Similar to automated data processing of energy bills, which provide helpful insights to property managers, for example, other valuable data can be used from them to focus on other things. On what? The detailed recording of line item data, which enables organizations to determine their CO2 footprint. The CO2 footprint provides clarity on how much CO2 is generated by which activities and in which business areas in a given company, how much emission is emitted. Something that is becoming increasingly important. Sustainability is important to most companies, as is the need for certification to prove an optimized CO2 footprint.

The carbon footprint – something like the company’s supervisor for sustainable business

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Focus not on invoice amounts, but on content details

Up to now, the financial accounting department of a company is aware that they have billed the total amount X for water in the accounting period Z, but they do not know how much m³ of water was consumed. Just as little do they know, with the data extracted so far, how the consumption is distributed among the twenty branches or how they differ, because depending on the location, this can simply be a simple water price issue and does not necessarily have to result from additional consumption. With fuel, the situation is similar: the accounting department bills the amount X for the period Y to Z, but they have no idea how much of it was unleaded 95, how much unleaded 98, how many liters of it were diesel. But it is precisely this detailed data that is essential for determining the carbon footprint.

What is true in goods management is also true for determining the carbon footprint: use data wisely and – above all – use all available data

Essential line item data to be extracted

The high significance for a company of being able to extract and process invoice data automatically remains, of course. In a further step, the additional line item data is added, which is required to be able to calculate and determine the CO2 footprint of a company exactly. There is enormous potential here, especially for companies with several branches. In order to identify where CO2 emissions are actually emitted and to reduce and optimize them, the following position data must be extracted:

  • Invoicing party
  • Property and property address: for companies with several branches (no matter if two, twenty, or two hundred branches)
  • Billing period: reference month or annual billing period
  • Definition for category or product: electricity, water, gas, heating oil, fuel, and so on and so forth
  • Distinctions: For fuel, consumption is distinguished between unleaded 95, unleaded 98, and diesel; for electricity, for example, between “gray” or “green” electricity; for gas, for example, between natural gas and biogas
  • Total consumption: depending on category or product in kWh, m³, liter, gallon
  • Conversion factors: for gas, consumption is often given in m³ and then converted to kWh, both item data are important

As individual factors, these line item data together have a significant influence on how much emissions the branches emit and how much energy a company consumes overall. With this data, the energy budget of a company, no matter how many branches it has, can be calculated, optimized, potential savings identified and the carbon footprint determined.

The exact extraction of detailed position data from energy bills – an efficient way to determine the CO2 footprint

The intelligent, AI-based OCR extracts accurately

To ensure that this position data can be extracted from the energy calculations in accurate detail, intelligent, AI-based OCR (Optical Character Recognition) comes to the rescue. The intelligent, AI-based OCR frees this “trapped” line item data from the documents so that it too can serve its intended purpose. Here’s what that looks like:

  • Energy bills are received at the individual company or at the various branch offices.
  • The intelligent, AI-based OCR performs quality improvement and page separation
  • All necessary energy bills from the different branches are classified
  • Automatic extraction of all previously listed and relevant data from the energy bills follows
  • Structured data is output, which in turn is used for the automatic determination of the CO2 footprint

The intelligent processing of energy bills is therefore a welcome means of determining the CO2 footprint

The automatic determination of the CO2 footprint helps twofold

The collected data can be used to make optimizations, such as switching to green electricity or natural gas. Thanks to intelligent, AI-based OCR, not only will energy bills be processed automatically in the future, but also high-emission business areas can be reduced, the CO2 footprint optimized and CO2 certifications sought. The automatic determination of the CO2 footprint thus helps companies both internally with their emissions optimization, which often means potential savings, and of course externally with the successful auditing and receipt of important CO2 certifications.