Image: Opensay flyer from welcome page. ‘Welcome to the open (energy) system analysis community forum

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Citation format: The Chicago Manual of Style, 17th Edition

Generation Research. ‘Civil Society Engagement Seeks Open Science for Rapid Decarbonization!’, 2020.

Opensay, a new open research community, recently launched to bring together civil society organisations and modelling researchers and apply Open Science practices to further 100% decarbonization planning and policy. Opensay: open (energy) system analysis community’ has come out of the predominantly European ‘openmod‘ open energy modelling community to partner with civil society organizations and bring open analytics practices to a wider audience. 

Launched this February (2020) with an online forum open to all—an invitation stands for anyone who want to find out more or get involved. Opensay has been formed in response to a disjuncture in current ‘zero-carbon’ planning between the apparent certainty of many such plans and their lack of verifiability. This situation quickly leads to a worrying question, which is: that using current analytical methods and tools—the state of the art—can it realistically be expected that anyone has a robust implementable, and sufficiently rapid ‘zero-carbon’ analysis and plan of action? The purpose of this new initiative is to plug the gap in ‘zero-carbon’ plans being developed by bringing together civil society organisations (CSOs) and modelling researchers and applying Open Science methods to ensure these plans are more robust and more negotiated and give CSOs and other players access to detailed analysis that is—verifiable, reproducible, and reusable.

This new community has emerged from the ‘openmod initiative‘, to-date a predominantly European and German open energy modelling community comprised of researchers from industry, research institutes, and the interested public. Openmod has pioneered Open Science practices in the field of energy modelling, created a community and open collaborative working practices for the last five years. This background of energy modelling is a foundation of the Opensay community, but Opensay is a distinct community stretching beyond energy modelling and will instead need to contend with new issues around ‘bridging’ the CSO and modelling worlds. In doing so, opensay will need to bring in a wider set of modelling disciplines: behavioral science, atmospheric science, integrated assessment, life cycle assessment, and CLEWS (climate, land, energy and water systems)​​​​​​​.

Futures and Systems Views Diagram
Image: Futures and Systems Views Diagram, 07, 4 February 2020, Robbie Morrison robbie.morrison AT, Creative Commons CC-BY-4.0. URL: – source:

As indicated, a key reason for forming this new community is to connect civil society and energy analysts. But not for public outreach in the conventional sense. Energy system modelling relies almost entirely on scenario analysis and it is civil society—not energy analysts—who should determine much of what makes up those scenarios: the high level objectives, what measures and technologies are acceptable or not, and how to trade‑off the various resulting pathways. Without this civil society partnership, the process looses legitimacy, not to mention trust and relevance.

Similarly, through this process of engagement, the public may become more knowledgeable and more likely to understand the consequences of social inertia and resistance. In this regard, one application that could be usefully explored is that of model‑mediated public consultation and sortition processes—including support for citizens’ assemblies.

Some early experiences suggest the interested public will also contribute, particularly those with skills in programming, data analysis, visualization, and technical communication. Much as happens in more conventional open source projects developing software collaboratively.

As an early example of Opensay’s interests Robbie Morrison, one of the community founders, has proposed ‘ɘinfach : an energy system model for education‘ (Morrison 2020) for a Jupyter Notebook-based energy modelling system for use in school education but equally as a tool for the interested public to familiarise themselves with modelling concepts.

In January (2020) the second ‘energy bridge’ event was held at DIW Berlin (German Institute for Economic Research) which attracted thirty participants. This one day workshop accompanied an earlier three day openmod workshop (10th European Open Energy Modelling Workshop) with one hundred and ninety researchers taking part and run as a grassroots, self organised research community event. There are a number of observations from the workshops which inform the questions facing the bridging efforts of Opensay. Firstly the openmod workshop was mainly made up of ‘early career researchers’ who are fired up by doing something really exciting, exploring a new epistemological field, and creating new technologies that literally can change the world—or else. Another aspect of the openmod workshop is that Open Science practices are at its core and the questions about Open Science have moved on from ‘pros and cons’ to ‘how to get it right’: which variety of FOSS tools can I use, how to get access to open data, utilize automated workflows, or how to close the loop and have tighter collaboration. Across both workshops the scale of the challenges involved are truly daunting, whether that is: getting an industry to turn around from closed to open practices, or seeing the lack of recent (last twenty years) reductions in energy use compared with what is needed for ‘rapid decarbonization’ plans to work.

In many ways the Open Science challenges for Opensay are meshed with the central mission of enabling CSOs with better knowledge, tools, and information to address questions around rapid decarbonization. In a modelling context Open Science is also accompanied by changes in computing infrastructures and methods, for example: Infrastructure as Code (IasC), cloud computing, Test Driven Development, and Continuous Integration. And maybe most important is data as a common pool resource, with considerable effort within the open energy modelling community to improve interoperability, traceability, persistence, and the development and use of distributed data architectures. Quite simply this conjoining reduces barriers to entry, speeds up the research cycle, and enable closer collaboration. For Open Science this joining with infrastructure and data can be seen in the introduction of the ‘computational paper’ (Konkol, Nüst, and Goulier 2020) currently exemplified by Jupyter Notebook using Binder (virtual machines) which can bring together data sets, models and explanatory narratives, as in the climate change modelling ’emissions data’ examples from Open Climate Data. (Gieseke n.d.)

There are also lessons to be learned from how Open Science, Open Access, and open licencing has fared in general in academia, which have largely progressed at a snail’s pace. Nearly twenty years ago the Budapest Open Access Initiative (Chan et al. 2002) set out the need for Open Access and to-date less than 30% of climate research publishing is open licenced. (Tai and Robinson 2018) When looking at the next twenty years which is the timeframe available to prevent temperature increases above the IPCC 1.5°C (IPCC 2018) it means things will have to be done differently from how they were done in the past in academia. The publishing industry and a certain resistance from of academia has meant that the open licencing of knowledge continues to be held back. The question of how to accelerate Open Science for climate change and a zero-carbon future is one for the Opensay community to address. Although there is hope and the pathways are available, such as ‘computational papers’ or ‘open science publishing’ where all aspects of a model or research cycle are at hand and open licenced, which offer rapid dissemination, collaboration, and a global reach for climate change knowledge and open energy modelling. We might not currently have robust ‘100% rapid decarbonization policies’, but what we do have is the social organisation, knowledge, data and tools to make them.


Thank you to Robbie Morrison of Opensay for technical guidance.


Morrison, Robbie. ‘ɘinfach : An Energy System Model for Education’. opensay community, 8 February 2020.

Konkol, Markus, Daniel Nüst, and Laura Goulier. ‘Publishing Computational Research — A Review of Infrastructures for Reproducible and Transparent Scholarly Communication’. ArXiv:2001.00484 [Cs], 2 January 2020.

Gieseke, Robert. ‘Open Climate Data: Emissions Data’. Potsdam Institute for Climate Impact Research. Accessed 18 February 2020.

Chan, Leslie, Darius Cuplinskas, Michael Eisen, and Yana Genova, et al. ‘Budapest Open Access Initiative’, 2002.

Tai, Travis C., and James P. W. Robinson. ‘Enhancing Climate Change Research With Open Science’. Frontiers in Environmental Science 6 (2018).

IPCC. ‘Global Warming of 1.5 oC’, 2018.

CLEWS = Climate, Land, Energy and Water Systems, developed for the Rio+20 conference and now assisting the UN Sustainable Development Goals (SDGs) process.