Methodologies Behind Our Datasets

The Physical Risk analysis framework includes robust and science-based climate change physical hazard characterization methodology, leveraging the latest available climate change models (CMIP6) and proprietary methodologies. It covers eight key climate change physical hazards at consistent resolution, globally: coastal flood, fluvial flood, extreme heat, extreme cold, tropical cyclone, wildfire, water stress and drought, and four climate change scenarios based on the IPCC Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP) scenarios.

The Sustainable1 Physical Risk: Country and Subnational Dataset provides information on the climate physical hazard exposure of country and subnational issuers. The dataset covers both hazards arising from longer term shifts in climate patterns, namely extreme heat, extreme cold, drought and wildfire conditions, and water stress, and acute event hazards such as tropical cyclone, and coastal, fluvial and pluvial flood. The dataset provides insights into the levels, trajectories, and comparative materiality of chronic and acute climate hazards faced by 201 countries and 2,098 subnational regions under four climate change scenarios and for all decades from the 2020s-2090s.

This document walks through the science-based climate change physical hazard characterization methodology, which leverages the latest available climate change models (CMIP6) and proprietary methodologies.

The Sustainable 1 Physical Risk: Municipal Dataset provides information on the climate physical hazard exposure of US county and State issuers and their general obligation municipal bond instruments, covering both hazards arising from longer term shifts in climate patterns, namely extreme heat, extreme cold, drought, and water stress, and acute event hazards such as tropical cyclone, wildfire and coastal, fluvial and pluvial flood. The dataset provides insights into the levels, trajectories, and comparative materiality of chronic and acute climate hazards faced by all 3,135 US counties and 50 US states under four climate change scenarios and for all decades from the 2020s-2090s.

This document walks through the science-based climate change physical hazard characterization methodology, which leverages the latest available climate change models (CMIP6) and proprietary methodologies.

Climanomics® is a risk analytics platform that calculates the financial impact of climate risk on physical assets and aggregates up to the portfolio level. The analysis is designed to support a range of use cases, including risk management, sustainable investin g, strategic decision-making, as well as compliance and reporting.

Analysis spans across eight decades for four emissions scenarios (High, Medium-High, Medium and Low Climate Change) globally, enabling users to quantify the future financial Impacts of climate change by:

• Integrating terabytes of climate and socioeconomic data on climate-related hazards
• Driving econometric models with hazard inputs and business data
• Translating risk into financial terms to provide decision-relevant insights

The Nature & Biodiversity Risk Dataset methodology is a methodology for profiling nature-related risks associated with location-specific business activities, and draws heavily on the principles outlined by the Taskforce on Nature-related Financial Disclosures (TNFD) in its Beta framework. The methodology rests on two core building blocks for profiling nature-related risks: dependencies on nature and impacts on nature. These are broken down into components that help to profile nature-related risks that can be assessed using company data and global nature-related datasets.

The S&P Global Sustainable1 Nature Risk Platform helps companies and financial institutions profile nature-related risks associated with location-specific business activities. It draws heavily on the principles outlined by the Taskforce on Nature-related Financial Disclosures (TNFD), and the Nature Risk Profile methodology launched by S&P Global Sustainable1 and UNEP in January 2023, to produce a set of decision-useful metrics that quantify impact and dependency risks on nature and biodiversity.

The Nature & Biodiversity Risk GICS Sector and Country Averages dataset provides a comprehensive analysis of average nature-related impact and dependency metrics across four GICS levels – Sub-industry, Industry, Industry Group, and Sector – at country and territory, regional, and a global level.

This dataset enables companies to benchmark against their relevant GICS sector averages and model nature and biodiversity risks for private companies based on their geographic operations and GICS sector classification. It supports companies, investors, and entities in understanding and managing nature-related risks and impacts, aligning with the Taskforce on Nature-related Financial Disclosures (TNFD) framework for enhanced transparency and accountability.

Trucost Environmental Data contains quantitative information on the environmental performance of over 18,000 of the world’s largest listed companies, representing 95% of global market capitalization. The Trucost Environmental Data is associated with approximately 500 distinct industry sectors across over 100 environmental key performance indicators (KPIs). The data covers hundreds of environmental issues encompassing greenhouse gas emissions, pollution to air, land & water, waste generation, and other natural resource use.

The Trucost environmental dataset is underpinned by multiple methodologies and models, for which upcoming changes are described below:

Notice of Methodology Enhancements: Revenue-Based Environmental Intensity Factors

Notice of Methodology Enhancements: Supply Chain EEIO Model

Notice of Methodology Enhancements: Environmental Profile Calculation Engine V3

Trucost Environmental Data – Private Companies contains quantitative information on the environmental performance of private companies. The Trucost Environmental Data – Private Companies is associated with approximately 500 distinct industry sectors across over 100 environmental key performance indicators (KPIs). The data covers hundreds of environmental issues encompassing greenhouse gas emissions, pollution to air, land & water, waste generation, and other natural resource use.

The Scope 3 Greenhouse Gas (GHG) Emissions data set provides users with emissions data across the value chain, including Scope 3 upstream and downstream absolute emissions, intensities expressed in metric tons of carbon dioxide equivalent per million USD revenue (tCO2e/USD mn) and disclosure sources. Data from a variety of sources is utilzed, such as company disclosures, third-party providers, and calculated emissions data from S&P Global’s proprietary environmental profiling model, in a four-step process including: Researching the environmental reporting of companies; standardizing the reported data and correcting reporting errors; completing environmental reporting gaps; engaging with companies to verify their environmental performance profile.

The Trucost Paris Alignment dataset enables investors to track their portfolios and benchmarks against the goal of limiting global warming to below 1.5°C or 2°C from pre-industrial levels, as well as other climate change scenario outcomes. The approach taken can be described as a transition pathway assessment, which examines the adequacy of the rate of emissions reductions over time in meeting decarbonization rates required to meet a range of possible temperature outcomes.

The S&P Global Sustainable1 Net Zero Commitments Dataset offers data on the emissions reduction commitments and future emissions trajectories of companies identified as having net zero or equivalent targets and commitments: The dataset provides corporate climate targets and indicators of their comprehensiveness, adequacy, credibility, and completion, and examines long-term forecasts of Scopes 1, 2, and 3 greenhouse gas (GHG) emissions through to 2050.

The GICS Sector GHG Average Dataset presents yearly average emissions across the four GICS levels: Subindustry, Industry, Industry Group and Sector. The data solution provides intensity emission levels (tCO2e per USD million) for each business activity in the four GICS levels for the following type of emissions: Direct Emissions, First Tier Indirect, Scope 1, Scope 2, Scope 3 downstream, Scope 3 upstream, Total Indirect.

The S&P Global Sustainable1 Carbon Earnings at Risk dataset enables investors to quantify the potential impact of rising carbon pricing regulations (carbon taxes, emissions trading schemes and fossil fuel taxes) on company operating expenses and earnings. The analysis combines company reported and estimated greenhouse gas emissions data by country and sector with projected changes in carbon prices across regions, scenarios and time periods. The output of the analysis is projections for the unpriced carbon cost exposure of each company and the potential impact on company earnings.

The Trucost Sovereign Carbon Exposure dataset produces estimates for sovereign territorial GHG emissions, broken out by sector along with estimates for imported and exported GHG emissions. This data in turn underpins the analysis of GHG impact of investments in sovereign bonds, which takes into account both territorial (domestic-consumption and export-related emissions) and imported emissions. The model allows for evaluating both direct (Tier 1 emissions that occur within its territories) and indirect (Tier N emissions linked with products imported from other countries) imported emissions.

The Trucost Green Bond dataset provides robust and comprehensive environmental data and analytics that support the assessment of the potential positive impacts of projects financed by green or sustainable bonds. Public disclosures from issuers, such as green bond reports, environmental data sources (corporate social responsibility, sustainability, or environmental reports), and data published on company websites or other public sources are collected and analysed, and life cycle analysis databases are used to model the carbon/environmental impact of projects and derive their potential benefits.