By 2007, Microsoft had come under increasing pressure to commit to sustainable practices, something the company had never done before. The scrutiny—from both stakeholders and environmental organization Greenpeace—prompted a lot of internal questions.
“How do we monitor all our carbon and electricity use around the globe? What systems must we put in place to get a handle on that?” asked Robert Bernard, who’d been at Microsoft for about a decade before becoming the company’s chief sustainability officer that year.
Microsoft had loads of data, but without hard numbers detailing the company’s climate impact, it would be impossible to make demonstrable change. Bernard and his team needed to apply the best technology for collecting its data and use it to decrease negative environmental impacts across Microsoft’s entire ecosystem, including its partners and suppliers.
Using technology including artificial intelligence (AI) and machine learning (ML) to learn from its climate-related data, Microsoft became “the first company to implement internal carbon taxes and become carbon neutral at scale globally,” Bernard says.
Since then, other companies have followed suit thanks to their own data collection practices, an effort made ever more urgent as severe and deadly weather events grow increasingly detrimental to their ecosystems and the environment. Governments, companies, and individuals are finally taking meaningful steps to address this climate crisis, but change is hard and, so far, slow.
That’s why many companies are looking closely at how technology and data can not only keep them on track, but also push them forward—to a place where they can eliminate their negative impact on the planet while still making employees, shareholders, regulators, and customers happy.
AWARENESS LEADS TO CHANGE
Decarbonizing the economy won’t be cheap. Consulting firm McKinsey estimates the effort will cost $9.2 trillion per year to reach this goal by 2050. Katie Stein, chief strategy officer at professional services firm Genpact, says that technology plays a key role in helping companies solve this crisis in three ways: “One is decarbonizing the technology footprint, which includes the move to the cloud. The second is investing in greener technologies and energy sources. And the third is rethinking business and operating models to have sustainability at their core.”
Tackling these issues requires data collection. But aggregating data across global companies isn’t easy. For instance, many organizations must track multiple supply chains, while working with numerous partners that also have their own ecosystems. In addition, they operate out of multiple buildings in different countries, full of employees with a variety of commutes and business trips.
That’s why companies tend to break data into subsets, observing how each aspect affects the environment before looking at their operations as a whole. At the employee level, for example, some businesses give individuals the tools to track their environmental impact at work.
“Our employees [want us] to reduce our company’s carbon footprint, but many of them also want to participate in it,” says Kimberly Evans, EVP, head of corporate sustainability, inclusion, and social impact at financial services company Northern Trust. The company works with Climate Vault and Genpact, which have come together to help organizations both reduce and remove their carbon emissions. Climate Vault purchases the corresponding number of credits from compliance carbon markets that are then funneled into the most effective carbon-removal technologies. Since it was founded in 2021, Climate Vault has reduced roughly 820,000 metric tons of carbon emissions on behalf of companies including Northern Trust.
Genpact is using digital technologies to help Climate Vault scale, giving more organizations and people access to the solution. For Northern Trust, that could mean making it available to employees, who can use it to, say, determine the potential environmental impact of a business trip. This information empowers employees to use data in all climate-related work decisions. “We can drive behavior change through awareness,” Evans says.
THE TECHNOLOGICAL EDGE
Awareness alone, however, isn’t enough. Data must also be easily accessible and digestible to drive change. Using cloud technology, for instance, companies can store vast sums of data that can be integrated with external sources, allowing the information to drive a company’s decision-making. From there, AI and ML can uncover more advanced insights to guide practices like energy management.
Bernard followed this model at Microsoft when gathering 500 million data points per day across the 125 buildings the company operated. By running algorithms against the data, the company could diagnose about 50% of incoming information in less than 60 seconds—rather than hours or days. Over time, the accuracy of the algorithms and pattern detection improved, identifying faults and assessing climate impacts like a building’s ultimate cooling load. “By using data and machine learning, the company reduced energy consumption by over 20%,” Bernard says.
AI processes data that is too robust to examine manually. Bernard cites the 64,000-square-mile Chesapeake Bay, which has more than 3,600 species, as an example. Using AI to process information gathered from aerial images and water-flow sensors, researchers have mapped the region to better understand how climate change and water pollution affect it, how best to manage the area, and where to leave it alone—a daunting task without such technology.
EFFICIENT SUPPLY CHAINS
The emissions a company produces indirectly through its value chain, known as Scope 3 emissions, are particularly difficult to track. For instance, some vendors might not want to share data, resulting in an incomplete picture of their impact. Genpact, for example, used digital technologies to help an online fashion retailer screen suppliers to identify vendors that did not meet its financial, ethical, and operational standards, including its sustainability objectives. It found that 20% fell into medium- or high-risk categories, which helped the business act.
“We automated processes so that when someone in the business places an order to a supplier, they’re only served suppliers that meet that gold seal of approval,” Stein says. “The fashion retailer could then say to its customers, ‘All our products have been sourced responsibly.’ ”
Supply chains have changed in other ways since the pandemic started, as more people now rely on online shopping, adding more endpoints (individual consumers’ addresses) to supply chain journeys. Transportation is responsible for one-third of all U.S. carbon emissions, 50% of which is freight. “Using customer location data can help companies designate pick-up spots for products to shorten journeys and cut emissions,” Stein says. “This allows companies to set up a kiosk pick-up site in your neighborhood, enabling them to reaggregate products and ship once.”
Ultimately, we’re living in a global economy. Every company’s ecosystem is, in some way, interconnected, informing not just its own operations but how everyone—from the heads of governments to individual consumers—make environmentally sound decisions.
“We can’t manually get our way to a decarbonized economy,” Bernard says. “We’ve got to take these massive data sets and processing power [and] transfer that information across not only companies but also research institutions and everything else with APIs,” Bernard says. “[Then], the data flows seamlessly and everybody understands their climate impact.”
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