Data Innovation in Industrial Ecology

Friday, July 31, 2020


The Journal of Industrial Ecology invites you to submit articles for a special issue on Data Innovation.

Data innovation and data science are gaining in importance for all scientific disciplines. Influential trends that change the possibilities of scientific advance and scrutiny include the massive availability of data with increasing resolution, the adoption of machine learning, more powerful computers and software, advanced modeling of manufacturing, infrastructure and food systems, environmental processes, logistics and social dynamics, as well as complex decision problems. The increased digitalization of processes, the rise of Internet of Things and Industry 4.0, and the publication of increasingly interoperable open, linked data are generating new research opportunities and facilitating exchanges between disciplines.

These trends are already transforming industrial ecology (IE) research, with a growing number of studies leveraging data science innovations. Many of the core methods used in industrial ecology (LCA, MFA, IO) are increasingly using massive datasets, hybridized databases, and agile, open software development to facilitate integrated and comprehensive assessments.

The Journal of Industrial Ecology is at the forefront of data innovation in sustainability research. Recent publications in industrial ecology call for research to be transparent and reproducible and thereby facilitate cumulative research  (e.g., Hertwich et al. 2018 and Pauliuk et al. 2019).  The journal has supported such calls by establishing data accessibility requirements  as well as the Data Openness Badges to highlight good data practices (see, e.g., Mayer et al. 2019). Meanwhile, further advances in data science will present new opportunities and challenges for the alignment of IE with Open Science principles.

Data innovation comes with opportunities (e.g., new types of analysis) and challenges (e.g., intellectual property, privacy), but also with many questions as to how the new data science will be integrated into industrial ecology methods and meaningfully applied to inform policies and actions that support sustainable development on a finite planet. This Special Issue will explore these aspects and submissions are welcomed on both fundamental work on data innovation in industrial ecology, and work that illustrates successful applications to sustainable systems research. Examples of suitable contributions include:

  • Data analytics; data mining and processing
  • Novel data frameworks and ontologies, data models, databases, data architecture and information systems (e.g., blockchain, semantic web) for IE
  • Big data analysis with industrial ecology applications; machine and statistical learning
  • Geo-spatial data analysis, geographical information systems (GIS)
  • Process digitalization, digital twins, Internet of Things, and Industry 4.0
  • Prescriptive analytics; data frameworks for action and decision making; agent-based modelling and discrete event simulations; operations research and optimization
  • Open science: work that is addressing transparency, reproducibility, and cumulative research in IE research; data quality and veracity assessment and communication.
  • Data fusion and information integration; bridging of data-centered methods, such as hybrid physical/monetary IO or hybrid MFA and LCA
  • The application of data innovations in various fields within industrial ecology (e.g., circular economy, sustainable urban systems, sustainable consumption-production, life cycle management, and others).
  • The assessment of the improvements and setbacks that have been observed following the application of data innovations in real-world IE applications.


  • Call for Papers: December 2019
  • Full manuscript submission deadline: 31 July 2020.
  • Papers posted online when accepted
  • Issue publication expected by end of 2020

We encourage, but do not require, potential authors to submit an extended abstract in order to receive feedback about the scope and suitability for this Special Issue. Extended abstracts should be in English and be no more than 1,000 words in length not including a figure or table.

Special Issue Editorial Team

  • Niko Heeren, ETH Zurich
  • Anu Ramaswami, Princeton University
  • Jean-Marc Frayret, Polytechnique Montreal
  • Guillaume Majeaux-Bettez, NTNU & CIRAIG, Polytechnique Montreal
  • Yang Li, Harvard University

How to Submit

Authors who would like to receive feedback before submitting a full paper can submit an (optional) extended abstract  as a text file (pdf, Word, txt) to

Submission of completed manuscripts (31st July) should follow JIE author guidelines. Authors should submit manuscripts through the JIE’s manuscript management website and indicate that their contribution is intended for the Special Issue on Data Innovation in the submission system. We strongly encourage all authors to consider sharing their data and methods and applying for the JIE’s data transparency badge:

Submission implies that the manuscript has not been submitted for publication elsewhere and that it will not be submitted elsewhere while the review process is underway.

For further information and abstract submission, please contact the following mailing list: