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How to Map GRI Disclosures to CSRD Requirements and Eliminate Duplication

GRI-ESRS interoperability: where the standards overlap, where they diverge, and a practical mapping approach.

The Reality of Multi-Framework Reporting

The vast majority of companies engaged in sustainability reporting report under multiple frameworks. While GRI Standards remain the most widely used sustainability reporting framework globally, ESRS under CSRD has become a legal requirement in Europe. Reporting separately under both frameworks can double data collection processes and create unnecessary workload for reporting teams.

The good news: GRI and EFRAG have been working together since 2022 to address this problem. In their joint statement, both organizations committed to ensuring a high degree of alignment between their standards and facilitating interoperability (GRI-EFRAG Joint Statement, 2022).

GRI and ESRS: Common Ground

Architectural Similarities

The 2021 revision of GRI Standards and ESRS Set 1 share several structural similarities:

  • Both frameworks are modular — consisting of general standards and topical standards
  • Both frameworks require a materiality assessment
  • Both frameworks adopt a value chain perspective
  • Both frameworks are organized around governance, strategy, risk management, and metrics

Areas of Strong Overlap

The strongest overlap between GRI and ESRS exists in:

Environmental topics:

  • Greenhouse gas emissions (GRI 305 ↔ ESRS E1): Scope 1, 2, 3 emissions data are largely equivalent
  • Energy consumption (GRI 302 ↔ ESRS E1): Energy data requirements are nearly identical
  • Water use (GRI 303 ↔ ESRS E3): Withdrawal, discharge, and consumption data are aligned
  • Waste management (GRI 306 ↔ ESRS E5): Waste types and disposal methods run in parallel

Governance topics:

  • Anti-corruption (GRI 205 ↔ ESRS G1): Policies and training data overlap
  • Tax transparency (GRI 207 ↔ ESRS G1): Country-by-country tax reporting is aligned

Data collected under GRI in these areas can be used in ESRS reporting with minimal adjustment (GRI Standards 2021; ESRS Set 1, 2023).

GRI and ESRS: Key Differences

1. Materiality Approach

This is the most fundamental difference between the two frameworks:

  • GRI: Impact materiality — the company's impacts on the economy, environment, and people
  • ESRS: Double materiality — impact materiality AND financial materiality (effects on the company's financial position)

GRI's impact materiality is a subset of ESRS's double materiality. A topic found material under GRI will also be material from the impact perspective under ESRS. However, ESRS additionally requires assessment from the financial perspective.

Practical implication: A company that has conducted a GRI materiality assessment should add the financial materiality dimension for ESRS. There is no need to start from scratch.

2. ESRS-Specific Data Points

ESRS requires certain specific data points not found in GRI:

  • Financial effects: Estimated financial impacts of sustainability risks and opportunities
  • Transition plans: Detailed description of the climate transition plan (ESRS E1-6 through E1-9)
  • EU Taxonomy alignment: Taxonomy-aligned revenue, capital expenditure, and operating expenditure ratios
  • Sector-agnostic metrics: Certain mandatory disclosures in ESRS 2 have no GRI counterpart

3. Reporting Boundaries

GRI allows companies to set boundaries from a "significant impacts" perspective, while ESRS aligns the consolidation scope with financial reporting boundaries. This difference can create inconsistencies regarding the inclusion of certain subsidiaries and joint ventures.

A Practical Mapping Approach

Phase 1: Data Inventory

List all data points currently collected under GRI. For each data point:

  • GRI disclosure number
  • Data source and collection frequency
  • Data quality level

Phase 2: Cross-Reference Table

Using the interoperability guidance published by EFRAG, map each GRI disclosure to the corresponding ESRS data point. This guidance shows in detail which GRI disclosures satisfy which ESRS requirements (EFRAG Interoperability Guidance, 2023).

Typical mapping results fall into three categories:

  1. Full match: GRI data directly satisfies the ESRS requirement (no additional data needed)
  2. Partial match: GRI data provides the foundation, but ESRS requires additional detail
  3. No match: ESRS-specific data point with no GRI counterpart (new data collection required)

Phase 3: Gap Analysis

For partial matches and non-matches:

  • Define additional data requirements
  • Expand data collection processes
  • Integrate ESRS supplementary requirements into the existing GRI data collection cycle

Phase 4: Unified Data Collection Process

Instead of two separate data collection processes, design a single process that meets the requirements of both frameworks. This approach:

  • Increases data consistency
  • Reduces cross-departmental workload
  • Shortens the reporting cycle

Overlap Rates: By the Numbers

According to EFRAG's interoperability analysis:

  • Approximately 70-80 percent of GRI environmental disclosures overlap directly or at a high level with ESRS
  • Approximately 50-60 percent of GRI social disclosures overlap with ESRS
  • Approximately 60-70 percent of GRI governance disclosures overlap with ESRS

These rates demonstrate that companies already reporting under GRI hold a significant advantage in transitioning to ESRS.

Points to Watch

  1. Track GRI updates: GRI continues to update its standards to increase ESRS alignment
  2. Build financial materiality capability: ESRS's financial perspective may require competencies not developed through GRI experience alone
  3. Digital reporting readiness: ESRS will require digital tagging in XBRL format — this obligation does not exist in GRI reporting
  4. Assurance requirements differ: Assurance is mandatory under CSRD, while it remains voluntary for GRI reporting

Bottom Line: The high degree of overlap between GRI and ESRS makes multi-framework reporting manageable. With a systematic mapping approach, duplicate data collection can be eliminated, and the requirements of both frameworks can be met through a single data collection cycle.


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