The future of industrial ecology research will be more collaborative. Tackling pressing research questions with high quality science will require more work and knowledge than what the typical PhD candidate-postdoc-supervisor constellation can encompass, and deep collaboration in large multi-disciplinary teams will be common. This change in working mode is necessary to embed IE knowledge to contribute to solving pressing societal problems, such as the adaptation of new and more efficient technologies, the link of environmental and social policy via carbon taxation and subsequent revenue recycling , the assessment of the impact of future urbanisation and sustainable development strategies on material cycles [2,3], the multi-disciplinary design and assessment of demand-side solutions and sustainable lifestyles [4,5], breakdown of climate targets and other SDGs to local governments, and many more.
Other disciplines have ushered in substantial change already, as a recent piece on collaboration in climate change research shows . Here, the authors argue that “high-impact research has become increasingly dependent on team work, and self-organising social networks of researchers have become a key feature of the research system as a whole” Moreover, in light if the global challenges addressed in rather short time, more and more science is undertaken in “large research collaborations that span countries, continents and disciplines” and “grants are therefore shifting to coordinated networks of research partners, rather than individual projects”. “There is another trend, and it might be the game-changer. In the field of climate change, researchers from the global South are key to successful research.” They conclude that these trends “fundamentally change the way that research is practiced, who has a place at global research tables, how researchers perceive their role in society, and how they understand their relationships with one another.” It is clear that such deep and large collaboration needs powerful research infrastructure in form of databases, analysis tools, and dissemination platforms.
Successful collaboration in larger teams that change from project to project requires more research infrastructure than what is available today. Over the last three decades, industrial ecology researchers have gathered a substantial database on industrial processes, material cycles, product inventories, societal material flows, etc. Unfortunately, this database is only partly available to the community and beyond, there is a huge transparency gap .
Data, research tools, and dissemination channels must be much easier to share and reuse if researchers don’t want to spend ever more time on repetitive tasks such as re-formatting a dataset from its original source for the x-th time or re-developing and coding an analysis tool that exists in identical form at ten other places already. Research time should rather be spent on making new data available, deepening and refining analysis tools, systematically studying the uncertainty of incoming data and assessment results, and making detailed quantitative results available to others. In short, more cumulative research is needed! The compilation of process and market data into life cycle database and the available MRIO tables are important examples of how (data) infrastructure has boosted entire fields, and collaborative research infrastructure is already being developed in form of the Industrial Ecology Virtual Laboratory (IELab) and the openLCA Collaboration Server .
Continuing and rising efforts are needed to ensure the persistence of the existing successful efforts and to expand research infrastructure to other methods in order to move IE research as a whole to the next level. Identifying infrastructure needs and gathering momentum for making IE research infrastructure fit for the future should therefore be an important discussion topic at this year's two major conferences, which are coming up in Berlin and Beijing. We need to discuss what types of research infrastructure the IE community needs!
From previous experience, I see three broad areas
+ Data infrastructure at intermediate level of aggregation: linked, formatted, classified data spanning all major IE fields and research questions.
+ Modular research and analysis tools: peer reviewed, tested, with well-documented interfaces.
+ Platform for interactive visualisation of results to disseminate and explore multi-dimensions results and gather community and external expert feedback on detailed results that are commonly not shown in papers or supplementary material.
What else is needed? How do we link infrastructure projects to ongoing work and grants? How can we make our research more cumulative in general?
These are all questions that should echo through the conference halls this year.
 Distributional effects of carbon taxation, by Wang et al. (2016), DOI 10.1016/j.apenergy.2016.06.083
 On the materials basis of modern society, by Graedel et al. (2013), DOI 10.1073/pnas.1312752110
 Circular Economy Rebound, by Trevor Zink and Roland Geyer, (2017), DOI 10.1111/jiec.12545
 Towards demand-side solutions for mitigating climate change, by Felix Creutzig et al. (2018), DOI 10.1038/s41558-018-0121-1
 Human well‐being and climate change mitigation, by William F. Lamb and Julia K. Steinberger (2017), DOI 10.1002/wcc.485
 Nullius in Verba - Advancing Data Transparency in Industrial Ecology, by Edgar Hertwich et al. (2018), Journal of Industrial Ecology
 Compiling and using input-output frameworks through collaborative virtual laboratories, by Manfred Lenzen et al. (2014), Science of the Total Environment, DOI 10.1016/j.scitotenv.2014.03.062
 http://www.openlca.org/collaboration-server/ Accessed May 9, 2019.
 The future is collaborative, by G. Cundill et al., Nature Climate Change, Volume 9, pages 343–345 (2019).