This thesis explores how Artificial Intelligence (AI) can support stakeholders in detecting greenwashing in corporate sustainability reports. This phenomenon, where companies misrepresent their environmental and social commitments, undermines the credibility of Environmental, Social, and Governance (ESG) ratings. Through AI tools, this study seeks to empower stakeholders to distinguish authentic sustainability efforts from misleading claims, thereby fostering transparency and trust in ESG evaluations. The research analyzes the correlation between the MSCI and Eikon Controversies ESG domains and sub-domains of the first 100 companies listed in the MSCI World ESG Leaders. Hypothesis 1 states that ESG ratings are influenced by companies’ misrepresentation or exaggeration of their sustainability efforts, resulting in biased ratings that obscure the actual presence of greenwashing. However, findings of the analysis reveal a rejection of this hypothesis. Hypothesis 2 posits that ESG leaders, as identified by the MSCI World ESG Leaders Index, demonstrate significantly higher ESG performance across all domains compared to their industry peers. This finding demonstrates that ESG leaders outperform their competitors, thanks in part to enhanced sustainability indicators which reduce greenwashing risks. These indicators have helped decrease greenwashing and support truly eco-friendly companies. To address greenwashing, third-party ESG rating agencies are improving transparency and standardizing methodologies, utilizing AI. Hypothesis 3 examines the variability in consistency and accuracy of ESG ratings across different rating agencies. The investigation confirms significant incongruences, with some alignment observed between MSCI and Eikon ratings, but overall variability in performance assessments. This underlines the necessity for stakeholders to examine ESG ratings carefully and consider multiple ratings to get a more accurate evaluation of corporate sustainability. The importance of this research lies in its potential to improve the transparency and reliability of ESG ratings and corporate sustainability reports, providing stakeholders with critical insights to discern genuine sustainability efforts from greenwashing. The integration of AI-driven tools for detailed analysis further enhances the ability to detect discrepancies, facilitating more informed decision-making
Questa tesi esplora come l'Intelligenza Artificiale (IA) possa aiutare gli stakeholder a individuare il greenwashing nei report di sostenibilità aziendale. Il greenwashing è la pratica con cui le aziende comunicano in modo fuorviante i propri impegni ambientali e sociali, minando la credibilità delle valutazioni ESG (ambientali, sociali e di governance). Questo studio mira a fornire strumenti basati su IA che permettano agli stakeholder di distinguere gli impegni di sostenibilità autentici da quelli fittizi, promuovendo così trasparenza e fiducia nelle valutazioni ESG. La ricerca analizza la correlazione tra i domini ESG di MSCI e Eikon Controversies per le prime 100 aziende nell'indice MSCI World ESG Leaders. La prima ipotesi suggerisce che le valutazioni ESG siano influenzate da una rappresentazione distorta degli sforzi di sostenibilità aziendale, portando a valutazioni fuorvianti che mascherano la presenza effettiva di greenwashing. Tuttavia, i risultati dell'analisi smentiscono questa ipotesi. La seconda ipotesi sostiene che le aziende leader ESG, identificate dall'indice MSCI World ESG Leaders, abbiano prestazioni ESG significativamente migliori rispetto ai concorrenti di settore. I risultati mostrano che questi leader superano la concorrenza grazie a indicatori di sostenibilità potenziati, che aiutano a ridurre i rischi di greenwashing e a sostenere aziende autenticamente eco-sostenibili. Le agenzie di valutazione ESG stanno inoltre migliorando la trasparenza e standardizzando le metodologie, anche grazie all’utilizzo dell’IA, per contrastare il greenwashing. La terza ipotesi analizza la coerenza e l'accuratezza delle valutazioni ESG tra diverse agenzie di rating, riscontrando incongruenze significative: esistono alcune convergenze tra le valutazioni MSCI ed Eikon, ma permangono differenze nelle valutazioni complessive delle prestazioni ESG. Questo evidenzia la necessità per gli stakeholder di considerare più fonti per ottenere una valutazione accurata della sostenibilità aziendale. Questa ricerca è importante perché può migliorare la trasparenza e l'affidabilità delle valutazioni ESG e dei rapporti di sostenibilità, offrendo agli stakeholder strumenti utili per riconoscere gli impegni di sostenibilità autentici dal greenwashing. L’integrazione di strumenti basati su IA per l'analisi approfondita rende più semplice individuare le discrepanze, supportando decisioni informate e consapevoli
Greenwashing nei Report di Sostenibilità Aziendale: Aumentare la Consapevolezza degli Stakeholders con il Supporto dell'IA
BOARI, ALESSIA
2023/2024
Abstract
This thesis explores how Artificial Intelligence (AI) can support stakeholders in detecting greenwashing in corporate sustainability reports. This phenomenon, where companies misrepresent their environmental and social commitments, undermines the credibility of Environmental, Social, and Governance (ESG) ratings. Through AI tools, this study seeks to empower stakeholders to distinguish authentic sustainability efforts from misleading claims, thereby fostering transparency and trust in ESG evaluations. The research analyzes the correlation between the MSCI and Eikon Controversies ESG domains and sub-domains of the first 100 companies listed in the MSCI World ESG Leaders. Hypothesis 1 states that ESG ratings are influenced by companies’ misrepresentation or exaggeration of their sustainability efforts, resulting in biased ratings that obscure the actual presence of greenwashing. However, findings of the analysis reveal a rejection of this hypothesis. Hypothesis 2 posits that ESG leaders, as identified by the MSCI World ESG Leaders Index, demonstrate significantly higher ESG performance across all domains compared to their industry peers. This finding demonstrates that ESG leaders outperform their competitors, thanks in part to enhanced sustainability indicators which reduce greenwashing risks. These indicators have helped decrease greenwashing and support truly eco-friendly companies. To address greenwashing, third-party ESG rating agencies are improving transparency and standardizing methodologies, utilizing AI. Hypothesis 3 examines the variability in consistency and accuracy of ESG ratings across different rating agencies. The investigation confirms significant incongruences, with some alignment observed between MSCI and Eikon ratings, but overall variability in performance assessments. This underlines the necessity for stakeholders to examine ESG ratings carefully and consider multiple ratings to get a more accurate evaluation of corporate sustainability. The importance of this research lies in its potential to improve the transparency and reliability of ESG ratings and corporate sustainability reports, providing stakeholders with critical insights to discern genuine sustainability efforts from greenwashing. The integration of AI-driven tools for detailed analysis further enhances the ability to detect discrepancies, facilitating more informed decision-makingFile | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14239/27005