The integration of AI into business operations is no longer a question of if, but how. Companies across industries are increasingly recognising the potential of AI to deliver significant business benefits. Applying AI to emissions data can unlock valuable insights that help organisations reduce their environmental impact and capitalise on emerging opportunities in the sustainability space.
Navigating the Challenges of Emissions Data
Organisations face two primary challenges when managing emissions data. The first is regulatory compliance. Governments worldwide are implementing stricter emissions reporting requirements, and businesses must demonstrate ongoing reductions.
To meet these demands, companies need a clear understanding of their current emissions footprint and the areas within their operations or supply chain where changes can lead to reductions. Moreover, they must implement these changes and track their progress over time.
The second challenge involves identifying business opportunities linked to emissions data. For example, the US’ Inflation Reduction Act offers investment credits for initiatives like carbon sequestration and storage, presenting significant financial incentives for companies that can efficiently manage and analyse their emissions data.
AI plays a pivotal role in addressing both challenges. By processing vast emissions datasets, AI can pinpoint areas within a company’s operations that offer the greatest potential for emissions reduction. It can also identify investment opportunities that align with sustainability initiatives. However, the effectiveness of AI depends on the quality and consistency of the emissions data.
The Role of Data Consistency in AI-Driven Insights
Before AI can be applied effectively to emissions data, the data must be well-organised and standardised. Consistency is critical, not only in the data itself but also in the associated metadata—such as units of measurement, emissions calculation formulas, and categories of emissions components. Additionally, emissions data must align with the organisational structure, covering factors like location, facility, equipment, and product life cycles.
Inconsistent data hinders the performance of AI models, leading to unreliable results. As Robert Seltzer highlights in his article Ensuring Data Consistency and Standardisation in AI Systems, overcoming challenges like diverse data sources, inconsistent data models, and a lack of standardisation protocols is essential for improving AI performance. When applied to emissions data, these challenges become even more pronounced. While greenhouse gas (GHG) data standards exist, the absence of a ubiquitous data model means that businesses often struggle with inconsistent data formats, especially when managing scope 3 emissions data from suppliers.
Implementing Standardised Data Models
One solution is the adoption of standardised data models, such as the Open Footprint Data Model.
This model ensures consistency in data naming, units of measurement, and relationships between data elements, all of which are essential for applying AI effectively to emissions data. By standardising data, companies can eliminate the need for manual conversion processes, accelerating the time to value for AI-driven insights.
Use Cases for AI in Emissions Data
Consider the example of a large multinational corporation with an extensive supply chain. This company wants to use AI to analyse the emissions profiles of its suppliers and identify which suppliers are effectively reducing emissions over time.
For AI to deliver meaningful insights, the emissions data from each supplier must be consistent in terms of definitions, metadata, and units of measure. Without a standardised approach, companies relying on spreadsheets would face labour-intensive data conversion efforts before AI could even be applied.
In another scenario, a company seeks to evaluate its scope 1 and 2 emissions across various business units, identifying areas where capital investments could yield the greatest emissions reductions.
Here, it’s essential that emissions data from different parts of the business be comparable, requiring consistent data definitions, units of measure, and calculation methods. As with the previous example, the use of a standard data model simplifies this process, making the data AI-ready and reducing the need for manual intervention.
The Business Case for a Standard Emissions Data Model
Adopting a standard emissions data model offers numerous advantages. Not only does it reduce the complexity of collecting and managing data from across an organisation and its supply chain, but it also facilitates the application of AI, enabling advanced analytics that drive emissions reductions and uncover new business opportunities.
For companies seeking to maximise the value of their emissions data, standardisation is a critical first step.
By embracing a standardised data framework, businesses can overcome the barriers that prevent AI from unlocking the full potential of their emissions data, ultimately leading to more sustainable practices and improved financial outcomes.
- Data & AI
