Are you looking to to learn more about the breadth and depth of our ESG data and analytics? And do you want to see how we can help you and your clients with a holistic overview of both their outside-in and inside-out impacts?
Join Jenny Frings (Head of Consulting Partnerships), Mary Hunter Hieronimus (Senior Manager – Sales) and Sneha Phalke (Head of Environment) to dive into new data and insights – such as our recently launched supply chain portfolio analytics tool.
In this webinar, we show how we can help your clients explore:
• Inside-out impacts through our:
1. Corporate environmental and social data
2. Quantified impact of environmental pressures (GHG emissions, water consumption etc.) on:
a. Human wellbeing, in monetary terms
b. Nature, in terms of biodiversity footprints
• Outside-in impacts:
We can identify vulnerabilities to climate or nature risk across the value chain, by using our database of over 3+ million verified corporate assets.
In addition, we show how to use our data-driven Double Materiality scores and Impacts, Risks and Opportunities registry – supporting key use cases such as benchmarking, supply chain and portfolio analysis and insights, and regulatory and voluntary reporting.
Recapping the Q&As we received during this webinar
Q: What are the responsibilities of a survey enumerator, when there are gaps in the data?
We have step-by-step datagap filling logic in place for any data gaps. We look into historical trends, secondary estimates using other reported indicators, sectoral trends, etc., to ensure data is consistent. This ensures all the data we provide can be compared on a like-for-like basis (this avoids penalising companies with better disclosure on impact).
Q: How do you translate GHG emissions into $ impact? Same for air pollutants etc?
We use Driver (release of pollutants)- outcome (change in baseline)-impact ($ impacts) based framework. use damage functions to convert the driver data into impacts.
For example, for air pollution- we look into source type (vehicle/ factory), regional weather data to understand how these pollutants get disperse (like wind, temperature) and use dispersion modelling to understand change in concentrations. Afterwards, we use health studies to link this to change in the health conditions, say increase in cardiovascular conditions and life years lost. We then use valuation approaches like production loss vs health cost to calculate the $ impacts as a result of increased health concerns associated with air emissions.
Q: Please expand on the jargons and abbreviations used?
KBAs – Key biodiversity area
PDF – potentially disappeared fraction of species
BII – Biodiversity Intactness Index
MSA – mean species abundance
Q: Could expand on the role of AI/ML in your generation of estimated data? Could you also explain in more detail how you measure the performance of your modelled data?
We use nearest neighbour approach and look into companies from various lenses – size of company, detailed information on business type, geography etc., to generate a more accurate data than traditional sectoral average approach. We have statistical parameters in place to measure performance of the model. For example our R2 is 90% for GHG emissions
Q: Is the company coverage limited to publicly listed companies / companies with public sustainability reports?
We largely cover public companies and private companies with sustainability report – both traceable to source. for private companies which do not have any sustainability report- we do have AL/ML driven estimation capabilities in place. We also provide off the platform analytical capabilities where client wants to do analysis using internal data (which is not in public domain).
Q: How do you oversee/govern the AI-generated reports on IROs?
AI-generated IRO reports are overseen through defined governance controls, including human review from our analysts, validation checks, and final approval from our sector experts. We also provide traceability from where a particular IRO is taken from. Model is designed to handle common hallucinations with help of various safeguards.
Q: For the scores regarding social, water, biodiversity, climate, etc. you showed earlier in the DMA section, is the baseline assessed relative to peers or across all topics?
The baseline scores are assessed within each topic, not across topics, and are primarily relative to defined criteria and peer/ sector benchmarks for that specific area. As a result, the scores are not directly comparable across different topics (e.g., flooding vs. biodiversity), as each topic uses distinct metrics, scales, and risk drivers.
Q: Does your dependencies assessment include other ecosystem services such as pollination, soil quality (not just erosion), vegetation for flood/storm management etc. - metrics more important for agricultural based supply chains
Yes, we do cover dependencies around all 25 categories defined by Encore model including- flood control, pollination, soil quality regulation etc.
