Comparison

Emmi vs ISS ESG: A Clear Comparison

Emmi and ISS ESG Climate solutions both offer climate and emissions data, yet we tackle the challenge using different approaches. Recognising the implications of these differences is important for investors in selecting the right solution.

This page is deprecated. We have developed a pdf instead. If you want access, please email us at info@emmi.io

Emmi provides financed emissions data and climate risk analysis across all major public and private asset classes. Emmi combines reported emissions with machine-learning estimation models. This creates accurate and comprehensive coverage in public markets. Emmi uses similar methods to provide extensive private market coverage.

Emmi translates emissions data into climate risk based on climate and pricing scenarios. Emmi data is quantitative, provides actionable insights, and is suited to investment management processes.

Emmi was purpose-built for financed emissions and carbon risk data. We focus on the intersection of climate and investment risk. Our methodology is grounded in financial materiality. Emmi’s products are backed by our team of climate and finance experts.

Emmi has a close distribution and integration partnership with FactSet. This means a purchase via FactSet is seamless, and comes with no premium. Contract directly with FactSet, who enable data access, provides setup assistance, and product support.

Emmi

ISS ESG is well-known for its ESG ratings, proxy advisory services, and governance analytics. Its ESG offering encompasses carbon footprinting and climate risk analysis, based on company reports and intensity-based estimates for those not reporting. While ISS ESG data covers many ESG areas, its climate data faces common industry challenges. Gaps from voluntary reporting, reliance on stale data, and methodology standardisation issues can impact the data's completeness and timeliness.

ISS ESG was established as a comprehensive ESG research and governance advisory provider. Its approach to financed emissions data and climate risk analysis emerged later, incorporating these capabilities into its broader ESG as complementary services rather than as its primary focus.

ISS ESG data is available on FactSet. However, integrating it requires configuration from both ISS ESG and FactSet. Contracts and support are managed separately. Clients whose priorities align more with governance and ESG assessments than specialised climate analytics may find ISS ESG’s offerings suitable for their needs.

ISS ESG

Choosing the Right Solution

Emmi presents a industry-leading approach for investors concerned about climate risk. It emphasises investments and grants straightforward access via FactSet.

For those seeking broader ESG ratings and governance insights, ISS ESG might be an option. A broader overall offering can mean limitations in climate data transparency and quality. Even a straightforward ISS / FactSet integration means two contracts, with typically higher friction customer setup, experience and support.

What Sets Emmi Apart

Public Equities:
At Emmi, combining reported emissions with machine learning models delivers comprehensive climate data and analytics for public markets. We cover 48,000+ companies and offer the depth and granularity required for investment decisions beyond major indices. Built on objective, complete, accurate, and timely data principles, our methodology ensures that every data point is transparent and reusable.

Comparatively, ISS ESG offers fewer than 38,000 companies.
Public Fixed Income:
Emmi provides Fixed Income coverage via attribution of financed emissions and risk of the underlying entity. Our high coverage in public markets, mixed with publicly available sovereign data sets brings our fixed income coverage to over 450,000 public securities across corporates and sovereigns.

Our approach utilises transparent, opinion-free quantitative models to encompass the entire fixed-income landscape.

Comparatively, ISS ESG typically limit coverage of public fixed income to sovereign nations’ debt with limited coverage of corporate debt.
Private Equity, Private Debt, Infrastructure & Property:
Emmi’s machine learning methods for emissions estimation apply equally well in private markets. Private equity, infrastructure and private debt data are based on a private equities universe, and data-processing based on specific customer inputs. Property is estimated via a more traditional approach, but similarly uses standard property financials like square footage, or energy efficiency ratings.

Results can be generated for almost any holding with a single underlying entity. This makes private markets coverage a strategic tradeoff between coverage and data collection effort.

In contrast, traditional providers like ISS ESG rely on static data sets that often limit coverage and detail. This limited coverage misses the nuanced analysis these complex asset classes require.
Climate scenario analysis at Emmi is across 10 scenarios (3x IPCC, 6x NGFS, 1x PRI). Comparing risk data for each scenario side-by-side allows for clear understanding of the difference between the scenarios. Our methodology was designed to accommodate any number of climate scenarios. This makes generating risk profiles based on custom climate scenarios possible (carbon price, carbon budget over time).

In comparison, while ISS ESG provides many scenarios, there are no customisation capabilities beyond scenario selection.
Climate Focus & Expertise:
Emmi was purpose-built by a team of climate and finance experts to address the financial risks associated with carbon emissions. We translate emissions into financial implications. This provides actionable insights that help investors understand and manage climate risk. Our methodology is based on objective, quantitative analysis and is designed to be transparent and traceable. Our data supports both climate reporting and investment management decisions.

In contrast, ISS ESG has traditionally focused on ESG ratings and governance metrics, more recently expanding into climate specific analytics. The breadth of data offered can tick more ESG boxes, but the sprawl of data can lead to a data usability challenge. Additionally, the number of methodologies and processes required to produce it all can create a sense of subjectivity and opaqueness.
Emissions Calculation Methodology:
Emmi uses machine-learning models to fill gaps in reported emissions data. Our models are trained on over 10,000 public companies that reported their emissions. This lets us calculate emissions more accurately than intensity-based models, using financial data for the company.

For high materiality sectors that are known for systematically underreporting Scope 3 emissions, we revert to a hand-picked intensity model that gives a more accurate indication of complete Scope 3 emissions than reported numbers. This ensures Scope 3 risks are better incorporated into overall transition risk profiles.

In contrast, ISS ESG relies on static intensity factors and a universal Scope 3 estimation technique, where all emissions are modelled, rather than any selective integration of reported data occurring. This inflexible framework lacks adaptability, making it less effective at identifying misreported or missing emissions.
Risk Methodology:
At Emmi, we assess risk using economic metrics. Our approach provides objective financial outputs, including emissions measured in tonnes and potential carbon liabilities (PCL) from carbon prices being applied beyond an allocated carbon allocation. This ensures risk is applied uniformly across the holdings, which helps with traceability, and transparency. By minimising subjective assumptions, we provide investors with foundational data that they can use as a foundation to build their unique risk perspectives.

ISS ESG takes a bottom-up, categorisation-focused approach to classifying industries. Analysis is completed at an industry level, and this risk is applied uniformly across every entity in the industry. This industry-based methodology can miss nuances in financial and climate risks within an industry.
Emmi recognises that sector-specific dynamics are critical in assessing climate risk and financial impact, but believes all of these nuances can be captured by applying a carbon budget and price across all three emissions scopes. By example, low-quality iron ore that requires coal to manufacture steel will almost certainly lose market share in an electrifying steel industry [Reported Emissions Data Integration].

Anchoring our methodology to carbon price and budget generates objective, quantitative, and high-coverage data that naturally incorporates the nuances of different sectors. Additionally, sector specific budgets in climate scenarios (NGFS) go further to reflect those nuances over time. For example, the energy sector has a low carbon budget at 2030 compared to the rest of the economy because their path to decarbonisation is clearer. This results in higher transition risk in the energy sector, sooner.

Comparatively, ISS develop fixed assumptions and broad sector averages, applying them uniformly to all entities within the sector. Sectoral analysis is in-depth and can capture much nuance, but inherently reflects analytical biases. The static (multi-period) sectorial approach is prone to losing relevance in an environment of continuous policy, market and technological shifts.
Let's Highlight this Difference with a Transport Sector Example:
With Emmi, the objective analysis has observable risk trends. Our transport sector risk profiles emerge as an observable trend in our output, based on our data-driven analysis. Our consistent methodology is applied economy-wide, objectively identifying risk whenever it exists.

In contrast, ISS applies analyst-specific assumptions and uplift factors that predetermine risk trends as model inputs, the risk patterns subsequently are an artifact of the biases built into the model.
We assess chronic physical risk damages on asset values alongside our holding-level transition risk analysis. Using the London School of Economics model, we quantify the climate Value-at-Risk of financial assets in hot-house climate scenarios by comparing global GDP growth with and without climate change impacts.

Institutional portfolios benefit from geographic and asset diversification, which protects them from acute localised physical risks like floods and storms, making such risks an immaterial part of assets under management (AUM). Acute risks remain isolated geographically and aren’t systemic until well after 2050. Thus, our analysis focuses on chronic physical damages at the portfolio level, since we perform climate risk when it is materially relevant for financial institutions.

Our approach emphasises transition risk, since it has far more significant impact on diversified portfolios, where transition risk is between 1000-4000 times greater materiality than physical risk up until 2050.

In contrast, ISS ESG provides the appearance of robust, granular physical risk analysis by assessing the entire corporate exposure to physical risk based on the Headquarters location, only at 2050, based on acute-risk increases under a single future scenario.

Our uniform approach to calculating chronic physical risk is proportionate to the immaterial, and long-term impact of physical risk on diversified portfolios.