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Abstract Climate action to achieve the Paris Agreement should respect the United Nations Sustainable Development Goals. Here, we use an integrated assessment modelling framework comprising nine climate policy models and quantify the impacts of decarbonisation pathways on Sustainable Development Goals in the European Union at regional and national levels. We show that scenario-consistent assumptions of future socio-economic trends and current climate policies would improve energy- and carbon-related aspects of sustainability and reduce inequalities. Ambitious net-zero emissions pathways would further improve health and agricultural productivity. Furthermore, countries currently lagging in achieving sustainable development goals would see the greatest benefits from ambitious climate action. Negative socio-economic impacts from climate action on poverty, hunger, and economic growth will require specific corrective policies. While our analysis does not quantify the negative effects of less ambitious climate policy, it demonstrates where co-benefits and trade-offs of greenhouse gas mitigation and sustainable development agenda exist and can guide policy formulation.

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-to guidance. 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 messiness of 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.

The extent to which modelled future pathways support effective policymaking for sustainability transitions has been questioned for a long time, with one major issue being the insufficient integration with the perspectives of policymakers and other stakeholders. One proposal to address this issue has been to set up facilitative dialogues with stakeholders to extend model-based pathways to socio-technical scenarios. This paper presents the results of a first series of such co-creation workshops, where stakeholders discussed bottlenecks for model-based decarbonisation pathways and ways to overcome these bottlenecks through tailored policy mixes. The workshops took place in five countries: Brazil, Canada, Greece, Germany, and the UK, each with a specific sector focus. In all five workshops, it became clear that substantial tensions exist between the “ideal” modelled decarbonisation pathways and the real-world situation on the ground. Also, adverse political framework conditions, uncertainty of future policies and resistance of powerful actors were emphasised as overarching bottlenecks in most workshops. At the same time, in several instances stakeholders pointed out important aspects of transformative trajectories that are not covered by the models. Some challenges and solutions stand out in all countries in spite of the strong diversity of contexts: allocation of capital towards massive investments into low-carbon solutions; infrastructure development for generation and transport of hydrogen, capture and use of CO2 as well as electricity grid and storage adapted to renewable energy solutions; stakeholder and citizen dialogues, where agreement is reached on cornerstones of long-term decarbonisation trajectories; and demand-side measures complementing investments into low-carbon processes.

Canada has pledged ambitious emission targets, aiming to achieve a reduction of at least 40–45% below 2005 levels by 2030 and net-zero emissions by 2050. Being amongst the major economies with high dependence on fossil fuels, however, this path is far from straightforward. This research employs NATEM, a TIMES-based regional energy system model for North America with explicit representation of Canada, as well as knowledge produced and shared by stakeholders during a targeted workshop dedicated to identifying decarbonisation bottlenecks, to compare the paths to net zero on the basis of whether stakeholder perceptions are considered or not. We find that the path to net-zero is technically feasible but critically entails the use of negative emissions technologies, like (bioenergy with) carbon capture and storage (CCS) and direct air capture, in addition to the large-scale deployment of a large range of mitigation options already available today. Based on the feedback received from the stakeholders, around both the use of CCS-based technologies and the potential of demand-side measures such as modal shifts in transportation and better urban planning, we impose a set of additional conditions and restrictions. We find that the co-created net-zero pathway is also technically feasible while relying less on technologies that may trigger bottlenecks prioritised by the stakeholders; notably, despite yielding a similar emissions trajectory, it entails significantly different sectoral and technological configurations to the non-co-created net-zero scenario, requiring an acceleration of near-term abatement measures, mainly through electrification and quicker rollout of renewable and other clean energy technologies.

Climate change mitigation entails different meanings for developed and developing countries. As a major emitting, high-income, developing economy that is largely dependant on hydrocarbons, Russia currently sits in the middle of the two groups, needing not only to drastically reduce emissions but also to ensure necessary economic growth to finance decarbonisation. This study explores two mitigation scenarios, one reflecting a cautious and the other a more ambitious decarbonisation pathway for Russia. These scenarios are co-created with a group of 135 national stakeholders, who inform the underlying assumptions based on their perceptions, expectations, and reservations: the more conservative scenario reflects the average of all input, while the ambitious scenario represents the optimistic end of the stakeholder input range. The two scenarios are modelled in CONTO, an input-output system of interconnected macro-structural calculations at the national level, to analyse the interplay between Russia's economy and decarbonisation progress, shedding light on the implications of mitigation for socioeconomic development. We find that, even for a country as dependant on hydrocarbons and under the most ambitious pathway that is still within experts’ realistic reach, Russia can achieve drastic reduction in absolute emissions and reach net-zero closely after 2050, while also achieving positive economic development in the long run. We highlight the need to prioritise a diverse set of mitigation options currently available and relevant to the Russian context, including energy efficiency and intensity improvements, electrification, and nuclear power, as well as to exploit the large potential lying within the Russian ecosystem's carbon sinks.

While fossil fuel prices soar during the 2022 global energy crisis, the European Union activates all available fossil-fuel levers and Greece still plans to use natural gas as a transition fuel for delignitisation, with strong concerns over potential exacerbation of energy poverty and hurdles to progress in climate action. This study assesses the trajectory of the Greek electricity mix and its reliance on natural gas under the current policy framework on the one hand, and an ambitious scenario aiming for complete decarbonisation by 2035 on the other. We model these scenarios using an energy system modelling framework, comprising LEAP and OSeMOSYS model implementations for Greece, and use a stakeholder-informed fuzzy cognitive mapping exercise to uncover transition uncertainties. While power generation from natural gas is projected to increase by almost 50% until 2030 under existing policies, the proposed decarbonisation scenario has the potential to achieve complete independence from Russian gas by 2026 while also leading to a cleaner and considerably cheaper power sector. This ‘higher climate ambition’ scenario is found feasible and more robust in case high fossil fuel prices persist post-2022, even if bottlenecks stressed by stakeholders such as community acceptance or technological constraints emerge and potentially constrain the expansion of certain renewable energy technologies. Apart from the added value of stakeholder input in modelling science, as reflected in the impact of barriers Greek stakeholders critically highlighted, our results emphasise that a diversified energy-supply mix alongside bold energy efficiency strategies are key to rapid and feasible decarbonisation in the country.

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.

Energy behaviours will play a key role in decarbonising the building sector but require the provision of tailored insights to assist occupants to reduce their energy use. Energy disaggregation has been proposed to provide such information on the appliance level without needing a smart meter plugged in to each load. However, the use of public datasets with pre-collected data employed for energy disaggregation is associated with limitations regarding its compatibility with random households, while gathering data on the ground still requires extensive, and hitherto under-deployed, equipment and time commitments. Going beyond these two approaches, here, we propose a novel data acquisition protocol based on multiplexing appliances’ signals to create an artificial database for energy disaggregation implementations tailored to each household and dedicated to performing under conditions of time and equipment constraints, requiring that only one smart meter be used and for less than a day. In a case study of a Greek household, we train and compare four common algorithms based on the data gathered through this protocol and perform two tests: an out-of-sample test in the artificially multiplexed signal, and an external test to predict the household’s appliances’ operation based on the time series of a real total consumption signal. We find accurate monitoring of the operation and the power consumption level of high-power appliances, while in low-power appliances the operation is still found to be followed accurately but is also associated with some incorrect triggers. These insights attest to the efficacy of the protocol and its ability to produce meaningful tips for changing energy behaviours even under constraints, while in said conditions, we also find that long short-term memory neural networks consistently outperform all other algorithms, with decision trees closely following.

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.

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.

Industrial processes are associated with high amounts of energy consumed and greenhouse gases emitted, stressing the urgent need for low-carbon sectoral transitions. This research reviews the energy-intensive iron and steel, cement and chemicals industries of Germany and the United Kingdom, two major emitting countries with significant activity, yet with different recent orientation. Our socio-technical analysis, based on the Sectoral Innovation Systems and the Systems Failure framework, aims to capture existing and potential drivers of or barriers to diffusion of sustainable industrial technologies and extract implications for policy. Results indicate that actor structures and inconsistent policies have limited low-carbon innovation. A critical factor for the successful decarbonisation of German industry lies in overcoming lobbying and resistance to technological innovation caused by strong networks. By contrast, a key to UK industrial decarbonisation is to drive innovation and investment in the context of an industry in decline and in light of Brexit-related uncertainty.

The transition of the energy system into a more efficient state requires innovative ideas to finance new schemes and engage people into adjusting their behavioural patterns concerning consumption. Effective energy management combined with Information and Communication Technologies (ICTs) open new opportunities for local and regional authorities, but also for energy suppliers, utilities and other obligated parties, or even energy cooperatives, to implement mechanisms that allow people to become more efficient either by producing and trading energy or by reducing their energy consumption. In this paper, a novel framework is proposed connecting energy savings with a digital energy currency. This framework builds reward schemes where the energy end-users could benefit financially from saving energy, by receiving coins according to their real consumption compared to the predicted consumption if no actions were to take place. A pilot appraisal of such a scheme is presented for the case of Bahrain, so as to simulate the behaviour of the proposed framework in order for it to become a viable choice for intelligent energy management in future action plans.

Seaports will play a pivotal role in the low-carbon transition of maritime shipping, the policy landscape in which is currently being shaped. In this context, we introduce a multi-criteria decision support framework for seaport sustainability planning, to identify the competitiveness of interventions under uncertainty and evaluate the direction of the sector's policy context in terms of required actions. The framework, based on the 2-tuple TOPSIS model, heterogeneous variables, and a Monte Carlo robustness analysis, is empirically applied to the port of Piraeus, Greece, to explore the most competitive interventions and their vulnerability to uncertainties. To inform port authorities and policymakers in the sector, we emphasise the added value of selected, inexpensive actions for energy efficiency and hybrid mobility. Furthermore, we find costly and seemingly obligatory actions under current European legislation, like cold ironing and LNG, to be robust and in the right direction if perception of non-financial risks is reduced. [ABSTRACT FROM AUTHOR]