly, choosing a scenario close to the current policy implied a strong dependency on imports, with net imports constituting 50 % of the energy supply. On the other hand, regional self-sufficiency implied spatial implications beyond stakeholder expectations. For example, land use associated with onshore wind energy was ∼13 % of the provincial land. The stakeholder interaction process highlighted capacity investments via other harmonized model linkages and the importance of the science-policy interfaces. Compared with contemporary models, the major advancements are spatial interfacing and the inclusion of land-use planning and policy constraints.

Off-grid hydrogen supply from solar or wind sources to hydrogen-based steelmaking can reduce CO₂ emissions. However, the techno-economic feasibility of different supply chain configurations remains uncertain. This study evaluates 61 off-grid hydrogen supply chains for a 15 Mt. steel/year plant in 2030, considering renewable energy sources (onshore/offshore wind, solar, and overseas options), transmission technologies (cables, pipelines, trucks, and ships), storage technologies (compressed gaseous hydrogen, liquid hydrogen, ammonia, methanol, and liquid organic hydrogen carriers), and seasonal storage locations (at the energy source or steelmaking plant). Onshore truck transmission of hydrogen is found to be unpromising due to the significantly higher cost compared to alternative transmission technologies. When the transmission technology is not truck, chains with underground compressed hydrogen storage achieve the lowest levelized cost of hydrogen (LCOH) at 3.8–5.6 €2020/kg H₂, outperforming other options. When underground hydrogen storage is not feasible, liquid organic hydrogen carriers present the next lowest cost. Chains utilizing ammonia, methanol, and liquid hydrogen exhibit lower efficiency, higher renewables capacity requirement, and consequently higher LCOH, making them less attractive. Electricity transmission lowers the LCOH of compressed hydrogen chains compared to hydrogen pipeline transmission, but for other chains the trend is reversed. Hydrogen storage near the steelmaking plant reduces costs by enabling the reuse of boil-off hydrogen in liquid hydrogen chains, but for other chains storing hydrogen near the renewable energy source lowers the cost. Impacts of input uncertainties on the LCOH, limitations of this study, and suggestions for future studies are also presented.