IBM announced it can help organizations accelerate the capture, calculation, and analysis of Scope 3 greenhouse gas (GHG) emissions through the integration of NLP in the IBM Envizi ESG Suite. Â The new text classification capabilities are designed to help enable a leap forward in efficiency and accuracy by helping organizations automatically ingest, organize, and manage the spend data required for emissions calculations and external disclosures.
New findings released as a preview of the IBM 2023 Sustainable Business Snapshot(1), conducted by Morning Consult on behalf of IBM, revealed that there’s a perception gap among corporate sustainability professionals, including sustainability and IT decisionmakers. 93% of respondents think their company is somewhat or very mature in using data to track sustainability progress. But only 45% say they are ready to report on Scope 3 emissions.
IBM Envizi has been helping organizations to collect, calculate, analyze and report on GHG emissions for over a decade. IBM Envizi offers coverage for all 15 Scope 3 categories, using a calculation engine to apply GHG Protocol calculation methods that are underpinned by a robust data management system. It includes automated data capture, supplier survey data capture, Scope 3 emissions factor libraries, reporting framework templates, a dedicated Scope 3 analytics dashboard, audit tools and functionality to support supplier benchmarking analysis.
The new functionality in Envizi announced today helps address one of the key challenges organizations face with Scope 3 emissions, which is the categorization of spend data drawn from financial or ERP systems.
With the addition of automated text classification using NLP, Â IBM Envizi can help streamline this process by aggregating, managing, categorizing and preparing spend data, ultimately to help produce a unified and up-to-date dataset that maps to country and industry-specific emissions factors. To complement this functionality, IBM Envizi now includes an embedded spend-based emissions factor library that is harmonized across over 180 countries, creating uniformity in Scope 3 calculations for purchased good and services. These datasets are designed to help organizations to produce their emissions calculations quickly, accurately and efficiently.
“Growing regulatory requirements and external interests are driving organizations to disclose GHG emissions, including those from Scope 3,” said Christina Shim, Vice President and Global Head, Product Management and Strategy, IBM Sustainability Software. “As requirements increase and as organizations prioritize enhancing operational insights, so does the need for robust, auditable data management, calculation, and reporting processes. IBM Envizi is now integrated with the new capabilities to help organizations seamlessly garner insights from spend data to facilitate Scope 3 emissions calculations for ESG reporting.”
For example, a manufacturer may buy computer and electronic components from various suppliers. In one entry, these components may be recorded as ‘chips,’ and in another, they may be called ‘hard drives.’Â Ultimately, they all need to be in the category of ‘computer and electronic components’ so the correct emissions factor can be applied. This application of text classification can help to automatically categorize potentially thousands of lines of spend data so teams can focus on higher value work.
IBM Envizi complements IBM’s existing portfolio of sustainability solutions and consulting expertise, including IBM Planning Analytics, IBM Supply Chain Intelligence Suite, IBM Maximo Application Suite, and IBM Turbonomic, that help organizations set, operationalize, and drive their environmental sustainability goals.
You can view a preview of the report HERE. For more information about the IBM Envizi ESG Suite, click HERE.
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- Methodology for the Morning Consult “Sustainable Business Snapshot:” This poll was conducted online by Morning Consult in August 2023 among a sample of 3,250 global business leaders, employed at companies with 1,000 or more employees. The sample includes 250 participants from 13 markets, including US, UK, UAE, Australia, Brazil, France, Germany, India, Italy, Japan, Mexico, Singapore, and Spain. Results have a margin of error of +/- 3 points.
