Merge branch 'gh-pages' of https://github.com/dkrees/SL-blog into gh-…

…pages

_drafts/carbon-emissions-of-end-user-devices-part-one-swd-method.markdown

@@ -11,6 +11,18 @@ summary: This is the first of a series of blog posts that examine the various me
 author: drees
 ---
 
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 # Introduction
 
 This series of blog posts discusses the methods of estimating carbon emissions of end-user devices. Specifically, this looks at web user interfaces, such as websites and web applications, and the devices we use to access them. After intending to write a single blog post, the research journey prompted me to reconsider how to present this to an audience. So, this is the first of a series of blog posts that broach this subject and look at the various methods, concluding with a comparison of the techniques.
@@ -35,17 +47,17 @@ The method has two parts: calculating the energy use and then applying the grid
 
 The key metric is a simple calculation. The estimated annual global energy use for the internet (1,988 TWh) is divided by the estimated global yearly data transferred (2,444 exabytes (EB)):
 
-\$$
+$$
 1988TWh / 2444EB = 0.81kWh/GB
-\$$
+$$
 
 This straightforward equation proportionally marries energy use to data transferred.
 
 So, to estimate the energy used (kWh), multiply the data transferred in gigabytes (GB) by 0.81.
 
-\$$
-E = D_t \\times 0.81
-\$$
+$$
+E = D_t \times 0.81
+$$
 
 That’s too easy, right?
 
@@ -59,9 +71,9 @@ However, the energy grid is typically served by a mixture of these sources, and
 
 So, the product of the estimated energy use and the carbon intensity of that energy gives us an estimated carbon emission.
 
-\$$
-C = E \\times C_i
-\$$
+$$
+C = E \times C_i
+$$
 
 Where:
 
@@ -77,9 +89,9 @@ Again, it is a simple formula for a complex problem.
 
 The example use of the SWD method is a little more complicated, but it demonstrates how it can be used to get more accurate results. It applies some assumptions: that 75% of visits to a web page are from new visitors or, more precisely, uncached visits. For the 25% returning visitors, the data transferred will only be 2% of the uncached data transfer, meaning a significant reduction of data transfer due to cached files.
 
-\$$
-E_v = (D_v \\times 0.81 \\times 0.75) \+ (D_v \\times 0.81 \\times 0.25 \\times 0.02)
-\$$
+$$
+E_v = (D_v \times 0.81 \times 0.75) + (D_v \times 0.81 \times 0.25 \times 0.02)
+$$
 
 where:
 
@@ -91,9 +103,9 @@ Of course, if you have actual metrics, you can easily replace these assumptions
 
 After calculating the energy per visit, we can apply the carbon intensity. We can use the global carbon intensity value (490gCO2e/kWh) for a generalised worldwide perspective or if we assume a global distribution and audience.
 
-\$$
-C_v = E_v \\times 490
-\$$
+$$
+C_v = E_v \times 490
+$$
 
 If we’re confident about where our web page and data are stored and where our users are accessing the web page, we can use a more specific carbon intensity.
 
@@ -133,9 +145,9 @@ Using [websitecarbon.com](http://websitecarbon.com) (which implements the SWD me
 
 With this data, we can use the SWD method to calculate the estimated carbon emissions of the blog page as follows:
 
-\$$
-E_v = (E_t \\times D_n \\times V_n) \+ (E_t \\times D_r \\times V_r)
-\$$
+$$
+E_v = (E_t \times D_n \times V_n) + (E_t \times D_r \times V_r)
+$$
 
 Where:
 
@@ -151,29 +163,29 @@ Where:
 
 * $V_r$ is the percentage of returning visits (25%)
 
-\$$
-E_v = (0.81 \\times 0.0043 \\times 0.75) \+ (0.81 \\times 0.0000106 \\times 0.25) = 0.0026kWh
-\$$
+$$
+E_v = (0.81 \times 0.0043 \times 0.75) + (0.81 \times 0.0000106 \times 0.25) = 0.0026kWh
+$$
 
 Using the system boundaries, we can break down that energy use as follows:
 
-* User device energy: $0.0026 \\times 0.52 = 0.001352kWh$
+* User device energy: $0.0026 \times 0.52 = 0.001352kWh$
 
-* Network energy: $0.0026 \\times 0.14 = 0.000364kWh$
+* Network energy: $0.0026 \times 0.14 = 0.000364kWh$
 
-* Data centre energy: $0.0026 \\times 0.15 = 0.00039kWh$
+* Data centre energy: $0.0026 \times 0.15 = 0.00039kWh$
 
-* Hardware production energy: $0.0026 \\times 0.19 = 0.000494kWh$
+* Hardware production energy: $0.0026 \times 0.19 = 0.000494kWh$
 
 While we have a known energy carbon intensity for the user device, the network, and the data centre where the blog is hosted, we will assume the hardware production energy using the global carbon intensity of 490gCO2e/kWh (given the lack of specific evidence otherwise). With this, we can calculate the estimated CO2 emissions per visit per system:
 
-* User device carbon emissions: $0.001352 \\times 238 = 0.322gCO2e$
+* User device carbon emissions: $0.001352 \times 238 = 0.322gCO2e$
 
-* Network carbon emissions: $0.000364 \\times 238 = 0.087gCO2e$
+* Network carbon emissions: $0.000364 \times 238 = 0.087gCO2e$
 
-* Data centre carbon emissions: $0.00039 \\times 50 = 0.019gCO2e$
+* Data centre carbon emissions: $0.00039 \times 50 = 0.019gCO2e$
 
-* Hardware production carbon emissions: $0.000494 \\times 490 = 0.242gCO2e$
+* Hardware production carbon emissions: $0.000494 \times 490 = 0.242gCO2e$
 
 Giving a total estimated carbon emissions per visit of 0.67gCO2e.