“We, as Americans, we tend to not like overbearing regulations,” he continued. “But luckily, so many of the large companies that operate internationally follow the international guidelines, despite what’s going on in the U.S. So, fortunately, international corporations are kind of leading the efforts, and that will help with things here.” That said, “We don’t have actual legislation,” Bunger continued. “We have targets but no actual legislation.” According to Josh Mahan, managing principal at data center provider C&C Technology, official statistics are not compiled regarding national and global levels of data center energy consumption, which requires those who estimate the energy consumed to use mathematical models. The “Bottom-up” models account for installed IT devices in data centers and each one’s energy use characteristics to estimate the total energy consumption. The extrapolation-based models estimate total energy use by scaling bottom-up values based on market growth indicators such as data center investments and global IP traffic. Because these models are simpler, they typically get used to fill the gaps left behind by the more sporadic bottom-up studies. According to Mahan, three factors primarily affect the change in energy use: IT device energy efficiency has improved substantially; server virtualization software reduces the energy intensity of hosted applications by allowing multiple to run on one server; and cloud and hyperscale class data centers tend to use more efficient cooling systems to minimize their energy consumption. Still, data centers have to find a way to provide the electricity that keeps their operations running, and that’s where “power-purchase agreements” come in. Balancing purchases with usage In a power-purchase agreement, the company knows it is using so many megawatt hours per year, so it purchases them ahead of time from renewable sources – solar, wind, hydro, etc. However, what it purchases may not always match its estimated usage. When that occurs, the company will “net it out,” Bunger explained. Over the year, it purchases so much power, and it tries to match its usage with the actual hourby-hour demand. Offsets come into play when the company ends up using carbonbased fuel for things for which it has no control. “It’s along the lines of ‘how do I pay somebody else to use less carbon to offset carbon for what I use now?’” Bunger explained. “Not everybody can do that, but the market is becoming a little more formalized on what is viewed as a more legitimate offset vs. something that is fly by night.” CORE COMMUNICATIONS Environmental Impact of Data Storage Data storage impacts the environment in various ways, including: • Carbon Emissions – Data storage is responsible for 0.3 percent of overall CO2 emissions. These emissions come from energy use and operations. • E-waste – Data storage produces significant amounts of toxic electronic waste. Not only is it non-biodegradable, but it also accumulates in the environment, affecting the soil and air quality of an area. • Battery Back-ups –Data centers use batteries as a backup when power outages occur. Once disposed of, these batteries end up in landfills and begin impacting the environment since they contain toxic, corrosive and hazardous materials, such as lead, lithium, mercury and cadmium. Source: 8Billion Trees Source: Info-Tech Research Group 58% 44% 41% 41% 39% 31% Cloud Computing Artificial Intelligence (AI) or Machine Learning Data Lake/Lakehouse Application Programming Interfaces (APIs) Next-Gen Cybersecurity No-Code/Low-Code Platforms Number of Data Centers Worldwide Source: 8Billion Trees 3000 2701 487 U.S. Germany UK China Canada Australia Netherlands France Japan Russia Mexico Brazil India Poland Italy 456 443 328 287 281 264 207 172 153 150 138 136 131 2500 2000 1500 1000 500 0 Cumulative Power System ‘Embodied’ Carbon Profile Over Time Source: Schneider Electric; Energy Management Research Center; shows the cumulative power system embodied carbon broken out by equipment as a value and percentage respectively over time. (a) Broken out by equipment as value (b) Broken out by equipment as percentage 0 1 5 10 Year 15 20 1 5 10 Year 15 20 200 400 600 800 1,000 0% 20% 40% 60% 80% 100% Carbon emissions (t CO2e) MV/LV transformer (30) UPS (12) Generator (20) VRLA battery (4) LV switchgear (20) Critical power distribution (20) 130 35 107 212 51 73 130 70 107 212 51 73 130 105 107 212 51 73 130 140 214 212 51 73 130 176 214 212 51 73 21% 6% 18% 35% 8% 12% 20% 11% 17% 33% 8% 11% 19% 16% 16% 31% 7% 11% 16% 17% 26% 26% 6% 9% 15% 21% 25% 25% 6% 8% 56 CHANNELVISION | JULY - AUGUST 2023
RkJQdWJsaXNoZXIy NTg4Njc=