The global commitment to achieving net-zero emissions, as outlined in The Paris Agreement, places pressure on corporations, governments, and consumers alike to ensure accurate and consistent emissions reporting, and ultimately emissions reductions. As businesses strive to meet these sustainability goals, the application of artificial intelligence (AI) and analytics presents a transformative opportunity to address the complexities and inconsistencies that often plague emissions data.
The Challenges of Emissions Reporting
The accuracy of emissions reporting is a significant challenge for organizations worldwide, particularly in two key areas: regulatory compliance and identifying business opportunities tied to emissions data.
Governments are tightening emissions reporting regulations, as a result businesses must not only track and report their current emissions but also demonstrate a clear pathway to reductions. To comply, companies must develop a comprehensive understanding of their emissions footprint and pinpoint where improvements in operations or supply chains can lead to lower emissions. This ongoing process requires precise monitoring to track progress and verify reductions over time.
Beyond compliance, businesses also face the challenge of uncovering financial opportunities within emissions data. The U.S. Inflation Reduction Act, for instance, offers tax credits for carbon sequestration and other sustainability initiatives, providing substantial incentives for businesses that can effectively track, manage and analyze data for sequestered carbon.
Artificial intelligence is uniquely positioned to address both of these challenges. Through the analysis of large volumes of emissions data, AI can identify opportunities for emissions reduction across various aspects of a company’s operations, while also highlighting potential financial opportunities for sustainability investments.
The Importance of Data Consistency
While AI’s potential is vast, its success hinges on the quality of the underlying data. For AI to provide accurate insights into emissions data, that data must be organized, consistent, and standardized. This applies not just to the emissions data itself, but also to the associated metadata—such as units of measurement, emissions calculation formulas, and emissions categories.
Additionally, the data must accurately represent the organization’s structure, including factors such as geographical locations, facilities, and product life cycles. This information matters because the final number that an emissions calculation delivers is not actionable unless you also have a clear sense of how, where, and when those emissions were produced.
Without consistency in emissions data, AI models risk producing inaccurate or unreliable results. The challenges of managing inconsistent data are well-documented, particularly in the context of greenhouse gas reporting. While there are recognized standards for greenhouse gas data, the lack of a universal data model means that businesses often face difficulties in aligning data formats, particularly when dealing with scope 3 emissions which originate from points of activity which lie outside the direct monitoring or control of the business in question.
Adopting Standardized Data Models for Emissions
A practical solution to these challenges lies in the adoption of standardized data models, such as the Open Footprint Data Model which ensures consistency in critical data elements, including naming conventions, units of measurement, and relationships between data points, all of which are essential for AI’s success in emissions data analysis. With a standardized framework, businesses can bypass the need for manual data conversion, allowing them to apply AI more quickly and effectively.
Real-World Use Cases for AI-Driven Emissions Insights
Take, for example, a large multinational corporation that seeks analyze emissions data from its extensive supply chain. The number of datapoints involved, and the need to understand each of them in their proper context, makes this an ideal opportunity to apply AI. However, to identify which suppliers are effectively reducing emissions, the company needs access to consistent emissions data from each supplier. Without a standardized approach to data collection, the company would face the cumbersome task of manually converting and aligning data before AI could be successfully leveraged.
In another example, a company aiming to assess its scope 1 and 2 emissions—that is, on-site emissions and energy usage-related emissions—across multiple business units would need comparable data from different segments. To identify the areas with the greatest potential for emissions reductions, the data must be consistent in terms of definitions, units of measure, and calculation methods. A standardized data model simplifies this process, making the data ready for AI analysis and minimizing manual intervention.
Why Standardization Matters for Business Success
The business case for adopting a standardized emissions data model is compelling. Standardization not only reduces the complexity of gathering and managing emissions data across a company’s operations and supply chain but also enhances the effectiveness of AI. With a standardized approach, businesses can unlock deeper insights from their emissions data, which in turn drives sustainability initiatives and identifies new business opportunities.
For companies aiming to harness the full potential of their emissions data, standardization is a foundational step. By creating a consistent data framework, organizations can overcome the barriers that prevent AI from delivering actionable insights, leading to more effective emissions reductions, better compliance with regulations, and improved financial performance.
