Sustainability assurance is evolving from a voluntary add‑on to a mechanism that makes rapidly expanding disclosure regimes credible. ESRS pushes firms to assess double materiality, while IFRS S1/S2 keep the lens on enterprise value; this split forces assurers to juggle heterogeneous scopes, evidence types, and assurance levels. Principles-based frameworks such as ISSA 5000 and topic‑specific guides like ISAE 3410 offer scaffolds, yet they struggle with forward‑looking, qualitative claims and sprawling datasets—think of estimating Scope 3 emissions across diffuse value chains or verifying “just transition” narratives built on scenario analysis. The chapter argues that methodological innovation, interdisciplinary teams, and clearer expectation management are needed to prevent procedural box‑ticking from crowding out substantive scrutiny. Assurance matters because it turns mandated transparency into accountable, decision‑useful information—for example, by curbing inflated decarbonization stories when underlying activity data are weak.

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Sustainability Assurance

  • Othmar M. Lehner

摘要

Sustainability assurance is evolving from a voluntary add‑on to a mechanism that makes rapidly expanding disclosure regimes credible. ESRS pushes firms to assess double materiality, while IFRS S1/S2 keep the lens on enterprise value; this split forces assurers to juggle heterogeneous scopes, evidence types, and assurance levels. Principles-based frameworks such as ISSA 5000 and topic‑specific guides like ISAE 3410 offer scaffolds, yet they struggle with forward‑looking, qualitative claims and sprawling datasets—think of estimating Scope 3 emissions across diffuse value chains or verifying “just transition” narratives built on scenario analysis. The chapter argues that methodological innovation, interdisciplinary teams, and clearer expectation management are needed to prevent procedural box‑ticking from crowding out substantive scrutiny. Assurance matters because it turns mandated transparency into accountable, decision‑useful information—for example, by curbing inflated decarbonization stories when underlying activity data are weak.