Data innovation in industrial ecology

Abstract

IE research is increasingly taking advantage of new strategies to acquire and curate data, enabling our field to quantitatively tackle research questions that could only be approached qualitatively or conceptually until recently. . . . The different articles of this special issue effectively document (1) practical applications of data innovations in real-world IE practice; (2) transformations and new opportunities for IE research; and (3) the development and consolidation of IE datasets.The ever-increasing ease with which massive datasets can be collected and analyzed is radically transforming the level of insight that industries, governments, and citizen-consumers can have into their activities. In fact, under the rather vague umbrella terms of big data, industry 4.0, internet of things (IoT), artificial intelligence (AI), and machine learning (ML), a diversity of very concrete data innovations are increasingly guiding decisions that affect the sustainability of our socioeconomic metabolism.In and of itself, there is nothing inherently virtuous about the rise of big data. There are legitimate fears that it could even be used to further promote an unsustainable growth in conspicuous material consumption (Kish, 2020). Artificial intelligence and other data-driven tools have the potential to contribute both positively and negatively to the sustainability of our development (Vinuesa et al., 2020). However, as has long been recognized by the Industrial Ecology (IE) community (Cooper et al., 2013;Xu et al., 2015), this increased digitalization can also be leveraged to achieve gains in efficiency and to facilitate the application of IE principles. A first motivation for this special issue therefore resides in the need to document the successes and failures of industries and communities that have pioneered novel uses of data in support to real-world IE initiatives.Furthermore, it stands to reason that IE research-a data-intensive, holistic field of research striving to capture complex system dynamics-is poised to benefit from the increased digitalization of industrial practices and of everyday life. From the increased access to raw data to advanced data curation and analysis techniques, the novel tools at our disposal are already transforming the nature of IE research. Putting forward articles that demonstrate this transformation and key applications of data innovation in IE research constitutes a second objective of this special issue. This transformation of IE research, with ever-larger datasets and more complex tools and algorithms, brings not only opportunities but also challenges for our scientific community: challenges in terms of transparency and reproducibility of research, of interoperability and compatibility of tools, and of data fusion and method integration. We must avoid having the sheer volume of data become a barrier to communication, interdisciplinary collaboration, and scientific scrutiny. Therefore, a third objective of this special issue is to follow the latest innovations pertaining to the compilati...

Agent‐based modeling and simulation for the circular economy: Lessons learned and path forward

“…Other modeling tools, such as machine learning (ML) and operations research (OR), have been introduced into industrial ecology to overcome some of these limitations. A special issue of the Journal of Industrial Ecology was dedicated to these tools (Majeau‐Bettez et al., 2022). They can model the complex relationships between many variables in a system to quantify the impact of adjusting variables on an objective function and search for an optimal combination of values.…”

The future of artificial intelligence in the context of industrial ecology

“…The recent (2022) special issue of Data Innovation in the Journal of Industrial Ecology addresses multiple of these questions with special attention to opportunities (Majeau‐Bettez et al., 2022), and similar efforts are also addressed in other disciplines under the environmental science umbrella (Hsieh, 2022; McGovern et al., 2022; Rolnick et al., 2019). The present article builds on these efforts and summarizes the seminar discussion on “The Future of Artificial Intelligence (AI) in the Context of Industrial Ecology (IE),” which took place on June 21, 2021, during the Industrial Ecology Day (ISIE, 2021).…”