Company Carbon Footprint
Calculating emissions can be complicated and full of inaccuracies. Greenometer provides you with accurate results according to international standards GHG Protocol and ISO 14064. Our platform combines software and know-how from hundreds of successful projects, so your data is always structured, verifiable, and ready for audit. This gives you not only the certainty of compliance, but also a competitive advantage—results that investors, customers, and regulators trust.
What We Do
Complete CO₂ footprint of the company, including its subsidiaries and assets (Scope 1–3) – according to the GHG Protocol and ISO 14064
01
Data Collection
Having all emission data in one place is often the most difficult step. That is why our consultants will come directly to you, go through all relevant data sources with you, and explain what needs to be monitored and how. We will train your teams so that data collection runs smoothly, without errors or unnecessary delays.
02
Data evaluation and visualization
Carbon footprint calculations are complex and prone to inaccuracies. Greenometer is more than just software—it combines know-how from hundreds of projects with clear methodology based on the GHG Protocol and ISO 14064. The platform evaluates all collected data and ensures that reports, graphs, and results are accurate, consistent, and ready for audit.
03
Audit and reporting
At the end, you will get more than just numbers. We will deliver customized, auditable reports, supplemented with clear graphs, detailed outputs, and comparisons of products with alternatives. Recommendations for reducing emissions and cost estimates are also included, so you have documentation that can be used for regulators, investors, and PR.
We know what AI does not tell you
Emission factors
AI does not automatically know which emission factors should be used for electricity, gas, heat, materials, LCA or EPD data, because concrete is not just concrete and heat is not just heat.
Consolidation process
AI does not automatically understand the group consolidation logic, including operational control, intra-group transaction eliminations and resale of services within the holding.
Data collection
AI does not know how to manage the first data collection process without clear deadlines, open-door sessions, internal guidance and mapping of accounting codes.
Common errors
AI does not automatically detect all common mistakes, such as wrong units, reporting periods, material types, supplier data or averaged employee commuting inputs.
We know what AI does not tell you
Emission factors
AI does not automatically know which emission factors should be used for electricity, gas, heat, materials, LCA or EPD data, because concrete is not just concrete and heat is not just heat.
Consolidation process
AI does not automatically understand the group consolidation logic, including operational control, intra-group transaction eliminations and resale of services within the holding.
Data collection
AI does not know how to manage the first data collection process without clear deadlines, open-door sessions, internal guidance and mapping of accounting codes.
Common errors
AI does not automatically detect all common mistakes, such as wrong units, reporting periods, material types, supplier data or averaged employee commuting inputs.
We know what AI does not tell you
Emission factors
AI does not automatically know which emission factors should be used for electricity, gas, heat, materials, LCA or EPD data, because concrete is not just concrete and heat is not just heat.
Consolidation process
AI does not automatically understand the group consolidation logic, including operational control, intra-group transaction eliminations and resale of services within the holding.
Data collection
AI does not know how to manage the first data collection process without clear deadlines, open-door sessions, internal guidance and mapping of accounting codes.
Common errors
AI does not automatically detect all common mistakes, such as wrong units, reporting periods, material types, supplier data or averaged employee commuting inputs.
There's a better way