Merge pull request #72 from Green-Software-Foundation/claude/wonderful-tesla-w2te5p

Standardize spelling: British to American English

Author: gosia-f

src/content/articles/en/call-to-action-on-green-software-establishing-a-centre-of-excellence/index.md

@@ -104,7 +104,7 @@ One approach you can use is recipe cards or small activities that you can promot
 
 This is the start of your journey by pulling together a like minded team. Whether that's informally as a Community of Practice, a formal Center of Excellence, or some other vehicle which works for your organization, you now have a direction and the start of a plan.
 
-In future articles, I'll be focussing on more detailed activities and approaches that your teams can adopt, including observability, instrumentation, devops practices as well as incentives for change and the use of nudge theory. The team you pull together will be key to adopting these techniques. 
+In future articles, I'll be focusing on more detailed activities and approaches that your teams can adopt, including observability, instrumentation, devops practices as well as incentives for change and the use of nudge theory. The team you pull together will be key to adopting these techniques. 
 
  As your community comes together, please remember that we, at the Green Software Foundation, need your help. Your community will be well placed to support our open-source projects including: 
 

src/content/articles/en/green-software-advocate-series-an-interview-with-chris-lloyd-jones/index.md

@@ -15,7 +15,7 @@ I first started thinking about technology's environmental impact during the lock
 
 ## What’s the underlying philosophy?
 
-We need to find a new stability and status quo in our use of resources - stewarding the limited resources we have to support environmental sustainability - making technology circular, deciding when it's best **not** to create something new, or focussing on renewal and repair. It's about creating a new norm where we carefully consider our resource consumption, energy efficiency, and carbon emissions without compromising technological advancements. Establishing this new balance meshes with my passion for driving societal change.
+We need to find a new stability and status quo in our use of resources - stewarding the limited resources we have to support environmental sustainability - making technology circular, deciding when it's best **not** to create something new, or focusing on renewal and repair. It's about creating a new norm where we carefully consider our resource consumption, energy efficiency, and carbon emissions without compromising technological advancements. Establishing this new balance meshes with my passion for driving societal change.
 
 ## Which recent developments were catalytic in advancing a green transition in software?
 

src/content/articles/en/introducing-the-software-water-intensity-swi-project/index.md

@@ -5,7 +5,7 @@ date: 2026-06-10
 published: true
 summary: Led by the Software Standards Working Group, the Software Water Intensity (SWI) project aims to develop a consistent way to measure and reduce software's water footprint.
 mainImage: SWE-Announcement-cover.png
-mainImageAlt: Isometric illustration showing a data center stack at the centre, with water flowing from its base into a dark blue pool below. Small figures interact with the infrastructure — one examining a document, another observing the environment. Trees and natural elements surround the scene, rendered in green tones. Green Software Foundation logo in the bottom left corner.
+mainImageAlt: Isometric illustration showing a data center stack at the center, with water flowing from its base into a dark blue pool below. Small figures interact with the infrastructure — one examining a document, another observing the environment. Trees and natural elements surround the scene, rendered in green tones. Green Software Foundation logo in the bottom left corner.
 featured: false
 tags:
   - standards
@@ -22,7 +22,7 @@ Software's environmental impact has long been understood through a single lens:
 
 Measuring water impacts requires accounting for both direct and indirect sources. Data centers [contribute to water consumption in three main ways](https://www.eesi.org/articles/view/data-centers-and-water-consumption): cooling, electricity generation, and semiconductor manufacturing. Many facilities use evaporative cooling systems that remove heat from servers through water evaporation, and beyond the facility, power generation itself is water-intensive. Earlier in the supply chain, the production process of semiconductors used for compute hardware also requires water. 
 
-As a result, large data centers can have a substantial water footprint, in some cases reaching [millions of litres per day](https://arxiv.org/abs/2506.22773), depending on cooling design, location, and workload mix.
+As a result, large data centers can have a substantial water footprint, in some cases reaching [millions of liters per day](https://arxiv.org/abs/2506.22773), depending on cooling design, location, and workload mix.
 
 Yet the industry has no shared, reliable way to measure software's contribution to water consumption.
 

src/content/articles/en/syngenio-ag-joins-the-green-software-foundation/index.md

@@ -22,7 +22,7 @@ authors:
         link: "https://diyunuwablog.com/"
 ---
 
-New Green Software Foundation member [Syngenio AG](http://www.syngenio.de) is an IT consulting and software development house based in Germany with subsidiaries in seven cities. Since 2001, Syngenio has focused on bringing IT and business departments together, specialising in digital payments, next generation banking, the Internet of Things (IoT) and green software. Syngenio continuously improves its climate impact since 2019 and is climate neutral since 2021.
+New Green Software Foundation member [Syngenio AG](http://www.syngenio.de) is an IT consulting and software development house based in Germany with subsidiaries in seven cities. Since 2001, Syngenio has focused on bringing IT and business departments together, specializing in digital payments, next generation banking, the Internet of Things (IoT) and green software. Syngenio continuously improves its climate impact since 2019 and is climate neutral since 2021.
 
 In this interview with Jürgen Funke, a technology enthusiast and Board member at Syngenio, we discuss Syngenio’s plans with the Green Software Foundation and why green software is a key consideration for the company and its people. 
 

src/content/articles/en/texas-state-university-deems-gsf-sci-an-effective-metric-to-evaluate-the-carbon-i/index.md

@@ -16,7 +16,7 @@ Foundation models are large software applications for artificial intelligence (A
 
 Added capabilities have led to an exponential increase in model size and complexity in the past ten years. Increased size and complexity automatically bring more computing. [OpenAI](https://openai.com/blog/ai-and-compute/) reported that the required computing to train state-of-the-art deep learning models had increased 300,000-fold since 2012. For example, training the GPT-3 model consumed approximately 190,000 kWh of energy and produced 85,000 kg of CO2.
 
-But what about after the training phase, when a model is deployed? This is where we have been facing an information gap. Previous studies had primarily focussed on energy use during the training phase of the model. The TSU study is bridging the gap by taking on the inference stage, where the model is put into action on live data to produce actionable output.
+But what about after the training phase, when a model is deployed? This is where we have been facing an information gap. Previous studies had primarily focused on energy use during the training phase of the model. The TSU study is bridging the gap by taking on the inference stage, where the model is put into action on live data to produce actionable output.
 
 ## Natural Language Processing (NLP) focus of study
 

src/content/articles/en/tokens-and-greens-measuring-the-impacts-of-agentic-ai/index.md

@@ -38,7 +38,7 @@ Read those numbers again. The same business outcome that used to cost one LLM ca
 
 This is what we call the agentic multiplier. Planning loops, tool calls, reflection, retries, multi-agent debate, context that compounds with every turn—each is defensible on its own. When stacked together, they turn a 2,000-token interaction into a 2,000,000-token workflow.
 
-Every one of those tokens requires GPU time. Every GPU-second is energy drawn from a grid, carbon emitted somewhere along the way, and water consumed to cool the silicon that produced it. **The token is the unit we bill on. The kilowatt-hour, the gram of CO₂e, and the litre of water are the units that cost the planet.** 
+Every one of those tokens requires GPU time. Every GPU-second is energy drawn from a grid, carbon emitted somewhere along the way, and water consumed to cool the silicon that produced it. **The token is the unit we bill on. The kilowatt-hour, the gram of CO₂e, and the liter of water are the units that cost the planet.** 
 
 ## **Why Energy, Carbon, and Water All Matter**
 
@@ -52,7 +52,7 @@ These three resource dimensions are connected, but distinct, and treating any of
 
 **Carbon is the climate consequence.** A kilowatt-hour from a solar-heavy grid at midday carries a fraction of the carbon of one drawn from coal at night. Where and when an agent runs matters as much as how much energy it consumes. Embodied carbon, the emissions baked into the hardware, is rising rapidly as the industry expands AI data centre capacity.
 
-**Water is the local impact, and its use by AI is increasingly drawing community attention.** Agentic workloads, with their long execution times and sustained GPU utilization, generate heat that needs cooling. In response, many data centers rely on evaporative cooling, which can consume millions of litres of freshwater per facility per day. Water impact is also place-specific—a litre consumed in a stressed watershed is not interchangeable with a litre elsewhere. The available alternatives are more energy-intensive and increase the carbon cost instead.
+**Water is the local impact, and its use by AI is increasingly drawing community attention.** Agentic workloads, with their long execution times and sustained GPU utilization, generate heat that needs cooling. In response, many data centers rely on evaporative cooling, which can consume millions of liters of freshwater per facility per day. Water impact is also place-specific—a liter consumed in a stressed watershed is not interchangeable with a liter elsewhere. The available alternatives are more energy-intensive and increase the carbon cost instead.
 
 You cannot optimize for one resource and assume the other two will follow. A carbon-aware schedule that shifts workloads to a renewable-heavy region in a drought zone can lower emissions while worsening water stress. An efficiency gain that reduces tokens but routes inference to newer hardware with high embodied emissions can lower energy and raise total lifecycle carbon. **Green software practice has to hold all three in view.**
 

src/content/research/en/sci-csrd-compliance.md

@@ -114,7 +114,7 @@ Despite the simplifications, EFRAG has reaffirmed that certain elements remain m
 | **E1-9** | Financial Effects | Quantifying how future carbon pricing affects the operational cost of software |
 
 > [!NOTE]
-> While these core requirements (E1-1 through E1-9) remain in effect, the 2025 Omnibus I updates have simplified the specific metrics required under each, prioritising 'gross' reporting and financial control boundaries over previous, more complex additive approaches.
+> While these core requirements (E1-1 through E1-9) remain in effect, the 2025 Omnibus I updates have simplified the specific metrics required under each, prioritizing 'gross' reporting and financial control boundaries over previous, more complex additive approaches.
 
 ### 1.3 The Software Dimension
 

src/content/stories/sci-for-ai.md

@@ -217,10 +217,10 @@ cta:
   ctaHref: "/standards/sci-ai/"
 ---
 
-AI workloads were exploding across the software industry, but nobody had a standardized way to measure their carbon footprint. Training GPT-3 alone produced approximately 500 tonnes of CO₂ emissions — equivalent to a petrol car driving roughly 2 million kilometres — and close to 1,300 megawatt hours of electricity. And that was just one model from 2020.
+AI workloads were exploding across the software industry, but nobody had a standardized way to measure their carbon footprint. Training GPT-3 alone produced approximately 500 tonnes of CO₂ emissions — equivalent to a petrol car driving roughly 2 million kilometers — and close to 1,300 megawatt hours of electricity. And that was just one model from 2020.
 
 The problem was not that organizations were unaware. Many GSF member organizations had sustainability commitments. But multi-million pound infrastructure decisions were being made without understanding relative carbon efficiency. Existing measurement approaches each captured only a slice of the picture: some covered only inference, others only training, and none addressed the full AI lifecycle.
 
-The fragmentation was paralysing. Multiple metrics existed — the Green AI Index, EcoLogits, EnergyScore — but none were consensus-built, none had a pathway to policy or certification, and none incentivized the full range of engineering optimizations that could actually reduce emissions. The question was whether the industry could agree on a consistent, trustworthy way to measure that footprint so they could systematically reduce it.
+The fragmentation was paralyzing. Multiple metrics existed — the Green AI Index, EcoLogits, EnergyScore — but none were consensus-built, none had a pathway to policy or certification, and none incentivized the full range of engineering optimizations that could actually reduce emissions. The question was whether the industry could agree on a consistent, trustworthy way to measure that footprint so they could systematically reduce it.
 
 > "While efforts can make AI more environmentally responsible, they will still leave a footprint behind." — Chris McClean, Global Lead for Digital Ethics, Avanade