Introduction

The report examines challenges affecting the quality, consistency and accessibility of environmental, social and governance (ESG) data used in corporate analysis and investment decision-making. Based on the experiences of the We Mean Business Coalition, it highlights systemic problems across company reporting, data providers and structural systems. The analysis aims to improve the reliability of ESG data so it can support credible corporate assessments and investment strategies.

Background

The research originated from an attempt to analyse whether companies that commit to climate initiatives reduce emissions faster than those that do not. A database of more than 18,000 companies with climate commitments was assembled and supplemented with financial and non-financial data from major providers and NGO sources. However, historical emissions data proved unreliable. For example, the rate of obvious data issues in emissions datasets was around 7% in 2019 compared with about 0.25% in 2021–2022. Some reported company emissions exceeded total global emissions due to extraction errors. These problems limited meaningful time-series analysis and led the researchers to focus instead on identifying systemic ESG data quality issues.

ESG Data – How To Access It?

ESG data is typically available in three forms: raw company-reported data, calculated or estimated data derived from peer models, and ESG ratings or rankings that aggregate multiple indicators. Most investors and analysts obtain this information through commercial data providers because manual extraction from company reports is costly and time-consuming.

Financial data is often available in structured digital formats such as XBRL through national authorities, whereas ESG data remains difficult and expensive to obtain. Reporting coverage is uneven: large listed companies in developed markets report most frequently, while private companies, SMEs and firms in developing regions often do not disclose ESG information. To fill gaps, providers frequently estimate missing metrics, yet research indicates estimated emissions are often unreliable substitutes for company-reported data. ESG ratings further simplify analysis but rely on subjective weighting and underlying data quality.

Issues Stemming From Company Data Reporting

Several data quality problems originate from companies themselves. Many companies do not report ESG data at all, producing skewed datasets dominated by large listed firms. Even when reporting occurs, companies often omit important key performance indicators (KPIs), making cross-company comparisons difficult.

Inconsistent calculation methods further reduce comparability. Firms may change methodologies without updating historical figures, complicating time-series analysis. Misinterpretation of reporting requirements is also common. Examples include reporting only carbon dioxide rather than carbon-dioxide equivalents, failing to include all Kyoto gases, or combining emissions scopes into a single figure.

Presentation formats can also hinder analysis. ESG information is frequently embedded in narrative text or graphics designed for communication rather than data extraction. Emerging regulations such as the EU Corporate Sustainability Reporting Directive (CSRD) and the adoption of International Sustainability Standards Board (ISSB) standards aim to improve reporting quality. Companies are encouraged to strengthen internal controls, clearly define KPI calculations, adopt tabular reporting formats and engage with stakeholders to clarify required disclosures.

Issues Stemming From Data Providers

Data providers introduce additional challenges. ESG datasets are often delayed; company information reported for a given year may only become fully available more than 1.5 years later. Manual extraction, machine reading and survey collection methods can introduce errors such as unit mistakes or missing data.

Historically, quality checks have been insufficient. Some provider databases contained values that were logically impossible, such as company emissions exceeding global totals. While recent improvements have reduced obvious errors, historical data often remains uncorrected. Providers may also offer insufficient granularity, for example failing to separate location-based and market-based Scope 2 emissions.

Adoption of XBRL tagging and the creation of central repositories could reduce extraction errors and delays. Providers are encouraged to strengthen validation procedures, update historical data when accounting changes occur and collaborate with users to ensure adequate data detail.

Structural Issues

Structural limitations also affect ESG data reliability. Currently, there is no widely accessible central repository for ESG information, forcing analysts to rely on costly commercial datasets. Initiatives such as the EU’s European Single Access Point and the Net Zero Data Public Utility aim to improve accessibility.

Another challenge is the limited validation of ESG reports by national authorities compared with financial reporting oversight. Expanding mandatory assurance and improving regulatory capacity could enhance reliability. In addition, greenhouse gas reporting boundaries often differ from financial reporting boundaries, complicating integrated financial and environmental analysis. Aligning these frameworks would improve comparability and analytical usefulness.

Conclusion

The report concludes that ESG data quality problems arise from company reporting practices, data provider processes and broader structural weaknesses. Although regulatory reforms, digital reporting standards and public repositories are expected to improve data quality, coordinated action by companies, regulators, data providers and investors remains necessary to produce reliable ESG datasets for financial analysis and decision-making.