Financed Emissions (FE) are the Greenhouse Gas (GHG) emissions attributable to financial institutions due to their involvement in providing capital and/or financing to GHG-emitting companies. FE figure in the GHG Protocol’s downstream scope 3 breakdown as category 15 which covers corporate investments. They must therefore be disclosed to comply with major regulations (i.e., the Corporate Sustainability Reporting Directive) and standards (i.e., International Sustainability Standards Board).
Why Measure FE?
Following the notion that what doesn’t get measured doesn’t get managed, FE provide financial institutions with a critical indicator to understand, monitor, and steer their activity to enable the economy’s decarbonization. Accurately and transparently measuring FE is thus of strategic importance as they notably relate to financial institutions’ transition risk exposure. As a result, FE reduction is incentivized by way of targets to goalpost financial institutions’ participation in the economy’s transition.
This said, measuring FE is a novel practice that was established by the Partnership for Carbon Accounting Financials (PCAF) in 2020, when a global coalition of over 500 signatories launched the first methodology and global standard. They defined FE formulas for six different financial instruments: listed equity and corporate bonds, business loans and unlisted equity, project finance, commercial real estate, mortgages, motor vehicle loans, and sovereign debt.
Common Pain Points When Measuring FE
Today, finance and compliance teams commonly own responsibility for FE disclosure. To accomplish this, they rely on ESG analytics teams who typically encounter four major pain points:
- Source data discovery and access
- Data wrangling at scale
- Data quality monitoring
- Production of decision-useful stories
Before diving into how Dataiku can help overcome these, we recommend consulting our recent article that presents our approach to building an ESG data foundation to facilitate strategic environmental sustainability projects in the context of regulatory readiness.
Now, let us provide a working example of how Dataiku can help ESG analytics teams measure FE by focusing on the business loans asset class.
How Can Dataiku Help ESG Analytics Teams?
1. Facilitate data access with the data catalog and tag systems.
Dataiku helps teams unlock data access thanks to its inherent extensibility to our users’ data infrastructure ecosystem and its partnership network. Dataiku’s data catalog and tag systems eases teams’ collaboration by allowing them to safely share and discover golden copies of data that are ready to use towards accurate FE measurement.
Our FE demo starts by pooling different datasets, which we have purposefully simplified: We start with an extract snapshot of the bank’s business loans, metadata on their clients’ profiles, an extract on reported client emissions, an emission factor library extract that covers sectors and country data, and an editable dataset of the bank’s FE targets.
2. Streamline data wrangling with visual recipes.
The next challenge for ESG analytics teams is to streamline the cleaning and standardization of these different data sources to make them ready for use. Dataiku’s visual recipes, embedded workflow logs, and optional generation of natural language explanations allow for major time efficiency gains. After being cleaned with the prepare recipe, the different data sources are aggregated with join recipes that both allow coders and non-coders to fast-forward repetitive preparation steps and focus on more complex stages.
In this case, applying the right PCAF formula depending on data availability, and corresponding data quality score, is made easy thanks to the visual “if, then, else” formula. These can be fully customized to the user’s formula of choice. Furthermore, the analysis’ lens can be adjusted at will thanks to a “group by” recipe that lets users leverage conventional, pre-made aggregations or code their own. At any point, coders could choose to code their own recipe step in the notebook of their choice. In this way, our visual recipes streamline the aggregation of FE in two important ways: First, in absolute terms at the group or portfolio level and, second, by the Weighted Average Carbon Intensity, which gives a relative understanding to revenues within the portfolio.
Beyond our demo project, we draw the reader’s attention to two recent additions to Dataiku that will benefit analysts' experience. AI Prepare harnesses GenAI to turn user prompts into fully editable prepare recipes and flow zones, while Visual Edit partially automates data reconciliation to speed up entity resolution that often arises when joining datasets from different sources.
3. Ensure data quality and traceability with visual tables.
Core to analytics’ responsibility is to ensure data quality and accuracy throughout its transformation steps. Given the volume, diversity, and frequency of these sources’ updates, there are high stakes of time efficiency linked to reliably and consistently streamlining this stage. This is a notorious challenge in ESG analytics given these new sources of data’s incompleteness and varying degrees of accuracy. Teams usually address this by pulling different data sources together to build reliable proxies and imputation methods. These different strands of work further complexify quality control tracking.
This is where Dataiku’s data quality dashboards are a game-changer for analytics teams. With the click of a button, teams can apply ready-made or customized data quality checks that they can then automate with scenarios and centralize their visual review in a dedicated dashboard. Furthermore, trigger warnings can be set to directly inform an assigned data steward on the event of a dataset or recipe producing data quality below a set threshold. This feature, alongside embedded logs mentioned previously, frees analytics teams of otherwise tedious, often manual tasks of data checks.
4. Enhance data interpretability with compelling dashboards.
ESG analytics teams are also tasked with bringing this FE data to life to support finance teams’ decision-making and fulfill their compliance duties. Dataiku’s visualizations, dashboards, and recent Dataiku Stories feature lets users build fully customizable presentations of their projects.
In the context of our project, we consolidated resulting FE measurements in a threefold dashboard.
- We start by providing an overview of all business loans FEs alongside the bank’s overall performance against its targets.
- The second slide allows users to drill down into a selected portfolio’s carbon intensity for a more granular understanding of FE drivers and their corresponding PCAF quality score.
- The third slide lets the reader refine their analysis down to a client-level to understand their contribution to the bank’s FE in relation to their loan’s outstanding amount and term.
Conclusion
Dataiku’s advanced analytics capabilities — from its visual, reusable preparation pipelines to its data quality monitoring dashboards and datalogs — streamlines ESG analytics teams’ work in a traceable and auditable way. Not only does this enhance their deliverables’ accuracy, it can help them focus their attention and apply their expertise to tackling the more pressing, complex issues of applying analytics to drive the bank’s actual decarbonization.