WillCO 2 st: leveraging freely available real-time carbon forecasting for research emissions reporting

Summary (Abstract) An increasing number of research institutions and funding bodies are making sustainability a focal point. The rapid rollout of auditing processes, such as the Laboratory Efficiency Assessment Framework (LEAF), and changing institutional policies, are creating demand for sustainability accounting upon researchers. Despite the increasing expectations on researchers to actively report their sustainability and demonstrate improvement, easy-to-use, and accurate reporting tools for electricity-associated emissions (scope 2) accounting are not widely available. Here, we created WillCO 2 st - a free program that enables fast, publication-read sustainability reports. WillCO 2 st makes use of real-time, region-specific conversion factor data freely available through resources like the Carbon Intensity and Electricity Maps API to create experimental carbon footprinting estimates using metadata from saved experimental files. Data can be simply click-and-dragged into WillCO 2 st to create an experimental sustainability audit within minutes. Further, we exemplified the utility of WillCO 2 st’s live-nudging feature by quantifying the retrospective theoretical reduction in electricity-driven emissions from shifting experimental timing to times of day with lower carbon intensities. Overall, WillCO 2 st eases time and resource burden on researchers and serves as a model for how researchers can effectively and accurately engage with research sustainability auditing. Highlights ⍰ WillCO 2 st is a free software platform that enables fast, publication-ready carbon emissions reporting for papers, grants, and internal audits. ⍰ WillCO 2 st leverages various energy-to-emissions database to provide real-time, region-specific estimations of carbon intensity for the upcoming 48 hours, nudging users to switch to lower carbon times for research activities. ⍰ WillCO 2 st integrates CodeCarbon to automatically audit Python-based coding scripts for experimental and computational-wide sustainability audits. ⍰ Partial conformity (21%) to switching experimental time would have disproportionately produced a large carbon saving (50%) in our most recent publication’s experimental carbon footprint.