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Greenwashing Detection in UK Corporate Sustainability Reports

Systematic verification of non-financial disclosures constitutes a fundamental prerequisite for corporate accountability in sustainable finance. Algorithmic and textual evaluation frameworks reveal significant discrepancies between qualitative corporate rhetoric and underlying operational realities. Addressing these discrepancies requires robust detection methodologies that integrate multimodal auditing standards into non-financial assurance frameworks.

Object & subject

Corporate sustainability reporting and non-financial disclosure practices. — Methodological frameworks and indicators for detecting greenwashing in corporate non-financial disclosures.

Scientific novelty

Synthesises multimodal and textual detection criteria across environmental, social, and governance pillars within corporate reporting literature.

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Undergraduate Dissertation

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Greenwashing Detection in UK Corporate Sustainability Reports

Author:

Group

First M. Last

Advisor:

Dr. First Last

City, 2026

Contents

Introduction
1. Theoretical Foundations of Corporate Sustainability and Greenwashing
1.1 Conceptualising Greenwashing and Symbolic Disclosures in Corporate Governance
1.2 Institutional and Stakeholder Perspectives on Environmental Narrative Assurance
1.3 Regulatory Frameworks Governing Non-Financial Reporting in the United Kingdom
1.4 Typologies of Greenwashing in Environmental, Social, and Governance Disclosures
2. Methodological Approaches to Automated Greenwashing Detection
2.1 Comparative Analysis of Natural Language Processing and Multimodal Models
2.2 Constructing Corpus Criteria from UK Corporate Sustainability Filings
2.3 Detection Metrics for Narrative Inconsistency and Selective Omission
2.4 Methodological Constraints and Reliability in Algorithmic Auditing
3. Analytical Evaluation of Greenwashing Indicators in Corporate Practice
3.1 Disconnect Between Qualitative ESG Narratives and Verified Quantitative Metrics
3.2 Cross-Pillar Inconsistencies Across Environmental, Social, and Governance Pillars
3.3 Visual Rhetoric and Multimodal Framing in Corporate Sustainability Reports
4. Strategic, Policy, and Practical Implications for UK Reporting Standards
4.1 Recommendations for Enhancing UK Sustainability Disclosure Requirements
4.2 Implementation of Automated Screening Protocols for Financial and ESG Auditors
4.3 Mitigating Reputational and Financial Risks for UK Listed Enterprises
Conclusion
Bibliography

Introduction

Corporate sustainability reporting in the United Kingdom has evolved rapidly under heightened stakeholder expectations and tightening regulatory oversight regarding non-financial performance. Within this context, misleading environmental claims and selective disclosures distort capital allocation and undermine stakeholder trust in corporate accountability [6]. The proliferation of non-financial reporting mandates has created an environment where organisations may prioritise symbolic compliance over substantive operational change, complicating the assessment of genuine environmental stewardship across the corporate landscape.

Automated approaches, including natural language processing and advanced machine learning models, are increasingly applied to detect deceptive communication patterns within corporate narratives [2]. Nevertheless, systematic greenwashing detection remains challenged by complex narrative structures and multimodal reporting techniques that obscure genuine environmental impacts [1]. Current evaluation frameworks frequently struggle to disentangle rhetorical sophistication from intentional concealment, generating inconsistent assessments across differing environmental, social, and governance indicators [1].

This study investigates the structural markers and methodological requirements for detecting greenwashing in corporate non-financial disclosures. Utilizing comparative qualitative analytical methods grounded in international and UK reporting frameworks [4], this investigation evaluates automated detection protocols against qualitative textual inconsistencies. The findings provide structural recommendations for standard setters, assurance providers, and governance bodies seeking to verify corporate non-financial claims.

3.2 Cross-Pillar Inconsistencies Across Environmental, Social, and Governance Pillars

The analytical assessment of corporate sustainability disclosures reveals significant structural tensions across environmental, social, and governance reporting dimensions. When corporate entities construct non-financial narratives, the prioritization of environmental metrics often obscures underlying deficiencies in corporate governance and social accountability. Methodological investigations into automated assurance indicate that analytical frameworks struggle with dimensional consistency across non-financial pillars. Specifically, comparative empirical evaluations demonstrate that advanced artificial intelligence systems produce inconsistent and contradictory evaluations across environmental, social, and governance pillars, frequently conflating sophisticated communication strategies with intentional deception rather than accurately reflecting substantive operational achievements (Evaluating Multimodal AI for Greenwashing Detection, 2025). This analytical vulnerability highlights how symbolic visual rhetoric and elaborate qualitative narratives can distort automated oversight mechanisms. Furthermore, systematic literature reviews confirm that current research and practical deployments remain heavily concentrated on the environmental pillar, leaving social and governance dimensions comparatively unexplored in automated verification environments (Artificial Intelligence in ESG, 2026). Consequently, algorithmic auditing models risk generating misleading assurance outcomes when deployed without robust domain-specific calibrations and rigorous contextual parameters. In corporate reporting practice, firms often leverage complex multimodal presentation formats to project normative compliance, thereby exploiting the limitations of current detection technologies that cannot adequately distinguish communicative polish from genuine corporate accountability. Addressing these persistent analytical distortions necessitates the integration of nuanced, multi-pillar evaluation standards that systematically evaluate qualitative narrative assertions against verified institutional baselines across all reporting dimensions.

References

  1. Evaluating Multimodal AI for Greenwashing Detection: A Comparative Analysis of ChatGPT, Claude, and Gemini in ESG Reports
    Jacek Krzysztof Jakubczak, Dorota Chmielewska-Muciek, Katarzyna Iwanicka
    DOI Link
  2. ARTIFICIAL INTELLIGENCE IN ESG, SUSTAINABILITY REPORTING, AND GREENWASHING DETECTION A Systematic Literature Review
    shirin shirin
    DOI Link
  3. Automated Detection of Greenwashing in Indian Corporate Sustainability Reports Using Natural Language Processing
    Ravi Shankar, Qian Xu
    DOI Link
  4. Advancing Sustainability Through ESG Reporting: Insights From India’s Regulatory Evolution and Corporate Practices
    Shukrant Jagotra
  5. Selective Silence as a Signal: ESG Issue-Level Greenwashing Detection through LLM Analysis of Corporate Narratives and Controversies
    Tsuyoshi Iwata, Eiji Sakihama
  6. Greenwashing in Corporate Sustainability: Implications for Financial Reporting and Accounting Practices
    Muhammed Zakir, Hossain, Md. Jobaer Rahman, Rashed, Annatul Islam, Hredoy
  7. The Impact of Corporate ESG Greenwashing on Financial Reporting Quality
    shuangpeng zhang, Wang Nuanxin, Peigong Li et al.
  8. Green Financial Policies and Corporate ESG Reporting ‘Greenwashing’: Empirical Evidence from Chinese Listed Companies
    Zhen Yu, Xiuling Li, Lang Yu

Bibliography

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