How can reliable estimates of greenhouse gas emissions be obtained? The answer is not so straightforward, especially for SMEs, which often struggle to find accurate data and appropriate tools.
Reporting emissions without a solid method means exposing oneself to inaccurate estimates, risking transparency issues and losing funding opportunities. This is why modefinance has developed GHG Nowcasting, an innovative solution that enables real-time estimation of companies’ Scope 1, 2, and 3 emissions. Based on the analysis of purchase invoices and artificial intelligence, GHG Nowcasting allows for precise and simple monitoring and management of environmental impact.
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GHG Nowcasting validation process
The dataset used to validate modefinance’s GHG Nowcasting was built by combining data from different sources to ensure a reliable comparison between estimated emissions and actual values. The process is based on the comparison between simulated data and real emissions data obtained from various sources, including Refinitiv, Transpareens, the DNF Observatory, and internal data.
The sample of companies considered for estimating emissions data based on invoice information consists of approximately 60,000 Italian companies, categorized by size, sector, and location. The sample’s representativeness faithfully reflects the population of Italian businesses, classified into eight categories based on revenue value, following and expanding the European Union’s classification rules for micro, small, and medium-sized enterprises.
The selected companies are distributed across all Italian regions, with a particular concentration in Lombardy. The most represented sectors include wholesale and retail trade, construction, and services.
The graph below compares two groups of companies:
- The sample used to estimate emissions (represented by orange bars)
- The sample of companies for which actual emissions data is available (indicated by blue bars).
Most of the actual data pertains to large enterprises, which are legally required to monitor and report their emissions. This situation highlights the usefulness of GHG Nowcasting, which can help fill data gaps by providing reliable estimates even for companies without official data.
The consistency of estimates with actual data was verified using two methods:
- Numerical comparison, conducted for groups of companies with a sufficient number of real data points
- Sectoral analysis, aimed at verifying whether the ranking of the sectors most responsible for emissions, obtained from simulated estimates, is comparable with the ranking derived from official data (source: Eurostat).
This process validated the model, demonstrating a high degree of overlap with official statistics and justifying any discrepancies through increased data granularity and refined estimation methodologies.
FIRST APPROACH
In the first approach, the comparison focused specifically on the manufacturing sector and companies of sizes 4, 5, and 6, where the overlap with the benchmark was most significant.
The median value of emissions estimated from invoices was compared with the median benchmark data. The results obtained are similar, considering statistical uncertainty. The tables below report the median Scope 1 and Scope 2 values for each analyzed group.
For Scope 1 emissions, discrepancies between estimated data and the benchmark may be due to differences in the types of fuels considered in the two datasets—estimated versus actual reference data. Regarding Scope 2 emissions, validation showed a high consistency between estimates and the benchmark, confirmed by the significant overlap of the confidence intervals of their respective distributions. This result suggests that the methodological approach based on electricity consumption is solid and comparable to real values.
SECOND APPROACH
The second approach involved verifying the ranking of sectors with the highest environmental impact, comparing the model results with official Eurostat data. This part of the analysis is based on calculating the overlap ratio, a metric indicating the degree of alignment between the two rankings, considering both the top five and top ten positions.
Additionally, the analysis covered both micro-enterprises (sizes 1-4) and small and medium-sized enterprises (sizes 5-7). Scope 1 and Scope 2 emissions were considered, while Scope 3 was excluded to align with Eurostat data.
The results show a high consistency between the two rankings, with an overlap rate of 60% for micro-enterprises and 80% for SMEs in the top five most environmentally impactful sectors. This percentage rises to 90% when considering the top ten sectors.
Thus, the industrial sectors identified as the largest greenhouse gas emitters by GHG Nowcasting were compared with those detected at the European level, achieving a high degree of overlap.
Thanks to this dual validation, GHG Nowcasting proves to be a reliable tool for estimating greenhouse gas emissions, offering companies a transparent and data-driven method to monitor their environmental impact.
Why choose GHG Nowcasting?
modefinance’s GHG Nowcasting is an innovative and reliable solution for estimating greenhouse gas emissions, designed to address the challenges that companies—particularly SMEs—face in environmental reporting. The lack of granular data and the complexity of traditional methodologies often hinder corporate transparency and access to sustainable financing. Without an effective method, there is a risk of obtaining inaccurate estimates, compromising the management of environmental impact.
This is where GHG Nowcasting comes into play. The system is based on a rigorous methodology, using information extracted from company invoices to provide reliable, easy-to-obtain estimates that align with real data. Thanks to a robust validation process, the model demonstrates a high degree of accuracy, with significant alignment with industry benchmarks and Eurostat data.
GHG Nowcasting is not just a measurement tool—it is a strategic partner for tackling sustainability challenges and transforming them into growth opportunities. Thanks to its accessibility and precision, it enables companies to enhance their ESG reputation, comply with regulations, and gain easier access to sustainable financing.
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