To get a greater understanding of how these scopes are outlined, let’s check out what each contains and the way they’re estimated within the context of a Google Cloud datacenter.
Scope 1 contains the direct emissions from sources managed by a company.
In a typical workplace constructing, scope 1 might embody the gasoline vary in a kitchen, or gas used to run shuttles. Within the context of a Google datacenter, scope 1 would come with an onsite backup generator.
Scope 2 captures the oblique emissions from the acquisition of electrical energy, heating, and cooling
Once we speak about carbon and computing, scope 2 is essentially the most related as a result of it contains the emissions associated to the electrical energy utilization in Google Cloud datacenter on account of being related to the grid.
Within the Carbon Footprint device, the scope 2 worth is estimated from the GHG emissions produced from electrical energy offered by the native grid the place your compute workload was executed.
Scope three captures the oblique emissions from belongings not managed by your group.
This contains classes like gadgets purchased from suppliers, waste disposal, and enterprise journey. Within the case of Google Cloud, this might embody the emissions related to producing the GPUs that we have now in our information facilities.
Within the Carbon Footprint device, scopes 1 and three are calculated by taking the overall Google Cloud scope 1 and three emissions and apportioning them based mostly on utilization on your particular billing group.
What can I find out about my carbon footprint?
I wished to research the emissions particularly from my Google Cloud initiatives. As a result of I share a billing account with my fellow Cloud Developer Advocates, and I wished to take a look at extra than simply emissions from the previous month, I wanted one thing extra granular than the Carbon Footprint overview web page.
I exported the information by way of the BigQuery Knowledge Switch Service, which creates a month-to-month partitioned desk referred to as carbon_footprint within the BigQuery dataset of your alternative. To get historic information, I ran an information backfill to 2021.