Growth in energy use has slowed owing to efficiency gains that smart policies can help maintain in the near term

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Bitcoin mining is becoming an increasingly energy-intensive process 1,2,3 whose future implications for energy use and CO2 emissions remain poorly understood. This is in part because-like many IT systems-its computational efficiencies and service demands have been evolving rapidly. Therefore, scenario analyses that explore these implications can fill pressing knowledge gaps, but they must be approached with care. History has shown that poorly constructed scenarios of future IT energy useoften due to overly-simplistic extrapolations of early rapid growth trends-can do more harm than good by spreading misinformation and driving ill-informed decisions. 4,5,6 Indeed, the utility of an energy demand scenario is directly proportional to its credibility, which is typically demonstrated through careful attention to technology characteristics and evolution, analytical rigor and transparency, and designing scenarios that align with plausible future outcomes. Regrettably, the Bitcoin CO2 emissions scenarios presented in the recent Mora et al. article 7 lack such credibility and should not be taken seriously by the research and policy communities. We arrived at this conclusion by replicating in detail Mora et al.'s methods, which revealed numerous flaws in the design and execution of their analysis as documented in the Supplementary Information. We describe the five most significant issues below.

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Global digitalization has given birth to the explosion of digital services in approximately every sector of contemporary life. Applications of artificial intelligence, blockchain technologies, and internet of things are promising to accelerate digitalization further. As a consequence, the number of data centers, which provide the services of data processing, storage, and communication services, is also increasing rapidly. Because data centers are energyintensive with significant and growing electricity demand, an energy model of data centers with temporal, spatial, and predictive analysis capability is critical for guiding industry and governmental authorities for making technology investment decisions. However, current models fail to provide consistent and high dimensional energy analysis for data centers due to severe data gaps. This can be further attributed to the lack of the modeling capabilities for energy analysis of data center components including IT equipment and data center cooling and power provisioning infrastructure in current energy models. In this research, a technology-based modeling framework, in hybrid with a data-driven approach, is proposed to address the knowledge gaps in current data center energy models. The research aims to provide policy makers and data center energy analysts with comprehensive understanding of data center energy use and efficiency opportunities and a better understanding of macro-level data center energy demand and energy saving potentials, in addition to the technological barriers for adopting energy efficiency measures.