' | remove: '
' }} - {% if item.link and item.linktext%}[{{ item.linktext }}]({{ item.link }}){:target="_blank"}{% endif %} + {% if item.link and item.linktext%}[{{ item.linktext }}]({{ item.link }}){:target="_blank"}{% endif %} {% endfor %} \ No newline at end of file diff --git a/pages/resources/tools.md b/pages/resources/tools.md new file mode 100644 index 0000000..e25c75d --- /dev/null +++ b/pages/resources/tools.md @@ -0,0 +1,8 @@ +--- +layout: default +title: Tools +permalink: /resources/tools +published: false +--- + +# Tools \ No newline at end of file diff --git a/pages/views/roles/architecture.md b/pages/roles/architecture/architecture.md similarity index 72% rename from pages/views/roles/architecture.md rename to pages/roles/architecture/architecture.md index f735f96..6b6fc05 100644 --- a/pages/views/roles/architecture.md +++ b/pages/roles/architecture/architecture.md @@ -1,8 +1,10 @@ --- -layout: default +layout: sideNavigation title: Architecture View -permalink: views/roles/architecture +permalink: /roles/architecture published: false +redirect_from: + - /views/roles/architecture --- # Architecture Views @@ -13,14 +15,14 @@ As an architect you can exert significant influence on a company's tech carbon e - Setting the tech strategy - e.g. cloud vs on-prem, build vs buy - Promoting best practice and setting tech principles - - e.g. princples around resilience, data retention etc + - e.g. principles around resilience, data retention etc - Leading technical selection, e.g. of a cloud vendor or a SaaS product - Architecture Design (from enterprise to technical) - Setting non-functional requirements (NFRs) -The first couple are about strategy and the other 3 about NFRs - tech selection and solution design are about weighing up options against the non functionals. Therefore 2 views are provied to cover these 2 areas: +The first couple are about strategy and the other 3 about NFRs - tech selection and solution design are about weighing up options against the non functionals. Therefore 2 views are provided to cover these 2 areas: -- [Architecture View - Strategy](/views/roles/architecture_strategy) +- [Architecture View - Strategy](/roles/architecture/strategy) - How to incorporate sustainability into technology strategy -- [Architecture View - NFRs](/views/roles/architecture_nfrs) +- [Architecture View - NFRs](/roles/architecture/nfrs) - How to incorporate sustainability when setting NFRs \ No newline at end of file diff --git a/pages/views/roles/architecture_nfrs.md b/pages/roles/architecture/architecture_nfrs.md similarity index 84% rename from pages/views/roles/architecture_nfrs.md rename to pages/roles/architecture/architecture_nfrs.md index 1d793af..b2138bd 100644 --- a/pages/views/roles/architecture_nfrs.md +++ b/pages/roles/architecture/architecture_nfrs.md @@ -1,17 +1,19 @@ --- -layout: default +layout: sideNavigation title: Architecture View -permalink: views/roles/architecture_nfrs +permalink: /roles/architecture/nfrs published: false +redirect_from: + - /views/roles/architecture_nfrs --- # Architecture View - Setting NFRs {% include carbonStandard.html variant=site.data.overlays.architecture_nfrs hideOutOfScope=true %} -This view presents some ideas for settings NFRs (also known as Quality Attributes) in order to promote energy efficiency and low carbon emissions. They derive from the strategy and principles described in [Architecture View - Strategy](/views/roles/architecture). What follows are just suggestions and examples; this is a new area and so there's no established NFRs like, for example 1/2/3/4 9s availability. Bear in mind that measuring carbon NFRs can be hard so think about your maturity in measurement and monitoring before setting NFRs - there's no point setting something where you'll never know if you met the requirement. +This view presents some ideas for settings NFRs (also known as Quality Attributes) in order to promote energy efficiency and low carbon emissions. They derive from the strategy and principles described in [Architecture View - Strategy](/roles/architecture). What follows are just suggestions and examples; this is a new area and so there's no established NFRs like, for example 1/2/3/4 9s availability. Bear in mind that measuring carbon NFRs can be hard so think about your maturity in measurement and monitoring before setting NFRs - there's no point setting something where you'll never know if you met the requirement. -The diagram maps NFRs onto specific TCS categories such as embodied server hardware but be aware that some NFRs may actually benefit more than one category; for example energy efficent software may allow you to squeeze more software onto less hardware reducing embodied carbon as well as operational. +The diagram maps NFRs onto specific TCS categories such as embodied server hardware but be aware that some NFRs may actually benefit more than one category; for example energy efficient software may allow you to squeeze more software onto less hardware reducing embodied carbon as well as operational. ## Upstream Emissions @@ -26,7 +28,7 @@ The architect is at the center of vendor selection and so can promote green requ - **Prefer vendors with a sustainability strategy:** In vendor selection prefer vendors with a clear carbon sustainability strategy including carbon reduction and clear reporting. This creates market pressure for vendors to be reporting and reducing emissions. -- **Prefer a higher user base:** Adding NFR around user base size means you'll be getting something well tested and more likely to be supported but can also influence carbon emissions. If you pay an organisation to create bespoke software for you or train a bespoke model for you the incurred emissions will only benefit your user base. On the other hand adopting open source software used by 100 companies and their users means there's a small attribution to you and there's less duplicated work. The same applies for internal build vs buy although in this case there's *Category O* emissions and other non tech ones like buildings in [GHGP Scope 3](/glossary#greenhouse-gas-ghg-protocols). +- **Prefer a higher user base:** Adding NFR around user base size means you'll be getting something well tested and more likely to be supported but can also influence carbon emissions. If you pay an organisation to create bespoke software for you or train a bespoke model for you the incurred emissions will only benefit your user base. On the other hand adopting open source software used by 100 companies and their users means there's a small attribution to you and there's less duplicated work. The same applies for internal build vs buy although in this case there's *Category O* emissions and other non tech ones like buildings in [GHGP Scope 3](/resources/glossary#greenhouse-gas-ghg-protocols). ### Hardware Manufacture, Transport and Installation @@ -49,7 +51,7 @@ For operational emissions you can directly target energy or emissions with your When targetting carbon or energy, NFRs could include: -- **Energy or carbon per service should be < X gCO2e:** Can go more or less granualar as needed and dependent on measurement sophistication, e.g. per API REST endpoint call. +- **Energy or carbon per service should be < X gCO2e:** Can go more or less granular as needed and dependent on measurement sophistication, e.g. per API REST endpoint call. Such an NFR pushes development teams to optimise their apps. It says carbon **or** energy because you may wish to target both. Energy is comparable from day to day whereas carbon will depend on the electricity mix. You might want to consider carbon or grid awareness to reduce carbon intensity of energy alongside developing efficient code to minimise electricity. @@ -57,8 +59,8 @@ Such an NFR pushes development teams to optimise their apps. It says carbon **or {% include categoryLabel.html label="CatO" %} At the level of the data centre, NFRs could include the following: -- **Data centre [PUE](/glossary#power-usage-effectiveness-pue) should be < 1.3:** When building out a new data centre you can set a maximum Power Usage Effectiveness. That said, be wary of over using PUE without considering factors like water usage. -- **[Carbon intensity](/glossary#carbon-intensity) should be < X kgCO2e/kWh:** Setting an NFR around the carbon intensity of the electricty influences choices on location or installation of wind turbines or solar. +- **Data centre [PUE](/resources/glossary#power-usage-effectiveness-pue) should be < 1.3:** When building out a new data centre you can set a maximum Power Usage Effectiveness. That said, be wary of over using PUE without considering factors like water usage. +- **[Carbon intensity](/resources/glossary#carbon-intensity) should be < X kgCO2e/kWh:** Setting an NFR around the carbon intensity of the electricity influences choices on location or installation of wind turbines or solar. {% include categoryItem.html item="CatOOnsiteEmployeeDevices" id="EmployeeDevices" noLink=true %} @@ -74,7 +76,7 @@ Again utilisation or power/carbon usage targets can be set: - **Power per port should be < 2W:** Power per Gbps throughput or per port could be targeted just as an embodied carbon can be also. -Some application targetted NFRs may also reduce network traffic and so reduce the need to have more network hardware - this benfits operational and embodied carbon. e.g. +Some application targetted NFRs may also reduce network traffic and so reduce the need to have more network hardware - this benefits operational and embodied carbon. e.g. - **Data compression of 50% vs raw text in API payloads:** promote zipping and/or efficient protocols like gRPC - **Normalise async messages, no one-to-many entity to message mappings:** avoids one entity change causing a storm of messages @@ -131,7 +133,7 @@ End user devices like customer laptops, these are out of our control but there a {% include categoryItem.html item="CatDEndUserDevices" id="EndUserDevices" noLink=true %} NFRs for devices are one of the easier things to set and test because you can obtain real representative hardware to do measurements. Possible NFRs are: -- **Single page view uses < X kWh:** Promote pages that don't use unneccessary energy. +- **Single page view uses < X kWh:** Promote pages that don't use unnecessary energy. - **No energy usage when user not on page:** Ensure that no energy is used when the user is not on a webpage. Note: modern browsers will help enforce this anyway. diff --git a/pages/views/roles/architecture_strategy.md b/pages/roles/architecture/architecture_strategy.md similarity index 97% rename from pages/views/roles/architecture_strategy.md rename to pages/roles/architecture/architecture_strategy.md index 59c84b6..a47cd59 100644 --- a/pages/views/roles/architecture_strategy.md +++ b/pages/roles/architecture/architecture_strategy.md @@ -1,15 +1,17 @@ --- -layout: default +layout: sideNavigation title: Architecture View -permalink: views/roles/architecture_strategy +permalink: /roles/architecture/strategy published: false +redirect_from: + - /views/roles/architecture_strategy --- # Architecture View - setting strategy {% include carbonStandard.html variant=site.data.overlays.architecture hideOutOfScope=true %} -This view shows how architecture strategy and technology principles can influence the various categories in the TCS. See [Architecture View - NFRs](/views/roles/architecture_nfrs) for a mapping of the NFRs that can derive from the strategy. +This view shows how architecture strategy and technology principles can influence the various categories in the TCS. See [Architecture View - NFRs](/roles/architecture_nfrs) for a mapping of the NFRs that can derive from the strategy. ## Upstream Emissions diff --git a/pages/roles/roles.md b/pages/roles/roles.md new file mode 100644 index 0000000..256cbaa --- /dev/null +++ b/pages/roles/roles.md @@ -0,0 +1,16 @@ +--- +layout: sideNavigation +title: Roles +permalink: /roles +published: false +--- + +# Roles + + + +- [Architecture ](/roles/architecture) + +- [Architecture - Strategy](/roles/architecture/strategy) + +- [Architecture - NFRs](/roles/architecture/nfrs) \ No newline at end of file diff --git a/pages/information/cloud.md b/pages/technology_categories/cloud/cloud.md similarity index 95% rename from pages/information/cloud.md rename to pages/technology_categories/cloud/cloud.md index d978a8d..c2c9278 100644 --- a/pages/information/cloud.md +++ b/pages/technology_categories/cloud/cloud.md @@ -1,7 +1,9 @@ --- -layout: category +layout: sideNavigation title: Cloud Services -permalink: /information/cloud +permalink: /technology-categories/cloud +redirect_from: + - /information/cloud --- # Cloud Services @@ -29,7 +31,7 @@ The environmental impact and carbon emissions associated with cloud computing ar At the same time, cloud computing presents some opportunities for improved sustainability compared to traditional on-premises data centres. Cloud providers can leverage economies of scale to invest in more energy-efficient hardware, cooling systems, and renewable energy sources. The flexibility of the cloud also allows organisations to dynamically provision resources and optimise their usage, potentially reducing idle capacity and associated emissions. -Focusing specifically on energy efficiency, Cloud providers often use custom hardware with a lower power demand. Generally speaking, it is in their interests to use the most energy efficient hardware possible to reduce their own operating costs. This also allows users to switch to lower powered hardware without the initial outlay for new hardware. Improved energy efficiency also extends to the typical [Power Usage Effectiveness (PUE)](/glossary#power-usage-effectiveness-pue) of a cloud-based data centre, which can be lower than typical on-premise data centres. +Focusing specifically on energy efficiency, Cloud providers often use custom hardware with a lower power demand. Generally speaking, it is in their interests to use the most energy efficient hardware possible to reduce their own operating costs. This also allows users to switch to lower powered hardware without the initial outlay for new hardware. Improved energy efficiency also extends to the typical [Power Usage Effectiveness (PUE)](/resources/glossary#power-usage-effectiveness-pue) of a cloud-based data centre, which can be lower than typical on-premise data centres. | Provider | PUE | | ----------------------------- | ---------- | @@ -47,7 +49,7 @@ Focusing specifically on energy efficiency, Cloud providers often use custom har While Cloud PUE figures may be lower than many on-premise data centres, these figures should be considered carefully when sourced from the Cloud Providers themselves. For example, they may apply to specific data centres only, rather than being an average. In future it is hoped that these figures can be further reduced by recapturing the waste heat from data centres, with [Deep Green](https://deepgreen.energy/) being an example of a smaller Cloud provider whose micro data centres extract heat to provide hot water for a range of purposes, which can give PUE figures as low as 1.001. -The region that the services are run from likely has an even more significant impact on carbon emissions, depending on the mix of renewable energy sources that power the underlying data centres. One advantage of using cloud is that is easier to move infrastructure to a new location with a better energy mix than it would be when hosting those servers on-premise. Regardless of the grid mix of the location, some cloud providers may report that they use 'zero carbon' energy but it is important to distinguish between direct renewable power to the data centre and that which has been offset via [Power Purchase Agreements](/glossary#power-purchase-agreements-ppa) or [Renewable Energy Certificates](/glossary#renewable-energy-certificates-recs). +The region that the services are run from likely has an even more significant impact on carbon emissions, depending on the mix of renewable energy sources that power the underlying data centres. One advantage of using cloud is that is easier to move infrastructure to a new location with a better energy mix than it would be when hosting those servers on-premise. Regardless of the grid mix of the location, some cloud providers may report that they use 'zero carbon' energy but it is important to distinguish between direct renewable power to the data centre and that which has been offset via [Power Purchase Agreements](/resources/glossary#power-purchase-agreements-ppa) or [Renewable Energy Certificates](/resources/glossary#renewable-energy-certificates-recs). The types of services used can also have an impact on emissions, with Infrastructure as a Service offerings potentially providing negligible benefits over already virtualised on-premise hardware. These services may allow the most control over cloud resources but they retain the same issues that can be seen when running on-premise, with instances being allocated to cope with peak load going underutilised for the majority of time. diff --git a/pages/information/paas.md b/pages/technology_categories/cloud/paas.md similarity index 99% rename from pages/information/paas.md rename to pages/technology_categories/cloud/paas.md index 613cc32..4b6577f 100644 --- a/pages/information/paas.md +++ b/pages/technology_categories/cloud/paas.md @@ -1,7 +1,9 @@ --- -layout: category +layout: sideNavigation title: Platform as a Service (PaaS) -permalink: /information/cloud/pass +permalink: /technology-categories/cloud/paas +redirect_from: + - /information/cloud/pass published: false --- diff --git a/pages/views/roles/data.md b/pages/technology_categories/data.md similarity index 98% rename from pages/views/roles/data.md rename to pages/technology_categories/data.md index 8c643fb..c8f8bf2 100644 --- a/pages/views/roles/data.md +++ b/pages/technology_categories/data.md @@ -1,8 +1,9 @@ --- -layout: default +layout: sideNavigation title: Data View -permalink: views/roles/data -published: false +permalink: /technology-categories/data +redirect_from: + - /views/roles/data --- # Data View diff --git a/pages/information/estate.md b/pages/technology_categories/estate.md similarity index 99% rename from pages/information/estate.md rename to pages/technology_categories/estate.md index c9d2117..bec6f3c 100644 --- a/pages/information/estate.md +++ b/pages/technology_categories/estate.md @@ -1,7 +1,9 @@ --- -layout: category +layout: sideNavigation title: Estimating Estate Emissions -permalink: /information/estate +permalink: /technology-categories/estate +redirect_from: + - /information/estate published: false --- diff --git a/pages/information/hardware/embodied.md b/pages/technology_categories/lifecycle/embodied.md similarity index 75% rename from pages/information/hardware/embodied.md rename to pages/technology_categories/lifecycle/embodied.md index 0261633..08c9afd 100644 --- a/pages/information/hardware/embodied.md +++ b/pages/technology_categories/lifecycle/embodied.md @@ -1,23 +1,25 @@ --- -layout: category +layout: sideNavigation title: Embodied Carbon Emissions -permalink: /information/lifecycle/embodied +permalink: /technology-categories/lifecycle/embodied +redirect_from: + - /information/lifecycle/embodied --- # Hardware Embodied Carbon Emissions -Embodied carbon refers to the emissions associated with the manufacture, transportation, installation, maintenance, and end-of-life of a device or product. Embodied carbon excludes emissions associated with the usage of a device or product, which instead falls under [usage carbon](/glossary#usage-carbon). [Read more about life cycle emissions.](../lifecycle) +Embodied carbon refers to the emissions associated with the manufacture, transportation, installation, maintenance, and end-of-life of a device or product. Embodied carbon excludes emissions associated with the usage of a device or product, which instead falls under [usage carbon](/resources/glossary#usage-carbon). [Read more about life cycle emissions.](/technology-categories/lifecycle) Technology hardware can be associated with large embodied carbon emissions due to resource-intensive electronics production and complex global supply chains. In 2021, the World Economic Forum (WEF) reported that 77% of greenhouse gas (GHG) emissions from the electronics industry are attributed to the supply chain, including raw materials mining, manufacture, assembly, and transportation of the finished product. Supply-chain decarbonisation presents a huge opportunity to reduce climate impacts. Notably, 35% of supply-chain emissions could be abated through renewable power, and efficiency improvements could further reduce emissions by ~20%[^WEF]. -End-of-life processes like reuse, recycling, and disposal contribute a relatively small portion of a product's embodied carbon emissions compared to the supply chain. Manufacturers typically provide expected life spans for devices, ranging from 3 to 5 years in most cases. Extending a device's life span can reduce the annual or [amortised](/glossary#amortisation) embodied carbon emissions. However, factors such as market demand for newer technology, built-in obsolescence, performance requirements, and non-repairable design often limit device life spans, impeding potential carbon savings. +End-of-life processes like reuse, recycling, and disposal contribute a relatively small portion of a product's embodied carbon emissions compared to the supply chain. Manufacturers typically provide expected life spans for devices, ranging from 3 to 5 years in most cases. Extending a device's life span can reduce the annual or [amortised](/resources/glossary#amortisation) embodied carbon emissions. However, factors such as market demand for newer technology, built-in obsolescence, performance requirements, and non-repairable design often limit device life spans, impeding potential carbon savings. Focusing carbon reduction efforts on only usage emissions fails to capture the full carbon footprint. While difficult to quantify, considering embodied carbon emissions is critical for comprehensive carbon accounting and driving responsible procurement and development of lower-impact technologies. {% include linkedHeading.html heading="A High-Level Approach for Estimating Embodied Emissions" level=2 %} -The [ratio of embodied carbon to usage carbon](../lifecycle#embodied-vs-usage-carbon) varies across different device types. -In the absence of more granular emissions data (such as [Product Carbon Footprint (PCF)](/glossary#product-carbon-footprint-pcf) data), industry-wide ratios can be applied to estimated usage emissions to approximate embodied emissions. Typically, it can be easier to [estimate usage emissions at a high level](usage#a-high-level-approach-for-estimating-usage-emissions). +The [ratio of embodied carbon to usage carbon](/technology-categories/lifecycle#embodied-vs-usage-carbon) varies across different device types. +In the absence of more granular emissions data (such as [Product Carbon Footprint (PCF)](/resources/glossary#product-carbon-footprint-pcf) data), industry-wide ratios can be applied to estimated usage emissions to approximate embodied emissions. Typically, it can be easier to [estimate usage emissions at a high level](/technology-categories/lifecycle/usage#a-high-level-approach-for-estimating-usage-emissions). With these ratios and usage carbon data, the following formula can be used to estimate embodied carbon: @@ -37,9 +39,9 @@ It is important to emphasise this is a high-level approach. In reality, ratios c {% include linkedHeading.html heading="Using Manufacturer Data for Estimating Embodied Emissions" level=2 %} -Many manufacturers choose to provide life cycle emissions data such as information relating to the [Product Carbon Footprint (PCF)](/glossary#product-carbon-footprint-pcf) of a product or device. [Read more about life cycle emissions data.](../lifecycle#life-cycle-emissions-data) +Many manufacturers choose to provide life cycle emissions data such as information relating to the [Product Carbon Footprint (PCF)](/resources/glossary#product-carbon-footprint-pcf) of a product or device. [Read more about life cycle emissions data.](/technology-categories/lifecycle#life-cycle-emissions-data) -Typically, PCF documentation breaks down the total carbon footprint of a product or device into life cycle stages: manufacturing, transportation, use, and end-of-life. An embodied carbon estimate can be derived by combining the emissions estimates not attributable to the use stage (typically manufacturing, transportation, and end-of-life emissions). Reported figures typically represent lifetime emissions and can be [amortised](/glossary#amortisation) to estimate annual contributions based on the assumed device lifespan outlined in the PCF. +Typically, PCF documentation breaks down the total carbon footprint of a product or device into life cycle stages: manufacturing, transportation, use, and end-of-life. An embodied carbon estimate can be derived by combining the emissions estimates not attributable to the use stage (typically manufacturing, transportation, and end-of-life emissions). Reported figures typically represent lifetime emissions and can be [amortised](/resources/glossary#amortisation) to estimate annual contributions based on the assumed device lifespan outlined in the PCF. Embodied carbon varies between different devices, as illustrated by the following example: @@ -47,13 +49,13 @@ Embodied carbon varies between different devices, as illustrated by the followin *Source: Dell Product Carbon Footprint (PCF) documents for an example model of Laptop[^laptop], Desktop[^desktop], Monitor[^monitor], and Server[^server].* -Servers have relatively high embodied carbon emissions due to their complex and energy-intensive manufacturing processes, as well as the use of specialised components and materials. However, their usage carbon emissions still contribute the larger portion of their overall carbon footprint. In contrast, for user devices like laptops and desktops, embodied carbon forms a larger component than usage carbon. [Read more about embodied vs usage carbon emissions.](../lifecycle#embodied-vs-usage-carbon) +Servers have relatively high embodied carbon emissions due to their complex and energy-intensive manufacturing processes, as well as the use of specialised components and materials. However, their usage carbon emissions still contribute the larger portion of their overall carbon footprint. In contrast, for user devices like laptops and desktops, embodied carbon forms a larger component than usage carbon. [Read more about embodied vs usage carbon emissions.](/technology-categories/lifecycle#embodied-vs-usage-carbon) {% include linkedHeading.html heading=" Worked Examples" level=2 %} -[Click here to see a worked example of estimating embodied emissions for a laptop.](../lifecycle/example/employee#embodied-carbon-emissions) +[Click here to see a worked example of estimating embodied emissions for a laptop.](/technology-categories/lifecycle/example/employee#embodied-carbon-emissions) -[Click here to see a worked example of estimating embodied emissions for a server.](../lifecycle/example/server#embodied-carbon-emissions) +[Click here to see a worked example of estimating embodied emissions for a server.](/technology-categories/lifecycle/example/server#embodied-carbon-emissions) ## References diff --git a/pages/information/hardware/examples/employee.md b/pages/technology_categories/lifecycle/examples/employee.md similarity index 61% rename from pages/information/hardware/examples/employee.md rename to pages/technology_categories/lifecycle/examples/employee.md index bae71d9..c3fec35 100644 --- a/pages/information/hardware/examples/employee.md +++ b/pages/technology_categories/lifecycle/examples/employee.md @@ -1,20 +1,22 @@ --- -layout: category +layout: sideNavigation title: Employee Hardware -permalink: /information/lifecycle/example/employee +permalink: /technology-categories/lifecycle/example/employee +redirect_from: + - /information/lifecycle/example/employee --- # Employee Hardware Example To understand and estimate the carbon footprint associated with employee hardware like computers (desktops, laptops), monitors, and printers, organisations must consider both the usage emissions from powering the devices and the embodied emissions from manufacturing, transportation, and end-of-life across the full product life cycle. -[Read more about hardware life cycle emissions.](../../lifecycle) +[Read more about hardware life cycle emissions.](/technology-categories/lifecycle) -Building on the approaches discussed in the [usage carbon emissions](../usage) and [embodied carbon emissions](../embodied) sections, this page provides an example of estimating emissions from employee devices, using both industry averages and a specific laptop model to illustrate different approaches and considerations. +Building on the approaches discussed in the [usage carbon emissions](/technology-categories/lifecycle/usage) and [embodied carbon emissions](/technology-categories/lifecycle/embodied) sections, this page provides an example of estimating emissions from employee devices, using both industry averages and a specific laptop model to illustrate different approaches and considerations. {% include linkedHeading.html heading="Usage Carbon Emissions" level=2 %} -Usage emissions refer to the [greenhouse gas (GHG)](/glossary#greenhouse-gases-ghgs) emissions associated with operating a device over its lifetime. [Read more about usage emissions.](../usage) +Usage emissions refer to the [greenhouse gas (GHG)](/resources/glossary#greenhouse-gases-ghgs) emissions associated with operating a device over its lifetime. [Read more about usage emissions.](/technology-categories/lifecycle/usage) {% include linkedHeading.html heading="Example Using a High-Level Approach for Estimating Usage Emissions" level=3 %} @@ -31,7 +33,7 @@ The power consumption of user devices can vary considerably. Typically, smartpho *Power range and average power of devices, data compiled from Dimpact [^dimpact], Scope3 [^scope3], Fershad Irani [^fershad], and our own device energy use research.* -Consider the [high-level approach for estimating usage emissions](../usage#a-high-level-approach-for-estimating-usage-emissions) discussed in the usage carbon emissions section. Assuming a typical laptop draws 17.1 W and runs for 8 hours a day over a working year (220 days), the annual energy consumption can be estimated as $$ 0.0171\ kW \times 8 \times 220 = 30\ kWh $$. In Europe, with an average [carbon intensity](/glossary#carbon-intensity) of 0.328 kg CO2e per kWh (2022)[^ember], the estimated annual usage emissions would be $$ 30 \times 0.328 = 9.87\ kg\ CO2e $$ per device. If there are 50 employees in Europe, each owning 1 laptop, the total annual usage emissions across the estate for this device in this location would be approximately $$ 9.87 \times 50 = 494\ kg\ CO2e $$. +Consider the [high-level approach for estimating usage emissions](/technology-categories/lifecycle/usage#a-high-level-approach-for-estimating-usage-emissions) discussed in the usage carbon emissions section. Assuming a typical laptop draws 17.1 W and runs for 8 hours a day over a working year (220 days), the annual energy consumption can be estimated as $$ 0.0171\ kW \times 8 \times 220 = 30\ kWh $$. In Europe, with an average [carbon intensity](/resources/glossary#carbon-intensity) of 0.328 kg CO2e per kWh (2022)[^ember], the estimated annual usage emissions would be $$ 30 \times 0.328 = 9.87\ kg\ CO2e $$ per device. If there are 50 employees in Europe, each owning 1 laptop, the total annual usage emissions across the estate for this device in this location would be approximately $$ 9.87 \times 50 = 494\ kg\ CO2e $$. When specific device models are known, manufacturer specifications and data sheets can be used to derive average power draw and estimate emissions more accurately. The table below shows an example of aggregating emissions across different employee device types and locations using this high-level approach above assuming business usage (8 hours a day for 220 days), with average power draw figures derived from referenced manufacturer specifications. @@ -46,7 +48,7 @@ This approach does not account for the variability in device usage patterns, as {% include linkedHeading.html heading="Example Using Manufacturer Data for Estimating Usage Emissions" level=3 %} -Many manufacturers choose to provide [life cycle emissions data](../../lifecycle#life-cycle-emissions-data) such as information relating to the [Product Carbon Footprint (PCF)](/glossary#product-carbon-footprint-pcf) of a product. A PCF provides an estimate of the total greenhouse gas emissions associated with a product across its entire life cycle, including the proportional allocation of emissions to each distinct life cycle stage. Consider the [PCF for a Dell XPS 13 9310 Laptop](https://www.delltechnologies.com/asset/en-us/products/laptops-and-2-in-1s/technical-support/xps-13-9310.pdf). +Many manufacturers choose to provide [life cycle emissions data](/technology-categories/lifecycle#life-cycle-emissions-data) such as information relating to the [Product Carbon Footprint (PCF)](/resources/glossary#product-carbon-footprint-pcf) of a product. A PCF provides an estimate of the total greenhouse gas emissions associated with a product across its entire life cycle, including the proportional allocation of emissions to each distinct life cycle stage. Consider the [PCF for a Dell XPS 13 9310 Laptop](https://www.delltechnologies.com/asset/en-us/products/laptops-and-2-in-1s/technical-support/xps-13-9310.pdf).  @@ -68,27 +70,27 @@ To break down the emissions into embodied (manufacturing, transportation, and en | Embodied | 86.1 | 277 | | Usage | 13.9 | 45 | -The embodied-to-usage ratio of 86.1/13.9 (embodied being approximately six times larger than usage) deviates slightly from the typical 80/20 ratio for laptops mentioned previously in [embodied vs usage carbon](../../lifecycle#embodied-vs-usage-carbon). +The embodied-to-usage ratio of 86.1/13.9 (embodied being approximately six times larger than usage) deviates slightly from the typical 80/20 ratio for laptops mentioned previously in [embodied vs usage carbon](/technology-categories/lifecycle#embodied-vs-usage-carbon). According to the PCF, usage emissions are estimated to be approximately 45 kg CO2e per device.  -The PCF document outlines the key assumptions used in approximating life cycle emissions of the device. With the 4-year lifetime assumed by the manufacturer, usage emissions can be [amortised](/glossary#amortisation) over the expected lifespan and allocated as approximately 11 kg CO2e per year. +The PCF document outlines the key assumptions used in approximating life cycle emissions of the device. With the 4-year lifetime assumed by the manufacturer, usage emissions can be [amortised](/resources/glossary#amortisation) over the expected lifespan and allocated as approximately 11 kg CO2e per year. -A [Typical Energy Consumption (TEC)](/glossary#typical-energy-consumption-tec) of 21.43 kWh per year for this laptop model has been assumed. As discussed in the [usage emissions section](../usage#typical-energy-consumption-tec), this figure may be lower than expected of business usage, and more typical of standard household usage. Employing the [high-level approach discussed earlier](#example-using-a-high-level-approach-for-estimating-usage-emissions), assuming business usage (8 hours a day) and a typical power draw of 15W[^dellxps], the estimated annual energy consumption is 26.4 kWh. Assuming the embodied emissions remain the same, this higher usage estimate would shift the percentage of emissions due to the use phase to approximately 17% as opposed to 13.9%, and embodied carbon emissions down to approximately 83% from 86.1%. This example demonstrates how a product's carbon footprint composition and proportions can vary depending on factors like usage patterns. +A [Typical Energy Consumption (TEC)](/resources/glossary#typical-energy-consumption-tec) of 21.43 kWh per year for this laptop model has been assumed. As discussed in the [usage emissions section](/technology-categories/lifecycle/usage#typical-energy-consumption-tec), this figure may be lower than expected of business usage, and more typical of standard household usage. Employing the [high-level approach discussed earlier](#example-using-a-high-level-approach-for-estimating-usage-emissions), assuming business usage (8 hours a day) and a typical power draw of 15W[^dellxps], the estimated annual energy consumption is 26.4 kWh. Assuming the embodied emissions remain the same, this higher usage estimate would shift the percentage of emissions due to the use phase to approximately 17% as opposed to 13.9%, and embodied carbon emissions down to approximately 83% from 86.1%. This example demonstrates how a product's carbon footprint composition and proportions can vary depending on factors like usage patterns. -The PCF assumes Europe as usage location. Continuing with the high-level approach by applying an average [carbon intensity](/glossary#carbon-intensity) factor for Europe (0.328 kg CO2e per kWh in 2022)[^ember] to the annual energy consumption estimate of 26.4 kWh translates to approximately 8.66 kg CO2e of annual usage emissions per device. Applying the same method to the manufacturer's TEC figure (21.43 kWh) would result in an annual usage emissions estimate of approximately 7 kg CO2e per device. +The PCF assumes Europe as usage location. Continuing with the high-level approach by applying an average [carbon intensity](/resources/glossary#carbon-intensity) factor for Europe (0.328 kg CO2e per kWh in 2022)[^ember] to the annual energy consumption estimate of 26.4 kWh translates to approximately 8.66 kg CO2e of annual usage emissions per device. Applying the same method to the manufacturer's TEC figure (21.43 kWh) would result in an annual usage emissions estimate of approximately 7 kg CO2e per device. The discrepancies between the emissions estimates derived from the high-level approach and the PCF data highlights the impact of using different assumptions and data sources when estimating usage emissions. It underscores the importance of cross-referencing values and clarifying assumptions made by manufacturers to ensure accurate and consistent emissions reporting. {% include linkedHeading.html heading="Embodied Carbon Emissions" level=2 %} -Embodied carbon refers to the emissions associated with the manufacture, transportation, installation, maintenance, and end-of-life of a device. [Read more about embodied carbon emissions.](../embodied) +Embodied carbon refers to the emissions associated with the manufacture, transportation, installation, maintenance, and end-of-life of a device. [Read more about embodied carbon emissions.](/technology-categories/lifecycle/embodied) {% include linkedHeading.html heading="Example Using a High-Level Approach for Estimating Embodied Emissions" level=3 %} -For devices such as laptops, embodied carbon typically accounts for around 80% of the total life cycle emissions, while usage emissions make up only 20%. Using this 80/20 ratio, [amortised](/glossary#amortisation) embodied emissions can be approximated as four times the annual usage emissions. For example, the amortised embodied carbon for a laptop with annual usage emissions of 10 kg CO2e per year can be allocated as approximately 40 kg CO2e per year. [Read more about this high-level approach for estimating embodied emissions.](../embodied#a-high-level-approach-for-estimating-embodied-emissions) +For devices such as laptops, embodied carbon typically accounts for around 80% of the total life cycle emissions, while usage emissions make up only 20%. Using this 80/20 ratio, [amortised](/resources/glossary#amortisation) embodied emissions can be approximated as four times the annual usage emissions. For example, the amortised embodied carbon for a laptop with annual usage emissions of 10 kg CO2e per year can be allocated as approximately 40 kg CO2e per year. [Read more about this high-level approach for estimating embodied emissions.](/technology-categories/lifecycle/embodied#a-high-level-approach-for-estimating-embodied-emissions) However, this 80/20 ratio assumes a certain life span for the device. If the life span changes, the ratio will change as well. For instance, if the life span doubles from the baseline assumption, and assuming the same usage pattern, the ratio would shift to around 67/33 (67% embodied emissions and 33% usage emissions). @@ -98,9 +100,9 @@ The ratio can also vary considerably depending on other factors such as hardware Refer to the [Product Carbon Footprint (PCF) breakdown for a Dell XPS 13 9310 Laptop](#example-using-manufacturer-data-for-estimating-usage-emissions) discussed above. -Based on this manufacturer data, the embodied carbon per device is approximately 277 kg CO2e. [Amortising](/glossary#amortisation) the embodied emissions over the expected 4-year life span, the annual embodied carbon emissions can be allocated as approximately 69 kg CO2e per year. +Based on this manufacturer data, the embodied carbon per device is approximately 277 kg CO2e. [Amortising](/resources/glossary#amortisation) the embodied emissions over the expected 4-year life span, the annual embodied carbon emissions can be allocated as approximately 69 kg CO2e per year. -As discussed in the [life cycle emissions data](../../lifecycle#life-cycle-emissions-data) section, while reputable manufacturers derive PCFs using methodologies driven by international standards, their estimates come with caveats and significant variance. These values should not be treated as definitive due to uncertainties around device supply chains, operational contexts, and potential truncation errors, which can all vary depending on the specific device. +As discussed in the [life cycle emissions data](/technology-categories/lifecycle#life-cycle-emissions-data) section, while reputable manufacturers derive PCFs using methodologies driven by international standards, their estimates come with caveats and significant variance. These values should not be treated as definitive due to uncertainties around device supply chains, operational contexts, and potential truncation errors, which can all vary depending on the specific device. ## References diff --git a/pages/information/hardware/examples/server.md b/pages/technology_categories/lifecycle/examples/server.md similarity index 64% rename from pages/information/hardware/examples/server.md rename to pages/technology_categories/lifecycle/examples/server.md index e83094c..5a6ccfd 100644 --- a/pages/information/hardware/examples/server.md +++ b/pages/technology_categories/lifecycle/examples/server.md @@ -1,20 +1,22 @@ --- -layout: category +layout: sideNavigation title: Data Centre and Server Hardware -permalink: /information/lifecycle/example/server +permalink: /technology-categories/lifecycle/example/server +redirect_from: + - /information/lifecycle/example/server --- # Data Centre and Server Hardware Example To understand and estimate the carbon footprint associated with data centre hardware like servers, organisations must consider both the usage emissions from powering the infrastructure and the embodied emissions from manufacturing, transportation, and end-of-life across the full product life cycle. -[Read more about hardware life cycle emissions.](../../lifecycle) +[Read more about hardware life cycle emissions.](/technology-categories/lifecycle) -Building on the approaches discussed in the [usage carbon emissions](../usage) and [embodied carbon emissions](../embodied) sections, this page provides an example of estimating emissions from servers, using a specific server model to illustrate different approaches and considerations. +Building on the approaches discussed in the [usage carbon emissions](/technology-categories/lifecycle/usage) and [embodied carbon emissions](/technology-categories/lifecycle/embodied) sections, this page provides an example of estimating emissions from servers, using a specific server model to illustrate different approaches and considerations. {% include linkedHeading.html heading="Usage Carbon Emissions" level=2 %} -Usage emissions refer to the [greenhouse gas (GHG)](/glossary#greenhouse-gases-ghgs) emissions associated with operating a device over its lifetime. [Read more about usage emissions.](../usage) +Usage emissions refer to the [greenhouse gas (GHG)](/resources/glossary#greenhouse-gases-ghgs) emissions associated with operating a device over its lifetime. [Read more about usage emissions.](/technology-categories/lifecycle/usage) {% include linkedHeading.html heading="Example Using a High-Level Approach for Estimating Usage Emissions" level=3 %} @@ -24,13 +26,13 @@ Here's an example of a [SPEC Power benchmark summary](https://www.spec.org/power  -Using the [high-level approach for estimating usage emissions](../usage#a-high-level-approach-for-estimating-usage-emissions) discussed in the usage carbon emissions section, consider a server operating at an average load of 50% corresponding to an average power draw of 237 W as per the SPEC Power benchmark. Assuming it runs for 24 hours daily for a year, the annual energy consumption would be approximately $$ 0.237\ kW \times 24 \times 365 = 2,076\ kWh $$. If this server is operating in Europe, with an average [carbon intensity](/glossary#carbon-intensity) factor of 0.328 kg CO2e per kWh (2022)[^ember], the estimated annual usage emissions would be $$ 2,076 \times 0.328 = 681\ kg\ CO2e $$ per device. For 10 such servers, the total annual usage emissions would be approximately $$ 681 \times 10 = 6,810\ kg\ CO2e $$. If these servers operate in a data centre with a [Power Usage Effectiveness (PUE)](/glossary#power-usage-effectiveness-pue) of 1.6 (using a global average[^pue]), the annual usage emissions attributed to the data centre infrastructure would be approximately $$ 6,810 \times 1.6 = 10,895\ kg\ CO2e $$ (or approximately 1,090 kg CO2e per device). +Using the [high-level approach for estimating usage emissions](/technology-categories/lifecycle/usage#a-high-level-approach-for-estimating-usage-emissions) discussed in the usage carbon emissions section, consider a server operating at an average load of 50% corresponding to an average power draw of 237 W as per the SPEC Power benchmark. Assuming it runs for 24 hours daily for a year, the annual energy consumption would be approximately $$ 0.237\ kW \times 24 \times 365 = 2,076\ kWh $$. If this server is operating in Europe, with an average [carbon intensity](/resources/glossary#carbon-intensity) factor of 0.328 kg CO2e per kWh (2022)[^ember], the estimated annual usage emissions would be $$ 2,076 \times 0.328 = 681\ kg\ CO2e $$ per device. For 10 such servers, the total annual usage emissions would be approximately $$ 681 \times 10 = 6,810\ kg\ CO2e $$. If these servers operate in a data centre with a [Power Usage Effectiveness (PUE)](/resources/glossary#power-usage-effectiveness-pue) of 1.6 (using a global average[^pue]), the annual usage emissions attributed to the data centre infrastructure would be approximately $$ 6,810 \times 1.6 = 10,895\ kg\ CO2e $$ (or approximately 1,090 kg CO2e per device). This approach does not account for the variability in usage patterns but can still be a useful starting point for identifying emissions improvement opportunities within an organisation's technology footprint. Continued refinement of this baseline estimate through more granular monitoring and profiling of actual usage would be required to enable rollout of more targeted carbon reduction initiatives. {% include linkedHeading.html heading="Example Using Manufacturer Data for Estimating Usage Emissions" level=3 %} -Many manufacturers choose to provide [life cycle emissions data](../../lifecycle#life-cycle-emissions-data) such as information relating to the [Product Carbon Footprint (PCF)](/glossary#product-carbon-footprint-pcf) of a product. A PCF provides an estimate of the total greenhouse gas emissions associated with a product across its entire life cycle, including the proportional allocation of emissions to each distinct life cycle stage. Consider the [PCF for a HPE ProLiant DL380 Gen10 server](https://www.hpe.com/psnow/doc/a50004545enw) (the same model as [discussed above](#example-using-a-high-level-approach-for-estimating-usage-emissions)). +Many manufacturers choose to provide [life cycle emissions data](/technology-categories/lifecycle#life-cycle-emissions-data) such as information relating to the [Product Carbon Footprint (PCF)](/resources/glossary#product-carbon-footprint-pcf) of a product. A PCF provides an estimate of the total greenhouse gas emissions associated with a product across its entire life cycle, including the proportional allocation of emissions to each distinct life cycle stage. Consider the [PCF for a HPE ProLiant DL380 Gen10 server](https://www.hpe.com/psnow/doc/a50004545enw) (the same model as [discussed above](#example-using-a-high-level-approach-for-estimating-usage-emissions)).  @@ -54,15 +56,15 @@ Consider a mainstream server operating in the United States. The total product c | Embodied | 30.5 | 1,726 | | Usage | 69.5 | 3,931 | -The embodied-to-usage ratio of 30.5/69.5 (usage being just over twice embodied) deviates slightly from the typical 20/80 ratio for servers mentioned previously in [embodied vs usage carbon](../../lifecycle#embodied-vs-usage-carbon). +The embodied-to-usage ratio of 30.5/69.5 (usage being just over twice embodied) deviates slightly from the typical 20/80 ratio for servers mentioned previously in [embodied vs usage carbon](/technology-categories/lifecycle#embodied-vs-usage-carbon). According to the PCF, usage emissions are estimated to be approximately 3,931 kg CO2e per device.  -The PCF document outlines the key assumptions used in approximating life cycle emissions of the device. With the 4-year lifetime assumed by the manufacturer, usage emissions can be [amortised](/glossary#amortisation) over the expected lifespan and allocated as approximately 983 kg CO2e per year. +The PCF document outlines the key assumptions used in approximating life cycle emissions of the device. With the 4-year lifetime assumed by the manufacturer, usage emissions can be [amortised](/resources/glossary#amortisation) over the expected lifespan and allocated as approximately 983 kg CO2e per year. -A [Typical Energy Consumption (TEC)](/glossary#typical-energy-consumption-tec) of 1,605 kWh per year for this mainstream server model has been assumed, based on modelling using the HPE Power Advisor tool and assuming 30% utilisation, 24/7 operation, and PUE values from industry sources. As discussed in the [usage emissions section](../usage#typical-energy-consumption-tec), this figure may be lower than expected if the server operates at higher average utilisation levels than those assumed in the TEC calculations, and may vary based on PUE of the data centre hosting the server infrastructure. +A [Typical Energy Consumption (TEC)](/resources/glossary#typical-energy-consumption-tec) of 1,605 kWh per year for this mainstream server model has been assumed, based on modelling using the HPE Power Advisor tool and assuming 30% utilisation, 24/7 operation, and PUE values from industry sources. As discussed in the [usage emissions section](/technology-categories/lifecycle/usage#typical-energy-consumption-tec), this figure may be lower than expected if the server operates at higher average utilisation levels than those assumed in the TEC calculations, and may vary based on PUE of the data centre hosting the server infrastructure. Consider the server operates at a higher utilisation (50%) than has been assumed by the manufacturer (30%). Assuming a linear relationship between load and utilisation (note that this does not always hold true), the [SPEC Power benchmark summary](https://www.spec.org/power_ssj2008/results/res2018q3/power_ssj2008-20180828-00850.html) for a similar mainstream HPE ProLiant DL380 Gen10 server suggests an average power draw of 237W at 50% load. This is the same example as considered in the [high-level approach discussed earlier](#example-using-a-high-level-approach-for-estimating-usage-emissions), where annual energy consumption was estimated to be approximately 2,076 kWh based on 24/7 operation and 50% load. This is higher than the quoted TEC, which is to be expected due to higher utilisation. The same example earlier considered a server operating in Europe in a data centre with a PUE of 1.6[^pue], resulting in estimated annual usage emissions of approximately 1,090 kg CO2e per device. @@ -70,11 +72,11 @@ The variability between the emissions estimate derived from the high-level appro {% include linkedHeading.html heading="Embodied Carbon Emissions" level=2 %} -Embodied carbon refers to the emissions associated with the manufacture, transportation, installation, maintenance, and end-of-life of a device. [Read more about embodied carbon emissions.](../embodied) +Embodied carbon refers to the emissions associated with the manufacture, transportation, installation, maintenance, and end-of-life of a device. [Read more about embodied carbon emissions.](/technology-categories/lifecycle/embodied) {% include linkedHeading.html heading="Example Using a High-Level Approach for Estimating Embodied Emissions" level=3 %} -For servers, embodied carbon typically accounts for around 20% of the total life cycle emissions, while usage emissions make up 80%. Using this 20/80 ratio, [amortised](/glossary#amortisation) embodied emissions can be approximated as a quarter of annual usage emissions. For example, the amortised embodied carbon for a server with annual usage emissions of 1,000 kg CO2e per year can be allocated as approximately 250 kg CO2e per year. [Read more about this high-level approach for estimating embodied emissions.](../embodied#a-high-level-approach-for-estimating-embodied-emissions) +For servers, embodied carbon typically accounts for around 20% of the total life cycle emissions, while usage emissions make up 80%. Using this 20/80 ratio, [amortised](/resources/glossary#amortisation) embodied emissions can be approximated as a quarter of annual usage emissions. For example, the amortised embodied carbon for a server with annual usage emissions of 1,000 kg CO2e per year can be allocated as approximately 250 kg CO2e per year. [Read more about this high-level approach for estimating embodied emissions.](/technology-categories/lifecycle/embodied#a-high-level-approach-for-estimating-embodied-emissions) However, this 20/80 ratio assumes a certain life span for the server. If the life span changes, the ratio will change as well. For instance, if the life span doubles from the baseline assumption, and assuming the same usage pattern, the ratio would shift to around 33/67 (33% embodied emissions and 67% usage emissions). @@ -86,9 +88,9 @@ Refer to the [Product Carbon Footprint (PCF) breakdown for an HPE ProLiant DL380 Embodied emissions (primarily from manufacturing, with small contributions from transportation and end-of-life) range from 15.4% for a performance server in Japan to 43.3% for a base server in Europe of total lifecycle emissions. Transportation emissions are estimated based on product packaging weight, shipping mode, distance travelled, and emissions intensity data, accounting for varying emissions associated with different transportation modes and routes based on the usage location. -Consider a mainstream server operating in the United States. The total product carbon footprint is approximately 5,657 kg CO2e with 1,726 kg CO2e (30.5%) attributed to embodied carbon emissions. [Amortising](/glossary#amortisation) these emissions over the expected 4-year life span, the annual embodied carbon emissions can be allocated as approximately 432 kg CO2e per year. +Consider a mainstream server operating in the United States. The total product carbon footprint is approximately 5,657 kg CO2e with 1,726 kg CO2e (30.5%) attributed to embodied carbon emissions. [Amortising](/resources/glossary#amortisation) these emissions over the expected 4-year life span, the annual embodied carbon emissions can be allocated as approximately 432 kg CO2e per year. -As discussed in the [life cycle emissions data](../../lifecycle#life-cycle-emissions-data) section, while reputable manufacturers derive PCFs using methodologies driven by international standards, their estimates come with caveats and significant variance. These values should not be treated as definitive due to uncertainties around device supply chains, operational contexts, and potential truncation errors, which can all vary depending on the specific device. +As discussed in the [life cycle emissions data](/technology-categories/lifecycle#life-cycle-emissions-data) section, while reputable manufacturers derive PCFs using methodologies driven by international standards, their estimates come with caveats and significant variance. These values should not be treated as definitive due to uncertainties around device supply chains, operational contexts, and potential truncation errors, which can all vary depending on the specific device. ## References diff --git a/pages/information/hardware/lifecycle.md b/pages/technology_categories/lifecycle/lifecycle.md similarity index 75% rename from pages/information/hardware/lifecycle.md rename to pages/technology_categories/lifecycle/lifecycle.md index 13fdfaa..644372f 100644 --- a/pages/information/hardware/lifecycle.md +++ b/pages/technology_categories/lifecycle/lifecycle.md @@ -1,14 +1,16 @@ --- -layout: category +layout: sideNavigation title: Hardware Life Cycle Emissions -permalink: /information/lifecycle +permalink: /technology-categories/lifecycle +redirect_from: + - /information/lifecycle --- # Hardware Life Cycle Emissions -The carbon footprint of an organisation's technology encompasses total emissions across the entire [product life cycle](/glossary#product-life-cycle), from raw material extraction through manufacture, transportation, usage, and disposal. +The carbon footprint of an organisation's technology encompasses total emissions across the entire [product life cycle](/resources/glossary#product-life-cycle), from raw material extraction through manufacture, transportation, usage, and disposal. -Broadly, the full life cycle view can be divided into two main groups: [embodied carbon](/glossary#embodied-carbon) and [usage carbon](/glossary#usage-carbon). +Broadly, the full life cycle view can be divided into two main groups: [embodied carbon](/resources/glossary#embodied-carbon) and [usage carbon](/resources/glossary#usage-carbon). $$ C = C_E + C_U $$ @@ -19,13 +21,13 @@ Where To measure an organisation's technology carbon footprint, it is essential to consider the full product life cycle emissions. -[Read more about embodied carbon emissions.](embodied) +[Read more about embodied carbon emissions.](/technology-categories/lifecycle/embodied) -[Read more about usage carbon emissions.](usage) +[Read more about usage carbon emissions.](/technology-categories/lifecycle/usage) {% include linkedHeading.html heading="Life Cycle Emissions Data" level=2 %} -A comprehensive [Life Cycle Assessment (LCA)](/glossary#life-cycle-assessment-lca) is necessary to fully quantify the environmental impacts across all stages of a product's life cycle. Many manufacturers choose to provide [Product Carbon Footprint (PCF)](/glossary#product-carbon-footprint-pcf) data for their products. A PCF provides an estimate of the greenhouse gas emissions attributable to a product over its full life cycle. This differs from an LCA, which considers broader environmental impacts beyond just greenhouse gas emissions (for example, water consumption, and land usage). +A comprehensive [Life Cycle Assessment (LCA)](/resources/glossary#life-cycle-assessment-lca) is necessary to fully quantify the environmental impacts across all stages of a product's life cycle. Many manufacturers choose to provide [Product Carbon Footprint (PCF)](/resources/glossary#product-carbon-footprint-pcf) data for their products. A PCF provides an estimate of the greenhouse gas emissions attributable to a product over its full life cycle. This differs from an LCA, which considers broader environmental impacts beyond just greenhouse gas emissions (for example, water consumption, and land usage). Several international standards provide guidelines for estimating environmental impacts such as [ISO 14040](https://www.iso.org/standard/37456.html), [ISO 14044](https://www.iso.org/standard/38498.html), [ISO 14067](https://www.iso.org/standard/71206.html), and [IEC TR 62921](https://webstore.iec.ch/publication/25994). These standards outline different approaches and outputs, which can be difficult to reconcile. A widely used methodology in the ICT industry to quantify greenhouse gas (GHG) emissions and estimate product carbon footprints is the [Product Attribute to Impact Algorithm (PAIA)](https://msl.mit.edu/projects/paia/main.html), although it is not strictly compliant with such standards. Methodologies can still vary between manufacturers so published figures require careful scrutiny to determine comparability. @@ -40,7 +42,7 @@ Reported emissions figures typically represent the mean value of carbon footprin {% include linkedHeading.html heading="Embodied vs Usage Carbon" level=2 %} -The total embodied carbon of an asset can be [amortised](/glossary#amortisation) over its expected lifetime, if information about its typical refresh cycle or life span is available. This enables understanding the annual carbon emissions attributed to embodied carbon. By amortising the embodied carbon over the product's lifetime, it allows a like-for-like comparison against annual usage emissions. +The total embodied carbon of an asset can be [amortised](/resources/glossary#amortisation) over its expected lifetime, if information about its typical refresh cycle or life span is available. This enables understanding the annual carbon emissions attributed to embodied carbon. By amortising the embodied carbon over the product's lifetime, it allows a like-for-like comparison against annual usage emissions. The ratio of embodied carbon to usage carbon varies across different types of device or product. In some cases, the embodied carbon associated with a product can exceed the carbon emissions from its usage, underscoring the importance of considering both when assessing the total carbon footprint. @@ -58,9 +60,9 @@ For devices such as laptops, embodied carbon typically accounts for around 80% o {% include linkedHeading.html heading=" Worked Examples" level=2 %} -[Click here to see a worked example of estimating life cycle emissions for a laptop.](../lifecycle/example/employee) +[Click here to see a worked example of estimating life cycle emissions for a laptop.](/technology-categories/lifecycle/example/employee) -[Click here to see a worked example of estimating life cycle emissions for a server.](../lifecycle/example/server) +[Click here to see a worked example of estimating life cycle emissions for a server.](/technology-categories/lifecycle/example/server) ## References diff --git a/pages/information/hardware/usage.md b/pages/technology_categories/lifecycle/usage.md similarity index 76% rename from pages/information/hardware/usage.md rename to pages/technology_categories/lifecycle/usage.md index 3d79435..4184108 100644 --- a/pages/information/hardware/usage.md +++ b/pages/technology_categories/lifecycle/usage.md @@ -1,14 +1,16 @@ --- -layout: category +layout: sideNavigation title: Usage Carbon Emissions -permalink: /information/lifecycle/usage +permalink: /technology-categories/lifecycle/usage +redirect_from: + - /information/lifecycle/usage --- # Hardware Usage Carbon Emissions -Usage emissions refer to the [greenhouse gas (GHG)](/glossary#greenhouse-gases-ghgs) emissions associated with the actual operation and use of a device or product over its lifetime. Usage carbon excludes emissions from the manufacturing, transportation, maintenance, consumables/replacements, and end-of-life disposal phases, which instead fall under [embodied carbon](/glossary#embodied-carbon). [Read more about life cycle emissions.](../lifecycle) +Usage emissions refer to the [greenhouse gas (GHG)](/resources/glossary#greenhouse-gases-ghgs) emissions associated with the actual operation and use of a device or product over its lifetime. Usage carbon excludes emissions from the manufacturing, transportation, maintenance, consumables/replacements, and end-of-life disposal phases, which instead fall under [embodied carbon](/resources/glossary#embodied-carbon). [Read more about life cycle emissions.](/technology-categories/lifecycle) -Factors influencing usage emissions include the device's energy efficiency, usage patterns, life span, and the [carbon intensity](/glossary#carbon-intensity) of the energy grid powering it. +Factors influencing usage emissions include the device's energy efficiency, usage patterns, life span, and the [carbon intensity](/resources/glossary#carbon-intensity) of the energy grid powering it. The ICT sector's electricity consumption was estimated at 1,183 TWh in 2022, accounting for 4.7 percent of the world's total electricity use in 2021[^worldbank]. With the accelerating pace of digitalisation and the increasing reliance on ICT services and infrastructure, the energy demands of the sector are projected to rise further in the coming years. Minimising usage emissions through efficiency improvements and cleaner energy sources is therefore crucial, in conjunction with reducing embodied emissions, to comprehensively reduce the greenhouse gas impact of a product or device across its complete life cycle. @@ -25,7 +27,7 @@ Where The average power draw (P) can be obtained from industry averages, manufacturer data, or actual readings, in increasing order of precision but also complexity. Organisations should balance the need for precision with practical considerations when selecting the source for P, keeping in mind the goal of effectively directing technology optimisation efforts. -Estimated energy consumption can be combined with a [carbon intensity](/glossary#carbon-intensity) factor to estimate usage emissions using the following formula: +Estimated energy consumption can be combined with a [carbon intensity](/resources/glossary#carbon-intensity) factor to estimate usage emissions using the following formula: $$ U = E \times C_I $$ @@ -34,7 +36,7 @@ Where - E is energy consumption (kWh). - CI is carbon intensity (kg CO2e per kWh). -For data centre infrastructure, such as server hardware, multiply the estimated usage emissions by the data centre's [Power Usage Effectiveness (PUE)](/glossary#power-usage-effectiveness-pue) to assess the overall climate impact beyond a sole focus on the hardware equipment itself. According to recent data, the global average PUE stood at 1.58 in 2023[^pue], indicating that for every unit of energy consumed by the IT equipment, an additional 0.58 units of energy were consumed by the data centre facility's overhead systems. +For data centre infrastructure, such as server hardware, multiply the estimated usage emissions by the data centre's [Power Usage Effectiveness (PUE)](/resources/glossary#power-usage-effectiveness-pue) to assess the overall climate impact beyond a sole focus on the hardware equipment itself. According to recent data, the global average PUE stood at 1.58 in 2023[^pue], indicating that for every unit of energy consumed by the IT equipment, an additional 0.58 units of energy were consumed by the data centre facility's overhead systems. The per device estimates can be scaled up to give a total estimate of the usage emissions across an organisation's estate **per hardware model and location**. Combined, the formula becomes: @@ -51,7 +53,7 @@ While this approach relies on key assumptions and does not account for the varia {% include linkedHeading.html heading="Using Manufacturer Data for Estimating Usage Emissions" level=2 %} -Many manufacturers choose to provide life cycle emissions data such as information relating to the [Product Carbon Footprint (PCF)](/glossary#product-carbon-footprint-pcf) of a product or device. [Read more about life cycle emissions data.](../lifecycle#life-cycle-emissions-data) +Many manufacturers choose to provide life cycle emissions data such as information relating to the [Product Carbon Footprint (PCF)](/resources/glossary#product-carbon-footprint-pcf) of a product or device. [Read more about life cycle emissions data.](/technology-categories/lifecycle#life-cycle-emissions-data) Typically, PCF documentation breaks down the total carbon footprint of a product or device into life cycle stages: manufacturing, transportation, use, and end-of-life. Reported use or usage stage emissions estimates often represent lifetime emissions; annual contributions can be estimated based on the assumed device lifespan outlined in the PCF. @@ -61,11 +63,11 @@ Usage carbon varies between different devices, as illustrated by the following e *Source: Dell Product Carbon Footprint (PCF) documents for an example model of Laptop[^laptop], Desktop[^desktop], Monitor[^monitor], and Server[^server].* -Servers can be associated with high usage emissions compared to devices like laptops or smartphones. This is largely due to higher power consumption associated with more powerful processors, large amounts of RAM, and multiple hard drives to store and process data, as well as more continuous usage patterns. Typically, server usage carbon emissions contribute the larger portion of their overall carbon footprint. In contrast, for user devices like laptops and desktops, embodied carbon forms a larger component than usage carbon. [Read more about embodied vs usage carbon emissions.](../lifecycle#embodied-vs-usage-carbon) +Servers can be associated with high usage emissions compared to devices like laptops or smartphones. This is largely due to higher power consumption associated with more powerful processors, large amounts of RAM, and multiple hard drives to store and process data, as well as more continuous usage patterns. Typically, server usage carbon emissions contribute the larger portion of their overall carbon footprint. In contrast, for user devices like laptops and desktops, embodied carbon forms a larger component than usage carbon. [Read more about embodied vs usage carbon emissions.](/technology-categories/lifecycle#embodied-vs-usage-carbon) {% include linkedHeading.html heading="Typical Energy Consumption (TEC)" level=3 %} -When providing PCF data, manufacturers should include the assumptions and inputs used in the methodology. With regards to usage emissions, one such assumption of note is the [Typical Energy Consumption (TEC)](/glossary#typical-energy-consumption-tec) figure. TEC is an estimate of a device's expected annual energy consumption based on standard usage patterns. For devices like desktops, laptops, and smartphones, TEC is based on standard household usage. For servers, the estimation of TEC relies on several assumptions, including server utilisation, operational duration, and PUE of the data centre. +When providing PCF data, manufacturers should include the assumptions and inputs used in the methodology. With regards to usage emissions, one such assumption of note is the [Typical Energy Consumption (TEC)](/resources/glossary#typical-energy-consumption-tec) figure. TEC is an estimate of a device's expected annual energy consumption based on standard usage patterns. For devices like desktops, laptops, and smartphones, TEC is based on standard household usage. For servers, the estimation of TEC relies on several assumptions, including server utilisation, operational duration, and PUE of the data centre. The TEC figure can be used in place of the estimated annual energy consumption figure approximated through the [high-level approach discussed earlier](#a-high-level-approach-for-estimating-usage-emissions). However, it's important to note that in some cases TEC may underestimate energy consumption compared to actual business usage patterns. This may stand for employee devices, where devices are typically used for longer durations (for example, 8 hours a day), or for servers operating at higher average utilisation levels than those assumed in the TEC calculations. Therefore, using TEC in subsequent estimations of emissions arising from business operations could result in lower emissions estimates than expected. @@ -73,9 +75,9 @@ To ensure accurate and comparable emissions estimates across all hardware assets {% include linkedHeading.html heading=" Worked Examples" level=2 %} -[Click here to see a worked example of estimating usage emissions for a laptop.](../lifecycle/example/employee#usage-carbon-emissions) +[Click here to see a worked example of estimating usage emissions for a laptop.](/technology-categories/lifecycle/example/employee#usage-carbon-emissions) -[Click here to see a worked example of estimating usage emissions for a server.](../lifecycle/example/server#usage-carbon-emissions) +[Click here to see a worked example of estimating usage emissions for a server.](/technology-categories/lifecycle/example/server#usage-carbon-emissions) ## References diff --git a/pages/information/networks/networks.md b/pages/technology_categories/networks.md similarity index 97% rename from pages/information/networks/networks.md rename to pages/technology_categories/networks.md index df1316a..e0306a6 100644 --- a/pages/information/networks/networks.md +++ b/pages/technology_categories/networks.md @@ -1,25 +1,27 @@ ---- -layout: category -title: Networks Emissions -permalink: /information/networks ---- -# Network Emissions -Networks can be challenging when considering carbon emissions for two reasons:- -- Networks are often shared. -- Network equipment can draw a constant amount of baseline power, regardless of its usage. - -There are several carbon estimators that calculate the carbon emissions of software my multiplying the payload size in megabytes by a number (such as 0.06) to obtain a value in KWh. This technique is based on previous research that looked at the wired backbone internet. It is a fine proxy to use initially when evaluating an IT estate to see where effort is best applied to reduce carbon emissions. It also works well as an attributional estimate, allowing the organisation to allocate responsibility of emissions to different parties. - -However, this kind of estimation should not be used in a consequential context. This means that it should not use a multiplier to determine that carbon emissions have been lowered in a system by simply reducing its page size. This is because routers are high availability devices that consume most of their power, regardless of their load. Under high load, a wired router will still consume more power, but not a great deal more than when it is idle. Therefore, halving the size of its payload will not halve the power it consumes or the carbon emissions of the system. - -This does not mean that reducing the payload of a system transmitted by a network is without merit. By reducing page sizes and the number of network calls, the pressure the system contributes to both the public internet infrastructure and the clients networking hardware is reduced. This may allow upstream dependencies and downstream dependents to run less networking infrastructure or renew their hardware with less frequency. - -{% include linkedHeading.html heading="Network Categories" level=2 %} - -When considering measuring network hardware, it is useful to split it into four categories: -- _The Operational Network_: This is the network of the organisation that owns the system. It includes routers, switches, WiFi access points and the like. This hardware can be catalogued and data sheets or power supplies can be inspected to determine the maximum power draw of each device. From this data, you can measure the baseline power draw and power draw under load of each device to calculate the carbon emissions of the network. -- _Backbone Internet_: This includes the main infrastructure of the wired internet from an internet supplier, across all intermediate wired networks, to the closest it can get to the destination client's network or device. As this network carries traffic for all users of the internet, and cannot be measured, an appropriate proxy can be used, such as multiplying page size in MB by 0.06 to gain an estimate in kWh. -- _Public Wireless Networks_: This includes all public wireless networks such as 4G and 5G that carries data from its source (likely the wired backbone internet), to the destination device or network. Again, measurements here are impossible, so estimation techniques and proxy measurements must be used. -- _The Client Network_: Finally, the destination client's network should be considered, if they are likely to have one. While client networks cannot be measured either, analytics and marketing data can provide insight into the kinds of customer who are using your system. This could be used to extrapolate an assumed network configuration for each segment of customers. For example, a B2B system aimed at small to medium sized enterprises can assume a network topology of a certain size, and multiply that up by the number of customers. Meanwhile a system that is used in homes could assume the presence of a home WiFi router and perhaps home mesh network. - +--- +layout: sideNavigation +title: Networks Emissions +permalink: /technology-categories/networks +redirect_from: + - /information/networks +--- +# Network Emissions +Networks can be challenging when considering carbon emissions for two reasons:- +- Networks are often shared. +- Network equipment can draw a constant amount of baseline power, regardless of its usage. + +There are several carbon estimators that calculate the carbon emissions of software my multiplying the payload size in megabytes by a number (such as 0.06) to obtain a value in KWh. This technique is based on previous research that looked at the wired backbone internet. It is a fine proxy to use initially when evaluating an IT estate to see where effort is best applied to reduce carbon emissions. It also works well as an attributional estimate, allowing the organisation to allocate responsibility of emissions to different parties. + +However, this kind of estimation should not be used in a consequential context. This means that it should not use a multiplier to determine that carbon emissions have been lowered in a system by simply reducing its page size. This is because routers are high availability devices that consume most of their power, regardless of their load. Under high load, a wired router will still consume more power, but not a great deal more than when it is idle. Therefore, halving the size of its payload will not halve the power it consumes or the carbon emissions of the system. + +This does not mean that reducing the payload of a system transmitted by a network is without merit. By reducing page sizes and the number of network calls, the pressure the system contributes to both the public internet infrastructure and the clients networking hardware is reduced. This may allow upstream dependencies and downstream dependents to run less networking infrastructure or renew their hardware with less frequency. + +{% include linkedHeading.html heading="Network Categories" level=2 %} + +When considering measuring network hardware, it is useful to split it into four categories: +- _The Operational Network_: This is the network of the organisation that owns the system. It includes routers, switches, WiFi access points and the like. This hardware can be catalogued and data sheets or power supplies can be inspected to determine the maximum power draw of each device. From this data, you can measure the baseline power draw and power draw under load of each device to calculate the carbon emissions of the network. +- _Backbone Internet_: This includes the main infrastructure of the wired internet from an internet supplier, across all intermediate wired networks, to the closest it can get to the destination client's network or device. As this network carries traffic for all users of the internet, and cannot be measured, an appropriate proxy can be used, such as multiplying page size in MB by 0.06 to gain an estimate in kWh. +- _Public Wireless Networks_: This includes all public wireless networks such as 4G and 5G that carries data from its source (likely the wired backbone internet), to the destination device or network. Again, measurements here are impossible, so estimation techniques and proxy measurements must be used. +- _The Client Network_: Finally, the destination client's network should be considered, if they are likely to have one. While client networks cannot be measured either, analytics and marketing data can provide insight into the kinds of customer who are using your system. This could be used to extrapolate an assumed network configuration for each segment of customers. For example, a B2B system aimed at small to medium sized enterprises can assume a network topology of a certain size, and multiply that up by the number of customers. Meanwhile a system that is used in homes could assume the presence of a home WiFi router and perhaps home mesh network. + By considering each part of the data journey from its origin system to the final device in these categories, a better estimation of the carbon emissions created by the IT system can be put together. Each category of network should have appropriate measurements and proxies used to best estimate the total carbon cost of data transmission. \ No newline at end of file diff --git a/pages/information/saas.md b/pages/technology_categories/saas.md similarity index 99% rename from pages/information/saas.md rename to pages/technology_categories/saas.md index 0dcd76e..b26c153 100644 --- a/pages/information/saas.md +++ b/pages/technology_categories/saas.md @@ -1,7 +1,9 @@ --- -layout: category +layout: sideNavigation title: Software as a Service (SaaS) -permalink: /information/saas +permalink: /technology-categories/saas +redirect_from: + - /information/saas published: false --- diff --git a/pages/technology_categories/technology_categories.md b/pages/technology_categories/technology_categories.md new file mode 100644 index 0000000..a6f47c5 --- /dev/null +++ b/pages/technology_categories/technology_categories.md @@ -0,0 +1,14 @@ +--- +layout: sideNavigation +title: Technology Categories +permalink: /technology-categories +redirect_from: + - /information +--- + +# Technology Categories + +- [Cloud Services](/technology-categories/cloud) +- [Hardware Life Cycle Emissions](/technology-categories/lifecycle) +- [Data](/technology-categories/data) +- [Networks](/technology-categories/networks) \ No newline at end of file diff --git a/tailwind.config.js b/tailwind.config.js index 82739ba..76d241c 100644 --- a/tailwind.config.js +++ b/tailwind.config.js @@ -12,6 +12,7 @@ module.exports = { './pages/*.md', './*.md', './*.html', + './**/resources.md' ], theme: { fontFamily: {