As companies attempt to take sustainability to the next level and gain a more complete view of their greenhouse gas emissions, there’s a growing need to quantify results and track progress.

“If you can’t measure it, you can’t manage it,” says Autumn Stanish, associate principal analyst at Gartner, Inc. “In order to take initiatives to the next level -- particularly as organizations look to expand beyond Scope 1 and Scope 2 tracking -- there’s a need for more advanced and granular measurement tools.”

It’s no small problem. Boston Consulting Group (BCG) reports that while 85% of companies are interested in reducing their emissions, only 9% of companies measure their total emissions comprehensively. Worse, only 11% have reduced their emissions in line with their goals over the last five years.

How can companies get a better handle on their carbon footprint? How can CIOs and other IT leaders ensure that tools are in place for tracking emissions comprehensively? Although developing a framework remains a challenge, the introduction of AI and machine learning are changing the picture. “Tracking tools are becoming more refined and more useful,” Stanish says.

Emerging Tech for Measuring Emissions

Gaining insight into sustainability is becoming easier. Tools for measuring Scope 1 emissions (produced by company facilities or vehicles) and Scope 2 categories (purchased energy) have advanced considerably over the last few years. Yet, most organizations still lack an extended view of external emissions, referred to as Scope 3. These emissions extend out to the value chain and include products that have been sold.

This lack of visibility is making it difficult for organizations to assemble a strategic framework and road map. BCG found that 57% of companies that measure all three types of Scope emissions see a significant decrease in emissions versus 31% that only partially measure emissions. Adding to the challenge: A measurement system must be accurate to pay dividends. Remarkably, firms BCG surveyed admitted a 30% to 40% error rate on their measurements.

“It’s difficult to obtain a comprehensive view of a company’s footprint, says Mike Lyons, a managing director at BCG. “It’s very easy to get the carbon accounting or a boundary wrong, especially as organizations attempt to get a handle on Scope 3 emissions and understand product and technology lifecycles at a granular level.” In addition, a lack of expertise within organizations, even among environmental, social, and governance (ESG) teams, serves as an impediment.

Most of today’s tools generate numbers based on widely used carbon accounting methodologies while allowing users to view their results against specific goals and targets. For example, software tools and platforms such as Salesforce Sustainability Cloud, Spherics, Envizi, Source Intelligence and Carbon Analytics provide dashboards that extend out to Scope 3 emission categories.

Cloud providers, including AWS, Azure and Google Cloud, also offer tools that provide insights into compute cycles, energy consumption, and carbon output. For example, Google has several tools that allow organizations to track carbon emissions, including Carbon Footprint, which highlights gross carbon emissions data in reports and disclosures, visualizes carbon insights via dashboards and charts, and offers tools designed to reduce gross emissions from cloud applications and infrastructure.

Tools tracking Scope 1 and Scope 2 emissions typically plug in power and fuel consumption, using power bills, meter readings and other sources. Many rely on aggregate and average figures collected from reports, documents, audits, and user inputs. Highly distributed businesses and organizations gauging Scope 3 emissions face steeper challenges. “Things can get difficult if you are a retailer and have thousands of stores, all with different bills at different rates, and you start peering into the supply chain,” says Casey Herman, ESG Leader at PwC US. “The question becomes, how do you accumulate all the data and convert everything into carbon output?”

It's critical to understand how equipment, data centers, systems, and devices consume greenhouse gas emissions on a more granular level, Herman points out. “Many tools use conversation factors that may or may not be accurate.” Although major equipment manufacturers often share data about their products, assembling all the pieces into a complete picture can prove daunting. “Many business and IT leaders realize that they are missing lots of data or they have the carbon accounting wrong,” Lyons says. For now, “They have no way to understand what is really taking place.”

Dialing Down Emissions

BCG found that 86% of organizations continue to use spreadsheets to track carbon emissions. Overall, 53% of business and IT leaders say that they have trouble making and tracking decisions. An incomplete picture of assets and consumption is partly to blame but business leaders also complained that measurements take place too infrequently, and a lack of automation is a problem.

More advanced platforms that incorporate AI and machine learning are emerging. BCG, for example, has introduced an artificial intelligence-based software platform called CO2 AI that strives for a more complete and accurate view across the supply chain. Its software connects to ERP systems and pulls operational data about materials that go into products; the physical movements of planes, trains, and trucks; e-waste streams, and much more. It essentially creates a digital twin of the enterprise.

Meanwhile, Tata Consultancy Services (TCS) has developed a suite of solutions, including a product called TCS Clever Energy, that tap the IoT, AI, machine learning, and the cloud to help organizations decipher intricate energy performance factors, including heating and cooling, process energy optimization, demand response, intelligent tariff management, emission management and sustainability compliances with integration to sensors, meters, and assets across the organization. It runs on the Azure Cloud platform.

The goal, Lyons says, is to gain a deeper understanding of how various options, trade-offs, and decisions impact the carbon reduction process. As organizations delve deeper into the space, there’s also an opportunity to run simulations and identify cost savings and potential funding issues. “It’s possible to view what-if scenarios and understand their impact in 2030 or 2050. An organization can spot gaps, including funding, and identify steps to address them,” he says.

Of course, as firms venture into the realm of Scope 3, success typically revolves around other companies sharing data, which can present obstacles. As Lyons puts it: “Right now, there’s no expectation of sharing data among companies and, in some cases, a business may do so at its peril.” He says that in order for businesses to further advance initiatives, there’s a need to develop ecosystems that allow organizations to share data securely and sometimes anonymously across partners and supply chains.

Herman says that organizations should focus on a strategy that incorporates tools and calculators but also presses vendors to provide more detailed information about the carbon footprint of their products. While there’s a need to gather, verify and vet various methods and data to ensure that everyone and everything is in sync, the approach helps build a framework for greenhouse gas emissions reduction. Along with training and an ongoing focus on integrating data into environmental, social, and governance programs, it’s possible to adopt a framework of continual improvement and progress.

Concludes Stanish: “We’re setting moonshot goals for greenhouse gas reduction. Organizations must adopt better tools and processes to gauge progress and deliver meaningful and actionable insights.”

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