Given the many layers of assumptions and allocations involved in modeling the emissions of the complex advertising supply chain, it is critical to understand the accuracy of an emissions “measurement” both for assessing provider quality and as a gating factor to incorporate data into corporate and regulatory reporting.

Model Quality Components

Each modeled request should include:

  • Grid mix model quality (1-5)
  • Organization model quality (1-5)
  • Property model quality (1-5)
  • Ad stack model quality (1-5)
  • Average ad platform quality (1-5)
  • Ad format model quality (1-5)
  • Input granularity score (1-5)

Grid mix

Grid intensity can fluctuate significantly on an hourly basis due to the variable nature of both renewable energy sources (sun, wind) and electricity demand. To achieve effective decarbonization, having hourly data is critical. However, this isn’t broadly available in many countries.

Data source and timescaleModel Quality
Country, monthly or annual2
Country, daily3
Country + region, daily4
Country + region, hourly5


Criteria for accurate sustainability data at the organization level:

  • An organization should provide a sustainability report that details its full carbon footprint, including scopes 1, 2, and all scope 3 categories. This report should clearly detail methodology for each category.

  • Any adjustments to the calculations for RECs, PPAs, offsets, or carbon credits should detail exactly what was purchased, from whom, and on what timeframe. Location-based scope 2 data should always be provided.

  • A sustainability report should be published within 6 months of the end of the previous calendar year to be considered for sustainability purposes.

Report elementModel Quality
Scope 1 and 2, market-based+1
Location-based scope 2+1
All scope 3 categories provided+1
Lines of business broken out+1

Ad Platform

An ad platform should provide 1) requests received, 2) requests sent (traffic shaping), and 3) emissions data from datacenters or cloud providers. The ad platform score starts at 1 and increments based on the presence of various modeled components.

Ad platform data elementModel Quality
Server emissions and request data+1
Traffic shaping data+1
All data provided monthly+1
All data provided regionally+1

Example: A company provides annual server emissions, requests received, and traffic shaping data. This would be model quality 3.

The average ad platform model quality is the average score of all direct ad platforms that the property works with.


For media properties, key metrics include session time, session weight, and ad load. For OOH screens, the key metric is power draw. A measured metric is acquired on a per-property basis - for instance, using Google Analytics on a website or a power meter on a screen. An estimated or average metric uses a third-party source or historical averages to project key metrics, for instance, using screen size and type to model power usage.

Property MetricsGeographyModel Quality
Not availableN/a1
Average or estimated activityGlobal3
Observed or measured activityGlobal4
Observed or measured activityRegional5

Ad Stack

The accuracy of the ad stack used by the publisher for the placement depends on accurate representation of all direct and indirect ad platforms. The ad stack score starts at 1 and increments based on modeled components.

Ad Stack ComponentModel Quality
Ad stack mapped via observed data+1
Ads.txt validated+1
Ad platforms mapped to region, device, and format+1
Placements mapped to GPID and ad platform+1

Ad Format

The ad format model should include all of elements included upon the initial render of the ad format. Advertiser-provided assets should be identified so that they can be replaced with actual data during the measurement process. All ad platforms that are part of the rendering process should be included, especially ad servers, real-time measurement providers, and video players.

Ad Format data elementModel Quality
Technical specs provided+1
Media assets identified+1
All static assets measured and included+1
Video player is identified and has model quality of at least 3+1

Input granularity

Provided inputs must match the underlying model in order to provide accurate output. For instance, if the user provides “ANZ” as a country, that might not match “AU” or “NZ”, causing a mapping issue that could cause a fallback to using worldwide grid mix and a significant loss of accuracy.

Granularity % is the percentage of recommended input fields that are provided and match valid values.

RegionFor countries with multiple grid regions, eg US, CA, AU
Device Type
TimeAt least hourly granularity
Ad format
Creative asset weights

Input granularity to model quality map

Granularity %Model Quality
< 30%1
30% - 50%2
50% - 70%3
70% - 90%4
90% - 100%5

Input granularity example

FieldProvided InputMatch
SellerGoogle AdXno
Device TypePhoneyes
Ad format320x50 banneryes
Creative weight(omitted)no

Input granularity percentage: 6/12 = 50%

Input granularity score: 2