• White paper

The State of Global
Sustainability Disclosures

80,000+ Companies Profiled. 200,000+ Reports Analyzed.

One AI-native climate intelligence layer that turns scattered disclosures into decision-ready insight.

Executive Summary

Sustainability disclosures have moved from the margins to the mainstream of corporate reporting. Companies across industries and geographies now publish reports on emissions, resource use, and climate goals with a frequency that would have been unthinkable even a decade ago. The challenge is no longer whether data exists, but whether it can be compared, trusted, and understood at scale.

This whitepaper draws on a dataset rarely assembled before: over 200,000 sustainability reports from more than 80,000 companies worldwide. These reports were analyzed using SustainSense, Sprih’s Climate AI engine, designed to extract, standardize, and interpret public sustainability data. The purpose of this work is two fold:

- To highlight what patterns emerge when such a large body of disclosures is analyzed together.
- To illustrate how technology can transform scattered, inconsistent reporting into decision-ready insights.

Key Insights Covered

  • Global patterns across Emissions, Energy, Water, Waste, Targets, Initiatives.
  • Regional contrasts: Europe, Asia, North America anchor >90% of disclosures; Scope 3 varies widely by region.
  • Sector realities: Industrials/Materials dense disclosure; finance strong on Scope 3 maturity signals.
  • Size effects: Larger firms disclose more consistently (renewables, targets, Scope 3 details).
  • Targets landscape: Near-term (to ~2030), long-term (~2050), and net-zero (~2050) adoption trends.
  • Inside SustainSense: How AI Turns Noise into Knowledge
  • Key Use Cases of the Global Climate Database
  • Stakeholder Perspectives: Benefit from a Structured Climate Database
Companies by region
Companies by sector
Companies by revenue range
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Key Findings

  • Emissions: A clear picture of how companies report across Scopes 1, 2, and 3; where disclosure is solid vs. patchy; and how intensity views let you compare differently sized companies. Expect highlights on regional/sector coverage, common value-chain blind spots, and what this means for strategy, risk, and target-setting.
  • Energy: How organizations describe the energy that powers operations—plus the transparency gap between total consumption and the renewable/non-renewable split. Expect sector and regional patterns that hint at transition readiness and guidance on where to improve energy data first.
  • Water: How firms disclose withdrawal, consumption, discharge, treatment, and harvesting—and where reporting drops off. You’ll see which industries carry the highest water-management relevance, how practices differ by region, and how normalized views support operational risk assessment.
  • Waste: How companies track total waste alongside hazardous, non-hazardous, recycled, and disposed streams. Expect sector differences in waste transparency, where recycling programs are most mature, and how disclosure quality can inform compliance and circularity initiatives.
  • Targets: The landscape of near-term, long-term, and net-zero commitments—who is setting them, who is still “committing to set,” and who isn’t disclosing at all. Expect patterns by sector, region, and size, plus implications for credibility, assurance, and investor scrutiny.
  • Initiatives: What on-the-ground actions companies say they’re taking—from efficiency and low-carbon energy to transport and fugitives management. Expect which levers are most common, where sector-specific approaches dominate, and how initiative portfolios align (or don’t) with stated targets.
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Inside SustainSense: How AI Turns Noise into Knowledge

The growth of sustainability reporting has expanded the volume of available information, but the data remains fragmented. Disclosures appear across corporate websites, filings, and stock exchanges, in multiple languages and formats, combining narrative text with quantitative metrics. This diversity makes comparability and analysis challenging for policymakers, auditors, and executives.

SustainSense addresses this by providing an AI-native architecture purpose-built for sustainability. Unlike general-purpose large language models, it is designed to extract, harmonize, and structure sustainability data at scale, turning dispersed disclosures into a coherent and decision-ready foundation.
Extract
Find signals in PDFs, HTML, scans; multilingual OCR/NLP.
Classify
Route to emissions, energy, water, waste, targets, initiatives, governance.
Validate
Units, anomalies, boundary/method flags; preserve gaps.
Normalize
Align to GHG Protocol, CSRD (ESRS E1+), ISSB, TCFD for comparability.
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Methodology

  • Time window: 2022–2024; 200k+ sustainability/integrated reports across 80k companies.
  • Sources: Corporate sites, exchange filings, regulatory repositories, public sustainability databases.
  • Normalization: Units/currencies; intensity denominators; S2 method labeling; organizational boundary flags.
  • Handling gaps: Preserve missing years; flag anomalies and restatements.
  • Bias control: Multilingual coverage (>30 languages) to avoid English‑only skew.

Value for Stakeholders

  • Executives (CFOs, CEOs, CSOs)
    Expect insights that connect disclosures directly to enterprise strategy and financial risk. This perspective shows how reporting influences cost of capital, investor trust, and competitive positioning, turning sustainability from a compliance box into a growth driver.
  • Investors & Financial Institutions:
    Expect a clear view of disclosure quality as a governance signal, plus tools like portfolio heatmaps and scenario modeling. The section shows how data maturity can reduce transition risk, inform valuation, and guide capital toward resilient companies.
  • Procurement & Supply Chain Leaders:
    Expect visibility into upstream risks and Scope 3 exposure. This perspective reveals how supplier benchmarking, maturity scoring, and deeper-tier insights can simplify engagement and strengthen supply-chain resilience.
  • Regulators & Auditors:
    Expect a roadmap to auditability and comparability. The section outlines how harmonized data supports policy evaluation, third‑party assurance, and cross‑border consistency, easing the burden on both regulators and reporting companies.
  • Technology Community:
    Expect a proof point for AI’s role in climate intelligence. This perspective demonstrates how agentic AI can transform fragmented disclosures into a global data layer, enabling future innovation and ecosystem tools.

Use Cases: How Stakeholders Apply the Insights

  • Sectoral Decarbonization
    Shows how industries benchmark peers, spot leaders and laggards, and design credible pathways aligned with climate goals. Expect sector examples like utilities phasing down coal or logistics electrification.
  • Reporting & Assurance
    Highlights how the database simplifies compliance prep and audits. Expect tools that flag disclosure gaps, provide traceable trails, and ensure year‑over‑year consistency.
  • Regulatory Benchmarking
    Reveals how disclosures map across regimes—CSRD, ISSB, TCFD, California laws—showing overlaps and gaps. Helps policymakers and companies streamline compliance.
  • Supply Chain Visibility
    Illustrates how Scope 3 blind spots are filled by consolidating supplier data. Expect supplier benchmarking, reduced survey fatigue, and visibility beyond Tier‑1.
  • Investor Risk & Allocation
    Explains how investors use disclosure maturity to guide capital. Expect portfolio heatmaps, scenario modeling, and transition‑risk signals for better allocation.

Frequently Asked Questions

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A Comparative Analysis of Global Climate Reporting Using 200,000+ Reports from 80,000+ Companies