Increasing concerns about climate change imply that decisions on the digitization of healthcare should consider evidence about its carbon footprint (CF). This study aims to develop a transparency catalogue for reporting CF calculations, to compare results, and to assess the transparency (reporting quality) of the current evidence of virtual care (VC) intervention. We developed a checklist of transparency criteria based on the consolidation of three established standards/norms for CF calculation. We conducted a systematic review of primary studies written in English or German on the CF of VC interventions to check applicability. Based on our checklist, we extracted methodological information. We compared the results and calculated a transparency score. The checklist comprises 22 items in the aim, scope, data and analysis categories. Twenty-three studies out of 1466 records were included, mostly addressing telemedicine. The mean transparency score was 38% (minimum 14%, maximum 68%). On average, 148 kg carbon dioxide equivalents per patient were saved. Digitization may have co-benefits, improving care and reducing the healthcare CF. However, the evidence for this is weak, and CF reports are heterogeneous. Our transparency checklist may serve as a reference for developing a standard to assess the CF of virtual and other healthcare and public health services.

The most important resource to improve technologies in the field of artificial intelligence is data. Two types of policies are crucial in this respect: privacy and data-sharing regulations, and the use of surveillance technologies for policing. Both types of policies vary substantially across countries and political regimes. In this paper, we examine how authoritarian and democratic political institutions can influence the quality of research in artificial intelligence, and the availability of large-scale datasets to improve and train deep learning algorithms. We focus mainly on the Chinese case, and find that -ceteris paribus -authoritarian political institutions continue to have a negative effect on innovation. They can, however, have a positive effect on research in deep learning, via the availability of large-scale datasets that have been obtained through government surveillance. We propose a research agenda to study which of the two effects might dominate in a race for leadership in artificial intelligence between countries with different political institutions, such as the United States and China.

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