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Citizen science and citizen energy communities are pluralistic terms that refer to a constellation of methods, projects, and outreach activities; however, citizen science and citizen energy communities are rarely, if ever, explicitly aligned. Our searches for “citizen science” and “energy” produced limited results and “citizen science” and “energy communities” produced zero. Therefore, to outline a future direction of citizen science, its potential alliances with energy communities, and their collaborative contributions to the Sustainable Development Goals, we performed a systematic literature review and analysis of “public participation” and “energy communities” using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRIMSA) guidelines. The results show four pathways through which current public participation in energy communities might be more explicitly aligned with citizen science projects: benefits and values, energy practices, intermediaries, and energy citizenship. Each of these pathways could engage citizen scientists in qualitative and quantitative research and increase scientific literacy about energy systems. Our call for citizen science to supplement current forms of participation builds from the “ecologies of participation” framework, itself an extension of co-productionist theories of science and technology studies. We conclude with a discussion of affordances and barriers to the alliances between citizen science and energy communities and their potential contributions to SDGs 7: Affordable and Clean Energy, 11: Sustainable Cities and Communities, 13: Climate Action, and 17: Partnerships for the Goals.

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During the past decade, pyrolysis routes have been identified as one of the most promising solutions for plastic waste management. However, the industrial adoption of such technologies has been limited and several unresolved blind spots hamper the commercial application of pyrolysis. Despite many years and efforts to explain pyrolysis models based on global kinetic approaches, recent advances in computational modelling such as machine learning and quantum mechanics offer new insights. For example, the kinetic and mechanistic information about plastic pyrolysis reactions necessary for scaling up processes is unravelling. This selective literature review reveals some of the foundational knowledge and accurate views on the reaction pathways, product yields, and other features of pyrolysis created by these new tools. Pyrolysis routes mapped by machine learning and quantum mechanics will gain more relevance in the coming years, especially studies that combine computational models with different time and scale resolutions governed by “first principles.” Existing research suggests that, as machine learning is further coupled to quantum mechanics, scientists and engineers will better predict products, yields, and compositions, as well as more complicated features such as ideal reactor design.

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