Anthony, thanks for these slick graphics!
While using operational carbon intensities for design decisions, say selecting energy-efficiency measures or building electrification, there are a couple other aspects to consider. These do require more granular data from energy models and somewhat complex analyses, but can can more accurately capture ‘real-world impact’.
One is using hourly emissions data instead of a single annual average. In California, for example, we know that there are enough renewables feeding the grid-during the daytime, so much so that they have to be curtailed at times. In late summer afternoons and early evenings, however, the grid is dominated by dirtier power plans, and using energy at that time has a much worse impact. See the image below showing this variability; green indicates clean power while red indicates higher grid emissions intensities.
The second, related aspect is utilizing marginal emissions factors which measure the actual environmental consequence of an energy-consuming or conserving action. Only some power plants will scale up or down in response to say, your sports area turning on or off its lights. The impact of this light turn on/off action should be measured using the emissions intensity of the marginal power plant that’s responding. WattTime, a company that provides this data, does a good job of explaining this concept. Our firm, Atelier Ten, partnered with them to write a white paper on how we use their data for these calculations.
Using hourly and marginal emissions factors can sometimes give surprising results. For instance, even though electrification makes sense in the long run, with cleaning-the-grid commitments as @Kjell_Anderson mentioned, we’ve found that in the near future something like electrifying food service can still be worse than traditional gas-powered. Electrification combined with on-site PVs, or say of HVAC using heat pumps still comes out on top, but the “2030 is the new 2050” mantra we’ve been following in the embodied carbon world adds some shades of grey.