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The European Union (EU) recently ratcheted its climate ambition to net-zero emissions by 2050, with a milestone of 55% emissions cuts in 2030. This study carries out a model inter-comparison to assess the EU’s path, from “Fit for 55” in 2030 to an intermediate milestone in 2040 and onto net zero in 2050, offering insights at sectoral and member-state levels. Our model results support the bloc’s ambition for its Emissions Trading System and Effort Sharing Regulation sectors while pointing to the need for near-complete decarbonization of electricity by 2040, enabled by considerable deployment of renewables (45%–65% in 2030, to 60%–70% in 2040, and to 75%–90% in 2050 in electricity generation) and carbon capture and storage (0.5–2 GtCO2/year by 2050). We also highlight the trade-offs between supply-side and harder-to-abate sectors, assess the ambition of member states for net zero and timing of coal phaseout, and reflect on the economic implications of investment, technical, and policy needs.

The COP26 Glasgow process resulted in many countries strengthening their 2030 emissions reduction targets and announcing net-zero pledges for 2050–2070 but it is not clear how this would impact future warming. Here, we use four diverse integrated assessment models (IAMs) to assess CO2emission trajectories in the near- and long-term on the basis of national policies and pledges, combined with a non-CO2infilling model and a simple climate model to assess the temperature implications. We also consider the feasibility of national long-term pledges towards net-zero. While near-term pledges alone lead to warming above 2 °C, the addition of long-term pledges leads to emissions trajectories compatible with a future well below 2 °C, across all four IAMs. However, while IAM heterogeneity translates to diverse decarbonization pathways towards long-term targets, all modelled pathways indicate several feasibility concerns, relating to the cost of mitigation and the rates and scales of deployed technologies and measures.

The silver lining of global crises is that they provide windows of opportunity for transformation. However, integrated assessment models (IAMs) are failing to successfully navigate these windows. We argue that a transdisciplinary approach is required to chart sustainable pathways through the polycrisis.

To meet the Paris temperature targets and recover from the effects of the pandemic, many countries have launched economic recovery plans, including specific elements to promote clean energy technologies and green jobs. However, how to successfully manage investment portfolios of green recovery packages to optimize both climate mitigation and employment benefits remains unclear. Here, we use three energy-economic models, combined with a portfolio analysis approach, to find optimal low-carbon technology subsidy combinations in six major emitting regions: Canada, China, the European Union (EU), India, Japan, and the United States (US). We find that, although numerical estimates differ given different model structures, results consistently show that a >50% investment in solar photovoltaics is more likely to enable CO2emissions reduction and green jobs, particularly in the EU and China. Our study illustrates the importance of strategically managing investment portfolios in recovery packages to enable optimal outcomes and foster a post-pandemic green economy.

Most of the integrated assessment modelling literature focuses on cost-effective pathways towards given temperature goals. Conversely, using seven diverse integrated assessment models, we project global energy CO2emissions trajectories on the basis of near-term mitigation efforts and two assumptions on how these efforts continue post-2030. Despite finding a wide range of emissions by 2050, nearly all the scenarios have median warming of less than 3 °C in 2100. However, the most optimistic scenario is still insufficient to limit global warming to 2 °C. We furthermore highlight key modelling choices inherent to projecting where emissions are headed. First, emissions are more sensitive to the choice of integrated assessment model than to the assumed mitigation effort, highlighting the importance of heterogeneous model intercomparisons. Differences across models reflect diversity in baseline assumptions and impacts of near-term mitigation efforts. Second, the common practice of using economy-wide carbon prices to represent policy exaggerates carbon capture and storage use compared with explicitly modelling policies.

The reduction of the EU's pipeline gas imports from Russia because of the Russian war against Ukraine has had severe economy-wide implications for the bloc. Using a multisector integrated assessment model (GCAM), we find that a potential complete cut-off of Russian pipeline gas exports to the EU unevenly impacts the energy mix and gas prices across subregions within the EU, depending on their access to alternative gas pipelines and liquefied natural gas infrastructure. The restrictions also affect global gas infrastructure capacity additions, asset stranding, and trade dynamics. Our results show that the Fit-for-55 policy framework already improves the EU's resilience against a cut-off of Russian pipeline gas, while additional improvements in energy efficiency and renewable targets could further soften impacts.

Energy system models are important tools to guide our understanding of current and future carbon dioxide emissions as well as to inform strategies for emissions reduction. These models offer a vital evidence base that increasingly underpins energy and climate policies in many countries. In light of this important role in policy formation, there is growing interest in, and demands for, energy modellers to integrate more diverse perspectives on possible and preferred futures into the modelling process. The main purpose of this is to ensure that the resultant policy decisions are both fairer and better reflect people's concerns and preferences. However, while there has been a focus in the literature on efforts to bring societal dimensions into modelling tools, there remains a limited number of examples of well-structured participatory energy systems modelling processes and no available how-toguidance. This paper addresses this gap by providing good practice guidance for integrating stakeholder and public involvement in energy systems modelling based on the reflections of a diverse range of experts from this emergent field. The framework outlined in this paper offers multiple entry points for modellers to incorporate participatory elements either throughout the process or in individual stages. Recognising the messinessof both fields (energy systems modelling and participatory research), the good practice principles are not comprehensive or set in stone, but rather pose important questions to steer this process. Finally, the reflections on key issues provide a summary of the crucial challenges and important areas for future research in this critical field.

Multi-criteria decision-making is a daily process in everyday life, in which different alternatives are evaluated over a set of conflicting criteria. Decision-making is becoming increasingly complex, and the apparition of uncertainty and vagueness is inevitable, especially when related to sustainability issues. To model such lack of information, decision makers often use linguistic information to express their opinions, closer to their way of thinking, giving place to linguistic decision-making. However, the participation of multiple experts usually involves disagreements within the group, leading to unreliable solutions. To assist in decision-making and reduce such complexities, A grouP decisiOn fuzzy TOoL in support of cLimate change pOlicy making (APOLLO), a fuzzy decision support system, is introduced to deal with such problems in climate change and policy. The tool implements a framework for group decision-making, using 2-tuple Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), coupled with a new consensus measuring model to increase robustness of selected solutions. The operation of the software tool is showcased in a real case carried out in Austria, where stakeholders were asked to assess the risks embedded in pathways for decarbonizing the country’s iron and steel sector. Results indicate that a coherent strategy addressing funding and competition issues is necessary, with experts displaying a consensus level of 85% in that these risks are the most threatening for the transition.

Clean energy technological innovations are widely acknowledged as a prerequisite to achieving ambitious long-term energy and climate targets. However, the optimal speed of their adoption has been parsimoniously studied in the literature. This study seeks to identify the optimal intensity of moving to a green hydrogen electricity sector in Greece, using the OSeMOSYS energy modeling framework. Green hydrogen policies are evaluated, first, on the basis of their robustness against uncertainty and, afterwards, against conflicting performance criteria and for different decision-making profiles towards risk, by applying the VIKOR and TOPSIS multi-criteria decision aid methods. Although our analysis focuses exclusively on the power sector and compares different rates of hydrogen penetration compared to a business-as-usual case without considering other game-changing innovations (such as other types of storage or carbon capture and storage), we find that a national transition to a green hydrogen economy can support Greece in potentially cutting at least 16 MtCO2while stimulating investments of EUR 10–13 bn. over 2030–2050.

Despite recent growth in respective investments, the Netherlands is not expected to achieve its near-term renewable energy targets, indicating that policy design tends to overlook specific uncertainties and implementation risks. In the aim of supporting policymaking towards a low-carbon transition of the Dutch power sector with a focus on solar power, this study employs a stakeholder-driven approach based on fuzzy cognitive maps, while considering the notion of time. Results appear to strongly depend on expectations of future socioeconomic developments and show that stakeholders favour soft measures over financial incentives.

Fuzzy Cognitive Mapping is a semi-quantitative modelling method, widely used for decision support in various domains. However, existing software applications have been criticised over inadequate handling of uncertain information, lack of accessibility, and inability to converge to solutions for all modelled systems. Here we present In-Cognitive, an open-source, web-based application for the creation, visualisation, and simulation of Fuzzy Cognitive Maps, ensuring solution convergence and allowing for Monte Carlo uncertainty analysis. The application is built in Python and Bokeh and provides an accessible and user-friendly interface to model various systems quickly and reliably and evaluate the robustness of the modelling solutions.

Following Russia's invasion of Ukraine and amidst COVID-19 recovery efforts, the energy crisis has put enormous pressure to policymakers to balance climate action, sustainable development, and management of the impacts of fuel supply disruptions and price shocks. Policy and market responses, such as liquefied natural gas infrastructure investments and use of every available fossil-fuel lever to make up for Russian gas supply cuts, are feared to trigger new lock-ins, jeopardising decarbonisation. This is also the case in Italy, which is highly dependent on Russia-imported gas. Energy models typically used to support such decisions take time to produce meaningful scenarios and, in times of crisis, are largely driven by highly uncertain parameters. This study uses fuzzy cognitive maps to engage with experts in a workshop and elicit their knowledge and perceptions with the aid of a questionnaire, towards simulating the impact of selected strategies and important uncertainties on the three pillars of Italy's progress to electricity-sector sustainability: decarbonisation, affordability, and reliability. In a framework of deliberation and simulation, experts displayed strong preference for renewable energy, compared to new gas infrastructure. Renewables were notably deemed to have positive impacts across all three sustainabiltiy dimensions and were found more robust against uncertainties, such as regulatory and political instability, which was highlighted as the biggest risk. Critically, despite their expectedly positive impact, demand-side transformations including demand reductions and broader behavioural shifts—a core component of the EU's current approach—may prove inadequate, should large system pressures from negative socio- and techno-economic developments persist.

Current technological improvements are yet to put the world on track to net-zero, which will require the uptake of transformative low-carbon innovations to supplement mitigation efforts. However, the role of such innovations is not yet fully understood; some of these ‘miracles’ are considered indispensable to Paris Agreement-compliant mitigation, but their limitations, availability, and potential remain a source of debate. We evaluate such potentially game-changing innovations from the experts' perspective, aiming to support the design of realistic decarbonisation scenarios and better-informed net-zero policy strategies. In a worldwide survey, 260 climate and energy experts assessed transformative innovations against their mitigation potential, at-scale availability and/or widescale adoption, and risk of delayed diffusion. Hierarchical clustering and multi-criteria decision-making revealed differences in perceptions of core technological innovations, with next-generation energy storage, alternative building materials, iron-ore electrolysis, and hydrogen in steelmaking emerging as top priorities. Instead, technologies highly represented in well-below-2°C scenarios seemingly feature considerable and impactful delays, hinting at the need to re-evaluate their role in future pathways. Experts' assessments appear to converge more on the potential role of other disruptive innovations, including lifestyle shifts and alternative economic models, indicating the importance of scenarios including non-technological and demand-side innovations. To provide insights for expert elicitation processes, we finally note caveats related to the level of representativeness among the 260 engaged experts, the level of their expertise that may have varied across the examined innovations, and the potential for subjective interpretation to which the employed linguistic scales may be prone to.

This paper presents AUGMECON-Py, a Python framework for solving large and complex multi-objective linear programming problems under uncertainty, optimally and robustly capturing all solutions. On the core of the AUGMECON-Py software lies the integration of a well-established optimisation algorithm (AUGMECON) with Monte Carlo analysis that helps maximise robustness against stochastic uncertainty, thereby avoiding the complexity of numerous cascading methods and code scripts. Using an object-oriented language, AUGMECON-Py overcomes limitations of its predecessors regarding memory requirements, and further extends the solution algorithm to ensure no efficient solution is left outside the solution grid. The framework is easily accessible, offering effortless data pre- and post-processing, management, and visualisation of results.

Letter to the Editor

Letter to the Editor

Quantitative systems modelling in support of climate policy has tended to focus more on the supply side in assessing interactions among technology, economy, environment, policy and society. By contrast, the demand side is usually underrepresented, often emphasising technological options for energy efficiency improvements. In this perspective, we argue that scientific support to climate action is not only about exploring capacity of “what”, in terms of policy and outcome, but also about assessing feasibility and desirability, in terms of “when”, “where” and especially for “whom”. Without the necessary behavioural and societal transformations, the world faces an inadequate response to the climate crisis challenge. This could result from poor uptake of low-carbon technologies, continued high-carbon intensive lifestyles, or economy-wide rebound effects. For this reason, we propose a framing for a holistic and transdisciplinary perspective on the role of human choices and behaviours in influencing the low-carbon transition, starting from the desires of individuals and communities, and analysing how these interact with the energy and economic landscape, leading to systemic change at the macro-level. In making a case for a political ecology agenda, we expand our scope, from comprehending the role of societal acceptance and uptake of end-use technologies, to co-developing knowledge with citizens from non-mainstream and marginalised communities, and to defining the modelling requirements to assess the decarbonisation potential of shifting lifestyle patterns in climate change and action.

•We employ stakeholder insights, multi-criteria analysis and fuzzy cognitive mapping.•Twenty risks associated with four selected climate policy strategies are evaluated.•Barriers of investment risks, monitoring capacity and intermittency are highlighted.•Stakeholders are also worried about land requirements, climate poverty and injustice.•Interplay of risks, policy and transition depends on future socioeconomic challenges.