Introducing:
AI Agents
for Sustainability Reporting

AI can be an invaluable tool for managing an organisation's environmental impact, especially for complex tasks like calculating Scope 3 emissions, gap analysis, and business sustainability report creation. It can automate data collection, mapping, and verification processes to enhance efficiency and accuracy in emissions management.

Beyond data management, AI can be used for strategic functions like forecasting future emissions, setting targets, and identifying decarbonisation opportunities across the value chain with unprecedented precision.

AI can also optimise logistics by identifying the most efficient transportation routes, reducing fuel consumption and emissions. In manufacturing, AI can optimise material usage to reduce waste and embodied carbon.

Scope 3 emissions are notoriously difficult to measure due to the complex, interconnected web of indirect emissions across an organisation's value chain. AI can streamline specific, resource-intensive tasks, such as:

Mapping financial data to emissions factors: AI can use Natural Language Processing (NLP) to analyze financial data from a general ledger and classify expenditure to align with the correct emission factor categories.

Filtering expenditure data: AI can identify and filter out expenses that are not associated with GHG emissions, such as taxes or payroll, to prevent double counting.

Verifying supplier-provided data: Anomaly detection algorithms can compare supplier data against industry averages to check for plausibility and highlight discrepancies.

Protecting sensitive company and supplier data is a critical consideration at NetNada. Robust data governance frameworks must be established to ensure responsible data handling and prevent data leakage, especially when integrating diverse supplier data. AI solutions can employ measures like data anonymisation, encryption, and strict access controls to safeguard information. It is crucial to work with AI providers that have a strong data privacy framework, adhere to legal standards and data protection laws, and conduct regular security audits.

The ethical use of AI is core about what we do at NetNada essential for building trust and achieving reliable results. Key concerns include:

‍Bias: AI models can be influenced by biased or incomplete input data, which may misrepresent emissions sources or overstate reduction impacts.

Transparency and Explainability: The complexity of AI models can make it difficult for stakeholders to understand how emissions estimates are generated.

‍Data Privacy: The use of extensive supplier and partner data for reporting raises significant privacy concerns.

‍AI's Environmental Footprint: It is important to acknowledge that AI technologies themselves have a climate and environmental footprint, including energy consumption and resource demands.