Implications for Artificial Intelligence and ESG Data

Macpherson, Martina; Gasperini, Andrea; Bosco, Matteo

doi:10.2139/ssrn.3863599

Cited by 14 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A subfield of AI that has emerged is machine learning (ML). ML uses algorithms to recognize patterns, make decisions, and imitate the way that humans learn and solve problems [7,8,52]. ML, like AI, has become increasingly prominent in the academic literature, as reflected by increases in the occurrences of the term as well as its evolution toward being a term that is sometimes viewed as being distinct or autonomous from AI within academic literature [11].…”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

Greenwashing, Sustainability Reporting, and Artificial Intelligence: A Systematic Literature Review

Moodaley

Telukdarie

2023

Sustainability

View full text Add to dashboard Cite

The rise of stakeholder interest globally in sustainable business practices has resulted in a rise in demands from stakeholders that companies report on the environmental and social impacts of their business activities. In certain cases, however, companies have resorted to the practice of providing inaccurate disclosures regarding sustainability as part of their corporate communications and sustainability reporting—commonly referred to as “greenwashing”. Concurrently, technological improvements in artificial intelligence have presented the means to rapidly and accurately analyze large volumes of text-based information, such as that contained in sustainability reports. Despite the possible impacts of artificial intelligence and machine learning on the fields of greenwashing and sustainability reporting, no literature to date has comprehensively and holistically addressed the interrelationship between these three important topics. This paper contributes to the body of knowledge by using bibliometric and thematic analyses to systematically analyze the interrelationship between those fields. The analysis is also used to conjecture a conceptual and thematic framework for the use of artificial intelligence with machine learning in relation to greenwashing and company sustainability reporting. This paper finds that the use of artificial intelligence in relation to greenwashing, and greenwashing within sustainability reporting, is an underexplored research field.

show abstract

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

“…The Global Reporting Initiative (GRI) states that the "foundation of sustainability reporting is for an organization to identify and prioritize its impacts on the economy, environment, and people to be transparent about their impacts" [6]. Many companies now choose to report on ESG factors in sustainability reports [7].…”

Section: Introductionmentioning

confidence: 99%

Greenwashing, Sustainability Reporting, and Artificial Intelligence: A Systematic Literature Review

Moodaley

Telukdarie

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…In contrast, greenwashing is "an umbrella term for a variety of misleading communications and practices that intentionally or not, induce false positive perceptions of an organization's environmental performance" (Nemes et al, 2022). Lyon and Maxwell (2011) define greenwashing as "selective disclosure of positive information about a company's environmental or social performance, without full disclosure of more information for stakeholders to analyse, and that information has become more complex (Macpherson et al, 2021). The challenge is compounded when such analyses consider multiple companies' reports across multiple time-periods (Ning et al, 2021).…”

Section: Sustainability Reporting and Greenwashingmentioning

confidence: 99%

“…The implication is that greenwashing threatens the objectives of both company sustainability initiatives, as well as the objectives of sustainability reporting, given the incongruence of the practice of greenwashing with those objectives. Detection of greenwashing within company sustainability disclosures is challenging, for a number of reasons which include the textual and qualitative nature of company disclosures and the volume and complexity of such disclosures (In & Schumacher, 2021) (Macpherson et al, 2021). Rapid and sustained progress made in the field of artificial intelligence (AI), and LLMs which use natural language processing (NLP), has demonstrated the ability of these technologies to effectively analyse large volumes of frequently complex text corpora, including those containing or consisting of sustainability disclosures.…”

Section: Introductionmentioning

confidence: 99%

A Conceptual Framework for Subdomain Specific Pre-Training of Large Language Models for Green Claim Detection

Moodaley,

Telukdarie

2023

EJSD

View full text Add to dashboard Cite

Detection of false or misleading green claims (referred to as “greenwashing”) within company sustainability disclosures is challenging for a number of reasons, which include the textual and qualitative nature, volume, and complexity of such disclosures. In recent years, notable progress made in the fields of artificial intelligence and specifically, large language models (LLMs), has showcased the capacity of these tools to effectively analyse extensive and intricate textual data, including the contents of sustainability disclosures. Transformer-based LLMs, such as Google’s BERT architecture, were trained on general domain text corpora. Subsequent research has shown that further pre-training of such LLMs on specific domains, such as the climate or sustainability domains, may improve performance. However, previous research often uses text corpora that exhibit significant variation across topics and language and which often consist of heterogeneous subdomains. We therefore propose a conceptual framework for further pre-training of transformer based LLMs using text corpora relating to specific sustainability subdomains i.e. subdomain specific pre-training. We do so as a basis for the improved performance of such models in analysing sustainability disclosures. The main contribution is a conceptual framework to advance the use of LLMs for the reliable identification of green claims and ultimately, greenwashing. Keywords: greenwashing, artificial intelligence, sustainability, sustainability reporting, sustainability disclosures.

show abstract

“…The pressure on investment managers to incorporate ESG factors into their portfolios is growing. AI can offer a solution in this case by acting as the trigger for scaled sustainable investing through analysis technologies that filter important data [66].…”

Section: Ai For Esgmentioning

confidence: 99%

Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape

et al. 2022

View full text Add to dashboard Cite

Currently, sustainability is a vital aspect for every nation and organization to accomplish Sustainable Development Goals (SDGs) by 2030. Environmental, social, and governance (ESG) metrics are used to evaluate the sustainability level of an organization. According to the statistics, 53% of respondents in the BlackRock survey are concerned about the availability of low ESG data, which is critical for determining the organization’s sustainability level. This obstacle can be overcome by implementing Industry 4.0 technologies, which enable real-time data, data authentication, prediction, transparency, authentication, and structured data. Based on the review of previous studies, it was determined that only a few studies discussed the implementation of Industry 4.0 technologies for ESG data and evaluation. The objective of the study is to discuss the significance of ESG data and report, which is used for the evaluation of the sustainability of an organization. In this regard, the assimilation of Industry 4.0 technologies (Internet of Things (IoT), artificial intelligence (AI), blockchain, and big data for obtaining ESG data by an organization is detailed presented to study the progress of advancement of these technologies for ESG. On the basis of analysis, this study concludes that consumers are concerned about the ESG data, as most organizations develop inaccurate ESG data and suggest that these digital technologies have a crucial role in framing an accurate ESG report. After analysis a few vital conclusions are drawn such as ESG investment has benefited from AI capabilities, which previously relied on self-disclosed, annualized company information that was susceptible to inherent data issues and biases. Finally, the article discusses the vital recommendations that can be implemented for future work.

show abstract

Implications for Artificial Intelligence and ESG Data

Cited by 14 publications

References 0 publications

Greenwashing, Sustainability Reporting, and Artificial Intelligence: A Systematic Literature Review

Greenwashing, Sustainability Reporting, and Artificial Intelligence: A Systematic Literature Review

A Conceptual Framework for Subdomain Specific Pre-Training of Large Language Models for Green Claim Detection

Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape

Contact Info

Product

Resources

About