HowGood Launches Scope 3 Emissions Reporting for Global Food Brands

HowGood, an independent research company with the world’s largest ingredient sustainability database, has released its Scope 3 Emissions Reporting feature for CPG food brands and foodservice companies to meet the growing demands of ESG reporting The new feature will allow organizations in the food industr y to accurately measure and report on their Scope 3 greenhouse gas emissions, the indirect emissions produced in the upstream agricultural supply chain

HowGood’s Scope 3 Reporting draws from the robust data available within its SaaS platform Latis, the leading sustainability intelligence platform featuring impact analysis on over 33,000 ingredients across eight environmental and social impact metrics

Emerging regulations in the United States and Europe, such as those proposed by the Securities Exchange Commission, are expected to mandate that companies report on their Scope 3 emissions With nearly 87 percent of a food company ’ s GHG emissions in the Scope 3 categor y, CPG food brands and foodser vice companies can face unique challenges in gathering the needed data from their various suppliers to provide an accurate report

HowGood’s Scope 3 Reporting removes the barriers of supplier data collection by drawing emission factors from its database of over 600 vetted data sources, including Life Cycle Assessment research, peer-reviewed environmental impact studies, government and NGO publications, and aggregated commercial databases

Instead of relying on global average emissions data or the spend-based method – practices commonly used to estimate scope 3 emissions – the specificity of HowGood’s ingredient database accounts for crop- and location-based emission factors and consideration of specific growing practices As the database is designed explicitly for food companies, this approach brings a level of granularity that is essential for companies looking to not only report on their emissions, but also measure and manage their impact over time using more accurate data GN