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. © 2021 The Authors This work was supported by the H2020 European Commis- sion projects “PARIS REINFORCE”(Grant Agreement No. 820846), “LOCOMOTION”(Grant Agreement No. 821105), “NDC ASPECTS”(Grant Agreement No. 101003866), and “newTRENDS”(Grant Agreement No. 893311); the European Research Council (ERC) project “FINEPRINT”(Grant Agreement No. 725525); the Hel- lenic Foundation for Research and Innovation (HFRI) and General Secretariat for Research and Technology (GSRT) project “ATOM”(Grant Agreement No. HFRI-FM17–2566); the Spanish Ministry of Science, Innovation, and Universities projects RTI2018–099858- A-I00 and RTI2018–093352-B-I00; the María de Maeztu excel- lence accreditation 2018–2022 (Ref. MDM-2017–0714), funded by MCIN/AEI/10.13039/50110 0 011033; and by the Basque Government through the BERC 2018–2021 program and BIDERATU project (KK- 2021/0 0 050, ELKARTEK programme 2021). The sole responsibility for the content of this paper lies with the authors; the paper does not necessarily reflect the opinions of the European Commission, the Basque Government, or the Spanish Government.