Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

This paper investigates the ecosystem dynamics of the Open‐source [COVID‐19] Medical Supplies network that arose to fill the institutional void revealed by state and private sector failures to stockpile and supply enough personal protective equipment. Theoretically, the paper adds correctives to extant institutional theory accounts of entrepreneurship filling institutional voids, showing that these can be filled rapidly and normatively by digital entrepreneurial ecosystems allied with peer production networks. These were able to transform the boundary conditions of a routinized system, refixing its autopoiesis innovatively. The COVID‐19 epidemic galvanized hundreds of thousands of volunteer “makers” around the world to cooperate to meet urgent demand for medical supplies. A digital entrepreneurial ecosystem arose in response to the problem of critical equipment shortages, connecting global, expert‐curated know‐how with local production equipment. We contribute to the theory of institutional voids by documenting and analyzing how the formation and emergent processes that created and sustained a Digital Peer Production Ecosystem based on self‐organization, expert curation and scalability, successfully catalyzed local initiatives worldwide. Institutional voids are not just barriers to entrepreneurship; they are also opportunities.

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A case study on small and medium metalworking firms in Ohio explores how US-based factory owners conceptualise automation and the impact that newly introduced technologies have on workers. The cost and risk of replacing entire production processes and the still-relevant capabilities of old equipment encourage the interviewed firms to complement rather than replace existing technologies and workers. An incremental, piecemeal strategy makes it less likely that companies will introduce more integrated automation systems that may presage fully-automated manufacturing.