September 28, 2022 - Data EngineeringManufacturing

Data Challenges of Carbon Accounting for Companies

Alessio Civitillo - September 28, 2022

To reach the goal of net-zero emissions, many large companies ignore the issue of collecting, analyzing, and distributing accurate carbon emission data. Therefore, addressing the fundamental challenges, in the areas of suppliers, logistics and data validation is essential. This article presents those three carbon accounting challenges and details steps on how to overcome them.

Companies like Microsoft, Amazon, and Disney have pledged to become net-zero carbon emitters and many other companies are starting to explore how they can become more sustainable as well. This has been prompted by the renewed focus on global warming across many countries and a growing trend that customers are increasingly choosing and advocating for organizations that live by sustainable values. To fulfill their pledges, companies are becoming more serious about their carbon accounting, which entails correctly measuring the total amount of greenhouse gases generated by operations.  

Carbon accounting or greenhouse gases (GHG) accounting measures the impact that the volume of GHG emissions has on climate change. Carbon dioxide emits the largest percentage  of gas into the atmosphere, so the terms carbon accounting and GHG accounting are often used interchangeably. 

Contrary to financial accounting, however, which has agreed-upon and regularly revisited standards, such as IFRS, carbon accounting has not yet benefited from agreed-upon standards, processes and methodologies. Additionally, accounting for greenhouse gases requires significantly more data sharing across companies and organizations. The reason is that a high percentage of emissions emanates from suppliers. For instance, 97% of Microsoft’s emissions are classified as scope 3, which means they are emitted from their suppliers and customers and not generated by the company directly.

Becoming net-zero or negative carbon emitters and instituting a system of carbon accounting has been hard for companies to fully grasp up to now.

The biggest hurdle has been how to collect accurate data from suppliers and logistics teams. The lack of standardization in how data should be validated is also a challenge to be faced and overcome.

Some companies do not include accurate data about their carbon footprint in their reporting. For example, when the data was obtained in 2020, it was discovered that Apple’s 2019 carbon footprint had increased by 7%. 

This article aims to explore why such inconsistencies exist and what can be done about them. You will learn: 

  • What challenges companies face in carbon footprint accounting.
  • What the effective solutions are for collecting data.
  • How companies can provide carbon accounting data accurately

Challenges faced by companies in the area of suppliers, logistics and data validation have a significant impact on providing accurate carbon accounting reports.

The Suppliers’ Challenge

The majority of companies “inherit” emissions from their suppliers. For instance, 73% of greenhouse gases are produced by the energy sector, not directly by manufacturing or service companies. But when reporting on their emissions footprints, companies are responsible for taking those suppliers’ energy emissions into account.

One of the biggest challenges is getting reliable suppliers’ emission data for each purchased product. This is especially challenging for manufacturing companies with deep and complex BOMs (Bill of Materials). These companies can use hundreds of thousands of components purchased from suppliers for manufacturing their products.

Currently, there is no easy way to share emissions data at product level for companies, so a variety of ways have been developed, including sharing of spreadsheet files.

The Logistics Challenge

Transportation accounts for 16% of total emissions. However, accounting for transportation emissions is not easy because different means of transportation have different emission levels. A truck transporting one 40-foot container is going to generate more greenhouse gases than a ship transporting hundreds of containers. This problem is further exacerbated by intermodal transportation; very rarely are goods transported using only one mode of transport. For example, steel shipping from Shanghai to Frankfurt could be transported firstly via truck, then via ship, and then via truck again.

The Audit Challenge

Carbon accounting does not currently follow any standards, in contrasts with financial information reporting which is heavily regulated by national and international standards.  

Here is a quote from the The Wall Street Journal:

“There is no set standard for how climate data should be verified, or by whom. Most of the S&P 500 companies that got climate data verified employed an engineering or consulting firm, rather than an accounting one.”

This puts additional pressure on data validation, especially suppliers’ data as it cannot be easily controlled and monitored by companies.

Some firms have taken advantage of the lack of standardization by employing carbon accounting methodologies that have been defined as “greenwashing”, which is using misleading positive environmental figures in order to present a positive public image.

This misleading information has earned a lack of trust by the public and now companies with true sustainability intentions have had to make an additional effort to validate their emissions data.

First steps in doing carbon accounting

The first step of every data initiative is Data Collection — obtaining correct, tested and validated data. The problem is we often see companies focusing on the end goal like a dashboard or ML/AI modeling. Without having the right level of Data Collection, there is no next step; it is not possible to build models to reduce emissions because there is no way to understand where the majority of emissions are coming from.

Obtaining good data on carbon accounting means focusing seriously on three things:

  1. Collecting good suppliers’ emissions data at the location and product level
  2. Collecting good emissions data of internal processes and operations
  3. Collecting good logistics emissions data during the transportation of goods and services

This is a significant undertaking but it can be done with the right team of data engineers. My advice is: don’t do this with a team comprised only of data scientists. 80% of this work is going to involve data engineering, which is the task of building pipelines to absorb, transform and validate data for reporting purposes.

In order to be auditable, this data should be versioned so that changes can be analyzed and explained. Versionability increases the size of the overall storage requirements, so it is a good idea to store carbon data in a cheap storage option like a data lake.


Data collection for emissions will require building interfaces with many internal and external providers. We can group the type of data providers like this:

  1. APIs—the easiest way to get data automatically
  2. Files—the messiest way to get data; formats and delivery dates have to be agreed
  3. Manual updates—the easiest way to make mistakes, but sometimes required

Given the significant number of data providers, it is advisable to have one person coordinating all of them to make sure that the emissions data flows correctly and that it is properly validated before being consolidated in the company sustainability reports.

How can dyvenia help you?

dyvenia is a data consultancy uniquely positioned to support you in developing solutions along the whole data value chain: from the first data MVP to a company-wide data strategy. Because we house all data capabilities, we can participate in any development stage, from ideation to implementation. 

We place significant importance on housing strong data engineering competencies. This allows us to deliver our data solutions in a fast, transparent, and flexible manner.

Our data engineers can work with your sustainability team to plan out, coordinate and deliver your sustainability initiatives in a scalable way. They will help you determine your goals, identify data sources and create a solid data foundation you can build your analytics’ capabilities upon. 


Carbon accounting is first and foremost a data engineering challenge as it requires diligent data collection from various sources.  Without a proper data collection process, pretty visualization dashboards or advanced models about carbon emissions are useless. Therefore, we advise you to introduce data engineering capabilities to your teams. They will ensure the process meets your goals. 


Alessio Civitillo

The founder and CEO of dyvenia. Because of his background in financial analytics, he strives to deliver fast, efficient and impactful solutions. Due to his programming experience, he believes that robust software engineering practices need to be introduced to the world of data. And because he wants to see that gap between the people, technology and data bridged someday, he loves to bring complex technical concepts to people so that they understand the big picture behind becoming data-fueled.

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