GIST Impact - Climate Risk Suite
Data category
- Environmental data
- Governance data
- Ratings
- Research data
- Social data
- Climate data
The data offers solutions for:
- Carbon footprinting
- Climate scenario analysis
- Environmental impact analysis and insight
- Geospatial/location data
- Investment decisions and portfolio insight
- Nature-based information
- Nature-based information: Biodiversity
- Nature-based information: Land use
- Nature-based information: Oceans
- Nature-based information: Water
- Physical risk
- Reporting: EU Regulations
- Reporting: ISSB standards
- Reporting: Other Regulations
- Reporting: SEC climate
- Reporting: TCFD
- Reporting: TNFD
- Transition plan assessments
- Basel II/III/IV; EBA; PCAF
Who are the data users?
- Corporates
- Financial institutions
- Investors
- Consulting firms; ESG data management platforms
Brief description of the data offering
The GIST Impact Climate Risk Suite offers a comprehensive assessment of climate-related risks at asset, company and portfolio levels, as well as across the upstream and downstream value chains, where required.
The suite comprises the following modules, which can be chosen separately and widely customised by the end user:
1. Risk Types
A three-fold assessment of current and emerging risks from portfolio to asset levels, including:
- Physical Risks - comprising 18 geospatial risk layers such as Water Stress, Riverine and Coastal Flood, Cyclones and Wildfires.
- Transition Risks - comprising 12 risk layers such as Policy Stringency, Consumer Preference and Environmental Policy Vulnerability.
- Supply Chain Risks - comprising 5 risk layers such as Geopolitical Risk and Inventory Shortage Risk.
Data sourced from institutional-grade databases and enhanced with proprietary algorithms where appropriate.
Analysis of risk across individual layers and risk types can be aggregated into compositive scores, with additional refinements (e.g. country-level vulnerability to physical risks) applied where appropriate.
2. Analytical Techniques
- Scenario Analysis - 12 pre-built scenarios from authoritative sources (e.g. NGFS, IPCC) as well as the ability to create custom scenarios, with full flexibility over constituent parameters, weighting and time horizon.
- Stress Testing - top-down and bottom-up stress testing approaches that integrate supervised and unsupervised machine learning methods for enhanced Monte Carlo simulations and sensitivity analyses.
3. Risk Parameters
- Key Risk Metrics - mapping of climate-related exposure to critical metrics such as PD, RWA and CVaR as well as capital and liquidity ratios of choice.
- Credit Risk Migration - credit risk assessments that show the impact of climate-related exposures on proprietary and external credit ratings.
4. Risk Integration
- Transmission Channels - translation of physical, transition and supply chain risk drivers into measurable effects across key financial risk categories — including credit, market, liquidity, operational, counterparty and capital risks.
- Regulatory Alignment - mapping to key standards and regulations such as the latest Basel III core principles, EBA Pillar 3 disclosure requirements as well as others such as TCFD, PCAF and IFRS S1/S2.
- Due Diligence - data-driven analytics that reveal risks and opportunities across the asset lifecycle and inform critical processes such as underwriting, capital allocation, and sustainability strategies.
5. Risk Strategy
- Risk Appetite - translation of complex climate-related risks into data-driven exposure limits at team-specific and enterprise-wide levels.
- Target-Setting - establishment of goals and pathways relating to e.g. PCAF-aligned financed emissions exposure and decarbonisation, wider sustainability performance and specific nature and biodiversity metrics.
- Dynamic Accounting - integration of climate-related risks into core financial analysis such as balance sheet projections, income forecasts and cash flow models.
6. Data Extraction
- Ingestion and processing of data from existing client systems using diverse methods (OCR + AI; API; Data Feeds).
- Integration of GIST Impact data - from carbon emissions data to performance across hundreds of environmental, social and governance metrics, to science-backed nature and biodiversity insights.
7. Visualisation and Reporting
- Customisable dashboards purpose-built for specific teams, with real-time insights and housed on-premise where required.
- Automated reporting for internal and external audiences, tailored to specific regulatory requirements where required.
Where and how do you source your data?
As a baseline, data is sourced from corporate disclosures. Where disclosures are missing, ML-powered estimation models provide accurate gap-fill across all relevant metrics.
At present, GIST Impact collects data on 18,000+ corporates worldwide. Data is available from 2016 until present.
Asset-level data is compiled from external and internal sources and validated for accuracy by GIST Impact (location; asset type).
Derived analytics (such as nature and biodiversity insights) leverage a compilation of internally curated methodologies and partner-led datasets. Sources for the latter include IBAT, Forest IQ and London's Natural History Museum.
For the analysis of proprietary investment and lending portfolios, as well as for the enhancement of existing risk strategies, GIST Impact is able to offer a data extraction module that offers integration and processing capabilities as well as real-time insights. Further information is outlined data offering description, above.
What is the cost for your data offering?
Please enquire for pricing.
