Across the month of September 2024, Tailpipe’s own use of cloud computing produced 8.31 kgCO2e of emissions, of which:
1.82 kgCO2e were embodied emissions, and
6.49 kgCO2e were operational emissions.
That’s the equivalent of driving 33 miles (53 km) in a typical petrol car.
Tailpipe calculated this by assessing the eight AWS EC2 instances that it utilized across the 30 days of September 2024. These instances were: t3.micro, t2.micro, c6a.metal, c6i.metal, c7i.metal-48xl, m7g.large, t4g.nano, t4g.small.
Operational Emissions
Operational emissions result from the energy generation process that provides the electricity that cloud service providers’ data centers need to power cloud computing services.
First, let’s break down how Tailpipe calculated its operational emissions.
Instance
Using its central database, Tailpipe could identify the following variables for each instance’s hardware:
|
Instance |
CPU |
TDP |
RAM (GB) |
DDR |
GPUs |
SSD (GB) |
Instance vCPU |
Server vCPU |
NICs |
|
t3.micro |
Intel Xeon Platinum 8175M |
240 |
1 |
4 |
0 |
0 |
2 |
8 |
1 |
|
t2.micro |
Intel Xeon E5-2676 v3 |
145 |
1 |
4 |
0 |
0 |
8 |
1 |
1 |
|
c6a.metal |
AMD EPYC 7R13 |
225 |
384 |
4 |
0 |
0 |
192 |
192 |
1 |
|
c6i.metal |
Intel Xeon Platinum 8375C |
300 |
256 |
4 |
0 |
0 |
128 |
123 |
1 |
|
c7i.metal-48xl |
Intel Xeon Platinum 8488C |
385 |
384 |
5 |
0 |
0 |
192 |
192 |
1 |
|
m7g.large |
Graviton3 |
250 |
8 |
5 |
0 |
0 |
2 |
64 |
1 |
|
t4g.nano |
Graviton2 |
150 |
1 |
4 |
0 |
0 |
2 |
8 |
1 |
|
t4g.small |
Gravtion2 |
150 |
2 |
4 |
0 |
0 |
2 |
8 |
1 |
It then multiplies these variables by power draw data, depending on the CPU utilization of each of Tailpipe’s instances for every hour of the month. For example, for the m7g.large instance:
|
Component |
Total Power Draw (Wh) |
|
CPU |
1006.8 |
|
GPU |
0 |
|
RAM |
1079.77 |
|
SSD |
0 |
|
Motherboard |
954.92 |
|
NIC |
7333.8 |
|
Total |
10375.29 |
The total power draw of the m7g.large instance for the month of September 2024 is therefore 10375.29 Wh.
Network Storage
Tailpipe then adds the impact of network storage. The m7g.large instance does not have any SSDs or HDDs, so its storage is maintained on the AWS Elastic Block Storage infrastructure. In September, Tailpipe stored 57.19 GB of data in the m7g.large instance’s network storage.
This quantity is applied to the Tailpipe methodology:
((0.0029 * GB of Network Storage) * Hours of Utilization) * Power Supply Efficiency Factor
((0.0029 * 57.19) * 720) *1.1 = 131.35 Wh
Instances in the Same Data Center
Tailpipe’s m7g.large instance was based in a data center in Stockholm in September 2024. The next stage of the methodology is to add together the impacts of the instances that were based in the same data center, and the networking that originated from that data center, during the same period.
Two other Tailpipe instances were based in the Stockholm data center in September: t3.micro and t4g.nano.
|
Name |
Instance (Wh) |
Network Storage (Wh) |
|
t3.micro |
34.15 |
0.0 |
|
t4g.nano |
175.49 |
0.0 |
|
m7g.large |
10375.29 |
131.35 |
|
Total |
10584.93 |
131.35 |
Data Transfer and Networking
The impact of networking must then be calculated. Tailpipe multiplies the GB of data transferred by specific figures for each networking type, accounting for the different power draws of networking across these different networks.
Instance Networking from Stockholm
|
Type |
GB |
Formula |
Power Draw (Wh) |
|
Intra-region |
0.00 |
(0.0006 * Intra-region GB) / 1000 |
0 |
|
Inter-region |
119.86 |
(0.0006 * Inter-region GB) / 1000 |
0.00007 |
|
External |
0.71 |
(0.0058 * External GB) / 1000 |
0.000004 |
Non-Instance Networking from Stockholm
|
Type |
GB |
Formula |
Power Draw (Wh) |
|
Intra-region |
0.00 |
(0.0006 * Intra-region GB) / 1000 |
0 |
|
Inter-region |
121.03 |
(0.0006 * Inter-region GB) / 1000 |
0.00007 |
|
External |
0.64 |
(0.0058 * External GB) / 1000 |
0.000004 |
Total Operational Emissions
The complete Tailpipe operational emissions methodology is:
((Instance + Network Storage + Intra-Region Data Transfer + Intra-Region Non-Instance Networking) * PUE) + (Inter-Region Data Transfer + External Data Transfer + Inter-Region Non-Instance Networking + External Non-Instance Networking)) * Carbon Intensity * Power Transmission Losses)
Where PUE (the Power Usage Effectiveness of the Stockholm data center) is 1.1.
Adding the figures above into this formula gives the following result:
((10584.93 + 131.35 + 0 + 0) * 1.1) + (0.00007 + 0.000004 + 0.00007 + 0.000004)) = 11787.91 Wh
This figure must then be multiplied by the carbon intensity of the Swedish grid mix in September 2024. This was 0.00001984 kgCO2e/Wh.
11787.91 * 0.00001984 = 0.23 kgCO2e
After applying the same formula to the five other instances Tailpipe utilizes, Tailpipe’s total operational emissions for September 2024 worked out to be 6.49 kgCO2e.
Embodied Emissions
Tailpipe then calculates the embodied emissions of its instances. Embodied emissions are released during the manufacture, shipping and disposal of the physical hardware that hosts cloud computing services.
Taking the m7g.large instance as an example, Tailpipe first calculates the embodied emissions of the server that hosts it. This is the m7g.metal server. It takes embodied emissions values from the Green Cloud Computing study and multiplies them based on the specific hardware in the m7g.metal server.
Component | Formula | Values | Result (kgCO2e) |
CPU | CPU Units * ((CPU Die Size) * 0.0197 + 9.14) | 1 * ((347.3) * 0.0197 + 9.14) | 15.98 |
RAM | RAM Units * ((RAM Capacity/RAM Density) * 2.2 + 5.22) | 1 * ((256/22.37) * 2.2 + 5.22) | 30.4 |
SSD | SSD Units * ((SSD Capacity/SSD Density) * 2.2 + 6.34) | 0 * ((0/0) * 2.2 + 6.34) | 0 |
HDD | HDD Units * 31.11 | 0 * 31.11 | 0 |
Motherboard | 66.10 | 66.1 | 66.1 |
PSU | Power Supply Units * (Weight * 24.3) | 2 * (2.99 * 24.3) | 145.31 |
Assembly | 6.68 | 6.68 | 6.68 |
Enclosure | 150 | 150 | 150 |
Total |
|
| 430.45 |
These figures are then scaled down for the specific m7g.large instance. The instance is a virtual machine, meaning that it uses the resources of the m7g.metal server, without having physical machinery of its own.
Component | Formula | Values | Result (kgCO2e) |
CPU | 15.98 * (Instance vCPU/Server vCPU) | 15.98 * (2/64) | 0.5 |
RAM | 30.4 * (Instance Memory/Server Memory) | 30.4 * (8/256) | 0.95 |
Motherboard | 66.1 * (Instance vCPU/Server vCPU) | 66.1 * (2/64) | 2.07 |
PSU | 145.31 * (Instance vCPU/Server vCPU) | 145.31 * (2/64) | 4.54 |
Assembly | 6.68 * (Instance vCPU/Server vCPU) | 6.68 * (2/64) | 0.2 |
Enclosure | 150 * (Instance vCPU/Server vCPU) | 150 * (2/64) | 4.69 |
Total |
|
| 12.95 |
Because the m7g.large instance hosts network storage (rather than onboard storage in the SSDs or HDDs of the m7g.metal server), the impact of this needs to be added too:
Network Storage Units * 0.0028
= 57.19 * 0.0028
= 0.16
Finally, the impact of networking equipment is added. This is 10.12 kg CO2e per instance, to account for an average of two switch ports per instance.
The total embodied emissions of the m7g.large instance across its entire lifespan are therefore:
12.95 + 0.16 + 10.12 = 23.23 kgCO2e
Disposal
Tailpipe then adds the impact of disposal onto this figure. This is a 1.8% reduction in the total carbon emissions of the physical hardware:
23.23 – (23.23 * 0.018) = 22.81 kgCO2e
Time Share
The final step in the embodied emissions calculation is to factor in Tailpipe’s use of its instances as a percentage of their total 6-year lifespan.
22.81 * (customer usage period/instance lifetime)
22.81 * (720/52596) = 0.31 kgCO2e
This means that in September 2024, the m7g.large instance generated 0.31 kgCO2e through its embodied emissions, and 0.23 kgCO2e through its operational emissions – producing a total of 0.54 kgCO2e. This result is unusual in that the embodied emissions are larger than the operational emissions, which suggests that the m7g.large instance was not being utilized efficiently in this month.
The same embodied emissions formula was applied to the seven other instances that Tailpipe utilized in September. The resulting figure for Tailpipe’s total embodied emissions for September 2024 was 1.82 kgCO2e.
Find Out More
For a more detailed breakdown of how Tailpipe calculates emissions, see the Tailpipe Methodology. For an explanation of how Tailpipe can reduce an organization’s cloud spend and carbon emissions, see Tailpipe’s Recommendations.
To discuss what Tailpipe can do to measure and reduce your cloud computing spend and emissions, get in touch with us here.
