Environmentally extended input–output analysis (EEIOA) can be applied to assess the economic and environmental implications of a transition towards a circular economy. In spite of the existence of several such applications, a systematic assessment of the opportunities and limitations of EEIOA to quantify the impacts of circularity strategies is currently missing. This article brings the current state of EEIOA-based studies for assessing circularity interventions up to date and is organised around four categories: residual waste management, closing supply chains, product lifetime extension, and resource efficiency. Our findings show that residual waste management can be modelled by increasing the amount of waste flows absorbed by the waste treatment sector. Closing supply chains can be modelled by adjusting input and output coefficients to reuse and recycling activities and specifying such actions in the EEIOA model if they are not explicitly presented. Product lifetime extension can be modelled by combining an adapted final demand with adjusted input coefficients in production. The impacts of resource efficiency can be modelled by lowering input coefficients for a given output. The major limitation we found was that most EEIOA studies are performed using monetary units, while circularity policies are usually defined in physical units. This problem affects all categories of circularity interventions, but is particularly relevant for residual waste management, due to the disconnect between the monetary and physical value of waste flows. For future research, we therefore suggest the incorporation of physical and hybrid tables in the assessment of circularity interventions when using EEIOA.

Life cycle assessment (LCA) databases and software evolve. We analyzed to which extent software and evolving life cycle inventory databases affect the comparison of technology alternatives, using a comparative LCA on permanent magnets as a case study, with two selected software tools: CMLCA and Brightway LCA. We migrated the system models from the CMLCA to Brightway LCA software and alternated between the ecoinvent database versions 2.2 and 3.1 to 3.6 in the system background. When using ecoinvent v3.6 instead of v2.2, the change of the indicator results ranged from to 283%. The evolution of the ecoinvent database impacted the absolute amounts of the characterized results and the relative performance between alternatives. The impact category with the highest variability was ionizing radiation, which even showed a ranking inversion with ecoinvent v3.4. In contrast, the impact of using CMLCA or Brightway was negligible because the same data and modeling assumptions caused percentage differences below 0.4%. During the semi‐automated data migration to Brightway, we identified 23 environmental flows in the CMLCA model that were not paired with their corresponding characterization factors in the published study of reference. This error had led to an underestimation of 63% in the photochemical oxidation indicator of one of the alternatives. This underestimation relates to an interoperability issue regarding the nomenclature of environmental flows in software alternatives and is a matter of data implementation rather than an issue intrinsic to the selected software. Finally, we identified improvement opportunities for the transparency and reusability of LCA models. This article met the requirements for a Gold‐Gold JIE data openness badge described at http://jie.click/badges .

Artificial intelligence (AI) applications and digital technologies (DTs) are increasingly present in the daily lives of citizens, in cities and in industries. These developments generate large amounts of data and enhance analytical capabilities that could benefit the industrial ecology (IE) community and sustainability research in general. With this communication, we would like to address some of the opportunities, challenges, and next steps that could be undertaken by the industrial ecology community in this realm. This article is an adapted summary of the discussion held by experts in industrial ecology, AI, and sustainability during the 2021 Industrial Ecology Day conference session titled “The Future of Artificial Intelligence in the Context of Industrial Ecology.” In brief, building on previous studies and communications, we advise the industrial ecology community to: (1) create internal committees and working groups to monitor and coordinate AI applications within and outside the community; (2) promote and ensure transdisciplinary efforts; (3) determine optimal infrastructure and governance of AI for IE to minimize undesired effects; and (4) act on effective representation and on reduction of digital divides.

Global environmental and resource problems ask for new ways of managing the production and consumption of resources. The implementation of new paradigms, such as the circular economy, requires decision‐makers at multiple levels to make complex decisions. For this, clear analyses and modeling of scenarios are of utmost importance. Meanwhile, as the sophistication of databases and models increases so does the need for user‐friendly tools to use them. The RaMa‐Scene web platform reduces these barriers by allowing users to visualize easily diverse impacts of implementing circular‐economy interventions. This online web platform makes use of the multi‐regional environmentally extended input–output database EXIOBASE version 3 in monetary units, which has been modified to show explicit transactions of raw materials from recycling activities.