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 portfolio analytics tool.
In this webinar, we show how we can help your clients explore:
Inside-out impacts through our:
- Corporate environmental and social data
- Quantified impact of environmental pressures (GHG emissions, water consumption etc.) on:
- Human wellbeing, in monetary terms
- 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 3.5+ million verified corporate assets.
- We can also help clients understand their dependencies on different ecosystem pressures
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 some of the Q&As we received during this webinar:
Q: How do you translate GHG emissions into $ impact? Same for air pollutants etc?
We use a Driver (e.g. release of pollutants) – Outcome (e.g. change in baseline) – Impact ($ impacts) based framework. We use damage functions to convert the driver data into impacts.
For example, for air pollution we look into source type (e.g. vehicle, factory), regional weather data (e.g. wind, temperature) to understand how these pollutants get dispersed, 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 pollution.
Q: Please expand on the jargons and abbreviations used?
KBA – 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 the nearest neighbour approach and look into companies from various lenses – size of company, detailed information on business type, geography etc., to generate more accurate data than a traditional sectoral average approach. We have statistical parameters in place to measure the performance of model – for example our R squared 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 publicly disclosed sustainability reports. For private companies which do not have any publicly available sustainability report, we do have AI/Machine Learning estimation capabilities in place. We also provide offline analytical capabilities if a client wants to do analysis using internal data which is not in the 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 with regards to where a particular IRO is taken from. Our model is designed to handle common hallucinations with the 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 the ENCORE model, including flood control, pollination, soil quality regulation etc.
