How sustainable is your company from 0 to 100?
Discover it in less than 2 hours, measure your company for free!
During the registration, when entering the VAT number, we connect to official databases and immediately identify the core characteristics of your company. Thanks to this automatic and real-time connection, we are able to automatically prepare calculation matrices, evaluation algorithms and customized questionnaires on the company. We only process and evaluete consistent and relevant input, providing a tailor-made self-assessment for your company.
Furthermore, with just one click, even before conducting the self-assessment, our algorithms can generate preliminary ESG risk indicators by cross-referencing specific company demographic data.
The system assesses the company by dynamically combining:
Every time an impact module is concluded, the algorithm instantly generates a customized report for the resolution of the critical issues detected, the improvements that can be implemented and the met criteria, arranged on 3 risk levels.
The degree of compliance is estimated, indicating the conformity to rules, regulations, and standards associated with each question and topic addressed.
The detailed report for each module also includes benchmarks that help understand how the company is positioned in relation to the European and Italian averages, competitors, the target market, and its size.
The company will receive over 150 different solutions related to:
Thanks to the over 200 pages of punctually categorized reports, the company will be able to use the information contained to quantify the impacts and make declarations on the intentions of sustainable development, drawing up a mini sustainability report to all intents and purposes (DNF).
Moreover, our tool will help you in drafting your impact assessment.
I questionari di ecomate
Ultima revisione dei questionari banche al 2023
Since the initial phase of the project, our rating model has been built with EU frameworks as a fundamental pillar: we can therefore strictly follow the guidelines for implementing the Taxonomy as written in the official documentation provided by the European Commission.
Our platform can already take these criteria into account during end-user clustering:
The Sustainable Finance Disclosure Regulation (SFDR) is a European regulation introduced to improve transparency in the market of sustainable investment products, prevent greenwashing and increase transparency on sustainability statements made by financial market participants.
Our PAI collector - the SFDR completion bar present in the Rating dashboard - displays the information available for PAI (Principal Adverse Impact) purposes after answering certain questionnaire modules. It is relevant for companies monitored by an investment fund under the SFDR regulation. If you currently do not have asset managers and investment funds among your shareholders, the PAI collector still serves as a valuable tool in the search for future investors.
To complete the PAI Collector, it is not necessary to respond to all questionnaire modules, but only the following ones:
Energy (E)
Environment (E)
Social responsibility (S)
Professional ethics (S)
Transparency (G)
While the average time to complete all modules is around 3 hours in order to get ESG-rated, by only submitting the above 5 modules, it should take you about 1 hour to only disclose the PAI KPIs.
This scale and grade is representing the risk level in regard of how the model is expressing its alignment and objectives.
There are 9 risk clusters, 11 grades and 10 scoring scales.
| AAA | 80-100 | VERY LOW RISK | Being fully in line with the European Union 2030 strategy and even anticipating some 2050 targets, the company can not only be an ESG leader, but also looking ahead with very positive internal and external impacts among the entire ESG materiality and a very high transparency level towards the stakeholders. |
| AA | 65-79 | ||
| A | 55-64 | LOW RISK | A resilient company capable of complying and reporting into different frameworks, understands how to deliver the right ESG strategy along with solid results. |
| BBB | 45-54 | MEDIUM/LOW RISK | The company has surely started a journey of sustainable development, which is opening the doors to new opportunities of growth. However, it needs to focus more for not being left behind. |
| BB | 35-44 | MEDIUM RISK | There is an initial awareness level towards sustainability issues, but the progress may be too slow. There is also a poor transparency towards the stakeholders. |
| B | 25-34 | MEDIUM/HIGH RISK | |
| CCC | 15-24 | HIGH RISK | While the company may comply with the minimum requirements of the national regulatory system, it is still vulnerable to one or more ESG negative events. These events can arise from an inability to meet compliance terms, achieve sustainability objectives, fulfill reporting obligations, or even face the risk of ESG litigation or ESG default. |
| CC | 6-14 | ||
| C | 0-5 | VERY HIGH RISK | |
| D | ANY | JUNK | There is a very high risk of fraud and/or the company has several negative events. |
| E | 00 | NOT APPLICABLE | Not enough information in order to rate the company or the issuer has evaluated that there is no significance in initiating a process of review. |
This table displays the product features in relation to the active subscription type
Subscription flexibility in the world's first algorithm designed for small and medium-sized enterprises
| Ecomate | Other Solutions | |
|---|---|---|
| Assessment depth | 4 weighted variables | Same for every company |
| Cluster variables | Revenues, employees, industry code and territory | None |
| Reference standard | Malta / Europe | Generic/International |
| EU Taxonomy compliant | Yes | No | SFDR compliant | Yes | No | ESRS (EFRAG) compliant | Yes | No |
| Dashboard detail | >200 KPI | <100 KPI |
| Rating ESG | Yes | No |
| Double materiality | Yes | No |
| Real-time improvement report | Yes | No |
| Educational content for SMEs | Yes | No |
| Multiple compliance | Yes, with open-standard | Yes |
| Technology | Al, crowd, open-data | Consulting approach |
| Fraud detection | Yes | No |
| API availability | Yes | No |
| Export/resell customers data outside of the EU | No | Yes |
| Rating model | Dynamic | Static |
The materiality analysis is the study of the relevance of sustainability issues within the company, and it is the process that underpins our assessment method.
We have incorporated over 70 sustainability thematic areas into our evaluation algorithms, and their weights are dynamically updated based on the characteristics of the company under evaluation. Every company is unique, with its specific areas of interest and material topics. For any of them, our dynamic materiality approach enables us to provide a precise and customized assessment.
In line with our values, we have decided to publish our technical-scientific platform architecture, regarding the ESG rating process.
In order to improve the current model, the experts of our open and decentralized technical-scientific committee contribute daily with their know-how.
At Ecomate, we put transparency first while speaking to our customers.
Our calculation system is built on a dynamic algorithm that provides companies with constantly updated information. This information is aligned with advancements in scientific research, as well as national and European legislation. In fact, our scientific committee is constantly integrating new regulations, directives, sustainable practices, and more. When new developments are introduced, companies can supplement with new findings and keep their status updated.
Our scoring model and policy allow the technical-scientific panel to fast and continuous improvement, aiming at the highest data refresh rate.
The conditions and events where the system prompts an update are for instance when a new NACE rule is included, or a new data input is added or removed.
To read more information, please check the methodological note.
Risk algorithm with multi-level fraud detection system to intercept unusual paths during qualitative and quantitative data input from self-assessment products.
On top of this, we perform cross-checks with data from the government's business registry and certain open data sources.
As our user database grows, we will integrate and incorporate machine learning for advanced functionalities.
Read methodological notes
English and cloud only
How does scientific evaluation work? Discover the open-standard ESG