GIST Impact - Data Foundations
Data category
- Environmental data
- Governance data
- Research data
- Social data
The data offers solutions for:
- Carbon footprinting
- Environmental impact analysis and insight
- Geospatial/location data
- Nature-based information: Biodiversity
- Reporting: EU Regulations
- Reporting: ISSB standards
- Reporting: Other Regulations
- Reporting: SEC climate
- Reporting: TCFD
- Temperature alignment
- Transition plan assessments
Who are the data users?
- Corporates
- Financial institutions
- Government
- Investors
- Consulting firms; ESG data management platforms
Brief description of the data offering
The GIST Impact Data Foundations Suite offers access to critical data and tools for essential sustainability insights. The suite comprises several standalone modules, which can be further broken down into constituent categories and delivered in a flexible combination tailored to specific needs.
Environmental, Social and Governance Data
1. 60+ quantitative environmental metrics across 18,000+ companies from 2016 until present year.
- Metrics span GHG emissions, air pollution, water consumption, waste generation, water and land pollution, and land use.
2. 70+ quantitative and qualitative social and governance metrics across 18,000+ companies from 2016 until present year.
- Quantitative metrics include age and gender diversity of employees.
- Qualitative metrics include business involvement with, and exposures to, themes such as deforestation, human rights abuses, political donations and supplier engagement.
One-Click Transparency Tools
Standalone module providing:
- Drill down from a given sustainability datapoint to the exact disclosure page, where data has been reported by the company.
- AI-powered explainability, including reasoning and traceability, of qualitative insights.
Data Gap Filling
Standalone module providing:
- ML-powered estimations of missing quantitative sustainability data, where this has not been disclosed by the company. This approach leverages tailored machine learning models that have been trained on a large, curated set of sustainability data, and which far outperform traditional benchmarking methods.
- AI-powered deduction of business involvement in, and exposures to, themes such as deforestation, human rights abuses, political donations and supplier engagement, where this has not been disclosed by the company. This approach leverages tailored large language models that have been trained on a curated mix of quantitative and qualitative data at company and sector levels, as well as other authoritative sources.
- Institutional-grade estimations of 60+ environmental metrics across a given company's entire upstream and downstream supply chain.
Data Validation
Standalone module providing:
- Validation of quantitative data using robust statistical methods that take into account company- and sector-wide trends.
- Validation of qualitative data using AI ensemble techniques.
- Validation of asset-specific data such as type and location using specialist AI workflows integrating geospatial and other reference data.
- Validation of real-time e.g. business news and sentiment data using AI ensemble techniques.
Data Collection
Standalone module providing:
- Purpose-built software for the automated collection of quantitative and qualitative data across environmental, social and governance metrics, with in-built confidence assessment of data veracity.
- Specialist tools for the automated collection of asset-specific data such as type and location.
- Specialist tools for the automated collection of real-time e.g. business news and sentiment data.
Where and how do you source your data?
Data is sourced directly from company disclosures, other official company information sources, and other third-party sources such as academic databases or news websites.
What is the cost for your data offering?
Please enquire for pricing.
Contacts
Mahima Sukhdev ([email protected])
