Our ESG cloud suite is coming soon... After completing the Private Beta rollout, we are now getting things ready for the public launch starting Q3 2022

ESG Risk

In modalità unsolicited, calcola il rischio ESG alla velocità della luce inserendo solamente la partita IVA di una impresa.

Grazie all'aggregazione di centinaia di fonti open-data l’algoritmo restituisce indicatori di rischio semaforici o forbici quantitative.

Il data proxy ESG a portata di click

ESG Risk Ecomate

ESG risk score of any company in seconds with our AI

ESG Risk Ecomate Step 1 ESG Risk Ecomate Step 2 ESG Risk Ecomate Step 3
Questo prodotto non è ancora disponibile al pubblico. Provalo in anteprima. Richiedi la versione Alpha o Pre-Ordina

How does it work?

The risk score is always related to the company's reference cluster: the intersection between industry code (NACE code), the number of employees, annual revenues and the territory. This is also why as a starting point we are asking a unique identification number (VAT) where the input information is verified and certified so we can be sure that we have correctly identified the company's cluster and benchmark.

ESG Risk Ecomate scoring model

Model highlights

  • Each source and indicator is linked to a macro-area (E,S or G)
  • Numerical data are transformed into qualitative indicators and vice-versa (QCA).
  • The ESG risks with benchmarking and rankings logics are directly linked to:
    a) the European strategy and objectives (EU Agenda and Framework 2030-2050)
    b) European and/or national average performance
    c) expected improvement coefficient across a defined reference timeframe
    d) historical series and likehoodness of non-financial defaults
  • The algorithm is adjusting the data quality dynamically: whenever the proxy data is not available for the highest level of detail, it automatically scales to the upper segment of data, providing the end user with a result.
  • Structural business statistics are necessary to calibrate the model according to the company’s cluster
  • Company specific data is necessary in order to precisely identify in which cluster the company is fitting
  • The risk scoring scale is dynamically adjusted according to each indicator

Technical specifications

Risk-score grade Upwards limit Downwards limit
Very low 0 16
Low 17 32
Medium/low 33 49
Medium 34 65
Medium/high 66 93
High 84 94
Very high 95 100
Product features
Technology AI
Potential coverage EU27 (18m companies)
Actual coverage Italy (2m companies)
KPI 80
Coefficient model Dynamically adjusted
EU NACE detail 4 levels
EU Class detail 4 levels (M-S-M-C)
EU Territory detail 4 levels (to municipality)

Data quality matrix

This table indicates the data reliability index score according to the type of data measured accross different sub-indexes.

Score Industry coverage GEO coverage Class coverage Transparency Authority Refresh rate
Level 4 (e.g. - B.05.10)
City 1 defined class Publicly accessible methodology, thoroughly described, well documented and with accurate biography. Official sources, EU government bodies or EU countries government bodies. Every year
Level 3 (e.g. B.05.1)
Municipality 2 defined classes Publicly accessible methodology, thoroughly described but does not include an accurate biography. EU Institutional sources (universities, research centers...) from accredited authors. Every 2 years
Level 2 (e.g. B.05)
Region 3 defined classes Publicly accessible methodology, but not thoroughly described and/or it does not include an accurate biography. Unofficial but academic EU sources but with reliable authors or projects with peer support. From 2 to 5 years
Level 1 (A,B, etc..)
Country 3 defined classes Methodology is not publicly accessible but access can be requested. White papers and/or single not-reviewed research where the author can be contacted and/or can be reconducted to other academic research. More than 10 years
1 Applicable to any/all NACE codes and/or other international industry classification Applicable to any/all EU countries and/or globally Applicable to any/all EU classes and/or other international classification Methodology is not publicly accessible and access cannot be requested. Generic but linked sources where the author is unknown and cannot be reconducted to other academic white-papers/research. More than 10 years
This product is also available through API for customised system integration projects. Contact us