remove repeated statement

contents/core/sustainable_ai/sustainable_ai.qmd

@@ -73,7 +73,7 @@ AI also enables better tracking of biodiversity [@silvestro2022improving], wildl
 
 Targeted investment in AI applications for environmental sustainability, cross-sector data sharing, and model accessibility can profoundly accelerate solutions to pressing ecological issues. Emphasizing AI for social good steers innovation in cleaner directions, guiding these world-shaping technologies towards ethical and responsible development.
 
-### Case Study
+### Case Study: DeepMind's AI for AI Energy Efficiency
 
 Google's data centers are foundational to powering products like Search, Gmail, and YouTube, which are used by billions daily. However, keeping the vast server farms up and running requires substantial energy, particularly for vital cooling systems. Google continuously strives to improve efficiency across operations. Yet progress was proving difficult through traditional methods alone, considering the complex, custom dynamics involved. This challenge prompted an ML breakthrough, yielding potential savings.
 
@@ -161,7 +161,7 @@ In reality, building both energy-efficient hardware and software and combining t
 
 ## Carbon Footprint {#carbon-footprint}
 
-The massive electricity demands of data centers can lead to significant environmental externalities absent an adequate renewable power supply. Many facilities rely heavily on nonrenewable energy sources like coal and natural gas. For example, data centers are estimated to produce up to [2% of total global $\textrm{CO}_2$ emissions](https://www.independent.co.uk/climate-change/news/global-warming-data-centres-to-consume-three-times-as-much-energy-in-next-decade-experts-warn-a6830086.html) which is [closing the gap with the airline industry](https://www.computerworld.com/article/3431148/why-data-centres-are-the-new-frontier-in-the-fight-against-climate-change.html). As mentioned in previous sections, the computational demands of AI are set to increase. The emissions of this surge are threefold. First, data centers are projected to increase in size [@liu2020energy]. Secondly, emissions during training are set to increase significantly [@patterson2022carbon]. Thirdly, inference calls to these models are set to increase dramatically.
+Data centers consume massive amounts of electricity, and without access to a renewable power supply, this demand can have substantial environmental impacts. Many facilities rely heavily on nonrenewable energy sources like coal and natural gas. For example, data centers are estimated to produce up to [2% of total global $\textrm{CO}_2$ emissions](https://www.independent.co.uk/climate-change/news/global-warming-data-centres-to-consume-three-times-as-much-energy-in-next-decade-experts-warn-a6830086.html) which is [closing the gap with the airline industry](https://www.computerworld.com/article/3431148/why-data-centres-are-the-new-frontier-in-the-fight-against-climate-change.html). As mentioned in previous sections, the computational demands of AI are set to increase. The emissions of this surge are threefold. First, data centers are projected to increase in size [@liu2020energy]. Secondly, emissions during training are set to increase significantly [@patterson2022carbon]. Thirdly, inference calls to these models are set to increase dramatically.
 
 Without action, this exponential demand growth risks ratcheting up the carbon footprint of data centers further to unsustainable levels. Major providers have pledged carbon neutrality and committed funds to secure clean energy, but progress remains incremental compared to overall industry expansion plans. More radical grid decarbonization policies and renewable energy investments may prove essential to counteracting the climate impact of the coming tide of new data centers aimed at supporting the next generation of AI.
 
@@ -193,7 +193,7 @@ Estimating AI systems' carbon footprint is critical in understanding their envir
 
 The carbon footprint of AI encompasses several key elements, each contributing to the overall environmental impact. First, energy is consumed during the AI model training and operational phases. The source of this energy heavily influences the carbon emissions. Once trained, these models, depending on their application and scale, continue to consume electricity during operation. Next to energy considerations, the hardware used stresses the environment as well.
 
-The carbon footprint varies significantly based on the energy sources used. The composition of the sources providing the energy used in the grid varies widely depending on geographical region and even time in a single day! For example, in the USA, [roughly 60 percent of the total energy supply is still covered by fossil fuels](https://www.eia.gov/tools/faqs/faq.php?id=427&t=3). Nuclear and renewable energy sources cover the remaining 40 percent. These fractions are not constant throughout the day. As renewable energy production usually relies on environmental factors, such as solar radiation and pressure fields, they do not provide a constant energy source.
+The carbon footprint varies significantly based on the energy sources used. The composition of the sources providing the energy used in the grid varies widely depending on geographical region and even time in a single day. For example, in the USA, [roughly 60 percent of the total energy supply is still covered by fossil fuels](https://www.eia.gov/tools/faqs/faq.php?id=427&t=3). Nuclear and renewable energy sources cover the remaining 40 percent. These fractions are not constant throughout the day. As renewable energy production usually relies on environmental factors, such as solar radiation and pressure fields, they do not provide a constant energy source.
 
 The variability of renewable energy production has been an ongoing challenge in the widespread use of these sources. Looking at @fig-energyprod, which shows data for the European grid, we see that it is supposed to be able to produce the required amount of energy throughout the day. While solar energy peaks in the middle of the day, wind energy has two distinct peaks in the mornings and evenings. Currently, we rely on fossil and coal-based energy generation methods to supplement the lack of energy during times when renewable energy does not meet requirements.
 
@@ -226,7 +226,7 @@ Beyond water consumption and chemical risks, fab operations also depend on rare
 
 ### Water Usage and Stress {#water-usage-and-stress}
 
-Semiconductor fabrication is an incredibly water-intensive process. Based on an article from 2009, a typical 300mm silicon wafer requires 8,328 liters of water, of which 5,678 liters is ultrapure water [@cope2009pure]. Today, a typical fab can use up to [four million gallons of pure water](https://wccftech.com/tsmc-arizona-foundry-205-million-approved/). To operate one facility, TSMC's latest fab in Arizona is projected to use 8.9 million gallons daily or nearly 3 percent of the city's current water production. To put things in perspective, Intel and [Quantis](https://quantis.com/) found that over 97% of their direct water consumption is attributed to semiconductor manufacturing operations within their fabrication facilities [@cooper2011semiconductor].
+Semiconductor fabrication is an incredibly water-intensive process. Based on an article from 2009, a typical 300mm silicon wafer requires 8,328 liters of water, of which 5,678 liters is ultrapure water [@cope2009pure]. While modern fabs like those mentioned earlier can use several million gallons of pure water daily, TSMC's latest fab in Arizona is projected to consume even more---8.9 million gallons per day---amounting to nearly 3 percent of the city's current water production. To put things in perspective, Intel and [Quantis](https://quantis.com/) found that over 97% of their direct water consumption is attributed to semiconductor manufacturing operations within their fabrication facilities [@cooper2011semiconductor].
 
 This water is repeatedly used to flush away contaminants in cleaning steps and also acts as a coolant and carrier fluid in thermal oxidation, chemical deposition, and chemical mechanical planarization processes. During peak summer months, this approximates the daily water consumption of a city with a population of half a million people.