Scaling Decarbonization with AI factsheet
AI offers innovative solutions to overcome the barriers to scaling decarbonization action across an organization
The knowledge and experience needed to drive decarbonization planning makes it inaccessible to non-sustainability experts. This is particularly relevant for Scope 3 or Supply Chain emissions, which are especially challenging to measure and manage. Data science, and in particular generative AI, offers a brand-new toolkit to support the collection, validation, and analysis of supplier data, and subsequently decarbonization planning.
Emissions data can be confusing and difficult for operational teams to action on
Building trust in the data
To create confidence that emissions calculations are accurate and error-free requires lengthy back-and-forth with data custodians and traceability of what data was used and why.
Explaining emission factors
Emission factor selection is a complex science as there is a vast number of options available, each having different versions, boundary conditions, and granularity.
Making emission data actionable
Extracting useful insights involves looking at diverse cuts of data, analyzing year-on-year changes in line-by-line detail, and preparing reports for multiple stakeholders and teams.
Comparing scenarios
Decarbonization planning can be frustrated by a lack of knowledge of abatement options, of relative strengths and weaknesses, as well as of relative costs required in their implementation.
Why leverage AI to scale decarbonization efforts?
Empower your teams with decision-useful insights that help turn emissions data into decarbonization action
Optimize data collection
AI-powered solutions can enhance the efficiency of emissions data collection efforts by providing real-time feedback on data quality, flagging discrepancies, helping plug data gaps, and suggesting where primary data from suppliers is most needed.
Emission factor matching
Automating the matching of business data with the most relevant emission factors, and surfacing the match confidence, enables you to focus attention on the outliers while AI takes care of the rest.
AI-generated decarbonization plans
Putting quantified decarbonization recommendations specific to your emissions hotspots and targets, tailored by industry, geography, and cost-effectiveness, into the hands of your teams, enables them to take the initiative on meeting sustainability goals.
Frequently Asked Questions
How can AI assist in determining what GHG emissions categories are material to measure?
AI can streamline the process of synthesizing information from various reporting standards and aligning it with a company’s business model. By mining and analyzing official information from diverse sources, AI can identify pertinent reporting scopes and categories, ensuring your company’s alignment with both regulatory mandates and strategic decarbonization goals.
In what ways can AI make emissions analytics more efficient and accurate?
Machine learning models can facilitate version management of emission factor databases, and provide insights for identifying measurement hotspots and material data improvements. AI can also be leveraged to generate algorithmically driven analyses and insights into emissions data, benchmarking, and progress evaluation toward reduction goals.
How can AI assist with decarbonization planning?
AI can support decarbonization planning by creating Business-as-Usual (BAU) scenarios, pinpointing abatement options, quantifying abatement potential and costs, and improving decision-making processes. By accessing extensive knowledge repositories and databases, AI organizes large volumes of data into customized scenarios and abatement strategies suited to your company's needs.
How can AI be leveraged for target setting?
Target-setting frameworks often require specific types of targets, such as absolute reduction or intensity targets, and prescribe mitigation methods tailored to a company’s circumstances, such as abating <10% through high-quality carbon credits. These frameworks can be challenging to navigate, especially when considering sector-specific guidelines like those for Forest, Land, and Agriculture (FLAG) emissions.
AI can recommend suitable targets for your company based on measurement data, sector, and target-setting frameworks. Recommendations are provided in clear and precise detail, enhancing usability and explainability.
What role can AI play in reporting and disclosure?
Carbon management software can automate data transformation for commonly used disclosure frameworks. However, since reporting requirements vary by sector, region, and evolve over time, AI-driven assistants can assist in adapting data to specific or new reporting formats. AI can also help enhance users’ understanding of different reporting requirements across frameworks.
Make decarbonization accessible to business and operations teams
Simulate and compare scenarios, understand the cost implications, and build your decarbonisation plan