How can emissions accounting be achieved with excellent data?

Content:

The importance of accurate data
The challenges in detail

Possible solution based on a specific example
The strategic added value of accurate GHG data
Conclusion: Time for change beyond regulatory requirements.

Sustainability is now the decisive factor for long-term corporate success and competitiveness. Let's look at the economic reality: regardless of regulatory conditions, sustainable companies demonstrate significantly greater resilience to crises. They are more resilient to supply chain disruptions, rising energy costs, and changing consumer preferences. Instead of making regulatory requirements the sole maxim for action, it is now necessary to establish structures for integrated sustainability management. Regulation then becomes an "end-of-pipe" task that can be fulfilled almost incidentally, while the actual focus is on substantive progress.

An important tool for sustainability management is continuous emissions accounting. It provides the figures that help sustainability managers to truly understand the company's emissions and thus its own climate impact. This also makes it the basis for any reduction strategy.

Without accurate and reliable data, however, any climate strategy remains a shot in the dark.

In this blog post, I would like to answer the following questions:

Why is accurate data necessary?
What challenges need to be overcome and what are the typical sources of error?
What concrete solutions might look like?

Woman concentrating on her laptop in bright surroundings with large indoor plants.

The importance of accurate data

The transition to data-driven sustainability management is not only necessary, it is a strategic imperative. It is the only way to create transparent and reliable GHG inventories (according to the GHG Protocol) and, at the same time, define clear reduction paths.

This paradigm shift requires solving four key challenges that directly influence data quality and thus audit trail security.

The challenges in detail

1. The challenge of data collection

Emissions-related data is often highly fragmented in modern corporate structures. It is stored in a variety of systems and formats:

ERP systems (SAP): These contain central consumption data for gas, electricity, and raw materials, as well as invoices and order items (important for Scope 3).
Decentralized storage: Mileage data for company cars, commuter data, and waste volumes are often stored in local Excel spreadsheets, whose consistency is difficult to ensure.
Physical meters: Liter readings for diesel, refrigerant losses, or specific plant data are read and transferred manually.

The consequences of manual and inaccurate data collection are significant. Leading studies and consulting experience show that up to 80% of errors in GHG balances occur during the data collection phase. An error in data collection can distort the total emissions balance by up to +/- 20%.

Particular complexity: Scope 3 and the supply chain
The biggest gap often arises in Scope 3 (upstream and downstream value chain). Here, data on purchased goods and services (Category 1) must be requested directly from suppliers. Companies face a dilemma:

Primary data: Product carbon footprints (PCF) from suppliers are the most accurate, but often difficult or impossible to obtain, as many suppliers do not yet carry out their own accounting.
Secondary data: If primary data is missing, industry averages or spend-based factors (emissions based on the monetary value of purchases) must be used. Although these are complete, they offer significantly lower accuracy and little potential for control.

The goal must be to create a robust digital infrastructure that automatically collects this fragmented data in a "single point of truth" and transparently closes the gaps in Scope 3.

2. The challenge of transformation and calculation

Raw data alone does not constitute a balance sheet. The major hurdle lies in the transformation and "translation" of pure activity data (e.g., liters of diesel, kWh of electricity, kg of raw materials) into correct CO2 equivalents. This requires the correct application of emission factors and compliance with global standards.

Activity data x emission factor = tCO2 equivalents

In addition, methodological decisions must be applied and documented consistently over many years to ensure comparability (and thus the pursuit of goals).

A common and audit-relevant error lies in the handling of purchased electricity (Scope 2):

Location-based: The calculation is based on the average emission mix of the national power grid at the location. This reflects the physical reality.
Market-based: The calculation is based on the company's contracts, e.g., guarantees of origin (GoO/RECs) for green electricity.

Companies with green electricity contracts must report both approaches in order to make the physical impact (location-based) and market development (market-based) transparent. Without a systematic logic, methodological inconsistencies lead to distorted results that do not stand up to external scrutiny. And then it should not just be about recording. The data should be used to pursue and achieve climate targets, e.g., according to SBTi. This requires anchoring, monitoring, and control in accordance with the GHG Protocol.

CO₂ lettering formed from white clouds in blue sky symbolizing greenhouse gases.

3. The challenge of analysis and control

Activity data can be collected at different levels and with varying degrees of precision. The more granular the data collection, the more specific the analysis and control of reduction measures can be.

Let's take the fictional company SkalaMotors AG as an example. It manufactures in various countries, operates its own logistics, and has several sales locations. The structure and hierarchy are as follows:

Organization chart of SkalaMotors AG with sales and production sites in Germany

It would be possible to collect the data for Scope 1 (mobile combustion) only at the AG level. You would then receive, for example, a single total figure for the diesel consumption of all locations and subsidiaries. This value gives the total emissions in tCO2e. However, the evaluability is then limited to this highest level. The critical question remains unanswered: Where do countermeasures need to be taken? Where are the hotspots or the low-hanging fruit?

It makes much more sense to create actionable knowledge and collect data at the lowest level that can still be controlled.

To do this, you have to ask yourself: At what levels can the team actually still control and influence? The finance department controls purchasing globally, but local facility management controls heating and lighting. Production management controls plant efficiency.

Example of granularity: Instead of the total electricity consumption for the entire Plant A, we need the consumption per production line or per individual machine. Only then can we establish a correlation between production efficiency (number of units) and energy consumption (kWh) and identify inefficiencies as emission hotspots.

The deeper the data is collected within the overall organization, the greater the control potential and ROI of sustainability measures.

4. The challenge of interpretation (technical complexity)

Interpreting data correctly requires expert knowledge and consideration of complex accounting and scientific rules. The complexity of the subject matter is often underestimated . After all, a number in the system is worthless if the context is missing or the wrong methodological derivation is made.

Without a deep understanding of the subject matter, companies run the risk of comparing apples with oranges or, worse still, unintentionally engaging in greenwashing because the scientific basis for the calculation is flawed. A common risk is the incorrect treatment of compensation projects or the inconsistent delineation of scopes 1, 2, and 3.

Focus of the auditors: Auditors will examine not only the figures, but also the underlying methodology (the "sustainability intelligence") to ensure that the balance sheet meets the requirements of the CSRD and the GHG Protocol. This is where double materiality comes into play: Not only the financial impact of climate risks, but also the company's impact on the climate must be accurately reflected.

This means that it takes more than just software that collects data; it also requires technical expertise and validation logic in the software to make this complexity manageable and ensure data quality.

Possible solution based on a specific example

Software solutions such as ID-Report are an effective way to overcome these challenges. They offer a high level of support, especially when combined with established ERP systems such as SAP. When used correctly, all data for sustainability management is collected centrally. It is provided in a homogeneous data set – the so-called "single point of truth."

The need for individual data models
The way forward: creating individual models and structures
Two ways of integrating data
Calculation and emission factors
Final analytical capabilities
The decisive advantage of granularity

The need for individual data models

Every company has different structures, data models, and interfaces. The software used must therefore offer sufficient flexibility and adaptability. This is an essential feature of the solution.

The way forward: creating individual models and structures

First, the desired granularity of evaluation and decision support must be defined. Let's look again at SkalaMotors AG:

If the emission-relevant data is only collected at the highest level of SkalaMotors AG, subsequent accounting for the individual areas (e.g., SkalaMotors Production) is no longer possible. The more finely granular the emission-relevant data collected within the organization, the more precise and controllable the emissions balance sheet becomes.

Two ways of integrating data

There are two basic ways to integrate data at the lowest levels:

Integration via technical interfaces.
Integration via data collection workflows:
- Surveys of data suppliers (e.g., questionnaires).
- Integration via file uploads or flat files.

In all cases, data models must be provided in order to collect the data. Put simply, a data model is a structured table. The more information you want to collect and evaluate, the larger this model becomes.

The more entities are integrated into the data collection processes, the more requirements arise for the data models. It is essential that the solution used – such as ID-Report – provides the creation and management of these models. This is the only way to prevent the main work from being done by the data suppliers rather than the software. The common data model is then the binding basis for all data, for example, for "fuel consumption."

Calculation and emission factors

Emissions can be calculated based on the collected data. Many solutions offer integrated factor catalogs. It is advantageous if these catalogs can be expanded individually.

There are a variety of emission factors. The better a factor is tailored to your own economic activities, the more realistic the resulting emissions balance will be. It may therefore be necessary to use your own factors, which come from associations, research projects, or dedicated databases.

Final analytical capabilities

The strength of a robust data infrastructure is manifested in its final analytical capabilities. Based on the data collected and harmonized via ETL (extract, transform, load) processes, homogeneous, multidimensional data models can be generated automatically.

The decisive advantage of granularity

Provided that the raw data has been recorded at a low, controllable level (e.g., not only the total electricity consumption of the corporation, but per location, per production hall, ideally per plant), as required in Challenge 3, the analytical model allows for dynamic evaluation at this level of detail.

This means that the complete emissions balance can be output at any desired organizational level at the touch of a button, without any further manual intervention:

Group level (AG):
Compliance report for CSRD/investors; total emissions tracking for SBTi targets.
Business unit:
Comparison of the efficiency of different divisions (benchmarking).
Location/plant:
Localization of specific emission hotspots; energy efficiency monitoring of facility management.
Plant/machine:
Correlation of CO₂ emissions and operating hours/production output; basis for investment decisions in new, more efficient technology.

This dynamic evaluability transforms the emissions balance sheet from a rigid, retrospective report into an agile control instrument. Sustainability managers are no longer limited to knowing the overall balance sheet, but can dive into the data. For example, they can perform ad hoc analyses to isolate the exact contribution of the logistics fleet in country X to Scope 1 or immediately quantify the impact of a specific raw material change (Scope 3).

With this high degree of measurability and flexibility, sustainability management is directly linked to the company's financial and operational decisions and generates an immediate, data-driven return on investment (ROI).

The need for individual data models

Every company has different structures, data models, and interfaces. The software used must therefore offer sufficient flexibility and adaptability. This is an essential feature of the solution.

The way forward: creating individual models and structures

First, the desired granularity of evaluation and decision support must be defined. Let's look again at SkalaMotors AG:

Two ways of integrating data

There are two basic ways to integrate data at the lowest levels:

Integration via technical interfaces.
Integration via data collection workflows:
- Surveys of data suppliers (e.g., questionnaires).
- Integration via file uploads or flat files.

Calculation and emission factors

Emissions can be calculated based on the collected data. Many solutions offer integrated factor catalogs. It is advantageous if these catalogs can be expanded individually.

Final analytical capabilities

The decisive advantage of granularity

This means that the complete emissions balance can be output at any desired organizational level at the touch of a button, without any further manual intervention:

Group level (AG):
Compliance report for CSRD/investors; total emissions tracking for SBTi targets.
Business unit:
Comparison of the efficiency of different divisions (benchmarking).
Location/plant:
Localization of specific emission hotspots; energy efficiency monitoring of facility management.
Plant/machine:
Correlation of CO₂ emissions and operating hours/production output; basis for investment decisions in new, more efficient technology.

The strategic added value of accurate GHG data

From reporting to control: The strategic added value of accurate GHG data
The initial effort to create a robust, future-proof database that follows clear rules and metrics pays off twice over. This foundation is the prerequisite for transparent reporting and real-time, data-driven management of your sustainability strategy. Essentially, it overcomes challenges and creates immediately measurable value in three areas:

1. Compliance and audit security (CSRD/ESRS)

Regulatory requirements in the area of sustainability are increasing. A sound information base will enable you not only to meet these requirements in the future, but also to master them easily and confidently.

Audit reliability: In the future, sustainability reports must be as auditable as financial reports. Good data guarantees complete traceability (audit trail) of every key figure from the source to the balance sheet. This creates transparency for all stakeholders and minimizes compliance risks. Even if you need an ad hoc report in the future, e.g., for your bank or investors, you can use the same audited data.

Two business people give each other a high-five in the office after a successful collaboration.

2. Economic efficiency, efficiency, and strategic control

Your sustainability management benefits from controllable data. This is the basis for:

Targeted savings in resources and costs: Correlating energy consumption with operating hours reveals inefficient systems. This data-driven optimization generates a direct return on investment (ROI) for your sustainability measures.
Actively and measurably advancing climate goals: Granularity allows you to apply reduction measures exactly where they have the greatest impact (hotspot management).
Ultimately, operate more economically and competitively in the market.

This means you move from pure "reporting" to an active, data-driven strategy – a real game changer for your future viability.

3. Strong stakeholder communication and green finance

Investors, banks, and customers demand quantitative evidence.

Access to capital: With precise key figures (e.g., greenhouse gas intensity), you can improve your ESG rating, secure access to green finance, and strengthen your brand's credibility.
Talent acquisition: It is also important to your employees today that your company treats the one habitat we have responsibly. Credible, audited data increases employee retention and attracts new talent.

Conclusion: Time for change beyond regulatory requirements

Making emissions or even sustainability measurable is not a one-time project, but a continuous process that stands or falls with the right technological infrastructure. The key lies in the technological trinity that solves the four central challenges of data quality:

Specialized data collection software (e.g., ID-Report): Provides the necessary flexible data models to capture highly granular data directly at the source.

Flexible ETL-Tools (Extract, Transform, Load): Enables the automated integration of fragmented data from ERP systems and Excel sources, applies the correct emission factors, and ensures an auditable audit trail.

Powerful analysis platform: Converts the collected, homogeneous data into dynamic reports that enable control at every organizational level (from the group to the individual plant).

This integration transforms tedious data collection from a burdensome compliance obligation into a strategic process. Companies that make this change create a robust foundation that kills two birds with one stone: they meet the strict requirements of CSRD and ESRS with confident audit security and, at the same time, gain actionable knowledge for actively reducing emissions.

Start building this consistent and future-proof database today. Put an end to the "patchwork" of manual processes and harness the power of accurate, actionable data to not only report on your green transformation, but also accelerate it strategically and economically.