10 Critical Scope 3 Emissions Mistakes and How to Avoid Them

Scope 3 emissions are complex – and mistakes can be costly. Companies face the challenge of correctly capturing their indirect emissions across the entire value chain. Errors not only lead to inaccurate reports but also to compliance risks, higher costs, and reputational damage. With the EU Taxonomy and the CSRD, requirements are becoming stricter, and precise accounting is becoming mandatory.

The most common mistakes and how to avoid them:

1. Incorrect data classification – Properly assign primary and secondary data.
2. Poor data quality – Ensure consistency and timeliness.
3. Weak supplier engagement – Structure collaboration systematically.
4. Departmental silos – Improve internal coordination.
5. Incorrect system boundaries – Include all relevant categories.
6. Ignoring investment emissions – Don't neglect Category 15.
7. Average values instead of precision – Prioritize specific data.
8. Outdated data – Plan regular updates.
9. Regulatory violations – Comply with regulations like CSRD and LkSG.
10. Lack of documentation – Ensure transparency and traceability.

Our tip: With clear processes, appropriate technologies, and close collaboration, these mistakes can be avoided. Those who act now save costs, reduce risks, and strengthen the trust of investors and partners.

1. Incorrect Classification of Primary and Secondary Data

The incorrect assignment of primary and secondary data can significantly distort the Scope 3 balance. Primary data comes directly from suppliers and reflects specific emission values for actually delivered products or services. Secondary data, on the other hand, is based on industry-wide average values or generic emission factors. When this data is incorrectly assigned, inaccuracies arise that make informed decarbonization decisions difficult. This has direct impacts on reporting accuracy.

Impact on Reporting Accuracy

Incorrect data classification can severely distort the emissions balance. Suppose high-quality primary data from a low-emission supplier is incorrectly classified as secondary data – in this case, valuable reduction potential remains unused. At the same time, overestimating data quality can lead to prioritizing measures in already optimized areas while actual emission drivers remain unnoticed. Primary data typically has lower uncertainties than secondary data, making their correct classification all the more important.

Compliance with German and EU Regulations

Regulatory requirements such as the CSRD require companies to disclose the quality of their data and the methods applied. The German Supply Chain Due Diligence Act also demands careful data collection. Incorrect classification can therefore not only jeopardize compliance with these regulations but also bring financial and legal risks. Companies risk incurring additional costs for corrections or even facing sanctions.

Cost Consequences of Error Correction

Correcting such errors is often complex and expensive, as it requires retroactive adjustments. Investment decisions based on incorrect data can be undermined. It becomes particularly problematic when decarbonization measures have already been implemented that later prove ineffective. Public corrections can also impair investor confidence and lead to worse financing conditions.

Feasibility of Avoidance Strategies

Clear classification guidelines are an effective means of minimizing these risks. Companies can develop standardized assessment matrices that define precise criteria for classifying primary and secondary data. A multi-stage validation process that checks data origin and quality across different departments helps identify errors early. Regular training of employees on relevant standards and criteria helps reduce human errors. Additionally, investments in specialized software solutions can save costs in the long term by preventing systematic errors and improving data quality. Such measures are technically feasible and should be at the top of the priority list.

2. Poor Data Quality and Consistency

Incomplete, outdated, or inconsistent data poses a major obstacle to precise Scope 3 balances. When companies work with such data or different departments access different data sources, systematic distortions in emission accounting arise. This directly affects the accuracy of calculations.

Impact on Reporting Accuracy

Poor data quality leads to significant uncertainties in emission calculations. A classic example: If complete transport data is missing and estimates are used instead, total emissions can be severely distorted. The situation becomes even more complicated when some suppliers provide detailed values while others only provide rough averages.

The result? Emission hotspots are incorrectly identified, and companies may make investment decisions based on incorrect priorities. Additionally, differences in data quality and collection methods between reporting years make it difficult to compare time series – a significant disadvantage in long-term planning.

Compliance with German and EU Regulations

The Corporate Sustainability Reporting Directive (CSRD) requires companies to disclose the quality of their underlying data and the estimation methods used. It must be clearly documented how much of the Scope 3 emissions comes from primary data, secondary data, or estimates. Poor data quality increases the risk of auditor objections.

Cost Consequences of Error Correction

Correcting incorrect data retroactively is expensive. Often external consultants must be brought in to close data gaps and standardize processes. Even more problematic: decarbonization measures based on incorrect data might need to be revised – with corresponding opportunity costs. And not to forget: public corrections damage reputation and can make financing sustainable projects more expensive.

Feasibility of Avoidance Strategies

A systematic improvement of data quality is technically feasible – and urgently needed. Automated validation rules can perform plausibility checks and identify outliers. Uniform data formats and templates for suppliers help reduce inconsistencies.

A tiered quality management system offers a practical solution: For the largest emission sources, the most detailed primary data should be used, while standardized secondary data may suffice for smaller suppliers. Regular data audits ensure that problems are identified and resolved early.

Including data quality metrics in reporting creates transparency and strengthens stakeholder confidence. Modern software solutions can automate many of these processes and reduce operating costs in the long term. Better coordination with suppliers can further strengthen this foundation.

3. Weak Supplier Engagement

Structured collaboration with suppliers is often underestimated in many companies – yet it is crucial for precise Scope 3 accounting. Without systematic integration of the supply chain, important emission data often remains either inaccessible or inaccurate. Superficial communication and the lack of clear requirements for suppliers significantly worsen the data situation. This not only affects reporting accuracy but also brings challenges for strategic corporate management.

Impact on Reporting Accuracy

Without reliable data from suppliers, companies must resort to general estimates. This can lead to complex emission profiles being distortedly represented and possible approaches for emission reduction being overlooked. Additionally, inconsistent data quality between different suppliers makes it difficult to identify central emission sources – so-called "hotspots" – and hinders the development of targeted measures.

Compliance with German and EU Regulations

Requirements from the CSRD (Corporate Sustainability Reporting Directive) and the Supply Chain Due Diligence Act (LkSG) make transparent integration of the supply chain indispensable. Insufficient or incorrect data from suppliers not only jeopardizes compliance with these regulations but can also entail financial and operational risks. Companies risk paying regulatory fines or damaging their reputation.

Cost Consequences of Error Correction

A lack of structured supplier cooperation often leads to higher costs for subsequent corrections. At the same time, long-term business relationships can be strained if suppliers are not actively involved in the process. Additionally, there are opportunity costs: without reliable data, decarbonization strategies cannot be effectively implemented, which can impair competitiveness.

Feasibility of Avoidance Strategies

Targeted supplier management is certainly feasible but requires a clear and gradual approach. Particularly emission-intensive suppliers should receive more intensive support, while standardized and less complex solutions may suffice for smaller partners. Digital platforms with uniform tools for data collection and automated plausibility checks can make the process more efficient. Additionally, measures such as training or support for suppliers' own climate accounting can improve collaboration. Cooperation with industry associations or industry initiatives also offers advantages: common standards can be developed and administrative effort significantly reduced.

4. Poor Coordination Between Departments

Scope 3 accounting requires close collaboration between different departments such as procurement, production, and finance. Without this coordination, data silos, contradictory information, and delays arise that hinder the entire process.

A common stumbling block is different methods and definitions for data collection. For example, the IT department might document energy consumption differently than facility management, while procurement applies its own standards for evaluating suppliers. This inconsistency directly affects the accuracy of the emission balance.

Impact on Reporting Accuracy

Without coordinated processes, data quality suffers significantly. When departments categorize the same emission sources differently, double counting or gaps threaten. Such inconsistencies often remain undetected because no uniform validation takes place. This can significantly impair confidence in the sustainability strategy, particularly during external audits or stakeholder inquiries.

Compliance with German and EU Regulations

The CSRD requires complete documentation of data origin and processing. Without cross-departmental coordination, it becomes difficult to create the required audit trails. The Supply Chain Due Diligence Act also places high demands on traceability along the entire value chain. Without coordination, regulatory problems quickly arise that are difficult to remedy.

Cost Consequences of Error Correction

Uncoordinated processes can become expensive. Subsequent data cleansing and harmonization of different systems cause high additional costs. Delays in the reporting process also lead to opportunity costs, as unreliable data makes well-founded decisions difficult. In the long term, this results in higher CO₂ costs and missed efficiency gains. To avoid such additional costs, clear structures and responsibilities are essential.

Feasibility of Avoidance Strategies

A central coordination point for Scope 3 accounting is an important step to improve collaboration between departments. This point should define clear responsibilities and introduce uniform data standards. A RACI matrix (Responsible, Accountable, Consulted, Informed) helps clearly assign the roles of individual departments.

Digital collaboration platforms can also facilitate information exchange. Shared dashboards and automated data validation help identify inconsistencies early. Regular coordination meetings, ideally monthly, ensure that data quality is continuously monitored and problems are resolved promptly.

Additionally, cross-departmental KPIs for data quality promote shared responsibility. When all stakeholders are co-responsible for the accuracy of the emission balance, motivation increases to work together systematically and achieve common goals.

5. Incorrect Boundary Setting and Category Selection

The precise definition of boundaries and selection of the right categories are essential for creating a reliable Scope 3 balance. However, the complexity of this task is underestimated by many companies, leading to hasty decisions – and these can become expensive later.

A common mistake is incomplete coverage of the value chain. While obvious categories like purchased goods and services (Category 1) are usually considered, less obvious areas, such as suppliers' business travel or disposal of sold products, are often left out. These gaps often arise from unclear processes or overly narrow organizational boundaries. The result? An inaccurate representation of emissions.

Impact on Reporting Accuracy

Incorrect boundary setting can significantly distort the balance. For example, excluding Category 11 (use of sold products) for energy-intensive goods leads to serious underestimations. Companies whose products cause high emissions during the use phase particularly risk distorted representation.

On the other hand, overly broad system boundaries can result in double counting. Such overlaps not only distort the overall balance but also make it difficult to identify the most effective emission reduction measures. Additionally, incorrect balances can jeopardize compliance with legal requirements.

Compliance with German and EU Regulations

The CSRD requires that all relevant emission sources be completely captured. Incomplete system boundaries can therefore lead to violations of these regulations. It becomes particularly critical during materiality analysis: if important categories are overlooked, credible reporting is no longer possible.

The Supply Chain Due Diligence Act further tightens these requirements. Incomplete category selection could be interpreted as lack of due diligence and result in legal consequences.

Cost Consequences of Error Correction

Errors in defining system boundaries are not only annoying, they can also be expensive. Subsequent adjustments mean that data for previously excluded categories must be collected again. Already created reports must also be revised – a time-consuming and costly process.

It becomes particularly problematic when historical data is missing. Many suppliers archive emission data only for limited periods, making complex estimation procedures or costly reconstructions necessary.

Practical Approaches to Avoidance

To avoid such problems, systematic analysis of the entire value chain is indispensable. This should cover all business processes from raw material procurement to product disposal and identify possible emission sources. A helpful tool is creating a process map that visualizes all relevant actors and material flows.

The materiality analysis should not only focus on absolute emission volume. Factors such as influenceability, data availability, or reputational risk also play a role. Even categories with relatively low emissions can be important if they have regulatory relevance or influence the company's image.

Pilot projects in selected business areas can help test the chosen approach. This allows evaluation of data quality, collection effort, and result significance before implementing the method company-wide. This step-by-step approach largely avoids costly wrong decisions.

6. Neglecting Investment Emissions (Category 15)

Category 15 is often overlooked, which can lead to significant misjudgments in companies with extensive investment portfolios. This category refers to emissions resulting from investments in other companies, real estate, or financial instruments – provided they are not already captured in other categories.

Many companies focus on obvious areas like purchased goods or business travel, while investment emissions are often dismissed as "too complicated" or "not relevant." However, this attitude is risky, especially for companies with diversified portfolios or strategic investments.

The variety of investment types requires individual calculation approaches. Without systematic capture, important emission components remain unconsidered. Here too – as with other categories – an integrated approach is crucial.

In the following, we examine the impact on reporting accuracy, regulatory requirements, financial consequences, and practical strategies for avoidance.

Impact on Reporting Accuracy

When investment emissions are ignored, this can significantly distort the entire CO₂ balance. Particularly for companies with large investments, these emissions can represent a relevant portion of Scope 3 emissions. If they are not fully captured, the actual CO₂ footprint is underestimated. This makes it difficult to evaluate climate targets and reduction measures, distorts industry benchmarks and ESG ratings, and hinders comparison with competitors.

Requirements from German and EU Regulations

The CSRD requires that all material emission sources – including investment emissions – be disclosed. Companies that do not consider Category 15 risk violations of reporting obligations. For financial institutions, specific requirements of the EU Taxonomy Regulation also apply. This demands transparent representation of financed emissions, and incomplete capture risks regulatory consequences.

Cost Consequences of Subsequent Correction

Subsequent collection of emission data can be expensive and complex. Without early secured data, estimation procedures or external consulting services are often necessary, which significantly increases costs for complex and diversified portfolios. While direct majority investments are usually easier to capture, broadly diversified portfolios with many positions require considerable personnel and financial effort. Collecting historical data retroactively causes additional effort and delays.

Practical Approaches to Avoidance

To avoid high correction costs, climate criteria should be integrated into investment strategy from the beginning. Already in the due diligence process, it is important to consider the availability of emission data as a central criterion.

A tiered approach has proven effective in practice: first, the largest and most influential investments are captured before smaller positions are added. Close collaboration with portfolio companies – such as through exercising shareholder rights, regular training, and exchanging best practices – can improve data quality and reduce organizational effort in the long term.

7. Excessive Dependence on Average Emission Factors

It may be tempting to work with generic emission factors – after all, they offer a quick start. But these average values carry significant risk for inaccuracies when adopted uncritically. Many companies resort to industry-wide average values out of convenience or time pressure, without considering the specific conditions of their supply chains.

The problem is obvious: average values are based on aggregated data and rarely represent the actual emissions of individual suppliers. Companies working with lower-emission suppliers could thereby underestimate their emissions. At the same time, the use of outdated technologies by other suppliers can lead to higher emissions, further impairing data quality.

Impact on Reporting Accuracy

Exclusive use of average values inevitably leads to distortions, particularly in international supply chains where regional differences in energy supply play a major role. An example: A German automotive company sourcing components from various countries could underestimate emissions from coal-intensive regions like Poland while overestimating emissions from countries with cleaner energy sources like Norway. Such inaccuracies not only make it difficult to evaluate one's own climate targets but can also lead to strategic wrong decisions. They also pose a challenge in meeting the strict requirements of the CSRD.

Requirements of the CSRD and Financial Impacts

The CSRD requires that emission data be collected as precisely as possible and based on the best available information. Companies are obligated to demonstrate continuous improvements in data quality – a point that is becoming increasingly important in external audits.

However, the transition from generic to specific emission factors involves costs. It requires investments in new data collection systems and building closer relationships with suppliers. It can become particularly expensive when historical data must be corrected retroactively. A gradual approach that integrates and strategically plans this transition is therefore advisable.

Practical Approaches to Reducing Dependence

How can the transition to more specific emission data be sensibly designed? An effective approach begins by identifying suppliers who contribute most to Scope 3 emissions. Detailed emission data should be requested from these key suppliers. For less significant positions, improved regional or industry-specific factors can be used.

Integrating this data into existing systems like ERP software can significantly reduce administrative effort. At the same time, long-term contracts with suppliers could include clauses ensuring regular provision of emission data. Additionally, training programs and technical support can help sustainably improve data quality throughout the supply chain. This not only facilitates compliance with legal requirements but also creates the foundation for well-founded strategic decisions.

8. Lack of Regular Emission Data Updates

Many companies view Scope 3 accounting as a one-time project: data is collected, a report is created – and then everything sits idle. But this static approach carries significant risks. Emission factors, supplier data, and regulatory requirements constantly change. Without regular updates, data gaps arise that can later only be corrected with great effort.

The dynamics of emission data are often underestimated. Energy mixes change depending on season and region, new technologies lower emission factors, and suppliers optimize their processes. These developments make it necessary to continuously review and adjust data.

Impact on Reporting Accuracy

Outdated emission data can distort the entire sustainability strategy. It becomes particularly critical when evaluating progress against climate targets. Companies might incorrectly assume their emissions have decreased when in reality only the underlying emission factors have changed.

The comparability of annual reports also suffers. Irregular updates lead to data series with fluctuating quality. This distorts trends and can lead to strategic wrong decisions.

Compliance with German and EU Regulations

The EU Sustainability Reporting Directive (CSRD) requires continuous improvement of data quality and regular updates. Companies must demonstrate that their data is always up to date. External auditors increasingly pay attention to whether systematic processes for data updating exist.

Additionally, the German Supply Chain Due Diligence Act tightens requirements. Companies are obligated to continuously monitor their supply chains. Outdated emission data could be interpreted as lack of due diligence and result in legal consequences – a risk that can also have financial impacts.

Cost Impact of Error Correction

Subsequent correction of outdated data is expensive. Retroactive cleansing and manual adjustments cause direct costs. At the same time, indirect costs arise, such as through delayed strategic decisions. Companies that don't know their actual emission situation precisely miss opportunities for targeted investments or supply chain optimizations.

Feasibility of Avoidance Strategies

The described challenges can be managed with a systematic update process. However, this requires strategic planning and clear responsibilities. A good approach begins with defining specific update cycles: energy data could be updated quarterly, while transport and logistics data are reviewed at longer intervals.

Technical solutions like automated data flows in ERP systems can significantly reduce manual effort. API interfaces enable continuous data streams and minimize errors.

Training involved teams is equally important. Employees should understand why regular updates are crucial and learn how to perform them efficiently. Clear escalation processes for missing or delayed data help identify and resolve problems early.

A gradual approach that initially focuses on the most emission-intensive categories facilitates implementation. This allows companies to quickly achieve measurable progress in data quality while creating the foundation for long-term improvements.

9. Failure to Comply with Regulatory Requirements

Besides internal processes, compliance with external regulations plays a central role in correctly accounting for Scope 3 emissions. Companies must consider all relevant EU and German regulations, which are also constantly evolving. Many organizations underestimate the complexity of these regulatory requirements – a mistake that can result in significant financial risks and reputational damage.

A particular problem lies in often overlapping regulations that bring different reporting obligations. For example, the Corporate Sustainability Reporting Directive (CSRD) explicitly requires reporting on Scope 3 emissions. Companies that do not consistently implement these requirements risk missing central requirements and exposing themselves to legal and financial consequences.

Impact on Reporting Accuracy

When regulatory requirements are not fully complied with, gaps in reporting arise. This can lead to important emission categories not being captured, which impairs data quality and makes comparison with other companies difficult. Additionally, different regulations define different minimum standards for data quality. If these are not met, there is a risk that the captured data does not meet regulatory requirements.

Compliance with German and EU Regulations

The CSRD explicitly requires consideration of Scope 3 emissions. Disregarding these requirements can lead to fines, liability claims, and significant reputational damage. For companies, this means that compliance with these regulations is not only a legal necessity but also a prerequisite for securing the trust of investors and the public.

Cost Impact of Error Correction

Subsequent adaptation to regulatory requirements is often associated with significant costs. Companies must revise their data collection and bring in external experts to close compliance gaps. It becomes particularly expensive when already published reports must be corrected. Besides direct costs, possible fines and damage claims are added. However, reputational damage in the long term – such as through greenwashing accusations – can be even more serious, as it sustainably impairs the trust of customers and investors. To avoid these risks, it is crucial to take early measures to comply with regulations.

Strategies to Avoid Compliance Violations

A systematic approach is key to meeting regulatory requirements. Companies should first identify all relevant regulations and record them in a compliance calendar with respective reporting deadlines and update cycles. An interdisciplinary team of lawyers, sustainability experts, and data analysts can help translate requirements into practical processes.

Regular employee training and the use of technical solutions, such as specialized software for monitoring changes in regulations, can further reduce the risk of violations. This approach seamlessly integrates compliance with regulatory requirements into the entire Scope 3 accounting strategy and creates a stable foundation for long-term compliance. This not only minimizes risk but also creates the basis for transparent and reliable reporting.

10. Poor Documentation and Transparency

Inadequate documentation in Scope 3 accounting can cost organizations dearly – both financially and in terms of their credibility. Although many companies collect extensive emission data, they often fail to consistently document the underlying methods, assumptions, and data sources. These transparency gaps not only jeopardize the credibility of reporting but can also result in legal and financial consequences.

The issue gains urgency as various stakeholders – from investors to auditors to regulatory authorities – place different requirements on the depth of documentation. Without a clear and structured approach, information gaps arise that can later only be closed with difficulty and at great expense. As with internal coordination and compliance with regulatory requirements, systematic documentation forms the foundation for reliable Scope 3 balances.

Impact on Reporting Accuracy and Regulatory Requirements

Missing documentation inevitably leads to inconsistencies in data collection and makes calculations difficult to trace. Particularly during personnel changes or changed responsibilities, essential information about methods used and data sources can be lost. Without clear documentation, reports cannot be validated, and it becomes difficult to reliably interpret trends. External auditors need detailed evidence for every calculation step – if these are missing, the entire accounting is quickly classified as unreliable.

Regulatory requirements such as the CSRD demand complete documentation of all methods, assumptions, and data sources. Additionally, the Supply Chain Due Diligence Act (LkSG) requires transparent documentation of environmental impacts along the supply chain. The European Sustainability Reporting Standards (ESRS) additionally require detailed disclosure of data quality and estimation methods used. Without complete documentation, it is nearly impossible to meet these requirements.

Costs from Subsequent Error Correction

Documentation errors can be extremely costly in hindsight. Creating comprehensive documentation after the fact can quickly cost hundreds of thousands of euros, especially with complex supply chains. It becomes even more expensive when already published sustainability reports must be corrected. Such corrections not only bring direct costs but also damage the company's reputation – damage that can have long-term effects on valuation.

Introducing new documentation systems is also resource-intensive. Besides financial investments, considerable personnel capacity is required to train employees, establish processes, and revise existing systems. However, this effort should not be avoided, as it leads to more stable and reliable reporting in the long term.

Strategies for Better Documentation

A robust documentation strategy is certainly feasible when approached systematically and structurally. The first step is to identify all relevant stakeholders and analyze their specific documentation requirements. A central document management system can help collect all information in one place and prepare it for different target groups.

Standardized templates and checklists are a helpful tool to ensure documentation consistency. They should cover all essential points: data sources, calculation methods, assumptions, uncertainties, and quality assessments. Regular internal audits can additionally ensure that documentation standards are maintained.

Documentation should not be viewed as an additional task but as an integral part of data collection and analysis. Modern software solutions can support this process by automatically creating audit trails and traceably documenting changes. With such a strategy, companies create the foundation for consistent and reliable reporting that meets the high requirements of stakeholders and regulatory authorities.

Comparison Table

Here you'll find a compact overview of the methodological differences explained above. Approaches to Scope 3 accounting vary in their complexity, accuracy, and associated costs. A clear comparison of the most important methods makes it easier to choose the right strategy for your reporting.

Comparison of Data Sources

Supplier Engagement

The type of supplier engagement has a major impact on data quality and long-term collaboration. Personal workshops often provide the best results but require considerable time investment. Online questionnaires are a quick but less effective alternative. Incentivized programs can create a good balance between effort and benefit.

Calculation of Investment Emissions (Category 15)

The calculation of emissions from investments varies depending on corporate structure. The equity share approach is particularly suitable for direct investments, while the enterprise value approach can deliver more precise results with more complex financial structures. In the service sector, a revenue-based approach based on sales data is frequently used.

Industry-specific emission factors can often deliver more precise results than global average values, as they consider local energy mixes and production conditions.

Depending on industry, supply chain complexity, and available resources, you can choose the appropriate approach. For companies with limited budgets, it's advisable to initially use secondary data and later switch to more detailed methods once the necessary structures are in place. This overview helps you find the right method for your Scope 3 accounting.

Conclusion

Scope 3 accounting is no longer optional but a business-critical necessity that goes far beyond pure compliance issues. Companies that avoid the most common mistakes not only secure a competitive advantage but also minimize risks and create trust with their stakeholders.

Successful Scope 3 accounting requires a smart strategy based on targeted selection of primary and secondary data. Equally crucial is close collaboration between departments – sustainability is not a task that can be solved by the sustainability team alone. Without clear processes and thorough preparation, unnecessary costs and inefficiencies threaten. But those who systematically collect data, update it regularly, and document it transparently not only save time and money but also strengthen the trust of investors and partners.

Looking at tightened regulations like the CSRD, one thing becomes clear: companies that don't act in time risk not only sanctions but also the loss of important business opportunities. The solution approaches presented directly address the described challenges. These are not theoretical models but proven practices that leading companies are already successfully implementing. The key lies in setting priorities and addressing the biggest risks first – step by step and with focus on what's essential.

Fiegenbaum Solutions stands by companies as a competent partner to effectively implement these strategies in practice. With their expertise in regulatory compliance and sustainability reporting, Johannes Fiegenbaum and his team support companies in reducing risks, meeting legal requirements, and simultaneously achieving measurable climate impact. From strategic planning to operational implementation – Fiegenbaum Solutions covers all aspects of modern sustainability strategies.

Now is the right time to act: Those who professionally invest in Scope 3 accounting not only create the foundation for regulatory compliance but also lay the groundwork for sustainable growth and long-term success.

FAQs

How can companies ensure that their data for Scope 3 accounting is accurate and up-to-date?

To ensure data quality and timeliness in Scope 3 accounting, it is essential to obtain primary data directly from your suppliers and update it regularly. Close collaboration with suppliers plays a central role in this process. Together, you should define clear standards for data provision and quality.

Modern data management tools can help you continuously monitor and systematically improve data quality. Additionally, it's helpful to pursue a data-driven strategy to identify and address potential gaps or inaccuracies early on. These measures form the foundation for accurate and reliable emissions accounting that will stand the test of time.

How can companies successfully integrate their suppliers into Scope 3 emissions accounting?

To successfully integrate your suppliers into Scope 3 accounting, it makes sense to first prioritize those suppliers that cause the largest share of emissions – typically around 80%. Clear goal agreements and open, transparent communication play a central role here. This way, you can not only strengthen your partners' engagement but also build trust.

Digital tools can help you significantly simplify data collection and monitoring. At the same time, long-term partnerships and regular training ensure that collaboration with your suppliers is intensified and the quality of the data provided increases. Close cooperation not only advances you in complying with standards like the GHG Protocol, but also lays the foundation for a more sustainable and environmentally friendly supply chain.

What are primary and secondary data in Scope 3 emissions capture, and why are they important?

Primary data comes directly from specific sources such as manufacturers or suppliers. It is characterized by high accuracy since it is obtained directly from actual processes. This data is indispensable when it comes to precise calculations and well-founded decisions.

Secondary data, on the other hand, is based on standardized average values or existing databases. It offers less detailed accuracy but is a quick and cost-effective alternative. It is particularly useful for initial assessments or when access to primary data is difficult.

For reliable Scope 3 accounting, the smart combination of both data types is crucial: Primary data provides the necessary precision, while secondary data can serve as a supplement or interim solution when detailed information is missing.