Coupling circularity performance and climate action : from disciplinary silos to transdisciplinary modelling science

Technological breakthroughs and policy measures targeting energy efficiency and clean energy alone will not suffice to deliver Paris Agreement-compliant greenhouse gas emissions trajectories in the next decades. Strong cases have recently been made for acknowledging the decarbonisation potential lying in transforming linear economic models into closed-loop industrial ecosystems and in shifting lifestyle patterns towards this direction. This perspective highlights the research capacity needed to inform on the role and potential of the circular economy for climate change mitigation and to enhance the scientific capabilities to quantitatively explore their synergies and trade-offs. This begins with establishing conceptual and methodological bridges amongst the relevant and currently fragmented research communities, thereby allowing an interdisciplinary integration and assessment of circularity, decarbonisation, and sustainable development. Following similar calls for science in support of climate action, a transdisciplinary scientific agenda is needed to co-create the goals and scientific processes underpinning the transition pathways towards a circular, net-zero economy with representatives from policy, industry, and civil society. Here, it is argued that such integration of disciplines, methods, and communities can then lead to new and/or structurally enhanced quantitative systems models that better represent critical industrial value chains, consumption patterns, and mitigation technologies. This will be a crucial advancement towards assessing the material implications of, and the contribution of enhanced circularity performance to, mitigation pathways that are compatible with the temperature goals of the Paris Agreement and the transition to a circular economy

In-Cognitive: A web-based Python application for fuzzy cognitive map design, simulation, and uncertainty analysis based on the Monte Carlo method

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

Advancing participatory energy systems modelling

ABSTRACT: 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.info:eu-repo/semantics/publishedVersio

Advancing participatory energy systems modelling

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

Three different directions in which the European Union could replace Russian natural gas

Russia's invasion of Ukraine fuelled an energy crisis, which considerably impacted Europe given its heavy reliance on Russian natural gas imports. This study uses an ensemble of four global integrated assessment models, which are further soft-linked to two sectoral models, and explores the synergies and trade-offs among three approaches to living without Russian gas in Europe: (a) replacing with other gas imports, (b) boosting domestic energy production, and (c) reducing demand and accelerating energy efficiency. We find that substituting Russian gas from other trade partners would miss an opportunity to accelerate decarbonisation in end-use sectors while risking further fossil-fuel lock-ins, despite featuring the lowest gas price spikes and potentially reducing heating costs for end-users in the near term. Boosting domestic, primarily renewable, energy production on the other hand would instead require considerable investments, potentially burdening consumers. Energy demand reductions, however, could offer considerable space for further emissions cuts at the lowest power-sector investment costs; nonetheless, an energy efficiency-driven strategy would also risk relocation of energy-intensive industries, an aspect of increasing relevance to EU policymakers.</p

RoboPol: AGN polarimetric monitoring data

Summarization: We present uniformly reprocessed and re-calibrated data from the RoboPol programme of optopolarimetric monitoring of active galactic nuclei (AGNs), covering observations between 2013, when the instrument was commissioned, and 2017. In total, the data set presented in this paper includes 5068 observations of 222 AGN with Dec. > −25○. We describe the current version of the RoboPol pipeline that was used to process and calibrate the entire data set, and we make the data publicly available for use by the astronomical community. Average quantities summarizing optopolarimetric behaviour (average degree of polarization, polarization variability index) are also provided for each source we have observed and for the time interval we have followed it.Presented on: Monthly Notices of the Royal Astronomical Societ